Fixing case in which Trainer hung while saving model in distributed training (#7365)

* remote debugging

* remote debugging

* moved _store_flos call

* moved _store_flos call

* moved _store_flos call

* removed debugging artefacts

.circleci/config.yml

@@ -244,7 +244,7 @@ jobs:
                   key: v0.3-code_quality-{{ checksum "setup.py" }}
                   paths:
                       - '~/.cache/pip'
-            - run: black --check --line-length 119 --target-version py35 examples templates tests src utils
+            - run: black --check examples templates tests src utils
             - run: isort --check-only examples templates tests src utils
             - run: flake8 examples templates tests src utils
             - run: python utils/check_copies.py

Makefile

@@ -3,7 +3,7 @@
 # Check that source code meets quality standards
 
 quality:
-	black --check --line-length 119 --target-version py35 examples templates tests src utils
+	black --check examples templates tests src utils
 	isort --check-only examples templates tests src utils
 	flake8 examples templates tests src utils
 	python utils/check_copies.py
@@ -12,7 +12,7 @@ quality:
 # Format source code automatically
 
 style:
-	black --line-length 119 --target-version py35 examples templates tests src utils
+	black examples templates tests src utils
 	isort examples templates tests src utils
 
 # Make marked copies of snippets of codes conform to the original

docs/README.md

@@ -88,20 +88,25 @@ The `huggingface/transformers` documentation follows the
 [Google documentation](https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html) style. It is
 mostly written in ReStructuredText 
 ([Sphinx simple documentation](https://www.sphinx-doc.org/en/master/usage/restructuredtext/index.html), 
-[Sourceforge complete documentation](https://docutils.sourceforge.io/docs/ref/rst/restructuredtext.html))
+[Sourceforge complete documentation](https://docutils.sourceforge.io/docs/ref/rst/restructuredtext.html)).
 
-### Adding a new section
 
-A section is a page held in the `Notes` toc-tree on the documentation. Adding a new section is done in two steps:
+### Adding a new tutorial
+
+Adding a new tutorial or section is done in two steps:
 
 - Add a new file under `./source`. This file can either be ReStructuredText (.rst) or Markdown (.md).
 - Link that file in `./source/index.rst` on the correct toc-tree.
 
+Make sure to put your new file under the proper section. It's unlikely to go in the first section (*Get Started*), so
+depending on the intended targets (beginners, more advanced users or researchers) it should go in section two, three or
+four.
+
 ### Adding a new model
 
 When adding a new model:
  
-- Create a file `xxx.rst` under `./source/model_doc`. 
+- Create a file `xxx.rst` under `./source/model_doc` (don't hesitate to copy an existing file as template). 
 - Link that file in `./source/index.rst` on the `model_doc` toc-tree.
 - Write a short overview of the model:
     - Overview with paper & authors
@@ -120,18 +125,18 @@ When adding a new model:
 These classes should be added using the RST syntax. Usually as follows:
 ```
 XXXConfig
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.XXXConfig
     :members:
 ```
 
-This will include every public method of the configuration. If for some reason you wish for a method not to be
-displayed in the documentation, you can do so by specifying which methods should be in the docs:
+This will include every public method of the configuration that is documented. If for some reason you wish for a method
+not to be displayed in the documentation, you can do so by specifying which methods should be in the docs:
 
 ```
 XXXTokenizer
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.XXXTokenizer
     :members: build_inputs_with_special_tokens, get_special_tokens_mask,
@@ -142,13 +147,17 @@ XXXTokenizer
 ### Writing source documentation
 
 Values that should be put in `code` should either be surrounded by double backticks: \`\`like so\`\` or be written as
-an object using the :obj: syntax: :obj:\`like so\`.
+an object using the :obj: syntax: :obj:\`like so\`. Note that argument names and objects like True, None or any strings
+should usually be put in `code`.
 
 When mentionning a class, it is recommended to use the :class: syntax as the mentioned class will be automatically
-linked by Sphinx: :class:\`transformers.XXXClass\`
+linked by Sphinx: :class:\`~transformers.XXXClass\`
 
-When mentioning a function, it is recommended to use the :func: syntax as the mentioned method will be automatically
-linked by Sphinx: :func:\`transformers.XXXClass.method\`
+When mentioning a function, it is recommended to use the :func: syntax as the mentioned function will be automatically
+linked by Sphinx: :func:\`~transformers.function\`.
+
+When mentioning a method, it is recommended to use the :meth: syntax as the mentioned method will be automatically
+linked by Sphinx: :meth:\`~transformers.XXXClass.method\`.
 
 Links should be done as so (note the double underscore at the end): \`text for the link <./local-link-or-global-link#loc>\`__
 
@@ -165,13 +174,34 @@ Here's an example showcasing everything so far:
         input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`):
             Indices of input sequence tokens in the vocabulary.
 
-            Indices can be obtained using :class:`transformers.AlbertTokenizer`.
-            See :func:`transformers.PreTrainedTokenizer.encode` and
-            :func:`transformers.PreTrainedTokenizer.__call__` for details.
+            Indices can be obtained using :class:`~transformers.AlbertTokenizer`.
+            See :meth:`~transformers.PreTrainedTokenizer.encode` and
+            :meth:`~transformers.PreTrainedTokenizer.__call__` for details.
 
             `What are input IDs? <../glossary.html#input-ids>`__
 ```
 
+For optional arguments or arguments with defaults we follow the following syntax: imagine we have a function with the
+following signature:
+
+```
+def my_function(x: str = None, a: float = 1):
+```
+
+then its documentation should look like this:
+
+```
+    Args:
+        x (:obj:`str`, `optional`):
+            This argument controls ...
+        a (:obj:`float`, `optional`, defaults to 1):
+            This argument is used to ...
+```
+
+Note that we always omit the "defaults to :obj:\`None\`" when None is the default for any argument. Also note that even
+if the first line describing your argument type and its default gets long, you can't break it on several lines. You can
+however write as many lines as you want in the indented description (see the example above with `input_ids`). 
+
 #### Writing a multi-line code block 
 
 Multi-line code blocks can be useful for displaying examples. They are done like so:
@@ -186,6 +216,9 @@ Example::
 
 The `Example` string at the beginning can be replaced by anything as long as there are two semicolons following it.
 
+We follow the [doctest](https://docs.python.org/3/library/doctest.html) syntax for the examples to automatically test
+the results stay consistent with the library.
+
 #### Writing a return block
 
 Arguments should be defined with the `Args:` prefix, followed by a line return and an indentation. 
@@ -207,5 +240,5 @@ Here's an example for a single value return:
 
 ```
     Returns:
-        A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+        :obj:`List[int]`: A list of integers in the range [0, 1] --- 1 for a special token, 0 for a sequence token.
 ```

docs/source/benchmarks.rst

@@ -1,12 +1,12 @@
 Benchmarks
-==========
+=======================================================================================================================
 
 Let's take a look at how 🤗 Transformer models can be benchmarked, best practices, and already available benchmarks.
 
 A notebook explaining in more detail how to benchmark 🤗 Transformer models can be found `here <https://github.com/huggingface/transformers/blob/master/notebooks/05-benchmark.ipynb>`__.
 
 How to benchmark 🤗 Transformer models
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 The classes :class:`~transformers.PyTorchBenchmark` and :class:`~transformers.TensorFlowBenchmark` allow to flexibly benchmark 🤗 Transformer models.
 The benchmark classes allow us to measure the `peak memory usage` and `required time` for both 
@@ -300,7 +300,7 @@ deciding for which configuration the model should be trained.
 
 
 Benchmark best practices
-~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 This section lists a couple of best practices one should be aware of when benchmarking a model.
 
@@ -311,7 +311,7 @@ This section lists a couple of best practices one should be aware of when benchm
 
 
 Sharing your benchmark
-~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Previously all available core models (10 at the time) have been benchmarked for `inference time`, across many different settings: using PyTorch, with
 and without TorchScript, using TensorFlow, with and without XLA. All of those tests were done across CPUs (except for

docs/source/bertology.rst

@@ -1,5 +1,5 @@
 BERTology
----------
+-----------------------------------------------------------------------------------------------------------------------
 
 There is a growing field of study concerned with investigating the inner working of large-scale transformers like BERT (that some call "BERTology"). Some good examples of this field are:
 

docs/source/converting_tensorflow_models.rst

@@ -1,5 +1,5 @@
 Converting Tensorflow Checkpoints
-================================================
+=======================================================================================================================
 
 A command-line interface is provided to convert original Bert/GPT/GPT-2/Transformer-XL/XLNet/XLM checkpoints in models than be loaded using the ``from_pretrained`` methods of the library.
 
@@ -10,7 +10,7 @@ A command-line interface is provided to convert original Bert/GPT/GPT-2/Transfor
     The documentation below reflects the **transformers-cli convert** command format.
 
 BERT
-^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 You can convert any TensorFlow checkpoint for BERT (in particular `the pre-trained models released by Google <https://github.com/google-research/bert#pre-trained-models>`_\ ) in a PyTorch save file by using the `convert_bert_original_tf_checkpoint_to_pytorch.py <https://github.com/huggingface/transformers/blob/master/src/transformers/convert_bert_original_tf_checkpoint_to_pytorch.py>`_ script.
 
@@ -34,7 +34,7 @@ Here is an example of the conversion process for a pre-trained ``BERT-Base Uncas
 You can download Google's pre-trained models for the conversion `here <https://github.com/google-research/bert#pre-trained-models>`__.
 
 ALBERT
-^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 Convert TensorFlow model checkpoints of ALBERT to PyTorch using the `convert_albert_original_tf_checkpoint_to_pytorch.py <https://github.com/huggingface/transformers/blob/master/src/transformers/convert_bert_original_tf_checkpoint_to_pytorch.py>`_ script.
 
@@ -54,7 +54,7 @@ Here is an example of the conversion process for the pre-trained ``ALBERT Base``
 You can download Google's pre-trained models for the conversion `here <https://github.com/google-research/albert#pre-trained-models>`__.
 
 OpenAI GPT
-^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 Here is an example of the conversion process for a pre-trained OpenAI GPT model, assuming that your NumPy checkpoint save as the same format than OpenAI pretrained model (see `here <https://github.com/openai/finetune-transformer-lm>`__\ )
 
@@ -70,7 +70,7 @@ Here is an example of the conversion process for a pre-trained OpenAI GPT model,
 
 
 OpenAI GPT-2
-^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 Here is an example of the conversion process for a pre-trained OpenAI GPT-2 model (see `here <https://github.com/openai/gpt-2>`__\ )
 
@@ -85,7 +85,7 @@ Here is an example of the conversion process for a pre-trained OpenAI GPT-2 mode
      [--finetuning_task_name OPENAI_GPT2_FINETUNED_TASK]
 
 Transformer-XL
-^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 Here is an example of the conversion process for a pre-trained Transformer-XL model (see `here <https://github.com/kimiyoung/transformer-xl/tree/master/tf#obtain-and-evaluate-pretrained-sota-models>`__\ )
 
@@ -101,7 +101,7 @@ Here is an example of the conversion process for a pre-trained Transformer-XL mo
 
 
 XLNet
-^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 Here is an example of the conversion process for a pre-trained XLNet model:
 
@@ -118,7 +118,7 @@ Here is an example of the conversion process for a pre-trained XLNet model:
 
 
 XLM
-^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 Here is an example of the conversion process for a pre-trained XLM model:
 

docs/source/custom_datasets.rst

@@ -1,5 +1,5 @@
 Fine-tuning with custom datasets
-================================
+=======================================================================================================================
 
 .. note::
 
@@ -24,7 +24,7 @@ We include several examples, each of which demonstrates a different type of comm
 .. _seq_imdb:
 
 Sequence Classification with IMDb Reviews
------------------------------------------
+-----------------------------------------------------------------------------------------------------------------------
 
 .. note::
 
@@ -139,7 +139,7 @@ Now that our datasets our ready, we can fine-tune a model either with the 🤗
 .. _ft_trainer:
 
 Fine-tuning with Trainer
-~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 The steps above prepared the datasets in the way that the trainer is expected. Now all we need to do is create a
 model to fine-tune, define the :class:`~transformers.TrainingArguments`/:class:`~transformers.TFTrainingArguments`
@@ -200,7 +200,7 @@ and instantiate a :class:`~transformers.Trainer`/:class:`~transformers.TFTrainer
 .. _ft_native:
 
 Fine-tuning with native PyTorch/TensorFlow
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 We can also train use native PyTorch or TensorFlow:
 
@@ -244,7 +244,7 @@ We can also train use native PyTorch or TensorFlow:
 .. _tok_ner:
 
 Token Classification with W-NUT Emerging Entities
--------------------------------------------------
+-----------------------------------------------------------------------------------------------------------------------
 
 .. note::
 
@@ -443,7 +443,7 @@ sequence classification example above.
 .. _qa_squad:
 
 Question Answering with SQuAD 2.0
----------------------------------
+-----------------------------------------------------------------------------------------------------------------------
 
 .. note::
 
@@ -655,7 +655,7 @@ multiple model outputs.
 .. _resources:
 
 Additional Resources
---------------------
+-----------------------------------------------------------------------------------------------------------------------
 
   - `How to train a new language model from scratch using Transformers and Tokenizers
     <https://huggingface.co/blog/how-to-train>`_. Blog post showing the steps to load in Esperanto data and train a
@@ -666,7 +666,7 @@ Additional Resources
 .. _nlplib:
 
 Using the 🤗 NLP Datasets & Metrics library
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 This tutorial demonstrates how to read in datasets from various raw text formats and prepare them for training with
 🤗 Transformers so that you can do the same thing with your own custom datasets. However, we recommend users use the

docs/source/glossary.rst

@@ -1,8 +1,8 @@
 Glossary
-^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 General terms
--------------
+-----------------------------------------------------------------------------------------------------------------------
 
 - autoencoding models: see MLM
 - autoregressive models: see CLM
@@ -27,7 +27,7 @@ General terms
   or a punctuation symbol.
 
 Model inputs
-------------
+-----------------------------------------------------------------------------------------------------------------------
 
 Every model is different yet bears similarities with the others. Therefore most models use the same inputs, which are
 detailed here alongside usage examples.
@@ -35,7 +35,7 @@ detailed here alongside usage examples.
 .. _input-ids:
 
 Input IDs
-~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 The input ids are often the only required parameters to be passed to the model as input. *They are token indices,
 numerical representations of tokens building the sequences that will be used as input by the model*.
@@ -43,7 +43,7 @@ numerical representations of tokens building the sequences that will be used as
 Each tokenizer works differently but the underlying mechanism remains the same. Here's an example using the BERT
 tokenizer, which is a `WordPiece <https://arxiv.org/pdf/1609.08144.pdf>`__ tokenizer:
 
-::
+.. code-block::
 
     >>> from transformers import BertTokenizer
     >>> tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
@@ -52,7 +52,7 @@ tokenizer, which is a `WordPiece <https://arxiv.org/pdf/1609.08144.pdf>`__ token
 
 The tokenizer takes care of splitting the sequence into tokens available in the tokenizer vocabulary.
 
-::
+.. code-block::
 
     >>> tokenized_sequence = tokenizer.tokenize(sequence)
 
@@ -60,7 +60,7 @@ The tokens are either words or subwords. Here for instance, "VRAM" wasn't in the
 in "V", "RA" and "M". To indicate those tokens are not separate words but parts of the same word, a double-hash prefix is
 added for "RA" and "M":
 
-::
+.. code-block::
 
     >>> print(tokenized_sequence)
     ['A', 'Titan', 'R', '##T', '##X', 'has', '24', '##GB', 'of', 'V', '##RA', '##M']
@@ -69,14 +69,14 @@ These tokens can then be converted into IDs which are understandable by the mode
 the sentence to the tokenizer, which leverages the Rust implementation of
 `huggingface/tokenizers <https://github.com/huggingface/tokenizers>`__ for peak performance.
 
-::
+.. code-block::
 
     >>> inputs = tokenizer(sequence)
 
 The tokenizer returns a dictionary with all the arguments necessary for its corresponding model to work properly. The
 token indices are under the key "input_ids":
 
-::
+.. code-block::
 
     >>> encoded_sequence = inputs["input_ids"]
     >>> print(encoded_sequence)
@@ -87,13 +87,13 @@ IDs the model sometimes uses.
 
 If we decode the previous sequence of ids,
 
-::
+.. code-block::
 
     >>> decoded_sequence = tokenizer.decode(encoded_sequence)
 
 we will see
 
-::
+.. code-block::
 
     >>> print(decoded_sequence)
     [CLS] A Titan RTX has 24GB of VRAM [SEP]
@@ -103,14 +103,14 @@ because this is the way a :class:`~transformers.BertModel` is going to expect it
 .. _attention-mask:
 
 Attention mask
-~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 The attention mask is an optional argument used when batching sequences together. This argument indicates to the
 model which tokens should be attended to, and which should not.
 
 For example, consider these two sequences:
 
-::
+.. code-block::
 
     >>> from transformers import BertTokenizer
     >>> tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
@@ -123,7 +123,7 @@ For example, consider these two sequences:
 
 The encoded versions have different lengths:
 
-::
+.. code-block::
 
     >>> len(encoded_sequence_a), len(encoded_sequence_b)
     (8, 19)
@@ -134,13 +134,13 @@ of the second one, or the second one needs to be truncated down to the length of
 In the first case, the list of IDs will be extended by the padding indices. We can pass a list to the tokenizer and ask
 it to pad like this:
 
-::
+.. code-block::
 
     >>> padded_sequences = tokenizer([sequence_a, sequence_b], padding=True)
 
 We can see that 0s have been added on the right of the first sentence to make it the same length as the second one:
 
-::
+.. code-block::
 
     >>> padded_sequences["input_ids"]
     [[101, 1188, 1110, 170, 1603, 4954, 119, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [101, 1188, 1110, 170, 1897, 1263, 4954, 119, 1135, 1110, 1120, 1655, 2039, 1190, 1103, 4954, 138, 119, 102]]
@@ -150,7 +150,7 @@ the position of the padded indices so that the model does not attend to them. Fo
 :class:`~transformers.BertTokenizer`, :obj:`1` indicates a value that should be attended to, while :obj:`0` indicates
 a padded value. This attention mask is in the dictionary returned by the tokenizer under the key "attention_mask":
 
-::
+.. code-block::
 
     >>> padded_sequences["attention_mask"]
     [[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
@@ -158,20 +158,20 @@ a padded value. This attention mask is in the dictionary returned by the tokeniz
 .. _token-type-ids:
 
 Token Type IDs
-~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Some models' purpose is to do sequence classification or question answering. These require two different sequences to
 be joined in a single "input_ids" entry, which usually is performed with the help of special tokens, such as the classifier (``[CLS]``) and separator (``[SEP]``)
 tokens. For example, the BERT model builds its two sequence input as such:
 
-::
+.. code-block::
 
    >>> # [CLS] SEQUENCE_A [SEP] SEQUENCE_B [SEP]
 
 We can use our tokenizer to automatically generate such a sentence by passing the two sequences to ``tokenizer`` as two arguments (and
 not a list, like before) like this:
 
-::
+.. code-block::
 
     >>> from transformers import BertTokenizer
     >>> tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
@@ -183,7 +183,7 @@ not a list, like before) like this:
 
 which will return:
 
-::
+.. code-block::
 
     >>> print(decoded)
     [CLS] HuggingFace is based in NYC [SEP] Where is HuggingFace based? [SEP]
@@ -194,7 +194,7 @@ mask identifying the two types of sequence in the model.
 
 The tokenizer returns this mask as the "token_type_ids" entry:
 
-::
+.. code-block::
 
     >>> encoded_dict['token_type_ids']
     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]
@@ -207,7 +207,7 @@ Some models, like :class:`~transformers.XLNetModel` use an additional token repr
 .. _position-ids:
 
 Position IDs
-~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Contrary to RNNs that have the position of each token embedded within them,
 transformers are unaware of the position of each token. Therefore, the position IDs (``position_ids``) are used by the model to identify each token's position in the list of tokens.
@@ -221,7 +221,7 @@ use other types of positional embeddings, such as sinusoidal position embeddings
 .. _feed-forward-chunking:
 
 Feed Forward Chunking
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 In each residual attention block in transformers the self-attention layer is usually followed by 2 feed forward layers.
 The intermediate embedding size of the feed forward layers is often bigger than the hidden size of the model (e.g.,

docs/source/index.rst

@@ -1,5 +1,5 @@
 Transformers
-================================================================================================================================================
+=======================================================================================================================
 
 State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0.
 
@@ -11,7 +11,7 @@ TensorFlow 2.0 and PyTorch.
 This is the documentation of our repository `transformers <https://github.com/huggingface/transformers>`_.
 
 Features
----------------------------------------------------
+-----------------------------------------------------------------------------------------------------------------------
 
 - High performance on NLU and NLG tasks
 - Low barrier to entry for educators and practitioners
@@ -36,7 +36,7 @@ Choose the right framework for every part of a model's lifetime:
 - Seamlessly pick the right framework for training, evaluation, production
 
 Contents
----------------------------------
+-----------------------------------------------------------------------------------------------------------------------
 
 The documentation is organized in five parts:
 

docs/source/internal/modeling_utils.rst

@@ -1,13 +1,13 @@
 Custom Layers and Utilities
----------------------------
+-----------------------------------------------------------------------------------------------------------------------
 
 This page lists all the custom layers used by the library, as well as the utility functions it provides for modeling.
 
 Most of those are only useful if you are studying the code of the models in the library.
 
 
-``Pytorch custom modules``
-~~~~~~~~~~~~~~~~~~~~~~~~~~
+Pytorch custom modules
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.modeling_utils.Conv1D
 
@@ -29,8 +29,8 @@ Most of those are only useful if you are studying the code of the models in the
     :members: forward
 
 
-``PyTorch Helper Functions``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+PyTorch Helper Functions
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autofunction:: transformers.apply_chunking_to_forward
 
@@ -42,8 +42,8 @@ Most of those are only useful if you are studying the code of the models in the
 
 .. autofunction:: transformers.modeling_utils.prune_linear_layer
 
-``TensorFlow custom layers``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+TensorFlow custom layers
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.modeling_tf_utils.TFConv1D
 
@@ -54,8 +54,8 @@ Most of those are only useful if you are studying the code of the models in the
     :members: call
 
 
-``TensorFlow loss functions``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+TensorFlow loss functions
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.modeling_tf_utils.TFCausalLanguageModelingLoss
     :members:
@@ -76,8 +76,8 @@ Most of those are only useful if you are studying the code of the models in the
     :members:
 
 
-``TensorFlow Helper Functions``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+TensorFlow Helper Functions
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autofunction:: transformers.modeling_tf_utils.cast_bool_to_primitive
 

docs/source/internal/pipelines_utils.rst

@@ -1,40 +1,40 @@
-Utilities for pipelines
------------------------
-
-This page lists all the utility functions the library provides for pipelines.
-
-Most of those are only useful if you are studying the code of the models in the library.
-
-
-Argument handling
-~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.pipelines.ArgumentHandler
-
-.. autoclass:: transformers.pipelines.ZeroShotClassificationArgumentHandler
-
-.. autoclass:: transformers.pipelines.QuestionAnsweringArgumentHandler
-
-
-Data format
-~~~~~~~~~~~
-
-.. autoclass:: transformers.pipelines.PipelineDataFormat
-    :members:
-
-.. autoclass:: transformers.pipelines.CsvPipelineDataFormat
-    :members:
-
-.. autoclass:: transformers.pipelines.JsonPipelineDataFormat
-    :members:
-
-.. autoclass:: transformers.pipelines.PipedPipelineDataFormat
-    :members:
-
-
-Utilities
-~~~~~~~~~
-
-.. autofunction:: transformers.pipelines.get_framework
-
-.. autoclass:: transformers.pipelines.PipelineException
+Utilities for pipelines
+-----------------------------------------------------------------------------------------------------------------------
+
+This page lists all the utility functions the library provides for pipelines.
+
+Most of those are only useful if you are studying the code of the models in the library.
+
+
+Argument handling
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.pipelines.ArgumentHandler
+
+.. autoclass:: transformers.pipelines.ZeroShotClassificationArgumentHandler
+
+.. autoclass:: transformers.pipelines.QuestionAnsweringArgumentHandler
+
+
+Data format
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.pipelines.PipelineDataFormat
+    :members:
+
+.. autoclass:: transformers.pipelines.CsvPipelineDataFormat
+    :members:
+
+.. autoclass:: transformers.pipelines.JsonPipelineDataFormat
+    :members:
+
+.. autoclass:: transformers.pipelines.PipedPipelineDataFormat
+    :members:
+
+
+Utilities
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autofunction:: transformers.pipelines.get_framework
+
+.. autoclass:: transformers.pipelines.PipelineException

docs/source/internal/tokenization_utils.rst

@@ -1,38 +1,38 @@
-Utilities for Tokenizers
-------------------------
-
-This page lists all the utility functions used by the tokenizers, mainly the class
-:class:`~transformers.tokenization_utils_base.PreTrainedTokenizerBase` that implements the common methods between
-:class:`~transformers.PreTrainedTokenizer` and :class:`~transformers.PreTrainedTokenizerFast` and the mixin
-:class:`~transformers.tokenization_utils_base.SpecialTokensMixin`.
-
-Most of those are only useful if you are studying the code of the tokenizers in the library.
-
-``PreTrainedTokenizerBase``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.tokenization_utils_base.PreTrainedTokenizerBase
-    :special-members: __call__
-    :members:
-
-
-``SpecialTokensMixin``
-~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.tokenization_utils_base.SpecialTokensMixin
-    :members:
-
-
-Enums and namedtuples
-~~~~~~~~~~~~~~~~~~~~~
-.. autoclass:: transformers.tokenization_utils_base.ExplicitEnum
-
-.. autoclass:: transformers.tokenization_utils_base.PaddingStrategy
-
-.. autoclass:: transformers.tokenization_utils_base.TensorType
-
-.. autoclass:: transformers.tokenization_utils_base.TruncationStrategy
-
-.. autoclass:: transformers.tokenization_utils_base.CharSpan
-
-.. autoclass:: transformers.tokenization_utils_base.TokenSpan
+Utilities for Tokenizers
+-----------------------------------------------------------------------------------------------------------------------
+
+This page lists all the utility functions used by the tokenizers, mainly the class
+:class:`~transformers.tokenization_utils_base.PreTrainedTokenizerBase` that implements the common methods between
+:class:`~transformers.PreTrainedTokenizer` and :class:`~transformers.PreTrainedTokenizerFast` and the mixin
+:class:`~transformers.tokenization_utils_base.SpecialTokensMixin`.
+
+Most of those are only useful if you are studying the code of the tokenizers in the library.
+
+PreTrainedTokenizerBase
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.tokenization_utils_base.PreTrainedTokenizerBase
+    :special-members: __call__
+    :members:
+
+
+SpecialTokensMixin
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.tokenization_utils_base.SpecialTokensMixin
+    :members:
+
+
+Enums and namedtuples
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. autoclass:: transformers.tokenization_utils_base.ExplicitEnum
+
+.. autoclass:: transformers.tokenization_utils_base.PaddingStrategy
+
+.. autoclass:: transformers.tokenization_utils_base.TensorType
+
+.. autoclass:: transformers.tokenization_utils_base.TruncationStrategy
+
+.. autoclass:: transformers.tokenization_utils_base.CharSpan
+
+.. autoclass:: transformers.tokenization_utils_base.TokenSpan

docs/source/main_classes/configuration.rst

@@ -1,5 +1,5 @@
 Configuration
-----------------------------------------------------
+-----------------------------------------------------------------------------------------------------------------------
 
 The base class :class:`~transformers.PretrainedConfig` implements the common methods for loading/saving a configuration
 either from a local file or directory, or from a pretrained model configuration provided by the library (downloaded
@@ -7,7 +7,7 @@ from HuggingFace's AWS S3 repository).
 
 
 PretrainedConfig
-~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.PretrainedConfig
     :members:

docs/source/main_classes/logging.rst

@@ -1,18 +1,20 @@
 Logging
--------
+-----------------------------------------------------------------------------------------------------------------------
 
 🤗 Transformers has a centralized logging system, so that you can setup the verbosity of the library easily.
 
 Currently the default verbosity of the library is ``WARNING``.
 
-To change the level of verbosity, just use one of the direct setters. For instance, here is how to change the verbosity to the INFO level.
+To change the level of verbosity, just use one of the direct setters. For instance, here is how to change the verbosity
+to the INFO level.
 
 .. code-block:: python
 
     import transformers
     transformers.logging.set_verbosity_info()
 
-You can also use the environment variable ``TRANSFORMERS_VERBOSITY`` to override the default verbosity. You can set it to one of the following: ``debug``, ``info``, ``warning``, ``error``, ``critical``. For example:
+You can also use the environment variable ``TRANSFORMERS_VERBOSITY`` to override the default verbosity. You can set it
+to one of the following: ``debug``, ``info``, ``warning``, ``error``, ``critical``. For example:
 
 .. code-block:: bash
                
@@ -32,7 +34,7 @@ verbose to the most verbose), those levels (with their corresponding int values
 - :obj:`transformers.logging.DEBUG` (int value, 10): report all information.
 
 Base setters
-~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autofunction:: transformers.logging.set_verbosity_error
 
@@ -43,7 +45,7 @@ Base setters
 .. autofunction:: transformers.logging.set_verbosity_debug
 
 Other functions
-~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autofunction:: transformers.logging.get_verbosity
 

docs/source/main_classes/model.rst

@@ -1,5 +1,5 @@
 Models
-----------------------------------------------------
+-----------------------------------------------------------------------------------------------------------------------
 
 The base classes :class:`~transformers.PreTrainedModel` and :class:`~transformers.TFPreTrainedModel` implement the
 common methods for loading/saving a model either from a local file or directory, or from a pretrained model
@@ -17,36 +17,36 @@ for text generation, :class:`~transformers.generation_utils.GenerationMixin` (fo
 :class:`~transformers.generation_tf_utils.TFGenerationMixin` (for the TensorFlow models)
 
 
-``PreTrainedModel``
-~~~~~~~~~~~~~~~~~~~~~
+PreTrainedModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.PreTrainedModel
     :members:
 
 
-``ModuleUtilsMixin``
-~~~~~~~~~~~~~~~~~~~~
+ModuleUtilsMixin
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.modeling_utils.ModuleUtilsMixin
     :members:
 
 
-``TFPreTrainedModel``
-~~~~~~~~~~~~~~~~~~~~~
+TFPreTrainedModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFPreTrainedModel
     :members:
 
 
-``TFModelUtilsMixin``
-~~~~~~~~~~~~~~~~~~~~~
+TFModelUtilsMixin
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.modeling_tf_utils.TFModelUtilsMixin
     :members:
 
 
 Generative models
-~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.generation_utils.GenerationMixin
     :members:

docs/source/main_classes/optimizer_schedules.rst

@@ -1,5 +1,5 @@
 Optimization
-----------------------------------------------------
+-----------------------------------------------------------------------------------------------------------------------
 
 The ``.optimization`` module provides:
 
@@ -7,29 +7,29 @@ The ``.optimization`` module provides:
 - several schedules in the form of schedule objects that inherit from ``_LRSchedule``:
 - a gradient accumulation class to accumulate the gradients of multiple batches
 
-``AdamW`` (PyTorch)
-~~~~~~~~~~~~~~~~~~~
+AdamW (PyTorch)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.AdamW
     :members:
 
-``AdaFactor`` (PyTorch)
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+AdaFactor (PyTorch)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.Adafactor
 
-``AdamWeightDecay`` (TensorFlow)
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+AdamWeightDecay (TensorFlow)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.AdamWeightDecay
 
 .. autofunction:: transformers.create_optimizer
 
 Schedules
-~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Learning Rate Schedules (Pytorch)
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 .. autofunction:: transformers.get_constant_schedule
 
@@ -62,16 +62,16 @@ Learning Rate Schedules (Pytorch)
     :target: /imgs/warmup_linear_schedule.png
     :alt:
 
-``Warmup`` (TensorFlow)
-^^^^^^^^^^^^^^^^^^^^^^^
+Warmup (TensorFlow)
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 .. autoclass:: transformers.WarmUp
     :members:
 
 Gradient Strategies
-~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-``GradientAccumulator`` (TensorFlow)
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+GradientAccumulator (TensorFlow)
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 .. autoclass:: transformers.GradientAccumulator

docs/source/main_classes/output.rst

@@ -1,5 +1,5 @@
 Model outputs
--------------
+-----------------------------------------------------------------------------------------------------------------------
 
 PyTorch models have outputs that are instances of subclasses of :class:`~transformers.file_utils.ModelOutput`. Those
 are data structures containing all the information returned by the model, but that can also be used as tuples or
@@ -44,98 +44,217 @@ values. Here for instance, it has two keys that are ``loss`` and ``logits``.
 We document here the generic model outputs that are used by more than one model type. Specific output types are
 documented on their corresponding model page.
 
-``ModelOutput``
-~~~~~~~~~~~~~~~
+ModelOutput
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.file_utils.ModelOutput
     :members:
 
-``BaseModelOutput``
-~~~~~~~~~~~~~~~~~~~
+
+BaseModelOutput
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.modeling_outputs.BaseModelOutput
     :members:
 
-``BaseModelOutputWithPooling``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+BaseModelOutputWithPooling
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.modeling_outputs.BaseModelOutputWithPooling
     :members:
 
-``BaseModelOutputWithPast``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+BaseModelOutputWithPast
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.modeling_outputs.BaseModelOutputWithPast
     :members:
 
-``Seq2SeqModelOutput``
-~~~~~~~~~~~~~~~~~~~~~~
+Seq2SeqModelOutput
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.modeling_outputs.Seq2SeqModelOutput
     :members:
 
-``CausalLMOutput``
-~~~~~~~~~~~~~~~~~~
+
+CausalLMOutput
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.modeling_outputs.CausalLMOutput
     :members:
 
-``CausalLMOutputWithPast``
-~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+CausalLMOutputWithPast
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.modeling_outputs.CausalLMOutputWithPast
     :members:
 
-``MaskedLMOutput``
-~~~~~~~~~~~~~~~~~~
+
+MaskedLMOutput
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.modeling_outputs.MaskedLMOutput
     :members:
 
-``Seq2SeqLMOutput``
-~~~~~~~~~~~~~~~~~~~
+
+Seq2SeqLMOutput
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.modeling_outputs.Seq2SeqLMOutput
     :members:
 
-``NextSentencePredictorOutput``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+NextSentencePredictorOutput
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.modeling_outputs.NextSentencePredictorOutput
     :members:
 
-``SequenceClassifierOutput``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+SequenceClassifierOutput
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.modeling_outputs.SequenceClassifierOutput
     :members:
 
-``Seq2SeqSequenceClassifierOutput``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Seq2SeqSequenceClassifierOutput
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput
     :members:
 
-``MultipleChoiceModelOutput``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+MultipleChoiceModelOutput
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.modeling_outputs.MultipleChoiceModelOutput
     :members:
 
-``TokenClassifierOutput``
-~~~~~~~~~~~~~~~~~~~~~~~~~
+
+TokenClassifierOutput
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.modeling_outputs.TokenClassifierOutput
     :members:
 
-``QuestionAnsweringModelOutput``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+QuestionAnsweringModelOutput
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.modeling_outputs.QuestionAnsweringModelOutput
     :members:
 
-``Seq2SeqQuestionAnsweringModelOutput``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Seq2SeqQuestionAnsweringModelOutput
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput
     :members:
+
+
+TFBaseModelOutput
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.modeling_tf_outputs.TFBaseModelOutput
+    :members:
+
+
+TFBaseModelOutputWithPooling
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling
+    :members:
+
+
+TFBaseModelOutputWithPast
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.modeling_tf_outputs.TFBaseModelOutputWithPast
+    :members:
+
+
+TFSeq2SeqModelOutput
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.modeling_tf_outputs.TFSeq2SeqModelOutput
+    :members:
+
+
+TFCausalLMOutput
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.modeling_tf_outputs.TFCausalLMOutput
+    :members:
+
+
+TFCausalLMOutputWithPast
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.modeling_tf_outputs.TFCausalLMOutputWithPast
+    :members:
+
+
+TFMaskedLMOutput
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.modeling_tf_outputs.TFMaskedLMOutput
+    :members:
+
+
+TFSeq2SeqLMOutput
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.modeling_tf_outputs.TFSeq2SeqLMOutput
+    :members:
+
+
+TFNextSentencePredictorOutput
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.modeling_tf_outputs.TFNextSentencePredictorOutput
+    :members:
+
+
+TFSequenceClassifierOutput
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.modeling_tf_outputs.TFSequenceClassifierOutput
+    :members:
+
+
+TFSeq2SeqSequenceClassifierOutput
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.modeling_tf_outputs.TFSeq2SeqSequenceClassifierOutput
+    :members:
+
+
+TFMultipleChoiceModelOutput
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput
+    :members:
+
+
+TFTokenClassifierOutput
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.modeling_tf_outputs.TFTokenClassifierOutput
+    :members:
+
+
+TFQuestionAnsweringModelOutput
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput
+    :members:
+
+
+TFSeq2SeqQuestionAnsweringModelOutput
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.modeling_tf_outputs.TFSeq2SeqQuestionAnsweringModelOutput
+    :members:

docs/source/main_classes/pipelines.rst

@@ -1,5 +1,5 @@
 Pipelines
-----------------------------------------------------
+-----------------------------------------------------------------------------------------------------------------------
 
 The pipelines are a great and easy way to use models for inference. These pipelines are objects that abstract most
 of the complex code from the library, offering a simple API dedicated to several tasks, including Named Entity
@@ -24,7 +24,7 @@ There are two categories of pipeline abstractions to be aware about:
     - :class:`~transformers.Text2TextGenerationPipeline`
 
 The pipeline abstraction
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 The `pipeline` abstraction is a wrapper around all the other available pipelines. It is instantiated as any
 other pipeline but requires an additional argument which is the `task`.
@@ -33,10 +33,10 @@ other pipeline but requires an additional argument which is the `task`.
 
 
 The task specific pipelines
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 ConversationalPipeline
-==========================================
+=======================================================================================================================
 
 .. autoclass:: transformers.Conversation
 
@@ -45,76 +45,76 @@ ConversationalPipeline
     :members:
 
 FeatureExtractionPipeline
-==========================================
+=======================================================================================================================
 
 .. autoclass:: transformers.FeatureExtractionPipeline
     :special-members: __call__
     :members:
 
 FillMaskPipeline
-==========================================
+=======================================================================================================================
 
 .. autoclass:: transformers.FillMaskPipeline
     :special-members: __call__
     :members:
 
 NerPipeline
-==========================================
+=======================================================================================================================
 
 This class is an alias of the :class:`~transformers.TokenClassificationPipeline` defined below. Please refer to that
 pipeline for documentation and usage examples.
 
 QuestionAnsweringPipeline
-==========================================
+=======================================================================================================================
 
 .. autoclass:: transformers.QuestionAnsweringPipeline
     :special-members: __call__
     :members:
 
 SummarizationPipeline
-==========================================
+=======================================================================================================================
 
 .. autoclass:: transformers.SummarizationPipeline
     :special-members: __call__
     :members:
 
 TextClassificationPipeline
-==========================================
+=======================================================================================================================
 
 .. autoclass:: transformers.TextClassificationPipeline
     :special-members: __call__
     :members:
 
 TextGenerationPipeline
-==========================================
+=======================================================================================================================
 
 .. autoclass:: transformers.TextGenerationPipeline
     :special-members: __call__
     :members:
 
 Text2TextGenerationPipeline
-==========================================
+=======================================================================================================================
 
 .. autoclass:: transformers.Text2TextGenerationPipeline
     :special-members: __call__
     :members:
 
 TokenClassificationPipeline
-==========================================
+=======================================================================================================================
 
 .. autoclass:: transformers.TokenClassificationPipeline
     :special-members: __call__
     :members:
 
 ZeroShotClassificationPipeline
-==========================================
+=======================================================================================================================
 
 .. autoclass:: transformers.ZeroShotClassificationPipeline
     :special-members: __call__
     :members:
 
 Parent class: :obj:`Pipeline`
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.Pipeline
     :members:

docs/source/main_classes/processors.rst

@@ -1,11 +1,11 @@
 Processors
-----------------------------------------------------
+-----------------------------------------------------------------------------------------------------------------------
 
 This library includes processors for several traditional tasks. These processors can be used to process a dataset into
 examples that can be fed to a model.
 
 Processors
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 All processors follow the same architecture which is that of the
 :class:`~transformers.data.processors.utils.DataProcessor`. The processor returns a list
@@ -26,7 +26,7 @@ of :class:`~transformers.data.processors.utils.InputExample`. These
 
 
 GLUE
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 `General Language Understanding Evaluation (GLUE) <https://gluebenchmark.com/>`__ is a benchmark that evaluates
 the performance of models across a diverse set of existing NLU tasks. It was released together with the paper
@@ -52,13 +52,13 @@ Additionally, the following method  can be used to load values from a data file
 .. automethod:: transformers.data.processors.glue.glue_convert_examples_to_features
 
 Example usage
-^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 An example using these processors is given in the `run_glue.py <https://github.com/huggingface/pytorch-transformers/blob/master/examples/text-classification/run_glue.py>`__ script.
 
 
 XNLI
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 `The Cross-Lingual NLI Corpus (XNLI) <https://www.nyu.edu/projects/bowman/xnli/>`__ is a benchmark that evaluates
 the quality of cross-lingual text representations. 
@@ -78,7 +78,7 @@ An example using these processors is given in the
 
 
 SQuAD
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 `The Stanford Question Answering Dataset (SQuAD) <https://rajpurkar.github.io/SQuAD-explorer//>`__ is a benchmark that evaluates
 the performance of models on question answering. Two versions are available, v1.1 and v2.0. The first version (v1.1) was released together with the paper
@@ -88,7 +88,7 @@ the paper `Know What You Don't Know: Unanswerable Questions for SQuAD <https://a
 This library hosts a processor for each of the two versions:
 
 Processors
-^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 Those processors are:
     - :class:`~transformers.data.processors.utils.SquadV1Processor`
@@ -109,7 +109,7 @@ Examples are given below.
 
 
 Example usage
-^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 Here is an example using the processors as well as the conversion method using data files:
 
 Example::

docs/source/main_classes/tokenizer.rst

@@ -1,5 +1,5 @@
 Tokenizer
-----------------------------------------------------
+-----------------------------------------------------------------------------------------------------------------------
 
 A tokenizer is in charge of preparing the inputs for a model. The library contains tokenizers for all the models. Most
 of the tokenizers are available in two flavors: a full python implementation and a "Fast" implementation based on the
@@ -36,24 +36,24 @@ alignment methods which can be used to map between the original string (characte
 getting the index of the token comprising a given character or the span of characters corresponding to a given token).
 
 
-``PreTrainedTokenizer``
-~~~~~~~~~~~~~~~~~~~~~~~~
+PreTrainedTokenizer
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.PreTrainedTokenizer
     :special-members: __call__
     :members:
 
 
-``PreTrainedTokenizerFast``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+PreTrainedTokenizerFast
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.PreTrainedTokenizerFast
     :special-members: __call__
     :members:
 
 
-``BatchEncoding``
-~~~~~~~~~~~~~~~~~~~~~~~~
+BatchEncoding
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.BatchEncoding
     :members:

docs/source/main_classes/trainer.rst

@@ -1,75 +1,75 @@
-Trainer
-----------
-
-The :class:`~transformers.Trainer` and :class:`~transformers.TFTrainer` classes provide an API for feature-complete
-training in most standard use cases. It's used in most of the :doc:`example scripts <../examples>`.
-
-Before instantiating your :class:`~transformers.Trainer`/:class:`~transformers.TFTrainer`, create a 
-:class:`~transformers.TrainingArguments`/:class:`~transformers.TFTrainingArguments` to access all the points of
-customization during training.
-
-The API supports distributed training on multiple GPUs/TPUs, mixed precision through `NVIDIA Apex
-<https://github.com/NVIDIA/apex>`__ for PyTorch and :obj:`tf.keras.mixed_precision` for TensorFlow.
-
-Both :class:`~transformers.Trainer` and :class:`~transformers.TFTrainer` contain the basic training loop supporting the
-previous features. To inject custom behavior you can subclass them and override the following methods:
-
-- **get_train_dataloader**/**get_train_tfdataset** -- Creates the training DataLoader (PyTorch) or TF Dataset.
-- **get_eval_dataloader**/**get_eval_tfdataset** -- Creates the evaulation DataLoader (PyTorch) or TF Dataset.
-- **get_test_dataloader**/**get_test_tfdataset** -- Creates the test DataLoader (PyTorch) or TF Dataset.
-- **log** -- Logs information on the various objects watching training.
-- **setup_wandb** -- Setups wandb (see `here <https://docs.wandb.com/huggingface>`__ for more information).
-- **create_optimizer_and_scheduler** -- Setups the optimizer and learning rate scheduler if they were not passed at
-  init.
-- **compute_loss** - Computes the loss on a batch of training inputs.
-- **training_step** -- Performs a training step.
-- **prediction_step** -- Performs an evaluation/test step.
-- **run_model** (TensorFlow only) -- Basic pass through the model.
-- **evaluate** -- Runs an evaluation loop and returns metrics.
-- **predict** -- Returns predictions (with metrics if labels are available) on a test set.
-
-Here is an example of how to customize :class:`~transformers.Trainer` using a custom loss function:
-
-.. code-block:: python
-
-    from transformers import Trainer
-    class MyTrainer(Trainer):
-        def compute_loss(self, model, inputs):
-            labels = inputs.pop("labels")
-            outputs = models(**inputs)
-            logits = outputs[0]
-            return my_custom_loss(logits, labels)
-
-
-``Trainer`` 
-~~~~~~~~~~~
-
-.. autoclass:: transformers.Trainer
-    :members:
-
-``TFTrainer`` 
-~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFTrainer
-    :members:
-
-``TrainingArguments``
-~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TrainingArguments
-    :members:
-
-``TFTrainingArguments``
-~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFTrainingArguments
-    :members:
-
-Utilities
-~~~~~~~~~
-
-.. autoclass:: transformers.EvalPrediction
-
-.. autofunction:: transformers.set_seed
-
-.. autofunction:: transformers.torch_distributed_zero_first
+Trainer
+-----------------------------------------------------------------------------------------------------------------------
+
+The :class:`~transformers.Trainer` and :class:`~transformers.TFTrainer` classes provide an API for feature-complete
+training in most standard use cases. It's used in most of the :doc:`example scripts <../examples>`.
+
+Before instantiating your :class:`~transformers.Trainer`/:class:`~transformers.TFTrainer`, create a 
+:class:`~transformers.TrainingArguments`/:class:`~transformers.TFTrainingArguments` to access all the points of
+customization during training.
+
+The API supports distributed training on multiple GPUs/TPUs, mixed precision through `NVIDIA Apex
+<https://github.com/NVIDIA/apex>`__ for PyTorch and :obj:`tf.keras.mixed_precision` for TensorFlow.
+
+Both :class:`~transformers.Trainer` and :class:`~transformers.TFTrainer` contain the basic training loop supporting the
+previous features. To inject custom behavior you can subclass them and override the following methods:
+
+- **get_train_dataloader**/**get_train_tfdataset** -- Creates the training DataLoader (PyTorch) or TF Dataset.
+- **get_eval_dataloader**/**get_eval_tfdataset** -- Creates the evaulation DataLoader (PyTorch) or TF Dataset.
+- **get_test_dataloader**/**get_test_tfdataset** -- Creates the test DataLoader (PyTorch) or TF Dataset.
+- **log** -- Logs information on the various objects watching training.
+- **setup_wandb** -- Setups wandb (see `here <https://docs.wandb.com/huggingface>`__ for more information).
+- **create_optimizer_and_scheduler** -- Setups the optimizer and learning rate scheduler if they were not passed at
+  init.
+- **compute_loss** - Computes the loss on a batch of training inputs.
+- **training_step** -- Performs a training step.
+- **prediction_step** -- Performs an evaluation/test step.
+- **run_model** (TensorFlow only) -- Basic pass through the model.
+- **evaluate** -- Runs an evaluation loop and returns metrics.
+- **predict** -- Returns predictions (with metrics if labels are available) on a test set.
+
+Here is an example of how to customize :class:`~transformers.Trainer` using a custom loss function:
+
+.. code-block:: python
+
+    from transformers import Trainer
+    class MyTrainer(Trainer):
+        def compute_loss(self, model, inputs):
+            labels = inputs.pop("labels")
+            outputs = models(**inputs)
+            logits = outputs[0]
+            return my_custom_loss(logits, labels)
+
+
+Trainer
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.Trainer
+    :members:
+
+TFTrainer
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFTrainer
+    :members:
+
+TrainingArguments
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TrainingArguments
+    :members:
+
+TFTrainingArguments
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFTrainingArguments
+    :members:
+
+Utilities
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.EvalPrediction
+
+.. autofunction:: transformers.set_seed
+
+.. autofunction:: transformers.torch_distributed_zero_first

docs/source/model_doc/albert.rst

@@ -1,15 +1,16 @@
 ALBERT
-----------------------------------------------------
+-----------------------------------------------------------------------------------------------------------------------
 
 Overview
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-The ALBERT model was proposed in `ALBERT: A Lite BERT for Self-supervised Learning of Language Representations <https://arxiv.org/abs/1909.11942>`_
-by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut. It presents
-two parameter-reduction techniques to lower memory consumption and increase the training speed of BERT:
+The ALBERT model was proposed in `ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
+<https://arxiv.org/abs/1909.11942>`__ by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma,
+Radu Soricut. It presents two parameter-reduction techniques to lower memory consumption and increase the training
+speed of BERT:
 
-- Splitting the embedding matrix into two smaller matrices
-- Using repeating layers split among groups
+- Splitting the embedding matrix into two smaller matrices.
+- Using repeating layers split among groups.
 
 The abstract from the paper is the following:
 
@@ -30,17 +31,17 @@ Tips:
   similar to a BERT-like architecture with the same number of hidden layers as it has to iterate through the same
   number of (repeating) layers.
 
-The original code can be found `here <https://github.com/google-research/ALBERT>`_.
+The original code can be found `here <https://github.com/google-research/ALBERT>`__.
 
 AlbertConfig
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.AlbertConfig
     :members:
 
 
 AlbertTokenizer
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.AlbertTokenizer
     :members: build_inputs_with_special_tokens, get_special_tokens_mask,
@@ -48,7 +49,7 @@ AlbertTokenizer
 
 
 Albert specific outputs
-~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.modeling_albert.AlbertForPreTrainingOutput
     :members:
@@ -58,98 +59,98 @@ Albert specific outputs
 
 
 AlbertModel
-~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.AlbertModel
-    :members:
+    :members: forward
 
 
 AlbertForPreTraining
-~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.AlbertForPreTraining
-    :members:
+    :members: forward
 
 
 AlbertForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.AlbertForMaskedLM
-    :members:
+    :members: forward
 
 
 AlbertForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.AlbertForSequenceClassification
-    :members:
+    :members: forward
 
 
 AlbertForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.AlbertForMultipleChoice
     :members:
 
 
 AlbertForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.AlbertForTokenClassification
-    :members:
+    :members: forward
 
 
 AlbertForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.AlbertForQuestionAnswering
-    :members:
+    :members: forward
 
 
 TFAlbertModel
-~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFAlbertModel
-    :members:
+    :members: call
 
 
 TFAlbertForPreTraining
-~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFAlbertForPreTraining
-    :members:
+    :members: call
 
 
 TFAlbertForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFAlbertForMaskedLM
-    :members:
+    :members: call
 
 
 TFAlbertForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFAlbertForSequenceClassification
-    :members:
+    :members: call
 
 
 TFAlbertForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFAlbertForMultipleChoice
-    :members:
+    :members: call
 
 
 TFAlbertForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFAlbertForTokenClassification
-    :members:
+    :members: call
 
 
 TFAlbertForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFAlbertForQuestionAnswering
-    :members:
+    :members: call

docs/source/model_doc/auto.rst

@@ -1,5 +1,5 @@
 AutoClasses
------------
+-----------------------------------------------------------------------------------------------------------------------
 
 In many cases, the architecture you want to use can be guessed from the name or the path of the pretrained model you
 are supplying to the :obj:`from_pretrained()` method.
@@ -20,112 +20,112 @@ There is one class of :obj:`AutoModel` for each task, and for each backend (PyTo
 
 
 AutoConfig
-~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.AutoConfig
     :members:
 
 
 AutoTokenizer
-~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.AutoTokenizer
     :members:
 
 
 AutoModel
-~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.AutoModel
     :members:
 
 
 AutoModelForPreTraining
-~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.AutoModelForPreTraining
     :members:
 
 
 AutoModelWithLMHead
-~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.AutoModelWithLMHead
     :members:
 
 
 AutoModelForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.AutoModelForSequenceClassification
     :members:
 
 
 AutoModelForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.AutoModelForMultipleChoice
     :members:
 
 
 AutoModelForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.AutoModelForTokenClassification
     :members:
 
 
 AutoModelForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.AutoModelForQuestionAnswering
     :members:
 
 
 TFAutoModel
-~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFAutoModel
     :members:
 
 
 TFAutoModelForPreTraining
-~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFAutoModelForPreTraining
     :members:
 
 
 TFAutoModelWithLMHead
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFAutoModelWithLMHead
     :members:
 
 
 TFAutoModelForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFAutoModelForSequenceClassification
     :members:
 
 
 TFAutoModelForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFAutoModelForMultipleChoice
     :members:
 
 
 TFAutoModelForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFAutoModelForTokenClassification
     :members:
 
 
 TFAutoModelForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFAutoModelForQuestionAnswering
     :members:

docs/source/model_doc/bart.rst

@@ -1,11 +1,11 @@
 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

@@ -1,13 +1,13 @@
 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

@@ -1,24 +1,36 @@
 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

@@ -1,8 +1,8 @@
 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:

docs/source/model_doc/ctrl.rst

@@ -1,12 +1,12 @@
 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

@@ -1,8 +1,8 @@
 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

@@ -1,14 +1,15 @@
 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

@@ -1,13 +1,12 @@
 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

@@ -1,14 +1,14 @@
 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

docs/source/model_doc/encoderdecoder.rst

@@ -1,24 +1,30 @@
 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

@@ -1,12 +1,12 @@
 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

@@ -1,49 +1,61 @@
 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

docs/source/model_doc/funnel.rst

@@ -1,14 +1,13 @@
 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

docs/source/model_doc/gpt.rst

@@ -1,12 +1,14 @@
 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

@@ -1,14 +1,13 @@
 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

@@ -1,126 +1,155 @@
 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: 
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-*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 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.
 
-The Authors' code can be found `here <https://github.com/allenai/longformer>`_ .
+The abstract from the paper is the following:
+
+*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:
 
-  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')
+.. code-block::
 
-  loss = model(input_ids, labels=input_ids, masked_lm_labels=mlm_labels)[0]
+    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')
+
+    loss = model(input_ids, labels=input_ids, masked_lm_labels=mlm_labels)[0]
 
 
 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

@@ -1,65 +1,72 @@
 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

@@ -1,11 +1,11 @@
 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

@@ -1,11 +1,11 @@
 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

@@ -1,13 +1,13 @@
 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

docs/source/model_doc/pegasus.rst

@@ -1,12 +1,12 @@
 Pegasus
-----------------------------------------------------
+-----------------------------------------------------------------------------------------------------------------------
 **DISCLAIMER:** 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.
 
 
 Overview
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 The Pegasus model was proposed in `PEGASUS: Pre-training with Extracted Gap-sentences for
 Abstractive Summarization <https://arxiv.org/pdf/1912.08777.pdf>`_ by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019.
@@ -19,7 +19,7 @@ The Authors' code can be found `here <https://github.com/google-research/pegasus
 
 
 Checkpoints
-~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 All the `checkpoints <https://huggingface.co/models?search=pegasus>`_ are finetuned for summarization, besides ``pegasus-large``, whence the other checkpoints are finetuned.
 - Each checkpoint is 2.2 GB on disk and 568M parameters.
 - FP16 is not supported (help/ideas on this appreciated!).
@@ -29,7 +29,7 @@ The gap is likely because of different alpha/length_penalty implementations in b
 
 
 Implementation Notes
-~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 - All models are transformer encoder-decoders with 16 layers in each component.
 - The implementation is completely inherited from ``BartForConditionalGeneration``
@@ -43,7 +43,7 @@ Implementation Notes
 
 
 Usage Example
-~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. code-block:: python
 
@@ -63,7 +63,7 @@ Usage Example
     assert tgt_text[0] == "California's largest electricity provider has turned off power to hundreds of thousands of customers."
 
 PegasusForConditionalGeneration
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 This class inherits all functionality from ``BartForConditionalGeneration``, see that page for method signatures.
 Available models are listed at `Model List <https://huggingface.co/models?search=pegasus>`__
@@ -73,7 +73,7 @@ Available models are listed at `Model List <https://huggingface.co/models?search
 
 
 PegasusConfig
-~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 This config fully inherits from ``BartConfig``, but pegasus uses different default values:
 Up to date parameter values can be seen in `S3 <https://s3.amazonaws.com/models.huggingface.co/bert/google/pegasus-xsum/config.json>`_.
 As of Aug 10, 2020, they are:
@@ -107,7 +107,7 @@ As of Aug 10, 2020, they are:
 
 
 PegasusTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 warning: ``add_tokens`` does not work at the moment.
 
 .. autoclass:: transformers.PegasusTokenizer

docs/source/model_doc/rag.rst

@@ -4,11 +4,14 @@ RAG
 Overview
 ~~~~~~~~~~~~~~~~~~~~~
 
-Retrieval-augmented generation ("RAG") models combine the powers of pretrained dense retrieval (DPR) and Seq2Seq models.
-RAG models retrieve docs, pass them to a seq2seq model, then marginalize to generate outputs.
-The retriever and seq2seq modules are initialized from pretrained models, and fine-tuned jointly, allowing both retrieval and generation to adapt to downstream tasks.
+Retrieval-augmented generation ("RAG") models combine the powers of pretrained dense retrieval (DPR) and
+sequence-to-sequence models. RAG models retrieve documents, pass them to a seq2seq model, then marginalize to generate
+outputs. The retriever and seq2seq modules are initialized from pretrained models, and fine-tuned jointly, allowing
+both retrieval and generation to adapt to downstream tasks.
 
-It is based on the paper `Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks <https://arxiv.org/abs/2005.11401>`__ by Patrick Lewis, Ethan Perez, Aleksandara Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela.
+It is based on the paper `Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
+<https://arxiv.org/abs/2005.11401>`__ by Patrick Lewis, Ethan Perez, Aleksandara Piktus, Fabio Petroni, Vladimir
+Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela.
 
 The abstract from the paper is the following:
 
@@ -47,7 +50,7 @@ RagTokenizer
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.RagTokenizer
-    :members:
+    :members: prepare_seq2seq_batch
 
 
 Rag specific outputs

docs/source/model_doc/reformer.rst

@@ -1,20 +1,37 @@
 Reformer
-----------------------------------------------------
-**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 Reformer model was presented in `Reformer: The Efficient Transformer <https://arxiv.org/abs/2001.04451.pdf>`_ by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
-Here the abstract: 
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-*Large Transformer models routinely achieve state-of-the-art results on a number of tasks but training these models can be prohibitively costly, especially on long sequences. We introduce two techniques to improve the efficiency of Transformers. For one, we replace dot-product attention by one that uses locality-sensitive hashing, changing its complexity from O(L^2) to O(Llog(L)), where L is the length of the sequence. Furthermore, we use reversible residual layers instead of the standard residuals, which allows storing activations only once in the training process instead of N times, where N is the number of layers. The resulting model, the Reformer, performs on par with Transformer models while being much more memory-efficient and much faster on long sequences.*
+The Reformer model was proposed in the paper `Reformer: The Efficient Transformer
+<https://arxiv.org/abs/2001.04451.pdf>`__ by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
 
-The Authors' code can be found `here <https://github.com/google/trax/tree/master/trax/models/reformer>`_ .
+The abstract from the paper is the following: 
+
+*Large Transformer models routinely achieve state-of-the-art results on a number of tasks but training these models can
+be prohibitively costly, especially on long sequences. We introduce two techniques to improve the efficiency of
+Transformers. For one, we replace dot-product attention by one that uses locality-sensitive hashing, changing its
+complexity from O(L^2) to O(Llog(L)), where L is the length of the sequence. Furthermore, we use reversible residual
+layers instead of the standard residuals, which allows storing activations only once in the training process instead of
+N times, where N is the number of layers. The resulting model, the Reformer, performs on par with Transformer models
+while being much more memory-efficient and much faster on long sequences.*
+
+The Authors' code can be found `here <https://github.com/google/trax/tree/master/trax/models/reformer>`__.
 
 Axial Positional Encodings
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-Axial Positional Encodings were first implemented in Google's `trax library <https://github.com/google/trax/blob/4d99ad4965bab1deba227539758d59f0df0fef48/trax/layers/research/position_encodings.py#L29>`_ and developed by the authors of this model's paper. In models that are treating very long input sequences, the conventional position id encodings store an embedings vector of size :math:`d` being the ``config.hidden_size`` for every position :math:`i, \ldots, n_s`, with :math:`n_s` being ``config.max_embedding_size``. *E.g.*, having a sequence length of :math:`n_s = 2^{19} \approx 0.5M` and a ``config.hidden_size`` of :math:`d = 2^{10} \approx 1000` would result in a position encoding matrix:
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Axial Positional Encodings were first implemented in Google's `trax library
+<https://github.com/google/trax/blob/4d99ad4965bab1deba227539758d59f0df0fef48/trax/layers/research/position_encodings.py#L29>`__
+and developed by the authors of this model's paper. In models that are treating very long input sequences, the
+conventional position id encodings store an embedings vector of size :math:`d` being the :obj:`config.hidden_size` for
+every position :math:`i, \ldots, n_s`, with :math:`n_s` being :obj:`config.max_embedding_size`. This means that having
+a sequence length of :math:`n_s = 2^{19} \approx 0.5M` and a ``config.hidden_size`` of :math:`d = 2^{10} \approx 1000`
+would result in a position encoding matrix:
 
 .. math::
     X_{i,j}, \text{ with } i \in \left[1,\ldots, d\right] \text{ and } j \in \left[1,\ldots, n_s\right] 
@@ -42,94 +59,127 @@ Therefore the following holds:
                 X^{2}_{i - d^1, l}, & \text{if } i \ge d^1 \text{ with } l = \lfloor\frac{j}{n_s^1}\rfloor
               \end{cases}
 
-Intuitively, this means that a position embedding vector :math:`x_j \in \mathbb{R}^{d}` is now the composition of two factorized embedding vectors: :math:`x^1_{k, l} + x^2_{l, k}`, where as the ``config.max_embedding_size`` dimension :math:`j` is factorized into :math:`k \text{ and } l`.
-This design ensures that each position embedding vector :math:`x_j` is unique.
+Intuitively, this means that a position embedding vector :math:`x_j \in \mathbb{R}^{d}` is now the composition of two
+factorized embedding vectors: :math:`x^1_{k, l} + x^2_{l, k}`, where as the :obj:`config.max_embedding_size` dimension
+:math:`j` is factorized into :math:`k \text{ and } l`. This design ensures that each position embedding vector
+:math:`x_j` is unique.
 
-Using the above example again, axial position encoding with :math:`d^1 = 2^5, d^2 = 2^5, n_s^1 = 2^9, n_s^2 = 2^{10}` can drastically reduced the number of parameters to :math:`2^{14} + 2^{15} \approx 49000` parameters.
-
-In practice, the parameter ``config.axial_pos_embds_dim`` is set to ``list``:math:`(d^1, d^2)` which sum has to be equal to ``config.hidden_size`` and ``config.axial_pos_shape`` is set to ``list``:math:`(n_s^1, n_s^2)` and which product has to be equal to ``config.max_embedding_size`` which during training has to be equal to the ``sequence length`` of the ``input_ids``.
+Using the above example again, axial position encoding with :math:`d^1 = 2^5, d^2 = 2^5, n_s^1 = 2^9, n_s^2 = 2^{10}`
+can drastically reduced the number of parameters to :math:`2^{14} + 2^{15} \approx 49000` parameters.
 
+In practice, the parameter :obj:`config.axial_pos_embds_dim` is set to a tuple :math:`(d^1, d^2)` which sum has to
+be equal to :obj:`config.hidden_size` and :obj:`config.axial_pos_shape` is set to a tuple :math:`(n_s^1, n_s^2)` which
+product has to be equal to :obj:`config.max_embedding_size`, which during training has to be equal to the
+`sequence length` of the :obj:`input_ids`.
 
 
 LSH Self Attention
-~~~~~~~~~~~~~~~~~~~~
-In Locality sensitive hashing (LSH) self attention the key and query projection weights are tied. Therefore, the key query embedding vectors are also tied.
-LSH self attention uses the locality sensitive 
-hashing mechanism proposed in `Practical and Optimal LSH for Angular Distance <https://arxiv.org/abs/1509.02897>`_ to assign each of the tied key query embedding vectors to one of ``config.num_buckets`` possible buckets. The premise is that the more "similar" key query embedding vectors (in terms of *cosine similarity*) are to each other, the more likely they are assigned to the same bucket. 
-The accuracy of the LSH mechanism can be improved by increasing ``config.num_hashes`` or directly the argument ``num_hashes`` of the forward function so that the output of the LSH self attention better approximates the output of the "normal" full self attention.
-The buckets are then sorted and chunked into query key embedding vector chunks each of length ``config.lsh_chunk_length``. For each chunk, the query embedding vectors attend to its key vectors (which are tied to themselves) and to the key embedding vectors of ``config.lsh_num_chunks_before`` previous neighboring chunks and ``config.lsh_num_chunks_after`` following neighboring chunks.
-For more information, see the `original Paper <https://arxiv.org/abs/2001.04451>`_ or this great `blog post <https://www.pragmatic.ml/reformer-deep-dive/>`_.
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+In Locality sensitive hashing (LSH) self attention the key and query projection weights are tied. Therefore, the key
+query embedding vectors are also tied. LSH self attention uses the locality sensitive hashing mechanism proposed in
+`Practical and Optimal LSH for Angular Distance <https://arxiv.org/abs/1509.02897>`__ to assign each of the tied key
+query embedding vectors to one of :obj:`config.num_buckets` possible buckets. The premise is that the more "similar"
+key query embedding vectors (in terms of *cosine similarity*) are to each other, the more likely they are assigned to
+the same bucket. 
 
-Note that ``config.num_buckets`` can also be factorized into a ``list``:math:`(n_{\text{buckets}}^1, n_{\text{buckets}}^2)`. This way instead of assigning the query key embedding vectors to one of :math:`(1,\ldots, n_{\text{buckets}})` they are assigned to one of :math:`(1-1,\ldots, n_{\text{buckets}}^1-1, \ldots, 1-n_{\text{buckets}}^2, \ldots, n_{\text{buckets}}^1-n_{\text{buckets}}^2)`. This is crucial for very long sequences to save memory.
+The accuracy of the LSH mechanism can be improved by increasing :obj:`config.num_hashes` or directly the argument 
+:obj:`num_hashes` of the forward function so that the output of the LSH self attention better approximates the output
+of the "normal" full self attention. The buckets are then sorted and chunked into query key embedding vector chunks
+each of length :obj:`config.lsh_chunk_length`. For each chunk, the query embedding vectors attend to its key vectors
+(which are tied to themselves) and to the key embedding vectors of :obj:`config.lsh_num_chunks_before` previous
+neighboring chunks and :obj:`config.lsh_num_chunks_after` following neighboring chunks.
 
-When training a model from scratch, it is recommended to leave ``config.num_buckets=None``, so that depending on the sequence length a good value for ``num_buckets`` is calculated on the fly. This value will then automatically be saved in the config and should be reused for inference.
+For more information, see the `original Paper <https://arxiv.org/abs/2001.04451>`__ or this great `blog post
+<https://www.pragmatic.ml/reformer-deep-dive/>`__.
 
-Using LSH self attention, the memory and time complexity of the query-key matmul operation can be reduced from :math:`\mathcal{O}(n_s \times n_s)` to :math:`\mathcal{O}(n_s \times \log(n_s))`, which usually represents the memory and time bottleneck in a transformer model, with :math:`n_s` being the sequence length.
+Note that :obj:`config.num_buckets` can also be factorized into a list
+:math:`(n_{\text{buckets}}^1, n_{\text{buckets}}^2)`. This way instead of assigning the query key embedding vectors to
+one of :math:`(1,\ldots, n_{\text{buckets}})` they are assigned to one of
+:math:`(1-1,\ldots, n_{\text{buckets}}^1-1, \ldots, 1-n_{\text{buckets}}^2, \ldots, n_{\text{buckets}}^1-n_{\text{buckets}}^2)`.
+This is crucial for very long sequences to save memory.
+
+When training a model from scratch, it is recommended to leave :obj:`config.num_buckets=None`, so that depending on the
+sequence length a good value for :obj:`num_buckets` is calculated on the fly. This value will then automatically be
+saved in the config and should be reused for inference.
+
+Using LSH self attention, the memory and time complexity of the query-key matmul operation can be reduced from
+:math:`\mathcal{O}(n_s \times n_s)` to :math:`\mathcal{O}(n_s \times \log(n_s))`, which usually represents the memory
+and time bottleneck in a transformer model, with :math:`n_s` being the sequence length.
 
 
 Local Self Attention
-~~~~~~~~~~~~~~~~~~~~
-Local self attention is essentially a "normal" self attention layer with 
-key, query and value projections, but is chunked so that in each chunk of length ``config.local_chunk_length`` the query embedding vectors only attends to the key embedding vectors in its chunk and to the key embedding vectors of ``config.local_num_chunks_before`` previous neighboring chunks and ``config.local_num_chunks_after`` following neighboring chunks.
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Using Local self attention, the memory and time complexity of the query-key matmul operation can be reduced from :math:`\mathcal{O}(n_s \times n_s)` to :math:`\mathcal{O}(n_s \times \log(n_s))`, which usually represents the memory and time bottleneck in a transformer model, with :math:`n_s` being the sequence length.
+Local self attention is essentially a "normal" self attention layer with key, query and value projections, but is
+chunked so that in each chunk of length :obj:`config.local_chunk_length` the query embedding vectors only attends to
+the key embedding vectors in its chunk and to the key embedding vectors of :obj:`config.local_num_chunks_before`
+previous neighboring chunks and :obj:`config.local_num_chunks_after` following neighboring chunks.
+
+Using Local self attention, the memory and time complexity of the query-key matmul operation can be reduced from
+:math:`\mathcal{O}(n_s \times n_s)` to :math:`\mathcal{O}(n_s \times \log(n_s))`, which usually represents the memory
+and time bottleneck in a transformer model, with :math:`n_s` being the sequence length.
 
 
 Training
-~~~~~~~~~~~~~~~~~~~~
-During training, we must ensure that the sequence length is set to a value that can be divided by the least common multiple of ``config.lsh_chunk_length`` and ``config.local_chunk_length`` and that the parameters of the Axial Positional Encodings are correctly set as described above. Reformer is very memory efficient so that the model can easily be trained on sequences as long as 64000 tokens.
-For training, the ``ReformerModelWithLMHead`` should be used as follows: 
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-::
+During training, we must ensure that the sequence length is set to a value that can be divided by the least common
+multiple of :obj:`config.lsh_chunk_length` and :obj:`config.local_chunk_length` and that the parameters of the Axial
+Positional Encodings are correctly set as described above. Reformer is very memory efficient so that the model can
+easily be trained on sequences as long as 64000 tokens.
+
+For training, the :class:`~transformers.ReformerModelWithLMHead` should be used as follows: 
+
+.. code-block::
 
   input_ids = tokenizer.encode('This is a sentence from the training data', return_tensors='pt')
   loss = model(input_ids, labels=input_ids)[0]
 
 
 ReformerConfig
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.ReformerConfig
     :members:
 
 
 ReformerTokenizer
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.ReformerTokenizer
-    :members: 
+    :members: save_vocabulary
 
 
 ReformerModel
-~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.ReformerModel
-    :members:
+    :members: forward
 
 
 ReformerModelWithLMHead
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.ReformerModelWithLMHead
-    :members:
+    :members: forward
 
 
 ReformerForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.ReformerForMaskedLM
-    :members:
+    :members: forward
 
 
 ReformerForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.ReformerForSequenceClassification
-    :members:
+    :members: forward
 
 
 ReformerForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.ReformerForQuestionAnswering
-    :members:
+    :members: forward

docs/source/model_doc/retribert.rst

@@ -1,39 +1,40 @@
 RetriBERT
-----------------------------------------------------
+-----------------------------------------------------------------------------------------------------------------------
 
 Overview
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-The RetriBERT model was proposed in the blog post
-`Explain Anything Like I'm Five: A Model for Open Domain Long Form Question Answering <https://yjernite.github.io/lfqa.html>`__,
-RetriBERT is a small model that uses either a single or pair of Bert encoders with lower-dimension projection for dense semantic indexing of text.
+The RetriBERT model was proposed in the blog post `Explain Anything Like I'm Five: A Model for Open Domain Long Form
+Question Answering <https://yjernite.github.io/lfqa.html>`__. RetriBERT is a small model that uses either a single or
+pair of BERT encoders with lower-dimension projection for dense semantic indexing of text.
 
-Code to train and use the model can be found `here <https://github.com/huggingface/transformers/tree/master/examples/distillation>`_.
+Code to train and use the model can be found `here
+<https://github.com/huggingface/transformers/tree/master/examples/distillation>`__.
 
 
 RetriBertConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.RetriBertConfig
     :members:
 
 
 RetriBertTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.RetriBertTokenizer
     :members:
 
 
 RetriBertTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.RetriBertTokenizerFast
     :members:
 
 
 RetriBertModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.RetriBertModel
-    :members:
+    :members: forward

docs/source/model_doc/roberta.rst

@@ -1,12 +1,12 @@
 RoBERTa
-----------------------------------------------------
+-----------------------------------------------------------------------------------------------------------------------
 
 Overview
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-The RoBERTa model was proposed in `RoBERTa: A Robustly Optimized BERT Pretraining Approach <https://arxiv.org/abs/1907.11692>`_
-by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer,
-Veselin Stoyanov. It is based on Google's BERT model released in 2018.
+The RoBERTa model was proposed in `RoBERTa: A Robustly Optimized BERT Pretraining Approach
+<https://arxiv.org/abs/1907.11692>`_ by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer
+Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov. It is based on Google's BERT model released in 2018.
 
 It builds on BERT and modifies key hyperparameters, removing the next-sentence pretraining
 objective and training with much larger mini-batches and learning rates.
@@ -27,22 +27,23 @@ Tips:
 - This implementation is the same as :class:`~transformers.BertModel` with a tiny embeddings tweak as well as a
   setup for Roberta pretrained models.
 - RoBERTa has the same architecture as BERT, but uses a byte-level BPE as a tokenizer (same as GPT-2) and uses a
-  different pre-training scheme.
-- RoBERTa 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 `</s>`)
-- `Camembert <./camembert.html>`__ is a wrapper around RoBERTa. Refer to this page for usage examples.
+  different pretraining scheme.
+- RoBERTa 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:`</s>`)
+- :doc:`CamemBERT <camembert>` is a wrapper around RoBERTa. Refer to this page for usage examples.
 
 The original code can be found `here <https://github.com/pytorch/fairseq/tree/master/examples/roberta>`_.
 
 
 RobertaConfig
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.RobertaConfig
     :members:
 
 
 RobertaTokenizer
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.RobertaTokenizer
     :members: build_inputs_with_special_tokens, get_special_tokens_mask,
@@ -50,98 +51,98 @@ RobertaTokenizer
 
 
 RobertaTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.RobertaTokenizerFast
     :members: build_inputs_with_special_tokens
 
 
 RobertaModel
-~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.RobertaModel
-    :members:
+    :members: forward
 
 
 RobertaForCausalLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.RobertaForCausalLM
-    :members:
+    :members: forward
 
 
 RobertaForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.RobertaForMaskedLM
-    :members:
+    :members: forward
 
 
 RobertaForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.RobertaForSequenceClassification
-    :members:
+    :members: forward
 
 
 RobertaForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.RobertaForMultipleChoice
-    :members:
+    :members: forward
 
 
 RobertaForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.RobertaForTokenClassification
-    :members:
+    :members: forward
 
 
 RobertaForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.RobertaForQuestionAnswering
-    :members:
+    :members: forward
 
 
 TFRobertaModel
-~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFRobertaModel
-    :members:
+    :members: call
 
 
 TFRobertaForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFRobertaForMaskedLM
-    :members:
+    :members: call
 
 
 TFRobertaForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFRobertaForSequenceClassification
-    :members:
+    :members: call
 
 
 TFRobertaForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFRobertaForMultipleChoice
-    :members:
+    :members: call
 
 
 TFRobertaForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFRobertaForTokenClassification
-    :members:
+    :members: call
 
 
 TFRobertaForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFRobertaForQuestionAnswering
-    :members:
+    :members: call

docs/source/model_doc/t5.rst

@@ -1,47 +1,66 @@
 T5
-----------------------------------------------------
-**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 T5 model was presented in `Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer <https://arxiv.org/pdf/1910.10683.pdf>`_ by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu in 
-Here the abstract: 
+The T5 model was presented in `Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
+<https://arxiv.org/pdf/1910.10683.pdf>`_ by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang,
+Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu.
 
-*Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. 
-In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. 
-Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. 
-By combining the insights from our exploration with scale and our new "Colossal Clean Crawled Corpus", we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. 
-To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code.*
+The abstract from the paper is the following:
+
+*Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream
+task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning
+has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of
+transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a
+text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer
+approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration
+with scale and our new "Colossal Clean Crawled Corpus", we achieve state-of-the-art results on many benchmarks covering
+summarization, question answering, text classification, and more. To facilitate future work on transfer learning for
+NLP, we release our dataset, pre-trained models, and code.*
 
 Tips:
 
-- T5 is an encoder-decoder model pre-trained on a multi-task mixture of unsupervised 
-  and supervised tasks and for which each task is converted into a text-to-text format.
-  T5 works well on a variety of tasks out-of-the-box by prepending a different prefix to the input corresponding to each task, e.g.: for translation: *translate English to German: ..., summarize: ...*.
-  For more information about which prefix to use, it is easiest to look into Appendix D of the `paper <https://arxiv.org/pdf/1910.10683.pdf>`_ .
-- For sequence to sequence generation, it is recommended to use ``T5ForConditionalGeneration.generate()``. The method takes care of feeding the encoded input via cross-attention layers to the decoder and auto-regressively generates the decoder output.
+- T5 is an encoder-decoder model pre-trained on a multi-task mixture of unsupervised and supervised tasks and for which
+  each task is converted into a text-to-text format. T5 works well on a variety of tasks out-of-the-box by prepending a
+  different prefix to the input corresponding to each task, e.g., for translation: *translate English to German: ...*,
+  for summarization: *summarize: ...*.
+  
+  For more information about which prefix to use, it is easiest to look into Appendix D of the `paper
+  <https://arxiv.org/pdf/1910.10683.pdf>`__.
+- For sequence-to-sequence generation, it is recommended to use :obj:`T5ForConditionalGeneration.generate()``. This
+  method takes care of feeding the encoded input via cross-attention layers to the decoder and auto-regressively
+  generates the decoder output.
 - T5 uses relative scalar embeddings. Encoder input padding can be done on the left and on the right.
 
-The original code can be found `here <https://github.com/google-research/text-to-text-transfer-transformer>`_.
+The original code can be found `here <https://github.com/google-research/text-to-text-transfer-transformer>`__.
 
 Training
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-T5 is an encoder-decoder model and converts all NLP problems into a text-to-text format. It is trained using teacher forcing.
-This means that for training we always need an input sequence and a target sequence. 
-The input sequence is fed to the model using ``input_ids``. The target sequence is shifted to the right, *i.e.* prepended by a start-sequence token and fed to the decoder using the `decoder_input_ids`. In teacher-forcing style, the target sequence is then appended by the EOS token and corresponds to the ``labels``. The PAD token is hereby used as the start-sequence token.
-T5 can be trained / fine-tuned both in a supervised and unsupervised fashion.
+T5 is an encoder-decoder model and converts all NLP problems into a text-to-text format. It is trained using teacher
+forcing. This means that for training we always need an input sequence and a target sequence. The input sequence is fed
+to the model using :obj:`input_ids``. The target sequence is shifted to the right, i.e., prepended by a start-sequence
+token and fed to the decoder using the :obj:`decoder_input_ids`. In teacher-forcing style, the target sequence is then
+appended by the EOS token and corresponds to the :obj:`labels`. The PAD token is hereby used as the start-sequence
+token. T5 can be trained / fine-tuned both in a supervised and unsupervised fashion.
 
 - Unsupervised denoising training
 
   In this setup spans of the input sequence are masked by so-called sentinel tokens (*a.k.a* unique mask tokens) 
   and the output sequence is formed as a concatenation of the same sentinel tokens and the *real* masked tokens. 
-  Each sentinel token represents a unique mask token for this sentence and should start with ``<extra_id_0>``, ``<extra_id_1>``, ... up to ``<extra_id_99>``. As a default 100 sentinel tokens are available in ``T5Tokenizer``.
-  *E.g.* the sentence "The cute dog walks in the park" with the masks put on "cute dog" and "the" should be processed as follows: 
+  Each sentinel token represents a unique mask token for this sentence and should start with :obj:`<extra_id_0>`, 
+  :obj:`<extra_id_1>`, ... up to :obj:`<extra_id_99>`. As a default, 100 sentinel tokens are available in
+  :class:`~transformers.T5Tokenizer`.
+  
+  For instance, the sentence "The cute dog walks in the park" with the masks put on "cute dog" and "the" should be
+  processed as follows: 
 
-::
+.. code-block::
 
   input_ids = tokenizer.encode('The <extra_id_0> walks in <extra_id_1> park', return_tensors='pt')
   labels = tokenizer.encode('<extra_id_0> cute dog <extra_id_1> the <extra_id_2> </s>', return_tensors='pt')
@@ -50,11 +69,11 @@ T5 can be trained / fine-tuned both in a supervised and unsupervised fashion.
 
 - Supervised training
 
-  In this setup the input sequence and output sequence are standard sequence to sequence input output mapping.
-  In translation, *e.g.* the input sequence "The house is wonderful." and output sequence "Das Haus ist wunderbar." should 
-  be processed as follows:
+  In this setup the input sequence and output sequence are standard sequence-to-sequence input output mapping.
+  In translation, for instance with the input sequence "The house is wonderful." and output sequence "Das Haus ist
+  wunderbar.", the sentences should be processed as follows:
   
-::
+.. code-block::
 
   input_ids = tokenizer.encode('translate English to German: The house is wonderful. </s>', return_tensors='pt')
   labels = tokenizer.encode('Das Haus ist wunderbar. </s>', return_tensors='pt')
@@ -63,43 +82,43 @@ T5 can be trained / fine-tuned both in a supervised and unsupervised fashion.
 
 
 T5Config
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.T5Config
     :members:
 
 
 T5Tokenizer
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.T5Tokenizer
     :members: build_inputs_with_special_tokens, get_special_tokens_mask,
-        create_token_type_ids_from_sequences, save_vocabulary
+        create_token_type_ids_from_sequences, prepare_seq2seq_batch, save_vocabulary
 
 
 T5Model
-~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.T5Model
-    :members:
+    :members: forward
 
 
 T5ForConditionalGeneration
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.T5ForConditionalGeneration
-    :members:
+    :members: forward
 
 
 TFT5Model
-~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFT5Model
-    :members:
+    :members: call
 
 
 TFT5ForConditionalGeneration
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFT5ForConditionalGeneration
-    :members:
+    :members: call

docs/source/model_doc/transformerxl.rst

@@ -1,15 +1,14 @@
 Transformer XL
-----------------------------------------------------
+-----------------------------------------------------------------------------------------------------------------------
 
 Overview
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-The Transformer-XL model was proposed in
-`Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context <https://arxiv.org/abs/1901.02860>`__
-by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
-It's a causal (uni-directional) transformer with relative positioning (sinusoïdal) embeddings which can reuse
-previously computed hidden-states to attend to longer context (memory).
-This model also uses adaptive softmax inputs and outputs (tied).
+The Transformer-XL model was proposed in `Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context
+<https://arxiv.org/abs/1901.02860>`__ by Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan
+Salakhutdinov. It's a causal (uni-directional) transformer with relative positioning (sinusoïdal) embeddings which can
+reuse previously computed hidden-states to attend to longer context (memory). This model also uses adaptive softmax
+inputs and outputs (tied).
 
 The abstract from the paper is the following:
 
@@ -30,32 +29,32 @@ Tips:
   The original implementation trains on SQuAD with padding on the left, therefore the padding defaults are set to left.
 - Transformer-XL is one of the few models that has no sequence length limit.
 
-The original code can be found `here <https://github.com/kimiyoung/transformer-xl>`_.
+The original code can be found `here <https://github.com/kimiyoung/transformer-xl>`__.
 
 
 TransfoXLConfig
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TransfoXLConfig
     :members:
 
 
 TransfoXLTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TransfoXLTokenizer
     :members: save_vocabulary
 
 
 TransfoXLTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TransfoXLTokenizerFast
     :members:
 
 
 TransfoXL specific outputs
-~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.modeling_transfo_xl.TransfoXLModelOutput
     :members:
@@ -71,28 +70,28 @@ TransfoXL specific outputs
 
 
 TransfoXLModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TransfoXLModel
-    :members:
+    :members: forward
 
 
 TransfoXLLMHeadModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TransfoXLLMHeadModel
-    :members:
+    :members: forward
 
 
 TFTransfoXLModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFTransfoXLModel
-    :members:
+    :members: call
 
 
 TFTransfoXLLMHeadModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFTransfoXLLMHeadModel
-    :members:
+    :members: call

docs/source/model_doc/xlm.rst

@@ -1,15 +1,15 @@
 XLM
-----------------------------------------------------
+-----------------------------------------------------------------------------------------------------------------------
 
 Overview
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-The XLM model was proposed in `Cross-lingual Language Model Pretraining <https://arxiv.org/abs/1901.07291>`_
-by Guillaume Lample*, Alexis Conneau*. It's a transformer pre-trained using one of the following objectives:
+The XLM model was proposed in `Cross-lingual Language Model Pretraining <https://arxiv.org/abs/1901.07291>`__ by
+Guillaume Lample, Alexis Conneau. It's a transformer pretrained using one of the following objectives:
 
 - a causal language modeling (CLM) objective (next token prediction),
-- a masked language modeling (MLM) objective (Bert-like), or
-- a Translation Language Modeling (TLM) object (extension of Bert's MLM to multiple language inputs)
+- a masked language modeling (MLM) objective (BERT-like), or
+- a Translation Language Modeling (TLM) object (extension of BERT's MLM to multiple language inputs)
 
 The abstract from the paper is the following:
 
@@ -27,20 +27,20 @@ Tips:
 
 - XLM has many different checkpoints, which were trained using different objectives: CLM, MLM or TLM. Make sure to
   select the correct objective for your task (e.g. MLM checkpoints are not suitable for generation).
-- XLM has multilingual checkpoints which leverage a specific `lang` parameter. Check out the
-  `multi-lingual <../multilingual.html>`__ page for more information.
+- XLM has multilingual checkpoints which leverage a specific :obj:`lang` parameter. Check out the
+  :doc:`multi-lingual <../multilingual>` page for more information.
 
-The original code can be found `here <https://github.com/facebookresearch/XLM/>`_.
+The original code can be found `here <https://github.com/facebookresearch/XLM/>`__.
 
 
 XLMConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.XLMConfig
     :members:
 
 XLMTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.XLMTokenizer
     :members: build_inputs_with_special_tokens, get_special_tokens_mask,
@@ -48,99 +48,99 @@ XLMTokenizer
 
 
 XLM specific outputs
-~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.modeling_xlm.XLMForQuestionAnsweringOutput
     :members:
 
 
 XLMModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.XLMModel
-    :members:
+    :members: forward
 
 
 XLMWithLMHeadModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.XLMWithLMHeadModel
-    :members:
+    :members: forward
 
 
 XLMForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.XLMForSequenceClassification
-    :members:
+    :members: forward
 
 
 XLMForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.XLMForMultipleChoice
-    :members:
+    :members: forward
 
 
 XLMForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.XLMForTokenClassification
-    :members:
+    :members: forward
 
 
 XLMForQuestionAnsweringSimple
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.XLMForQuestionAnsweringSimple
-    :members:
+    :members: forward
 
 
 XLMForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.XLMForQuestionAnswering
-    :members:
+    :members: forward
 
 
 TFXLMModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFXLMModel
-    :members:
+    :members: call
 
 
 TFXLMWithLMHeadModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFXLMWithLMHeadModel
-    :members:
+    :members: call
 
 
 TFXLMForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFXLMForSequenceClassification
-    :members:
+    :members: call
 
 
 TFXLMForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFXLMForMultipleChoice
-    :members:
+    :members: call
 
 
 TFXLMForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFXLMForTokenClassification
-    :members:
+    :members: call
 
 
 
 TFXLMForQuestionAnsweringSimple
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFXLMForQuestionAnsweringSimple
-    :members:
+    :members: call

docs/source/model_doc/xlmroberta.rst

@@ -1,13 +1,14 @@
 XLM-RoBERTa
-------------------------------------------
+-----------------------------------------------------------------------------------------------------------------------
 
 Overview
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-The XLM-RoBERTa model was proposed in `Unsupervised Cross-lingual Representation Learning at Scale <https://arxiv.org/abs/1911.02116>`__
-by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán,
-Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov. It is based on Facebook's RoBERTa model released in 2019.
-It is a large multi-lingual language model, trained on 2.5TB of filtered CommonCrawl data.
+The XLM-RoBERTa model was proposed in `Unsupervised Cross-lingual Representation Learning at Scale
+<https://arxiv.org/abs/1911.02116>`__ by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume
+Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov. It is based on Facebook's
+RoBERTa model released in 2019. It is a large multi-lingual language model, trained on 2.5TB of filtered CommonCrawl
+data.
 
 The abstract from the paper is the following:
 
@@ -25,24 +26,24 @@ and XNLI benchmarks. We will make XLM-R code, data, and models publicly availabl
 
 Tips:
 
-- XLM-R is a multilingual model trained on 100 different languages. Unlike some XLM multilingual models, it does
-  not require `lang` tensors to understand which language is used, and should be able to determine the correct
+- XLM-RoBERTa is a multilingual model trained on 100 different languages. Unlike some XLM multilingual models, it does
+  not require :obj:`lang` tensors to understand which language is used, and should be able to determine the correct
   language from the input ids.
-- 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://github.com/pytorch/fairseq/tree/master/examples/xlmr>`_.
+The original code can be found `here <https://github.com/pytorch/fairseq/tree/master/examples/xlmr>`__.
 
 
 XLMRobertaConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.XLMRobertaConfig
     :members:
 
 
 XLMRobertaTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.XLMRobertaTokenizer
     :members: build_inputs_with_special_tokens, get_special_tokens_mask,
@@ -50,91 +51,91 @@ XLMRobertaTokenizer
 
 
 XLMRobertaModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.XLMRobertaModel
-    :members:
+    :members: forward
 
 
 XLMRobertaForCausalLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.XLMRobertaForCausalLM
-    :members:
+    :members: forward
 
 
 XLMRobertaForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.XLMRobertaForMaskedLM
-    :members:
+    :members: forward
 
 
 XLMRobertaForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.XLMRobertaForSequenceClassification
-    :members:
+    :members: forward
 
 
 XLMRobertaForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.XLMRobertaForMultipleChoice
-    :members:
+    :members: forward
 
 
 XLMRobertaForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.XLMRobertaForTokenClassification
-    :members:
+    :members: forward
 
 
 XLMRobertaForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.XLMRobertaForQuestionAnswering
-    :members:
+    :members: forward
 
 
 TFXLMRobertaModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFXLMRobertaModel
-    :members:
+    :members: call
 
 
 TFXLMRobertaForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFXLMRobertaForMaskedLM
-    :members:
+    :members: call
 
 
 TFXLMRobertaForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFXLMRobertaForSequenceClassification
-    :members:
+    :members: call
 
 
 TFXLMRobertaForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFXLMRobertaForMultipleChoice
-    :members:
+    :members: call
 
 
 TFXLMRobertaForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFXLMRobertaForTokenClassification
-    :members:
+    :members: call
 
 
 TFXLMRobertaForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFXLMRobertaForQuestionAnswering
-    :members:
+    :members: call

docs/source/model_doc/xlnet.rst

@@ -1,14 +1,14 @@
 XLNet
-----------------------------------------------------
+-----------------------------------------------------------------------------------------------------------------------
 
 Overview
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-The XLNet model was proposed in `XLNet: Generalized Autoregressive Pretraining for Language Understanding <https://arxiv.org/abs/1906.08237>`_
-by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
-XLnet is an extension of the Transformer-XL model pre-trained using an autoregressive method
-to learn bidirectional contexts by maximizing the expected likelihood over all permutations
-of the input sequence factorization order.
+The XLNet model was proposed in `XLNet: Generalized Autoregressive Pretraining for Language Understanding
+<https://arxiv.org/abs/1906.08237>`_ by Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov,
+Quoc V. Le. XLnet is an extension of the Transformer-XL model pre-trained using an autoregressive method to learn
+bidirectional contexts by maximizing the expected likelihood over all permutations of the input sequence factorization
+order.
 
 The abstract from the paper is the following:
 
@@ -24,26 +24,26 @@ a large margin, including question answering, natural language inference, sentim
 
 Tips:
 
-- The specific attention pattern can be controlled at training and test time using the `perm_mask` input.
-- Due to the difficulty of training a fully auto-regressive model over various factorization order,
-  XLNet is pretrained using only a sub-set of the output tokens as target which are selected
-  with the `target_mapping` input.
-- To use XLNet for sequential decoding (i.e. not in fully bi-directional setting), use the `perm_mask` and
-  `target_mapping` inputs to control the attention span and outputs (see examples in `examples/text-generation/run_generation.py`)
+- The specific attention pattern can be controlled at training and test time using the :obj:`perm_mask` input.
+- Due to the difficulty of training a fully auto-regressive model over various factorization order, XLNet is pretrained
+  using only a sub-set of the output tokens as target which are selected with the :obj:`target_mapping` input.
+- To use XLNet for sequential decoding (i.e. not in fully bi-directional setting), use the :obj:`perm_mask` and
+  :obj:`target_mapping` inputs to control the attention span and outputs (see examples in
+  `examples/text-generation/run_generation.py`)
 - XLNet is one of the few models that has no sequence length limit.
 
-The original code can be found `here <https://github.com/zihangdai/xlnet/>`_.
+The original code can be found `here <https://github.com/zihangdai/xlnet/>`__.
 
 
 XLNetConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.XLNetConfig
     :members:
 
 
 XLNetTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.XLNetTokenizer
     :members: build_inputs_with_special_tokens, get_special_tokens_mask,
@@ -51,7 +51,7 @@ XLNetTokenizer
 
 
 XLNet specific outputs
-~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.modeling_xlnet.XLNetModelOutput
     :members:
@@ -94,91 +94,91 @@ XLNet specific outputs
 
 
 XLNetModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.XLNetModel
-    :members:
+    :members: forward
 
 
 XLNetLMHeadModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.XLNetLMHeadModel
-    :members:
+    :members: forward
 
 
 XLNetForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.XLNetForSequenceClassification
-    :members:
+    :members: forward
 
 
 XLNetForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.XLNetForMultipleChoice
-    :members:
+    :members: forward
 
 
 XLNetForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.XLNetForTokenClassification
-    :members:
+    :members: forward
 
 
 XLNetForQuestionAnsweringSimple
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.XLNetForQuestionAnsweringSimple
-    :members:
+    :members: forward
 
 
 XLNetForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.XLNetForQuestionAnswering
-    :members:
+    :members: forward
 
 
 TFXLNetModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFXLNetModel
-    :members:
+    :members: call
 
 
 TFXLNetLMHeadModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFXLNetLMHeadModel
-    :members:
+    :members: call
 
 
 TFXLNetForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFXLNetForSequenceClassification
-    :members:
+    :members: call
 
 
 TFLNetForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFXLNetForMultipleChoice
-    :members:
+    :members: call
 
 
 TFXLNetForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFXLNetForTokenClassification
-    :members:
+    :members: call
 
 
 TFXLNetForQuestionAnsweringSimple
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFXLNetForQuestionAnsweringSimple
-    :members:
+    :members: call

docs/source/model_sharing.rst

@@ -1,224 +1,224 @@
-Model sharing and uploading
-===========================
-
-In this page, we will show you how to share a model you have trained or fine-tuned on new data with the community on
-the `model hub <https://huggingface.co/models>`__.
-
-.. note::
-
-    You will need to create an account on `huggingface.co <https://huggingface.co/join>`__ for this.
-
-    Optionally, you can join an existing organization or create a new one.
-
-Prepare your model for uploading
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-We have seen in the :doc:`training tutorial <training>`: how to fine-tune a model on a given task. You have probably
-done something similar on your task, either using the model directly in your own training loop or using the
-:class:`~.transformers.Trainer`/:class:`~.transformers.TFTrainer` class. Let's see how you can share the result on
-the `model hub <https://huggingface.co/models>`__.
-
-Basic steps
-^^^^^^^^^^^
-
-.. 
-    When #5258 is merged, we can remove the need to create the directory.
-
-First, pick a directory with the name you want your model to have on the model hub (its full name will then be
-`username/awesome-name-you-picked` or `organization/awesome-name-you-picked`) and create it with either
-
-::
-
-    mkdir path/to/awesome-name-you-picked
-
-or in python
-
-::
-
-    import os
-    os.makedirs("path/to/awesome-name-you-picked")
-
-then you can save your model and tokenizer with:
-
-::
-
-    model.save_pretrained("path/to/awesome-name-you-picked")
-    tokenizer.save_pretrained("path/to/awesome-name-you-picked")
-
-Or, if you're using the Trainer API
-
-::
-
-    trainer.save_model("path/to/awesome-name-you-picked")
-    tokenizer.save_pretrained("path/to/awesome-name-you-picked")
-
-Make your model work on all frameworks
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. 
-    TODO Sylvain: make this automatic during the upload
-
-You probably have your favorite framework, but so will other users! That's why it's best to upload your model with both
-PyTorch `and` TensorFlow checkpoints to make it easier to use (if you skip this step, users will still be able to load
-your model in another framework, but it will be slower, as it will have to be converted on the fly). Don't worry, it's super easy to do (and in a future version,
-it will all be automatic). You will need to install both PyTorch and TensorFlow for this step, but you don't need to
-worry about the GPU, so it should be very easy. Check the
-`TensorFlow installation page <https://www.tensorflow.org/install/pip#tensorflow-2.0-rc-is-available>`__ 
-and/or the `PyTorch installation page <https://pytorch.org/get-started/locally/#start-locally>`__ to see how.
-
-First check that your model class exists in the other framework, that is try to import the same model by either adding
-or removing TF. For instance, if you trained a :class:`~transformers.DistilBertForSequenceClassification`, try to
-type
-
-::
-
-    from transformers import TFDistilBertForSequenceClassification
-
-and if you trained a :class:`~transformers.TFDistilBertForSequenceClassification`, try to
-type
-
-::
-
-    from transformers import DistilBertForSequenceClassification
-
-This will give back an error if your model does not exist in the other framework (something that should be pretty rare
-since we're aiming for full parity between the two frameworks). In this case, skip this and go to the next step.
-
-Now, if you trained your model in PyTorch and have to create a TensorFlow version, adapt the following code to your
-model class:
-
-::
-
-    tf_model = TFDistilBertForSequenceClassification.from_pretrained("path/to/awesome-name-you-picked", from_pt=True)
-    tf_model.save_pretrained("path/to/awesome-name-you-picked")
-
-and if you trained your model in TensorFlow and have to create a PyTorch version, adapt the following code to your
-model class:
-
-::
-
-    pt_model = DistilBertForSequenceClassification.from_pretrained("path/to/awesome-name-you-picked", from_tf=True)
-    pt_model.save_pretrained("path/to/awesome-name-you-picked")
-
-That's all there is to it!
-
-Check the directory before uploading
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Make sure there are no garbage files in the directory you'll upload. It should only have:
-
-- a `config.json` file, which saves the :doc:`configuration <main_classes/configuration>` of your model ;
-- a `pytorch_model.bin` file, which is the PyTorch checkpoint (unless you can't have it for some reason) ;
-- a `tf_model.h5` file, which is the TensorFlow checkpoint (unless you can't have it for some reason) ;
-- a `special_tokens_map.json`, which is part of your :doc:`tokenizer <main_classes/tokenizer>` save;
-- a `tokenizer_config.json`, which is part of your :doc:`tokenizer <main_classes/tokenizer>` save;
-- a `vocab.txt`, which is the vocabulary of your tokenizer, part of your :doc:`tokenizer <main_classes/tokenizer>`
-  save;
-- maybe a `added_tokens.json`, which is part of your :doc:`tokenizer <main_classes/tokenizer>` save.
-
-Other files can safely be deleted.
-
-Upload your model with the CLI
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Now go in a terminal and run the following command. It should be in the virtual enviromnent where you installed 🤗
-Transformers, since that command :obj:`transformers-cli` comes from the library.
-
-::
-
-    transformers-cli login
-
-Then log in using the same credentials as on huggingface.co. To upload your model, just type
-
-::
-
-    transformers-cli upload path/to/awesome-name-you-picked/
-
-This will upload the folder containing the weights, tokenizer and configuration we prepared in the previous section.
-
-By default you will be prompted to confirm that you want these files to be uploaded. If you are uploading multiple models and need to script that process, you can add `-y` to bypass the prompt. For example:
-
-::
-
-    transformers-cli upload -y path/to/awesome-name-you-picked/
-
-
-If you want to upload a single file (a new version of your model, or the other framework checkpoint you want to add),
-just type:
-
-::
-
-    transformers-cli upload path/to/awesome-name-you-picked/that-file 
-
-or
-
-::
-
-   transformers-cli upload path/to/awesome-name-you-picked/that-file --filename awesome-name-you-picked/new_name
-
-if you want to change its filename.
-
-This uploads the model to your personal account. If you want your model to be namespaced by your organization name
-rather than your username, add the following flag to any command:
-
-::
-
-    --organization organization_name
-
-so for instance:
-
-::
-
-    transformers-cli upload path/to/awesome-name-you-picked/ --organization organization_name
-
-Your model will then be accessible through its identifier, which is, as we saw above,
-`username/awesome-name-you-picked` or `organization/awesome-name-you-picked`.
-
-Add a model card
-^^^^^^^^^^^^^^^^
-
-To make sure everyone knows what your model can do, what its limitations and potential bias or ethetical
-considerations, please add a README.md model card to the 🤗 Transformers repo under `model_cards/`. It should then be
-placed in a subfolder with your username or organization, then another subfolder named like your model
-(`awesome-name-you-picked`). Or just click on the "Create a model card on GitHub" button on the model page, it will
-get you directly to the right location. If you need one, `here <https://github.com/huggingface/model_card>`__ is a
-model card template (meta-suggestions are welcome).
-
-If your model is fine-tuned from another model coming from the model hub (all 🤗 Transformers pretrained models do),
-don't forget to link to its model card so that people can fully trace how your model was built.
-
-If you have never made a pull request to the 🤗 Transformers repo, look at the
-:doc:`contributing guide <contributing>` to see the steps to follow.
-
-.. Note::
-
-    You can also send your model card in the folder you uploaded with the CLI by placing it in a `README.md` file
-    inside `path/to/awesome-name-you-picked/`.
-
-Using your model
-^^^^^^^^^^^^^^^^
-
-Your model now has a page on huggingface.co/models 🔥
-
-Anyone can load it from code:
-
-::
-
-    tokenizer = AutoTokenizer.from_pretrained("namespace/awesome-name-you-picked")
-    model = AutoModel.from_pretrained("namespace/awesome-name-you-picked")
-
-Additional commands
-^^^^^^^^^^^^^^^^^^^
-
-You can list all the files you uploaded on the hub like this:
-
-::
-
-    transformers-cli s3 ls
-
-You can also delete unneeded files with
-
-::
-
-    transformers-cli s3 rm awesome-name-you-picked/filename
-
+Model sharing and uploading
+=======================================================================================================================
+
+In this page, we will show you how to share a model you have trained or fine-tuned on new data with the community on
+the `model hub <https://huggingface.co/models>`__.
+
+.. note::
+
+    You will need to create an account on `huggingface.co <https://huggingface.co/join>`__ for this.
+
+    Optionally, you can join an existing organization or create a new one.
+
+Prepare your model for uploading
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+We have seen in the :doc:`training tutorial <training>`: how to fine-tune a model on a given task. You have probably
+done something similar on your task, either using the model directly in your own training loop or using the
+:class:`~.transformers.Trainer`/:class:`~.transformers.TFTrainer` class. Let's see how you can share the result on
+the `model hub <https://huggingface.co/models>`__.
+
+Basic steps
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. 
+    When #5258 is merged, we can remove the need to create the directory.
+
+First, pick a directory with the name you want your model to have on the model hub (its full name will then be
+`username/awesome-name-you-picked` or `organization/awesome-name-you-picked`) and create it with either
+
+.. code-block::
+
+    mkdir path/to/awesome-name-you-picked
+
+or in python
+
+.. code-block::
+
+    import os
+    os.makedirs("path/to/awesome-name-you-picked")
+
+then you can save your model and tokenizer with:
+
+.. code-block::
+
+    model.save_pretrained("path/to/awesome-name-you-picked")
+    tokenizer.save_pretrained("path/to/awesome-name-you-picked")
+
+Or, if you're using the Trainer API
+
+.. code-block::
+
+    trainer.save_model("path/to/awesome-name-you-picked")
+    tokenizer.save_pretrained("path/to/awesome-name-you-picked")
+
+Make your model work on all frameworks
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. 
+    TODO Sylvain: make this automatic during the upload
+
+You probably have your favorite framework, but so will other users! That's why it's best to upload your model with both
+PyTorch `and` TensorFlow checkpoints to make it easier to use (if you skip this step, users will still be able to load
+your model in another framework, but it will be slower, as it will have to be converted on the fly). Don't worry, it's super easy to do (and in a future version,
+it will all be automatic). You will need to install both PyTorch and TensorFlow for this step, but you don't need to
+worry about the GPU, so it should be very easy. Check the
+`TensorFlow installation page <https://www.tensorflow.org/install/pip#tensorflow-2.0-rc-is-available>`__ 
+and/or the `PyTorch installation page <https://pytorch.org/get-started/locally/#start-locally>`__ to see how.
+
+First check that your model class exists in the other framework, that is try to import the same model by either adding
+or removing TF. For instance, if you trained a :class:`~transformers.DistilBertForSequenceClassification`, try to
+type
+
+.. code-block::
+
+    from transformers import TFDistilBertForSequenceClassification
+
+and if you trained a :class:`~transformers.TFDistilBertForSequenceClassification`, try to
+type
+
+.. code-block::
+
+    from transformers import DistilBertForSequenceClassification
+
+This will give back an error if your model does not exist in the other framework (something that should be pretty rare
+since we're aiming for full parity between the two frameworks). In this case, skip this and go to the next step.
+
+Now, if you trained your model in PyTorch and have to create a TensorFlow version, adapt the following code to your
+model class:
+
+.. code-block::
+
+    tf_model = TFDistilBertForSequenceClassification.from_pretrained("path/to/awesome-name-you-picked", from_pt=True)
+    tf_model.save_pretrained("path/to/awesome-name-you-picked")
+
+and if you trained your model in TensorFlow and have to create a PyTorch version, adapt the following code to your
+model class:
+
+.. code-block::
+
+    pt_model = DistilBertForSequenceClassification.from_pretrained("path/to/awesome-name-you-picked", from_tf=True)
+    pt_model.save_pretrained("path/to/awesome-name-you-picked")
+
+That's all there is to it!
+
+Check the directory before uploading
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Make sure there are no garbage files in the directory you'll upload. It should only have:
+
+- a `config.json` file, which saves the :doc:`configuration <main_classes/configuration>` of your model ;
+- a `pytorch_model.bin` file, which is the PyTorch checkpoint (unless you can't have it for some reason) ;
+- a `tf_model.h5` file, which is the TensorFlow checkpoint (unless you can't have it for some reason) ;
+- a `special_tokens_map.json`, which is part of your :doc:`tokenizer <main_classes/tokenizer>` save;
+- a `tokenizer_config.json`, which is part of your :doc:`tokenizer <main_classes/tokenizer>` save;
+- a `vocab.txt`, which is the vocabulary of your tokenizer, part of your :doc:`tokenizer <main_classes/tokenizer>`
+  save;
+- maybe a `added_tokens.json`, which is part of your :doc:`tokenizer <main_classes/tokenizer>` save.
+
+Other files can safely be deleted.
+
+Upload your model with the CLI
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Now go in a terminal and run the following command. It should be in the virtual enviromnent where you installed 🤗
+Transformers, since that command :obj:`transformers-cli` comes from the library.
+
+.. code-block::
+
+    transformers-cli login
+
+Then log in using the same credentials as on huggingface.co. To upload your model, just type
+
+.. code-block::
+
+    transformers-cli upload path/to/awesome-name-you-picked/
+
+This will upload the folder containing the weights, tokenizer and configuration we prepared in the previous section.
+
+By default you will be prompted to confirm that you want these files to be uploaded. If you are uploading multiple models and need to script that process, you can add `-y` to bypass the prompt. For example:
+
+.. code-block::
+
+    transformers-cli upload -y path/to/awesome-name-you-picked/
+
+
+If you want to upload a single file (a new version of your model, or the other framework checkpoint you want to add),
+just type:
+
+.. code-block::
+
+    transformers-cli upload path/to/awesome-name-you-picked/that-file 
+
+or
+
+.. code-block::
+
+   transformers-cli upload path/to/awesome-name-you-picked/that-file --filename awesome-name-you-picked/new_name
+
+if you want to change its filename.
+
+This uploads the model to your personal account. If you want your model to be namespaced by your organization name
+rather than your username, add the following flag to any command:
+
+.. code-block::
+
+    --organization organization_name
+
+so for instance:
+
+.. code-block::
+
+    transformers-cli upload path/to/awesome-name-you-picked/ --organization organization_name
+
+Your model will then be accessible through its identifier, which is, as we saw above,
+`username/awesome-name-you-picked` or `organization/awesome-name-you-picked`.
+
+Add a model card
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+To make sure everyone knows what your model can do, what its limitations and potential bias or ethetical
+considerations, please add a README.md model card to the 🤗 Transformers repo under `model_cards/`. It should then be
+placed in a subfolder with your username or organization, then another subfolder named like your model
+(`awesome-name-you-picked`). Or just click on the "Create a model card on GitHub" button on the model page, it will
+get you directly to the right location. If you need one, `here <https://github.com/huggingface/model_card>`__ is a
+model card template (meta-suggestions are welcome).
+
+If your model is fine-tuned from another model coming from the model hub (all 🤗 Transformers pretrained models do),
+don't forget to link to its model card so that people can fully trace how your model was built.
+
+If you have never made a pull request to the 🤗 Transformers repo, look at the
+:doc:`contributing guide <contributing>` to see the steps to follow.
+
+.. Note::
+
+    You can also send your model card in the folder you uploaded with the CLI by placing it in a `README.md` file
+    inside `path/to/awesome-name-you-picked/`.
+
+Using your model
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Your model now has a page on huggingface.co/models 🔥
+
+Anyone can load it from code:
+
+.. code-block::
+
+    tokenizer = AutoTokenizer.from_pretrained("namespace/awesome-name-you-picked")
+    model = AutoModel.from_pretrained("namespace/awesome-name-you-picked")
+
+Additional commands
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+You can list all the files you uploaded on the hub like this:
+
+.. code-block::
+
+    transformers-cli s3 ls
+
+You can also delete unneeded files with
+
+.. code-block::
+
+    transformers-cli s3 rm awesome-name-you-picked/filename
+

docs/source/multilingual.rst

@@ -1,5 +1,5 @@
 Multi-lingual models
-================================================
+=======================================================================================================================
 
 Most of the models available in this library are mono-lingual models (English, Chinese and German). A few
 multi-lingual models are available and have a different mechanisms than mono-lingual models.
@@ -8,13 +8,13 @@ This page details the usage of these models.
 The two models that currently support multiple languages are BERT and XLM.
 
 XLM
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 XLM has a total of 10 different checkpoints, only one of which is mono-lingual. The 9 remaining model checkpoints can
 be split in two categories: the checkpoints that make use of language embeddings, and those that don't
 
 XLM & Language Embeddings
-------------------------------------------------
+-----------------------------------------------------------------------------------------------------------------------
 
 This section concerns the following checkpoints:
 
@@ -82,7 +82,7 @@ The example `run_generation.py <https://github.com/huggingface/transformers/blob
 can generate text using the CLM checkpoints from XLM, using the language embeddings.
 
 XLM without Language Embeddings
-------------------------------------------------
+-----------------------------------------------------------------------------------------------------------------------
 
 This section concerns the following checkpoints:
 
@@ -94,7 +94,7 @@ sentence representations, differently from previously-mentioned XLM checkpoints.
 
 
 BERT
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 BERT has two checkpoints that can be used for multi-lingual tasks:
 
@@ -105,7 +105,7 @@ These checkpoints do not require language embeddings at inference time. They sho
 used in the context and infer accordingly.
 
 XLM-RoBERTa
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 XLM-RoBERTa was trained on 2.5TB of newly created clean CommonCrawl data in 100 languages. It provides strong
 gains over previously released multi-lingual models like mBERT or XLM on downstream taks like classification,

docs/source/perplexity.rst

@@ -1,5 +1,5 @@
 Perplexity of fixed-length models
-=================================
+=======================================================================================================================
 
 Perplexity (PPL) is one of the most common metrics for evaluating language
 models. Before diving in, we should note that the metric applies specifically
@@ -31,7 +31,7 @@ relationship to Bits Per Character (BPC) and data compression, check out this
 <https://thegradient.pub/understanding-evaluation-metrics-for-language-models/>`_.
 
 Calculating PPL with fixed-length models
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 If we weren't limited by a model's context size, we would evaluate the
 model's perplexity by autoregressively factorizing a sequence and
@@ -83,7 +83,7 @@ time. This allows computation to procede much faster while still giving the
 model a large context to make predictions at each step.
 
 Example: Calculating perplexity with GPT-2 in 🤗 Transformers
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 Let's demonstrate this process with GPT-2.
 

docs/source/philosophy.rst

@@ -1,5 +1,5 @@
 Philosophy
-==========
+=======================================================================================================================
 
 🤗 Transformers is an opinionated library built for:
 
@@ -48,7 +48,7 @@ A few other goals:
 - Switch easily between PyTorch and TensorFlow 2.0, allowing training using one framework and inference using another.
 
 Main concepts
-~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 The library is built around three types of classes for each model:
 

docs/source/preprocessing.rst

@@ -1,343 +1,343 @@
-Preprocessing data
-==================
-
-In this tutorial, we'll explore how to preprocess your data using 🤗 Transformers. The main tool for this is what we
-
-call a :doc:`tokenizer <main_classes/tokenizer>`. You can build one using the tokenizer class associated to the model
-you would like to use, or directly with the :class:`~transformers.AutoTokenizer` class.
-
-As we saw in the :doc:`quicktour </quicktour>`, the tokenizer will first split a given text in words (or part of words,
-punctuation symbols, etc.) usually called `tokens`. Then it will convert those `tokens` into numbers, to be able to
-build a tensor out of them and feed them to the model. It will also add any additional inputs the model might expect to
-work properly.
-
-.. note::
-
-    If you plan on using a pretrained model, it's important to use the associated pretrained tokenizer: it will split
-    the text you give it in tokens the same way for the pretraining corpus, and it will use the same correspondence
-    token to index (that we usually call a `vocab`) as during pretraining.
-
-To automatically download the vocab used during pretraining or fine-tuning a given model, you can use the 
-:func:`~transformers.AutoTokenizer.from_pretrained` method:
-
-.. code-block::
-
-    from transformers import AutoTokenizer
-    tokenizer = AutoTokenizer.from_pretrained('bert-base-cased')
-
-Base use
-~~~~~~~~
-
-A :class:`~transformers.PreTrainedTokenizer` has many methods, but the only one you need to remember for preprocessing
-is its ``__call__``: you just need to feed your sentence to your tokenizer object.
-
-.. code-block::
-
-    >>> encoded_input = tokenizer("Hello, I'm a single sentence!")
-    >>> print(encoded_input)
-    {'input_ids': [101, 138, 18696, 155, 1942, 3190, 1144, 1572, 13745, 1104, 159, 9664, 2107, 102], 
-     'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 
-     'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
-
-This returns a dictionary string to list of ints.
-The `input_ids <glossary.html#input-ids>`__ are the indices corresponding to each token in our sentence. We will see
-below what the `attention_mask <glossary.html#attention-mask>`__ is used for and in
-:ref:`the next section <sentence-pairs>` the goal of `token_type_ids <glossary.html#token-type-ids>`__.
-
-The tokenizer can decode a list of token ids in a proper sentence:
-
-.. code-block::
-
-    >>> tokenizer.decode(encoded_input["input_ids"])
-    "[CLS] Hello, I'm a single sentence! [SEP]"
-
-As you can see, the tokenizer automatically added some special tokens that the model expect. Not all model need special
-tokens; for instance, if we had used` gtp2-medium` instead of `bert-base-cased` to create our tokenizer, we would have
-seen the same sentence as the original one here. You can disable this behavior (which is only advised if you have added
-those special tokens yourself) by passing ``add_special_tokens=False``.
-
-If you have several sentences you want to process, you can do this efficiently by sending them as a list to the
-tokenizer:
-
-.. code-block::
-
-    >>> batch_sentences = ["Hello I'm a single sentence",
-    ...                    "And another sentence",
-    ...                    "And the very very last one"]
-    >>> encoded_inputs = tokenizer(batch_sentences)
-    >>> print(encoded_inputs)
-    {'input_ids': [[101, 8667, 146, 112, 182, 170, 1423, 5650, 102],
-                   [101, 1262, 1330, 5650, 102],
-                   [101, 1262, 1103, 1304, 1304, 1314, 1141, 102]],
-     'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0],
-                        [0, 0, 0, 0, 0],
-                        [0, 0, 0, 0, 0, 0, 0, 0]],
-     'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1],
-                        [1, 1, 1, 1, 1],
-                        [1, 1, 1, 1, 1, 1, 1, 1]]}
-
-We get back a dictionary once again, this time with values being list of list of ints.
-
-If the purpose of sending several sentences at a time to the tokenizer is to build a batch to feed the model, you will
-probably want:
-
-- To pad each sentence to the maximum length there is in your batch.
-- To truncate each sentence to the maximum length the model can accept (if applicable).
-- To return tensors.
-
-You can do all of this by using the following options when feeding your list of sentences to the tokenizer:
-
-.. code-block::
-
-    >>> ## PYTORCH CODE
-    >>> batch = tokenizer(batch_sentences, padding=True, truncation=True, return_tensors="pt")
-    >>> print(batch)
-    {'input_ids': tensor([[ 101, 8667,  146,  112,  182,  170, 1423, 5650,  102],
-                          [ 101, 1262, 1330, 5650,  102,    0,    0,    0,    0],
-                          [ 101, 1262, 1103, 1304, 1304, 1314, 1141,  102,    0]]),
-     'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0],
-                               [0, 0, 0, 0, 0, 0, 0, 0, 0],
-                               [0, 0, 0, 0, 0, 0, 0, 0, 0]]), 
-     'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1],
-                               [1, 1, 1, 1, 1, 0, 0, 0, 0],
-                               [1, 1, 1, 1, 1, 1, 1, 1, 0]])}
-    >>> ## TENSORFLOW CODE
-    >>> batch = tokenizer(batch_sentences, padding=True, truncation=True, return_tensors="tf")
-    >>> print(batch)
-    {'input_ids': tf.Tensor([[ 101, 8667,  146,  112,  182,  170, 1423, 5650,  102],
-                          [ 101, 1262, 1330, 5650,  102,    0,    0,    0,    0],
-                          [ 101, 1262, 1103, 1304, 1304, 1314, 1141,  102,    0]]),
-     'token_type_ids': tf.Tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0],
-                               [0, 0, 0, 0, 0, 0, 0, 0, 0],
-                               [0, 0, 0, 0, 0, 0, 0, 0, 0]]), 
-     'attention_mask': tf.Tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1],
-                               [1, 1, 1, 1, 1, 0, 0, 0, 0],
-                               [1, 1, 1, 1, 1, 1, 1, 1, 0]])}
-
-It returns a dictionary string to tensor. We can now see what the `attention_mask <glossary.html#attention-mask>`__ is
-all about: it points out which tokens the model should pay attention to and which ones it should not (because they
-represent padding in this case).
-
-
-Note that if your model does not have a maximum length associated to it, the command above will throw a warning. You
-can safely ignore it. You can also pass ``verbose=False`` to stop the tokenizer to throw those kinds of warnings.
-
-.. _sentence-pairs:
-
-Preprocessing pairs of sentences
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Sometimes you need to feed pair of sentences to your model. For instance, if you want to classify if two sentences in a
-pair are similar, or for question-answering models, which take a context and a question. For BERT models, the input is
-then represented like this: :obj:`[CLS] Sequence A [SEP] Sequence B [SEP]`
-
-You can encode a pair of sentences in the format expected by your model by supplying the two sentences as two arguments
-(not a list since a list of two sentences will be interpreted as a batch of two single sentences, as we saw before).
-This will once again return a dict string to list of ints:
-
-.. code-block::
-
-    >>> encoded_input = tokenizer("How old are you?", "I'm 6 years old")
-    >>> print(encoded_input)
-    {'input_ids': [101, 1731, 1385, 1132, 1128, 136, 102, 146, 112, 182, 127, 1201, 1385, 102], 
-     'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1], 
-     'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
-
-This shows us what the `token_type_ids <glossary.html#token-type-ids>`__ are for: they indicate to the model which part
-of the inputs correspond to the first sentence and which part corresponds to the second sentence. Note that
-`token_type_ids` are not required or handled by all models. By default, a tokenizer will only return the inputs that
-its associated model expects. You can force the return (or the non-return) of any of those special arguments by
-using ``return_input_ids`` or ``return_token_type_ids``.
-
-If we decode the token ids we obtained, we will see that the special tokens have been properly added.
-
-.. code-block::
-
-    >>> tokenizer.decode(encoded_input["input_ids"])
-    "[CLS] How old are you? [SEP] I'm 6 years old [SEP]"
-
-If you have a list of pairs of sequences you want to process, you should feed them as two lists to your tokenizer: the
-list of first sentences and the list of second sentences:
-
-.. code-block::
-
-    >>> batch_sentences = ["Hello I'm a single sentence",
-    ...                    "And another sentence",
-    ...                    "And the very very last one"]
-    >>> batch_of_second_sentences = ["I'm a sentence that goes with the first sentence",
-    ...                              "And I should be encoded with the second sentence",
-    ...                              "And I go with the very last one"]
-    >>> encoded_inputs = tokenizer(batch_sentences, batch_of_second_sentences)
-    >>> print(encoded_inputs)
-    {'input_ids': [[101, 8667, 146, 112, 182, 170, 1423, 5650, 102, 146, 112, 182, 170, 5650, 1115, 2947, 1114, 1103, 1148, 5650, 102], 
-                   [101, 1262, 1330, 5650, 102, 1262, 146, 1431, 1129, 12544, 1114, 1103, 1248, 5650, 102], 
-                   [101, 1262, 1103, 1304, 1304, 1314, 1141, 102, 1262, 146, 1301, 1114, 1103, 1304, 1314, 1141, 102]], 
-    'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 
-                       [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 
-                       [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 
-    'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 
-                       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 
-                       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
-
-As we can see, it returns a dictionary with the values being list of lists of ints.
-
-To double-check what is fed to the model, we can decode each list in `input_ids` one by one:
-
-.. code-block::
-
-    >>> for ids in encoded_inputs["input_ids"]:
-    >>>     print(tokenizer.decode(ids))
-    [CLS] Hello I'm a single sentence [SEP] I'm a sentence that goes with the first sentence [SEP]
-    [CLS] And another sentence [SEP] And I should be encoded with the second sentence [SEP]
-    [CLS] And the very very last one [SEP] And I go with the very last one [SEP]
-
-Once again, you can automatically pad your inputs to the maximum sentence length in the batch, truncate to the maximum
-length the model can accept and return tensors directly with the following:
-
-.. code-block::
-
-    ## PYTORCH CODE
-    batch = tokenizer(batch_sentences, batch_of_second_sentences, padding=True, truncation=True, return_tensors="pt")
-    ## TENSORFLOW CODE
-    batch = tokenizer(batch_sentences, batch_of_second_sentences, padding=True, truncation=True, return_tensors="tf")
-
-Everything you always wanted to know about padding and truncation
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-We have seen the commands that will work for most cases (pad your batch to the length of the maximum sentence and
-
-truncate to the maximum length the mode can accept). However, the API supports more strategies if you need them. The
-three arguments you need to know for this are :obj:`padding`, :obj:`truncation` and :obj:`max_length`.
-
-- :obj:`padding` controls the padding. It can be a boolean or a string which should be:
-
-    - :obj:`True` or :obj:`'longest'` to pad to the longest sequence in the batch (doing no padding if you only provide
-      a single sequence).
-    - :obj:`'max_length'` to pad to a length specified by the :obj:`max_length` argument or the maximum length accepted
-      by the model if no :obj:`max_length` is provided (``max_length=None``). If you only provide a single sequence,
-      padding will still be applied to it. 
-    - :obj:`False` or :obj:`'do_not_pad'` to not pad the sequences. As we have seen before, this is the default
-      behavior.
-
-- :obj:`truncation` controls the truncation. It can be a boolean or a string which should be:
-
-    - :obj:`True` or :obj:`'only_first'` truncate to a maximum length specified by the :obj:`max_length` argument or
-      the maximum length accepted by the model if no :obj:`max_length` is provided (``max_length=None``). This will
-      only truncate the first sentence of a pair if a pair of sequence (or a batch of pairs of sequences) is provided.
-    - :obj:`'only_second'` truncate to a maximum length specified by the :obj:`max_length` argument or the maximum
-      length accepted by the model if no :obj:`max_length` is provided (``max_length=None``). This will only truncate
-      the second sentence of a pair if a pair of sequence (or a batch of pairs of sequences) is provided.
-    - :obj:`'longest_first'` truncate to a maximum length specified by the :obj:`max_length` argument or the maximum
-      length accepted by the model if no :obj:`max_length` is provided (``max_length=None``). This will truncate token
-      by token, removing a token from the longest sequence in the pair until the proper length is reached.
-    - :obj:`False` or :obj:`'do_not_truncate'` to not truncate the sequences. As we have seen before, this is the
-      default behavior.
-
-- :obj:`max_length` to control the length of the padding/truncation. It can be an integer or :obj:`None`, in which case
-  it will default to the maximum length the model can accept. If the model has no specific maximum input length,
-  truncation/padding to :obj:`max_length` is deactivated.
-
-Here is a table summarizing the recommend way to setup padding and truncation. If you use pair of inputs sequence in
-any of the following examples, you can replace :obj:`truncation=True` by a :obj:`STRATEGY` selected in 
-:obj:`['only_first', 'only_second', 'longest_first']`, i.e. :obj:`truncation='only_second'` or
-:obj:`truncation= 'longest_first'` to control how both sequence in the pair are truncated as detailed before.
-
-+--------------------------------------+-----------------------------------+---------------------------------------------------------------------------------------------+
-| Truncation                           | Padding                           | Instruction                                                                                 |
-+======================================+===================================+=============================================================================================+
-| no truncation                        | no padding                        | :obj:`tokenizer(batch_sentences)`                                                           |
-|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
-|                                      | padding to max sequence in batch  | :obj:`tokenizer(batch_sentences, padding=True)` or                                          |
-|                                      |                                   | :obj:`tokenizer(batch_sentences, padding='longest')`                                        |
-|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
-|                                      | padding to max model input length | :obj:`tokenizer(batch_sentences, padding='max_length')`                                     |
-|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
-|                                      | padding to specific length        | :obj:`tokenizer(batch_sentences, padding='max_length', max_length=42)`                      |
-+--------------------------------------+-----------------------------------+---------------------------------------------------------------------------------------------+
-| truncation to max model input length | no padding                        | :obj:`tokenizer(batch_sentences, truncation=True)` or                                       |
-|                                      |                                   | :obj:`tokenizer(batch_sentences, truncation=STRATEGY)`                                      |
-|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
-|                                      | padding to max sequence in batch  | :obj:`tokenizer(batch_sentences, padding=True, truncation=True)` or                         |
-|                                      |                                   | :obj:`tokenizer(batch_sentences, padding=True, truncation=STRATEGY)`                        |
-|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
-|                                      | padding to max model input length | :obj:`tokenizer(batch_sentences, padding='max_length', truncation=True)` or                 |
-|                                      |                                   | :obj:`tokenizer(batch_sentences, padding='max_length', truncation=STRATEGY)`                |
-|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
-|                                      | padding to specific length        | Not possible                                                                                |
-+--------------------------------------+-----------------------------------+---------------------------------------------------------------------------------------------+
-| truncation to specific length        | no padding                        | :obj:`tokenizer(batch_sentences, truncation=True, max_length=42)` or                        |
-|                                      |                                   | :obj:`tokenizer(batch_sentences, truncation=STRATEGY, max_length=42)`                       |
-|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
-|                                      | padding to max sequence in batch  | :obj:`tokenizer(batch_sentences, padding=True, truncation=True, max_length=42)` or          |
-|                                      |                                   | :obj:`tokenizer(batch_sentences, padding=True, truncation=STRATEGY, max_length=42)`         |
-|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
-|                                      | padding to max model input length | Not possible                                                                                |
-|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
-|                                      | padding to specific length        | :obj:`tokenizer(batch_sentences, padding='max_length', truncation=True, max_length=42)` or  |
-|                                      |                                   | :obj:`tokenizer(batch_sentences, padding='max_length', truncation=STRATEGY, max_length=42)` |
-+--------------------------------------+-----------------------------------+---------------------------------------------------------------------------------------------+
-
-Pre-tokenized inputs
-~~~~~~~~~~~~~~~~~~~~
-
-The tokenizer also accept pre-tokenized inputs. This is particularly useful when you want to compute labels and extract
-predictions in `named entity recognition (NER) <https://en.wikipedia.org/wiki/Named-entity_recognition>`__ or
-`part-of-speech tagging (POS tagging) <https://en.wikipedia.org/wiki/Part-of-speech_tagging>`__.
-
-.. warning::
-
-    Pre-tokenized does not mean your inputs are already tokenized (you wouldn't need to pass them though the tokenizer
-    if that was the case) but just split into words (which is often the first step in subword tokenization algorithms
-    like BPE).
-
-If you want to use pre-tokenized inputs, just set :obj:`is_split_into_words=True` when passing your inputs to the
-tokenizer. For instance, we have:
-
-.. code-block::
-
-    >>> encoded_input = tokenizer(["Hello", "I'm", "a", "single", "sentence"], is_split_into_words=True)
-    >>> print(encoded_input)
-    {'input_ids': [101, 8667, 146, 112, 182, 170, 1423, 5650, 102],
-     'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0], 
-     'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1]}
-
-Note that the tokenizer still adds the ids of special tokens (if applicable) unless you pass
-``add_special_tokens=False``.
-
-This works exactly as before for batch of sentences or batch of pairs of sentences. You can encode a batch of sentences
-like this:
-
-.. code-block::
-
-    batch_sentences = [["Hello", "I'm", "a", "single", "sentence"],
-                       ["And", "another", "sentence"],
-                       ["And", "the", "very", "very", "last", "one"]]
-    encoded_inputs = tokenizer(batch_sentences, is_split_into_words=True)
-
-or a batch of pair sentences like this:
-
-.. code-block::
-
-    batch_of_second_sentences = [["I'm", "a", "sentence", "that", "goes", "with", "the", "first", "sentence"],
-                                 ["And", "I", "should", "be", "encoded", "with", "the", "second", "sentence"],
-                                 ["And", "I", "go", "with", "the", "very", "last", "one"]]
-    encoded_inputs = tokenizer(batch_sentences, batch_of_second_sentences, is_split_into_words=True)
-
-And you can add padding, truncation as well as directly return tensors like before:
-
-.. code-block::
-
-    ## PYTORCH CODE
-    batch = tokenizer(batch_sentences,
-                      batch_of_second_sentences,
-                      is_split_into_words=True,
-                      padding=True,
-                      truncation=True,
-                      return_tensors="pt")
-    ## TENSORFLOW CODE
-    batch = tokenizer(batch_sentences,
-                      batch_of_second_sentences,
-                      is_split_into_words=True,
-                      padding=True,
-                      truncation=True,
-                      return_tensors="tf")
+Preprocessing data
+=======================================================================================================================
+
+In this tutorial, we'll explore how to preprocess your data using 🤗 Transformers. The main tool for this is what we
+
+call a :doc:`tokenizer <main_classes/tokenizer>`. You can build one using the tokenizer class associated to the model
+you would like to use, or directly with the :class:`~transformers.AutoTokenizer` class.
+
+As we saw in the :doc:`quicktour </quicktour>`, the tokenizer will first split a given text in words (or part of words,
+punctuation symbols, etc.) usually called `tokens`. Then it will convert those `tokens` into numbers, to be able to
+build a tensor out of them and feed them to the model. It will also add any additional inputs the model might expect to
+work properly.
+
+.. note::
+
+    If you plan on using a pretrained model, it's important to use the associated pretrained tokenizer: it will split
+    the text you give it in tokens the same way for the pretraining corpus, and it will use the same correspondence
+    token to index (that we usually call a `vocab`) as during pretraining.
+
+To automatically download the vocab used during pretraining or fine-tuning a given model, you can use the 
+:func:`~transformers.AutoTokenizer.from_pretrained` method:
+
+.. code-block::
+
+    from transformers import AutoTokenizer
+    tokenizer = AutoTokenizer.from_pretrained('bert-base-cased')
+
+Base use
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+A :class:`~transformers.PreTrainedTokenizer` has many methods, but the only one you need to remember for preprocessing
+is its ``__call__``: you just need to feed your sentence to your tokenizer object.
+
+.. code-block::
+
+    >>> encoded_input = tokenizer("Hello, I'm a single sentence!")
+    >>> print(encoded_input)
+    {'input_ids': [101, 138, 18696, 155, 1942, 3190, 1144, 1572, 13745, 1104, 159, 9664, 2107, 102], 
+     'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 
+     'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
+
+This returns a dictionary string to list of ints.
+The `input_ids <glossary.html#input-ids>`__ are the indices corresponding to each token in our sentence. We will see
+below what the `attention_mask <glossary.html#attention-mask>`__ is used for and in
+:ref:`the next section <sentence-pairs>` the goal of `token_type_ids <glossary.html#token-type-ids>`__.
+
+The tokenizer can decode a list of token ids in a proper sentence:
+
+.. code-block::
+
+    >>> tokenizer.decode(encoded_input["input_ids"])
+    "[CLS] Hello, I'm a single sentence! [SEP]"
+
+As you can see, the tokenizer automatically added some special tokens that the model expect. Not all model need special
+tokens; for instance, if we had used` gtp2-medium` instead of `bert-base-cased` to create our tokenizer, we would have
+seen the same sentence as the original one here. You can disable this behavior (which is only advised if you have added
+those special tokens yourself) by passing ``add_special_tokens=False``.
+
+If you have several sentences you want to process, you can do this efficiently by sending them as a list to the
+tokenizer:
+
+.. code-block::
+
+    >>> batch_sentences = ["Hello I'm a single sentence",
+    ...                    "And another sentence",
+    ...                    "And the very very last one"]
+    >>> encoded_inputs = tokenizer(batch_sentences)
+    >>> print(encoded_inputs)
+    {'input_ids': [[101, 8667, 146, 112, 182, 170, 1423, 5650, 102],
+                   [101, 1262, 1330, 5650, 102],
+                   [101, 1262, 1103, 1304, 1304, 1314, 1141, 102]],
+     'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0],
+                        [0, 0, 0, 0, 0],
+                        [0, 0, 0, 0, 0, 0, 0, 0]],
+     'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1],
+                        [1, 1, 1, 1, 1],
+                        [1, 1, 1, 1, 1, 1, 1, 1]]}
+
+We get back a dictionary once again, this time with values being list of list of ints.
+
+If the purpose of sending several sentences at a time to the tokenizer is to build a batch to feed the model, you will
+probably want:
+
+- To pad each sentence to the maximum length there is in your batch.
+- To truncate each sentence to the maximum length the model can accept (if applicable).
+- To return tensors.
+
+You can do all of this by using the following options when feeding your list of sentences to the tokenizer:
+
+.. code-block::
+
+    >>> ## PYTORCH CODE
+    >>> batch = tokenizer(batch_sentences, padding=True, truncation=True, return_tensors="pt")
+    >>> print(batch)
+    {'input_ids': tensor([[ 101, 8667,  146,  112,  182,  170, 1423, 5650,  102],
+                          [ 101, 1262, 1330, 5650,  102,    0,    0,    0,    0],
+                          [ 101, 1262, 1103, 1304, 1304, 1314, 1141,  102,    0]]),
+     'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0],
+                               [0, 0, 0, 0, 0, 0, 0, 0, 0],
+                               [0, 0, 0, 0, 0, 0, 0, 0, 0]]), 
+     'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1],
+                               [1, 1, 1, 1, 1, 0, 0, 0, 0],
+                               [1, 1, 1, 1, 1, 1, 1, 1, 0]])}
+    >>> ## TENSORFLOW CODE
+    >>> batch = tokenizer(batch_sentences, padding=True, truncation=True, return_tensors="tf")
+    >>> print(batch)
+    {'input_ids': tf.Tensor([[ 101, 8667,  146,  112,  182,  170, 1423, 5650,  102],
+                          [ 101, 1262, 1330, 5650,  102,    0,    0,    0,    0],
+                          [ 101, 1262, 1103, 1304, 1304, 1314, 1141,  102,    0]]),
+     'token_type_ids': tf.Tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0],
+                               [0, 0, 0, 0, 0, 0, 0, 0, 0],
+                               [0, 0, 0, 0, 0, 0, 0, 0, 0]]), 
+     'attention_mask': tf.Tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1],
+                               [1, 1, 1, 1, 1, 0, 0, 0, 0],
+                               [1, 1, 1, 1, 1, 1, 1, 1, 0]])}
+
+It returns a dictionary string to tensor. We can now see what the `attention_mask <glossary.html#attention-mask>`__ is
+all about: it points out which tokens the model should pay attention to and which ones it should not (because they
+represent padding in this case).
+
+
+Note that if your model does not have a maximum length associated to it, the command above will throw a warning. You
+can safely ignore it. You can also pass ``verbose=False`` to stop the tokenizer to throw those kinds of warnings.
+
+.. _sentence-pairs:
+
+Preprocessing pairs of sentences
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Sometimes you need to feed pair of sentences to your model. For instance, if you want to classify if two sentences in a
+pair are similar, or for question-answering models, which take a context and a question. For BERT models, the input is
+then represented like this: :obj:`[CLS] Sequence A [SEP] Sequence B [SEP]`
+
+You can encode a pair of sentences in the format expected by your model by supplying the two sentences as two arguments
+(not a list since a list of two sentences will be interpreted as a batch of two single sentences, as we saw before).
+This will once again return a dict string to list of ints:
+
+.. code-block::
+
+    >>> encoded_input = tokenizer("How old are you?", "I'm 6 years old")
+    >>> print(encoded_input)
+    {'input_ids': [101, 1731, 1385, 1132, 1128, 136, 102, 146, 112, 182, 127, 1201, 1385, 102], 
+     'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1], 
+     'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
+
+This shows us what the `token_type_ids <glossary.html#token-type-ids>`__ are for: they indicate to the model which part
+of the inputs correspond to the first sentence and which part corresponds to the second sentence. Note that
+`token_type_ids` are not required or handled by all models. By default, a tokenizer will only return the inputs that
+its associated model expects. You can force the return (or the non-return) of any of those special arguments by
+using ``return_input_ids`` or ``return_token_type_ids``.
+
+If we decode the token ids we obtained, we will see that the special tokens have been properly added.
+
+.. code-block::
+
+    >>> tokenizer.decode(encoded_input["input_ids"])
+    "[CLS] How old are you? [SEP] I'm 6 years old [SEP]"
+
+If you have a list of pairs of sequences you want to process, you should feed them as two lists to your tokenizer: the
+list of first sentences and the list of second sentences:
+
+.. code-block::
+
+    >>> batch_sentences = ["Hello I'm a single sentence",
+    ...                    "And another sentence",
+    ...                    "And the very very last one"]
+    >>> batch_of_second_sentences = ["I'm a sentence that goes with the first sentence",
+    ...                              "And I should be encoded with the second sentence",
+    ...                              "And I go with the very last one"]
+    >>> encoded_inputs = tokenizer(batch_sentences, batch_of_second_sentences)
+    >>> print(encoded_inputs)
+    {'input_ids': [[101, 8667, 146, 112, 182, 170, 1423, 5650, 102, 146, 112, 182, 170, 5650, 1115, 2947, 1114, 1103, 1148, 5650, 102], 
+                   [101, 1262, 1330, 5650, 102, 1262, 146, 1431, 1129, 12544, 1114, 1103, 1248, 5650, 102], 
+                   [101, 1262, 1103, 1304, 1304, 1314, 1141, 102, 1262, 146, 1301, 1114, 1103, 1304, 1314, 1141, 102]], 
+    'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 
+                       [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 
+                       [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 
+    'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 
+                       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 
+                       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
+
+As we can see, it returns a dictionary with the values being list of lists of ints.
+
+To double-check what is fed to the model, we can decode each list in `input_ids` one by one:
+
+.. code-block::
+
+    >>> for ids in encoded_inputs["input_ids"]:
+    >>>     print(tokenizer.decode(ids))
+    [CLS] Hello I'm a single sentence [SEP] I'm a sentence that goes with the first sentence [SEP]
+    [CLS] And another sentence [SEP] And I should be encoded with the second sentence [SEP]
+    [CLS] And the very very last one [SEP] And I go with the very last one [SEP]
+
+Once again, you can automatically pad your inputs to the maximum sentence length in the batch, truncate to the maximum
+length the model can accept and return tensors directly with the following:
+
+.. code-block::
+
+    ## PYTORCH CODE
+    batch = tokenizer(batch_sentences, batch_of_second_sentences, padding=True, truncation=True, return_tensors="pt")
+    ## TENSORFLOW CODE
+    batch = tokenizer(batch_sentences, batch_of_second_sentences, padding=True, truncation=True, return_tensors="tf")
+
+Everything you always wanted to know about padding and truncation
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+We have seen the commands that will work for most cases (pad your batch to the length of the maximum sentence and
+
+truncate to the maximum length the mode can accept). However, the API supports more strategies if you need them. The
+three arguments you need to know for this are :obj:`padding`, :obj:`truncation` and :obj:`max_length`.
+
+- :obj:`padding` controls the padding. It can be a boolean or a string which should be:
+
+    - :obj:`True` or :obj:`'longest'` to pad to the longest sequence in the batch (doing no padding if you only provide
+      a single sequence).
+    - :obj:`'max_length'` to pad to a length specified by the :obj:`max_length` argument or the maximum length accepted
+      by the model if no :obj:`max_length` is provided (``max_length=None``). If you only provide a single sequence,
+      padding will still be applied to it. 
+    - :obj:`False` or :obj:`'do_not_pad'` to not pad the sequences. As we have seen before, this is the default
+      behavior.
+
+- :obj:`truncation` controls the truncation. It can be a boolean or a string which should be:
+
+    - :obj:`True` or :obj:`'only_first'` truncate to a maximum length specified by the :obj:`max_length` argument or
+      the maximum length accepted by the model if no :obj:`max_length` is provided (``max_length=None``). This will
+      only truncate the first sentence of a pair if a pair of sequence (or a batch of pairs of sequences) is provided.
+    - :obj:`'only_second'` truncate to a maximum length specified by the :obj:`max_length` argument or the maximum
+      length accepted by the model if no :obj:`max_length` is provided (``max_length=None``). This will only truncate
+      the second sentence of a pair if a pair of sequence (or a batch of pairs of sequences) is provided.
+    - :obj:`'longest_first'` truncate to a maximum length specified by the :obj:`max_length` argument or the maximum
+      length accepted by the model if no :obj:`max_length` is provided (``max_length=None``). This will truncate token
+      by token, removing a token from the longest sequence in the pair until the proper length is reached.
+    - :obj:`False` or :obj:`'do_not_truncate'` to not truncate the sequences. As we have seen before, this is the
+      default behavior.
+
+- :obj:`max_length` to control the length of the padding/truncation. It can be an integer or :obj:`None`, in which case
+  it will default to the maximum length the model can accept. If the model has no specific maximum input length,
+  truncation/padding to :obj:`max_length` is deactivated.
+
+Here is a table summarizing the recommend way to setup padding and truncation. If you use pair of inputs sequence in
+any of the following examples, you can replace :obj:`truncation=True` by a :obj:`STRATEGY` selected in 
+:obj:`['only_first', 'only_second', 'longest_first']`, i.e. :obj:`truncation='only_second'` or
+:obj:`truncation= 'longest_first'` to control how both sequence in the pair are truncated as detailed before.
+
++--------------------------------------+-----------------------------------+---------------------------------------------------------------------------------------------+
+| Truncation                           | Padding                           | Instruction                                                                                 |
++======================================+===================================+=============================================================================================+
+| no truncation                        | no padding                        | :obj:`tokenizer(batch_sentences)`                                                           |
+|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
+|                                      | padding to max sequence in batch  | :obj:`tokenizer(batch_sentences, padding=True)` or                                          |
+|                                      |                                   | :obj:`tokenizer(batch_sentences, padding='longest')`                                        |
+|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
+|                                      | padding to max model input length | :obj:`tokenizer(batch_sentences, padding='max_length')`                                     |
+|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
+|                                      | padding to specific length        | :obj:`tokenizer(batch_sentences, padding='max_length', max_length=42)`                      |
++--------------------------------------+-----------------------------------+---------------------------------------------------------------------------------------------+
+| truncation to max model input length | no padding                        | :obj:`tokenizer(batch_sentences, truncation=True)` or                                       |
+|                                      |                                   | :obj:`tokenizer(batch_sentences, truncation=STRATEGY)`                                      |
+|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
+|                                      | padding to max sequence in batch  | :obj:`tokenizer(batch_sentences, padding=True, truncation=True)` or                         |
+|                                      |                                   | :obj:`tokenizer(batch_sentences, padding=True, truncation=STRATEGY)`                        |
+|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
+|                                      | padding to max model input length | :obj:`tokenizer(batch_sentences, padding='max_length', truncation=True)` or                 |
+|                                      |                                   | :obj:`tokenizer(batch_sentences, padding='max_length', truncation=STRATEGY)`                |
+|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
+|                                      | padding to specific length        | Not possible                                                                                |
++--------------------------------------+-----------------------------------+---------------------------------------------------------------------------------------------+
+| truncation to specific length        | no padding                        | :obj:`tokenizer(batch_sentences, truncation=True, max_length=42)` or                        |
+|                                      |                                   | :obj:`tokenizer(batch_sentences, truncation=STRATEGY, max_length=42)`                       |
+|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
+|                                      | padding to max sequence in batch  | :obj:`tokenizer(batch_sentences, padding=True, truncation=True, max_length=42)` or          |
+|                                      |                                   | :obj:`tokenizer(batch_sentences, padding=True, truncation=STRATEGY, max_length=42)`         |
+|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
+|                                      | padding to max model input length | Not possible                                                                                |
+|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
+|                                      | padding to specific length        | :obj:`tokenizer(batch_sentences, padding='max_length', truncation=True, max_length=42)` or  |
+|                                      |                                   | :obj:`tokenizer(batch_sentences, padding='max_length', truncation=STRATEGY, max_length=42)` |
++--------------------------------------+-----------------------------------+---------------------------------------------------------------------------------------------+
+
+Pre-tokenized inputs
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The tokenizer also accept pre-tokenized inputs. This is particularly useful when you want to compute labels and extract
+predictions in `named entity recognition (NER) <https://en.wikipedia.org/wiki/Named-entity_recognition>`__ or
+`part-of-speech tagging (POS tagging) <https://en.wikipedia.org/wiki/Part-of-speech_tagging>`__.
+
+.. warning::
+
+    Pre-tokenized does not mean your inputs are already tokenized (you wouldn't need to pass them though the tokenizer
+    if that was the case) but just split into words (which is often the first step in subword tokenization algorithms
+    like BPE).
+
+If you want to use pre-tokenized inputs, just set :obj:`is_split_into_words=True` when passing your inputs to the
+tokenizer. For instance, we have:
+
+.. code-block::
+
+    >>> encoded_input = tokenizer(["Hello", "I'm", "a", "single", "sentence"], is_split_into_words=True)
+    >>> print(encoded_input)
+    {'input_ids': [101, 8667, 146, 112, 182, 170, 1423, 5650, 102],
+     'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0], 
+     'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1]}
+
+Note that the tokenizer still adds the ids of special tokens (if applicable) unless you pass
+``add_special_tokens=False``.
+
+This works exactly as before for batch of sentences or batch of pairs of sentences. You can encode a batch of sentences
+like this:
+
+.. code-block::
+
+    batch_sentences = [["Hello", "I'm", "a", "single", "sentence"],
+                       ["And", "another", "sentence"],
+                       ["And", "the", "very", "very", "last", "one"]]
+    encoded_inputs = tokenizer(batch_sentences, is_split_into_words=True)
+
+or a batch of pair sentences like this:
+
+.. code-block::
+
+    batch_of_second_sentences = [["I'm", "a", "sentence", "that", "goes", "with", "the", "first", "sentence"],
+                                 ["And", "I", "should", "be", "encoded", "with", "the", "second", "sentence"],
+                                 ["And", "I", "go", "with", "the", "very", "last", "one"]]
+    encoded_inputs = tokenizer(batch_sentences, batch_of_second_sentences, is_split_into_words=True)
+
+And you can add padding, truncation as well as directly return tensors like before:
+
+.. code-block::
+
+    ## PYTORCH CODE
+    batch = tokenizer(batch_sentences,
+                      batch_of_second_sentences,
+                      is_split_into_words=True,
+                      padding=True,
+                      truncation=True,
+                      return_tensors="pt")
+    ## TENSORFLOW CODE
+    batch = tokenizer(batch_sentences,
+                      batch_of_second_sentences,
+                      is_split_into_words=True,
+                      padding=True,
+                      truncation=True,
+                      return_tensors="tf")

docs/source/pretrained_models.rst

@@ -1,5 +1,5 @@
 Pretrained models
-================================================
+=======================================================================================================================
 
 Here is the full list of the currently provided pretrained models together with a short presentation of each model.
 

docs/source/quicktour.rst

@@ -1,5 +1,5 @@
 Quick tour
-==========
+=======================================================================================================================
 
 Let's have a quick look at the 🤗 Transformers library features. The library downloads pretrained models for
 Natural Language Understanding (NLU) tasks, such as analyzing the sentiment of a text, and Natural Language Generation (NLG),
@@ -14,7 +14,7 @@ will dig a little bit more and see how the library gives you access to those mod
     not, the code is expected to work for both backends without any change needed.
 
 Getting started on a task with a pipeline
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 The easiest way to use a pretrained model on a given task is to use :func:`~transformers.pipeline`. 🤗 Transformers
 provides the following tasks out of the box:
@@ -123,7 +123,7 @@ to share your fine-tuned model on the hub with the community, using :doc:`this t
 .. _pretrained-model:
 
 Under the hood: pretrained models
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Let's now see what happens beneath the hood when using those pipelines. As we saw, the model and tokenizer are created
 using the :obj:`from_pretrained` method:
@@ -142,7 +142,7 @@ using the :obj:`from_pretrained` method:
     >>> tokenizer = AutoTokenizer.from_pretrained(model_name)
 
 Using the tokenizer
-^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 We mentioned the tokenizer is responsible for the preprocessing of your texts. First, it will split a given text in
 words (or part of words, punctuation symbols, etc.) usually called `tokens`. There are multiple rules that can govern
@@ -210,7 +210,7 @@ padding token the model was pretrained with. The attention mask is also adapted
 You can learn more about tokenizers :doc:`here <preprocessing>`.
 
 Using the model
-^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 Once your input has been preprocessed by the tokenizer, you can send it directly to the model. As we mentioned, it will
 contain all the relevant information the model needs. If you're using a TensorFlow model, you can pass the
@@ -330,7 +330,7 @@ Lastly, you can also ask the model to return all hidden states and all attention
     >>> all_hidden_states, all_attentions = tf_outputs[-2:]
 
 Accessing the code
-^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 The :obj:`AutoModel` and :obj:`AutoTokenizer` classes are just shortcuts that will automatically work with any
 pretrained model. Behind the scenes, the library has one model class per combination of architecture plus class, so the
@@ -358,7 +358,7 @@ without the auto magic:
     >>> tokenizer = DistilBertTokenizer.from_pretrained(model_name)
 
 Customizing the model
-^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 If you want to change how the model itself is built, you can define your custom configuration class. Each architecture
 comes with its own relevant configuration (in the case of DistilBERT, :class:`~transformers.DistilBertConfig`) which

docs/source/serialization.rst

@@ -1,9 +1,9 @@
-**********************************************
+***********************************************************************************************************************
 Exporting transformers models
-**********************************************
+***********************************************************************************************************************
 
 ONNX / ONNXRuntime
-==============================================
+=======================================================================================================================
 
 Projects `ONNX (Open Neural Network eXchange) <http://onnx.ai>`_ and `ONNXRuntime (ORT) <https://microsoft.github.io/onnxruntime/>`_ are part of an effort from leading industries in the AI field
 to provide a unified and community-driven format to store and, by extension, efficiently execute neural network leveraging a variety
@@ -42,7 +42,7 @@ Also, the conversion tool supports different options which let you tune the beha
 
 
 Optimizations
-------------------------------------------------
+-----------------------------------------------------------------------------------------------------------------------
 
 ONNXRuntime includes some transformers-specific transformations to leverage optimized operations in the graph.
 Below are some of the operators which can be enabled to speed up inference through ONNXRuntime (*see note below*):
@@ -68,7 +68,7 @@ Optimizations can then be enabled when loading the model through ONNX runtime fo
     For more information about the optimizations enabled by ONNXRuntime, please have a look at the (`ONNXRuntime Github <https://github.com/microsoft/onnxruntime/tree/master/onnxruntime/python/tools/transformers>`_)
 
 Quantization
-------------------------------------------------
+-----------------------------------------------------------------------------------------------------------------------
 
 ONNX exporter supports generating a quantized version of the model to allow efficient inference.
 
@@ -116,7 +116,7 @@ Example of quantized BERT model export:
 
 
 TorchScript
-=======================================
+=======================================================================================================================
 
 .. note::
     This is the very beginning of our experiments with TorchScript and we are still exploring its capabilities
@@ -141,10 +141,10 @@ These necessities imply several things developers should be careful about. These
 
 
 Implications
-------------------------------------------------
+-----------------------------------------------------------------------------------------------------------------------
 
 TorchScript flag and tied weights
-------------------------------------------------
+-----------------------------------------------------------------------------------------------------------------------
 This flag is necessary because most of the language models in this repository have tied weights between their
 ``Embedding`` layer and their ``Decoding`` layer. TorchScript does not allow the export of models that have tied weights, therefore
 it is necessary to untie and clone the weights beforehand.
@@ -157,7 +157,7 @@ This is not the case for models that do not have a Language Model head, as those
 can be safely exported without the ``torchscript`` flag.
 
 Dummy inputs and standard lengths
-------------------------------------------------
+-----------------------------------------------------------------------------------------------------------------------
 
 The dummy inputs are used to do a model forward pass. While the inputs' values are propagating through the layers,
 Pytorch keeps track of the different operations executed on each tensor. These recorded operations are then used
@@ -178,12 +178,12 @@ It is recommended to be careful of the total number of operations done on each i
 when exporting varying sequence-length models.
 
 Using TorchScript in Python
--------------------------------------------------
+-----------------------------------------------------------------------------------------------------------------------
 
 Below is an example, showing how to save, load models as well as how to use the trace for inference.
 
 Saving a model
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 This snippet shows how to use TorchScript to export a ``BertModel``. Here the ``BertModel`` is instantiated
 according to a ``BertConfig`` class and then saved to disk under the filename ``traced_bert.pt``
@@ -229,7 +229,7 @@ according to a ``BertConfig`` class and then saved to disk under the filename ``
     torch.jit.save(traced_model, "traced_bert.pt")
 
 Loading a model
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 This snippet shows how to load the ``BertModel`` that was previously saved to disk under the name ``traced_bert.pt``.
 We are re-using the previously initialised ``dummy_input``.
@@ -242,7 +242,7 @@ We are re-using the previously initialised ``dummy_input``.
     all_encoder_layers, pooled_output = loaded_model(*dummy_input)
 
 Using a traced model for inference
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 Using the traced model for inference is as simple as using its ``__call__`` dunder method:
 

docs/source/task_summary.rst

@@ -1,5 +1,5 @@
 Summary of the tasks
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 This page shows the most frequent use-cases when using the library. The models available allow for many different
 configurations and a great versatility in use-cases. The most simple ones are presented here, showcasing usage
@@ -38,7 +38,7 @@ Both approaches are showcased here.
     This would produce random output.
 
 Sequence Classification
---------------------------
+-----------------------------------------------------------------------------------------------------------------------
 
 Sequence classification is the task of classifying sequences according to a given number of classes. An example
 of sequence classification is the GLUE dataset, which is entirely based on that task. If you would like to fine-tune
@@ -152,7 +152,7 @@ of each other. The process is the following:
     is paraphrase: 6%
 
 Extractive Question Answering
-----------------------------------------------------
+-----------------------------------------------------------------------------------------------------------------------
 
 Extractive Question Answering is the task of extracting an answer from a text given a question. An example of a
 question answering dataset is the SQuAD dataset, which is entirely based on that task. If you would like to fine-tune
@@ -297,7 +297,7 @@ Here is an example of question answering using a model and a tokenizer. The proc
 
 
 Language Modeling
-----------------------------------------------------
+-----------------------------------------------------------------------------------------------------------------------
 
 Language modeling is the task of fitting a model to a corpus, which can be domain specific. All popular transformer-based
 models are trained using a variant of language modeling, e.g. BERT with masked language modeling, GPT-2 with
@@ -308,7 +308,7 @@ domain-specific: using a language model trained over a very large corpus, and th
 or on scientific papers e.g. `LysandreJik/arxiv-nlp <https://huggingface.co/lysandre/arxiv-nlp>`__.
 
 Masked Language Modeling
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Masked language modeling is the task of masking tokens in a sequence with a masking token, and prompting the model to
 fill that mask with an appropriate token. This allows the model to attend to both the right context (tokens on the
@@ -421,7 +421,7 @@ This prints five sequences, with the top 5 tokens predicted by the model:
 
 
 Causal Language Modeling
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Causal language modeling is the task of predicting the token following a sequence of tokens. In this situation, the
 model only attends to the left context (tokens on the left of the mask). Such a training is particularly interesting
@@ -493,7 +493,7 @@ This outputs a (hopefully) coherent next token following the original sequence,
 In the next section, we show how this functionality is leveraged in :func:`~transformers.PreTrainedModel.generate` to generate multiple tokens up to a user-defined length.
 
 Text Generation
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 In text generation (*a.k.a* *open-ended text generation*) the goal is to create a coherent portion of text that is a continuation from the given context. The following example shows how *GPT-2* can be used in pipelines to generate text. As a default all models apply *Top-K* sampling when used in pipelines, as configured in their respective configurations (see `gpt-2 config <https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-config.json>`__ for example).
 
@@ -576,7 +576,7 @@ For more information on how to apply different decoding strategies for text gene
 
 
 Named Entity Recognition
-----------------------------------------------------
+-----------------------------------------------------------------------------------------------------------------------
 
 Named Entity Recognition (NER) is the task of classifying tokens according to a class, for example, identifying a
 token as a person, an organisation or a location.
@@ -723,7 +723,7 @@ following array should be the output:
     [('[CLS]', 'O'), ('Hu', 'I-ORG'), ('##gging', 'I-ORG'), ('Face', 'I-ORG'), ('Inc', 'I-ORG'), ('.', 'O'), ('is', 'O'), ('a', 'O'), ('company', 'O'), ('based', 'O'), ('in', 'O'), ('New', 'I-LOC'), ('York', 'I-LOC'), ('City', 'I-LOC'), ('.', 'O'), ('Its', 'O'), ('headquarters', 'O'), ('are', 'O'), ('in', 'O'), ('D', 'I-LOC'), ('##UM', 'I-LOC'), ('##BO', 'I-LOC'), (',', 'O'), ('therefore', 'O'), ('very', 'O'), ('##c', 'O'), ('##lose', 'O'), ('to', 'O'), ('the', 'O'), ('Manhattan', 'I-LOC'), ('Bridge', 'I-LOC'), ('.', 'O'), ('[SEP]', 'O')]
 
 Summarization
-----------------------------------------------------
+-----------------------------------------------------------------------------------------------------------------------
 
 Summarization is the task of summarizing a document or an article into a shorter text.
 
@@ -798,7 +798,7 @@ In this example we use Google`s T5 model. Even though it was pre-trained only on
     >>> outputs = model.generate(inputs, max_length=150, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True)
 
 Translation
-----------------------------------------------------
+-----------------------------------------------------------------------------------------------------------------------
 
 Translation is the task of translating a text from one language to another.
 

docs/source/testing.rst

@@ -1,5 +1,5 @@
 Testing
-==========
+=======================================================================================================================
 
 
 Let's take a look at how 🤗 Transformer models are tested and how you can write new tests and improve the existing ones.
@@ -10,7 +10,7 @@ There are 2 test suites in the repository:
 2. ``examples`` -- tests primarily for various applications that aren't part of the API
 
 How transformers are tested
----------------------------
+-----------------------------------------------------------------------------------------------------------------------
 
 1. Once a PR is submitted it gets tested with 9 CircleCi jobs. Every new commit to that PR gets retested. These jobs are defined in this `config file <https://github.com/huggingface/transformers/blob/master/.circleci/config.yml>`__, so that if needed you can reproduce the same environment on your machine.
    
@@ -34,14 +34,14 @@ How transformers are tested
 
 
 Running tests
--------------
+-----------------------------------------------------------------------------------------------------------------------
 
 
 
 
 
 Choosing which tests to run
-~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 This document goes into many details of how tests can be run. If after reading everything, you need even more details you will find them `here <https://docs.pytest.org/en/latest/usage.html>`__.
 
@@ -75,7 +75,7 @@ which tells pytest to:
 
 
 Getting the list of all tests
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 All tests of the test suite:
 
@@ -92,7 +92,7 @@ All tests of a given test file:
 
    
 Run a specific test module
-~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 To run an individual test module:
 
@@ -102,7 +102,7 @@ To run an individual test module:
    
 
 Run specific tests
-~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Since unittest is used inside most of the tests, to run specific subtests you need to know the name of the unittest class containing those tests. For example, it could be:
 
@@ -156,7 +156,7 @@ And you can combine the two patterns in one:
 
 
 Run only modified tests
-~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 You can run the tests related to the unstaged files or the current branch (according to Git) by using `pytest-picked <https://github.com/anapaulagomes/pytest-picked>`__. This is a great way of quickly testing your changes didn't break anything, since it won't run the tests related to files you didn't touch.
 
@@ -172,7 +172,7 @@ All tests will be run from files and folders which are modified, but not
 yet committed.
 
 Automatically rerun failed tests on source modification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 `pytest-xdist <https://github.com/pytest-dev/pytest-xdist>`__ provides a
 very useful feature of detecting all failed tests, and then waiting for
@@ -212,7 +212,7 @@ alternative implementation of this functionality.
 
 
 Skip a test module
-~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 If you want to run all test modules, except a few you can exclude them by giving an explicit list of tests to run. For example, to run all except ``test_modeling_*.py`` tests:
 
@@ -222,7 +222,7 @@ If you want to run all test modules, except a few you can exclude them by giving
 
 
 Clearing state
-~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 CI builds and when isolation is important (against speed), cache should
 be cleared:
@@ -232,7 +232,7 @@ be cleared:
     pytest --cache-clear tests
 
 Running tests in parallel
-~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 As mentioned earlier ``make test`` runs tests in parallel via ``pytest-xdist`` plugin (``-n X`` argument, e.g. ``-n 2`` to run 2 parallel jobs).
 
@@ -246,7 +246,7 @@ tests in the same order, which should help with then somehow reducing
 that failing sequence to a minimum.
 
 Test order and repetition
-~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 It's good to repeat the tests several times, in sequence, randomly, or
 in sets, to detect any potential inter-dependency and state-related bugs
@@ -255,7 +255,7 @@ detect some problems that get uncovered by randomness of DL.
 
 
 Repeat tests
-^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 * `pytest-flakefinder <https://github.com/dropbox/pytest-flakefinder>`__:
 
@@ -277,7 +277,7 @@ And then run every test multiple times (50 by default):
 
 
 Run tests in a random order
-^^^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 .. code-block:: bash
 
@@ -333,10 +333,10 @@ details please see its `documentation <https://github.com/jbasko/pytest-random-o
 Another randomization alternative is: ``pytest-randomly`` <https://github.com/pytest-dev/pytest-randomly>`__. This module has a very similar functionality/interface, but it doesn't have the bucket modes available in ``pytest-random-order``. It has the same problem of imposing itself once installed.
 
 Look and feel variations
-~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 pytest-sugar
-^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 `pytest-sugar <https://github.com/Frozenball/pytest-sugar>`__ is a
 plugin that improves the look-n-feel, adds a progressbar, and show tests
@@ -358,7 +358,7 @@ or uninstall it.
 
 
 Report each sub-test name and its progress
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 For a single or a group of tests via ``pytest`` (after
 ``pip install pytest-pspec``):
@@ -370,7 +370,7 @@ For a single or a group of tests via ``pytest`` (after
 
 
 Instantly shows failed tests
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 `pytest-instafail <https://github.com/pytest-dev/pytest-instafail>`__
 shows failures and errors instantly instead of waiting until the end of
@@ -385,7 +385,7 @@ test session.
     pytest --instafail
 
 To GPU or not to GPU
-~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 On a GPU-enabled setup, to test in CPU-only mode add ``CUDA_VISIBLE_DEVICES=""``:
 
@@ -403,14 +403,24 @@ This is handy when you want to run different tasks on different GPUs.
     
 And we have these decorators that require the condition described by the marker.
 
-```
+``
 @require_torch
 @require_tf
 @require_multigpu
 @require_non_multigpu
 @require_torch_tpu
 @require_torch_and_cuda
-```
+``
+
+Some decorators like ``@parametrized`` rewrite test names, therefore ``@require_*`` skip decorators have to be listed last for them to work correctly. Here is an example of the correct usage:
+
+.. code-block:: python
+
+    @parameterized.expand(...)
+    @require_multigpu
+    def test_integration_foo():
+    
+There is no problem whatsoever with ``@pytest.mark.parametrize`` (but it only works with non-unittests) - can use it in any order.
 
 This section will be expanded soon once our work in progress on those decorators is finished.
 
@@ -427,7 +437,7 @@ Inside tests:
 
 
 Output capture
-~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 During test execution any output sent to ``stdout`` and ``stderr`` is
 captured. If a test or a setup method fails, its according captured
@@ -448,7 +458,7 @@ To send test results to JUnit format output:
 
 
 Color control
-~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 To have no color (e.g., yellow on white background is not readable):
 
@@ -459,7 +469,7 @@ To have no color (e.g., yellow on white background is not readable):
 
 
 Sending test report to online pastebin service
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Creating a URL for each test failure:
 
@@ -480,7 +490,7 @@ Creating a URL for a whole test session log:
 
 
 Writing tests
--------------
+-----------------------------------------------------------------------------------------------------------------------
 
 🤗 transformers tests are based on ``unittest``, but run by ``pytest``, so most of the time features from both systems can be used.
 
@@ -488,7 +498,7 @@ You can read `here <https://docs.pytest.org/en/stable/unittest.html>`__ which fe
 
 
 Parametrization
-~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Often, there is a need to run the same test multiple times, but with different arguments. It could be done from within the test, but then there is no way of running that test for just one set of arguments.
 
@@ -586,7 +596,7 @@ as in the previous example.
     
 
 Temporary files and directories
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Using unique temporary files and directories are essential for parallel test running, so that the tests won't overwrite each other's data. Also we want to get the temp files and directories removed at the end of each test that created them. Therefore, using packages like ``tempfile``, which address these needs is essential.
 
@@ -636,7 +646,7 @@ In this and all the following scenarios the temporary directory will be auto-rem
 
 
 Skipping tests
-~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 This is useful when a bug is found and a new test is written, yet the
 bug is not fixed yet. In order to be able to commit it to the main
@@ -663,7 +673,7 @@ causes some bad state that will affect other tests, do not use
 ``xfail``.
 
 Implementation
-^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 - Here is how to skip whole test unconditionally:
 
@@ -739,7 +749,7 @@ or skip the whole module:
 More details, example and ways are `here <https://docs.pytest.org/en/latest/skipping.html>`__.
 
 Custom markers
-~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 * Slow tests
 
@@ -757,7 +767,7 @@ To run such tests set ``RUN_SLOW=1`` env var, e.g.:
 
     RUN_SLOW=1 pytest tests
     
-It's important that the decorator ``@slow`` appears last in the stack of decorators, as some decorators like ``parametrized`` may interfere with its normal functioning. Here is an example of the correct usage:
+Some decorators like ``@parametrized`` rewrite test names, therefore ``@slow`` and the rest of the skip decorators ``@require_*`` have to be listed last for them to work correctly. Here is an example of the correct usage:
 
 .. code-block:: python
 
@@ -766,7 +776,7 @@ It's important that the decorator ``@slow`` appears last in the stack of decorat
     def test_integration_foo():
 
 Testing the stdout/stderr output
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 In order to test functions that write to ``stdout`` and/or ``stderr``,
 the test can access those streams using the ``pytest``'s `capsys
@@ -875,7 +885,7 @@ If you need to capture both streams at once, use the parent
 
 
 Capturing logger stream
-~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 If you need to validate the output of a logger, you can use :obj:`CaptureLogger`:
 
@@ -893,7 +903,7 @@ If you need to validate the output of a logger, you can use :obj:`CaptureLogger`
 
 
 Testing with environment variables
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 If you want to test the impact of environment variables for a specific test you can use a helper decorator ``transformers.testing_utils.mockenv``
 
@@ -907,7 +917,7 @@ If you want to test the impact of environment variables for a specific test you
 
 
 Getting reproducible results
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 In some situations you may want to remove randomness for your tests. To
 get identical reproducable results set, you will need to fix the seed:
@@ -934,7 +944,7 @@ get identical reproducable results set, you will need to fix the seed:
     tf.random.set_seed(seed)
 
 Debugging tests
-~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 To start a debugger at the point of the warning, do this:
 

docs/source/tokenizer_summary.rst

@@ -1,243 +1,243 @@
-Tokenizer summary
------------------
-
-In this page, we will have a closer look at tokenization. As we saw in
-:doc:`the preprocessing tutorial <preprocessing>`, tokenizing a text is splitting it into words or subwords, which then
-are converted to ids. The second part is pretty straightforward, here we will focus on the first part. More
-specifically, we will look at the three main different kinds of tokenizers used in 🤗 Transformers:
-:ref:`Byte-Pair Encoding (BPE) <byte-pair-encoding>`, :ref:`WordPiece <wordpiece>` and
-:ref:`SentencePiece <sentencepiece>`, and provide examples of models using each of those.
-
-Note that on each model page, you can look at the documentation of the associated tokenizer to know which of those
-algorithms the pretrained model used. For instance, if we look at :class:`~transformers.BertTokenizer`, we can see it's
-using :ref:`WordPiece <wordpiece>`.
-
-Introduction to tokenization
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Splitting a text in smaller chunks is a task that's harder than it looks, and there are multiple ways of doing it. For
-instance, let's look at the sentence "Don't you love 🤗 Transformers? We sure do." A first simple way of tokenizing
-this text is just to split it by spaces, which would give:
-
-::
-
-    ["Don't", "you", "love", "🤗", "Transformers?", "We", "sure", "do."]
-
-This is a nice first step, but if we look at the tokens "Transformers?" or "do.", we can see we can do better. Those
-will be different than the tokens "Transformers" and "do" for our model, so we should probably take the punctuation
-into account. This would give:
-
-::
-
-    ["Don", "'", "t", "you", "love", "🤗", "Transformers", "?", "We", "sure", "do", "."]
-
-which is better already. One thing that is annoying though is how it dealt with "Don't". "Don't" stands for do not, so
-it should probably be better tokenized as ``["Do", "n't"]``. This is where things start getting more complicated, and
-part of the reason each kind of model has its own tokenizer class. Depending on the rules we apply to split our texts
-into tokens, we'll get different tokenized versions of the same text. And of course, a given pretrained model won't
-perform properly if you don't use the exact same rules as the persons who pretrained it.
-
-`spaCy <https://spacy.io/>`__ and `Moses <http://www.statmt.org/moses/?n=Development.GetStarted>`__ are two popular
-rule-based tokenizers. On the text above, they'd output something like:
-
-::
-
-    ["Do", "n't", "you", "love", "🤗", "Transformers", "?", "We", "sure", "do", "."]
-
-Space/punctuation-tokenization and rule-based tokenization are both examples of word tokenization, which is splitting a
-sentence into words. While it's the most intuitive way to separate texts in smaller chunks, it can have a problem when
-you have a huge corpus: it usually yields a very big vocabulary (the set of all unique tokens used).
-:doc:`Transformer XL <model_doc/transformerxl>` for instance uses space/punctuation-tokenization, and has a vocabulary
-size of 267,735!
-
-A huge vocabulary size means a huge embedding matrix at the start of the model, which will cause memory problems.
-TransformerXL deals with it by using a special kind of embeddings called adaptive embeddings, but in general,
-transformers models rarely have a vocabulary size greater than 50,000, especially if they are trained on a single
-language.
-
-So if tokenizing on words is unsatisfactory, we could go on the opposite direction and simply tokenize on characters.
-While it's very simple and would save a lot of memory, this doesn't allow the model to learn representations of texts
-as meaningful as when using a word tokenization, leading to a loss of performance. So to get the best of both worlds,
-all transformers models use a hybrid between word-level and character-level tokenization called subword tokenization.
-
-Subword tokenization
-^^^^^^^^^^^^^^^^^^^^
-
-Subword tokenization algorithms rely on the principle that most common words should be left as is, but rare words
-should be decomposed in meaningful subword units. For instance "annoyingly" might be considered a rare word and
-decomposed as "annoying" and "ly". This is especially useful in agglutinative languages such as Turkish, where you can
-form (almost) arbitrarily long complex words by stringing together some subwords.
-
-This allows the model to keep a reasonable vocabulary while still learning useful representations for common words or
-subwords. This also enables the model to process words it has never seen before, by decomposing them into
-subwords it knows. For instance, the base :class:`~transformers.BertTokenizer` will tokenize "I have a new GPU!" like
-this:
-
-.. code-block::
-
-    >>> from transformers import BertTokenizer
-    >>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-    >>> tokenizer.tokenize("I have a new GPU!")
-    ['i', 'have', 'a', 'new', 'gp', '##u', '!']
-
-Since we are considering the uncased model, the sentence was lowercased first. Then all the words were present in the
-vocabulary of the tokenizer, except for "gpu", so the tokenizer split it in subwords it knows: "gp" and "##u". The "##"
-means that the rest of the token should be attached to the previous one, without space (for when we need to decode
-predictions and reverse the tokenization).
-
-Another example is when we use the base :class:`~transformers.XLNetTokenizer` to tokenize our previous text:
-
-.. code-block::
-
-    >>> from transformers import XLNetTokenizer
-    >>> tokenizer = XLNetTokenizer.from_pretrained('xlnet-base-cased')
-    >>> tokenizer.tokenize("Don't you love 🤗 Transformers? We sure do.")
-    ['▁Don', "'", 't', '▁you', '▁love', '▁', '🤗', '▁', 'Transform', 'ers', '?', '▁We', '▁sure', '▁do', '.']
-
-We'll get back to the meaning of those '▁' when we look at :ref:`SentencePiece <sentencepiece>` but you can see
-Transformers has been split into "Transform" and "ers".
-
-Let's now look at how the different subword tokenization algorithms work. Note that they all rely on some form of
-training which is usually done on the corpus the corresponding model will be trained on.
-
-.. _byte-pair-encoding:
-
-Byte-Pair Encoding
-~~~~~~~~~~~~~~~~~~
-
-Byte-Pair Encoding was introduced in `this paper <https://arxiv.org/abs/1508.07909>`__. It relies on a pretokenizer
-splitting the training data into words, which can be a simple space tokenization
-(:doc:`GPT-2 <model_doc/gpt2>` and :doc:`Roberta <model_doc/roberta>` uses this for instance) or a rule-based tokenizer
-(:doc:`XLM <model_doc/xlm>` use Moses for most languages, as does :doc:`FlauBERT <model_doc/flaubert>`),
-
-:doc:`GPT <model_doc/gpt>` uses Spacy and ftfy, and counts the frequency of each word in the training corpus.
-
-It then begins from the list of all characters, and will learn merge rules to form a new token from two symbols in the
-vocabulary until it has learned a vocabulary of the desired size (this is a hyperparameter to pick).
-
-Let's say that after the pre-tokenization we have the following words (the number indicating the frequency of each
-word):
-
-::
-
-    ('hug', 10), ('pug', 5), ('pun', 12), ('bun', 4), ('hugs', 5)
-
-Then the base vocabulary is ['b', 'g', 'h', 'n', 'p', 's', 'u'] and all our words are first split by character:
-
-::
-
-    ('h' 'u' 'g', 10), ('p' 'u' 'g', 5), ('p' 'u' 'n', 12), ('b' 'u' 'n', 4), ('h' 'u' 'g' 's', 5)
-
-We then take each pair of symbols and look at the most frequent. For instance 'hu' is present `10 + 5 = 15` times (10
-times in the 10 occurrences of 'hug', 5 times in the 5 occurrences of 'hugs'). The most frequent here is 'ug', present
-`10 + 5 + 5 = 20` times in total. So the first merge rule the tokenizer learns is to group all 'u' and 'g' together
-then it adds 'ug' to the vocabulary. Our corpus then becomes
-
-::
-
-    ('h' 'ug', 10), ('p' 'ug', 5), ('p' 'u' 'n', 12), ('b' 'u' 'n', 4), ('h' 'ug' 's', 5)
-
-and we continue by looking at the next most common pair of symbols. It's 'un', present 16 times, so we merge those two
-and add 'un' to the vocabulary. Then it's 'hug' (as 'h' + 'ug'), present 15 times, so we merge those two and add 'hug'
-to the vocabulary.
-
-At this stage, the vocabulary is ``['b', 'g', 'h', 'n', 'p', 's', 'u', 'ug', 'un', 'hug']`` and our corpus is
-represented as
-
-::
-
-    ('hug', 10), ('p' 'ug', 5), ('p' 'un', 12), ('b' 'un', 4), ('hug' 's', 5)
-
-If we stop there, the tokenizer can apply the rules it learned to new words (as long as they don't contain characters that
-were not in the base vocabulary). For instance 'bug' would be tokenized as ``['b', 'ug']`` but mug would be tokenized as
-``['<unk>', 'ug']`` since the 'm' is not in the base vocabulary. This doesn't happen to letters in general (since the
-base corpus uses all of them), but to special characters like emojis.
-
-As we said before, the vocabulary size (which is the base vocabulary size + the number of merges) is a hyperparameter
-to choose. For instance :doc:`GPT <model_doc/gpt>` has a vocabulary size of 40,478 since they have 478 base characters
-and chose to stop the training of the tokenizer at 40,000 merges.
-
-Byte-level BPE
-^^^^^^^^^^^^^^
-
-To deal with the fact the base vocabulary needs to get all base characters, which can be quite big if one allows for
-all unicode characters, the
-`GPT-2 paper <https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf>`__
-introduces a clever trick, which is to use bytes as the base vocabulary (which gives a size of 256). With some
-additional rules to deal with punctuation, this manages to be able to tokenize every text without needing an unknown
-token. For instance, the :doc:`GPT-2 model <model_doc/gpt>` has a vocabulary size of 50,257, which corresponds to the
-256 bytes base tokens, a special end-of-text token and the symbols learned with 50,000 merges.
-
-.. _wordpiece:
-
-WordPiece
-=========
-
-WordPiece is the subword tokenization algorithm used for :doc:`BERT <model_doc/bert>` (as well as
-:doc:`DistilBERT <model_doc/distilbert>` and :doc:`Electra <model_doc/electra>`) and was outlined in
-`this paper <https://static.googleusercontent.com/media/research.google.com/ja//pubs/archive/37842.pdf>`__. It relies
-on the same base as BPE, which is to initialize the vocabulary to every character present in the corpus and
-progressively learn a given number of merge rules, the difference is that it doesn't choose the pair that is the most
-frequent but the one that will maximize the likelihood on the corpus once merged.
-
-What does this mean? Well, in the previous example, it means we would only merge 'u' and 'g' if the probability of
-having 'ug' divided by the probability of having 'u' then 'g' is greater than for any other pair of symbols. It's
-subtly different from what BPE does in the sense that it evaluates what it "loses" by merging two symbols and makes
-sure it's `worth it`.
-
-.. _unigram:
-
-Unigram
-=======
-
-Unigram is a subword tokenization algorithm introduced in `this paper <https://arxiv.org/pdf/1804.10959.pdf>`__.
-Instead of starting with a group of base symbols and learning merges with some rule, like BPE or WordPiece, it starts
-from a large vocabulary (for instance, all pretokenized words and the most common substrings) that it will trim down
-progressively. It's not used directly for any of the pretrained models in the library, but it's used in conjunction
-with :ref:`SentencePiece <sentencepiece>`.
-
-More specifically, at a given step, unigram computes a loss from the corpus we have and the current vocabulary, then,
-for each subword, evaluate how much the loss would augment if the subword was removed from the vocabulary. It then
-sorts the subwords by this quantity (that represents how worse the loss becomes if the token is removed) and removes
-all the worst p tokens (for instance p could be 10% or 20%). It then repeats the process until the vocabulary has
-reached the desired size, always keeping the base characters (to be able to tokenize any word written with them, like
-BPE or WordPiece).
-
-Contrary to BPE and WordPiece that work out rules in a certain order that you can then apply in the same order when
-tokenizing new text, Unigram will have several ways of tokenizing a new text. For instance, if it ends up with the
-vocabulary
-
-::
-
-    ['b', 'g', 'h', 'n', 'p', 's', 'u', 'ug', 'un', 'hug']
-
-we had before, it could tokenize "hugs" as ``['hug', 's']``, ``['h', 'ug', 's']`` or ``['h', 'u', 'g', 's']``. So which
-one choose? On top of saving the vocabulary, the trained tokenizer will save the probability of each token in the
-training corpus. You can then give a probability to each tokenization (which is the product of the probabilities of the
-tokens forming it) and pick the most likely one (or if you want to apply some data augmentation, you could sample one
-of the tokenization according to their probabilities).
-
-Those probabilities define the loss that trains the tokenizer: if our corpus consists of the
-words :math:`x_{1}, \dots, x_{N}` and if for the word :math:`x_{i}` we note :math:`S(x_{i})` the set of all possible
-tokenizations of :math:`x_{i}` (with the current vocabulary), then the loss is defined as
-
-.. math::
-    \mathcal{L} = -\sum_{i=1}^{N} \log \left ( \sum_{x \in S(x_{i})} p(x) \right )
-
-.. _sentencepiece:
-
-SentencePiece
-=============
-
-All the methods we have been looking at so far required some form of pretokenization, which has a central problem: not
-all languages use spaces to separate words. This is a problem :doc:`XLM <model_doc/xlm>` solves by using specific
-pretokenizers for each of those languages (in this case, Chinese, Japanese and Thai). To solve this problem,
-SentencePiece (introduced in `this paper <https://arxiv.org/pdf/1808.06226.pdf>`__) treats the input as a raw stream,
-includes the space in the set of characters to use, then uses BPE or unigram to construct the appropriate vocabulary.
-
-That's why in the example we saw before using :class:`~transformers.XLNetTokenizer` (which uses SentencePiece), we had
-the '▁' character, that represents space. Decoding a tokenized text is then super easy: we just have to concatenate
-all of them together and replace '▁' with space.
-
-All transformers models in the library that use SentencePiece use it with unigram. Examples of models using it are
-:doc:`ALBERT <model_doc/albert>`, :doc:`XLNet <model_doc/xlnet>` or the :doc:`Marian framework <model_doc/marian>`.
+Tokenizer summary
+-----------------------------------------------------------------------------------------------------------------------
+
+In this page, we will have a closer look at tokenization. As we saw in
+:doc:`the preprocessing tutorial <preprocessing>`, tokenizing a text is splitting it into words or subwords, which then
+are converted to ids. The second part is pretty straightforward, here we will focus on the first part. More
+specifically, we will look at the three main different kinds of tokenizers used in 🤗 Transformers:
+:ref:`Byte-Pair Encoding (BPE) <byte-pair-encoding>`, :ref:`WordPiece <wordpiece>` and
+:ref:`SentencePiece <sentencepiece>`, and provide examples of models using each of those.
+
+Note that on each model page, you can look at the documentation of the associated tokenizer to know which of those
+algorithms the pretrained model used. For instance, if we look at :class:`~transformers.BertTokenizer`, we can see it's
+using :ref:`WordPiece <wordpiece>`.
+
+Introduction to tokenization
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Splitting a text in smaller chunks is a task that's harder than it looks, and there are multiple ways of doing it. For
+instance, let's look at the sentence "Don't you love 🤗 Transformers? We sure do." A first simple way of tokenizing
+this text is just to split it by spaces, which would give:
+
+.. code-block::
+
+    ["Don't", "you", "love", "🤗", "Transformers?", "We", "sure", "do."]
+
+This is a nice first step, but if we look at the tokens "Transformers?" or "do.", we can see we can do better. Those
+will be different than the tokens "Transformers" and "do" for our model, so we should probably take the punctuation
+into account. This would give:
+
+.. code-block::
+
+    ["Don", "'", "t", "you", "love", "🤗", "Transformers", "?", "We", "sure", "do", "."]
+
+which is better already. One thing that is annoying though is how it dealt with "Don't". "Don't" stands for do not, so
+it should probably be better tokenized as ``["Do", "n't"]``. This is where things start getting more complicated, and
+part of the reason each kind of model has its own tokenizer class. Depending on the rules we apply to split our texts
+into tokens, we'll get different tokenized versions of the same text. And of course, a given pretrained model won't
+perform properly if you don't use the exact same rules as the persons who pretrained it.
+
+`spaCy <https://spacy.io/>`__ and `Moses <http://www.statmt.org/moses/?n=Development.GetStarted>`__ are two popular
+rule-based tokenizers. On the text above, they'd output something like:
+
+.. code-block::
+
+    ["Do", "n't", "you", "love", "🤗", "Transformers", "?", "We", "sure", "do", "."]
+
+Space/punctuation-tokenization and rule-based tokenization are both examples of word tokenization, which is splitting a
+sentence into words. While it's the most intuitive way to separate texts in smaller chunks, it can have a problem when
+you have a huge corpus: it usually yields a very big vocabulary (the set of all unique tokens used).
+:doc:`Transformer XL <model_doc/transformerxl>` for instance uses space/punctuation-tokenization, and has a vocabulary
+size of 267,735!
+
+A huge vocabulary size means a huge embedding matrix at the start of the model, which will cause memory problems.
+TransformerXL deals with it by using a special kind of embeddings called adaptive embeddings, but in general,
+transformers models rarely have a vocabulary size greater than 50,000, especially if they are trained on a single
+language.
+
+So if tokenizing on words is unsatisfactory, we could go on the opposite direction and simply tokenize on characters.
+While it's very simple and would save a lot of memory, this doesn't allow the model to learn representations of texts
+as meaningful as when using a word tokenization, leading to a loss of performance. So to get the best of both worlds,
+all transformers models use a hybrid between word-level and character-level tokenization called subword tokenization.
+
+Subword tokenization
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Subword tokenization algorithms rely on the principle that most common words should be left as is, but rare words
+should be decomposed in meaningful subword units. For instance "annoyingly" might be considered a rare word and
+decomposed as "annoying" and "ly". This is especially useful in agglutinative languages such as Turkish, where you can
+form (almost) arbitrarily long complex words by stringing together some subwords.
+
+This allows the model to keep a reasonable vocabulary while still learning useful representations for common words or
+subwords. This also enables the model to process words it has never seen before, by decomposing them into
+subwords it knows. For instance, the base :class:`~transformers.BertTokenizer` will tokenize "I have a new GPU!" like
+this:
+
+.. code-block::
+
+    >>> from transformers import BertTokenizer
+    >>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+    >>> tokenizer.tokenize("I have a new GPU!")
+    ['i', 'have', 'a', 'new', 'gp', '##u', '!']
+
+Since we are considering the uncased model, the sentence was lowercased first. Then all the words were present in the
+vocabulary of the tokenizer, except for "gpu", so the tokenizer split it in subwords it knows: "gp" and "##u". The "##"
+means that the rest of the token should be attached to the previous one, without space (for when we need to decode
+predictions and reverse the tokenization).
+
+Another example is when we use the base :class:`~transformers.XLNetTokenizer` to tokenize our previous text:
+
+.. code-block::
+
+    >>> from transformers import XLNetTokenizer
+    >>> tokenizer = XLNetTokenizer.from_pretrained('xlnet-base-cased')
+    >>> tokenizer.tokenize("Don't you love 🤗 Transformers? We sure do.")
+    ['▁Don', "'", 't', '▁you', '▁love', '▁', '🤗', '▁', 'Transform', 'ers', '?', '▁We', '▁sure', '▁do', '.']
+
+We'll get back to the meaning of those '▁' when we look at :ref:`SentencePiece <sentencepiece>` but you can see
+Transformers has been split into "Transform" and "ers".
+
+Let's now look at how the different subword tokenization algorithms work. Note that they all rely on some form of
+training which is usually done on the corpus the corresponding model will be trained on.
+
+.. _byte-pair-encoding:
+
+Byte-Pair Encoding
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Byte-Pair Encoding was introduced in `this paper <https://arxiv.org/abs/1508.07909>`__. It relies on a pretokenizer
+splitting the training data into words, which can be a simple space tokenization
+(:doc:`GPT-2 <model_doc/gpt2>` and :doc:`Roberta <model_doc/roberta>` uses this for instance) or a rule-based tokenizer
+(:doc:`XLM <model_doc/xlm>` use Moses for most languages, as does :doc:`FlauBERT <model_doc/flaubert>`),
+
+:doc:`GPT <model_doc/gpt>` uses Spacy and ftfy, and counts the frequency of each word in the training corpus.
+
+It then begins from the list of all characters, and will learn merge rules to form a new token from two symbols in the
+vocabulary until it has learned a vocabulary of the desired size (this is a hyperparameter to pick).
+
+Let's say that after the pre-tokenization we have the following words (the number indicating the frequency of each
+word):
+
+.. code-block::
+
+    ('hug', 10), ('pug', 5), ('pun', 12), ('bun', 4), ('hugs', 5)
+
+Then the base vocabulary is ['b', 'g', 'h', 'n', 'p', 's', 'u'] and all our words are first split by character:
+
+.. code-block::
+
+    ('h' 'u' 'g', 10), ('p' 'u' 'g', 5), ('p' 'u' 'n', 12), ('b' 'u' 'n', 4), ('h' 'u' 'g' 's', 5)
+
+We then take each pair of symbols and look at the most frequent. For instance 'hu' is present `10 + 5 = 15` times (10
+times in the 10 occurrences of 'hug', 5 times in the 5 occurrences of 'hugs'). The most frequent here is 'ug', present
+`10 + 5 + 5 = 20` times in total. So the first merge rule the tokenizer learns is to group all 'u' and 'g' together
+then it adds 'ug' to the vocabulary. Our corpus then becomes
+
+.. code-block::
+
+    ('h' 'ug', 10), ('p' 'ug', 5), ('p' 'u' 'n', 12), ('b' 'u' 'n', 4), ('h' 'ug' 's', 5)
+
+and we continue by looking at the next most common pair of symbols. It's 'un', present 16 times, so we merge those two
+and add 'un' to the vocabulary. Then it's 'hug' (as 'h' + 'ug'), present 15 times, so we merge those two and add 'hug'
+to the vocabulary.
+
+At this stage, the vocabulary is ``['b', 'g', 'h', 'n', 'p', 's', 'u', 'ug', 'un', 'hug']`` and our corpus is
+represented as
+
+.. code-block::
+
+    ('hug', 10), ('p' 'ug', 5), ('p' 'un', 12), ('b' 'un', 4), ('hug' 's', 5)
+
+If we stop there, the tokenizer can apply the rules it learned to new words (as long as they don't contain characters that
+were not in the base vocabulary). For instance 'bug' would be tokenized as ``['b', 'ug']`` but mug would be tokenized as
+``['<unk>', 'ug']`` since the 'm' is not in the base vocabulary. This doesn't happen to letters in general (since the
+base corpus uses all of them), but to special characters like emojis.
+
+As we said before, the vocabulary size (which is the base vocabulary size + the number of merges) is a hyperparameter
+to choose. For instance :doc:`GPT <model_doc/gpt>` has a vocabulary size of 40,478 since they have 478 base characters
+and chose to stop the training of the tokenizer at 40,000 merges.
+
+Byte-level BPE
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+To deal with the fact the base vocabulary needs to get all base characters, which can be quite big if one allows for
+all unicode characters, the
+`GPT-2 paper <https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf>`__
+introduces a clever trick, which is to use bytes as the base vocabulary (which gives a size of 256). With some
+additional rules to deal with punctuation, this manages to be able to tokenize every text without needing an unknown
+token. For instance, the :doc:`GPT-2 model <model_doc/gpt>` has a vocabulary size of 50,257, which corresponds to the
+256 bytes base tokens, a special end-of-text token and the symbols learned with 50,000 merges.
+
+.. _wordpiece:
+
+WordPiece
+=======================================================================================================================
+
+WordPiece is the subword tokenization algorithm used for :doc:`BERT <model_doc/bert>` (as well as
+:doc:`DistilBERT <model_doc/distilbert>` and :doc:`Electra <model_doc/electra>`) and was outlined in
+`this paper <https://static.googleusercontent.com/media/research.google.com/ja//pubs/archive/37842.pdf>`__. It relies
+on the same base as BPE, which is to initialize the vocabulary to every character present in the corpus and
+progressively learn a given number of merge rules, the difference is that it doesn't choose the pair that is the most
+frequent but the one that will maximize the likelihood on the corpus once merged.
+
+What does this mean? Well, in the previous example, it means we would only merge 'u' and 'g' if the probability of
+having 'ug' divided by the probability of having 'u' then 'g' is greater than for any other pair of symbols. It's
+subtly different from what BPE does in the sense that it evaluates what it "loses" by merging two symbols and makes
+sure it's `worth it`.
+
+.. _unigram:
+
+Unigram
+=======================================================================================================================
+
+Unigram is a subword tokenization algorithm introduced in `this paper <https://arxiv.org/pdf/1804.10959.pdf>`__.
+Instead of starting with a group of base symbols and learning merges with some rule, like BPE or WordPiece, it starts
+from a large vocabulary (for instance, all pretokenized words and the most common substrings) that it will trim down
+progressively. It's not used directly for any of the pretrained models in the library, but it's used in conjunction
+with :ref:`SentencePiece <sentencepiece>`.
+
+More specifically, at a given step, unigram computes a loss from the corpus we have and the current vocabulary, then,
+for each subword, evaluate how much the loss would augment if the subword was removed from the vocabulary. It then
+sorts the subwords by this quantity (that represents how worse the loss becomes if the token is removed) and removes
+all the worst p tokens (for instance p could be 10% or 20%). It then repeats the process until the vocabulary has
+reached the desired size, always keeping the base characters (to be able to tokenize any word written with them, like
+BPE or WordPiece).
+
+Contrary to BPE and WordPiece that work out rules in a certain order that you can then apply in the same order when
+tokenizing new text, Unigram will have several ways of tokenizing a new text. For instance, if it ends up with the
+vocabulary
+
+.. code-block::
+
+    ['b', 'g', 'h', 'n', 'p', 's', 'u', 'ug', 'un', 'hug']
+
+we had before, it could tokenize "hugs" as ``['hug', 's']``, ``['h', 'ug', 's']`` or ``['h', 'u', 'g', 's']``. So which
+one choose? On top of saving the vocabulary, the trained tokenizer will save the probability of each token in the
+training corpus. You can then give a probability to each tokenization (which is the product of the probabilities of the
+tokens forming it) and pick the most likely one (or if you want to apply some data augmentation, you could sample one
+of the tokenization according to their probabilities).
+
+Those probabilities define the loss that trains the tokenizer: if our corpus consists of the
+words :math:`x_{1}, \dots, x_{N}` and if for the word :math:`x_{i}` we note :math:`S(x_{i})` the set of all possible
+tokenizations of :math:`x_{i}` (with the current vocabulary), then the loss is defined as
+
+.. math::
+    \mathcal{L} = -\sum_{i=1}^{N} \log \left ( \sum_{x \in S(x_{i})} p(x) \right )
+
+.. _sentencepiece:
+
+SentencePiece
+=======================================================================================================================
+
+All the methods we have been looking at so far required some form of pretokenization, which has a central problem: not
+all languages use spaces to separate words. This is a problem :doc:`XLM <model_doc/xlm>` solves by using specific
+pretokenizers for each of those languages (in this case, Chinese, Japanese and Thai). To solve this problem,
+SentencePiece (introduced in `this paper <https://arxiv.org/pdf/1808.06226.pdf>`__) treats the input as a raw stream,
+includes the space in the set of characters to use, then uses BPE or unigram to construct the appropriate vocabulary.
+
+That's why in the example we saw before using :class:`~transformers.XLNetTokenizer` (which uses SentencePiece), we had
+the '▁' character, that represents space. Decoding a tokenized text is then super easy: we just have to concatenate
+all of them together and replace '▁' with space.
+
+All transformers models in the library that use SentencePiece use it with unigram. Examples of models using it are
+:doc:`ALBERT <model_doc/albert>`, :doc:`XLNet <model_doc/xlnet>` or the :doc:`Marian framework <model_doc/marian>`.

docs/source/training.rst

@@ -1,5 +1,5 @@
 Training and fine-tuning
-========================
+=======================================================================================================================
 
 Model classes in 🤗 Transformers are designed to be compatible with native
 PyTorch and TensorFlow 2 and can be used seemlessly with either. In this
@@ -24,7 +24,7 @@ Sections:
 .. _pytorch:
 
 Fine-tuning in native PyTorch
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 Model classes in 🤗 Transformers that don't begin with ``TF`` are
 `PyTorch Modules <https://pytorch.org/docs/master/generated/torch.nn.Module.html>`_,
@@ -141,7 +141,7 @@ with features like mixed precision and easy tensorboard logging.
 
 
 Freezing the encoder
---------------------
+-----------------------------------------------------------------------------------------------------------------------
 
 In some cases, you might be interested in keeping the weights of the
 pre-trained encoder frozen and optimizing only the weights of the head
@@ -158,7 +158,7 @@ submodule on any task-specific model in the library:
 .. _tensorflow:
 
 Fine-tuning in native TensorFlow 2
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 Models can also be trained natively in TensorFlow 2. Just as with PyTorch,
 TensorFlow models can be instantiated with
@@ -210,7 +210,7 @@ can even save the model and then reload it as a PyTorch model (or vice-versa):
 .. _trainer:
 
 Trainer
-^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 We also provide a simple but feature-complete training and evaluation
 interface through :func:`~transformers.Trainer` and
@@ -303,7 +303,7 @@ launching tensorboard in your specified ``logging_dir`` directory.
 .. _additional-resources:
 
 Additional resources
-^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 - `A lightweight colab demo <https://colab.research.google.com/drive/1-JIJlao4dI-Ilww_NnTc0rxtp-ymgDgM?usp=sharing>`_
   which uses ``Trainer`` for IMDb sentiment classification.

examples/benchmarking/run_benchmark.py

@@ -20,7 +20,25 @@ from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArg
 
 def main():
     parser = HfArgumentParser(PyTorchBenchmarkArguments)
-    benchmark_args = parser.parse_args_into_dataclasses()[0]
+    try:
+        benchmark_args = parser.parse_args_into_dataclasses()[0]
+    except ValueError as e:
+        arg_error_msg = "Arg --no_{0} is no longer used, please use --no-{0} instead."
+        begin_error_msg = " ".join(str(e).split(" ")[:-1])
+        full_error_msg = ""
+        depreciated_args = eval(str(e).split(" ")[-1])
+        wrong_args = []
+        for arg in depreciated_args:
+            # arg[2:] removes '--'
+            if arg[2:] in PyTorchBenchmarkArguments.deprecated_args:
+                # arg[5:] removes '--no_'
+                full_error_msg += arg_error_msg.format(arg[5:])
+            else:
+                wrong_args.append(arg)
+        if len(wrong_args) > 0:
+            full_error_msg = full_error_msg + begin_error_msg + str(wrong_args)
+        raise ValueError(full_error_msg)
+
     benchmark = PyTorchBenchmark(args=benchmark_args)
     benchmark.run()
 

examples/benchmarking/run_benchmark_tf.py

@@ -22,6 +22,24 @@ def main():
     parser = HfArgumentParser(TensorFlowBenchmarkArguments)
     benchmark_args = parser.parse_args_into_dataclasses()[0]
     benchmark = TensorFlowBenchmark(args=benchmark_args)
+    try:
+        benchmark_args = parser.parse_args_into_dataclasses()[0]
+    except ValueError as e:
+        arg_error_msg = "Arg --no_{0} is no longer used, please use --no-{0} instead."
+        begin_error_msg = " ".join(str(e).split(" ")[:-1])
+        full_error_msg = ""
+        depreciated_args = eval(str(e).split(" ")[-1])
+        wrong_args = []
+        for arg in depreciated_args:
+            # arg[2:] removes '--'
+            if arg[2:] in TensorFlowBenchmark.deprecated_args:
+                # arg[5:] removes '--no_'
+                full_error_msg += arg_error_msg.format(arg[5:])
+            else:
+                wrong_args.append(arg)
+        if len(wrong_args) > 0:
+            full_error_msg = full_error_msg + begin_error_msg + str(wrong_args)
+        raise ValueError(full_error_msg)
     benchmark.run()
 
 

examples/seq2seq/callbacks.py

@@ -8,6 +8,8 @@ import torch
 from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
 from pytorch_lightning.utilities import rank_zero_only
 
+from utils import save_json
+
 
 def count_trainable_parameters(model):
     model_parameters = filter(lambda p: p.requires_grad, model.parameters())
@@ -72,8 +74,15 @@ class Seq2SeqLoggingCallback(pl.Callback):
 
     @rank_zero_only
     def on_test_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule):
+        save_json(pl_module.metrics, pl_module.metrics_save_path)
         return self._write_logs(trainer, pl_module, "test")
 
+    @rank_zero_only
+    def on_validation_end(self, trainer: pl.Trainer, pl_module):
+        save_json(pl_module.metrics, pl_module.metrics_save_path)
+        # Uncommenting this will save val generations
+        # return self._write_logs(trainer, pl_module, "valid")
+
 
 def get_checkpoint_callback(output_dir, metric, save_top_k=1, lower_is_better=False):
     """Saves the best model by validation ROUGE2 score."""

examples/seq2seq/finetune.py

@@ -30,7 +30,6 @@ from utils import (
     lmap,
     pickle_save,
     save_git_info,
-    save_json,
     use_task_specific_params,
 )
 
@@ -189,7 +188,7 @@ class SummarizationModule(BaseTransformer):
         losses.update(generative_metrics)
         all_metrics = {f"{prefix}_avg_{k}": x for k, x in losses.items()}
         all_metrics["step_count"] = self.step_count
-        self.save_metrics(all_metrics, prefix)  # writes to self.metrics_save_path
+        self.metrics[prefix].append(all_metrics)  # callback writes this to self.metrics_save_path
         preds = flatten_list([x["preds"] for x in outputs])
         return {
             "log": all_metrics,
@@ -198,10 +197,6 @@ class SummarizationModule(BaseTransformer):
             f"{prefix}_{self.val_metric}": metric_tensor,
         }
 
-    def save_metrics(self, latest_metrics, type_path) -> None:
-        self.metrics[type_path].append(latest_metrics)
-        save_json(self.metrics, self.metrics_save_path)
-
     def calc_generative_metrics(self, preds, target) -> Dict:
         return calculate_rouge(preds, target)
 

examples/seq2seq/run_distributed_eval.py

@@ -38,6 +38,9 @@ def eval_data_dir(
     fp16=False,
     task="summarization",
     local_rank=None,
+    src_lang=None,
+    tgt_lang=None,
+    prefix="",
     **generate_kwargs,
 ) -> Dict:
     """Run evaluation on part of the data for one gpu and save to {save_dir}/rank_{rank}_output.json"""
@@ -57,6 +60,8 @@ def eval_data_dir(
     use_task_specific_params(model, task)  # update config with task specific params
     if max_source_length is None:
         max_source_length = tokenizer.model_max_length
+    if prefix is None:
+        prefix = prefix or getattr(model.config, "prefix", "") or ""
     ds = Seq2SeqDataset(
         tokenizer,
         data_dir,
@@ -64,7 +69,9 @@ def eval_data_dir(
         max_target_length=1024,
         type_path=type_path,
         n_obs=n_obs,
-        prefix=model.config.prefix,
+        src_lang=src_lang,
+        tgt_lang=tgt_lang,
+        prefix=prefix,
     )
     # I set shuffle=True for a more accurate progress bar.
     # If all the longest samples are first, the prog bar estimate is too high at the beginning.
@@ -118,6 +125,11 @@ def run_generate():
         required=False,
         help="How long should master process wait for other processes to finish.",
     )
+    parser.add_argument("--src_lang", type=str, default=None, required=False)
+    parser.add_argument("--tgt_lang", type=str, default=None, required=False)
+    parser.add_argument(
+        "--prefix", type=str, required=False, default=None, help="will be added to the begininng of src examples"
+    )
     parser.add_argument("--fp16", action="store_true")
     parser.add_argument("--debug", action="store_true")
     start_time = time.time()
@@ -144,6 +156,9 @@ def run_generate():
         local_rank=args.local_rank,
         n_obs=args.n_obs,
         max_source_length=args.max_source_length,
+        prefix=args.prefix,
+        src_lang=args.src_lang,
+        tgt_lang=args.tgt_lang,
         **generate_kwargs,
     )
 

model_cards/blinoff/roberta-base-russian-v0/README.md

@@ -0,0 +1,61 @@
+---
+language: ru
+widget:
+- text: "Мозг — это машина <mask>, которая пытается снизить ошибку в прогнозе."
+---
+
+# RoBERTa-like language model trained on part of part of TAIGA corpus
+
+## Training Details
+
+- about 60k steps
+
+![]()
+
+## Example pipeline
+
+```python
+from transformers import pipeline
+from transformers import RobertaTokenizerFast
+
+tokenizer = RobertaTokenizerFast.from_pretrained('blinoff/roberta-base-russian-v0', max_len=512)
+
+fill_mask = pipeline(
+    "fill-mask",
+    model="blinoff/roberta-base-russian-v0",
+    tokenizer=tokenizer
+)
+
+fill_mask("Мозг — это машина <mask>, которая пытается снизить ошибку в прогнозе.")
+
+# {
+#     'sequence': '<s>Мозг — это машина города, которая пытается снизить ошибку в прогнозе.</s>',
+#     'score': 0.012859329581260681,
+#     'token': 2144,
+#     'token_str': 'ĠгоÑĢода'
+# },
+# {
+#     'sequence': '<s>Мозг — это машина человека, которая пытается снизить ошибку в прогнозе.</s>',
+#     'score': 0.01185101643204689,
+#     'token': 1470,
+#     'token_str': 'ĠÑĩеловека'
+# },
+# {
+#     'sequence': '<s>Мозг — это машина дома, которая пытается снизить ошибку в прогнозе.</s>',
+#     'score': 0.009940559044480324,
+#     'token': 1411,
+#     'token_str': 'Ġдома'
+# },
+# {
+#     'sequence': '<s>Мозг — это машина женщина, которая пытается снизить ошибку в прогнозе.</s>',
+#     'score': 0.007794599514454603,
+#     'token': 2707,
+#     'token_str': 'ĠженÑīина'
+# },
+# {
+#     'sequence': '<s>Мозг — это машина женщины, которая пытается снизить ошибку в прогнозе.</s>',
+#     'score': 0.007725382689386606,
+#     'token': 3546,
+#     'token_str': 'ĠженÑīинÑĭ'
+# }
+```

pyproject.toml

@@ -0,0 +1,3 @@
+[tool.black]
+line-length = 119
+target-version = ['py35']

src/transformers/__init__.py

@@ -17,6 +17,16 @@ else:
     absl.logging.set_stderrthreshold("info")
     absl.logging._warn_preinit_stderr = False
 
+# Integrations: this needs to come before other ml imports
+# in order to allow any 3rd-party code to initialize properly
+from .integrations import (  # isort:skip
+    is_comet_available,
+    is_optuna_available,
+    is_ray_available,
+    is_tensorboard_available,
+    is_wandb_available,
+)
+
 # Configurations
 from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig
 from .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, CONFIG_MAPPING, AutoConfig
@@ -96,15 +106,6 @@ from .file_utils import (
 )
 from .hf_argparser import HfArgumentParser
 
-# Integrations
-from .integrations import (
-    is_comet_available,
-    is_optuna_available,
-    is_ray_available,
-    is_tensorboard_available,
-    is_wandb_available,
-)
-
 # Model Cards
 from .modelcard import ModelCard
 
@@ -120,27 +121,27 @@ from .modeling_tf_pytorch_utils import (
 )
 
 # Pipelines
-# from .pipelines import (
-#     Conversation,
-#     ConversationalPipeline,
-#     CsvPipelineDataFormat,
-#     FeatureExtractionPipeline,
-#     FillMaskPipeline,
-#     JsonPipelineDataFormat,
-#     NerPipeline,
-#     PipedPipelineDataFormat,
-#     Pipeline,
-#     PipelineDataFormat,
-#     QuestionAnsweringPipeline,
-#     SummarizationPipeline,
-#     Text2TextGenerationPipeline,
-#     TextClassificationPipeline,
-#     TextGenerationPipeline,
-#     TokenClassificationPipeline,
-#     TranslationPipeline,
-#     ZeroShotClassificationPipeline,
-#     pipeline,
-# )
+from .pipelines import (
+    Conversation,
+    ConversationalPipeline,
+    CsvPipelineDataFormat,
+    FeatureExtractionPipeline,
+    FillMaskPipeline,
+    JsonPipelineDataFormat,
+    NerPipeline,
+    PipedPipelineDataFormat,
+    Pipeline,
+    PipelineDataFormat,
+    QuestionAnsweringPipeline,
+    SummarizationPipeline,
+    Text2TextGenerationPipeline,
+    TextClassificationPipeline,
+    TextGenerationPipeline,
+    TokenClassificationPipeline,
+    TranslationPipeline,
+    ZeroShotClassificationPipeline,
+    pipeline,
+)
 
 # Retriever
 from .retrieval_rag import RagRetriever

src/transformers/activations.py

@@ -40,6 +40,10 @@ def gelu_fast(x):
     return 0.5 * x * (1.0 + torch.tanh(x * 0.7978845608 * (1.0 + 0.044715 * x * x)))
 
 
+def mish(x):
+    return x * torch.tanh(torch.nn.functional.softplus(x))
+
+
 ACT2FN = {
     "relu": F.relu,
     "swish": swish,
@@ -47,6 +51,7 @@ ACT2FN = {
     "tanh": torch.tanh,
     "gelu_new": gelu_new,
     "gelu_fast": gelu_fast,
+    "mish": mish,
 }
 
 

src/transformers/benchmark/benchmark.py

@@ -229,7 +229,7 @@ class PyTorchBenchmark(Benchmark):
             if self.args.is_tpu:
                 # tpu
                 raise NotImplementedError(
-                    "Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking with `--no_memory` or `args.no_memory=True`"
+                    "Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking with `--no-memory` or `args.memory=False`"
                 )
             elif self.args.is_gpu:
                 if not is_py3nvml_available():

src/transformers/benchmark/benchmark_args.py

@@ -34,6 +34,34 @@ logger = logging.get_logger(__name__)
 
 @dataclass
 class PyTorchBenchmarkArguments(BenchmarkArguments):
+
+    deprecated_args = [
+        "no_inference",
+        "no_cuda",
+        "no_tpu",
+        "no_speed",
+        "no_memory",
+        "no_env_print",
+        "no_multi_process",
+    ]
+
+    def __init__(self, **kwargs):
+        """This __init__ is there for legacy code. When removing
+        deprecated args completely, the class can simply be deleted
+        """
+        for deprecated_arg in self.deprecated_args:
+            if deprecated_arg in kwargs:
+                positive_arg = deprecated_arg[3:]
+                setattr(self, positive_arg, not kwargs.pop(deprecated_arg))
+                logger.warning(
+                    f"{deprecated_arg} is depreciated. Please use --no-{positive_arg} or {positive_arg}={kwargs[positive_arg]}"
+                )
+
+        self.torchscript = kwargs.pop("torchscript", self.torchscript)
+        self.torch_xla_tpu_print_metrics = kwargs.pop("torch_xla_tpu_print_metrics", self.torch_xla_tpu_print_metrics)
+        self.fp16_opt_level = kwargs.pop("fp16_opt_level", self.fp16_opt_level)
+        super().__init__(**kwargs)
+
     torchscript: bool = field(default=False, metadata={"help": "Trace the models using torchscript"})
     torch_xla_tpu_print_metrics: bool = field(default=False, metadata={"help": "Print Xla/PyTorch tpu metrics"})
     fp16_opt_level: str = field(
@@ -50,7 +78,7 @@ class PyTorchBenchmarkArguments(BenchmarkArguments):
     @torch_required
     def _setup_devices(self) -> Tuple["torch.device", int]:
         logger.info("PyTorch: setting up devices")
-        if self.no_cuda:
+        if not self.cuda:
             device = torch.device("cpu")
             n_gpu = 0
         elif is_torch_tpu_available():
@@ -63,7 +91,7 @@ class PyTorchBenchmarkArguments(BenchmarkArguments):
 
     @property
     def is_tpu(self):
-        return is_torch_tpu_available() and not self.no_tpu
+        return is_torch_tpu_available() and self.tpu
 
     @property
     @torch_required

src/transformers/benchmark/benchmark_args_tf.py

@@ -31,6 +31,34 @@ logger = logging.get_logger(__name__)
 
 @dataclass
 class TensorFlowBenchmarkArguments(BenchmarkArguments):
+
+    deprecated_args = [
+        "no_inference",
+        "no_cuda",
+        "no_tpu",
+        "no_speed",
+        "no_memory",
+        "no_env_print",
+        "no_multi_process",
+    ]
+
+    def __init__(self, **kwargs):
+        """This __init__ is there for legacy code. When removing
+        deprecated args completely, the class can simply be deleted
+        """
+        for deprecated_arg in self.deprecated_args:
+            if deprecated_arg in kwargs:
+                positive_arg = deprecated_arg[3:]
+                kwargs[positive_arg] = not kwargs.pop(deprecated_arg)
+                logger.warning(
+                    f"{deprecated_arg} is depreciated. Please use --no-{positive_arg} or {positive_arg}={kwargs[positive_arg]}"
+                )
+        self.tpu_name = kwargs.pop("tpu_name", self.tpu_name)
+        self.device_idx = kwargs.pop("device_idx", self.device_idx)
+        self.eager_mode = kwargs.pop("eager_mode", self.eager_mode)
+        self.use_xla = kwargs.pop("use_xla", self.use_xla)
+        super().__init__(**kwargs)
+
     tpu_name: str = field(
         default=None,
         metadata={"help": "Name of TPU"},
@@ -50,7 +78,7 @@ class TensorFlowBenchmarkArguments(BenchmarkArguments):
     @cached_property
     @tf_required
     def _setup_tpu(self) -> Tuple["tf.distribute.cluster_resolver.TPUClusterResolver"]:
-        if not self.no_tpu:
+        if self.tpu:
             try:
                 if self.tpu_name:
                     tpu = tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name)
@@ -98,7 +126,7 @@ class TensorFlowBenchmarkArguments(BenchmarkArguments):
     @property
     @tf_required
     def n_gpu(self) -> int:
-        if not self.no_cuda:
+        if self.cuda:
             return len(self.gpu_list)
         return 0
 

src/transformers/benchmark/benchmark_args_utils.py

@@ -1,131 +1,147 @@
-# coding=utf-8
-# Copyright 2018 The HuggingFace Inc. team.
-# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import dataclasses
-import json
-from dataclasses import dataclass, field
-from time import time
-from typing import List
-
-from ..utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-
-def list_field(default=None, metadata=None):
-    return field(default_factory=lambda: default, metadata=metadata)
-
-
-@dataclass
-class BenchmarkArguments:
-    """
-    BenchMarkArguments are arguments we use in our benchmark scripts
-    **which relate to the training loop itself**.
-
-    Using `HfArgumentParser` we can turn this class
-    into argparse arguments to be able to specify them on
-    the command line.
-    """
-
-    models: List[str] = list_field(
-        default=[],
-        metadata={
-            "help": "Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version of all available models"
-        },
-    )
-
-    batch_sizes: List[int] = list_field(
-        default=[8], metadata={"help": "List of batch sizes for which memory and time performance will be evaluated"}
-    )
-
-    sequence_lengths: List[int] = list_field(
-        default=[8, 32, 128, 512],
-        metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"},
-    )
-
-    no_inference: bool = field(default=False, metadata={"help": "Don't benchmark inference of model"})
-    no_cuda: bool = field(default=False, metadata={"help": "Whether to run on available cuda devices"})
-    no_tpu: bool = field(default=False, metadata={"help": "Whether to run on available tpu devices"})
-    fp16: bool = field(default=False, metadata={"help": "Use FP16 to accelerate inference."})
-    training: bool = field(default=False, metadata={"help": "Benchmark training of model"})
-    verbose: bool = field(default=False, metadata={"help": "Verbose memory tracing"})
-    no_speed: bool = field(default=False, metadata={"help": "Don't perform speed measurements"})
-    no_memory: bool = field(default=False, metadata={"help": "Don't perform memory measurements"})
-    trace_memory_line_by_line: bool = field(default=False, metadata={"help": "Trace memory line by line"})
-    save_to_csv: bool = field(default=False, metadata={"help": "Save result to a CSV file"})
-    log_print: bool = field(default=False, metadata={"help": "Save all print statements in a log file"})
-    no_env_print: bool = field(default=False, metadata={"help": "Don't print environment information"})
-    no_multi_process: bool = field(
-        default=False,
-        metadata={
-            "help": "Don't use multiprocessing for memory and speed measurement. It is highly recommended to use multiprocessing for accurate CPU and GPU memory measurements. This option should only be used for debugging / testing and on TPU."
-        },
-    )
-    inference_time_csv_file: str = field(
-        default=f"inference_time_{round(time())}.csv",
-        metadata={"help": "CSV filename used if saving time results to csv."},
-    )
-    inference_memory_csv_file: str = field(
-        default=f"inference_memory_{round(time())}.csv",
-        metadata={"help": "CSV filename used if saving memory results to csv."},
-    )
-    train_time_csv_file: str = field(
-        default=f"train_time_{round(time())}.csv",
-        metadata={"help": "CSV filename used if saving time results to csv for training."},
-    )
-    train_memory_csv_file: str = field(
-        default=f"train_memory_{round(time())}.csv",
-        metadata={"help": "CSV filename used if saving memory results to csv for training."},
-    )
-    env_info_csv_file: str = field(
-        default=f"env_info_{round(time())}.csv",
-        metadata={"help": "CSV filename used if saving environment information."},
-    )
-    log_filename: str = field(
-        default=f"log_{round(time())}.csv",
-        metadata={"help": "Log filename used if print statements are saved in log."},
-    )
-    repeat: int = field(default=3, metadata={"help": "Times an experiment will be run."})
-    only_pretrain_model: bool = field(
-        default=False,
-        metadata={
-            "help": "Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain model weights."
-        },
-    )
-
-    def to_json_string(self):
-        """
-        Serializes this instance to a JSON string.
-        """
-        return json.dumps(dataclasses.asdict(self), indent=2)
-
-    @property
-    def model_names(self):
-        assert (
-            len(self.models) > 0
-        ), "Please make sure you provide at least one model name / model identifier, *e.g.* `--models bert-base-cased` or `args.models = ['bert-base-cased']."
-        return self.models
-
-    @property
-    def do_multi_processing(self):
-        if self.no_multi_process:
-            return False
-        elif self.is_tpu:
-            logger.info("Multiprocessing is currently not possible on TPU.")
-            return False
-        else:
-            return True
+# coding=utf-8
+# Copyright 2018 The HuggingFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import dataclasses
+import json
+from dataclasses import dataclass, field
+from time import time
+from typing import List
+
+from ..utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+def list_field(default=None, metadata=None):
+    return field(default_factory=lambda: default, metadata=metadata)
+
+
+@dataclass
+class BenchmarkArguments:
+    """
+    BenchMarkArguments are arguments we use in our benchmark scripts
+    **which relate to the training loop itself**.
+
+    Using `HfArgumentParser` we can turn this class
+    into argparse arguments to be able to specify them on
+    the command line.
+    """
+
+    models: List[str] = list_field(
+        default=[],
+        metadata={
+            "help": "Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version of all available models"
+        },
+    )
+
+    batch_sizes: List[int] = list_field(
+        default=[8], metadata={"help": "List of batch sizes for which memory and time performance will be evaluated"}
+    )
+
+    sequence_lengths: List[int] = list_field(
+        default=[8, 32, 128, 512],
+        metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"},
+    )
+
+    inference: bool = field(
+        default=True,
+        metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."},
+    )
+    cuda: bool = field(
+        default=True,
+        metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."},
+    )
+    tpu: bool = field(
+        default=True, metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."}
+    )
+    fp16: bool = field(default=False, metadata={"help": "Use FP16 to accelerate inference."})
+    training: bool = field(default=False, metadata={"help": "Benchmark training of model"})
+    verbose: bool = field(default=False, metadata={"help": "Verbose memory tracing"})
+    speed: bool = field(
+        default=True,
+        metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."},
+    )
+    memory: bool = field(
+        default=True,
+        metadata={
+            "help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
+        },
+    )
+    trace_memory_line_by_line: bool = field(default=False, metadata={"help": "Trace memory line by line"})
+    save_to_csv: bool = field(default=False, metadata={"help": "Save result to a CSV file"})
+    log_print: bool = field(default=False, metadata={"help": "Save all print statements in a log file"})
+    env_print: bool = field(default=False, metadata={"help": "Whether to print environment information"})
+    multi_process: bool = field(
+        default=True,
+        metadata={
+            "help": "Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled for debugging / testing and on TPU."
+        },
+    )
+    inference_time_csv_file: str = field(
+        default=f"inference_time_{round(time())}.csv",
+        metadata={"help": "CSV filename used if saving time results to csv."},
+    )
+    inference_memory_csv_file: str = field(
+        default=f"inference_memory_{round(time())}.csv",
+        metadata={"help": "CSV filename used if saving memory results to csv."},
+    )
+    train_time_csv_file: str = field(
+        default=f"train_time_{round(time())}.csv",
+        metadata={"help": "CSV filename used if saving time results to csv for training."},
+    )
+    train_memory_csv_file: str = field(
+        default=f"train_memory_{round(time())}.csv",
+        metadata={"help": "CSV filename used if saving memory results to csv for training."},
+    )
+    env_info_csv_file: str = field(
+        default=f"env_info_{round(time())}.csv",
+        metadata={"help": "CSV filename used if saving environment information."},
+    )
+    log_filename: str = field(
+        default=f"log_{round(time())}.csv",
+        metadata={"help": "Log filename used if print statements are saved in log."},
+    )
+    repeat: int = field(default=3, metadata={"help": "Times an experiment will be run."})
+    only_pretrain_model: bool = field(
+        default=False,
+        metadata={
+            "help": "Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain model weights."
+        },
+    )
+
+    def to_json_string(self):
+        """
+        Serializes this instance to a JSON string.
+        """
+        return json.dumps(dataclasses.asdict(self), indent=2)
+
+    @property
+    def model_names(self):
+        assert (
+            len(self.models) > 0
+        ), "Please make sure you provide at least one model name / model identifier, *e.g.* `--models bert-base-cased` or `args.models = ['bert-base-cased']."
+        return self.models
+
+    @property
+    def do_multi_processing(self):
+        if not self.multi_process:
+            return False
+        elif self.is_tpu:
+            logger.info("Multiprocessing is currently not possible on TPU.")
+            return False
+        else:
+            return True

src/transformers/benchmark/benchmark_tf.py

@@ -248,7 +248,7 @@ class TensorFlowBenchmark(Benchmark):
                 if self.args.is_tpu:
                     # tpu
                     raise NotImplementedError(
-                        "Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking with `args.no_memory=True`"
+                        "Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking with `args.memory=False`"
                     )
                 elif self.args.is_gpu:
                     # gpu

src/transformers/configuration_albert.py

@@ -32,54 +32,55 @@ ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
 
 class AlbertConfig(PretrainedConfig):
     r"""
-    This is the configuration class to store the configuration of a :class:`~transformers.AlbertModel`.
-    It is used to instantiate an ALBERT model according to the specified arguments, defining the model
-    architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of
-    the ALBERT `xxlarge <https://huggingface.co/albert-xxlarge-v2>`__ architecture.
+    This is the configuration class to store the configuration of a :class:`~transformers.AlbertModel` or a
+    :class:`~transformers.TFAlbertModel`. It is used to instantiate an ALBERT model according to the specified
+    arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar
+    configuration to that of the ALBERT `xxlarge <https://huggingface.co/albert-xxlarge-v2>`__ architecture.
 
     Configuration objects inherit from  :class:`~transformers.PretrainedConfig` and can be used
     to control the model outputs. Read the documentation from  :class:`~transformers.PretrainedConfig`
     for more information.
 
-
     Args:
-        vocab_size (:obj:`int`, optional, defaults to 30000):
-            Vocabulary size of the ALBERT model. Defines the different tokens that
-            can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.AlbertModel`.
-        embedding_size (:obj:`int`, optional, defaults to 128):
+        vocab_size (:obj:`int`, `optional`, defaults to 30000):
+            Vocabulary size of the ALBERT model. Defines the number of different tokens that can be represented by the
+            :obj:`inputs_ids` passed when calling :class:`~transformers.AlbertModel` or
+            :class:`~transformers.TFAlbertModel`.
+        embedding_size (:obj:`int`, `optional`, defaults to 128):
             Dimensionality of vocabulary embeddings.
-        hidden_size (:obj:`int`, optional, defaults to 4096):
+        hidden_size (:obj:`int`, `optional`, defaults to 4096):
             Dimensionality of the encoder layers and the pooler layer.
-        num_hidden_layers (:obj:`int`, optional, defaults to 12):
+        num_hidden_layers (:obj:`int`, `optional`, defaults to 12):
             Number of hidden layers in the Transformer encoder.
-        num_hidden_groups (:obj:`int`, optional, defaults to 1):
+        num_hidden_groups (:obj:`int`, `optional`, defaults to 1):
             Number of groups for the hidden layers, parameters in the same group are shared.
-        num_attention_heads (:obj:`int`, optional, defaults to 64):
+        num_attention_heads (:obj:`int`, `optional`, defaults to 64):
             Number of attention heads for each attention layer in the Transformer encoder.
-        intermediate_size (:obj:`int`, optional, defaults to 16384):
-            The dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
-        inner_group_num (:obj:`int`, optional, defaults to 1):
+        intermediate_size (:obj:`int`, `optional`, defaults to 16384):
+            The dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
+        inner_group_num (:obj:`int`, `optional`, defaults to 1):
             The number of inner repetition of attention and ffn.
-        hidden_act (:obj:`str` or :obj:`function`, optional, defaults to "gelu_new"):
+        hidden_act (:obj:`str` or :obj:`Callable`, `optional`, defaults to :obj:`"gelu_new"`):
             The non-linear activation function (function or string) in the encoder and pooler.
-            If string, "gelu", "relu", "swish" and "gelu_new" are supported.
-        hidden_dropout_prob (:obj:`float`, optional, defaults to 0):
+            If string, :obj:`"gelu"`, :obj:`"relu"`, :obj:`"swish"` and :obj:`"gelu_new"` are supported.
+        hidden_dropout_prob (:obj:`float`, `optional`, defaults to 0):
             The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
-        attention_probs_dropout_prob (:obj:`float`, optional, defaults to 0):
+        attention_probs_dropout_prob (:obj:`float`, `optional`, defaults to 0):
             The dropout ratio for the attention probabilities.
-        max_position_embeddings (:obj:`int`, optional, defaults to 512):
+        max_position_embeddings (:obj:`int`, `optional`, defaults to 512):
             The maximum sequence length that this model might ever be used with. Typically set this to something
             large (e.g., 512 or 1024 or 2048).
-        type_vocab_size (:obj:`int`, optional, defaults to 2):
-            The vocabulary size of the `token_type_ids` passed into :class:`~transformers.AlbertModel`.
-        initializer_range (:obj:`float`, optional, defaults to 0.02):
+        type_vocab_size (:obj:`int`, `optional`, defaults to 2):
+            The vocabulary size of the :obj:`token_type_ids` passed when calling :class:`~transformers.AlbertModel` or
+            :class:`~transformers.TFAlbertModel`.
+        initializer_range (:obj:`float`, `optional`, defaults to 0.02):
             The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        layer_norm_eps (:obj:`float`, optional, defaults to 1e-12):
+        layer_norm_eps (:obj:`float`, `optional`, defaults to 1e-12):
             The epsilon used by the layer normalization layers.
-        classifier_dropout_prob (:obj:`float`, optional, defaults to 0.1):
+        classifier_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
             The dropout ratio for attached classifiers.
 
-    Example::
+    Examples::
 
         >>> from transformers import AlbertConfig, AlbertModel
         >>> # Initializing an ALBERT-xxlarge style configuration

src/transformers/configuration_bert.py

@@ -50,10 +50,10 @@ BERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
 
 class BertConfig(PretrainedConfig):
     r"""
-    This is the configuration class to store the configuration of a :class:`~transformers.BertModel`.
-    It is used to instantiate an BERT model according to the specified arguments, defining the model
-    architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of
-    the BERT `bert-base-uncased <https://huggingface.co/bert-base-uncased>`__ architecture.
+    This is the configuration class to store the configuration of a :class:`~transformers.BertModel` or a
+    :class:`~transformers.TFBertModel`. It is used to instantiate a BERT model according to the specified
+    arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar
+    configuration to that of the BERT `bert-base-uncased <https://huggingface.co/bert-base-uncased>`__ architecture.
 
     Configuration objects inherit from  :class:`~transformers.PretrainedConfig` and can be used
     to control the model outputs. Read the documentation from  :class:`~transformers.PretrainedConfig`
@@ -61,37 +61,39 @@ class BertConfig(PretrainedConfig):
 
 
     Args:
-        vocab_size (:obj:`int`, optional, defaults to 30522):
-            Vocabulary size of the BERT model. Defines the different tokens that
-            can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.BertModel`.
-        hidden_size (:obj:`int`, optional, defaults to 768):
+        vocab_size (:obj:`int`, `optional`, defaults to 30522):
+            Vocabulary size of the BERT model. Defines the number of different tokens that can be represented by the
+            :obj:`inputs_ids` passed when calling :class:`~transformers.BertModel` or
+            :class:`~transformers.TFBertModel`.
+        hidden_size (:obj:`int`, `optional`, defaults to 768):
             Dimensionality of the encoder layers and the pooler layer.
-        num_hidden_layers (:obj:`int`, optional, defaults to 12):
+        num_hidden_layers (:obj:`int`, `optional`, defaults to 12):
             Number of hidden layers in the Transformer encoder.
-        num_attention_heads (:obj:`int`, optional, defaults to 12):
+        num_attention_heads (:obj:`int`, `optional`, defaults to 12):
             Number of attention heads for each attention layer in the Transformer encoder.
-        intermediate_size (:obj:`int`, optional, defaults to 3072):
-            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
-        hidden_act (:obj:`str` or :obj:`function`, optional, defaults to "gelu"):
+        intermediate_size (:obj:`int`, `optional`, defaults to 3072):
+            Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
+        hidden_act (:obj:`str` or :obj:`Callable`, `optional`, defaults to :obj:`"gelu"`):
             The non-linear activation function (function or string) in the encoder and pooler.
-            If string, "gelu", "relu", "swish" and "gelu_new" are supported.
-        hidden_dropout_prob (:obj:`float`, optional, defaults to 0.1):
+            If string, :obj:`"gelu"`, :obj:`"relu"`, :obj:`"swish"` and :obj:`"gelu_new"` are supported.
+        hidden_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
             The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
-        attention_probs_dropout_prob (:obj:`float`, optional, defaults to 0.1):
+        attention_probs_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
             The dropout ratio for the attention probabilities.
-        max_position_embeddings (:obj:`int`, optional, defaults to 512):
+        max_position_embeddings (:obj:`int`, `optional`, defaults to 512):
             The maximum sequence length that this model might ever be used with.
             Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
-        type_vocab_size (:obj:`int`, optional, defaults to 2):
-            The vocabulary size of the `token_type_ids` passed into :class:`~transformers.BertModel`.
-        initializer_range (:obj:`float`, optional, defaults to 0.02):
+        type_vocab_size (:obj:`int`, `optional`, defaults to 2):
+            The vocabulary size of the :obj:`token_type_ids` passed when calling :class:`~transformers.BertModel` or
+            :class:`~transformers.TFBertModel`.
+        initializer_range (:obj:`float`, `optional`, defaults to 0.02):
             The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        layer_norm_eps (:obj:`float`, optional, defaults to 1e-12):
+        layer_norm_eps (:obj:`float`, `optional`, defaults to 1e-12):
             The epsilon used by the layer normalization layers.
-        gradient_checkpointing (:obj:`bool`, optional, defaults to :obj:`False`):
+        gradient_checkpointing (:obj:`bool`, `optional`, defaults to :obj:`False`):
             If True, use gradient checkpointing to save memory at the expense of slower backward pass.
 
-    Example::
+    Examples::
 
         >>> from transformers import BertModel, BertConfig
 

src/transformers/configuration_bert_generation.py

@@ -19,18 +19,18 @@ from .configuration_utils import PretrainedConfig
 
 class BertGenerationConfig(PretrainedConfig):
     r"""
-    This is the configuration class to store the configuration of a :class:`~transformers.BertGenerationPreTrainedModel`.
-    It is used to instantiate a BertGenerationConfig model according to the specified arguments, defining the model architecture.
+    This is the configuration class to store the configuration of a
+    :class:`~transformers.BertGenerationPreTrainedModel`. It is used to instantiate a BertGeneration model according to
+    the specified arguments, defining the model architecture.
 
     Configuration objects inherit from  :class:`~transformers.PretrainedConfig` and can be used
     to control the model outputs. Read the documentation from  :class:`~transformers.PretrainedConfig`
     for more information.
 
-
     Args:
         vocab_size (:obj:`int`, `optional`, defaults to 50358):
-            Vocabulary size of the BertGeneration model. Defines the different tokens that
-            can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.BertGeneration`.
+            Vocabulary size of the BERT model. Defines the number of different tokens that can be represented by the
+            :obj:`inputs_ids` passed when calling :class:`~transformers.BertGeneration`.
         hidden_size (:obj:`int`, `optional`, defaults to 1024):
             Dimensionality of the encoder layers and the pooler layer.
         num_hidden_layers (:obj:`int`, `optional`, defaults to 24):
@@ -38,7 +38,7 @@ class BertGenerationConfig(PretrainedConfig):
         num_attention_heads (:obj:`int`, `optional`, defaults to 16):
             Number of attention heads for each attention layer in the Transformer encoder.
         intermediate_size (:obj:`int`, `optional`, defaults to 3072):
-            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
+            Dimensionality of the "intermediate" (often called feed-forward) layer in the Transformer encoder.
         hidden_act (:obj:`str` or :obj:`function`, `optional`, defaults to :obj:`"gelu"`):
             The non-linear activation function (function or string) in the encoder and pooler.
             If string, :obj:`"gelu"`, :obj:`"relu"`, :obj:`"swish"` and :obj:`"gelu_new"` are supported.
@@ -56,7 +56,7 @@ class BertGenerationConfig(PretrainedConfig):
         gradient_checkpointing (:obj:`bool`, `optional`, defaults to :obj:`False`):
             If :obj:`True`, use gradient checkpointing to save memory at the expense of slower backward pass.
 
-    Example::
+    Examples::
 
         >>> from transformers import BertGenerationConfig, BertGenerationEncoder
 

src/transformers/configuration_ctrl.py

@@ -25,44 +25,45 @@ CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP = {"ctrl": "https://s3.amazonaws.com/models.h
 
 class CTRLConfig(PretrainedConfig):
     """
-    This is the configuration class to store the configuration of a :class:`~transformers.CTRLModel`.
-    It is used to instantiate an CTRL model according to the specified arguments, defining the model
-    architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of
-    the `ctrl <https://huggingface.co/ctrl>`__ architecture from SalesForce.
+    This is the configuration class to store the configuration of a :class:`~transformers.CTRLModel` or a
+    :class:`~transformers.TFCTRLModel`. It is used to instantiate a CTRL model according to the specified
+    arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar
+    configuration to that of the `ctrl <https://huggingface.co/ctrl>`__ architecture from SalesForce.
 
     Configuration objects inherit from  :class:`~transformers.PretrainedConfig` and can be used
     to control the model outputs. Read the documentation from  :class:`~transformers.PretrainedConfig`
     for more information.
 
     Args:
-        vocab_size (:obj:`int`, optional, defaults to 246534):
-            Vocabulary size of the CTRL model. Defines the different tokens that
-            can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.CTRLModel`.
-        n_positions (:obj:`int`, optional, defaults to 256):
+        vocab_size (:obj:`int`, `optional`, defaults to 246534):
+            Vocabulary size of the CTRL model. Defines the number of different tokens that can be represented by the
+            :obj:`inputs_ids` passed when calling :class:`~transformers.CTRLModel` or
+            :class:`~transformers.TFCTRLModel`.
+        n_positions (:obj:`int`, `optional`, defaults to 256):
             The maximum sequence length that this model might ever be used with.
             Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
-        n_ctx (:obj:`int`, optional, defaults to 256):
+        n_ctx (:obj:`int`, `optional`, defaults to 256):
             Dimensionality of the causal mask (usually same as n_positions).
-        n_embd (:obj:`int`, optional, defaults to 1280):
+        n_embd (:obj:`int`, `optional`, defaults to 1280):
             Dimensionality of the embeddings and hidden states.
-        dff (:obj:`int`, optional, defaults to 8192):
-            Dimensionality of the inner dimension of the FFN.
-        n_layer (:obj:`int`, optional, defaults to 48):
+        dff (:obj:`int`, `optional`, defaults to 8192):
+            Dimensionality of the inner dimension of the feed forward networks (FFN).
+        n_layer (:obj:`int`, `optional`, defaults to 48):
             Number of hidden layers in the Transformer encoder.
-        n_head (:obj:`int`, optional, defaults to 16):
+        n_head (:obj:`int`, `optional`, defaults to 16):
             Number of attention heads for each attention layer in the Transformer encoder.
-        resid_pdrop (:obj:`float`, optional, defaults to 0.1):
+        resid_pdrop (:obj:`float`, `optional`, defaults to 0.1):
             The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
-        embd_pdrop (:obj:`int`, optional, defaults to 0.1):
+        embd_pdrop (:obj:`int`, `optional`, defaults to 0.1):
             The dropout ratio for the embeddings.
-        attn_pdrop (:obj:`float`, optional, defaults to 0.1):
+        attn_pdrop (:obj:`float`, `optional`, defaults to 0.1):
             The dropout ratio for the attention.
-        layer_norm_epsilon (:obj:`float`, optional, defaults to 1e-6):
+        layer_norm_epsilon (:obj:`float`, `optional`, defaults to 1e-6):
             The epsilon to use in the layer normalization layers
-        initializer_range (:obj:`float`, optional, defaults to 0.02):
+        initializer_range (:obj:`float`, `optional`, defaults to 0.02):
             The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
 
-    Example::
+    Examples::
 
         >>> from transformers import CTRLModel, CTRLConfig
 

src/transformers/configuration_distilbert.py

@@ -33,50 +33,51 @@ DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
 
 class DistilBertConfig(PretrainedConfig):
     r"""
-    This is the configuration class to store the configuration of a :class:`~transformers.DistilBertModel`.
-    It is used to instantiate a DistilBERT model according to the specified arguments, defining the model
-    architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of
-    the DistilBERT `distilbert-base-uncased <https://huggingface.co/distilbert-base-uncased>`__ architecture.
+    This is the configuration class to store the configuration of a :class:`~transformers.DistilBertModel` or a
+    :class:`~transformers.TFDistilBertModel`. It is used to instantiate a DistilBERT model according to the specified
+    arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar
+    configuration to that of the DistilBERT
+    `distilbert-base-uncased <https://huggingface.co/distilbert-base-uncased>`__ architecture.
 
     Configuration objects inherit from  :class:`~transformers.PretrainedConfig` and can be used
     to control the model outputs. Read the documentation from  :class:`~transformers.PretrainedConfig`
     for more information.
 
-
     Args:
-        vocab_size (:obj:`int`, optional, defaults to 30522):
-            Vocabulary size of the DistilBERT model. Defines the different tokens that
-            can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.BertModel`.
-        max_position_embeddings (:obj:`int`, optional, defaults to 512):
+        vocab_size (:obj:`int`, `optional`, defaults to 30522):
+            Vocabulary size of the DistilBERT model. Defines the number of different tokens that can be represented by the
+            :obj:`inputs_ids` passed when calling :class:`~transformers.DistilBertModel` or
+            :class:`~transformers.TFDistilBertModel`.
+        max_position_embeddings (:obj:`int`, `optional`, defaults to 512):
             The maximum sequence length that this model might ever be used with.
             Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
-        sinusoidal_pos_embds (:obj:`boolean`, optional, defaults to :obj:`False`):
+        sinusoidal_pos_embds (:obj:`boolean`, `optional`, defaults to :obj:`False`):
             Whether to use sinusoidal positional embeddings.
-        n_layers (:obj:`int`, optional, defaults to 6):
+        n_layers (:obj:`int`, `optional`, defaults to 6):
             Number of hidden layers in the Transformer encoder.
-        n_heads (:obj:`int`, optional, defaults to 12):
+        n_heads (:obj:`int`, `optional`, defaults to 12):
             Number of attention heads for each attention layer in the Transformer encoder.
-        dim (:obj:`int`, optional, defaults to 768):
+        dim (:obj:`int`, `optional`, defaults to 768):
             Dimensionality of the encoder layers and the pooler layer.
-        hidden_dim (:obj:`int`, optional, defaults to 3072):
-            The size of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
-        dropout (:obj:`float`, optional, defaults to 0.1):
+        hidden_dim (:obj:`int`, `optional`, defaults to 3072):
+            The size of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
+        dropout (:obj:`float`, `optional`, defaults to 0.1):
             The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
-        attention_dropout (:obj:`float`, optional, defaults to 0.1):
+        attention_dropout (:obj:`float`, `optional`, defaults to 0.1):
             The dropout ratio for the attention probabilities.
-        activation (:obj:`str` or :obj:`function`, optional, defaults to "gelu"):
+        activation (:obj:`str` or :obj:`Callable`, `optional`, defaults to :obj:`"gelu"`):
             The non-linear activation function (function or string) in the encoder and pooler.
-            If string, "gelu", "relu", "swish" and "gelu_new" are supported.
-        initializer_range (:obj:`float`, optional, defaults to 0.02):
+            If string, :obj:`"gelu"`, :obj:`"relu"`, :obj:`"swish"` and :obj:`"gelu_new"` are supported.
+        initializer_range (:obj:`float`, `optional`, defaults to 0.02):
             The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        qa_dropout (:obj:`float`, optional, defaults to 0.1):
+        qa_dropout (:obj:`float`, `optional`, defaults to 0.1):
             The dropout probabilities used in the question answering model
             :class:`~transformers.DistilBertForQuestionAnswering`.
-        seq_classif_dropout (:obj:`float`, optional, defaults to 0.2):
+        seq_classif_dropout (:obj:`float`, `optional`, defaults to 0.2):
             The dropout probabilities used in the sequence classification and the multiple choice model
             :class:`~transformers.DistilBertForSequenceClassification`.
 
-    Example::
+    Examples::
 
         >>> from transformers import DistilBertModel, DistilBertConfig
 

src/transformers/configuration_dpr.py

@@ -32,8 +32,12 @@ class DPRConfig(PretrainedConfig):
     :class:`~transformers.DPRConfig` is the configuration class to store the configuration of a
     `DPRModel`.
 
-    This is the configuration class to store the configuration of a `DPRContextEncoder`, `DPRQuestionEncoder`, or a `DPRReader`.
-    It is used to instantiate the components of the DPR model.
+    This is the configuration class to store the configuration of a :class:`~transformers.DPRContextEncoder`,
+    :class:`~transformers.DPRQuestionEncoder`, or a :class:`~transformers.DPRReader`. It is used to instantiate the
+    components of the DPR model.
+
+    This class is a subclass of :class:`~transformers.BertConfig`. Please check the
+    superclass for the documentation of all kwargs.
 
     Args:
         vocab_size (:obj:`int`, `optional`, defaults to 30522):

src/transformers/configuration_electra.py

@@ -33,11 +33,11 @@ ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP = {
 
 class ElectraConfig(PretrainedConfig):
     r"""
-    This is the configuration class to store the configuration of a :class:`~transformers.ElectraModel`.
-    It is used to instantiate an ELECTRA model according to the specified arguments, defining the model
-    architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of
-    the ELECTRA `google/electra-small-discriminator <https://huggingface.co/google/electra-small-discriminator>`__
-    architecture.
+    This is the configuration class to store the configuration of a :class:`~transformers.ElectraModel` or a
+    :class:`~transformers.TFElectraModel`. It is used to instantiate a ELECTRA model according to the specified
+    arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar
+    configuration to that of the ELECTRA
+    `google/electra-small-discriminator <https://huggingface.co/google/electra-small-discriminator>`__ architecture.
 
     Configuration objects inherit from  :class:`~transformers.PretrainedConfig` and can be used
     to control the model outputs. Read the documentation from  :class:`~transformers.PretrainedConfig`
@@ -45,59 +45,61 @@ class ElectraConfig(PretrainedConfig):
 
 
     Args:
-        vocab_size (:obj:`int`, optional, defaults to 30522):
-            Vocabulary size of the ELECTRA model. Defines the different tokens that
-            can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.ElectraModel`.
-        embedding_size (:obj:`int`, optional, defaults to 128):
+        vocab_size (:obj:`int`, `optional`, defaults to 30522):
+            Vocabulary size of the ELECTRA model. Defines the number of different tokens that can be represented by the
+            :obj:`inputs_ids` passed when calling :class:`~transformers.ElectraModel` or
+            :class:`~transformers.TFElectraModel`.
+        embedding_size (:obj:`int`, `optional`, defaults to 128):
             Dimensionality of the encoder layers and the pooler layer.
-        hidden_size (:obj:`int`, optional, defaults to 256):
+        hidden_size (:obj:`int`, `optional`, defaults to 256):
             Dimensionality of the encoder layers and the pooler layer.
-        num_hidden_layers (:obj:`int`, optional, defaults to 12):
+        num_hidden_layers (:obj:`int`, `optional`, defaults to 12):
             Number of hidden layers in the Transformer encoder.
-        num_attention_heads (:obj:`int`, optional, defaults to 4):
+        num_attention_heads (:obj:`int`, `optional`, defaults to 4):
             Number of attention heads for each attention layer in the Transformer encoder.
-        intermediate_size (:obj:`int`, optional, defaults to 1024):
+        intermediate_size (:obj:`int`, `optional`, defaults to 1024):
             Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
-        hidden_act (:obj:`str` or :obj:`function`, optional, defaults to "gelu"):
+        hidden_act (:obj:`str` or :obj:`Callable`, `optional`, defaults to :obj:`"gelu"`):
             The non-linear activation function (function or string) in the encoder and pooler.
-            If string, "gelu", "relu", "swish" and "gelu_new" are supported.
-        hidden_dropout_prob (:obj:`float`, optional, defaults to 0.1):
+            If string, :obj:`"gelu"`, :obj:`"relu"`, :obj:`"swish"` and :obj:`"gelu_new"` are supported.
+        hidden_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
             The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
-        attention_probs_dropout_prob (:obj:`float`, optional, defaults to 0.1):
+        attention_probs_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
             The dropout ratio for the attention probabilities.
-        max_position_embeddings (:obj:`int`, optional, defaults to 512):
+        max_position_embeddings (:obj:`int`, `optional`, defaults to 512):
             The maximum sequence length that this model might ever be used with.
             Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
-        type_vocab_size (:obj:`int`, optional, defaults to 2):
-            The vocabulary size of the `token_type_ids` passed into :class:`~transformers.ElectraModel`.
-        initializer_range (:obj:`float`, optional, defaults to 0.02):
+        type_vocab_size (:obj:`int`, `optional`, defaults to 2):
+            The vocabulary size of the :obj:`token_type_ids` passed when calling :class:`~transformers.ElectraModel` or
+            :class:`~transformers.TFElectraModel`.
+        initializer_range (:obj:`float`, `optional`, defaults to 0.02):
             The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        layer_norm_eps (:obj:`float`, optional, defaults to 1e-12):
+        layer_norm_eps (:obj:`float`, `optional`, defaults to 1e-12):
             The epsilon used by the layer normalization layers.
-        summary_type (:obj:`string`, optional, defaults to "first"):
-            Argument used when doing sequence summary. Used in for the multiple choice head in
-            :class:`~transformers.ElectraForMultipleChoice`.
-            Is one of the following options:
+        summary_type (:obj:`str`, `optional`, defaults to :obj:`"first"`):
+            Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.
 
-                - 'last' => take the last token hidden state (like XLNet)
-                - 'first' => take the first token hidden state (like Bert)
-                - 'mean' => take the mean of all tokens hidden states
-                - 'cls_index' => supply a Tensor of classification token position (GPT/GPT-2)
-                - 'attn' => Not implemented now, use multi-head attention
-        summary_use_proj (:obj:`boolean`, optional, defaults to :obj:`True`):
-            Argument used when doing sequence summary. Used in for the multiple choice head in
-            :class:`~transformers.ElectraForMultipleChoice`.
-            Add a projection after the vector extraction
-        summary_activation (:obj:`string` or :obj:`None`, optional):
-            Argument used when doing sequence summary. Used in for the multiple choice head in
-            :class:`~transformers.ElectraForMultipleChoice`.
-            'gelu' => add a gelu activation to the output, Other => no activation.
-        summary_last_dropout (:obj:`float`, optional, defaults to 0.0):
-            Argument used when doing sequence summary. Used in for the multiple choice head in
-            :class:`~transformers.ElectraForMultipleChoice`.
-            Add a dropout after the projection and activation
+            Has to be one of the following options:
 
-    Example::
+                - :obj:`"last"`: Take the last token hidden state (like XLNet).
+                - :obj:`"first"`: Take the first token hidden state (like BERT).
+                - :obj:`"mean"`: Take the mean of all tokens hidden states.
+                - :obj:`"cls_index"`: Supply a Tensor of classification token position (like GPT/GPT-2).
+                - :obj:`"attn"`: Not implemented now, use multi-head attention.
+        summary_use_proj (:obj:`bool`, `optional`, defaults to :obj:`True`):
+            Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.
+
+            Whether or not to add a projection after the vector extraction.
+        summary_activation (:obj:`str`, `optional`):
+            Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.
+
+            Pass :obj:`"gelu"` for a gelu activation to the output, any other value will result in no activation.
+        summary_last_dropout (:obj:`float`, `optional`, defaults to 0.0):
+            Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.
+
+            The dropout ratio to be used after the projection and activation.
+
+    Examples::
 
         >>> from transformers import ElectraModel, ElectraConfig
 

src/transformers/configuration_encoder_decoder.py

@@ -25,22 +25,24 @@ logger = logging.get_logger(__name__)
 
 class EncoderDecoderConfig(PretrainedConfig):
     r"""
-    :class:`~transformers.EncoderDecoderConfig` is the configuration class to store the configuration of a `EncoderDecoderModel`.
+    :class:`~transformers.EncoderDecoderConfig` is the configuration class to store the configuration of a
+    :class:`~transformers.EncoderDecoderModel`. It is used to instantiate an Encoder Decoder model according to the
+    specified arguments, defining the encoder and decoder configs.
 
-    It is used to instantiate an Encoder Decoder model according to the specified arguments, defining the encoder and decoder configs.
-    Configuration objects inherit from  :class:`~transformers.PretrainedConfig`
-    and can be used to control the model outputs.
-    See the documentation for :class:`~transformers.PretrainedConfig` for more information.
+    Configuration objects inherit from  :class:`~transformers.PretrainedConfig` and can be used
+    to control the model outputs. Read the documentation from  :class:`~transformers.PretrainedConfig`
+    for more information.
 
     Args:
         kwargs (`optional`):
-            Remaining dictionary of keyword arguments. Notably:
-                encoder (:class:`PretrainedConfig`, optional, defaults to `None`):
-                    An instance of a configuration object that defines the encoder config.
-                decoder (:class:`PretrainedConfig`, optional, defaults to `None`):
-                    An instance of a configuration object that defines the decoder config.
+            Dictionary of keyword arguments. Notably:
 
-    Example::
+                - **encoder** (:class:`~transformers.PretrainedConfig`, `optional`) -- An instance of a configuration
+                  object that defines the encoder config.
+                - **decoder** (:class:`~transformers.PretrainedConfig`, `optional`) -- An instance of a configuration
+                  object that defines the decoder config.
+
+    Examples::
 
         >>> from transformers import BertConfig, EncoderDecoderConfig, EncoderDecoderModel
 

src/transformers/configuration_flaubert.py

@@ -30,11 +30,9 @@ FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
 
 class FlaubertConfig(XLMConfig):
     """
-    Configuration class to store the configuration of a `FlaubertModel`.
-    This is the configuration class to store the configuration of a :class:`~transformers.XLMModel`.
-    It is used to instantiate an XLM model according to the specified arguments, defining the model
-    architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of
-    the `xlm-mlm-en-2048 <https://huggingface.co/xlm-mlm-en-2048>`__ architecture.
+    This is the configuration class to store the configuration of a :class:`~transformers.FlaubertModel` or a
+    :class:`~transformers.TFFlaubertModel`. It is used to instantiate a FlauBERT model according to the specified
+    arguments, defining the model architecture.
 
     Configuration objects inherit from  :class:`~transformers.PretrainedConfig` and can be used
     to control the model outputs. Read the documentation from  :class:`~transformers.PretrainedConfig`
@@ -47,95 +45,95 @@ class FlaubertConfig(XLMConfig):
         layerdrop (:obj:`float`, `optional`, defaults to 0.0):
             Probability to drop layers during training (Fan et al., Reducing Transformer Depth on Demand
             with Structured Dropout. ICLR 2020)
-        vocab_size (:obj:`int`, optional, defaults to 30145):
-            Vocabulary size of the Flaubert model. Defines the different tokens that
-            can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.FlaubertModel`.
-        emb_dim (:obj:`int`, optional, defaults to 2048):
+        vocab_size (:obj:`int`, `optional`, defaults to 30145):
+            Vocabulary size of the FlauBERT model. Defines the number of different tokens that can be represented by the
+            :obj:`inputs_ids` passed when calling :class:`~transformers.FlaubertModel` or
+            :class:`~transformers.TFFlaubertModel`.
+        emb_dim (:obj:`int`, `optional`, defaults to 2048):
             Dimensionality of the encoder layers and the pooler layer.
-        n_layer (:obj:`int`, optional, defaults to 12):
+        n_layer (:obj:`int`, `optional`, defaults to 12):
             Number of hidden layers in the Transformer encoder.
-        n_head (:obj:`int`, optional, defaults to 16):
+        n_head (:obj:`int`, `optional`, defaults to 16):
             Number of attention heads for each attention layer in the Transformer encoder.
-        dropout (:obj:`float`, optional, defaults to 0.1):
+        dropout (:obj:`float`, `optional`, defaults to 0.1):
             The dropout probability for all fully connected
             layers in the embeddings, encoder, and pooler.
-        attention_dropout (:obj:`float`, optional, defaults to 0.1):
+        attention_dropout (:obj:`float`, `optional`, defaults to 0.1):
             The dropout probability for the attention mechanism
-        gelu_activation (:obj:`boolean`, optional, defaults to :obj:`True`):
-            The non-linear activation function (function or string) in the
-            encoder and pooler. If set to `True`, "gelu" will be used instead of "relu".
-        sinusoidal_embeddings (:obj:`boolean`, optional, defaults to :obj:`False`):
-            Whether to use sinusoidal positional embeddings instead of absolute positional embeddings.
-        causal (:obj:`boolean`, optional, defaults to :obj:`False`):
-            Set this to `True` for the model to behave in a causal manner.
+        gelu_activation (:obj:`bool`, `optional`, defaults to :obj:`True`):
+            Whether or not to use a `gelu` actibation instead of `relu`.
+        sinusoidal_embeddings (:obj:`bool`, `optional`, defaults to :obj:`False`):
+            Whether or not to use sinusoidal positional embeddings instead of absolute positional embeddings.
+        causal (:obj:`bool`, `optional`, defaults to :obj:`False`):
+            Whether or not the model shoul behave in a causal manner.
             Causal models use a triangular attention mask in order to only attend to the left-side context instead
             if a bidirectional context.
-        asm (:obj:`boolean`, optional, defaults to :obj:`False`):
-            Whether to use an adaptive log softmax projection layer instead of a linear layer for the prediction
+        asm (:obj:`bool`, `optional`, defaults to :obj:`False`):
+            Whether or not to use an adaptive log softmax projection layer instead of a linear layer for the prediction
             layer.
-        n_langs (:obj:`int`, optional, defaults to 1):
+        n_langs (:obj:`int`, `optional`, defaults to 1):
             The number of languages the model handles. Set to 1 for monolingual models.
-        use_lang_emb (:obj:`boolean`, optional, defaults to :obj:`True`)
+        use_lang_emb (:obj:`bool`, `optional`, defaults to :obj:`True`)
             Whether to use language embeddings. Some models use additional language embeddings, see
             `the multilingual models page <http://huggingface.co/transformers/multilingual.html#xlm-language-embeddings>`__
             for information on how to use them.
-        max_position_embeddings (:obj:`int`, optional, defaults to 512):
+        max_position_embeddings (:obj:`int`, `optional`, defaults to 512):
             The maximum sequence length that this model might
             ever be used with. Typically set this to something large just in case
             (e.g., 512 or 1024 or 2048).
-        embed_init_std (:obj:`float`, optional, defaults to 2048^-0.5):
+        embed_init_std (:obj:`float`, `optional`, defaults to 2048^-0.5):
             The standard deviation of the truncated_normal_initializer for
             initializing the embedding matrices.
-        init_std (:obj:`int`, optional, defaults to 50257):
+        init_std (:obj:`int`, `optional`, defaults to 50257):
             The standard deviation of the truncated_normal_initializer for
             initializing all weight matrices except the embedding matrices.
-        layer_norm_eps (:obj:`float`, optional, defaults to 1e-12):
+        layer_norm_eps (:obj:`float`, `optional`, defaults to 1e-12):
             The epsilon used by the layer normalization layers.
-        bos_index (:obj:`int`, optional, defaults to 0):
+        bos_index (:obj:`int`, `optional`, defaults to 0):
             The index of the beginning of sentence token in the vocabulary.
-        eos_index (:obj:`int`, optional, defaults to 1):
+        eos_index (:obj:`int`, `optional`, defaults to 1):
             The index of the end of sentence token in the vocabulary.
-        pad_index (:obj:`int`, optional, defaults to 2):
+        pad_index (:obj:`int`, `optional`, defaults to 2):
             The index of the padding token in the vocabulary.
-        unk_index (:obj:`int`, optional, defaults to 3):
+        unk_index (:obj:`int`, `optional`, defaults to 3):
             The index of the unknown token in the vocabulary.
-        mask_index (:obj:`int`, optional, defaults to 5):
+        mask_index (:obj:`int`, `optional`, defaults to 5):
             The index of the masking token in the vocabulary.
-        is_encoder(:obj:`boolean`, optional, defaults to :obj:`True`):
-            Whether the initialized model should be a transformer encoder or decoder as seen in Vaswani et al.
-        summary_type (:obj:`string`, optional, defaults to "first"):
-            Argument used when doing sequence summary. Used in for the multiple choice head in
-            :class:`~transformers.XLMForSequenceClassification`.
-            Is one of the following options:
+        is_encoder(:obj:`bool`, `optional`, defaults to :obj:`True`):
+            Whether or not the initialized model should be a transformer encoder or decoder as seen in Vaswani et al.
+        summary_type (:obj:`string`, `optional`, defaults to "first"):
+            Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.
 
-            - 'last' => take the last token hidden state (like XLNet)
-            - 'first' => take the first token hidden state (like Bert)
-            - 'mean' => take the mean of all tokens hidden states
-            - 'cls_index' => supply a Tensor of classification token position (GPT/GPT-2)
-            - 'attn' => Not implemented now, use multi-head attention
-        summary_use_proj (:obj:`boolean`, optional, defaults to :obj:`True`):
-            Argument used when doing sequence summary. Used in for the multiple choice head in
-            :class:`~transformers.XLMForSequenceClassification`.
-            Add a projection after the vector extraction
-        summary_activation (:obj:`string` or :obj:`None`, optional):
-            Argument used when doing sequence summary. Used in for the multiple choice head in
-            :class:`~transformers.XLMForSequenceClassification`.
-            'tanh' => add a tanh activation to the output, Other => no activation.
-        summary_proj_to_labels (:obj:`boolean`, optional, defaults to :obj:`True`):
-            Argument used when doing sequence summary. Used in for the multiple choice head in
-            :class:`~transformers.XLMForSequenceClassification`.
-            If True, the projection outputs to config.num_labels classes (otherwise to hidden_size). Default: False.
-        summary_first_dropout (:obj:`float`, optional, defaults to 0.1):
-            Argument used when doing sequence summary. Used in for the multiple choice head in
-            :class:`~transformers.XLMForSequenceClassification`.
-            Add a dropout before the projection and activation
-        start_n_top (:obj:`int`, optional, defaults to 5):
-            Used in the SQuAD evaluation script for XLM and XLNet.
-        end_n_top (:obj:`int`, optional, defaults to 5):
-            Used in the SQuAD evaluation script for XLM and XLNet.
-        mask_token_id (:obj:`int`, optional, defaults to 0):
+            Has to be one of the following options:
+
+                - :obj:`"last"`: Take the last token hidden state (like XLNet).
+                - :obj:`"first"`: Take the first token hidden state (like BERT).
+                - :obj:`"mean"`: Take the mean of all tokens hidden states.
+                - :obj:`"cls_index"`: Supply a Tensor of classification token position (like GPT/GPT-2).
+                - :obj:`"attn"`: Not implemented now, use multi-head attention.
+        summary_use_proj (:obj:`bool`, `optional`, defaults to :obj:`True`):
+            Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.
+
+            Whether or not to add a projection after the vector extraction.
+        summary_activation (:obj:`str`, `optional`):
+            Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.
+
+            Pass :obj:`"tanh"` for a tanh activation to the output, any other value will result in no activation.
+        summary_proj_to_labels (:obj:`bool`, `optional`, defaults to :obj:`True`):
+            Used in the sequence classification and multiple choice models.
+
+            Whether the projection outputs should have :obj:`config.num_labels` or :obj:`config.hidden_size` classes.
+        summary_first_dropout (:obj:`float`, `optional`, defaults to 0.1):
+            Used in the sequence classification and multiple choice models.
+
+            The dropout ratio to be used after the projection and activation.
+        start_n_top (:obj:`int`, `optional`, defaults to 5):
+            Used in the SQuAD evaluation script.
+        end_n_top (:obj:`int`, `optional`, defaults to 5):
+            Used in the SQuAD evaluation script.
+        mask_token_id (:obj:`int`, `optional`, defaults to 0):
             Model agnostic parameter to identify masked tokens when generating text in an MLM context.
-        lang_id (:obj:`int`, optional, defaults to 1):
+        lang_id (:obj:`int`, `optional`, defaults to 1):
             The ID of the language used by the model. This parameter is used when generating
             text in a given language.
     """

src/transformers/configuration_fsmt.py

@@ -18,7 +18,6 @@
 import copy
 
 from .configuration_utils import PretrainedConfig
-from .file_utils import add_start_docstrings_to_callable
 from .utils import logging
 
 
@@ -27,33 +26,54 @@ logger = logging.get_logger(__name__)
 FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
 
 
-FSMT_CONFIG_ARGS_DOC = r"""
+class DecoderConfig(PretrainedConfig):
+    r"""
+    Configuration class for FSMT's decoder specific things.
+    note: this is a private helper class
+    """
+    model_type = "fsmt_decoder"
+
+    def __init__(self, vocab_size=0, bos_token_id=0):
+        super().__init__()
+        self.vocab_size = vocab_size
+        self.bos_token_id = bos_token_id
+
+
+class FSMTConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a :class:`~transformers.FSMTModel`. It is used to
+    instantiate a FSMT model according to the specified arguments, defining the model architecture.
+
+    Configuration objects inherit from  :class:`~transformers.PretrainedConfig` and can be used
+    to control the model outputs. Read the documentation from  :class:`~transformers.PretrainedConfig`
+    for more information.
+
     Args:
         langs (:obj:`List[str]`):
-            source language, target_language (e.g. ['en', 'ru'])
+            A list with source language and target_language (e.g., ['en', 'ru']).
         src_vocab_size (:obj:`int`):
-            defines the different tokens that can be represented by `inputs_ids` passed to the forward
-            method in the encoder.
+            Vocabulary size of the encoder. Defines the number of different tokens that can be represented by the
+            :obj:`inputs_ids` passed to the forward method in the encoder.
         tgt_vocab_size (:obj:`int`):
-            defines the different tokens that can be represented by `inputs_ids` passed to the forward
-            method in the decoder.
+            Vocabulary size of the decoder. Defines the number of different tokens that can be represented by the
+            :obj:`inputs_ids` passed to the forward method in the decoder.
         d_model (:obj:`int`, `optional`, defaults to 1024):
             Dimensionality of the layers and the pooler layer.
         encoder_layers (:obj:`int`, `optional`, defaults to 12):
-            Number of encoder layers, 16 for pegasus, 6 for bart-base and marian
+            Number of encoder layers.
         decoder_layers (:obj:`int`, `optional`, defaults to 12):
-            Number of decoder layers, 16 for pegasus, 6 for bart-base and marian
+            Number of decoder layers.
         encoder_attention_heads (:obj:`int`, `optional`, defaults to 16):
             Number of attention heads for each attention layer in the Transformer encoder.
         decoder_attention_heads (:obj:`int`, `optional`, defaults to 16):
             Number of attention heads for each attention layer in the Transformer decoder.
         decoder_ffn_dim (:obj:`int`, `optional`, defaults to 4096):
-            Dimensionality of the "intermediate" (i.e., feed-forward) layer in decoder.
+            Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
         encoder_ffn_dim (:obj:`int`, `optional`, defaults to 4096):
-            Dimensionality of the "intermediate" (i.e., feed-forward) layer in decoder.
-        activation_function (:obj:`str` or :obj:`function`, `optional`, defaults to "relu"):
+            Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
+        activation_function (:obj:`str` or :obj:`Callable`, `optional`, defaults to :obj:`"relu"`):
             The non-linear activation function (function or string) in the encoder and pooler.
-            If string, "gelu", "relu", "swish" and "gelu_new" are supported.
+            If string, :obj:`"gelu"`, :obj:`"relu"`, :obj:`"swish"` and :obj:`"gelu_new"` are supported.
         dropout (:obj:`float`, `optional`, defaults to 0.1):
             The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
         attention_dropout (:obj:`float`, `optional`, defaults to 0.0):
@@ -74,7 +94,7 @@ FSMT_CONFIG_ARGS_DOC = r"""
         eos_token_id (:obj:`int`, `optional`, defaults to 2)
             End of stream token id.
         decoder_start_token_id (:obj:`int`, `optional`):
-            This model starts decoding with `eos_token_id`
+            This model starts decoding with :obj:`eos_token_id`
         encoder_layerdrop: (:obj:`float`, `optional`, defaults to 0.0):
             Google "layerdrop arxiv", as its not explainable in one line.
         decoder_layerdrop: (:obj:`float`, `optional`, defaults to 0.0):
@@ -92,26 +112,14 @@ FSMT_CONFIG_ARGS_DOC = r"""
         early_stopping (:obj:`bool`, `optional`, defaults to :obj:`False`)
             Flag that will be used by default in the :obj:`generate` method of the model. Whether to stop
             the beam search when at least ``num_beams`` sentences are finished per batch or not.
-"""
 
+        Examples::
 
-class DecoderConfig(PretrainedConfig):
-    r"""
-    Configuration class for FSMT's decoder specific things.
-    note: this is a private helper class
-    """
-    model_type = "fsmt_decoder"
+            >>> from transformers import FSMTConfig, FSMTModel
 
-    def __init__(self, vocab_size=0, bos_token_id=0):
-        super().__init__()
-        self.vocab_size = vocab_size
-        self.bos_token_id = bos_token_id
+            >>> config = FSMTConfig.from_pretrained('facebook/wmt19-en-ru')
+            >>> model = FSMTModel(config)
 
-
-@add_start_docstrings_to_callable(FSMT_CONFIG_ARGS_DOC)
-class FSMTConfig(PretrainedConfig):
-    r"""
-    Configuration class for FSMT.
     """
     model_type = "fsmt"
 
@@ -149,17 +157,6 @@ class FSMTConfig(PretrainedConfig):
         early_stopping=False,
         **common_kwargs
     ):
-        r"""
-        :class:`~transformers.FSMTConfig` is the configuration class for `FSMTModel`.
-
-        Examples::
-
-            >>> from transformers import FSMTConfig, FSMTModel
-
-            >>> config = FSMTConfig.from_pretrained('facebook/wmt19-en-ru')
-            >>> model = FSMTModel(config)
-
-        """
         if "hidden_size" in common_kwargs:
             raise ValueError("hidden size is called d_model")
         super().__init__(

src/transformers/configuration_funnel.py

@@ -36,20 +36,21 @@ FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP = {
 
 class FunnelConfig(PretrainedConfig):
     r"""
-    This is the configuration class to store the configuration of a :class:`~transformers.FunnelModel`.
-    It is used to instantiate an Funnel Transformer model according to the specified arguments, defining the model
-    architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of
-    the Funnel Transformer `funnel-transformer/small <https://huggingface.co/funnel-transformer/small>`__ architecture.
+    This is the configuration class to store the configuration of a :class:`~transformers.FunnelModel` or a
+    :class:`~transformers.TFBertModel`. It is used to instantiate a Funnel Transformer model according to the specified
+    arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar
+    configuration to that of the Funnel Transformer `funnel-transformer/small
+    <https://huggingface.co/funnel-transformer/small>`__ architecture.
 
     Configuration objects inherit from  :class:`~transformers.PretrainedConfig` and can be used
     to control the model outputs. Read the documentation from  :class:`~transformers.PretrainedConfig`
     for more information.
 
-
     Args:
         vocab_size (:obj:`int`, `optional`, defaults to 30522):
-            Vocabulary size of the Funnel transformer. Defines the different tokens that
-            can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.FunnelModel`.
+            Vocabulary size of the Funnel transformer. Defines the number of different tokens that can be represented
+            by the :obj:`inputs_ids` passed when calling :class:`~transformers.FunnelModel` or
+            :class:`~transformers.TFFunnelModel`.
         block_sizes (:obj:`List[int]`, `optional`, defaults to :obj:`[4, 4, 4]`):
             The sizes of the blocks used in the model.
         block_repeats (:obj:`List[int]`, `optional`):
@@ -77,7 +78,8 @@ class FunnelConfig(PretrainedConfig):
             The maximum sequence length that this model might ever be used with.
             Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
         type_vocab_size (:obj:`int`, `optional`, defaults to 3):
-            The vocabulary size of the `token_type_ids` passed into :class:`~transformers.FunnelModel`.
+            The vocabulary size of the :obj:`token_type_ids` passed when calling :class:`~transformers.FunnelModel` or
+            :class:`~transformers.TFFunnelModel`.
         initializer_range (:obj:`float`, `optional`, defaults to 0.1):
             The standard deviation of the `uniform initializer` for initializing all weight matrices in attention
             layers.

src/transformers/configuration_gpt2.py

@@ -32,10 +32,10 @@ GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP = {
 
 class GPT2Config(PretrainedConfig):
     """
-    This is the configuration class to store the configuration of a :class:`~transformers.GPT2Model`.
-    It is used to instantiate an GPT-2 model according to the specified arguments, defining the model
-    architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of
-    the GPT-2 `small <https://huggingface.co/gpt2>`__ architecture.
+    This is the configuration class to store the configuration of a :class:`~transformers.GPT2Model` or a
+    :class:`~transformers.TFGPT2Model`. It is used to instantiate a GPT-2 model according to the specified
+    arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar
+    configuration to that of the GPT-2 `small <https://huggingface.co/gpt2>`__ architecture.
 
     Configuration objects inherit from  :class:`~transformers.PretrainedConfig` and can be used
     to control the model outputs. Read the documentation from  :class:`~transformers.PretrainedConfig`
@@ -43,60 +43,66 @@ class GPT2Config(PretrainedConfig):
 
 
     Args:
-        vocab_size (:obj:`int`, optional, defaults to 50257):
-            Vocabulary size of the GPT-2 model. Defines the different tokens that
-            can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.GPT2Model`.
-        n_positions (:obj:`int`, optional, defaults to 1024):
+        vocab_size (:obj:`int`, `optional`, defaults to 50257):
+            Vocabulary size of the GPT-2 model. Defines the number of different tokens that can be represented by the
+            :obj:`inputs_ids` passed when calling :class:`~transformers.GPT2Model` or
+            :class:`~transformers.TFGPT2Model`.
+        n_positions (:obj:`int`, `optional`, defaults to 1024):
             The maximum sequence length that this model might ever be used with.
             Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
-        n_ctx (:obj:`int`, optional, defaults to 1024):
+        n_ctx (:obj:`int`, `optional`, defaults to 1024):
             Dimensionality of the causal mask (usually same as n_positions).
-        n_embd (:obj:`int`, optional, defaults to 768):
+        n_embd (:obj:`int`, `optional`, defaults to 768):
             Dimensionality of the embeddings and hidden states.
-        n_layer (:obj:`int`, optional, defaults to 12):
+        n_layer (:obj:`int`, `optional`, defaults to 12):
             Number of hidden layers in the Transformer encoder.
-        n_head (:obj:`int`, optional, defaults to 12):
+        n_head (:obj:`int`, `optional`, defaults to 12):
             Number of attention heads for each attention layer in the Transformer encoder.
-        n_inner (:obj:`int`, optional, defaults to None):
+        n_inner (:obj:`int`, `optional`, defaults to None):
             Dimensionality of the inner feed-forward layers. :obj:`None` will set it to 4 times n_embd
-        activation_function (:obj:`str`, optional, defaults to 'gelu'):
-            Activation function selected in the list ["relu", "swish", "gelu", "tanh", "gelu_new"].
-        resid_pdrop (:obj:`float`, optional, defaults to 0.1):
+        activation_function (:obj:`str`, `optional`, defaults to :obj:`"gelu"`):
+            Activation function, to be selected in the list :obj:`["relu", "swish", "gelu", "tanh", "gelu_new"]`.
+        resid_pdrop (:obj:`float`, `optional`, defaults to 0.1):
             The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
-        embd_pdrop (:obj:`int`, optional, defaults to 0.1):
+        embd_pdrop (:obj:`int`, `optional`, defaults to 0.1):
             The dropout ratio for the embeddings.
-        attn_pdrop (:obj:`float`, optional, defaults to 0.1):
+        attn_pdrop (:obj:`float`, `optional`, defaults to 0.1):
             The dropout ratio for the attention.
-        layer_norm_epsilon (:obj:`float`, optional, defaults to 1e-5):
+        layer_norm_epsilon (:obj:`float`, `optional`, defaults to 1e-5):
             The epsilon to use in the layer normalization layers
-        initializer_range (:obj:`float`, optional, defaults to 0.02):
+        initializer_range (:obj:`float`, `optional`, defaults to 0.02):
             The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        summary_type (:obj:`string`, optional, defaults to "cls_index"):
-            Argument used when doing sequence summary. Used in for the multiple choice head in
-            :class:`~transformers.GPT2DoubleHeadsModel`.
-            Is one of the following options:
+        summary_type (:obj:`string`, `optional`, defaults to :obj:`"cls_index"`):
+            Argument used when doing sequence summary, used in the models
+            :class:`~transformers.GPT2DoubleHeadsModel` and :class:`~transformers.TFGPT2DoubleHeadsModel`.
 
-            - 'last' => take the last token hidden state (like XLNet)
-            - 'first' => take the first token hidden state (like Bert)
-            - 'mean' => take the mean of all tokens hidden states
-            - 'cls_index' => supply a Tensor of classification token position (GPT/GPT-2)
-            - 'attn' => Not implemented now, use multi-head attention
-        summary_use_proj (:obj:`boolean`, optional, defaults to :obj:`True`):
+            Has to be one of the following options:
+
+                - :obj:`"last"`: Take the last token hidden state (like XLNet).
+                - :obj:`"first"`: Take the first token hidden state (like BERT).
+                - :obj:`"mean"`: Take the mean of all tokens hidden states.
+                - :obj:`"cls_index"`: Supply a Tensor of classification token position (like GPT/GPT-2).
+                - :obj:`"attn"`: Not implemented now, use multi-head attention.
+        summary_use_proj (:obj:`bool`, `optional`, defaults to :obj:`True`):
+            Argument used when doing sequence summary, used in the models
+            :class:`~transformers.GPT2DoubleHeadsModel` and :class:`~transformers.TFGPT2DoubleHeadsModel`.
+
+            Whether or not to add a projection after the vector extraction.
+        summary_activation (:obj:`str`, `optional`):
             Argument used when doing sequence summary. Used in for the multiple choice head in
             :class:`~transformers.GPT2DoubleHeadsModel`.
-            Add a projection after the vector extraction
-        summary_activation (:obj:`string` or :obj:`None`, optional):
-            Argument used when doing sequence summary. Used in for the multiple choice head in
-            :class:`~transformers.GPT2DoubleHeadsModel`.
-            'tanh' => add a tanh activation to the output, Other => no activation.
-        summary_proj_to_labels (:obj:`boolean`, optional, defaults to :obj:`True`):
-            Argument used when doing sequence summary. Used in for the multiple choice head in
-            :class:`~transformers.GPT2DoubleHeadsModel`.
-            If True, the projection outputs to config.num_labels classes (otherwise to hidden_size). Default: False.
-        summary_first_dropout (:obj:`float`, optional, defaults to 0.1):
-            Argument used when doing sequence summary. Used in for the multiple choice head in
-            :class:`~transformers.GPT2DoubleHeadsModel`.
-            Add a dropout before the projection and activation
+
+            Pass :obj:`"tanh"` for a tanh activation to the output, any other value will result in no activation.
+        summary_proj_to_labels (:obj:`bool`, `optional`, defaults to :obj:`True`):
+            Argument used when doing sequence summary, used in the models
+            :class:`~transformers.GPT2DoubleHeadsModel` and :class:`~transformers.TFGPT2DoubleHeadsModel`.
+
+            Whether the projection outputs should have :obj:`config.num_labels` or :obj:`config.hidden_size` classes.
+        summary_first_dropout (:obj:`float`, `optional`, defaults to 0.1):
+            Argument used when doing sequence summary, used in the models
+            :class:`~transformers.GPT2DoubleHeadsModel` and :class:`~transformers.TFGPT2DoubleHeadsModel`.
+
+            The dropout ratio to be used after the projection and activation.
 
     Example::
 

src/transformers/configuration_longformer.py

@@ -33,6 +33,10 @@ LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP = {
 
 class LongformerConfig(RobertaConfig):
     r"""
+    This is the configuration class to store the configuration of a :class:`~transformers.LongformerModel` or a
+    :class:`~transformers.TFLongformerModel`. It is used to instantiate a Longformer model according to the specified
+    arguments, defining the model architecture.
+
     This is the configuration class to store the configuration of a :class:`~transformers.LongformerModel`.
     It is used to instantiate an Longformer model according to the specified arguments, defining the model
     architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of
@@ -42,8 +46,8 @@ class LongformerConfig(RobertaConfig):
     It reuses the same defaults. Please check the parent class for more information.
 
     Args:
-        attention_window (:obj:`int` or :obj:`List[int]`, optional, defaults to 512):
-            Size of an attention window around each token. If :obj:`int`, use the same size for all layers.
+        attention_window (:obj:`int` or :obj:`List[int]`, `optional`, defaults to 512):
+            Size of an attention window around each token. If an :obj:`int`, use the same size for all layers.
             To specify a different window size for each layer, use a :obj:`List[int]` where
             ``len(attention_window) == num_hidden_layers``.
 

src/transformers/configuration_lxmert.py

@@ -29,83 +29,91 @@ LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
 
 class LxmertConfig(PretrainedConfig):
     r"""
-    This is the configuration class to store the configuration of a :class:`~transformers.BertModel`.
-    It is used to instantiate an Lxmert model according to the specified arguments, defining the model
-    architecture.
+    This is the configuration class to store the configuration of a :class:`~transformers.LxmertModel` or a
+    :class:`~transformers.TFLxmertModel`. It is used to instantiate a LXMERT model according to the specified
+    arguments, defining the model architecture.
+
+    Configuration objects inherit from  :class:`~transformers.PretrainedConfig` and can be used
+    to control the model outputs. Read the documentation from  :class:`~transformers.PretrainedConfig`
+    for more information.
 
 
     Args:
-        vocab_size (:obj:`int`, optional, defaults to 30522):
-            Vocabulary size of the BERT model. Defines the different tokens that
-            can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.BertModel`.
-        hidden_size (:obj:`int`, optional, defaults to 768):
+        vocab_size (:obj:`int`, `optional`, defaults to 30522):
+            Vocabulary size of the LXMERT model. Defines the number of different tokens that can be represented by the
+            :obj:`inputs_ids` passed when calling :class:`~transformers.LxmertModel` or
+            :class:`~transformers.TFLxmertModel`.
+        hidden_size (:obj:`int`, `optional`, defaults to 768):
             Dimensionality of the encoder layers and the pooler layer.
-        r_layers (:obj:`int`, optional, defaults to 5):
+        r_layers (:obj:`int`, `optional`, defaults to 5):
             Number of hidden layers in the Transformer visual encoder.
-        l_layers (:obj:`int`, optional, defaults to 9):
+        l_layers (:obj:`int`, `optional`, defaults to 9):
             Number of hidden layers in the Transformer language encoder.
-        x_layers (:obj:`int`, optional, defaults to 5):
+        x_layers (:obj:`int`, `optional`, defaults to 5):
             Number of hidden layers in the Transformer cross modality encoder.
-        num_attention_heads (:obj:`int`, optional, defaults to 5):
+        num_attention_heads (:obj:`int`, `optional`, defaults to 5):
             Number of attention heads for each attention layer in the Transformer encoder.
-        intermediate_size (:obj:`int`, optional, defaults to 3072):
-            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
-        hidden_act (:obj:`str` or :obj:`function`, optional, defaults to "gelu"):
+        intermediate_size (:obj:`int`, `optional`, defaults to 3072):
+            Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
+        hidden_act (:obj:`str` or :obj:`Callable`, `optional`, defaults to :obj:`"gelu"`):
             The non-linear activation function (function or string) in the encoder and pooler.
-            If string, "gelu", "relu", "swish" and "gelu_new" are supported.
-        hidden_dropout_prob (:obj:`float`, optional, defaults to 0.1):
+            If string, :obj:`"gelu"`, :obj:`"relu"`, :obj:`"swish"` and :obj:`"gelu_new"` are supported.
+        hidden_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
             The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
-        attention_probs_dropout_prob (:obj:`float`, optional, defaults to 0.1):
+        attention_probs_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
             The dropout ratio for the attention probabilities.
-        max_position_embeddings (:obj:`int`, optional, defaults to 512):
+        max_position_embeddings (:obj:`int`, `optional`, defaults to 512):
             The maximum sequence length that this model might ever be used with.
             Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
-        type_vocab_size (:obj:`int`, optional, defaults to 2):
+        type_vocab_size (:obj:`int`, `optional`, defaults to 2):
             The vocabulary size of the `token_type_ids` passed into :class:`~transformers.BertModel`.
-        initializer_range (:obj:`float`, optional, defaults to 0.02):
+        initializer_range (:obj:`float`, `optional`, defaults to 0.02):
             The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        layer_norm_eps (:obj:`float`, optional, defaults to 1e-12):
+        layer_norm_eps (:obj:`float`, `optional`, defaults to 1e-12):
             The epsilon used by the layer normalization layers.
-        visual_feat_dim (:obj:`int`, optional, defaults to 2048):
+        visual_feat_dim (:obj:`int`, `optional`, defaults to 2048):
             This represents the last dimension of the pooled-object features used as input for the model,
             representing the size of each object feature itself.
-        visual_pos_dim (:obj:`int`, optional, defaults to 4):
+        visual_pos_dim (:obj:`int`, `optional`, defaults to 4):
             This represents the number of spacial features that are mixed into the visual features.
             The default is set to 4 because most commonly this will represent the location of a bounding box.
-            i.e. (x, y, width, height)
-        visual_loss_normalizer (:obj:`float`, optional, defaults to 1/15):
+            i.e., (x, y, width, height)
+        visual_loss_normalizer (:obj:`float`, `optional`, defaults to 1/15):
             This represents the scaling factor in which each visual loss is multiplied by if during pretraining,
             one decided to train with multiple vision-based loss objectives.
-        num_qa_labels (:obj:`int`, optional, defaults to 9500):
-            This represents the total number of different question answering (QA) labels there are. If using more than one dataset with QA,
-            the user will need to account for the total number of labels that all of the datasets have in total.
-        num_object_labels (:obj:`int`, optional, defaults to 1600):
-            This represents the total number of semantically unique objects that lxmert will be able to classify a pooled-object feature
-            as belonging too.
-        num_attr_labels (:obj:`int`, optional, defaults to 400):
-            This represents the total number of semantically unique attributes that lxmert will be able to classify a pooled-object feature
-            as possessing.
-        task_matched (:obj:`bool`, optional, defaults to :obj:`True`):
-            This task is used for sentence-image matching. If the sentence correctly describes the image the label will be 1.
-            If the sentence does not correctly describe the image, the label will be 0.
-        task_mask_lm (:obj:`bool`, optional, defaults to :obj:`True`):
-            This task is the defacto masked langauge modeling used in pretraining models such as BERT.
-        task_obj_predict (:obj:`bool`, optional, defaults to :obj:`True`):
-            This task is set to true if the user would like to perform one of the following loss objectives:
-            object predicition, atrribute predicition, feature regression
-        task_qa (:obj:`bool`, optional, defaults to :obj:`True`):
-            This task specifies whether or not Lxmert will calculate the question-asnwering loss objective
-        visual_obj_loss (:obj:`bool`, optional, defaults to :obj:`True`):
-            This task specifies whether or not Lxmert will calculate the object-prediction loss objective
-        visual_attr_loss (:obj:`bool`, optional, defaults to :obj:`True`):
-            This task specifies whether or not Lxmert will calculate the attribute-prediction loss objective
-        visual_feat_loss (:obj:`bool`, optional, defaults to :obj:`True`):
-            This task specifies whether or not Lxmert will calculate the feature-regression loss objective
-        output_attentions (:obj:`bool`, optional, defaults to :obj:`False`):
-                if True, the vision, langauge, and cross-modality layers will be returned
-        output_hidden_states (:obj:`bool`, optional, defaults to :obj:`False`):
-                if True, final cross-modality hidden states for language and vision features will be returned
-
+        num_qa_labels (:obj:`int`, `optional`, defaults to 9500):
+            This represents the total number of different question answering (QA) labels there are. If using more than
+            one dataset with QA, the user will need to account for the total number of labels that all of the datasets
+            have in total.
+        num_object_labels (:obj:`int`, `optional`, defaults to 1600):
+            This represents the total number of semantically unique objects that lxmert will be able to classify a
+            pooled-object feature as belonging too.
+        num_attr_labels (:obj:`int`, `optional`, defaults to 400):
+            This represents the total number of semantically unique attributes that lxmert will be able to classify a
+            pooled-object feature as possessing.
+        task_matched (:obj:`bool`, `optional`, defaults to :obj:`True`):
+            This task is used for sentence-image matching. If the sentence correctly describes the image the label
+            will be 1. If the sentence does not correctly describe the image, the label will be 0.
+        task_mask_lm (:obj:`bool`, `optional`, defaults to :obj:`True`):
+            Whether or not to add masked language modeling (as used in pretraining models such as BERT) to the loss
+            objective.
+        task_obj_predict (:obj:`bool`, `optional`, defaults to :obj:`True`):
+            Whether or not to add object predicition, attribute predicition and feature regression to the loss
+            objective.
+        task_qa (:obj:`bool`, `optional`, defaults to :obj:`True`):
+            Whether or not to add the question-asnwering loss to the objective
+        visual_obj_loss (:obj:`bool`, `optional`, defaults to :obj:`True`):
+            Whether or not to calculate the object-prediction loss objective
+        visual_attr_loss (:obj:`bool`, `optional`, defaults to :obj:`True`):
+            Whether or not to calculate the attribute-prediction loss objective
+        visual_feat_loss (:obj:`bool`, `optional`, defaults to :obj:`True`):
+            Whether or not to calculate the feature-regression loss objective
+        output_attentions (:obj:`bool`, `optional`, defaults to :obj:`False`):
+            Whether or not the model should return the attentions from the vision, langauge, and cross-modality
+            layers should be returned.
+        output_hidden_states (:obj:`bool`, `optional`, defaults to :obj:`False`):
+            Whether or not the model should return the hidden states from the vision, langauge, and cross-modality
+            layers should be returned.
     """
 
     model_type = "lxmert"

src/transformers/configuration_mmbt.py

@@ -22,15 +22,16 @@ logger = logging.get_logger(__name__)
 
 
 class MMBTConfig(object):
-    """Configuration class to store the configuration of a `MMBT Model`.
+    """
+    This is the configuration class to store the configuration of a :class:`~transformers.MMBTModel`. It is used to
+    instantiate a MMBT model according to the specified arguments, defining the model architecture.
 
     Args:
-        config (:obj:`~transformers.PreTrainedConfig`):
-            Config of the underlying Transformer models. Its values are
-            copied over to use a single config.
-        num_labels (:obj:`int` or :obj:`None`, optional, defaults to `None`):
+        config (:class:`~transformers.PreTrainedConfig`):
+            Config of the underlying Transformer models. Its values are copied over to use a single config.
+        num_labels (:obj:`int`, `optional`):
             Size of final Linear layer for classification.
-        modal_hidden_size (:obj:`int`, optional, defautls to 2048):
+        modal_hidden_size (:obj:`int`, `optional`, defautls to 2048):
             Embedding dimension of the non-text modality encoder.
     """
 

src/transformers/configuration_mobilebert.py

@@ -25,9 +25,9 @@ MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
 
 class MobileBertConfig(PretrainedConfig):
     r"""
-    This is the configuration class to store the configuration of a :class:`~transformers.MobileBertModel`.
-    It is used to instantiate a MobileBERT model according to the specified arguments, defining the model
-    architecture.
+    This is the configuration class to store the configuration of a :class:`~transformers.MobileBertModel` or a
+    :class:`~transformers.TFMobileBertModel`. It is used to instantiate a MobileBERT model according to the specified
+    arguments, defining the model architecture.
 
     Configuration objects inherit from  :class:`~transformers.PretrainedConfig` and can be used
     to control the model outputs. Read the documentation from  :class:`~transformers.PretrainedConfig`
@@ -35,54 +35,56 @@ class MobileBertConfig(PretrainedConfig):
 
 
     Args:
-        vocab_size (:obj:`int`, optional, defaults to 30522):
-            Vocabulary size of the MobileBERT model. Defines the different tokens that
-            can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.MobileBertModel`.
-        hidden_size (:obj:`int`, optional, defaults to 512):
+        vocab_size (:obj:`int`, `optional`, defaults to 30522):
+            Vocabulary size of the MobileBERT model. Defines the number of different tokens that can be represented by
+            the :obj:`inputs_ids` passed when calling :class:`~transformers.MobileBertModel` or
+            :class:`~transformers.TFMobileBertModel`.
+        hidden_size (:obj:`int`, `optional`, defaults to 512):
             Dimensionality of the encoder layers and the pooler layer.
-        num_hidden_layers (:obj:`int`, optional, defaults to 24):
+        num_hidden_layers (:obj:`int`, `optional`, defaults to 24):
             Number of hidden layers in the Transformer encoder.
-        num_attention_heads (:obj:`int`, optional, defaults to 4):
+        num_attention_heads (:obj:`int`, `optional`, defaults to 4):
             Number of attention heads for each attention layer in the Transformer encoder.
-        intermediate_size (:obj:`int`, optional, defaults to 512):
-            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
-        hidden_act (:obj:`str` or :obj:`function`, optional, defaults to "relu"):
+        intermediate_size (:obj:`int`, `optional`, defaults to 512):
+            Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
+        hidden_act (:obj:`str` or :obj:`function`, `optional`, defaults to :obj:`"relu"`):
             The non-linear activation function (function or string) in the encoder and pooler.
-            If string, "gelu", "relu", "swish" and "gelu_new" are supported.
-        hidden_dropout_prob (:obj:`float`, optional, defaults to 0.0):
+            If string, :obj:`"gelu"`, :obj:`"relu"`, :obj:`"swish"` and :obj:`"gelu_new"` are supported.
+        hidden_dropout_prob (:obj:`float`, `optional`, defaults to 0.0):
             The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
-        attention_probs_dropout_prob (:obj:`float`, optional, defaults to 0.1):
+        attention_probs_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
             The dropout ratio for the attention probabilities.
-        max_position_embeddings (:obj:`int`, optional, defaults to 512):
+        max_position_embeddings (:obj:`int`, `optional`, defaults to 512):
             The maximum sequence length that this model might ever be used with.
             Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
-        type_vocab_size (:obj:`int`, optional, defaults to 2):
-            The vocabulary size of the `token_type_ids` passed into :class:`~transformers.MobileBertModel`.
-        initializer_range (:obj:`float`, optional, defaults to 0.02):
+        type_vocab_size (:obj:`int`, `optional`, defaults to 2):
+            The vocabulary size of the :obj:`token_type_ids` passed when calling :class:`~transformers.MobileBertModel`
+            or :class:`~transformers.TFMobileBertModel`.
+        initializer_range (:obj:`float`, `optional`, defaults to 0.02):
             The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        layer_norm_eps (:obj:`float`, optional, defaults to 1e-12):
+        layer_norm_eps (:obj:`float`, `optional`, defaults to 1e-12):
             The epsilon used by the layer normalization layers.
 
-        pad_token_id (:obj:`int`, optional, defaults to 0):
+        pad_token_id (:obj:`int`, `optional`, defaults to 0):
             The ID of the token in the word embedding to use as padding.
-        embedding_size (:obj:`int`, optional, defaults to 128):
+        embedding_size (:obj:`int`, `optional`, defaults to 128):
             The dimension of the word embedding vectors.
-        trigram_input (:obj:`bool`, optional, defaults to :obj:`True`):
+        trigram_input (:obj:`bool`, `optional`, defaults to :obj:`True`):
             Use a convolution of trigram as input.
-        use_bottleneck (:obj:`bool`, optional, defaults to :obj:`True`):
+        use_bottleneck (:obj:`bool`, `optional`, defaults to :obj:`True`):
             Whether to use bottleneck in BERT.
-        intra_bottleneck_size (:obj:`int`, optional, defaults to 128):
+        intra_bottleneck_size (:obj:`int`, `optional`, defaults to 128):
             Size of bottleneck layer output.
-        use_bottleneck_attention (:obj:`bool`, optional, defaults to :obj:`False`):
+        use_bottleneck_attention (:obj:`bool`, `optional`, defaults to :obj:`False`):
             Whether to use attention inputs from the bottleneck transformation.
-        key_query_shared_bottleneck (:obj:`bool`, optional, defaults to :obj:`True`):
+        key_query_shared_bottleneck (:obj:`bool`, `optional`, defaults to :obj:`True`):
             Whether to use the same linear transformation for query&key in the bottleneck.
-        num_feedforward_networks (:obj:`int`, optional, defaults to 4):
+        num_feedforward_networks (:obj:`int`, `optional`, defaults to 4):
             Number of FFNs in a block.
-        normalization_type (:obj:`str`, optional, defaults to "no_norm"):
-            The normalization type in BERT.
+        normalization_type (:obj:`str`, `optional`, defaults to :obj:`"no_norm"`):
+            The normalization type in MobileBERT.
 
-    Example:
+    Examples:
 
         >>> from transformers import MobileBertModel, MobileBertConfig
 

src/transformers/configuration_openai.py

@@ -28,73 +28,79 @@ OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
 
 class OpenAIGPTConfig(PretrainedConfig):
     """
-    This is the configuration class to store the configuration of a :class:`~transformers.OpenAIGPTModel`.
-    It is used to instantiate an GPT model according to the specified arguments, defining the model
-    architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of
-    the `GPT <https://huggingface.co/openai-gpt>`__ architecture from OpenAI.
+    This is the configuration class to store the configuration of a :class:`~transformers.OpenAIGPTModel` or a
+    :class:`~transformers.TFOpenAIGPTModel`. It is used to instantiate a GPT model according to the specified
+    arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar
+    configuration to that of the `GPT <https://huggingface.co/openai-gpt>`__ architecture from OpenAI.
 
     Configuration objects inherit from  :class:`~transformers.PretrainedConfig` and can be used
     to control the model outputs. Read the documentation from  :class:`~transformers.PretrainedConfig`
     for more information.
 
     Args:
-        vocab_size (:obj:`int`, optional, defaults to 40478):
-            Vocabulary size of the GPT model. Defines the different tokens that
-            can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.CTRLModel`.
-        n_positions (:obj:`int`, optional, defaults to 512):
+        vocab_size (:obj:`int`, `optional`, defaults to 40478):
+            Vocabulary size of the GPT-2 model. Defines the number of different tokens that can be represented by the
+            :obj:`inputs_ids` passed when calling :class:`~transformers.OpenAIGPTModel` or
+            :class:`~transformers.TFOpenAIGPTModel`.
+        n_positions (:obj:`int`, `optional`, defaults to 512):
             The maximum sequence length that this model might ever be used with.
             Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
-        n_ctx (:obj:`int`, optional, defaults to 512):
+        n_ctx (:obj:`int`, `optional`, defaults to 512):
             Dimensionality of the causal mask (usually same as n_positions).
-        n_embd (:obj:`int`, optional, defaults to 768):
+        n_embd (:obj:`int`, `optional`, defaults to 768):
             Dimensionality of the embeddings and hidden states.
-        n_layer (:obj:`int`, optional, defaults to 12):
+        n_layer (:obj:`int`, `optional`, defaults to 12):
             Number of hidden layers in the Transformer encoder.
-        n_head (:obj:`int`, optional, defaults to 12):
+        n_head (:obj:`int`, `optional`, defaults to 12):
             Number of attention heads for each attention layer in the Transformer encoder.
-        afn (:obj:`str` or :obj:`function`, optional, defaults to "gelu"):
+        afn (:obj:`str` or :obj:`Callable`, `optional`, defaults to :obj:`"gelu"`):
             The non-linear activation function (function or string) in the encoder and pooler.
-            If string, "gelu", "relu", "swish" and "gelu_new" are supported.
-        resid_pdrop (:obj:`float`, optional, defaults to 0.1):
+            If string, :obj:`"gelu"`, :obj:`"relu"`, :obj:`"swish"` and :obj:`"gelu_new"` are supported.
+        resid_pdrop (:obj:`float`, `optional`, defaults to 0.1):
             The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
-        embd_pdrop (:obj:`int`, optional, defaults to 0.1):
+        embd_pdrop (:obj:`int`, `optional`, defaults to 0.1):
             The dropout ratio for the embeddings.
-        attn_pdrop (:obj:`float`, optional, defaults to 0.1):
+        attn_pdrop (:obj:`float`, `optional`, defaults to 0.1):
             The dropout ratio for the attention.
-        layer_norm_epsilon (:obj:`float`, optional, defaults to 1e-5):
+        layer_norm_epsilon (:obj:`float`, `optional`, defaults to 1e-5):
             The epsilon to use in the layer normalization layers
-        initializer_range (:obj:`float`, optional, defaults to 0.02):
+        initializer_range (:obj:`float`, `optional`, defaults to 0.02):
             The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        predict_special_tokens (:obj:`boolean`, optional, defaults to :obj:`True`):
-            Whether special tokens should be predicted when the model is has a language modeling head.
-        summary_type (:obj:`string`, optional, defaults to "cls_index"):
-            Argument used when doing sequence summary. Used in for the multiple choice head in
-            :class:`~transformers.OpenAIGPTDoubleHeadsModel`.
-            Is one of the following options:
+        predict_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`True`):
+            Whether or not special tokens should be predicted when the model has a language modeling head.
+        summary_type (:obj:`str`, `optional`, defaults to :obj:`"cls_index"`):
+            Argument used when doing sequence summary, used in the models
+            :class:`~transformers.OpenAIGPTDoubleHeadsModel` and :class:`~transformers.OpenAIGPTDoubleHeadsModel`.
 
-            - 'last' => take the last token hidden state (like XLNet)
-            - 'first' => take the first token hidden state (like Bert)
-            - 'mean' => take the mean of all tokens hidden states
-            - 'cls_index' => supply a Tensor of classification token position (GPT/GPT-2)
-            - 'attn' => Not implemented now, use multi-head attention
-        summary_use_proj (:obj:`boolean`, optional, defaults to :obj:`True`):
-            Argument used when doing sequence summary. Used in for the multiple choice head in
-            :class:`~transformers.OpenAIGPTDoubleHeadsModel`.
-            Add a projection after the vector extraction
-        summary_activation (:obj:`string` or :obj:`None`, optional):
-            Argument used when doing sequence summary. Used in for the multiple choice head in
-            :class:`~transformers.OpenAIGPTDoubleHeadsModel`.
-            'tanh' => add a tanh activation to the output, Other => no activation.
-        summary_proj_to_labels (:obj:`boolean`, optional, defaults to :obj:`True`):
-            Argument used when doing sequence summary. Used in for the multiple choice head in
-            :class:`~transformers.OpenAIGPTDoubleHeadsModel`.
-            If True, the projection outputs to config.num_labels classes (otherwise to hidden_size). Default: False.
-        summary_first_dropout (:obj:`float`, optional, defaults to 0.1):
-            Argument used when doing sequence summary. Used in for the multiple choice head in
-            :class:`~transformers.OpenAIGPTDoubleHeadsModel`.
-            Add a dropout before the projection and activation
+            Has to be one of the following options:
 
-    Example::
+                - :obj:`"last"`: Take the last token hidden state (like XLNet).
+                - :obj:`"first"`: Take the first token hidden state (like BERT).
+                - :obj:`"mean"`: Take the mean of all tokens hidden states.
+                - :obj:`"cls_index"`: Supply a Tensor of classification token position (like GPT/GPT-2).
+                - :obj:`"attn"`: Not implemented now, use multi-head attention.
+        summary_use_proj (:obj:`bool`, `optional`, defaults to :obj:`True`):
+            Argument used when doing sequence summary, used in the models
+            :class:`~transformers.OpenAIGPTDoubleHeadsModel` and :class:`~transformers.OpenAIGPTDoubleHeadsModel`.
+
+            Whether or not to add a projection after the vector extraction.
+        summary_activation (:obj:`str`, `optional`):
+            Argument used when doing sequence summary, used in the models
+            :class:`~transformers.OpenAIGPTDoubleHeadsModel` and :class:`~transformers.OpenAIGPTDoubleHeadsModel`.
+
+            Pass :obj:`"tanh"` for a tanh activation to the output, any other value will result in no activation.
+        summary_proj_to_labels (:obj:`bool`, `optional`, defaults to :obj:`True`):
+            Argument used when doing sequence summary, used in the models
+            :class:`~transformers.OpenAIGPTDoubleHeadsModel` and :class:`~transformers.OpenAIGPTDoubleHeadsModel`.
+
+            Whether the projection outputs should have :obj:`config.num_labels` or :obj:`config.hidden_size` classes.
+        summary_first_dropout (:obj:`float`, `optional`, defaults to 0.1):
+            Argument used when doing sequence summary, used in the models
+            :class:`~transformers.OpenAIGPTDoubleHeadsModel` and :class:`~transformers.OpenAIGPTDoubleHeadsModel`.
+
+            The dropout ratio to be used after the projection and activation.
+
+    Examples::
 
         >>> from transformers import OpenAIGPTConfig, OpenAIGPTModel
 

src/transformers/configuration_rag.py

@@ -38,35 +38,39 @@ RAG_CONFIG_DOC = r"""
         retrieval_vector_size (:obj:`int`, `optional`, defaults to 768):
             Dimensionality of the document embeddings indexed by :class:`~transformers.RagRetriever`.
         retrieval_batch_size (:obj:`int`, `optional`, defaults to 8):
-            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index excapsulated :class:`~transformers.RagRetriever`.
+            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index excapsulated
+            :class:`~transformers.RagRetriever`.
         dataset (:obj:`str`, `optional`, defaults to :obj:`"wiki_dpr"`):
-            A datatset identifier of the indexed dataset on HuggingFace AWS bucket (list all available datasets and ids using :obj:`datasets.list_datasets()`).
-        dataset_split (:obj:`str`, `optional`, defaults to :obj:`train`)
-            Which split of the ``dataset`` to load.
-        index_name (:obj:`str`, `optional`, defaults to :obj:`compressed`)
-            The index_name of the index associated with the :obj:`dataset`. One can choose between :obj:`legacy`, :obj:`exact` and :obj:`compressed`.
+            A dataset identifier of the indexed dataset on HuggingFace AWS bucket (list all available datasets and
+            ids using :obj:`datasets.list_datasets()`).
+        dataset_split (:obj:`str`, `optional`, defaults to :obj:`"train"`)
+            Which split of the :obj:`dataset` to load.
+        index_name (:obj:`str`, `optional`, defaults to :obj:`"compressed"`)
+            The index name of the index associated with the :obj:`dataset`. One can choose between :obj:`"legacy"`,
+            :obj:`"exact"` and :obj:`"compressed"`.
         index_path (:obj:`str`, `optional`)
             The path to the serialized faiss index on disk.
         passages_path: (:obj:`str`, `optional`):
-            A path to text passages compatible with the faiss index. Required if using :class:`~transformers.retrieval_rag.LegacyIndex`
+            A path to text passages compatible with the faiss index. Required if using
+            :class:`~transformers.retrieval_rag.LegacyIndex`
         use_dummy_dataset (:obj:`bool`, `optional`, defaults to ``False``)
             Whether to load a "dummy" variant of the dataset specified by :obj:`dataset`.
         label_smoothing (:obj:`float`, `optional`, defaults to 0.0):
-            Only relevant if ``return_loss`` is set to :obj:`True`. Controls the ``epsilon`` parameter value for label smoothing in the loss calculation.
-            If set to ``0.0``, no label smoothing is performed.
+            Only relevant if ``return_loss`` is set to :obj:`True`. Controls the ``epsilon`` parameter value for label
+            smoothing in the loss calculation. If set to 0, no label smoothing is performed.
         do_marginalize (:obj:`bool`, `optional`, defaults to :obj:`False`):
             If :obj:`True`, the logits are marginalized over all documents
             by making use of ``torch.nn.functional.log_softmax``.
         reduce_loss (:obj:`bool`, `optional`, defaults to :obj:`False`):
-            If :obj:`True`, the NLL loss is reduced using the ``torch.Tensor.sum`` operation.
+            Whether or not to reduce the NLL loss using the ``torch.Tensor.sum`` operation.
         do_deduplication (:obj:`bool`, `optional`, defaults to :obj:`True`):
-            Controls whether we want to deduplicate the generations from different context documents for a given input.
+            Whether or not to deduplicate the generations from different context documents for a given input.
             Has to be set to :obj:`False` if used while training with distributed backend.
         exclude_bos_score (:obj:`bool`, `optional`, defaults to :obj:`False`):
-            If :obj:`True`, the score of the BOS token is disregarded when computing
-            the loss.
+            Whether or not to disregard the BOS token when computing the loss.
         output_retrieved(:obj:`bool`, `optional`, defaults to :obj:`False`):
-            If set to ``True``, :obj:`retrieved_doc_embeds`, :obj:`retrieved_doc_ids`, :obj:`context_input_ids` and :obj:`context_attention_mask` are returned. See returned tensors for more detail.
+            If set to ``True``, :obj:`retrieved_doc_embeds`, :obj:`retrieved_doc_ids`, :obj:`context_input_ids` and
+            :obj:`context_attention_mask` are returned. See returned tensors for more detail.
 """
 
 

src/transformers/configuration_reformer.py

@@ -29,96 +29,120 @@ REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP = {
 
 class ReformerConfig(PretrainedConfig):
     r"""
-    This is the configuration class to store the configuration of a :class:`~transformers.ReformerModel`.
-    It is used to instantiate an Reformer model according to the specified arguments, defining the model
-    architecture.
+    This is the configuration class to store the configuration of a :class:`~transformers.ReformerModel`. It is used to
+    instantiate a Reformer model according to the specified arguments, defining the model architecture.
 
     Configuration objects inherit from  :class:`~transformers.PretrainedConfig` and can be used
     to control the model outputs. Read the documentation from  :class:`~transformers.PretrainedConfig`
     for more information.
 
     Args:
-        attention_head_size (:obj:`int`, optional, defaults to 64):
+        attention_head_size (:obj:`int`, `optional`, defaults to 64):
             Dimensionality of the projected key, query and value vectors
-        attn_layers (:obj:`list(str)`, optional, defaults to ["local", "lsh", "local", "lsh", "local", "lsh"]):
+        attn_layers (:obj:`List[str]`, `optional`, defaults to :obj:`["local", "lsh", "local", "lsh", "local", "lsh"]`):
             List of attention layer types in ascending order. It can be chosen between a
-            LSHSelfAttention layer ("lsh") and a LocalSelfAttention layer ("local").
-            For more information on LSHSelfAttention layer, see `LSH Self Attention <reformer.html#lsh-self-attention>`__ .
-            For more information on LocalSelfAttention layer, see `Local Self Attention <reformer.html#local-sensitive-hashing-self-attention>`__ .
-        axial_pos_embds (:obj:`bool`, optional, defaults to :obj:`True`):
-            If `True` use axial position embeddings. For more information on how axial position embeddings work, see `Axial Position Encodings <reformer.html#axial-positional-encodings>`__
-        axial_norm_std (:obj:`float`, optional, defaluts to 1.0):
-            The standard deviation of the normal_initializer for initializing the weight matrices of the axial positional encodings.
-        axial_pos_shape (:obj:`list(int)`, optional, defaults to `[64, 64]`):
-            The position dims of the axial position encodings.
-            During training the product of the position dims has to equal the sequence length.
-            For more information on how axial position embeddings work, see `Axial Position Encodings <reformer.html#axial-positional-encodings>`__.
-        axial_pos_embds_dim (:obj:`list(int)`, optional, defaults to `[64, 192]`):
-            The embedding dims of the axial position encodings.
-            The sum of the embedding dims has to equal the hidden size.
-            For more information on how axial position embeddings work, see `Axial Position Encodings <reformer.html#axial-positional-encodings>`__.
-        chunk_size_lm_head (:obj:`int`, optional, defaults to 0):
+            LSHSelfAttention layer (:obj:`"lsh"`) and a LocalSelfAttention layer (:obj:`"local"`).
+
+            For more information on LSHSelfAttention layer, see `LSH Self Attention
+            <reformer.html#lsh-self-attention>`__. For more information on LocalSelfAttention layer, see `Local Self
+            Attention <reformer.html#local-sensitive-hashing-self-attention>`__.
+        axial_pos_embds (:obj:`bool`, `optional`, defaults to :obj:`True`):
+            Whether or not to use axial position embeddings. For more information on how axial position embeddings
+            work, see `Axial Position Encodings <reformer.html#axial-positional-encodings>`__.
+        axial_norm_std (:obj:`float`, `optional`, defaults to 1.0):
+            The standard deviation of the normal_initializer for initializing the weight matrices of the axial
+            positional encodings.
+        axial_pos_shape (:obj:`List[int]`, `optional`, defaults to :obj:`[64, 64]`):
+            The position dims of the axial position encodings. During training the product of the position dims has to
+            be equal to the sequence length.
+
+            For more information on how axial position embeddings work, see `Axial Position Encodings
+            <reformer.html#axial-positional-encodings>`__.
+        axial_pos_embds_dim (:obj:`List[int]`, `optional`, defaults to :obj:`[64, 192]`):
+            The embedding dims of the axial position encodings. The sum of the embedding dims has to be equal to the
+            hidden size.
+
+            For more information on how axial position embeddings work, see `Axial Position Encodings
+            <reformer.html#axial-positional-encodings>`__.
+        chunk_size_lm_head (:obj:`int`, `optional`, defaults to 0):
             The chunk size of the final language model feed forward head layer.
             A chunk size of 0 means that the feed forward layer is not chunked.
             A chunk size of n means that the feed forward layer processes n < sequence_length embeddings at a time.
-            For more information on feed forward chunking, see `How does Feed Forward Chunking work? <../glossary.html#feed-forward-chunking>`__ .
-        eos_token_id (:obj:`int`, optional, defaults to 2):
-            The token id for the <EOS> token.
-        feed_forward_size (:obj:`int`, optional, defaults to 512):
-            Dimensionality of the "feed_forward" (i.e., feed-forward) layer in the residual attention block.
-        hash_seed (:obj:`int`, optional, defaults to `None`):
-            Seed that can be used to make local sensitive hashing in LSHSelfAttention deterministic. This should only be set for testing purposed. For evaluation and training purposes `hash_seed` should be set to `None` to ensure fully random rotations in local sensitive hashing scheme.
-        hidden_act (:obj:`str` or :obj:`function`, optional, defaults to "relu"):
-            The non-linear activation function (function or string) in the feed forward layer in the residual attention block.
-            If string, "gelu", "relu", "swish", "gelu_new" and "gelu_fast" are supported.
-        hidden_dropout_prob (:obj:`float`, optional, defaults to 0.05):
+
+            For more information on feed forward chunking, see `How does Feed Forward Chunking work?
+            <../glossary.html#feed-forward-chunking>`__.
+        eos_token_id (:obj:`int`, `optional`, defaults to 2):
+            The token id for the end-of-sentence token.
+        feed_forward_size (:obj:`int`, `optional`, defaults to 512):
+            Dimensionality of the feed_forward layer in the residual attention block.
+        hash_seed (:obj:`int`, `optional`):
+            Seed that can be used to make local sensitive hashing in :obj:`LSHSelfAttention` deterministic. This should
+            only be set for testing purposed. For evaluation and training purposes :obj:`hash_seed` should be left as
+            :obj:`None` to ensure fully random rotations in local sensitive hashing scheme.
+        hidden_act (:obj:`str` or :obj:`Callable`, `optional`, defaults to :obj:`"relu"`):
+            The non-linear activation function (function or string) in the feed forward layer in the residual attention
+            block.
+            If string, :obj:`"gelu"`, :obj:`"relu"`, :obj:`"swish"` and :obj:`"gelu_new"` are supported.
+        hidden_dropout_prob (:obj:`float`, `optional`, defaults to 0.05):
             The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
-        hidden_size (:obj:`int`, optional, defaults to 256):
+        hidden_size (:obj:`int`, `optional`, defaults to 256):
             Dimensionality of the output hidden states of the residual attention blocks.
-        initializer_range (:obj:`float`, optional, defaults to 0.02):
+        initializer_range (:obj:`float`, `optional`, defaults to 0.02):
             The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        is_decoder (:obj:`bool`, optional, defaults to :obj:`False`):
-            If `is_decoder` is True, a causal mask is used in addition to `attention_mask`.
-            When using the Reformer for causal language modeling, `is_decoder` is set to `True`.
-        layer_norm_eps (:obj:`float`, optional, defaults to 1e-12):
+        is_decoder (:obj:`bool`, `optional`, defaults to :obj:`False`):
+            Whether ot not to use a causal mask in addition to the :obj:`attention_mask` passed to
+            :class:`~transformers.ReformerModel`. When using the Reformer for causal language modeling, this argument
+            should be set to :obj:`True`.
+        layer_norm_eps (:obj:`float`, `optional`, defaults to 1e-12):
             The epsilon used by the layer normalization layers.
-        local_chunk_length (:obj:`int`, optional, defaults to 64):
-            Length of chunk which attends to itself in LocalSelfAttention. Chunking reduces memory complexity from sequence length x sequence length (self attention) to chunk length x chunk length x sequence length / chunk length (chunked self attention).
-        local_num_chunks_before (:obj:`int`, optional, defaults to 1):
-            Number of previous neighbouring chunks to attend to in LocalSelfAttention layer to itself.
-        local_num_chunks_after (:obj:`int`, optional, defaults to 0):
-            Number of following neighbouring chunks to attend to in LocalSelfAttention layer in addition to itself.
-        local_attention_probs_dropout_prob (:obj:`float`, optional, defaults to 0.1):
-            The dropout ratio for the attention probabilities in LocalSelfAttention.
-        lsh_attn_chunk_length (:obj:`int`, optional, defaults to 64):
-            Length of chunk which attends to itself in LSHSelfAttention. Chunking reduces memory complexity from sequence length x sequence length (self attention) to chunk length x chunk length x sequence length / chunk length (chunked self attention).
-        lsh_num_chunks_before (:obj:`int`, optional, defaults to 1):
-            Number of previous neighbouring chunks to attend to in LSHSelfAttention layer to itself.
-        lsh_num_chunks_after (:obj:`int`, optional, defaults to 0):
-            Number of following neighbouring chunks to attend to in LSHSelfAttention layer to itself.
-        lsh_attention_probs_dropout_prob (:obj:`float`, optional, defaults to 0.1):
-            The dropout ratio for the attention probabilities in LSHSelfAttention.
-        max_position_embeddings (:obj:`int`, optional, defaults to 4096):
+        local_chunk_length (:obj:`int`, `optional`, defaults to 64):
+            Length of chunk which attends to itself in :obj:`LocalSelfAttention`. Chunking reduces memory complexity
+            from sequence length x sequence length (self attention) to
+            chunk length x chunk length x sequence length / chunk length (chunked self attention).
+        local_num_chunks_before (:obj:`int`, `optional`, defaults to 1):
+            Number of previous neighbouring chunks to attend to in :obj:`LocalSelfAttention` layer to itself.
+        local_num_chunks_after (:obj:`int`, `optional`, defaults to 0):
+            Number of following neighbouring chunks to attend to in :obj:`LocalSelfAttention` layer in addition to
+            itself.
+        local_attention_probs_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
+            The dropout ratio for the attention probabilities in :obj:`LocalSelfAttention`.
+        lsh_attn_chunk_length (:obj:`int`, `optional`, defaults to 64):
+            Length of chunk which attends to itself in :obj:`LSHSelfAttention`. Chunking reduces memory complexity from
+            sequence length x sequence length (self attention) to
+            chunk length x chunk length x sequence length / chunk length (chunked self attention).
+        lsh_num_chunks_before (:obj:`int`, `optional`, defaults to 1):
+            Number of previous neighbouring chunks to attend to in :obj:`LSHSelfAttention` layer to itself.
+        lsh_num_chunks_after (:obj:`int`, `optional`, defaults to 0):
+            Number of following neighbouring chunks to attend to in :obj:`LSHSelfAttention` layer to itself.
+        lsh_attention_probs_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
+            The dropout ratio for the attention probabilities in :obj:`LSHSelfAttention`.
+        max_position_embeddings (:obj:`int`, `optional`, defaults to 4096):
             The maximum sequence length that this model might ever be used with.
             Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
-        num_attention_heads (:obj:`int`, optional, defaults to 12):
+        num_attention_heads (:obj:`int`, `optional`, defaults to 12):
             Number of attention heads for each attention layer in the Transformer encoder.
-        num_buckets (:obj:`int` or :obj:`list(int)`, optional, defaults to `None`):
-            Number of buckets, the key query vectors can be "hashed into" using the locality sensitive hashing scheme. Each query key vector is hashed into a hash in `1, ..., num_buckets`.
-            The number of buckets can also be factorized into a list for improved memory complexity. In this case, each query key vector is hashed into a hash in `1-1, 1-2, ..., num_buckets[0]-1, ..., num_buckets[0]-num_buckets[1]` if `num_buckets` is factorized into two factors.
-            The number of buckets (or the product the factors) should approximately equal sequence length / lsh_chunk_length. If `num_buckets` is set to `None`, a good value for `num_buckets` is calculated on the fly.
-        num_hashes (:obj:`int`, optional, defaults to 1):
-            Number of hashing rounds (e.g. number of random rotations) in Local Sensitive Hashing scheme.
-            The higher `num_hashes`, the more accurate the `LSHSelfAttention` becomes, but also the more memory and time intensive the hashing becomes.
-        pad_token_id (:obj:`int`, optional, defaults to 0):
-            The token id for the <PAD> token.
-        vocab_size (:obj:`int`, optional, defaults to 320):
-            Vocabulary size of the Reformer model. Defines the different tokens that
-            can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.ReformerModel`.
+        num_buckets (:obj:`int` or :obj:`List[int]`, `optional`):
+            Number of buckets, the key query vectors can be "hashed into" using the locality sensitive hashing scheme.
+            Each query key vector is hashed into a hash in :obj:`1, ..., num_buckets`.
+            The number of buckets can also be factorized into a list for improved memory complexity. In this case, each
+            query key vector is hashed into a hash in
+            :obj:`1-1, 1-2, ..., num_buckets[0]-1, ..., num_buckets[0]-num_buckets[1]` if :obj:`num_buckets` is
+            factorized into two factors.
+            The number of buckets (or the product the factors) should approximately equal
+            sequence length / lsh_chunk_length. If :obj:`num_buckets` not set, a good value is calculated on the fly.
+        num_hashes (:obj:`int`, `optional`, defaults to 1):
+            Number of hashing rounds (e.g., number of random rotations) in Local Sensitive Hashing scheme.
+            The higher :obj:`num_hashes`, the more accurate the :obj:`LSHSelfAttention` becomes, but also the more
+            memory and time intensive the hashing becomes.
+        pad_token_id (:obj:`int`, `optional`, defaults to 0):
+            The token id for the padding token.
+        vocab_size (:obj:`int`, `optional`, defaults to 320):\
+            Vocabulary size of the BERT model. Defines the number of different tokens that can be represented by the
+            :obj:`inputs_ids` passed when calling :class:`~transformers.ReformerModel`.
         tie_word_embeddings (:obj:`bool`, `optional`, defaults to :obj:`False`):
             Whether to tie input and output embeddings.
 
-    Example::
+    Examples::
 
         >>> from transformers import ReformerModel, ReformerConfig