Models doc (#7345)

* Clean up model documentation

* Formatting

* Preparation work

* Long lines

* Main work on rst files

* Cleanup all config files

* Syntax fix

* Clean all tokenizers

* Work on first models

* Models beginning

* FaluBERT

* All PyTorch models

* All models

* Long lines again

* Fixes

* More fixes

* Update docs/source/model_doc/bert.rst
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Update docs/source/model_doc/electra.rst
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Last fixes
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

docs/source/model_doc/bart.rst

 Bart
-----------------------------------------------------
+-----------------------------------------------------------------------------------------------------------------------
 **DISCLAIMER:** If you see something strange,
 file a `Github Issue <https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title>`__ and assign
 @sshleifer
 
 Overview
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 The Bart model was `proposed <https://arxiv.org/abs/1910.13461>`_ by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer on 29 Oct, 2019.
 According to the abstract,
@@ -18,7 +18,7 @@ The Authors' code can be found `here <https://github.com/pytorch/fairseq/tree/ma
 
 
 Implementation Notes
-~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 - Bart doesn't use :obj:`token_type_ids` for sequence classification. Use BartTokenizer.encode to get the proper splitting.
 - The forward pass of ``BartModel`` will create decoder inputs (using the helper function ``transformers.modeling_bart._prepare_bart_decoder_inputs``)  if they are not passed. This is different than some other modeling APIs.
@@ -29,21 +29,21 @@ Implementation Notes
 
 
 BartForConditionalGeneration
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.BartForConditionalGeneration
     :members: forward
 
 
 BartConfig
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.BartConfig
     :members:
 
 
 BartTokenizer
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.BartTokenizer
     :members:
@@ -51,7 +51,7 @@ BartTokenizer
 
 
 BartModel
-~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.BartModel
     :members: forward
@@ -60,14 +60,14 @@ BartModel
 
 
 BartForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.BartForSequenceClassification
     :members: forward
 
 
 BartForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.BartForQuestionAnswering
     :members: forward

docs/source/model_doc/bert.rst

 BERT
-----------------------------------------------------
+-----------------------------------------------------------------------------------------------------------------------
 
 Overview
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-The BERT model was proposed in `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. It's a bidirectional transformer
-pre-trained using a combination of masked language modeling objective and next sentence prediction
-on a large corpus comprising the Toronto Book Corpus and Wikipedia.
+The BERT model was proposed in `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. It's a
+bidirectional transformer pretrained using a combination of masked language modeling objective and next sentence
+prediction on a large corpus comprising the Toronto Book Corpus and Wikipedia.
 
 The abstract from the paper is the following:
 
@@ -27,20 +27,20 @@ Tips:
 
 - BERT is a model with absolute position embeddings so it's usually advised to pad the inputs on
   the right rather than the left.
-- BERT was trained with the masked language modeling (MLM) and next sentence prediction (NSP) objectives. It is efficient at predicting masked
-  tokens and at NLU in general, but is not optimal for text generation.
+- BERT was trained with the masked language modeling (MLM) and next sentence prediction (NSP) objectives. It is
+  efficient at predicting masked tokens and at NLU in general, but is not optimal for text generation.
 
-The original code can be found `here <https://github.com/google-research/bert>`_.
+The original code can be found `here <https://github.com/google-research/bert>`__.
 
 BertConfig
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.BertConfig
     :members:
 
 
 BertTokenizer
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.BertTokenizer
     :members: build_inputs_with_special_tokens, get_special_tokens_mask,
@@ -48,14 +48,14 @@ BertTokenizer
 
 
 BertTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.BertTokenizerFast
     :members:
 
 
 Bert specific outputs
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.modeling_bert.BertForPreTrainingOutput
     :members:
@@ -65,127 +65,126 @@ Bert specific outputs
 
 
 BertModel
-~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.BertModel
-    :members:
+    :members: forward
 
 
 BertForPreTraining
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.BertForPreTraining
-    :members:
+    :members: forward
 
 
 BertModelLMHeadModel
-~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.BertLMHeadModel
-    :members:
+    :members: forward
 
 
 BertForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.BertForMaskedLM
-    :members:
+    :members: forward
 
 
 BertForNextSentencePrediction
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.BertForNextSentencePrediction
-    :members:
+    :members: forward
 
 
 BertForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.BertForSequenceClassification
-    :members:
+    :members: forward
 
 
 BertForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.BertForMultipleChoice
-    :members:
+    :members: forward
 
 
 BertForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.BertForTokenClassification
-    :members:
+    :members: forward
 
 
 BertForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.BertForQuestionAnswering
-    :members:
+    :members: forward
 
 
 TFBertModel
-~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFBertModel
-    :members:
+    :members: call
 
 
 TFBertForPreTraining
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFBertForPreTraining
-    :members:
+    :members: call
 
 
 TFBertModelLMHeadModel
-~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFBertLMHeadModel
-    :members:
+    :members: call
 
 
 TFBertForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFBertForMaskedLM
-    :members:
+    :members: call
 
 
 TFBertForNextSentencePrediction
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFBertForNextSentencePrediction
-    :members:
+    :members: call
 
 
 TFBertForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFBertForSequenceClassification
-    :members:
+    :members: call
 
 
 TFBertForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFBertForMultipleChoice
-    :members:
+    :members: call
 
 
 TFBertForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFBertForTokenClassification
-    :members:
+    :members: call
 
 
 TFBertForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFBertForQuestionAnswering
-    :members:
-
+    :members: call

docs/source/model_doc/bertgeneration.rst

 BertGeneration
-----------------------------------------------------
+-----------------------------------------------------------------------------------------------------------------------
 
 Overview
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-The BertGeneration model is a BERT model that can be leveraged for sequence-to-sequence tasks using :class:`~transformers.EncoderDecoderModel` as proposed in `Leveraging Pre-trained Checkpoints for Sequence Generation Tasks <https://arxiv.org/abs/1907.12461>`__ by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
+The BertGeneration model is a BERT model that can be leveraged for sequence-to-sequence tasks using
+:class:`~transformers.EncoderDecoderModel` as proposed in `Leveraging Pre-trained Checkpoints for Sequence Generation
+Tasks <https://arxiv.org/abs/1907.12461>`__ by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
 
 The abstract from the paper is the following:
 
-*Unsupervised pre-training of large neural models has recently revolutionized Natural Language Processing. By warm-starting from the publicly released checkpoints, NLP practitioners have pushed the state-of-the-art on multiple benchmarks while saving significant amounts of compute time. So far the focus has been mainly on the Natural Language Understanding tasks. In this paper, we demonstrate the efficacy of pre-trained checkpoints for Sequence Generation. We developed a Transformer-based sequence-to-sequence model that is compatible with publicly available pre-trained BERT, GPT-2 and RoBERTa checkpoints and conducted an extensive empirical study on the utility of initializing our model, both encoder and decoder, with these checkpoints. Our models result in new state-of-the-art results on Machine Translation, Text Summarization, Sentence Splitting, and Sentence Fusion.*
+*Unsupervised pre-training of large neural models has recently revolutionized Natural Language Processing. By
+warm-starting from the publicly released checkpoints, NLP practitioners have pushed the state-of-the-art on multiple
+benchmarks while saving significant amounts of compute time. So far the focus has been mainly on the Natural Language
+Understanding tasks. In this paper, we demonstrate the efficacy of pre-trained checkpoints for Sequence Generation. We
+developed a Transformer-based sequence-to-sequence model that is compatible with publicly available pre-trained BERT,
+GPT-2 and RoBERTa checkpoints and conducted an extensive empirical study on the utility of initializing our model, both
+encoder and decoder, with these checkpoints. Our models result in new state-of-the-art results on Machine Translation,
+Text Summarization, Sentence Splitting, and Sentence Fusion.*
 
 Usage:
 
-- The model can be used in combination with the :class:`~transformers.EncoderDecoderModel` to leverage two bert pretrained bert checkpoints for subsequent fine-tuning.
+- The model can be used in combination with the :class:`~transformers.EncoderDecoderModel` to leverage two pretrained
+  BERT checkpoints for subsequent fine-tuning.
 
-::
+:: code-block
   
   # leverage checkpoints for Bert2Bert model...
-  encoder = BertGenerationEncoder.from_pretrained("bert-large-uncased", bos_token_id=101, eos_token_id=102)  # use BERT's cls token as BOS token and sep token as EOS token
-  decoder = BertGenerationDecoder.from_pretrained("bert-large-uncased", add_cross_attention=True, is_decoder=True, bos_token_id=101, eos_token_id=102)  # add cross attention layers and use BERT's cls token as BOS token and sep token as EOS token
+  # use BERT's cls token as BOS token and sep token as EOS token
+  encoder = BertGenerationEncoder.from_pretrained("bert-large-uncased", bos_token_id=101, eos_token_id=102)
+  # add cross attention layers and use BERT's cls token as BOS token and sep token as EOS token
+  decoder = BertGenerationDecoder.from_pretrained("bert-large-uncased", add_cross_attention=True, is_decoder=True, bos_token_id=101, eos_token_id=102)
   bert2bert = EncoderDecoderModel(encoder=encoder, decoder=decoder)
   
   # create tokenizer...
@@ -32,10 +44,10 @@ Usage:
   loss.backward()
 
 
-- Pretrained :class:`~transformers.EncoderDecoderModel` are also directly available in the model hub, *e.g.*:
+- Pretrained :class:`~transformers.EncoderDecoderModel` are also directly available in the model hub, e.g.,
 
 
-::
+:: code-block
 
   # instantiate sentence fusion model
   sentence_fuser = EncoderDecoderModel.from_pretrained("google/roberta2roberta_L-24_discofuse")
@@ -50,33 +62,35 @@ Usage:
 
 Tips:
 
-- :class:`~transformers.BertGenerationEncoder` and :class:`~transformers.BertGenerationDecoder`  should be used in combination with :class:`~transformers.EncoderDecoder`.
-- For summarization, sentence splitting, sentence fusion and translation, no special tokens are required for the input. Therefore, no EOS token should be added to the end of the input.
+- :class:`~transformers.BertGenerationEncoder` and :class:`~transformers.BertGenerationDecoder` should be used in
+  combination with :class:`~transformers.EncoderDecoder`.
+- For summarization, sentence splitting, sentence fusion and translation, no special tokens are required for the input.
+  Therefore, no EOS token should be added to the end of the input.
 
 The original code can be found `here <https://tfhub.dev/s?module-type=text-generation&subtype=module,placeholder>`__.
 
 BertGenerationConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.BertGenerationConfig
     :members:
 
 
 BertGenerationTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.BertGenerationTokenizer
-    :members: 
+    :members: save_vocabulary
 
 BertGenerationEncoder
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.BertGenerationEncoder
-    :members:
+    :members: forward
 
 
 BertGenerationDecoder
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.BertGenerationDecoder
-    :members:
+    :members: forward

docs/source/model_doc/camembert.rst

 CamemBERT
-----------------------------------------------------
+-----------------------------------------------------------------------------------------------------------------------
 
 Overview
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 The CamemBERT model was proposed in `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
@@ -22,20 +22,20 @@ pretrained model for CamemBERT hoping to foster research and downstream applicat
 
 Tips:
 
-- This implementation is the same as RoBERTa. Refer to the `documentation of RoBERTa <./roberta.html>`__ for usage
+- This implementation is the same as RoBERTa. Refer to the :doc:`documentation of RoBERTa <roberta>` for usage
   examples as well as the information relative to the inputs and outputs.
 
-The original code can be found `here <https://camembert-model.fr/>`_.
+The original code can be found `here <https://camembert-model.fr/>`__.
 
 CamembertConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.CamembertConfig
     :members:
 
 
 CamembertTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.CamembertTokenizer
     :members: build_inputs_with_special_tokens, get_special_tokens_mask,
@@ -43,91 +43,91 @@ CamembertTokenizer
 
 
 CamembertModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.CamembertModel
     :members:
 
 
 CamembertForCausalLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.CamembertForCausalLM
     :members:
 
 
 CamembertForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.CamembertForMaskedLM
     :members:
 
 
 CamembertForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.CamembertForSequenceClassification
     :members:
 
 
 CamembertForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.CamembertForMultipleChoice
     :members:
 
 
 CamembertForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.CamembertForTokenClassification
     :members:
 
 
 CamembertForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.CamembertForQuestionAnswering
     :members:
 
 
 TFCamembertModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFCamembertModel
     :members:
 
 
 TFCamembertForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFCamembertForMaskedLM
     :members:
 
 
 TFCamembertForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFCamembertForSequenceClassification
     :members:
 
 
 TFCamembertForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFCamembertForMultipleChoice
     :members:
 
 
 TFCamembertForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFCamembertForTokenClassification
     :members:
 
 
 TFCamembertForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFCamembertForQuestionAnswering
     :members:
\ No newline at end of file

docs/source/model_doc/ctrl.rst

 CTRL
-----------------------------------------------------
+-----------------------------------------------------------------------------------------------------------------------
 
 Overview
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-CTRL model was proposed in `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.
-It's a causal (unidirectional) transformer pre-trained using language modeling on a very large
+CTRL model was proposed in `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. It's a causal (unidirectional) transformer pre-trained using language modeling on a very large
 corpus of ~140 GB of text data with the first token reserved as a control code (such as Links, Books, Wikipedia etc.).
 
 The abstract from the paper is the following:
@@ -31,50 +31,50 @@ Tips:
   it can be observed in the `run_generation.py` example script.
 - The PyTorch models can take the `past` as input, which is the previously computed key/value attention pairs. Using
   this `past` value prevents the model from re-computing pre-computed values in the context of text generation.
-  See `reusing the past in generative models <../quickstart.html#using-the-past>`_ for more information on the usage
+  See `reusing the past in generative models <../quickstart.html#using-the-past>`__ for more information on the usage
   of this argument.
 
-The original code can be found `here <https://github.com/salesforce/ctrl>`_.
+The original code can be found `here <https://github.com/salesforce/ctrl>`__.
 
 
 CTRLConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.CTRLConfig
     :members:
 
 
 CTRLTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.CTRLTokenizer
     :members: save_vocabulary
 
 
 CTRLModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.CTRLModel
-    :members:
+    :members: forward
 
 
 CTRLLMHeadModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.CTRLLMHeadModel
-    :members:
+    :members: forward
 
 
 TFCTRLModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFCTRLModel
-    :members:
+    :members: call
 
 
 TFCTRLLMHeadModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFCTRLLMHeadModel
-    :members:
+    :members: call
 

docs/source/model_doc/dialogpt.rst

 DialoGPT
-----------------------------------------------------
+-----------------------------------------------------------------------------------------------------------------------
 
 Overview
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 DialoGPT was proposed in
 `DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation <https://arxiv.org/abs/1911.00536>`_

docs/source/model_doc/distilbert.rst

 DistilBERT
-----------------------------------------------------
+-----------------------------------------------------------------------------------------------------------------------
 
 Overview
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 The DistilBERT model was proposed in the blog post
-`Smaller, faster, cheaper, lighter: Introducing DistilBERT, a distilled version of BERT <https://medium.com/huggingface/distilbert-8cf3380435b5>`__,
-and the paper `DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter <https://arxiv.org/abs/1910.01108>`__.
-DistilBERT is a small, fast, cheap and light Transformer model trained by distilling Bert base. It has 40% less
-parameters than `bert-base-uncased`, runs 60% faster while preserving over 95% of Bert's performances as measured on
+`Smaller, faster, cheaper, lighter: Introducing DistilBERT, a distilled version of BERT
+<https://medium.com/huggingface/distilbert-8cf3380435b5>`__, and the paper `DistilBERT, a distilled version of BERT:
+smaller, faster, cheaper and lighter <https://arxiv.org/abs/1910.01108>`__.
+DistilBERT is a small, fast, cheap and light Transformer model trained by distilling BERT base. It has 40% less
+parameters than `bert-base-uncased`, runs 60% faster while preserving over 95% of BERT's performances as measured on
 the GLUE language understanding benchmark.
 
 The abstract from the paper is the following:
@@ -27,113 +28,115 @@ on-device study.*
 
 Tips:
 
-- DistilBert doesn't have `token_type_ids`, you don't need to indicate which token belongs to which segment. Just separate your segments with the separation token `tokenizer.sep_token` (or `[SEP]`)
-- DistilBert doesn't have options to select the input positions (`position_ids` input). This could be added if necessary though, just let's us know if you need this option.
+- DistilBERT doesn't have :obj:`token_type_ids`, you don't need to indicate which token belongs to which segment. Just
+  separate your segments with the separation token :obj:`tokenizer.sep_token` (or :obj:`[SEP]`).
+- DistilBERT doesn't have options to select the input positions (:obj:`position_ids` input). This could be added if
+  necessary though, just let us know if you need this option.
 
-The original code can be found `here <https://github.com/huggingface/transformers/tree/master/examples/distillation>`_.
+The original code can be found `here <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__.
 
 
 DistilBertConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.DistilBertConfig
     :members:
 
 
 DistilBertTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.DistilBertTokenizer
     :members:
 
 
 DistilBertTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.DistilBertTokenizerFast
     :members:
 
 
 DistilBertModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.DistilBertModel
-    :members:
+    :members: forward
 
 
 DistilBertForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.DistilBertForMaskedLM
-    :members:
+    :members: forward
 
 
 DistilBertForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.DistilBertForSequenceClassification
-    :members:
+    :members: forward
 
 
 DistilBertForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.DistilBertForMultipleChoice
-    :members:
+    :members: forward
 
 
 DistilBertForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.DistilBertForTokenClassification
-    :members:
+    :members: forward
 
 
 DistilBertForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.DistilBertForQuestionAnswering
-    :members:
+    :members: forward
 
 TFDistilBertModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFDistilBertModel
-    :members:
+    :members: call
 
 
 TFDistilBertForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFDistilBertForMaskedLM
-    :members:
+    :members: call
 
 
 TFDistilBertForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFDistilBertForSequenceClassification
-    :members:
+    :members: call
 
 
 
 TFDistilBertForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFDistilBertForMultipleChoice
-    :members:
+    :members: call
 
 
 
 TFDistilBertForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFDistilBertForTokenClassification
-    :members:
+    :members: call
 
 
 TFDistilBertForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFDistilBertForQuestionAnswering
-    :members:
+    :members: call

docs/source/model_doc/dpr.rst

 DPR
-----------------------------------------------------
+-----------------------------------------------------------------------------------------------------------------------
 
 Overview
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Dense Passage Retrieval (DPR) - is a set of tools and models for state-of-the-art open-domain Q&A research.
-It is based on the following paper:
-
-Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih, Dense Passage Retrieval for Open-Domain Question Answering.
+Dense Passage Retrieval (DPR) is a set of tools and models for state-of-the-art open-domain Q&A research.
+It was intorduced in `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, Wen-tau Yih.
 
 The abstract from the paper is the following:
 
@@ -19,58 +18,58 @@ our dense retriever outperforms a strong Lucene-BM25 system largely by 9%-19% ab
 retrieval accuracy, and helps our end-to-end QA system establish new state-of-the-art on multiple open-domain QA
 benchmarks.*
 
-The original code can be found `here <https://github.com/facebookresearch/DPR>`_.
+The original code can be found `here <https://github.com/facebookresearch/DPR>`__.
 
 
 DPRConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.DPRConfig
     :members:
 
 
 DPRContextEncoderTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.DPRContextEncoderTokenizer
     :members:
 
 
 DPRContextEncoderTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.DPRContextEncoderTokenizerFast
     :members:
 
 DPRQuestionEncoderTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.DPRQuestionEncoderTokenizer
     :members:
 
 
 DPRQuestionEncoderTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.DPRQuestionEncoderTokenizerFast
     :members:
 
 DPRReaderTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.DPRReaderTokenizer
     :members:
 
 
 DPRReaderTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.DPRReaderTokenizerFast
     :members:
 
 
 DPR specific outputs
-~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.modeling_dpr.DPRContextEncoderOutput
     :members:
@@ -83,20 +82,20 @@ DPR specific outputs
 
 
 DPRContextEncoder
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.DPRContextEncoder
-    :members:
+    :members: forward
 
 DPRQuestionEncoder
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.DPRQuestionEncoder
-    :members:
+    :members: forward
 
 
 DPRReader
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.DPRReader
-    :members:
+    :members: forward

docs/source/model_doc/electra.rst

 ELECTRA
-----------------------------------------------------
+-----------------------------------------------------------------------------------------------------------------------
 
 Overview
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-The ELECTRA model was proposed in the paper.
-`ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators <https://openreview.net/pdf?id=r1xMH1BtvB>`__.
-ELECTRA is a new pre-training approach which trains two transformer models: the generator and the discriminator. The
-generator's role is to replace tokens in a sequence, and is therefore trained as a masked language model. The discriminator,
-which is the model we're interested in, tries to identify which tokens were replaced by the generator in the sequence.
+The ELECTRA model was proposed in the paper `ELECTRA: Pre-training Text Encoders as Discriminators Rather Than
+Generators <https://openreview.net/pdf?id=r1xMH1BtvB>`__. ELECTRA is a new pretraining approach which trains two
+transformer models: the generator and the discriminator. The generator's role is to replace tokens in a sequence, and
+is therefore trained as a masked language model. The discriminator, which is the model we're interested in, tries to
+identify which tokens were replaced by the generator in the sequence.
 
 The abstract from the paper is the following:
 
@@ -35,44 +35,45 @@ compute and outperforms them when using the same amount of compute.*
 
 Tips:
 
-- ELECTRA is the pre-training approach, therefore there is nearly no changes done to the underlying model: BERT. The
-  only change is the separation of the embedding size and the hidden size -> The embedding size is generally smaller,
+- ELECTRA is the pretraining approach, therefore there is nearly no changes done to the underlying model: BERT. The
+  only change is the separation of the embedding size and the hidden size: the embedding size is generally smaller,
   while the hidden size is larger. An additional projection layer (linear) is used to project the embeddings from
   their embedding size to the hidden size. In the case where the embedding size is the same as the hidden size, no
   projection layer is used.
 - The ELECTRA checkpoints saved using `Google Research's implementation <https://github.com/google-research/electra>`__
   contain both the generator and discriminator. The conversion script requires the user to name which model to export
   into the correct architecture. Once converted to the HuggingFace format, these checkpoints may be loaded into all
-  available ELECTRA models, however. This means that the discriminator may be loaded in the `ElectraForMaskedLM` model,
-  and the generator may be loaded in the `ElectraForPreTraining` model (the classification head will be randomly
-  initialized as it doesn't exist in the generator).
+  available ELECTRA models, however. This means that the discriminator may be loaded in the
+  :class:`~transformers.ElectraForMaskedLM` model, and the generator may be loaded in the
+  :class:`~transformers.ElectraForPreTraining` model (the classification head will be randomly initialized as it
+  doesn't exist in the generator).
 
-The original code can be found `here <https://github.com/google-research/electra>`_.
+The original code can be found `here <https://github.com/google-research/electra>`__.
 
 
 ElectraConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.ElectraConfig
     :members:
 
 
 ElectraTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.ElectraTokenizer
     :members:
 
 
 ElectraTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.ElectraTokenizerFast
     :members:
 
 
 Electra specific outputs
-~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.modeling_electra.ElectraForPreTrainingOutput
     :members:
@@ -82,98 +83,98 @@ Electra specific outputs
 
 
 ElectraModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.ElectraModel
-    :members:
+    :members: forward
 
 
 ElectraForPreTraining
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.ElectraForPreTraining
-    :members:
+    :members: forward
 
 
 ElectraForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.ElectraForMaskedLM
-    :members:
+    :members: forward
 
 
 ElectraForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.ElectraForSequenceClassification
-    :members:
+    :members: forward
 
 
 ElectraForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.ElectraForMultipleChoice
-    :members:
+    :members: forward
 
 
 ElectraForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.ElectraForTokenClassification
-    :members:
+    :members: forward
 
 
 ElectraForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.ElectraForQuestionAnswering
-    :members:
+    :members: forward
 
 
 TFElectraModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFElectraModel
-    :members:
+    :members: call
 
 
 TFElectraForPreTraining
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFElectraForPreTraining
-    :members:
+    :members: call
 
 
 TFElectraForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFElectraForMaskedLM
-    :members:
+    :members: call
 
 
 TFElectraForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFElectraForSequenceClassification
-    :members:
+    :members: call
 
 
 TFElectraForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFElectraForMultipleChoice
-    :members:
+    :members: call
 
 
 TFElectraForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFElectraForTokenClassification
-    :members:
+    :members: call
 
 
 TFElectraForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFElectraForQuestionAnswering
-    :members:
+    :members: call
\ No newline at end of file

docs/source/model_doc/encoderdecoder.rst

 Encoder Decoder Models
-------------------------
+-----------------------------------------------------------------------------------------------------------------------
 
-The :class:`~transformers.EncoderDecoderModel` can be used to initialize a sequence-to-sequence model with any pre-trained autoencoding model as the encoder and any pre-trained autoregressive model as the decoder.
+The :class:`~transformers.EncoderDecoderModel` can be used to initialize a sequence-to-sequence model with any
+pretrained autoencoding model as the encoder and any pretrained autoregressive model as the decoder.
 
-The effectiveness of initializing sequence-to-sequence models with pre-trained checkpoints for sequence generation tasks was shown in `Leveraging Pre-trained Checkpoints for Sequence Generation Tasks <https://arxiv.org/abs/1907.12461>`__ by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
+The effectiveness of initializing sequence-to-sequence models with pretrained checkpoints for sequence generation tasks
+was shown in `Leveraging Pre-trained Checkpoints for Sequence Generation Tasks <https://arxiv.org/abs/1907.12461>`__ by
+Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
 
-After such an :class:`~transformers.EncoderDecoderModel` has been trained / fine-tuned, it can be saved / loaded just like any other models (see Examples for more information).
+After such an :class:`~transformers.EncoderDecoderModel` has been trained/fine-tuned, it can be saved/loaded just like
+any other models (see the examples for more information).
 
-An application of this architecture could be to leverage two pre-trained :obj:`transformers.BertModel` models as the encoder and decoder for a summarization model as was shown in: `Text Summarization with Pretrained Encoders <https://arxiv.org/abs/1908.08345>`_ by Yang Liu and Mirella Lapata. 
+An application of this architecture could be to leverage two pretrained :class:`~transformers.BertModel` as the encoder
+and decoder for a summarization model as was shown in: `Text Summarization with Pretrained Encoders
+<https://arxiv.org/abs/1908.08345>`__ by Yang Liu and Mirella Lapata. 
 
 
-``EncoderDecoderConfig``
-~~~~~~~~~~~~~~~~~~~~~~~~~
+EncoderDecoderConfig
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.EncoderDecoderConfig
     :members:
 
 
-``EncoderDecoderModel``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+EncoderDecoderModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.EncoderDecoderModel
-    :members:
+    :members: forward

docs/source/model_doc/flaubert.rst

 FlauBERT
-----------------------------------------------------
+-----------------------------------------------------------------------------------------------------------------------
 
 Overview
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-The FlauBERT model was proposed in the paper
-`FlauBERT: Unsupervised Language Model Pre-training for French <https://arxiv.org/abs/1912.05372>`__ by Hang Le et al.
-It's a transformer pre-trained using a masked language modeling (MLM) objective (BERT-like).
+The FlauBERT model was proposed in the paper `FlauBERT: Unsupervised Language Model Pre-training for French
+<https://arxiv.org/abs/1912.05372>`__ by Hang Le et al. It's a transformer model pretrained using a masked language
+modeling (MLM) objective (like BERT).
 
 The abstract from the paper is the following:
 
@@ -23,109 +23,109 @@ of the time they outperform other pre-training approaches. Different versions of
 evaluation protocol for the downstream tasks, called FLUE (French Language Understanding Evaluation), are shared
 to the research community for further reproducible experiments in French NLP.*
 
-The original code can be found `here <https://github.com/getalp/Flaubert>`_.
+The original code can be found `here <https://github.com/getalp/Flaubert>`__.
 
 
 FlaubertConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.FlaubertConfig
     :members:
 
 
 FlaubertTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.FlaubertTokenizer
     :members:
 
 
 FlaubertModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.FlaubertModel
-    :members:
+    :members: forward
 
 
 FlaubertWithLMHeadModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.FlaubertWithLMHeadModel
-    :members:
+    :members: forward
 
 
 FlaubertForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.FlaubertForSequenceClassification
-    :members:
+    :members: forward
 
 
 FlaubertForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.FlaubertForMultipleChoice
-    :members:
+    :members: forward
 
 
 FlaubertForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.FlaubertForTokenClassification
-    :members:
+    :members: forward
 
 
 FlaubertForQuestionAnsweringSimple
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.FlaubertForQuestionAnsweringSimple
-    :members:
+    :members: forward
 
 
 FlaubertForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.FlaubertForQuestionAnswering
-    :members:
+    :members: forward
 
 
 TFFlaubertModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFFlaubertModel
-    :members:
+    :members: call
 
 
 TFFlaubertWithLMHeadModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFFlaubertWithLMHeadModel
-    :members:
+    :members: call
 
 
 TFFlaubertForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFFlaubertForSequenceClassification
-    :members:
+    :members: call
 
 
 TFFlaubertForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFFlaubertForMultipleChoice
-    :members:
+    :members: call
 
 
 TFFlaubertForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFFlaubertForTokenClassification
-    :members:
+    :members: call
 
 
 TFFlaubertForQuestionAnsweringSimple
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFFlaubertForQuestionAnsweringSimple
-    :members:
+    :members: call

docs/source/model_doc/fsmt.rst

 FSMT
-----------------------------------------------------
-**DISCLAIMER:** If you see something strange,
-file a `Github Issue <https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title>`__ and assign
+-----------------------------------------------------------------------------------------------------------------------
+
+**DISCLAIMER:** If you see something strange, file a `Github Issue
+<https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title>`__ and assign
 @stas00.
 
 Overview
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-FSMT (FairSeq MachineTranslation) models were introduced in "Facebook FAIR's WMT19 News Translation Task Submission" <this paper <https://arxiv.org/abs/1907.06616>__ by Nathan Ng, Kyra Yee, Alexei Baevski, Myle Ott, Michael Auli, Sergey Edunov.
+FSMT (FairSeq MachineTranslation) models were introduced in `Facebook FAIR's WMT19 News Translation Task Submission
+<https://arxiv.org/abs/1907.06616>`__ by Nathan Ng, Kyra Yee, Alexei Baevski, Myle Ott, Michael Auli, Sergey Edunov.
 
 The abstract of the paper is the following:
 
-    This paper describes Facebook FAIR's submission to the WMT19 shared news translation task. We participate in two language pairs and four language directions, English <-> German and English <-> Russian. Following our submission from last year, our baseline systems are large BPE-based transformer models trained with the Fairseq sequence modeling toolkit which rely on sampled back-translations. This year we experiment with different bitext data filtering schemes, as well as with adding filtered back-translated data. We also ensemble and fine-tune our models on domain-specific data, then decode using noisy channel model reranking. Our submissions are ranked first in all four directions of the human evaluation campaign. On En->De, our system significantly outperforms other systems as well as human translations. This system improves upon our WMT'18 submission by 4.5 BLEU points.
+*This paper describes Facebook FAIR's submission to the WMT19 shared news translation task. We participate in two
+language pairs and four language directions, English <-> German and English <-> Russian. Following our submission from
+last year, our baseline systems are large BPE-based transformer models trained with the Fairseq sequence modeling
+toolkit which rely on sampled back-translations. This year we experiment with different bitext data filtering schemes,
+as well as with adding filtered back-translated data. We also ensemble and fine-tune our models on domain-specific
+data, then decode using noisy channel model reranking. Our submissions are ranked first in all four directions of the
+human evaluation campaign. On En->De, our system significantly outperforms other systems as well as human translations.
+This system improves upon our WMT'18 submission by 4.5 BLEU points.*
 
 The original code can be found here <https://github.com/pytorch/fairseq/tree/master/examples/wmt19>__.
 
 Implementation Notes
-~~~~~~~~~~~~~~~~~~~~
-
-- FSMT uses source and target vocab pair, that aren't combined into one. It doesn't share embed tokens either. Its tokenizer is very similar to `XLMTokenizer` and the main model is derived from `BartModel`.
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-
-FSMTForConditionalGeneration
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FSMTForConditionalGeneration
-    :members: forward
+- FSMT uses source and target vocabulary pairs that aren't combined into one. It doesn't share embeddings tokens
+  either. Its tokenizer is very similar to :class:`~transformers.XLMTokenizer` and the main model is derived from
+  :class:`~transformers.BartModel`.
 
 
 FSMTConfig
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.FSMTConfig
     :members:
 
 
 FSMTTokenizer
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.FSMTTokenizer
-    :members:
+    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
+        create_token_type_ids_from_sequences, prepare_seq2seq_batch, save_vocabulary
 
 
 FSMTModel
-~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.FSMTModel
     :members: forward
+
+
+FSMTForConditionalGeneration
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FSMTForConditionalGeneration
+    :members: forward
\ No newline at end of file

docs/source/model_doc/funnel.rst

 Funnel Transformer
-------------------
+-----------------------------------------------------------------------------------------------------------------------
 
 Overview
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-The Funnel Transformer model was proposed in the paper
-`Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing
-<https://arxiv.org/abs/2006.03236>`__.
-It is a bidirectional transformer model, like BERT, but with a pooling operation after each block of layers, a bit
-like in traditional convolutional neural networks (CNN) in computer vision.
+The Funnel Transformer model was proposed in the paper `Funnel-Transformer: Filtering out Sequential Redundancy for
+Efficient Language Processing <https://arxiv.org/abs/2006.03236>`__. It is a bidirectional transformer model, like
+BERT, but with a pooling operation after each block of layers, a bit like in traditional convolutional neural networks
+(CNN) in computer vision.
 
 The abstract from the paper is the following:
 
@@ -38,18 +37,18 @@ Tips:
   :class:`~transformers.FunnelBaseModel`, :class:`~transformers.FunnelForSequenceClassification` and
   :class:`~transformers.FunnelForMultipleChoice`.
 
-The original code can be found `here <https://github.com/laiguokun/Funnel-Transformer>`_.
+The original code can be found `here <https://github.com/laiguokun/Funnel-Transformer>`__.
 
 
 FunnelConfig
-~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.FunnelConfig
     :members:
 
 
 FunnelTokenizer
-~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.FunnelTokenizer
     :members: build_inputs_with_special_tokens, get_special_tokens_mask,
@@ -57,14 +56,14 @@ FunnelTokenizer
 
 
 FunnelTokenizerFast
-~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.FunnelTokenizerFast
     :members:
 
 
 Funnel specific outputs
-~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.modeling_funnel.FunnelForPreTrainingOutput
     :members:
@@ -74,112 +73,112 @@ Funnel specific outputs
 
 
 FunnelBaseModel
-~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.FunnelBaseModel
-    :members:
+    :members: forward
 
 
 FunnelModel
-~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.FunnelModel
-    :members:
+    :members: forward
 
 
 FunnelModelForPreTraining
-~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.FunnelForPreTraining
-    :members:
+    :members: forward
 
 
 FunnelForMaskedLM
-~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.FunnelForMaskedLM
-    :members:
+    :members: forward
 
 
 FunnelForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.FunnelForSequenceClassification
-    :members:
+    :members: forward
 
 
 FunnelForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.FunnelForMultipleChoice
-    :members:
+    :members: forward
 
 
 FunnelForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.FunnelForTokenClassification
-    :members:
+    :members: forward
 
 
 FunnelForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.FunnelForQuestionAnswering
-    :members:
+    :members: forward
 
 
 TFFunnelBaseModel
-~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFFunnelBaseModel
-    :members:
+    :members: call
 
 
 TFFunnelModel
-~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFFunnelModel
-    :members:
+    :members: call
 
 
 TFFunnelModelForPreTraining
-~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFFunnelForPreTraining
-    :members:
+    :members: call
 
 
 TFFunnelForMaskedLM
-~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFFunnelForMaskedLM
-    :members:
+    :members: call
 
 
 TFFunnelForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFFunnelForSequenceClassification
-    :members:
+    :members: call
 
 
 TFFunnelForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFFunnelForMultipleChoice
-    :members:
+    :members: call
 
 
 TFFunnelForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFFunnelForTokenClassification
-    :members:
+    :members: call
 
 
 TFFunnelForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFFunnelForQuestionAnswering
-    :members:
+    :members: call
\ No newline at end of file

docs/source/model_doc/gpt.rst

 OpenAI GPT
-----------------------------------------------------
+-----------------------------------------------------------------------------------------------------------------------
 
 Overview
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-OpenAI GPT model was proposed in `Improving Language Understanding by Generative Pre-Training <https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf>`__
+OpenAI GPT model was proposed in `Improving Language Understanding by Generative Pre-Training
+<https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf>`__
 by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever. It's a causal (unidirectional)
-transformer pre-trained using language modeling on a large corpus will long range dependencies, the Toronto Book Corpus.
+transformer pre-trained using language modeling on a large corpus will long range dependencies, the Toronto Book
+Corpus.
 
 The abstract from the paper is the following:
 
@@ -36,7 +38,7 @@ Tips:
 `Write With Transformer <https://transformer.huggingface.co/doc/gpt>`__ is a webapp created and hosted by
 Hugging Face showcasing the generative capabilities of several models. GPT is one of them.
 
-The original code can be found `here <https://github.com/openai/finetune-transformer-lm>`_.
+The original code can be found `here <https://github.com/openai/finetune-transformer-lm>`__.
 
 Note:
 
@@ -46,33 +48,33 @@ If you want to reproduce the original tokenization process of the `OpenAI GPT` p
     pip install spacy ftfy==4.4.3
     python -m spacy download en
 
-If you don't install ``ftfy`` and ``SpaCy``, the :class:`transformers.OpenAIGPTTokenizer` will default to tokenize using 
-BERT's :obj:`BasicTokenizer` followed by Byte-Pair Encoding (which should be fine for most usage, don't 
+If you don't install ``ftfy`` and ``SpaCy``, the :class:`~transformers.OpenAIGPTTokenizer` will default to tokenize
+using BERT's :obj:`BasicTokenizer` followed by Byte-Pair Encoding (which should be fine for most usage, don't 
 worry).
 
 OpenAIGPTConfig
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.OpenAIGPTConfig
     :members:
 
 
 OpenAIGPTTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.OpenAIGPTTokenizer
     :members: save_vocabulary
 
 
 OpenAIGPTTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.OpenAIGPTTokenizerFast
     :members:
 
 
 OpenAI specific outputs
-~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.modeling_openai.OpenAIGPTDoubleHeadsModelOutput
     :members:
@@ -82,42 +84,42 @@ OpenAI specific outputs
 
 
 OpenAIGPTModel
-~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.OpenAIGPTModel
-    :members:
+    :members: forward
 
 
 OpenAIGPTLMHeadModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.OpenAIGPTLMHeadModel
-    :members:
+    :members: forward
 
 
 OpenAIGPTDoubleHeadsModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.OpenAIGPTDoubleHeadsModel
-    :members:
+    :members: forward
 
 
 TFOpenAIGPTModel
-~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFOpenAIGPTModel
-    :members:
+    :members: call
 
 
 TFOpenAIGPTLMHeadModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFOpenAIGPTLMHeadModel
-    :members:
+    :members: call
 
 
 TFOpenAIGPTDoubleHeadsModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFOpenAIGPTDoubleHeadsModel
-    :members:
+    :members: call

docs/source/model_doc/gpt2.rst

 OpenAI GPT2
-----------------------------------------------------
+-----------------------------------------------------------------------------------------------------------------------
 
 Overview
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-OpenAI GPT-2 model was proposed in
-`Language Models are Unsupervised Multitask Learners <https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf>`_
-by Alec Radford*, Jeffrey Wu*, Rewon Child, David Luan, Dario Amodei** and Ilya Sutskever**.
-It's a causal (unidirectional) transformer pre-trained using  language modeling on a very large
-corpus of ~40 GB of text data.
+OpenAI GPT-2 model was proposed in `Language Models are Unsupervised Multitask Learners
+<https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf>`_
+by Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei and Ilya Sutskever. It's a causal (unidirectional)
+transformer pretrained using  language modeling on a very large corpus of ~40 GB of text data.
 
 The abstract from the paper is the following:
 
@@ -27,39 +26,39 @@ Tips:
   it can be observed in the `run_generation.py` example script.
 - The PyTorch models can take the `past` as input, which is the previously computed key/value attention pairs. Using
   this `past` value prevents the model from re-computing pre-computed values in the context of text generation.
-  See `reusing the past in generative models <../quickstart.html#using-the-past>`_ for more information on the usage
+  See `reusing the past in generative models <../quickstart.html#using-the-past>`__ for more information on the usage
   of this argument.
 
 `Write With Transformer <https://transformer.huggingface.co/doc/gpt2-large>`__ is a webapp created and hosted by
 Hugging Face showcasing the generative capabilities of several models. GPT-2 is one of them and is available in five
-different sizes: small, medium, large, xl and a distilled version of the small checkpoint: distilgpt-2.
+different sizes: small, medium, large, xl and a distilled version of the small checkpoint: `distilgpt-2`.
 
-The original code can be found `here <https://openai.com/blog/better-language-models/>`_.
+The original code can be found `here <https://openai.com/blog/better-language-models/>`__.
 
 
 GPT2Config
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.GPT2Config
     :members:
 
 
 GPT2Tokenizer
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.GPT2Tokenizer
     :members: save_vocabulary
 
 
 GPT2TokenizerFast
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.GPT2TokenizerFast
     :members:
 
 
 GPT2 specific outputs
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.modeling_gpt2.GPT2DoubleHeadsModelOutput
     :members:
@@ -69,42 +68,42 @@ GPT2 specific outputs
 
 
 GPT2Model
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.GPT2Model
-    :members:
+    :members: forward
 
 
 GPT2LMHeadModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.GPT2LMHeadModel
-    :members:
+    :members: forward
 
 
 GPT2DoubleHeadsModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.GPT2DoubleHeadsModel
-    :members:
+    :members: forward
 
 
 TFGPT2Model
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFGPT2Model
-    :members:
+    :members: call
 
 
 TFGPT2LMHeadModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFGPT2LMHeadModel
-    :members:
+    :members: call
 
 
 TFGPT2DoubleHeadsModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFGPT2DoubleHeadsModel
-    :members:
+    :members: call

docs/source/model_doc/longformer.rst

 Longformer
-----------------------------------------------------
-**DISCLAIMER:** This model is still a work in progress, if you see something strange,
-file a `Github Issue <https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title>`_
+-----------------------------------------------------------------------------------------------------------------------
+
+**DISCLAIMER:** This model is still a work in progress, if you see something strange, file a `Github Issue
+<https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title>`__.
 
 Overview
-~~~~~~~~~
-The Longformer model was presented in `Longformer: The Long-Document Transformer <https://arxiv.org/pdf/2004.05150.pdf>`_ by Iz Beltagy, Matthew E. Peters, Arman Cohan.
-Here the abstract: 
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The Longformer model was presented in `Longformer: The Long-Document Transformer
+<https://arxiv.org/pdf/2004.05150.pdf>`__ by Iz Beltagy, Matthew E. Peters, Arman Cohan.
 
-*Transformer-based models are unable to process long sequences due to their self-attention operation, which scales quadratically with the sequence length. To address this limitation, we introduce the Longformer with an attention mechanism that scales linearly with sequence length, making it easy to process documents of thousands of tokens or longer. Longformer's attention mechanism is a drop-in replacement for the standard self-attention and combines a local windowed attention with a task motivated global attention. Following prior work on long-sequence transformers, we evaluate Longformer on character-level language modeling and achieve state-of-the-art results on text8 and enwik8. In contrast to most prior work, we also pretrain Longformer and finetune it on a variety of downstream tasks. Our pretrained Longformer consistently outperforms RoBERTa on long document tasks and sets new state-of-the-art results on WikiHop and TriviaQA.*
+The abstract from the paper is the following:
 
-The Authors' code can be found `here <https://github.com/allenai/longformer>`_ .
+*Transformer-based models are unable to process long sequences due to their self-attention operation, which scales
+quadratically with the sequence length. To address this limitation, we introduce the Longformer with an attention
+mechanism that scales linearly with sequence length, making it easy to process documents of thousands of tokens or
+longer. Longformer's attention mechanism is a drop-in replacement for the standard self-attention and combines a local
+windowed attention with a task motivated global attention. Following prior work on long-sequence transformers, we
+evaluate Longformer on character-level language modeling and achieve state-of-the-art results on text8 and enwik8. In
+contrast to most prior work, we also pretrain Longformer and finetune it on a variety of downstream tasks. Our
+pretrained Longformer consistently outperforms RoBERTa on long document tasks and sets new state-of-the-art results on
+WikiHop and TriviaQA.*
+
+The Authors' code can be found `here <https://github.com/allenai/longformer>`__.
 
 Longformer Self Attention
-~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
 Longformer self attention employs self attention on both a "local" context and a "global" context.
-Most tokens only attend "locally" to each other meaning that each token attends to its :math:`\frac{1}{2} w` previous tokens and :math:`\frac{1}{2} w` succeding tokens with :math:`w` being the window length as defined in `config.attention_window`. Note that `config.attention_window` can be of type ``list`` to define a different :math:`w` for each layer. 
-A selected few tokens attend "globally" to all other tokens, as it is conventionally done for all tokens in *e.g.* `BertSelfAttention`.
+Most tokens only attend "locally" to each other meaning that each token attends to its :math:`\frac{1}{2} w` previous
+tokens and :math:`\frac{1}{2} w` succeding tokens with :math:`w` being the window length as defined in
+:obj:`config.attention_window`. Note that :obj:`config.attention_window` can be of type :obj:`List` to define a
+different :math:`w` for each layer. A selected few tokens attend "globally" to all other tokens, as it is
+conventionally done for all tokens in :obj:`BertSelfAttention`.
 
 Note that "locally" and "globally" attending tokens are projected by different query, key and value matrices.
-Also note that every "locally" attending token not only attends to tokens within its window :math:`w`, but also to all "globally" attending tokens so that global attention is *symmetric*.
+Also note that every "locally" attending token not only attends to tokens within its window :math:`w`, but also to all
+"globally" attending tokens so that global attention is *symmetric*.
 
-The user can define which tokens attend "locally" and which tokens attend "globally" by setting the tensor `global_attention_mask` at run-time appropriately. `Longformer` employs the following logic for `global_attention_mask`: `0` - the token attends "locally", `1` - token attends "globally". For more information please also refer to :func:`~transformers.LongformerModel.forward` method.
+The user can define which tokens attend "locally" and which tokens attend "globally" by setting the tensor
+:obj:`global_attention_mask` at run-time appropriately. All Longformer models employ the following logic for
+:obj:`global_attention_mask`: 
 
-Using Longformer self attention, the memory and time complexity of the query-key matmul operation, which usually represents the memory and time bottleneck, can be reduced from :math:`\mathcal{O}(n_s \times n_s)` to :math:`\mathcal{O}(n_s \times w)`, with :math:`n_s` being the sequence length and :math:`w` being the average window size. It is assumed that the number of "globally" attending tokens is insignificant as compared to the number of "locally" attending tokens.
+- 0: the token attends "locally",
+- 1: the token attends "globally".
 
-For more information, please refer to the official `paper <https://arxiv.org/pdf/2004.05150.pdf>`_ .
+For more information please also refer to :meth:`~transformers.LongformerModel.forward` method.
+
+Using Longformer self attention, the memory and time complexity of the query-key matmul operation, which usually
+represents the memory and time bottleneck, can be reduced from :math:`\mathcal{O}(n_s \times n_s)` to
+:math:`\mathcal{O}(n_s \times w)`, with :math:`n_s` being the sequence length and :math:`w` being the average window
+size. It is assumed that the number of "globally" attending tokens is insignificant as compared to the number of
+"locally" attending tokens.
+
+For more information, please refer to the official `paper <https://arxiv.org/pdf/2004.05150.pdf>`__.
 
 
 Training
-~~~~~~~~~~~~~~~~~~~~
-``LongformerForMaskedLM`` is trained the exact same way, ``RobertaForMaskedLM`` is trained and 
-should be used as follows:
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-::
+:class:`~transformers.LongformerForMaskedLM` is trained the exact same way :class:`~transformers.RobertaForMaskedLM` is
+trained and should be used as follows:
+
+.. code-block::
 
     input_ids = tokenizer.encode('This is a sentence from [MASK] training data', return_tensors='pt')
     mlm_labels = tokenizer.encode('This is a sentence from the training data', return_tensors='pt')
@@ -42,85 +71,85 @@ should be used as follows:
 
 
 LongformerConfig
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.LongformerConfig
     :members:
 
 
 LongformerTokenizer
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.LongformerTokenizer
     :members: 
 
 
 LongformerTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.LongformerTokenizerFast
     :members: 
 
 
 LongformerModel
-~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.LongformerModel
-    :members:
+    :members: forward
 
 
 LongformerForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.LongformerForMaskedLM
-    :members:
+    :members: forward
 
 
 LongformerForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.LongformerForSequenceClassification
-    :members:
+    :members: forward
 
 
 LongformerForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.LongformerForMultipleChoice
-    :members:
+    :members: forward
 
 
 LongformerForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.LongformerForTokenClassification
-    :members:
+    :members: forward
 
 
 LongformerForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.LongformerForQuestionAnswering
-    :members:
+    :members: forward
 
 
 TFLongformerModel
-~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFLongformerModel
-    :members:
+    :members: call
 
 
 TFLongformerForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFLongformerForMaskedLM
-    :members:
+    :members: call
 
 
 TFLongformerForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFLongformerForQuestionAnswering
-    :members:
+    :members: call
 

docs/source/model_doc/lxmert.rst

 LXMERT
-----------------------------------------------------
+-----------------------------------------------------------------------------------------------------------------------
 
 Overview
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-The LXMERT model was proposed in `LXMERT: Learning Cross-Modality Encoder Representations from Transformers <https://arxiv.org/abs/1908.07490>`__
-by Hao Tan & Mohit Bansal. It is a series of bidirectional transformer encoders (one for the vision modality, one for the language modality, and then one to fuse both modalities)
-pre-trained using a combination of masked language modeling, visual-language text alignment, ROI-feature regression, masked visual-attribute modeling, masked visual-object modeling, and visual-question answering objectives.
-The pretraining consists of multiple multi-modal datasets: MSCOCO, Visual-Genome + Visual-Genome Question Answering, VQA 2.0, and GQA.
+The LXMERT model was proposed in `LXMERT: Learning Cross-Modality Encoder Representations from Transformers
+<https://arxiv.org/abs/1908.07490>`__ by Hao Tan & Mohit Bansal. It is a series of bidirectional transformer encoders
+(one for the vision modality, one for the language modality, and then one to fuse both modalities) pretrained using a
+combination of masked language modeling, visual-language text alignment, ROI-feature regression, masked
+visual-attribute modeling, masked visual-object modeling, and visual-question answering objectives.
+The pretraining consists of multiple multi-modal datasets: MSCOCO, Visual-Genome + Visual-Genome Question Answering,
+VQA 2.0, and GQA.
 
 The abstract from the paper is the following:
 
-*Vision-and-language reasoning requires an understanding of visual concepts, language semantics, and, most importantly, the alignment and relationships between these two
-modalities. We thus propose the LXMERT
-(Learning Cross-Modality Encoder Representations from Transformers) framework to learn
-these vision-and-language connections. In
-LXMERT, we build a large-scale Transformer
-model that consists of three encoders: an object relationship encoder, a language encoder,
-and a cross-modality encoder. Next, to endow our model with the capability of connecting vision and language semantics, we
-pre-train the model with large amounts of
-image-and-sentence pairs, via five diverse representative pre-training tasks: masked language modeling, masked object prediction
-(feature regression and label classification),
-cross-modality matching, and image question answering. These tasks help in learning both intra-modality and cross-modality relationships. After fine-tuning from our pretrained parameters, our model achieves the
-state-of-the-art results on two visual question answering datasets (i.e., VQA and GQA).
-We also show the generalizability of our pretrained cross-modality model by adapting it to
-a challenging visual-reasoning task, NLVR
-,
-and improve the previous best result by 22%
-absolute (54% to 76%). Lastly, we demonstrate detailed ablation studies to prove that
-both our novel model components and pretraining strategies significantly contribute to
-our strong results; and also present several
+*Vision-and-language reasoning requires an understanding of visual concepts, language semantics, and, most importantly,
+the alignment and relationships between these two modalities. We thus propose the LXMERT (Learning Cross-Modality
+Encoder Representations from Transformers) framework to learn these vision-and-language connections. In LXMERT, we
+build a large-scale Transformer model that consists of three encoders: an object relationship encoder, a language
+encoder, and a cross-modality encoder. Next, to endow our model with the capability of connecting vision and language
+semantics, we pre-train the model with large amounts of image-and-sentence pairs, via five diverse representative
+pre-training tasks: masked language modeling, masked object prediction (feature regression and label classification),
+cross-modality matching, and image question answering. These tasks help in learning both intra-modality and
+cross-modality relationships. After fine-tuning from our pretrained parameters, our model achieves the state-of-the-art
+results on two visual question answering datasets (i.e., VQA and GQA). We also show the generalizability of our
+pretrained cross-modality model by adapting it to a challenging visual-reasoning task, NLVR, and improve the previous
+best result by 22% absolute (54% to 76%). Lastly, we demonstrate detailed ablation studies to prove that both our novel
+model components and pretraining strategies significantly contribute to our strong results; and also present several
 attention visualizations for the different encoders*
 
 Tips:
 
-- Bounding boxes are not necessary to be used in the visual feature embeddings, any kind of visual-spacial features will work.
-- Both the language hidden states and the visual hidden states that LXMERT outputs are passed through the cross-modality layer, so they
-  contain information from both modalities. To access a modality that only attends to itself, select the vision/language hidden states from the first input in the tuple.
-- The bi-directional cross-modality encoder attention only returns attention values when the language modality is used as the input and the vision modality is used as the context vector. Further,
-  while the cross-modality encoder contains self-attention for each respective modality and cross-attention, only the cross attention is returned and both self attention outputs are disregarded.
+- Bounding boxes are not necessary to be used in the visual feature embeddings, any kind of visual-spacial features
+  will work.
+- Both the language hidden states and the visual hidden states that LXMERT outputs are passed through the
+  cross-modality layer, so they contain information from both modalities. To access a modality that only attends to
+  itself, select the vision/language hidden states from the first input in the tuple.
+- The bidirectional cross-modality encoder attention only returns attention values when the language modality is used
+  as the input and the vision modality is used as the context vector. Further, while the cross-modality encoder
+  contains self-attention for each respective modality and cross-attention, only the cross attention is returned and
+  both self attention outputs are disregarded.
 
-The code can be found `here <https://github.com/airsplay/lxmert>`__
+The original code can be found `here <https://github.com/airsplay/lxmert>`__.
 
 
 LxmertConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.LxmertConfig
     :members:
 
 
 LxmertTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.LxmertTokenizer
-    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
-        create_token_type_ids_from_sequences, save_vocabulary
+    :members:
+
+
+LxmertTokenizerFast
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.LxmertTokenizerFast
+    :members:
 
 
 Lxmert specific outputs
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.modeling_lxmert.LxmertModelOutput
     :members:
@@ -78,32 +85,32 @@ Lxmert specific outputs
 
 
 LxmertModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.LxmertModel
-    :members:
+    :members: forward
 
 LxmertForPreTraining
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.LxmertForPreTraining
-    :members:
+    :members: forward
 
 LxmertForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.LxmertForQuestionAnswering
-    :members:
+    :members: forward
 
 
 TFLxmertModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFLxmertModel
-    :members:
+    :members: call
 
 TFLxmertForPreTraining
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFLxmertForPreTraining
-    :members:
+    :members: call

docs/source/model_doc/marian.rst

 MarianMT
-----------------------------------------------------
+-----------------------------------------------------------------------------------------------------------------------
 **Bugs:** If you see something strange,
 file a `Github Issue <https://github.com/huggingface/transformers/issues/new?assignees=sshleifer&labels=&template=bug-report.md&title>`__ and assign
 @sshleifer. Translations should be similar, but not identical to, output in the test set linked to in each model card.
 
 Implementation Notes
-~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 - Each model is about 298 MB on disk, there are 1,000+ models.
 - The list of supported language pairs can be found `here <https://huggingface.co/Helsinki-NLP>`__.
 - models were originally trained by `Jörg Tiedemann <https://researchportal.helsinki.fi/en/persons/j%C3%B6rg-tiedemann>`__ using the `Marian <https://marian-nmt.github.io/>`_ C++ library, which supports fast training and translation.
@@ -19,14 +19,14 @@ Implementation Notes
 - Code to bulk convert models can be found in ``convert_marian_to_pytorch.py``
 
 Naming
-~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 - All  model names use the following format: ``Helsinki-NLP/opus-mt-{src}-{tgt}``
 - The language codes used to name models are inconsistent. Two digit codes can usually be found `here <https://developers.google.com/admin-sdk/directory/v1/languages>`_, three digit codes require googling "language code {code}".
 - Codes formatted like ``es_AR`` are usually ``code_{region}``. That one is spanish documents from Argentina.
 
 
 Multilingual Models
-~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 All  model names use the following format: ``Helsinki-NLP/opus-mt-{src}-{tgt}``:
     - if ``src`` is in all caps, the model supports multiple input languages, you can figure out which ones by looking at the model card, or the Group Members `mapping <https://gist.github.com/sshleifer/6d20e7761931b08e73c3219027b97b8a>`_ .
@@ -87,7 +87,7 @@ Code to see available pretrained models:
     multi_models = [f'{org}/{s}' for s in suffix if s != s.lower()]
 
 MarianMTModel
-~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Pytorch version of marian-nmt's transformer.h (c++). Designed for the OPUS-NMT translation checkpoints.
 Model API is identical to BartForConditionalGeneration.
@@ -95,13 +95,13 @@ Available models are listed at `Model List <https://huggingface.co/models?search
 This class inherits nearly all functionality from ``BartForConditionalGeneration``, see that page for method signatures.
 
 MarianConfig
-~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 .. autoclass:: transformers.MarianConfig
     :members:
 
 
 MarianTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.MarianTokenizer
     :members: prepare_seq2seq_batch

docs/source/model_doc/mbart.rst

 MBart
-----------------------------------------------------
+-----------------------------------------------------------------------------------------------------------------------
 **DISCLAIMER:** If you see something strange,
 file a `Github Issue <https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title>`__ and assign
 @sshleifer
 
 Overview
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 The MBart model was presented in `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. According to the abstract,
 
@@ -15,7 +15,7 @@ The Authors' code can be found `here <https://github.com/pytorch/fairseq/tree/ma
 
 
 Training
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 MBart is a multilingual encoder-decoder (seq-to-seq) model primarily intended for translation task. 
 As the model is multilingual it expects the sequences in a different format. A special language id token 
 is added in both the source and target text. The source text format is ``X [eos, src_lang_code]`` 
@@ -25,7 +25,7 @@ the sequences for seq-2-seq fine-tuning.
 
 - Supervised training
 
-::
+.. code-block::
 
     example_english_phrase = "UN Chief Says There Is No Military Solution in Syria"
     expected_translation_romanian = "Şeful ONU declară că nu există o soluţie militară în Siria"
@@ -41,7 +41,7 @@ the sequences for seq-2-seq fine-tuning.
     While generating the target text set the `decoder_start_token_id` to the target language id. 
     The following example shows how to translate English to Romanian using the ```facebook/mbart-large-en-ro``` model.
 
-::
+.. code-block::
 
     from transformers import MBartForConditionalGeneration, MBartTokenizer
     model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-en-ro")
@@ -54,21 +54,21 @@ the sequences for seq-2-seq fine-tuning.
 
 
 MBartConfig
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.MBartConfig
     :members:
 
 
 MBartTokenizer
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.MBartTokenizer
     :members: build_inputs_with_special_tokens, prepare_seq2seq_batch
 
 
 MBartForConditionalGeneration
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.MBartForConditionalGeneration
     :members: generate, forward

docs/source/model_doc/mobilebert.rst

 MobileBERT
-----------------------------------------------------
+-----------------------------------------------------------------------------------------------------------------------
 
 Overview
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-The MobileBERT model was proposed in `MobileBERT: a Compact Task-Agnostic BERT
-for Resource-Limited Devices <https://arxiv.org/abs/2004.02984>`__
-by Zhiqing Sun, Hongkun Yu, Xiaodan Song, Renjie Liu, Yiming Yang, and Denny Zhou. It's a bidirectional transformer
-based on the BERT model, which is compressed and accelerated using several approaches.
+The MobileBERT model was proposed in `MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices
+<https://arxiv.org/abs/2004.02984>`__ by Zhiqing Sun, Hongkun Yu, Xiaodan Song, Renjie Liu, Yiming Yang, and Denny
+Zhou. It's a bidirectional transformer based on the BERT model, which is compressed and accelerated using several
+approaches.
 
 The abstract from the paper is the following:
 
@@ -32,32 +32,31 @@ Tips:
   It is therefore efficient at predicting masked tokens and at NLU in general, but is not optimal for
   text generation. Models trained with a causal language modeling (CLM) objective are better in that regard.
 
-The original code can be found `here <https://github.com/google-research/mobilebert>`_.
+The original code can be found `here <https://github.com/google-research/mobilebert>`__.
 
 MobileBertConfig
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.MobileBertConfig
     :members:
 
 
 MobileBertTokenizer
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.MobileBertTokenizer
-    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
-        create_token_type_ids_from_sequences, save_vocabulary
+    :members:
 
 
 MobileBertTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.MobileBertTokenizerFast
     :members:
 
 
 MobileBert specific outputs
-~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.modeling_mobilebert.MobileBertForPreTrainingOutput
     :members:
@@ -67,113 +66,112 @@ MobileBert specific outputs
 
 
 MobileBertModel
-~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.MobileBertModel
-    :members:
+    :members: forward
 
 
 MobileBertForPreTraining
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.MobileBertForPreTraining
-    :members:
+    :members: forward
 
 
 MobileBertForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.MobileBertForMaskedLM
-    :members:
+    :members: forward
 
 
 MobileBertForNextSentencePrediction
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.MobileBertForNextSentencePrediction
-    :members:
+    :members: forward
 
 
 MobileBertForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.MobileBertForSequenceClassification
-    :members:
+    :members: forward
 
 
 MobileBertForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.MobileBertForMultipleChoice
-    :members:
+    :members: forward
 
 
 MobileBertForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.MobileBertForTokenClassification
-    :members:
+    :members: forward
 
 
 MobileBertForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.MobileBertForQuestionAnswering
-    :members:
+    :members: forward
 
 
 TFMobileBertModel
-~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFMobileBertModel
-    :members:
+    :members: call
 
 
 TFMobileBertForPreTraining
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFMobileBertForPreTraining
-    :members:
+    :members: call
 
 
 TFMobileBertForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFMobileBertForMaskedLM
-    :members:
+    :members: call
 
 
 TFMobileBertForNextSentencePrediction
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFMobileBertForNextSentencePrediction
-    :members:
+    :members: call
 
 
 TFMobileBertForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFMobileBertForSequenceClassification
-    :members:
+    :members: call
 
 
 TFMobileBertForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFMobileBertForMultipleChoice
-    :members:
+    :members: call
 
 
 TFMobileBertForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFMobileBertForTokenClassification
-    :members:
+    :members: call
 
 
 TFMobileBertForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFMobileBertForQuestionAnswering
-    :members:
-
+    :members: call