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diff --git a/docs/source/index.rst b/docs/source/index.rst
@@ -1,9 +1,38 @@
 Transformers
 ================================================================================================================================================
 
-Transformers is a library of state-of-the-art pre-trained models for Natural Language Processing (NLP).
+🤗 Transformers (formerly known as `pytorch-transformers` and `pytorch-pretrained-bert`) provides general-purpose architectures
+(BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural Language Understanding (NLU) and Natural Language Generation
+(NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between TensorFlow 2.0 and PyTorch.
 
-The library currently contains PyTorch implementations, pre-trained model weights, usage scripts and conversion utilities for the following models:
+Features
+---------------------------------------------------
+
+- As easy to use as pytorch-transformers
+- As powerful and concise as Keras
+- High performance on NLU and NLG tasks
+- Low barrier to entry for educators and practitioners
+
+State-of-the-art NLP for everyone
+- Deep learning researchers
+- Hands-on practitioners
+- AI/ML/NLP teachers and educators
+
+Lower compute costs, smaller carbon footprint
+- Researchers can share trained models instead of always retraining
+- Practitioners can reduce compute time and production costs
+- 8 architectures with over 30 pretrained models, some in more than 100 languages
+
+Choose the right framework for every part of a model's lifetime
+- Train state-of-the-art models in 3 lines of code
+- Deep interoperability between TensorFlow 2.0 and PyTorch models
+- Move a single model between TF2.0/PyTorch frameworks at will
+- Seamlessly pick the right framework for training, evaluation, production
+
+Contents
+---------------------------------
+
+The library currently contains PyTorch and Tensorflow implementations, pre-trained model weights, usage scripts and conversion utilities for the following models:
 
 1. `BERT <https://github.com/google-research/bert>`_ (from Google) released with the paper `BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding <https://arxiv.org/abs/1810.04805>`_ by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.
 2. `GPT <https://github.com/openai/finetune-transformer-lm>`_ (from OpenAI) released with the paper `Improving Language Understanding by Generative Pre-Training <https://blog.openai.com/language-unsupervised>`_ by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever.
@@ -14,6 +43,7 @@ The library currently contains PyTorch implementations, pre-trained model weight
 7. `RoBERTa <https://github.com/pytorch/fairseq/tree/master/examples/roberta>`_ (from Facebook), released together with the paper a `Robustly Optimized BERT Pretraining Approach <https://arxiv.org/abs/1907.11692>`_ by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
 8. `DistilBERT <https://huggingface.co/transformers/model_doc/distilbert.html>`_ (from HuggingFace) released together with the blog post `Smaller, faster, cheaper, lighter: Introducing DistilBERT, a distilled version of BERT <https://medium.com/huggingface/distilbert-8cf3380435b5>`_ by Victor Sanh, Lysandre Debut and Thomas Wolf.
 
+
 .. toctree::
     :maxdepth: 2
     :caption: Notes
@@ -37,6 +67,7 @@ The library currently contains PyTorch implementations, pre-trained model weight
     main_classes/model
     main_classes/tokenizer
     main_classes/optimizer_schedules
+    main_classes/processors
 
 .. toctree::
     :maxdepth: 2

diff --git a/docs/source/main_classes/model.rst b/docs/source/main_classes/model.rst
@@ -13,3 +13,9 @@ The base class ``PreTrainedModel`` implements the common methods for loading/sav
 
 .. autoclass:: transformers.PreTrainedModel
     :members:
+
+``TFPreTrainedModel``
+~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: pytorch_transformers.TFPreTrainedModel
+    :members:
diff --git a/docs/source/main_classes/processors.rst b/docs/source/main_classes/processors.rst
@@ -0,0 +1,58 @@
+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:`~pytorch_transformers.data.processors.utils.DataProcessor`. The processor returns a list
+of :class:`~pytorch_transformers.data.processors.utils.InputExample`. These
+:class:`~pytorch_transformers.data.processors.utils.InputExample` can be converted to
+:class:`~pytorch_transformers.data.processors.utils.InputFeatures` in order to be fed to the model.
+
+.. autoclass:: pytorch_transformers.data.processors.utils.DataProcessor
+    :members:
+
+
+.. autoclass:: pytorch_transformers.data.processors.utils.InputExample
+    :members:
+
+
+.. autoclass:: pytorch_transformers.data.processors.utils.InputFeatures
+    :members:
+
+
+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
+`GLUE: A multi-task benchmark and analysis platform for natural language understanding <https://openreview.net/pdf?id=rJ4km2R5t7>`__
+
+This library hosts a total of 10 processors for the following tasks: MRPC, MNLI, MNLI (mismatched),
+CoLA, SST2, STSB, QQP, QNLI, RTE and WNLI.
+
+Those processors are:
+    - :class:`~pytorch_transformers.data.processors.utils.MrpcProcessor`
+    - :class:`~pytorch_transformers.data.processors.utils.MnliProcessor`
+    - :class:`~pytorch_transformers.data.processors.utils.MnliMismatchedProcessor`
+    - :class:`~pytorch_transformers.data.processors.utils.Sst2Processor`
+    - :class:`~pytorch_transformers.data.processors.utils.StsbProcessor`
+    - :class:`~pytorch_transformers.data.processors.utils.QqpProcessor`
+    - :class:`~pytorch_transformers.data.processors.utils.QnliProcessor`
+    - :class:`~pytorch_transformers.data.processors.utils.RteProcessor`
+    - :class:`~pytorch_transformers.data.processors.utils.WnliProcessor`
+
+Additionally, the following method  can be used to load values from a data file and convert them to a list of
+:class:`~pytorch_transformers.data.processors.utils.InputExample`.
+
+.. automethod:: pytorch_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/run_glue.py>`__ script.
diff --git a/docs/source/model_doc/bert.rst b/docs/source/model_doc/bert.rst
@@ -70,3 +70,59 @@ BERT
 .. autoclass:: transformers.BertForQuestionAnswering
     :members:
 
+
+``TFBertModel``
+~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: pytorch_transformers.TFBertModel
+    :members:
+
+
+``TFBertForPreTraining``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: pytorch_transformers.TFBertForPreTraining
+    :members:
+
+
+``TFBertForMaskedLM``
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: pytorch_transformers.TFBertForMaskedLM
+    :members:
+
+
+``TFBertForNextSentencePrediction``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: pytorch_transformers.TFBertForNextSentencePrediction
+    :members:
+
+
+``TFBertForSequenceClassification``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: pytorch_transformers.TFBertForSequenceClassification
+    :members:
+
+
+``TFBertForMultipleChoice``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: pytorch_transformers.TFBertForMultipleChoice
+    :members:
+
+
+``TFBertForTokenClassification``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: pytorch_transformers.TFBertForTokenClassification
+    :members:
+
+
+``TFBertForQuestionAnswering``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: pytorch_transformers.TFBertForQuestionAnswering
+    :members:
+
diff --git a/docs/source/model_doc/distilbert.rst b/docs/source/model_doc/distilbert.rst
@@ -41,3 +41,30 @@ DistilBERT
 
 .. autoclass:: transformers.DistilBertForQuestionAnswering
     :members:
+
+``TFDistilBertModel``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: pytorch_transformers.TFDistilBertModel
+    :members:
+
+
+``TFDistilBertForMaskedLM``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: pytorch_transformers.TFDistilBertForMaskedLM
+    :members:
+
+
+``TFDistilBertForSequenceClassification``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: pytorch_transformers.TFDistilBertForSequenceClassification
+    :members:
+
+
+``TFDistilBertForQuestionAnswering``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: pytorch_transformers.TFDistilBertForQuestionAnswering
+    :members:
diff --git a/docs/source/model_doc/gpt.rst b/docs/source/model_doc/gpt.rst
@@ -34,3 +34,24 @@ OpenAI GPT
 
 .. autoclass:: transformers.OpenAIGPTDoubleHeadsModel
     :members:
+
+
+``TFOpenAIGPTModel``
+~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: pytorch_transformers.TFOpenAIGPTModel
+    :members:
+
+
+``TFOpenAIGPTLMHeadModel``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: pytorch_transformers.TFOpenAIGPTLMHeadModel
+    :members:
+
+
+``TFOpenAIGPTDoubleHeadsModel``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: pytorch_transformers.TFOpenAIGPTDoubleHeadsModel
+    :members:
diff --git a/docs/source/model_doc/gpt2.rst b/docs/source/model_doc/gpt2.rst
@@ -34,3 +34,24 @@ OpenAI GPT2
 
 .. autoclass:: transformers.GPT2DoubleHeadsModel
     :members:
+
+
+``TFGPT2Model``
+~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: pytorch_transformers.TFGPT2Model
+    :members:
+
+
+``TFGPT2LMHeadModel``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: pytorch_transformers.TFGPT2LMHeadModel
+    :members:
+
+
+``TFGPT2DoubleHeadsModel``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: pytorch_transformers.TFGPT2DoubleHeadsModel
+    :members:
diff --git a/docs/source/model_doc/roberta.rst b/docs/source/model_doc/roberta.rst
@@ -34,3 +34,24 @@ RoBERTa
 
 .. autoclass:: transformers.RobertaForSequenceClassification
     :members:
+
+
+``TFRobertaModel``
+~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: pytorch_transformers.TFRobertaModel
+    :members:
+
+
+``TFRobertaForMaskedLM``
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: pytorch_transformers.TFRobertaForMaskedLM
+    :members:
+
+
+``TFRobertaForSequenceClassification``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: pytorch_transformers.TFRobertaForSequenceClassification
+    :members:
diff --git a/docs/source/model_doc/transformerxl.rst b/docs/source/model_doc/transformerxl.rst
@@ -28,3 +28,17 @@ Transformer XL
 
 .. autoclass:: transformers.TransfoXLLMHeadModel
     :members:
+
+
+``TFTransfoXLModel``
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: pytorch_transformers.TFTransfoXLModel
+    :members:
+
+
+``TFTransfoXLLMHeadModel``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: pytorch_transformers.TFTransfoXLLMHeadModel
+    :members:
diff --git a/docs/source/model_doc/xlm.rst b/docs/source/model_doc/xlm.rst
@@ -39,3 +39,31 @@ XLM
 
 .. autoclass:: transformers.XLMForQuestionAnswering
     :members:
+
+
+``TFXLMModel``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: pytorch_transformers.TFXLMModel
+    :members:
+
+
+``TFXLMWithLMHeadModel``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: pytorch_transformers.TFXLMWithLMHeadModel
+    :members:
+
+
+``TFXLMForSequenceClassification``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: pytorch_transformers.TFXLMForSequenceClassification
+    :members:
+
+
+``TFXLMForQuestionAnsweringSimple``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: pytorch_transformers.TFXLMForQuestionAnsweringSimple
+    :members:
diff --git a/docs/source/model_doc/xlnet.rst b/docs/source/model_doc/xlnet.rst
@@ -41,3 +41,31 @@ XLNet
 
 .. autoclass:: transformers.XLNetForQuestionAnswering
     :members:
+
+
+``TFXLNetModel``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: pytorch_transformers.TFXLNetModel
+    :members:
+
+
+``TFXLNetLMHeadModel``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: pytorch_transformers.TFXLNetLMHeadModel
+    :members:
+
+
+``TFXLNetForSequenceClassification``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: pytorch_transformers.TFXLNetForSequenceClassification
+    :members:
+
+
+``TFXLNetForQuestionAnsweringSimple``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: pytorch_transformers.TFXLNetForQuestionAnsweringSimple
+    :members:
diff --git a/docs/source/pretrained_models.rst b/docs/source/pretrained_models.rst
@@ -44,15 +44,15 @@ Here is the full list of the currently provided pretrained models together with
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``bert-large-uncased-whole-word-masking-finetuned-squad``  | | 24-layer, 1024-hidden, 16-heads, 340M parameters.                                                                                   |
 |                   |                                                            | | The ``bert-large-uncased-whole-word-masking`` model fine-tuned on SQuAD                                                             |
-|                   |                                                            | (see details of fine-tuning in the `example section <https://github.com/huggingface/transformers/tree/master/examples>`__).   |
+|                   |                                                            | (see details of fine-tuning in the `example section <https://github.com/huggingface/transformers/tree/master/examples>`__).           |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``bert-large-cased-whole-word-masking-finetuned-squad``    | | 24-layer, 1024-hidden, 16-heads, 340M parameters                                                                                    |
 |                   |                                                            | | The ``bert-large-cased-whole-word-masking`` model fine-tuned on SQuAD                                                               |
-|                   |                                                            | (see `details of fine-tuning in the example section <https://huggingface.co/transformers/examples.html>`__)                   |
+|                   |                                                            | (see `details of fine-tuning in the example section <https://huggingface.co/transformers/examples.html>`__)                           |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``bert-base-cased-finetuned-mrpc``                         | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
 |                   |                                                            | | The ``bert-base-cased`` model fine-tuned on MRPC                                                                                    |
-|                   |                                                            | (see `details of fine-tuning in the example section <https://huggingface.co/transformers/examples.html>`__)                   |
+|                   |                                                            | (see `details of fine-tuning in the example section <https://huggingface.co/transformers/examples.html>`__)                           |
 +-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 | GPT               | ``openai-gpt``                                             | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
 |                   |                                                            | | OpenAI GPT English model                                                                                                            |