Merge branch 'master' of...

Merge branch 'master' of https://github.com/danai-antoniou/pytorch-transformers into add-duplicate-tokens-error

.circleci/config.yml

 version: 2
 jobs:
-    build_py3:
-        working_directory: ~/pytorch-transformers
+    build_py3_torch_and_tf:
+        working_directory: ~/transformers
         docker:
             - image: circleci/python:3.5
         resource_class: xlarge
         parallelism: 1
         steps:
             - checkout
+            - run: sudo pip install torch
+            - run: sudo pip install tensorflow==2.0.0-rc0
             - run: sudo pip install --progress-bar off .
             - run: sudo pip install pytest codecov pytest-cov
             - run: sudo pip install tensorboardX scikit-learn
-            - run: python -m pytest -sv ./pytorch_transformers/tests/ --cov
+            - run: python -m pytest -sv ./transformers/tests/ --cov
+            - run: codecov
+    build_py3_torch:
+        working_directory: ~/transformers
+        docker:
+            - image: circleci/python:3.5
+        resource_class: xlarge
+        parallelism: 1
+        steps:
+            - checkout
+            - run: sudo pip install torch
+            - run: sudo pip install --progress-bar off .
+            - run: sudo pip install pytest codecov pytest-cov
+            - run: sudo pip install tensorboardX scikit-learn
+            - run: python -m pytest -sv ./transformers/tests/ --cov
             - run: python -m pytest -sv ./examples/
             - run: codecov
-    build_py2:
-        working_directory: ~/pytorch-transformers
+    build_py3_tf:
+        working_directory: ~/transformers
+        docker:
+            - image: circleci/python:3.5
+        resource_class: xlarge
+        parallelism: 1
+        steps:
+            - checkout
+            - run: sudo pip install tensorflow==2.0.0-rc0
+            - run: sudo pip install --progress-bar off .
+            - run: sudo pip install pytest codecov pytest-cov
+            - run: sudo pip install tensorboardX scikit-learn
+            - run: python -m pytest -sv ./transformers/tests/ --cov
+            - run: codecov
+    build_py2_torch:
+        working_directory: ~/transformers
+        resource_class: large
+        parallelism: 1
+        docker:
+            - image: circleci/python:2.7
+        steps:
+            - checkout
+            - run: sudo pip install torch
+            - run: sudo pip install --progress-bar off .
+            - run: sudo pip install pytest codecov pytest-cov
+            - run: python -m pytest -sv ./transformers/tests/ --cov
+            - run: codecov
+    build_py2_tf:
+        working_directory: ~/transformers
         resource_class: large
         parallelism: 1
         docker:
             - image: circleci/python:2.7
         steps:
             - checkout
+            - run: sudo pip install tensorflow==2.0.0-rc0
             - run: sudo pip install --progress-bar off .
             - run: sudo pip install pytest codecov pytest-cov
-            - run: python -m pytest -sv ./pytorch_transformers/tests/ --cov
+            - run: python -m pytest -sv ./transformers/tests/ --cov
             - run: codecov
     deploy_doc:
-        working_directory: ~/pytorch-transformers
+        working_directory: ~/transformers
         docker:
             - image: circleci/python:3.5
         steps:
@@ -37,7 +81,6 @@ jobs:
             - checkout
             - run: sudo pip install --progress-bar off -r docs/requirements.txt
             - run: sudo pip install --progress-bar off -r requirements.txt
-            - run: cd docs/source && ln -s ../../examples/README.md examples.md && cd -
             - run: cd docs && make clean && make html && scp -r -oStrictHostKeyChecking=no _build/html/* $doc:$dir
 workflow_filters: &workflow_filters
     filters:
@@ -48,6 +91,9 @@ workflows:
     version: 2
     build_and_test:
         jobs:
-            - build_py3
-            - build_py2
+            - build_py3_torch_and_tf
+            - build_py3_torch
+            - build_py3_tf
+            - build_py2_torch
+            - build_py2_tf
             - deploy_doc: *workflow_filters
\ No newline at end of file

.coveragerc

 [run]
-source=pytorch_transformers
+source=transformers
 omit =
     # skip convertion scripts from testing for now
     */convert_*

.github/ISSUE_TEMPLATE/migration.md

 ---
 name: "\U0001F4DA Migration from PyTorch-pretrained-Bert"
-about: Report a problem when migrating from PyTorch-pretrained-Bert to PyTorch-Transformers
+about: Report a problem when migrating from PyTorch-pretrained-Bert to Transformers
 ---
 
 ## 📚 Migration

.gitignore

@@ -130,5 +130,5 @@ runs
 examples/runs
 
 # data
-data
+/data
 serialization_dir
\ No newline at end of file

docker/Dockerfile

@@ -2,6 +2,6 @@ FROM pytorch/pytorch:latest
 
 RUN git clone https://github.com/NVIDIA/apex.git && cd apex && python setup.py install --cuda_ext --cpp_ext
 
-RUN pip install pytorch_transformers
+RUN pip install transformers
 
 WORKDIR /workspace
\ No newline at end of file

docs/README.md

@@ -34,11 +34,11 @@ pip install recommonmark
 
 ## Building the documentation
 
-Make sure that there is a symlink from the `example` file (in /examples) inside the source folder. Run the followig 
+Make sure that there is a symlink from the `example` file (in /examples) inside the source folder. Run the following 
 command to generate it:
 
 ```bash
-ln -s ../../examples/README.md source/examples.md
+ln -s ../../examples/README.md examples.md
 ```
 
 Once you have setup `sphinx`, you can build the documentation by running the following command in the `/docs` folder:

docs/requirements.txt

@@ -27,3 +27,6 @@ sphinxcontrib-qthelp==1.0.2
 sphinxcontrib-serializinghtml==1.1.3
 urllib3==1.25.3
 sphinx-markdown-tables==0.0.9
+numpy==1.17.2
+tensorflow==2.0.0rc2
+torch==1.2.0
\ No newline at end of file

docs/source/_static/js/custom.js

 function addIcon() {
-    const huggingFaceLogo = "http://lysand.re/huggingface_logo.svg";
+    const huggingFaceLogo = "https://huggingface.co/assets/transformers-docs/huggingface_logo.svg";
     const image = document.createElement("img");
     image.setAttribute("src", huggingFaceLogo);
 
@@ -9,14 +9,14 @@ function addIcon() {
     div.style.paddingTop = '30px';
     div.style.backgroundColor = '#6670FF';
 
-    const scrollDiv = document.getElementsByClassName("wy-side-scroll")[0];
+    const scrollDiv = document.querySelector(".wy-side-scroll");
     scrollDiv.prepend(div);
 }
 
 function addCustomFooter() {
     const customFooter = document.createElement("div");
     const questionOrIssue = document.createElement("div");
-    questionOrIssue.innerHTML = "Stuck? Read our <a href='https://medium.com/huggingface'>Blog posts</a> or <a href='https://github.com/huggingface/pytorch_transformers'>Create an issue</a>";
+    questionOrIssue.innerHTML = "Stuck? Read our <a href='https://medium.com/huggingface'>Blog posts</a> or <a href='https://github.com/huggingface/transformers'>Create an issue</a>";
     customFooter.appendChild(questionOrIssue);
     customFooter.classList.add("footer");
 
@@ -24,10 +24,10 @@ function addCustomFooter() {
     social.classList.add("footer__Social");
 
     const imageDetails = [
-        { link: "https://huggingface.co", imageLink: "http://lysand.re/icons/website.svg" },
-        { link: "https://twitter.com/huggingface", imageLink: "http://lysand.re/icons/twitter.svg" },
-        { link: "https://github.com/huggingface", imageLink: "http://lysand.re/icons/github.svg" },
-        { link: "https://www.linkedin.com/company/huggingface/", imageLink: "http://lysand.re/icons/linkedin.svg" }
+        { link: "https://huggingface.co", imageLink: "https://huggingface.co/assets/transformers-docs/website.svg" },
+        { link: "https://twitter.com/huggingface", imageLink: "https://huggingface.co/assets/transformers-docs/twitter.svg" },
+        { link: "https://github.com/huggingface", imageLink: "https://huggingface.co/assets/transformers-docs/github.svg" },
+        { link: "https://www.linkedin.com/company/huggingface/", imageLink: "https://huggingface.co/assets/transformers-docs/linkedin.svg" }
     ];
 
     imageDetails.forEach(imageLinks => {
@@ -42,13 +42,38 @@ function addCustomFooter() {
     });
 
     customFooter.appendChild(social);
-    document.getElementsByTagName("footer")[0].appendChild(customFooter);
+    document.querySelector("footer").appendChild(customFooter);
 }
 
+function addGithubButton() {
+    const div = `
+        <div class="github-repo">
+            <a 
+                class="github-button"
+                href="https://github.com/huggingface/transformers" data-size="large" data-show-count="true" aria-label="Star huggingface/pytorch-transformers on GitHub">
+                Star
+            </a>
+        </div>
+    `;
+    document.querySelector(".wy-side-nav-search .icon-home").insertAdjacentHTML('afterend', div);
+}
+
+/*!
+ * github-buttons v2.2.10
+ * (c) 2019 なつき
+ * @license BSD-2-Clause
+ */
+/**
+ * modified to run programmatically
+ */
+function parseGithubButtons (){"use strict";var e=window.document,t=e.location,o=window.encodeURIComponent,r=window.decodeURIComponent,n=window.Math,a=window.HTMLElement,i=window.XMLHttpRequest,l="https://unpkg.com/github-buttons@2.2.10/dist/buttons.html",c=i&&i.prototype&&"withCredentials"in i.prototype,d=c&&a&&a.prototype.attachShadow&&!a.prototype.attachShadow.prototype,s=function(e,t,o){e.addEventListener?e.addEventListener(t,o):e.attachEvent("on"+t,o)},u=function(e,t,o){e.removeEventListener?e.removeEventListener(t,o):e.detachEvent("on"+t,o)},h=function(e,t,o){var r=function(n){return u(e,t,r),o(n)};s(e,t,r)},f=function(e,t,o){var r=function(n){if(t.test(e.readyState))return u(e,"readystatechange",r),o(n)};s(e,"readystatechange",r)},p=function(e){return function(t,o,r){var n=e.createElement(t);if(o)for(var a in o){var i=o[a];null!=i&&(null!=n[a]?n[a]=i:n.setAttribute(a,i))}if(r)for(var l=0,c=r.length;l<c;l++){var d=r[l];n.appendChild("string"==typeof 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docs/source/bertology.rst

@@ -15,4 +15,4 @@ In order to help this new field develop, we have included a few additional featu
 * accessing all the attention weights for each head of BERT/GPT/GPT-2,
 * retrieving heads output values and gradients to be able to compute head importance score and prune head as explained in https://arxiv.org/abs/1905.10650.
 
-To help you understand and use these features, we have added a specific example script: `bertology.py <https://github.com/huggingface/pytorch-transformers/blob/master/examples/run_bertology.py>`_ while extract information and prune a model pre-trained on GLUE.
+To help you understand and use these features, we have added a specific example script: `bertology.py <https://github.com/huggingface/transformers/blob/master/examples/run_bertology.py>`_ while extract information and prune a model pre-trained on GLUE.

docs/source/conf.py

@@ -19,14 +19,14 @@ sys.path.insert(0, os.path.abspath('../..'))
 
 # -- Project information -----------------------------------------------------
 
-project = u'pytorch-transformers'
+project = u'transformers'
 copyright = u'2019, huggingface'
 author = u'huggingface'
 
 # The short X.Y version
 version = u''
 # The full version, including alpha/beta/rc tags
-release = u'1.2.0'
+release = u'2.0.0'
 
 
 # -- General configuration ---------------------------------------------------
@@ -109,7 +109,7 @@ html_static_path = ['_static']
 # -- Options for HTMLHelp output ---------------------------------------------
 
 # Output file base name for HTML help builder.
-htmlhelp_basename = 'pytorch-transformersdoc'
+htmlhelp_basename = 'transformersdoc'
 
 
 # -- Options for LaTeX output ------------------------------------------------
@@ -136,7 +136,7 @@ latex_elements = {
 # (source start file, target name, title,
 #  author, documentclass [howto, manual, or own class]).
 latex_documents = [
-    (master_doc, 'pytorch-transformers.tex', u'pytorch-transformers Documentation',
+    (master_doc, 'transformers.tex', u'transformers Documentation',
      u'huggingface', 'manual'),
 ]
 
@@ -146,7 +146,7 @@ latex_documents = [
 # One entry per manual page. List of tuples
 # (source start file, name, description, authors, manual section).
 man_pages = [
-    (master_doc, 'pytorch-transformers', u'pytorch-transformers Documentation',
+    (master_doc, 'transformers', u'transformers Documentation',
      [author], 1)
 ]
 
@@ -157,8 +157,8 @@ man_pages = [
 # (source start file, target name, title, author,
 #  dir menu entry, description, category)
 texinfo_documents = [
-    (master_doc, 'pytorch-transformers', u'pytorch-transformers Documentation',
-     author, 'pytorch-transformers', 'One line description of project.',
+    (master_doc, 'transformers', u'transformers Documentation',
+     author, 'transformers', 'One line description of project.',
      'Miscellaneous'),
 ]
 

docs/source/converting_tensorflow_models.rst

@@ -6,7 +6,7 @@ A command-line interface is provided to convert original Bert/GPT/GPT-2/Transfor
 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_tf_checkpoint_to_pytorch.py <https://github.com/huggingface/pytorch-transformers/blob/master/pytorch_transformers/convert_tf_checkpoint_to_pytorch.py>`_ script.
+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_tf_checkpoint_to_pytorch.py <https://github.com/huggingface/transformers/blob/master/transformers/convert_tf_checkpoint_to_pytorch.py>`_ script.
 
 This CLI takes as input a TensorFlow checkpoint (three files starting with ``bert_model.ckpt``\ ) and the associated configuration file (\ ``bert_config.json``\ ), and creates a PyTorch model for this configuration, loads the weights from the TensorFlow checkpoint in the PyTorch model and saves the resulting model in a standard PyTorch save file that can be imported using ``torch.load()`` (see examples in `run_bert_extract_features.py <https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/examples/run_bert_extract_features.py>`_\ , `run_bert_classifier.py <https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/examples/run_bert_classifier.py>`_ and `run_bert_squad.py <https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/examples/run_bert_squad.py>`_\ ).
 
@@ -20,7 +20,7 @@ Here is an example of the conversion process for a pre-trained ``BERT-Base Uncas
 
    export BERT_BASE_DIR=/path/to/bert/uncased_L-12_H-768_A-12
 
-   pytorch_transformers bert \
+   transformers bert \
      $BERT_BASE_DIR/bert_model.ckpt \
      $BERT_BASE_DIR/bert_config.json \
      $BERT_BASE_DIR/pytorch_model.bin
@@ -36,7 +36,7 @@ Here is an example of the conversion process for a pre-trained OpenAI GPT model,
 
    export OPENAI_GPT_CHECKPOINT_FOLDER_PATH=/path/to/openai/pretrained/numpy/weights
 
-   pytorch_transformers gpt \
+   transformers gpt \
      $OPENAI_GPT_CHECKPOINT_FOLDER_PATH \
      $PYTORCH_DUMP_OUTPUT \
      [OPENAI_GPT_CONFIG]
@@ -50,7 +50,7 @@ Here is an example of the conversion process for a pre-trained OpenAI GPT-2 mode
 
    export OPENAI_GPT2_CHECKPOINT_PATH=/path/to/gpt2/pretrained/weights
 
-   pytorch_transformers gpt2 \
+   transformers gpt2 \
      $OPENAI_GPT2_CHECKPOINT_PATH \
      $PYTORCH_DUMP_OUTPUT \
      [OPENAI_GPT2_CONFIG]
@@ -64,7 +64,7 @@ Here is an example of the conversion process for a pre-trained Transformer-XL mo
 
    export TRANSFO_XL_CHECKPOINT_FOLDER_PATH=/path/to/transfo/xl/checkpoint
 
-   pytorch_transformers transfo_xl \
+   transformers transfo_xl \
      $TRANSFO_XL_CHECKPOINT_FOLDER_PATH \
      $PYTORCH_DUMP_OUTPUT \
      [TRANSFO_XL_CONFIG]
@@ -80,7 +80,7 @@ Here is an example of the conversion process for a pre-trained XLNet model, fine
    export TRANSFO_XL_CHECKPOINT_PATH=/path/to/xlnet/checkpoint
    export TRANSFO_XL_CONFIG_PATH=/path/to/xlnet/config
 
-   pytorch_transformers xlnet \
+   transformers xlnet \
      $TRANSFO_XL_CHECKPOINT_PATH \
      $TRANSFO_XL_CONFIG_PATH \
      $PYTORCH_DUMP_OUTPUT \
@@ -96,6 +96,6 @@ Here is an example of the conversion process for a pre-trained XLM model:
 
    export XLM_CHECKPOINT_PATH=/path/to/xlm/checkpoint
 
-   pytorch_transformers xlm \
+   transformers xlm \
      $XLM_CHECKPOINT_PATH \
      $PYTORCH_DUMP_OUTPUT \

docs/source/examples.md

+../../examples/README.md
\ No newline at end of file

docs/source/index.rst

-Pytorch-Transformers
+Transformers
 ================================================================================================================================================
 
-PyTorch-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:
+This is the documentation of our repository `transformers <https://github.com/huggingface/transformers>`__.
+
+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.
@@ -12,7 +46,8 @@ The library currently contains PyTorch implementations, pre-trained model weight
 5. `XLNet <https://github.com/zihangdai/xlnet>`_ (from Google/CMU) released with the paper `​XLNet: Generalized Autoregressive Pretraining for Language Understanding <https://arxiv.org/abs/1906.08237>`_ by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
 6. `XLM <https://github.com/facebookresearch/XLM>`_ (from Facebook) released together with the paper `Cross-lingual Language Model Pretraining <https://arxiv.org/abs/1901.07291>`_ by Guillaume Lample and Alexis Conneau.
 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/pytorch-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.
+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
@@ -37,6 +72,7 @@ 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

docs/source/installation.rst

 Installation
 ================================================
 
-PyTorch-Transformers is tested on Python 2.7 and 3.5+ (examples are tested only on python 3.5+) and PyTorch 1.1.0
+Transformers is tested on Python 2.7 and 3.5+ (examples are tested only on python 3.5+) and PyTorch 1.1.0
 
 With pip
 ^^^^^^^^
@@ -10,7 +10,7 @@ PyTorch Transformers can be installed using pip as follows:
 
 .. code-block:: bash
 
-   pip install pytorch-transformers
+   pip install transformers
 
 From source
 ^^^^^^^^^^^
@@ -19,15 +19,15 @@ To install from source, clone the repository and install with:
 
 .. code-block:: bash
 
-    git clone https://github.com/huggingface/pytorch-transformers.git
-    cd pytorch-transformers
+    git clone https://github.com/huggingface/transformers.git
+    cd transformers
     pip install [--editable] .
 
 
 Tests
 ^^^^^
 
-An extensive test suite is included to test the library behavior and several examples. Library tests can be found in the `tests folder <https://github.com/huggingface/pytorch-transformers/tree/master/pytorch_transformers/tests>`_ and examples tests in the `examples folder <https://github.com/huggingface/pytorch-transformers/tree/master/examples>`_.
+An extensive test suite is included to test the library behavior and several examples. Library tests can be found in the `tests folder <https://github.com/huggingface/transformers/tree/master/transformers/tests>`_ and examples tests in the `examples folder <https://github.com/huggingface/transformers/tree/master/examples>`_.
 
 Tests can be run using `pytest` (install pytest if needed with `pip install pytest`).
 
@@ -35,7 +35,7 @@ Run all the tests from the root of the cloned repository with the commands:
 
 .. code-block:: bash
 
-    python -m pytest -sv ./pytorch_transformers/tests/
+    python -m pytest -sv ./transformers/tests/
     python -m pytest -sv ./examples/
 
 

docs/source/main_classes/configuration.rst

@@ -6,5 +6,5 @@ The base class ``PretrainedConfig`` implements the common methods for loading/sa
 ``PretrainedConfig``
 ~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.PretrainedConfig
+.. autoclass:: transformers.PretrainedConfig
     :members:

docs/source/main_classes/model.rst

@@ -11,5 +11,11 @@ The base class ``PreTrainedModel`` implements the common methods for loading/sav
 ``PreTrainedModel``
 ~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.PreTrainedModel
+.. autoclass:: transformers.PreTrainedModel
+    :members:
+
+``TFPreTrainedModel``
+~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFPreTrainedModel
     :members:

docs/source/main_classes/optimizer_schedules.rst

@@ -9,7 +9,7 @@ The ``.optimization`` module provides:
 ``AdamW``
 ~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.AdamW
+.. autoclass:: transformers.AdamW
     :members:
 
 Schedules
@@ -18,11 +18,11 @@ Schedules
 Learning Rate Schedules
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-.. autoclass:: pytorch_transformers.ConstantLRSchedule
+.. autoclass:: transformers.ConstantLRSchedule
     :members:
 
 
-.. autoclass:: pytorch_transformers.WarmupConstantSchedule
+.. autoclass:: transformers.WarmupConstantSchedule
     :members:
 
 .. image:: /imgs/warmup_constant_schedule.png
@@ -30,7 +30,7 @@ Learning Rate Schedules
     :alt:
 
 
-.. autoclass:: pytorch_transformers.WarmupCosineSchedule
+.. autoclass:: transformers.WarmupCosineSchedule
     :members:
 
 .. image:: /imgs/warmup_cosine_schedule.png
@@ -38,7 +38,7 @@ Learning Rate Schedules
     :alt:
 
 
-.. autoclass:: pytorch_transformers.WarmupCosineWithHardRestartsSchedule
+.. autoclass:: transformers.WarmupCosineWithHardRestartsSchedule
     :members:
 
 .. image:: /imgs/warmup_cosine_hard_restarts_schedule.png
@@ -47,7 +47,7 @@ Learning Rate Schedules
 
 
 
-.. autoclass:: pytorch_transformers.WarmupLinearSchedule
+.. autoclass:: transformers.WarmupLinearSchedule
     :members:
 
 .. image:: /imgs/warmup_linear_schedule.png

docs/source/main_classes/processors.rst

+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
+of :class:`~transformers.data.processors.utils.InputExample`. These
+:class:`~transformers.data.processors.utils.InputExample` can be converted to
+:class:`~transformers.data.processors.utils.InputFeatures` in order to be fed to the model.
+
+.. autoclass:: transformers.data.processors.utils.DataProcessor
+    :members:
+
+
+.. autoclass:: transformers.data.processors.utils.InputExample
+    :members:
+
+
+.. autoclass:: 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:`~transformers.data.processors.utils.MrpcProcessor`
+    - :class:`~transformers.data.processors.utils.MnliProcessor`
+    - :class:`~transformers.data.processors.utils.MnliMismatchedProcessor`
+    - :class:`~transformers.data.processors.utils.Sst2Processor`
+    - :class:`~transformers.data.processors.utils.StsbProcessor`
+    - :class:`~transformers.data.processors.utils.QqpProcessor`
+    - :class:`~transformers.data.processors.utils.QnliProcessor`
+    - :class:`~transformers.data.processors.utils.RteProcessor`
+    - :class:`~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:`~transformers.data.processors.utils.InputExample`.
+
+.. 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/run_glue.py>`__ script.
\ No newline at end of file