Release: v4.0.0

Remove deprecated evalutate_during_training (#8852 )
* Remove deprecated `evalutate_during_training` * Update src/transformers/training_args_tf.py Co-authored-by: Lysandre Debut <lysandre@huggingface.co> Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
2020-11-30 11:33:35 -05:00 · 2020-11-30 11:17:43 -05:00 · 2020-11-30 10:40:02 -05:00 · 2020-11-30 10:39:52 -05:00 · 2020-11-30 09:45:12 +01:00 · 2020-11-29 20:13:31 -05:00
768 changed files with 7535 additions and 39481 deletions
@@ -3,7 +3,6 @@ orbs:
    gcp-gke: circleci/gcp-gke@1.0.4
    go: circleci/go@1.3.0

-
 # TPU REFERENCES
 references:
    checkout_ml_testing: &checkout_ml_testing
@@ -79,7 +78,6 @@ jobs:
                      - v0.4-{{ checksum "setup.py" }}
            - run: pip install --upgrade pip
            - run: pip install .[sklearn,tf-cpu,torch,testing,sentencepiece]
-            - run: pip install tapas torch-scatter -f https://pytorch-geometric.com/whl/torch-1.7.0+cpu.html
            - save_cache:
                key: v0.4-{{ checksum "setup.py" }}
                paths:
@@ -106,7 +104,6 @@ jobs:
                      - v0.4-{{ checksum "setup.py" }}
            - run: pip install --upgrade pip
            - run: pip install .[sklearn,torch,testing,sentencepiece]
-            - run: pip install tapas torch-scatter -f https://pytorch-geometric.com/whl/torch-1.7.0+cpu.html
            - save_cache:
                  key: v0.4-torch-{{ checksum "setup.py" }}
                  paths:
@@ -185,7 +182,6 @@ jobs:
                      - v0.4-{{ checksum "setup.py" }}
            - run: pip install --upgrade pip
            - run: pip install .[sklearn,torch,testing,sentencepiece]
-            - run: pip install tapas torch-scatter -f https://pytorch-geometric.com/whl/torch-1.7.0+cpu.html
            - save_cache:
                  key: v0.4-torch-{{ checksum "setup.py" }}
                  paths:
@@ -225,7 +221,7 @@ jobs:
    run_tests_custom_tokenizers:
        working_directory: ~/transformers
        docker:
-            - image: circleci/python:3.7
+            - image: circleci/python:3.6
        environment:
            RUN_CUSTOM_TOKENIZERS: yes
        steps:
@@ -263,7 +259,7 @@ jobs:
                      - v0.4-{{ checksum "setup.py" }}
            - run: pip install --upgrade pip
            - run: pip install .[sklearn,torch,sentencepiece,testing]
-            - run: pip install -r examples/_tests_requirements.txt
+            - run: pip install -r examples/requirements.txt
            - save_cache:
                  key: v0.4-torch_examples-{{ checksum "setup.py" }}
                  paths:
@@ -274,22 +270,6 @@ jobs:
            - store_artifacts:
                  path: ~/transformers/reports

-    run_tests_git_lfs:
-        working_directory: ~/transformers
-        docker:
-            - image: circleci/python:3.7
-        resource_class: xlarge
-        parallelism: 1
-        steps:
-            - checkout
-            - run: sudo apt-get install git-lfs
-            - run: |
-                git config --global user.email "ci@dummy.com"
-                git config --global user.name "ci"
-            - run: pip install --upgrade pip
-            - run: pip install .[testing]
-            - run: RUN_GIT_LFS_TESTS=1 python -m pytest -sv ./tests/test_hf_api.py -k "HfLargefilesTest"
-
    build_doc:
        working_directory: ~/transformers
        docker:
@@ -354,7 +334,6 @@ jobs:
            - run: flake8 examples tests src utils
            - run: python utils/style_doc.py src/transformers docs/source --max_len 119 --check_only
            - run: python utils/check_copies.py
-            - run: python utils/check_table.py
            - run: python utils/check_dummies.py
            - run: python utils/check_repo.py

@@ -418,7 +397,6 @@ workflows:
            - run_tests_flax
            - run_tests_pipelines_torch
            - run_tests_pipelines_tf
-            - run_tests_git_lfs
            - build_doc
            - deploy_doc: *workflow_filters
    tpu_testing_jobs:
@@ -52,5 +52,4 @@ deploy_doc "4b3ee9c" v3.1.0
 deploy_doc "3ebb1b3" v3.2.0
 deploy_doc "0613f05" v3.3.1
 deploy_doc "eb0e0ce" v3.4.0
-deploy_doc "818878d" v3.5.1
-deploy_doc "c781171" # v4.0.0 Latest stable release
+deploy_doc "818878d" # v3.5.1 Latest stable release
@@ -58,5 +58,5 @@ members/contributors which may be interested in your PR.
 tensorflow: @jplu
 examples/token-classification: @stefan-it
 documentation: @sgugger
- FSMT: @stas00
+ FSTM: @stas00
 -->
@@ -1 +0,0 @@
-$PYTHON setup.py install     # Python command to install the script.
@@ -1,48 +0,0 @@
-{% set name = "transformers" %}
-
-package:
-  name: "{{ name|lower }}"
-  version: "{{ TRANSFORMERS_VERSION }}"
-
-source:
-  path: ../../
-
-build:
-  noarch: python
-
-requirements:
-  host:
-    - python
-    - pip
-    - numpy
-    - dataclasses
-    - packaging
-    - filelock
-    - requests
-    - tqdm >=4.27
-    - sacremoses
-    - regex !=2019.12.17
-    - protobuf
-    - tokenizers ==0.9.4
-  run:
-    - python
-    - numpy
-    - dataclasses
-    - packaging
-    - filelock
-    - requests
-    - tqdm >=4.27
-    - sacremoses
-    - regex !=2019.12.17
-    - protobuf
-    - tokenizers ==0.9.4
-
-test:
-  imports:
-    - transformers
-
-about:
-  home: https://huggingface.co
-  license: Apache License 2.0
-  license_file: LICENSE
-  summary: "🤗Transformers: State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0."
@@ -1,65 +0,0 @@
-name: Model templates runner
-
-on:
-  push:
-    paths:
-      - "src/**"
-      - "tests/**"
-      - ".github/**"
-      - "templates/**"
-
-jobs:
-  run_tests_templates:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v1
-
-      - name: Install Python
-        uses: actions/setup-python@v1
-        with:
-          python-version: 3.6
-
-      - name: Loading cache.
-        uses: actions/cache@v2
-        id: cache
-        with:
-          path: ~/.cache/pip
-          key: v1.2-tests_templates
-          restore-keys: |
-            v1.2-tests_templates-${{ hashFiles('setup.py') }}
-            v1.2-tests_templates
-
-      - name: Install dependencies
-        run: |
-          pip install --upgrade pip
-          pip install .[dev]
-      - name: Create model files
-        run: |
-          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/encoder-bert-tokenizer.json --path=templates/adding_a_new_model
-          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/pt-encoder-bert-tokenizer.json --path=templates/adding_a_new_model
-          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/standalone.json --path=templates/adding_a_new_model
-          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/tf-encoder-bert-tokenizer.json --path=templates/adding_a_new_model
-          make style
-          python utils/check_table.py --fix_and_overwrite
-          python utils/check_dummies.py --fix_and_overwrite
-
-      - name: Run all non-slow tests
-        run: |
-          python -m pytest -n 2 --dist=loadfile -s --make-reports=tests_templates tests/*template*
-
-      - name: Run style changes
-        run: |
-          git fetch origin master:master
-          make fixup
-
-      - name: Failure short reports
-        if: ${{ always() }}
-        run: cat reports/tests_templates_failures_short.txt
-
-      - name: Test suite reports artifacts
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v2
-        with:
-          name: run_all_tests_templates_test_reports
-          path: reports
@@ -1,43 +0,0 @@
-name: Release - Conda
-
-on:
-  push:
-    tags:
-      - v*
-
-env:
-  ANACONDA_API_TOKEN: ${{ secrets.ANACONDA_API_TOKEN }}
-
-jobs:
-  build_and_package:
-    runs-on: ubuntu-latest
-    defaults:
-      run:
-        shell: bash -l {0}
-
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v1
-
-      - name: Install miniconda
-        uses: conda-incubator/setup-miniconda@v2
-        with:
-          auto-update-conda: true
-          auto-activate-base: false
-          activate-environment: "build-transformers"
-          channels: huggingface
-
-      - name: Setup conda env
-        run: |
-          conda install -c defaults anaconda-client conda-build
-
-      - name: Extract version
-        run: echo "TRANSFORMERS_VERSION=`python setup.py --version`" >> $GITHUB_ENV
-
-      - name: Build conda packages
-        run: |
-          conda info
-          conda build .github/conda
-
-      - name: Upload to Anaconda
-        run: anaconda upload `conda build .github/conda --output` --force
@@ -4,7 +4,7 @@ on:
  push:
    branches:
      - master
-      - ci_*
+      - model-templates
    paths:
      - "src/**"
      - "tests/**"
@@ -50,7 +50,6 @@ jobs:
          pip install --upgrade pip
          pip install .[torch,sklearn,testing,onnxruntime,sentencepiece]
          pip install git+https://github.com/huggingface/datasets
-          pip install pandas torch-scatter -f https://pytorch-geometric.com/whl/torch-$(python -c "import torch; print(''.join(torch.__version__)")+$(python -c "import torch; print(''.join(torch.version.cuda.split('.')))").html

      - name: Are GPUs recognized by our DL frameworks
        run: |
@@ -58,13 +57,13 @@ jobs:
          python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
          python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"

-#      - name: Create model files
-#        run: |
-#          source .env/bin/activate
-#          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/encoder-bert-tokenizer.json --path=templates/adding_a_new_model
-#          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/pt-encoder-bert-tokenizer.json --path=templates/adding_a_new_model
-#          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/standalone.json --path=templates/adding_a_new_model
-#          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/tf-encoder-bert-tokenizer.json --path=templates/adding_a_new_model
+      - name: Create model files
+        run: |
+          source .env/bin/activate
+          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/encoder-bert-tokenizer.json --path=templates/adding_a_new_model
+          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/pt-encoder-bert-tokenizer.json --path=templates/adding_a_new_model
+          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/standalone.json --path=templates/adding_a_new_model
+          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/tf-encoder-bert-tokenizer.json --path=templates/adding_a_new_model

      - name: Run all non-slow tests on GPU
        env:
@@ -130,10 +129,10 @@ jobs:
      - name: Create model files
        run: |
          source .env/bin/activate
-#          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/encoder-bert-tokenizer.json --path=templates/adding_a_new_model
-#          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/pt-encoder-bert-tokenizer.json --path=templates/adding_a_new_model
-#          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/standalone.json --path=templates/adding_a_new_model
-#          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/tf-encoder-bert-tokenizer.json --path=templates/adding_a_new_model
+          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/encoder-bert-tokenizer.json --path=templates/adding_a_new_model
+          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/pt-encoder-bert-tokenizer.json --path=templates/adding_a_new_model
+          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/standalone.json --path=templates/adding_a_new_model
+          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/tf-encoder-bert-tokenizer.json --path=templates/adding_a_new_model

      - name: Run all non-slow tests on GPU
        env:
@@ -188,7 +187,6 @@ jobs:
          pip install --upgrade pip
          pip install .[torch,sklearn,testing,onnxruntime,sentencepiece]
          pip install git+https://github.com/huggingface/datasets
-          pip install pandas torch-scatter -f https://pytorch-geometric.com/whl/torch-$(python -c "import torch; print(''.join(torch.__version__)")+$(python -c "import torch; print(''.join(torch.version.cuda.split('.')))").html

      - name: Are GPUs recognized by our DL frameworks
        run: |
@@ -6,6 +6,9 @@
 name: Self-hosted runner (scheduled)

 on:
+  push:
+    branches:
+      - ci_*
  repository_dispatch:
  schedule:
    - cron: "0 0 * * *"
@@ -159,6 +159,3 @@ tags

 # pre-commit
 .pre-commit*
-
-# .lock
-*.lock
@@ -1,19 +1,3 @@
-<!---
-Copyright 2020 The HuggingFace Team. 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.
-->
-
 # How to contribute to transformers?

 Everyone is welcome to contribute, and we value everybody's contribution. Code
@@ -141,7 +125,7 @@ Follow these steps to start contributing:
   $ git checkout -b a-descriptive-name-for-my-changes
   ```

-   **Do not** work on the `master` branch.
+   **do not** work on the `master` branch.

 4. Set up a development environment by running the following command in a virtual environment:

@@ -333,16 +317,3 @@ One way one can run the make command on Window is to pass by MSYS2:
 1. [Download MSYS2](https://www.msys2.org/), we assume to have it installed in C:\msys64
 2. Open the command line C:\msys64\msys2.exe (it should be available from the start menu)
 3. Run in the shell: `pacman -Syu` and install make with `pacman -S make`
-
-### Syncing forked master with upstream (HuggingFace) master
-
-To avoid pinging the upstream repository which adds reference notes to each upstream PR and sends unnessary notifications to the developers involved in these PRs, 
-when syncing the master branch of a forked repository, please, follow these steps:
-1. When possible, avoid syncing with the upstream using a branch and PR on the forked repository. Instead merge directly into the forked master.
-2. If a PR is absolutely necessary, use the following steps after checking out your branch:
-```
-$ git checkout -b your-branch-for-syncing
-$ git pull --squash --no-commit upstream master
-$ git commit -m '<your message without GitHub references>'
-$ git push --set-upstream origin your-branch-for-syncing
-```
@@ -1,4 +1,3 @@
-Copyright 2018- The Hugging Face team. All rights reserved.

                                 Apache License
                           Version 2.0, January 2004
@@ -1,4 +1,4 @@
-.PHONY: deps_table_update modified_only_fixup extra_quality_checks quality style fixup fix-copies test test-examples docs
+.PHONY: modified_only_fixup extra_quality_checks quality style fixup fix-copies test test-examples docs


 check_dirs := examples tests src utils
@@ -14,16 +14,10 @@ modified_only_fixup:
 		echo "No library .py files were modified"; \
 	fi

-# Update src/transformers/dependency_versions_table.py
-
-deps_table_update:
-	@python setup.py deps_table_update
-
 # Check that source code meets quality standards

-extra_quality_checks: deps_table_update
+extra_quality_checks:
 	python utils/check_copies.py
-	python utils/check_table.py
 	python utils/check_dummies.py
 	python utils/check_repo.py
 	python utils/style_doc.py src/transformers docs/source --max_len 119
@@ -38,7 +32,7 @@ quality:

 # Format source code automatically and check is there are any problems left that need manual fixing

-style: deps_table_update
+style:
 	black $(check_dirs)
 	isort $(check_dirs)
 	python utils/style_doc.py src/transformers docs/source --max_len 119
@@ -51,7 +45,6 @@ fixup: modified_only_fixup extra_quality_checks

 fix-copies:
 	python utils/check_copies.py --fix_and_overwrite
-	python utils/check_table.py --fix_and_overwrite
 	python utils/check_dummies.py --fix_and_overwrite

 # Run tests for the library
@@ -1,19 +1,3 @@
-<!---
-Copyright 2020 The HuggingFace Team. 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.
-->
-
 <p align="center">
    <br>
    <img src="https://raw.githubusercontent.com/huggingface/transformers/master/docs/source/imgs/transformers_logo_name.png" width="400"/>
@@ -47,6 +31,9 @@ limitations under the License.

 🤗 Transformers is backed by the two most popular deep learning libraries, [PyTorch](https://pytorch.org/) and [TensorFlow](https://www.tensorflow.org/), with a seamless integration between them, allowing you to train your models with one then load it for inference with the other.

+### Recent contributors
+[![](https://sourcerer.io/fame/clmnt/huggingface/transformers/images/0)](https://sourcerer.io/fame/clmnt/huggingface/transformers/links/0)[![](https://sourcerer.io/fame/clmnt/huggingface/transformers/images/1)](https://sourcerer.io/fame/clmnt/huggingface/transformers/links/1)[![](https://sourcerer.io/fame/clmnt/huggingface/transformers/images/2)](https://sourcerer.io/fame/clmnt/huggingface/transformers/links/2)[![](https://sourcerer.io/fame/clmnt/huggingface/transformers/images/3)](https://sourcerer.io/fame/clmnt/huggingface/transformers/links/3)[![](https://sourcerer.io/fame/clmnt/huggingface/transformers/images/4)](https://sourcerer.io/fame/clmnt/huggingface/transformers/links/4)[![](https://sourcerer.io/fame/clmnt/huggingface/transformers/images/5)](https://sourcerer.io/fame/clmnt/huggingface/transformers/links/5)[![](https://sourcerer.io/fame/clmnt/huggingface/transformers/images/6)](https://sourcerer.io/fame/clmnt/huggingface/transformers/links/6)[![](https://sourcerer.io/fame/clmnt/huggingface/transformers/images/7)](https://sourcerer.io/fame/clmnt/huggingface/transformers/links/7)
+
 ## Online demos

 You can test most of our models directly on their pages from the [model hub](https://huggingface.co/models). We also offer an [inference API](https://huggingface.co/pricing) to use those models.
@@ -150,16 +137,14 @@ The model itself is a regular [Pytorch `nn.Module`](https://pytorch.org/docs/sta

 ## Installation

-### With pip
-
 This repository is tested on Python 3.6+, PyTorch 1.0.0+ (PyTorch 1.3.1+ for [examples](https://github.com/huggingface/transformers/tree/master/examples)) and TensorFlow 2.0.

 You should install 🤗 Transformers in a [virtual environment](https://docs.python.org/3/library/venv.html). If you're unfamiliar with Python virtual environments, check out the [user guide](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/).

 First, create a virtual environment with the version of Python you're going to use and activate it.

-Then, you will need to install at least one of TensorFlow 2.0, PyTorch or Flax.
-Please refer to [TensorFlow installation page](https://www.tensorflow.org/install/pip#tensorflow-2.0-rc-is-available), [PyTorch installation page](https://pytorch.org/get-started/locally/#start-locally) regarding the specific install command for your platform and/or [Flax installation page](https://github.com/google/flax#quick-install).
+Then, you will need to install one of, or both, TensorFlow 2.0 and PyTorch.
+Please refer to [TensorFlow installation page](https://www.tensorflow.org/install/pip#tensorflow-2.0-rc-is-available) and/or [PyTorch installation page](https://pytorch.org/get-started/locally/#start-locally) regarding the specific install command for your platform.

 When TensorFlow 2.0 and/or PyTorch has been installed, 🤗 Transformers can be installed using pip as follows:

@@ -169,29 +154,12 @@ pip install transformers

 If you'd like to play with the examples, you must [install the library from source](https://huggingface.co/transformers/installation.html#installing-from-source).

-### With conda
-
-Since Transformers version v4.0.0, we now have a conda channel: `huggingface`.
-
-🤗 Transformers can be installed using conda as follows:
-
-```shell script
-conda install -c huggingface transformers
-```
-
-Follow the installation pages of TensorFlow, PyTorch or Flax to see how to install them with conda. 
-
 ## Models architectures

-**[All the model checkpoints](https://huggingface.co/models)** provided by 🤗 Transformers are seamlessly integrated from the huggingface.co [model hub](https://huggingface.co) where they are uploaded directly by [users](https://huggingface.co/users) and [organizations](https://huggingface.co/organizations).
-
-Current number of checkpoints: ![](https://img.shields.io/endpoint?url=https://huggingface.co/api/shields/models&color=brightgreen)
-
 🤗 Transformers currently provides the following architectures (see [here](https://huggingface.co/transformers/model_summary.html) for a high-level summary of each them):

 1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.
 1. **[BART](https://huggingface.co/transformers/model_doc/bart.html)** (from Facebook) released with the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/pdf/1910.13461.pdf) by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer.
-1. **[BARThez](https://huggingface.co/transformers/model_doc/barthez.html)** (from École polytechnique) released with the paper [BARThez: a Skilled Pretrained French Sequence-to-Sequence Model](https://arxiv.org/abs/2010.12321) by Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis.
 1. **[BERT](https://huggingface.co/transformers/model_doc/bert.html)** (from Google) released with the paper [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.
 1. **[BERT For Sequence Generation](https://huggingface.co/transformers/model_doc/bertgeneration.html)** (from Google) released with the paper [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
 1. **[Blenderbot](https://huggingface.co/transformers/model_doc/blenderbot.html)** (from Facebook) released with the paper [Recipes for building an open-domain chatbot](https://arxiv.org/abs/2004.13637) by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
@@ -213,7 +181,6 @@ Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
 1. **[LXMERT](https://huggingface.co/transformers/model_doc/lxmert.html)** (from UNC Chapel Hill) released with the paper [LXMERT: Learning Cross-Modality Encoder Representations from Transformers for Open-Domain Question Answering](https://arxiv.org/abs/1908.07490) by Hao Tan and Mohit Bansal.
 1. **[MarianMT](https://huggingface.co/transformers/model_doc/marian.html)** Machine translation models trained using [OPUS](http://opus.nlpl.eu/) data by Jörg Tiedemann. The [Marian Framework](https://marian-nmt.github.io/) is being developed by the Microsoft Translator Team.
 1. **[MBart](https://huggingface.co/transformers/model_doc/mbart.html)** (from Facebook) released with the paper [Multilingual Denoising Pre-training for Neural Machine Translation](https://arxiv.org/abs/2001.08210) by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
-1. **[MPNet](https://huggingface.co/transformers/model_doc/mpnet.html)** (from Microsoft Research) released with the paper [MPNet: Masked and Permuted Pre-training for Language Understanding](https://arxiv.org/abs/2004.09297) by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu.
 1. **[MT5](https://huggingface.co/transformers/model_doc/mt5.html)** (from Google AI) released with the paper [mT5: A massively multilingual pre-trained text-to-text transformer](https://arxiv.org/abs/2010.11934) by Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel.
 1. **[Pegasus](https://huggingface.co/transformers/model_doc/pegasus.html)** (from Google) released with the paper [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/abs/1912.08777)> by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu.
 1. **[ProphetNet](https://huggingface.co/transformers/model_doc/prophetnet.html)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
@@ -222,15 +189,15 @@ Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
 ultilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers/tree/master/examples/distillation) and a German version of DistilBERT.
 1. **[SqueezeBert](https://huggingface.co/transformers/model_doc/squeezebert.html)** released with the paper [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer.
 1. **[T5](https://huggingface.co/transformers/model_doc/t5.html)** (from Google AI) released with the paper [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
-1. **[TAPAS](https://huggingface.co/transformers/master/model_doc/tapas.html)** released with the paper [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://arxiv.org/abs/2004.02349) by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos.
 1. **[Transformer-XL](https://huggingface.co/transformers/model_doc/transformerxl.html)** (from Google/CMU) released with the paper [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
 1. **[XLM](https://huggingface.co/transformers/model_doc/xlm.html)** (from Facebook) released together with the paper [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample and Alexis Conneau.
 1. **[XLM-ProphetNet](https://huggingface.co/transformers/model_doc/xlmprophetnet.html)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
 1. **[XLM-RoBERTa](https://huggingface.co/transformers/model_doc/xlmroberta.html)** (from Facebook AI), released together with the paper [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Alexis Conneau*, Kartikay Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov.
 1. **[XLNet](https://huggingface.co/transformers/model_doc/xlnet.html)** (from Google/CMU) released with the paper [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
+1. **[Other community models](https://huggingface.co/models)**, contributed by the [community](https://huggingface.co/users).
 1. Want to contribute a new model? We have added a **detailed guide and templates** to guide you in the process of adding a new model. You can find them in the [`templates`](./templates) folder of the repository. Be sure to check the [contributing guidelines](./CONTRIBUTING.md) and contact the maintainers or open an issue to collect feedbacks before starting your PR.

-To check if each model has an implementation in PyTorch/TensorFlow/Flax or has an associated tokenizer backed by the 🤗 Tokenizers library, refer to [this table](https://huggingface.co/transformers/index.html#bigtable)
+To cehck if each model has an implementation in PyTorch/TensorFlow/Flax or has an associated tokenizer backed by the 🤗 Tokenizers library, refer to [this table](https://huggingface.co/transformers/index.html#bigtable)

 These implementations have been tested on several datasets (see the example scripts) and should match the performances of the original implementations. You can find more details on the performances in the Examples section of the [documentation](https://huggingface.co/transformers/examples.html).

@@ -249,17 +216,13 @@ These implementations have been tested on several datasets (see the example scri

 ## Citation

-We now have a [paper](https://www.aclweb.org/anthology/2020.emnlp-demos.6/) you can cite for the 🤗 Transformers library:
+We now have a [paper](https://arxiv.org/abs/1910.03771) you can cite for the 🤗 Transformers library:
 ```bibtex
-@inproceedings{wolf-etal-2020-transformers,
-    title = "Transformers: State-of-the-Art Natural Language Processing",
-    author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush",
-    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
-    month = oct,
-    year = "2020",
-    address = "Online",
-    publisher = "Association for Computational Linguistics",
-    url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6",
-    pages = "38--45"
+@article{Wolf2019HuggingFacesTS,
+  title={HuggingFace's Transformers: State-of-the-art Natural Language Processing},
+  author={Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush},
+  journal={ArXiv},
+  year={2019},
+  volume={abs/1910.03771}
 }
 ```
@@ -1,19 +1,3 @@
-<!---
-Copyright 2020 The HuggingFace Team. 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.
-->
-
 # Generating the documentation

 To generate the documentation, you first have to build it. Several packages are necessary to build the doc,
@@ -1,15 +1,14 @@
 // These two things need to be updated at each release for the version selector.
 // Last stable version
-const stableVersion = "v4.0.0"
-// Dictionary doc folder to label. The last stable version should have an empty key.
+const stableVersion = "v3.5.0"
+// Dictionary doc folder to label
 const versionMapping = {
    "master": "master",
-    "": "v4.0.0 (stable)",
-    "v3.5.1": "v3.5.0/v3.5.1",
+    "": "v3.5.0/v3.5.1",
    "v3.4.0": "v3.4.0",
    "v3.3.1": "v3.3.0/v3.3.1",
    "v3.2.0": "v3.2.0",
-    "v3.1.0": "v3.1.0",
+    "v3.1.0": "v3.1.0 (stable)",
    "v3.0.2": "v3.0.0/v3.0.1/v3.0.2",
    "v2.11.0": "v2.11.0",
    "v2.10.0": "v2.10.0",
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 Benchmarks
 =======================================================================================================================

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 BERTology
 -----------------------------------------------------------------------------------------------------------------------

@@ -20,7 +20,7 @@ sys.path.insert(0, os.path.abspath('../../src'))
 # -- Project information -----------------------------------------------------

 project = u'transformers'
-copyright = u'2020, The Hugging Face Team, Licenced under the Apache License, Version 2.0'
+copyright = u'2020, huggingface'
 author = u'huggingface'

 # The short X.Y version
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 Converting Tensorflow Checkpoints
 =======================================================================================================================

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 Fine-tuning with custom datasets
 =======================================================================================================================

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 Glossary
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

@@ -22,18 +22,6 @@ State-of-the-art NLP for everyone:
 - Hands-on practitioners
 - AI/ML/NLP teachers and educators

-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 Lower compute costs, smaller carbon footprint:

 - Researchers can share trained models instead of always retraining
@@ -49,14 +37,6 @@ Choose the right framework for every part of a model's lifetime:

 Experimental support for Flax with a few models right now, expected to grow in the coming months.

-`All the model checkpoints <https://huggingface.co/models>`__ are seamlessly integrated from the huggingface.co `model
-hub <https://huggingface.co>`__ where they are uploaded directly by `users <https://huggingface.co/users>`__ and
-`organizations <https://huggingface.co/organizations>`__.
-
-Current number of checkpoints: |checkpoints|
-
-.. |checkpoints| image:: https://img.shields.io/endpoint?url=https://huggingface.co/api/shields/models&color=brightgreen
-
 Contents
 -----------------------------------------------------------------------------------------------------------------------

@@ -88,112 +68,105 @@ and conversion utilities for the following models:
   Pre-training for Natural Language Generation, Translation, and Comprehension
   <https://arxiv.org/pdf/1910.13461.pdf>`__ by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman
   Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer.
-3. :doc:`BARThez <model_doc/barthez>` (from École polytechnique) released with the paper `BARThez: a Skilled Pretrained
-   French Sequence-to-Sequence Model <https://arxiv.org/abs/2010.12321>`__ by Moussa Kamal Eddine, Antoine J.-P.
-   Tixier, Michalis Vazirgiannis.
-4. :doc:`BERT <model_doc/bert>` (from Google) released with the paper `BERT: Pre-training of Deep Bidirectional
+3. :doc:`BERT <model_doc/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.
-5. :doc:`BERT For Sequence Generation <model_doc/bertgeneration>` (from Google) released with the paper `Leveraging
+4. :doc:`BERT For Sequence Generation <model_doc/bertgeneration>` (from Google) released with the paper `Leveraging
   Pre-trained Checkpoints for Sequence Generation Tasks <https://arxiv.org/abs/1907.12461>`__ by Sascha Rothe, Shashi
   Narayan, Aliaksei Severyn.
-6. :doc:`Blenderbot <model_doc/blenderbot>` (from Facebook) released with the paper `Recipes for building an
+5. :doc:`Blenderbot <model_doc/blenderbot>` (from Facebook) released with the paper `Recipes for building an
   open-domain chatbot <https://arxiv.org/abs/2004.13637>`__ by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary
   Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
-7. :doc:`CamemBERT <model_doc/camembert>` (from Inria/Facebook/Sorbonne) released with the paper `CamemBERT: a Tasty
+6. :doc:`CamemBERT <model_doc/camembert>` (from Inria/Facebook/Sorbonne) released with the paper `CamemBERT: a Tasty
   French Language Model <https://arxiv.org/abs/1911.03894>`__ by Louis Martin*, Benjamin Muller*, Pedro Javier Ortiz
   Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
-8. :doc:`CTRL <model_doc/ctrl>` (from Salesforce) released with the paper `CTRL: A Conditional Transformer Language
+7. :doc:`CTRL <model_doc/ctrl>` (from Salesforce) released with the paper `CTRL: A Conditional Transformer Language
   Model for Controllable Generation <https://arxiv.org/abs/1909.05858>`__ by Nitish Shirish Keskar*, Bryan McCann*,
   Lav R. Varshney, Caiming Xiong and Richard Socher.
-9. :doc:`DeBERTa <model_doc/deberta>` (from Microsoft Research) released with the paper `DeBERTa: Decoding-enhanced
+8. :doc:`DeBERTa <model_doc/deberta>` (from Microsoft Research) released with the paper `DeBERTa: Decoding-enhanced
   BERT with Disentangled Attention <https://arxiv.org/abs/2006.03654>`__ by Pengcheng He, Xiaodong Liu, Jianfeng Gao,
   Weizhu Chen.
-10. :doc:`DialoGPT <model_doc/dialogpt>` (from Microsoft Research) released with the paper `DialoGPT: Large-Scale
-    Generative Pre-training for Conversational Response Generation <https://arxiv.org/abs/1911.00536>`__ by Yizhe
-    Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan.
-11. :doc:`DistilBERT <model_doc/distilbert>` (from HuggingFace), released together with the paper `DistilBERT, a
+9. :doc:`DialoGPT <model_doc/dialogpt>` (from Microsoft Research) released with the paper `DialoGPT: Large-Scale
+   Generative Pre-training for Conversational Response Generation <https://arxiv.org/abs/1911.00536>`__ by Yizhe Zhang,
+   Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan.
+10. :doc:`DistilBERT <model_doc/distilbert>` (from HuggingFace), released together with the paper `DistilBERT, a
    distilled version of BERT: smaller, faster, cheaper and lighter <https://arxiv.org/abs/1910.01108>`__ by Victor
    Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into `DistilGPT2
    <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__, RoBERTa into `DistilRoBERTa
    <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__, Multilingual BERT into
    `DistilmBERT <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__ and a German
    version of DistilBERT.
-12. :doc:`DPR <model_doc/dpr>` (from Facebook) released with the paper `Dense Passage Retrieval for Open-Domain
+11. :doc:`DPR <model_doc/dpr>` (from Facebook) released with the paper `Dense Passage Retrieval for Open-Domain
    Question Answering <https://arxiv.org/abs/2004.04906>`__ by Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick
    Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
-13. :doc:`ELECTRA <model_doc/electra>` (from Google Research/Stanford University) released with the paper `ELECTRA:
+12. :doc:`ELECTRA <model_doc/electra>` (from Google Research/Stanford University) released with the paper `ELECTRA:
    Pre-training text encoders as discriminators rather than generators <https://arxiv.org/abs/2003.10555>`__ by Kevin
    Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning.
-14. :doc:`FlauBERT <model_doc/flaubert>` (from CNRS) released with the paper `FlauBERT: Unsupervised Language Model
+13. :doc:`FlauBERT <model_doc/flaubert>` (from CNRS) released with the paper `FlauBERT: Unsupervised Language Model
    Pre-training for French <https://arxiv.org/abs/1912.05372>`__ by Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne,
    Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, Didier Schwab.
-15. :doc:`Funnel Transformer <model_doc/funnel>` (from CMU/Google Brain) released with the paper `Funnel-Transformer:
+14. :doc:`Funnel Transformer <model_doc/funnel>` (from CMU/Google Brain) released with the paper `Funnel-Transformer:
    Filtering out Sequential Redundancy for Efficient Language Processing <https://arxiv.org/abs/2006.03236>`__ by
    Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le.
-16. :doc:`GPT <model_doc/gpt>` (from OpenAI) released with the paper `Improving Language Understanding by Generative
+15. :doc:`GPT <model_doc/gpt>` (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.
-17. :doc:`GPT-2 <model_doc/gpt2>` (from OpenAI) released with the paper `Language Models are Unsupervised Multitask
+16. :doc:`GPT-2 <model_doc/gpt2>` (from OpenAI) released with the paper `Language Models are Unsupervised Multitask
    Learners <https://blog.openai.com/better-language-models/>`__ by Alec Radford*, Jeffrey Wu*, Rewon Child, David
    Luan, Dario Amodei** and Ilya Sutskever**.
-18. :doc:`LayoutLM <model_doc/layoutlm>` (from Microsoft Research Asia) released with the paper `LayoutLM: Pre-training
+17. :doc:`LayoutLM <model_doc/layoutlm>` (from Microsoft Research Asia) released with the paper `LayoutLM: Pre-training
    of Text and Layout for Document Image Understanding <https://arxiv.org/abs/1912.13318>`__ by Yiheng Xu, Minghao Li,
    Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou.
-19. :doc:`Longformer <model_doc/longformer>` (from AllenAI) released with the paper `Longformer: The Long-Document
+18. :doc:`Longformer <model_doc/longformer>` (from AllenAI) released with the paper `Longformer: The Long-Document
    Transformer <https://arxiv.org/abs/2004.05150>`__ by Iz Beltagy, Matthew E. Peters, Arman Cohan.
-20. :doc:`LXMERT <model_doc/lxmert>` (from UNC Chapel Hill) released with the paper `LXMERT: Learning Cross-Modality
+19. :doc:`LXMERT <model_doc/lxmert>` (from UNC Chapel Hill) released with the paper `LXMERT: Learning Cross-Modality
    Encoder Representations from Transformers for Open-Domain Question Answering <https://arxiv.org/abs/1908.07490>`__
    by Hao Tan and Mohit Bansal.
-21. :doc:`MarianMT <model_doc/marian>` Machine translation models trained using `OPUS <http://opus.nlpl.eu/>`__ data by
+20. :doc:`MarianMT <model_doc/marian>` Machine translation models trained using `OPUS <http://opus.nlpl.eu/>`__ data by
    Jörg Tiedemann. The `Marian Framework <https://marian-nmt.github.io/>`__ is being developed by the Microsoft
    Translator Team.
-22. :doc:`MBart <model_doc/mbart>` (from Facebook) released with the paper `Multilingual Denoising Pre-training for
+21. :doc:`MBart <model_doc/mbart>` (from Facebook) released with the paper `Multilingual Denoising Pre-training for
    Neural Machine Translation <https://arxiv.org/abs/2001.08210>`__ by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li,
    Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
-23. :doc:`MPNet <model_doc/mpnet>` (from Microsoft Research) released with the paper `MPNet: Masked and Permuted
-    Pre-training for Language Understanding <https://arxiv.org/abs/2004.09297>`__ by Kaitao Song, Xu Tan, Tao Qin,
-    Jianfeng Lu, Tie-Yan Liu.
-24. :doc:`MT5 <model_doc/mt5>` (from Google AI) released with the paper `mT5: A massively multilingual pre-trained
+22. :doc:`MT5 <model_doc/mt5>` (from Google AI) released with the paper `mT5: A massively multilingual pre-trained
    text-to-text transformer <https://arxiv.org/abs/2010.11934>`__ by Linting Xue, Noah Constant, Adam Roberts, Mihir
    Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel.
-25. :doc:`Pegasus <model_doc/pegasus>` (from Google) released with the paper `PEGASUS: Pre-training with Extracted
+23. :doc:`Pegasus <model_doc/pegasus>` (from Google) released with the paper `PEGASUS: Pre-training with Extracted
    Gap-sentences for Abstractive Summarization <https://arxiv.org/abs/1912.08777>`__> by Jingqing Zhang, Yao Zhao,
    Mohammad Saleh and Peter J. Liu.
-26. :doc:`ProphetNet <model_doc/prophetnet>` (from Microsoft Research) released with the paper `ProphetNet: Predicting
+24. :doc:`ProphetNet <model_doc/prophetnet>` (from Microsoft Research) released with the paper `ProphetNet: Predicting
    Future N-gram for Sequence-to-Sequence Pre-training <https://arxiv.org/abs/2001.04063>`__ by Yu Yan, Weizhen Qi,
    Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
-27. :doc:`Reformer <model_doc/reformer>` (from Google Research) released with the paper `Reformer: The Efficient
+25. :doc:`Reformer <model_doc/reformer>` (from Google Research) released with the paper `Reformer: The Efficient
    Transformer <https://arxiv.org/abs/2001.04451>`__ by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
-28. :doc:`RoBERTa <model_doc/roberta>` (from Facebook), released together with the paper a `Robustly Optimized BERT
+26. :doc:`RoBERTa <model_doc/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. ultilingual BERT into `DistilmBERT
    <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__ and a German version of
    DistilBERT.
-29. :doc:`SqueezeBert <model_doc/squeezebert>` released with the paper `SqueezeBERT: What can computer vision teach NLP
+27. :doc:`SqueezeBert <model_doc/squeezebert>` released with the paper `SqueezeBERT: What can computer vision teach NLP
    about efficient neural networks? <https://arxiv.org/abs/2006.11316>`__ by Forrest N. Iandola, Albert E. Shaw, Ravi
    Krishna, and Kurt W. Keutzer.
-30. :doc:`T5 <model_doc/t5>` (from Google AI) released with the paper `Exploring the Limits of Transfer Learning with a
+28. :doc:`T5 <model_doc/t5>` (from Google AI) released with the paper `Exploring the Limits of Transfer Learning with a
    Unified Text-to-Text Transformer <https://arxiv.org/abs/1910.10683>`__ by Colin Raffel and Noam Shazeer and Adam
    Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
-31. `TAPAS <https://huggingface.co/transformers/master/model_doc/tapas.html>`__ released with the paper `TAPAS: Weakly
-    Supervised Table Parsing via Pre-training <https://arxiv.org/abs/2004.02349>`__ by Jonathan Herzig, Paweł Krzysztof
-    Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos.
-32. :doc:`Transformer-XL <model_doc/transformerxl>` (from Google/CMU) released with the paper `Transformer-XL:
+29. :doc:`Transformer-XL <model_doc/transformerxl>` (from Google/CMU) released with the paper `Transformer-XL:
    Attentive Language Models Beyond a Fixed-Length Context <https://arxiv.org/abs/1901.02860>`__ by Zihang Dai*,
    Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
-33. :doc:`XLM <model_doc/xlm>` (from Facebook) released together with the paper `Cross-lingual Language Model
+30. :doc:`XLM <model_doc/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.
-34. :doc:`XLM-ProphetNet <model_doc/xlmprophetnet>` (from Microsoft Research) released with the paper `ProphetNet:
+31. :doc:`XLM-ProphetNet <model_doc/xlmprophetnet>` (from Microsoft Research) released with the paper `ProphetNet:
    Predicting Future N-gram for Sequence-to-Sequence Pre-training <https://arxiv.org/abs/2001.04063>`__ by Yu Yan,
    Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
-35. :doc:`XLM-RoBERTa <model_doc/xlmroberta>` (from Facebook AI), released together with the paper `Unsupervised
+32. :doc:`XLM-RoBERTa <model_doc/xlmroberta>` (from Facebook AI), released together with the paper `Unsupervised
    Cross-lingual Representation Learning at Scale <https://arxiv.org/abs/1911.02116>`__ by Alexis Conneau*, Kartikay
    Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke
    Zettlemoyer and Veselin Stoyanov.
-36. :doc:`XLNet <model_doc/xlnet>` (from Google/CMU) released with the paper `XLNet: Generalized Autoregressive
+33. :doc:`XLNet <model_doc/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.
+34. `Other community models <https://huggingface.co/models>`__, contributed by the `community
+    <https://huggingface.co/users>`__.


 .. _bigtable:
@@ -246,8 +219,6 @@ TensorFlow and/or Flax.
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |         Longformer          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|            MPNet            |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |           Marian            |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |         MobileBERT          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
@@ -272,8 +243,6 @@ TensorFlow and/or Flax.
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |             T5              |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|            TAPAS            |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |       Transformer-XL        |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |             XLM             |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
@@ -289,6 +258,7 @@ TensorFlow and/or Flax.
 |             mT5             |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+

+
 .. toctree::
    :maxdepth: 2
    :caption: Get started
@@ -354,7 +324,6 @@ TensorFlow and/or Flax.
    model_doc/albert
    model_doc/auto
    model_doc/bart
-    model_doc/barthez
    model_doc/bert
    model_doc/bertgeneration
    model_doc/blenderbot
@@ -375,7 +344,6 @@ TensorFlow and/or Flax.
    model_doc/marian
    model_doc/mbart
    model_doc/mobilebert
-    model_doc/mpnet
    model_doc/mt5
    model_doc/gpt
    model_doc/gpt2
@@ -387,7 +355,6 @@ TensorFlow and/or Flax.
    model_doc/roberta
    model_doc/squeezebert
    model_doc/t5
-    model_doc/tapas
    model_doc/transformerxl
    model_doc/xlm
    model_doc/xlmprophetnet
@@ -1,19 +1,3 @@
-<!---
-Copyright 2020 The HuggingFace Team. 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.
-->
-
 # Installation

 🤗 Transformers is tested on Python 3.6+, and PyTorch 1.1.0+ or TensorFlow 2.0+.
@@ -28,10 +12,9 @@ must install it from source.
 ## Installation with pip

 First you need to install one of, or both, TensorFlow 2.0 and PyTorch.
-Please refer to [TensorFlow installation page](https://www.tensorflow.org/install/pip#tensorflow-2.0-rc-is-available), 
-[PyTorch installation page](https://pytorch.org/get-started/locally/#start-locally) and/or 
-[Flax installation page](https://github.com/google/flax#quick-install)
-regarding the specific install command for your platform.
+Please refer to [TensorFlow installation page](https://www.tensorflow.org/install/pip#tensorflow-2.0-rc-is-available) 
+and/or [PyTorch installation page](https://pytorch.org/get-started/locally/#start-locally) regarding the specific 
+install command for your platform.

 When TensorFlow 2.0 and/or PyTorch has been installed, 🤗 Transformers can be installed using pip as follows:

@@ -51,12 +34,6 @@ or 🤗 Transformers and TensorFlow 2.0 in one line with:
 pip install transformers[tf-cpu]
 ```

-or 🤗 Transformers and Flax in one line with:
-
-```bash
-pip install transformers[flax]
-```
-
 To check 🤗 Transformers is properly installed, run the following command:

 ```bash
@@ -89,19 +66,6 @@ python -c "from transformers import pipeline; print(pipeline('sentiment-analysis

 to check 🤗 Transformers is properly installed.

-
-## With conda
-
-Since Transformers version v4.0.0, we now have a conda channel: `huggingface`.
-
-🤗 Transformers can be installed using conda as follows:
-
-```
-conda install -c huggingface transformers
-```
-
-Follow the installation pages of TensorFlow, PyTorch or Flax to see how to install them with conda. 
-
 ## Caching models

 This library provides pretrained models that will be downloaded and cached locally. Unless you specify a location with
@@ -1,22 +1,9 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 Utilities for Generation
 -----------------------------------------------------------------------------------------------------------------------

 This page lists all the utility functions used by :meth:`~transformers.PretrainedModel.generate`,
 :meth:`~transformers.PretrainedModel.greedy_search`, :meth:`~transformers.PretrainedModel.sample`,
-:meth:`~transformers.PretrainedModel.beam_search`, :meth:`~transformers.PretrainedModel.beam_sample`, and
-:meth:`~transformers.PretrainedModel.group_beam_search`.
+:meth:`~transformers.PretrainedModel.beam_search`, and :meth:`~transformers.PretrainedModel.beam_sample`.

 Most of those are only useful if you are studying the code of the generate methods in the library.

@@ -32,9 +19,6 @@ generation.
 .. autoclass:: transformers.LogitsProcessorList
    :members: __call__

-.. autoclass:: transformers.LogitsWarper
-    :members: __call__
-
 .. autoclass:: transformers.MinLengthLogitsProcessor
    :members: __call__

@@ -56,12 +40,6 @@ generation.
 .. autoclass:: transformers.NoBadWordsLogitsProcessor
    :members: __call__

-.. autoclass:: transformers.PrefixConstrainedLogitsProcessor
-    :members: __call__
-
-.. autoclass:: transformers.HammingDiversityLogitsProcessor
-    :members: __call__
-
 BeamSearch
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -70,10 +48,3 @@ BeamSearch

 .. autoclass:: transformers.BeamSearchScorer
    :members: process, finalize
-
-Utilities
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autofunction:: transformers.top_k_top_p_filtering
-
-.. autofunction:: transformers.tf_top_k_top_p_filtering
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 Custom Layers and Utilities
 -----------------------------------------------------------------------------------------------------------------------

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 Utilities for pipelines
 -----------------------------------------------------------------------------------------------------------------------

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 Utilities for Tokenizers
 -----------------------------------------------------------------------------------------------------------------------

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 Utilities for Trainer
 -----------------------------------------------------------------------------------------------------------------------

@@ -22,8 +10,6 @@ Utilities

 .. autoclass:: transformers.EvalPrediction

-.. autoclass:: transformers.EvaluationStrategy
-
 .. autofunction:: transformers.set_seed

 .. autofunction:: transformers.torch_distributed_zero_first
@@ -34,15 +20,8 @@ Callbacks internals

 .. autoclass:: transformers.trainer_callback.CallbackHandler

-
 Distributed Evaluation
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.trainer_pt_utils.DistributedTensorGatherer
    :members:
-
-
-Distributed Evaluation
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.HfArgumentParser
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 Callbacks
 -----------------------------------------------------------------------------------------------------------------------

@@ -56,8 +44,6 @@ Here is the list of the available :class:`~transformers.TrainerCallback` in the

 .. autoclass:: transformers.ProgressCallback

-.. autoclass:: transformers.EarlyStoppingCallback
-
 .. autoclass:: transformers.integrations.TensorBoardCallback

 .. autoclass:: transformers.integrations.WandbCallback
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 Configuration
 -----------------------------------------------------------------------------------------------------------------------

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 Logging
 -----------------------------------------------------------------------------------------------------------------------

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 Models
 -----------------------------------------------------------------------------------------------------------------------

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 Optimization
 -----------------------------------------------------------------------------------------------------------------------

@@ -74,10 +62,6 @@ Learning Rate Schedules (Pytorch)
    :target: /imgs/warmup_linear_schedule.png
    :alt:

-
-.. autofunction:: transformers.get_polynomial_decay_schedule_with_warmup
-
-
 Warmup (TensorFlow)
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 Model outputs
 -----------------------------------------------------------------------------------------------------------------------

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 Pipelines
 -----------------------------------------------------------------------------------------------------------------------

@@ -73,9 +61,8 @@ FillMaskPipeline
 NerPipeline
 =======================================================================================================================

-.. autoclass:: transformers.NerPipeline
-
-See :class:`~transformers.TokenClassificationPipeline` for all details.
+This class is an alias of the :class:`~transformers.TokenClassificationPipeline` defined below. Please refer to that
+pipeline for documentation and usage examples.

 QuestionAnsweringPipeline
 =======================================================================================================================
@@ -119,13 +106,6 @@ TokenClassificationPipeline
    :special-members: __call__
    :members:

-TranslationPipeline
-=======================================================================================================================
-
-.. autoclass:: transformers.TranslationPipeline
-    :special-members: __call__
-    :members:
-
 ZeroShotClassificationPipeline
 =======================================================================================================================

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 Processors
 -----------------------------------------------------------------------------------------------------------------------

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 Tokenizer
 -----------------------------------------------------------------------------------------------------------------------

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 Trainer
 -----------------------------------------------------------------------------------------------------------------------

@@ -1,19 +1,3 @@
-<!---
-Copyright 2020 The HuggingFace Team. 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.
-->
-
 # Migrating from previous packages

 ## Migrating from transformers `v3.x` to `v4.x`
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 ALBERT
 -----------------------------------------------------------------------------------------------------------------------

@@ -60,13 +48,6 @@ AlbertTokenizer
        create_token_type_ids_from_sequences, save_vocabulary


-AlbertTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AlbertTokenizerFast
-    :members:
-
-
 Albert specific outputs
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 Auto Classes
 -----------------------------------------------------------------------------------------------------------------------

@@ -175,10 +163,3 @@ TFAutoModelForQuestionAnswering

 .. autoclass:: transformers.TFAutoModelForQuestionAnswering
    :members:
-
-
-FlaxAutoModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxAutoModel
-    :members:
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 BART
 -----------------------------------------------------------------------------------------------------------------------

@@ -98,12 +86,6 @@ BartTokenizer
    :members:


-BartTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BartTokenizerFast
-    :members:
-

 BartModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -111,6 +93,8 @@ BartModel
 .. autoclass:: transformers.BartModel
    :members: forward

+.. autofunction:: transformers.models.bart.modeling_bart._prepare_bart_decoder_inputs
+

 BartForConditionalGeneration
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -1,59 +0,0 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
-BARThez
-----------------------------------------------------------------------------------------------------------------------
-
-Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The BARThez model was proposed in `BARThez: a Skilled Pretrained French Sequence-to-Sequence Model`
-<https://arxiv.org/abs/2010.12321>`__ by Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis on 23 Oct,
-2020.
-
-The abstract of the paper:
-
-
-*Inductive transfer learning, enabled by self-supervised learning, have taken the entire Natural Language Processing
-(NLP) field by storm, with models such as BERT and BART setting new state of the art on countless natural language
-understanding tasks. While there are some notable exceptions, most of the available models and research have been
-conducted for the English language. In this work, we introduce BARThez, the first BART model for the French language
-(to the best of our knowledge). BARThez was pretrained on a very large monolingual French corpus from past research
-that we adapted to suit BART's perturbation schemes. Unlike already existing BERT-based French language models such as
-CamemBERT and FlauBERT, BARThez is particularly well-suited for generative tasks, since not only its encoder but also
-its decoder is pretrained. In addition to discriminative tasks from the FLUE benchmark, we evaluate BARThez on a novel
-summarization dataset, OrangeSum, that we release with this paper. We also continue the pretraining of an already
-pretrained multilingual BART on BARThez's corpus, and we show that the resulting model, which we call mBARTHez,
-provides a significant boost over vanilla BARThez, and is on par with or outperforms CamemBERT and FlauBERT.*
-
-The Authors' code can be found `here <https://github.com/moussaKam/BARThez>`__.
-
-
-Examples
-_______________________________________________________________________________________________________________________
-
- BARThez can be fine-tuned on sequence-to-sequence tasks in a similar way as BART, check: `examples/seq2seq/
-  <https://github.com/huggingface/transformers/blob/master/examples/seq2seq/README.md>`__.
-
-
-BarthezTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BarthezTokenizer
-    :members:
-
-
-BarthezTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BarthezTokenizerFast
-    :members:
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 BERT
 -----------------------------------------------------------------------------------------------------------------------

@@ -207,10 +195,3 @@ FlaxBertModel

 .. autoclass:: transformers.FlaxBertModel
    :members: __call__
-
-
-FlaxBertForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxBertForMaskedLM
-    :members: __call__
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 BertGeneration
 -----------------------------------------------------------------------------------------------------------------------

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 Blenderbot
 -----------------------------------------------------------------------------------------------------------------------

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 CamemBERT
 -----------------------------------------------------------------------------------------------------------------------

@@ -54,13 +42,6 @@ CamembertTokenizer
        create_token_type_ids_from_sequences, save_vocabulary


-CamembertTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.CamembertTokenizerFast
-    :members:
-
-
 CamembertModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 CTRL
 -----------------------------------------------------------------------------------------------------------------------

@@ -77,13 +65,6 @@ CTRLLMHeadModel
    :members: forward


-CTRLForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.CTRLForSequenceClassification
-    :members: forward
-
-
 TFCTRLModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 DeBERTa
 -----------------------------------------------------------------------------------------------------------------------

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 DialoGPT
 -----------------------------------------------------------------------------------------------------------------------

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 DistilBERT
 -----------------------------------------------------------------------------------------------------------------------

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 DPR
 -----------------------------------------------------------------------------------------------------------------------

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 ELECTRA
 -----------------------------------------------------------------------------------------------------------------------

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 Encoder Decoder Models
 -----------------------------------------------------------------------------------------------------------------------

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 FlauBERT
 -----------------------------------------------------------------------------------------------------------------------

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 FSMT
 -----------------------------------------------------------------------------------------------------------------------

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 Funnel Transformer
 -----------------------------------------------------------------------------------------------------------------------

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 OpenAI GPT
 -----------------------------------------------------------------------------------------------------------------------

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 OpenAI GPT2
 -----------------------------------------------------------------------------------------------------------------------

@@ -83,14 +71,14 @@ GPT2Model
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.GPT2Model
-    :members: forward, parallelize, deparallelize
+    :members: forward


 GPT2LMHeadModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.GPT2LMHeadModel
-    :members: forward, parallelize, deparallelize
+    :members: forward


 GPT2DoubleHeadsModel
@@ -126,15 +114,3 @@ TFGPT2DoubleHeadsModel

 .. autoclass:: transformers.TFGPT2DoubleHeadsModel
    :members: call
-
-TFGPT2ForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFGPT2ForSequenceClassification
-    :members: call
-
-TFSequenceClassifierOutputWithPast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_tf_outputs.TFSequenceClassifierOutputWithPast
-    :members:
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 LayoutLM
 -----------------------------------------------------------------------------------------------------------------------

@@ -57,13 +45,6 @@ LayoutLMTokenizer
    :members:


-LayoutLMTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.LayoutLMTokenizerFast
-    :members:
-
-
 LayoutLMModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 Longformer
 -----------------------------------------------------------------------------------------------------------------------

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 LXMERT
 -----------------------------------------------------------------------------------------------------------------------

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 MarianMT
 -----------------------------------------------------------------------------------------------------------------------

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 MBart
 -----------------------------------------------------------------------------------------------------------------------

@@ -90,13 +78,6 @@ MBartTokenizer
    :members: build_inputs_with_special_tokens, prepare_seq2seq_batch


-MBartTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MBartTokenizerFast
-    :members:
-
-
 MBartForConditionalGeneration
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 MobileBERT
 -----------------------------------------------------------------------------------------------------------------------

@@ -1,149 +0,0 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
-MPNet
-----------------------------------------------------------------------------------------------------------------------
-
-Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The MPNet model was proposed in `MPNet: Masked and Permuted Pre-training for Language Understanding
-<https://arxiv.org/abs/2004.09297>`__ by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu.
-
-MPNet adopts a novel pre-training method, named masked and permuted language modeling, to inherit the advantages of
-masked language modeling and permuted language modeling for natural language understanding.
-
-The abstract from the paper is the following:
-
-*BERT adopts masked language modeling (MLM) for pre-training and is one of the most successful pre-training models.
-Since BERT neglects dependency among predicted tokens, XLNet introduces permuted language modeling (PLM) for
-pre-training to address this problem. However, XLNet does not leverage the full position information of a sentence and
-thus suffers from position discrepancy between pre-training and fine-tuning. In this paper, we propose MPNet, a novel
-pre-training method that inherits the advantages of BERT and XLNet and avoids their limitations. MPNet leverages the
-dependency among predicted tokens through permuted language modeling (vs. MLM in BERT), and takes auxiliary position
-information as input to make the model see a full sentence and thus reducing the position discrepancy (vs. PLM in
-XLNet). We pre-train MPNet on a large-scale dataset (over 160GB text corpora) and fine-tune on a variety of
-down-streaming tasks (GLUE, SQuAD, etc). Experimental results show that MPNet outperforms MLM and PLM by a large
-margin, and achieves better results on these tasks compared with previous state-of-the-art pre-trained methods (e.g.,
-BERT, XLNet, RoBERTa) under the same model setting.*
-
-Tips:
-
- MPNet 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]`).
-
-The original code can be found `here <https://github.com/microsoft/MPNet>`__.
-
-MPNetConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MPNetConfig
-    :members:
-
-
-MPNetTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MPNetTokenizer
-    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
-        create_token_type_ids_from_sequences, save_vocabulary
-
-
-MPNetTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MPNetTokenizerFast
-    :members:
-
-
-MPNetModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MPNetModel
-    :members: forward
-
-
-MPNetForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MPNetForMaskedLM
-    :members: forward
-
-
-MPNetForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MPNetForSequenceClassification
-    :members: forward
-
-
-MPNetForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MPNetForMultipleChoice
-    :members: forward
-
-
-MPNetForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MPNetForTokenClassification
-    :members: forward
-
-
-MPNetForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MPNetForQuestionAnswering
-    :members: forward
-
-
-TFMPNetModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFMPNetModel
-    :members: call
-
-
-TFMPNetForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFMPNetForMaskedLM
-    :members: call
-
-
-TFMPNetForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFMPNetForSequenceClassification
-    :members: call
-
-
-TFMPNetForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFMPNetForMultipleChoice
-    :members: call
-
-
-TFMPNetForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFMPNetForTokenClassification
-    :members: call
-
-
-TFMPNetForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFMPNetForQuestionAnswering
-    :members: call
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 MT5
 -----------------------------------------------------------------------------------------------------------------------

@@ -37,22 +25,6 @@ MT5Config
    :members:


-MT5Tokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MT5Tokenizer
-
-See :class:`~transformers.T5Tokenizer` for all details.
-
-
-MT5TokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MT5TokenizerFast
-
-See :class:`~transformers.T5TokenizerFast` for all details.
-
-
 MT5Model
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -67,13 +39,6 @@ MT5ForConditionalGeneration
    :members:


-MT5EncoderModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MT5EncoderModel
-    :members:
-
-
 TFMT5Model
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -86,10 +51,3 @@ TFMT5ForConditionalGeneration

 .. autoclass:: transformers.TFMT5ForConditionalGeneration
    :members:
-
-
-TFMT5EncoderModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFMT5EncoderModel
-    :members:
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 Pegasus
 -----------------------------------------------------------------------------------------------------------------------

@@ -112,13 +100,6 @@ warning: ``add_tokens`` does not work at the moment.
    :members: __call__, prepare_seq2seq_batch


-PegasusTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.PegasusTokenizerFast
-    :members:
-
-
 PegasusForConditionalGeneration
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 ProphetNet
 -----------------------------------------------------------------------------------------------------------------------

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 RAG
 -----------------------------------------------------------------------------------------------------------------------

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 Reformer
 -----------------------------------------------------------------------------------------------------------------------

@@ -163,13 +151,6 @@ ReformerTokenizer
    :members: save_vocabulary


-ReformerTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.ReformerTokenizerFast
-    :members:
-
-
 ReformerModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 RetriBERT
 -----------------------------------------------------------------------------------------------------------------------

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 RoBERTa
 -----------------------------------------------------------------------------------------------------------------------

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 SqueezeBERT
 -----------------------------------------------------------------------------------------------------------------------

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 T5
 -----------------------------------------------------------------------------------------------------------------------

@@ -107,32 +95,20 @@ T5Tokenizer
        create_token_type_ids_from_sequences, prepare_seq2seq_batch, save_vocabulary


-T5TokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.T5TokenizerFast
-    :members:
-
-
 T5Model
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.T5Model
-    :members: forward, parallelize, deparallelize
+    :members: forward


 T5ForConditionalGeneration
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.T5ForConditionalGeneration
-    :members: forward, parallelize, deparallelize
-
-T5EncoderModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.T5EncoderModel
    :members: forward

+
 TFT5Model
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -145,9 +121,3 @@ TFT5ForConditionalGeneration

 .. autoclass:: transformers.TFT5ForConditionalGeneration
    :members: call
-
-TFT5EncoderModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFT5EncoderModel
-    :members: call
@@ -1,444 +0,0 @@
-TAPAS
-----------------------------------------------------------------------------------------------------------------------
-
-Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The TAPAS model was proposed in `TAPAS: Weakly Supervised Table Parsing via Pre-training
-<https://www.aclweb.org/anthology/2020.acl-main.398>`__ by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller,
-Francesco Piccinno and Julian Martin Eisenschlos. It's a BERT-based model specifically designed (and pre-trained) for
-answering questions about tabular data. Compared to BERT, TAPAS uses relative position embeddings and has 7 token types
-that encode tabular structure. TAPAS is pre-trained on the masked language modeling (MLM) objective on a large dataset
-comprising millions of tables from English Wikipedia and corresponding texts. For question answering, TAPAS has 2 heads
-on top: a cell selection head and an aggregation head, for (optionally) performing aggregations (such as counting or
-summing) among selected cells. TAPAS has been fine-tuned on several datasets: `SQA
-<https://www.microsoft.com/en-us/download/details.aspx?id=54253>`_ (Sequential Question Answering by Microsoft), `WTQ
-<https://github.com/ppasupat/WikiTableQuestions>`__ (Wiki Table Questions by Stanford University) and `WikiSQL
-<https://github.com/salesforce/WikiSQL>`__ (by Salesforce). It achieves state-of-the-art on both SQA and WTQ, while
-having comparable performance to SOTA on WikiSQL, with a much simpler architecture.
-
-The abstract from the paper is the following:
-
-*Answering natural language questions over tables is usually seen as a semantic parsing task. To alleviate the
-collection cost of full logical forms, one popular approach focuses on weak supervision consisting of denotations
-instead of logical forms. However, training semantic parsers from weak supervision poses difficulties, and in addition,
-the generated logical forms are only used as an intermediate step prior to retrieving the denotation. In this paper, we
-present TAPAS, an approach to question answering over tables without generating logical forms. TAPAS trains from weak
-supervision, and predicts the denotation by selecting table cells and optionally applying a corresponding aggregation
-operator to such selection. TAPAS extends BERT's architecture to encode tables as input, initializes from an effective
-joint pre-training of text segments and tables crawled from Wikipedia, and is trained end-to-end. We experiment with
-three different semantic parsing datasets, and find that TAPAS outperforms or rivals semantic parsing models by
-improving state-of-the-art accuracy on SQA from 55.1 to 67.2 and performing on par with the state-of-the-art on WIKISQL
-and WIKITQ, but with a simpler model architecture. We additionally find that transfer learning, which is trivial in our
-setting, from WIKISQL to WIKITQ, yields 48.7 accuracy, 4.2 points above the state-of-the-art.*
-
-In addition, the authors have further pre-trained TAPAS to recognize **table entailment**, by creating a balanced
-dataset of millions of automatically created training examples which are learned in an intermediate step prior to
-fine-tuning. The authors of TAPAS call this further pre-training intermediate pre-training (since TAPAS is first
-pre-trained on MLM, and then on another dataset). They found that intermediate pre-training further improves
-performance on SQA, achieving a new state-of-the-art as well as state-of-the-art on `TabFact
-<https://github.com/wenhuchen/Table-Fact-Checking>`__, a large-scale dataset with 16k Wikipedia tables for table
-entailment (a binary classification task). For more details, see their follow-up paper: `Understanding tables with
-intermediate pre-training <https://www.aclweb.org/anthology/2020.findings-emnlp.27/>`__ by Julian Martin Eisenschlos,
-Syrine Krichene and Thomas Müller.
-
-The original code can be found `here <https://github.com/google-research/tapas>`__.
-
-Tips:
-
- TAPAS is a model that uses relative position embeddings by default (restarting the position embeddings at every cell
-  of the table). Note that this is something that was added after the publication of the original TAPAS paper.
-  According to the authors, this usually results in a slightly better performance, and allows you to encode longer
-  sequences without running out of embeddings. This is reflected in the ``reset_position_index_per_cell`` parameter of
-  :class:`~transformers.TapasConfig`, which is set to ``True`` by default. The latest versions of the models available
-  in the `model hub <https://huggingface.co/models?search=tapas>`_ all use relative position embeddings. You can still
-  use the ones with absolute position embeddings by passing in a certain version when calling the
-  ``.from_pretrained()`` method as explained in the model cards. Note that it's usually advised to pad the inputs on
-  the right rather than the left.
- TAPAS is based on BERT, so ``TAPAS-base`` for example corresponds to a ``BERT-base`` architecture. Of course,
-  TAPAS-large will result in the best performance (the results reported in the paper are from TAPAS-large). Results of
-  the various sized models are shown on the `original Github repository <https://github.com/google-research/tapas>`_.
- TAPAS has checkpoints fine-tuned on SQA, which are capable of answering questions related to a table in a
-  conversational set-up. This means that you can ask follow-up questions such as "what is his age?" related to the
-  previous question. Note that the forward pass of TAPAS is a bit different in case of a conversational set-up: in that
-  case, you have to feed every table-question pair one by one to the model, such that the `prev_labels` token type ids
-  can be overwritten by the predicted `labels` of the model to the previous question. See "Usage" section for more
-  info.
- TAPAS is similar to BERT and therefore relies on the masked language modeling (MLM) objective. 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.
-
-
-Usage: fine-tuning
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Here we explain how you can fine-tune :class:`~transformers.TapasForQuestionAnswering` on your own dataset.
-
-**STEP 1: Choose one of the 3 ways in which you can use TAPAS - or experiment**
-
-Basically, there are 3 different ways in which one can fine-tune :class:`~transformers.TapasForQuestionAnswering`,
-corresponding to the different datasets on which Tapas was fine-tuned:
-
-1. SQA: if you're interested in asking follow-up questions related to a table, in a conversational set-up. For example
-   if you first ask "what's the name of the first actor?" then you can ask a follow-up question such as "how old is
-   he?". Here, questions do not involve any aggregation (all questions are cell selection questions).
-2. WTQ: if you're not interested in asking questions in a conversational set-up, but rather just asking questions
-   related to a table, which might involve aggregation, such as counting a number of rows, summing up cell values or
-   averaging cell values. You can then for example ask "what's the total number of goals Cristiano Ronaldo made in his
-   career?". This case is also called **weak supervision**, since the model itself must learn the appropriate
-   aggregation operator (SUM/COUNT/AVERAGE/NONE) given only the answer to the question as supervision.
-3. WikiSQL-supervised: this dataset is based on WikiSQL with the model being given the ground truth aggregation
-   operator during training. This is also called **strong supervision**. Here, learning the appropriate aggregation
-   operator is much easier.
-
-To summarize:
-
-+------------------------------------+----------------------+-------------------------------------------------------------------------------------------------------------------+
-| **Task**                           | **Example dataset**  | **Description**                                                                                                   |
-+------------------------------------+----------------------+-------------------------------------------------------------------------------------------------------------------+
-| Conversational                     | SQA                  | Conversational, only cell selection questions                                                                     |
-+------------------------------------+----------------------+-------------------------------------------------------------------------------------------------------------------+
-| Weak supervision for aggregation   | WTQ                  | Questions might involve aggregation, and the model must learn this given only the answer as supervision           |
-+------------------------------------+----------------------+-------------------------------------------------------------------------------------------------------------------+
-| Strong supervision for aggregation | WikiSQL-supervised   | Questions might involve aggregation, and the model must learn this given the gold aggregation operator            |
-+------------------------------------+----------------------+-------------------------------------------------------------------------------------------------------------------+
-
-Initializing a model with a pre-trained base and randomly initialized classification heads from the model hub can be
-done as follows (be sure to have installed the `torch-scatter dependency <https://github.com/rusty1s/pytorch_scatter>`_
-for your environment):
-
-.. code-block::
-
-        >>> from transformers import TapasConfig, TapasForQuestionAnswering
-
-        >>> # for example, the base sized model with default SQA configuration
-        >>> model = TapasForQuestionAnswering.from_pretrained('google/tapas-base')
-
-        >>> # or, the base sized model with WTQ configuration
-        >>> config = TapasConfig(
-        ...            num_aggregation_labels = 4,
-        ...            use_answer_as_supervision = True,
-        ...            answer_loss_cutoff = 0.664694,
-        ...            cell_selection_preference = 0.207951,
-        ...            huber_loss_delta = 0.121194,
-        ...            init_cell_selection_weights_to_zero = True,
-        ...            select_one_column = True,
-        ...            allow_empty_column_selection = False,
-        ...            temperature = 0.0352513,
-        ... )
-        >>> model = TapasForQuestionAnswering.from_pretrained('google/tapas-base', config=config)
-
-        >>> # or, the base sized model with WikiSQL configuration
-        >>> config = TapasConfig(
-        ...            num_aggregation_labels = 4
-        ...            use_answer_as_supervision = False
-        ...            answer_loss_cutoff = 36.4519
-        ...            cell_selection_preference = 0.903421
-        ...            huber_loss_delta = 222.088
-        ...            init_cell_selection_weights_to_zero = True
-        ...            select_one_column = True
-        ...            allow_empty_column_selection = True
-        ...            temperature = 0.763141
-        ... )
-        >>> model = TapasForQuestionAnswering.from_pretrained('google/tapas-base', config=config)
-
-
-Of course, you don't necessarily have to follow one of these three ways in which TAPAS was fine-tuned. You can also
-experiment by defining any hyperparameters you want when initializing :class:`~transformers.TapasConfig`, and then
-create a :class:`~transformers.TapasForQuestionAnswering` based on that configuration. For example, if you have a
-dataset that has both conversational questions and questions that might involve aggregation, then you can do it this
-way. Here's an example:
-
-.. code-block::
-
-        >>> from transformers import TapasConfig, TapasForQuestionAnswering
-
-        >>> # you can initialize the classification heads any way you want (see docs of TapasConfig)
-        >>> config = TapasConfig(num_aggregation_labels=3, average_logits_per_cell=True, select_one_column=False)
-        >>> # initializing the pre-trained base sized model with our custom classification heads
-        >>> model = TapasForQuestionAnswering.from_pretrained('google/tapas-base', config=config)
-
-What you can also do is start from an already fine-tuned checkpoint. A note here is that the already fine-tuned
-checkpoint on WTQ has some issues due to the L2-loss which is somewhat brittle. See `here
-<https://github.com/google-research/tapas/issues/91#issuecomment-735719340>`__ for more info.
-
-For a list of all pre-trained and fine-tuned TAPAS checkpoints available in the HuggingFace model hub, see `here
-<https://huggingface.co/models?search=tapas>`__.
-
-**STEP 2: Prepare your data in the SQA format**
-
-Second, no matter what you picked above, you should prepare your dataset in the `SQA format
-<https://www.microsoft.com/en-us/download/details.aspx?id=54253>`__. This format is a TSV/CSV file with the following
-columns:
-
- ``id``: optional, id of the table-question pair, for bookkeeping purposes.
- ``annotator``: optional, id of the person who annotated the table-question pair, for bookkeeping purposes.
- ``position``: integer indicating if the question is the first, second, third,... related to the table. Only required
-  in case of conversational setup (SQA). You don't need this column in case you're going for WTQ/WikiSQL-supervised.
- ``question``: string
- ``table_file``: string, name of a csv file containing the tabular data
- ``answer_coordinates``: list of one or more tuples (each tuple being a cell coordinate, i.e. row, column pair that is
-  part of the answer)
- ``answer_text``: list of one or more strings (each string being a cell value that is part of the answer)
- ``aggregation_label``: index of the aggregation operator. Only required in case of strong supervision for aggregation
-  (the WikiSQL-supervised case)
- ``float_answer``: the float answer to the question, if there is one (np.nan if there isn't). Only required in case of
-  weak supervision for aggregation (such as WTQ and WikiSQL)
-
-The tables themselves should be present in a folder, each table being a separate csv file. Note that the authors of the
-TAPAS algorithm used conversion scripts with some automated logic to convert the other datasets (WTQ, WikiSQL) into the
-SQA format. The author explains this `here
-<https://github.com/google-research/tapas/issues/50#issuecomment-705465960>`__. Interestingly, these conversion scripts
-are not perfect (the ``answer_coordinates`` and ``float_answer`` fields are populated based on the ``answer_text``),
-meaning that WTQ and WikiSQL results could actually be improved.
-
-**STEP 3: Convert your data into PyTorch tensors using TapasTokenizer**
-
-Third, given that you've prepared your data in this TSV/CSV format (and corresponding CSV files containing the tabular
-data), you can then use :class:`~transformers.TapasTokenizer` to convert table-question pairs into :obj:`input_ids`,
-:obj:`attention_mask`, :obj:`token_type_ids` and so on. Again, based on which of the three cases you picked above,
-:class:`~transformers.TapasForQuestionAnswering` requires different inputs to be fine-tuned:
-
-+------------------------------------+----------------------------------------------------------------------------------------------+
-| **Task**                           | **Required inputs**                                                                          |
-+------------------------------------+----------------------------------------------------------------------------------------------+
-| Conversational                     | ``input_ids``, ``attention_mask``, ``token_type_ids``, ``labels``                            |
-+------------------------------------+----------------------------------------------------------------------------------------------+
-| Weak supervision for aggregation   | ``input_ids``, ``attention_mask``, ``token_type_ids``, ``labels``, ``numeric_values``,       |
-|                                    | ``numeric_values_scale``, ``float_answer``                                                   |
-+------------------------------------+----------------------------------------------------------------------------------------------+
-| Strong supervision for aggregation | ``input ids``, ``attention mask``, ``token type ids``, ``labels``, ``aggregation_labels``    |
-+------------------------------------+----------------------------------------------------------------------------------------------+
-
-:class:`~transformers.TapasTokenizer` creates the ``labels``, ``numeric_values`` and ``numeric_values_scale`` based on
-the ``answer_coordinates`` and ``answer_text`` columns of the TSV file. The ``float_answer`` and ``aggregation_labels``
-are already in the TSV file of step 2. Here's an example:
-
-.. code-block::
-
-        >>> from transformers import TapasTokenizer
-        >>> import pandas as pd
-
-        >>> model_name = 'google/tapas-base'
-        >>> tokenizer = TapasTokenizer.from_pretrained(model_name)
-
-        >>> data = {'Actors': ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"], 'Number of movies': ["87", "53", "69"]}
-        >>> queries = ["What is the name of the first actor?", "How many movies has George Clooney played in?", "What is the total number of movies?"]
-        >>> answer_coordinates = [[(0, 0)], [(2, 1)], [(0, 1), (1, 1), (2, 1)]]
-        >>> answer_text = [["Brad Pitt"], ["69"], ["209"]]
-        >>> table = pd.DataFrame.from_dict(data)
-        >>> inputs = tokenizer(table=table, queries=queries, answer_coordinates=answer_coordinates, answer_text=answer_text, padding='max_length', return_tensors='pt')
-        >>> inputs
-        {'input_ids': tensor([[ ... ]]), 'attention_mask': tensor([[...]]), 'token_type_ids': tensor([[[...]]]),
-        'numeric_values': tensor([[ ... ]]), 'numeric_values_scale: tensor([[ ... ]]), labels: tensor([[ ... ]])}
-
-Note that :class:`~transformers.TapasTokenizer` expects the data of the table to be **text-only**. You can use
-``.astype(str)`` on a dataframe to turn it into text-only data. Of course, this only shows how to encode a single
-training example. It is advised to create a PyTorch dataset and a corresponding dataloader:
-
-.. code-block::
-
-        >>> import torch
-        >>> import pandas as pd
-
-        >>> tsv_path = "your_path_to_the_tsv_file"
-        >>> table_csv_path = "your_path_to_a_directory_containing_all_csv_files"
-
-        >>> class TableDataset(torch.utils.data.Dataset):
-        ...     def __init__(self, data, tokenizer):
-        ...         self.data = data
-        ...         self.tokenizer = tokenizer
-        ...
-        ...     def __getitem__(self, idx):
-        ...         item = data.iloc[idx]
-        ...         table = pd.read_csv(table_csv_path + item.table_file).astype(str) # be sure to make your table data text only
-        ...         encoding = self.tokenizer(table=table, 
-        ...                                   queries=item.question, 
-        ...                                   answer_coordinates=item.answer_coordinates, 
-        ...                                   answer_text=item.answer_text,
-        ...                                   truncation=True,
-        ...                                   padding="max_length",
-        ...                                   return_tensors="pt"
-        ...         )
-        ...         # remove the batch dimension which the tokenizer adds by default
-        ...         encoding = {key: val.squeeze(0) for key, val in encoding.items()}
-        ...         # add the float_answer which is also required (weak supervision for aggregation case)
-        ...         encoding["float_answer"] = torch.tensor(item.float_answer) 
-        ...         return encoding
-        ...
-        ...     def __len__(self):
-        ...        return len(self.data)
-
-        >>> data = pd.read_csv(tsv_path, sep='\t')
-        >>> train_dataset = TableDataset(data, tokenizer)
-        >>> train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=32)
-
-Note that here, we encode each table-question pair independently. This is fine as long as your dataset is **not
-conversational**. In case your dataset involves conversational questions (such as in SQA), then you should first group
-together the ``queries``, ``answer_coordinates`` and ``answer_text`` per table (in the order of their ``position``
-index) and batch encode each table with its questions. This will make sure that the ``prev_labels`` token types (see
-docs of :class:`~transformers.TapasTokenizer`) are set correctly.
-
-**STEP 4: Train (fine-tune) TapasForQuestionAnswering**
-
-You can then fine-tune :class:`~transformers.TapasForQuestionAnswering` using native PyTorch as follows (shown here for
-the weak supervision for aggregation case):
-
-.. code-block::
-
-        >>> from transformers import TapasConfig, TapasForQuestionAnswering, AdamW
-
-        >>> # this is the default WTQ configuration
-        >>> config = TapasConfig(
-        ...            num_aggregation_labels = 4,
-        ...            use_answer_as_supervision = True,
-        ...            answer_loss_cutoff = 0.664694,
-        ...            cell_selection_preference = 0.207951,
-        ...            huber_loss_delta = 0.121194,
-        ...            init_cell_selection_weights_to_zero = True,
-        ...            select_one_column = True,
-        ...            allow_empty_column_selection = False,
-        ...            temperature = 0.0352513,
-        ... )
-        >>> model = TapasForQuestionAnswering.from_pretrained("google/tapas-base", config=config)
-
-        >>> optimizer = AdamW(model.parameters(), lr=5e-5)
-
-        >>> for epoch in range(2):  # loop over the dataset multiple times
-        ...    for idx, batch in enumerate(train_dataloader):
-        ...         # get the inputs; 
-        ...         input_ids = batch["input_ids"]
-        ...         attention_mask = batch["attention_mask"]
-        ...         token_type_ids = batch["token_type_ids"]
-        ...         labels = batch["labels"]
-        ...         numeric_values = batch["numeric_values"]
-        ...         numeric_values_scale = batch["numeric_values_scale"]
-        ...         float_answer = batch["float_answer"]
-
-        ...         # zero the parameter gradients
-        ...         optimizer.zero_grad()
-
-        ...         # forward + backward + optimize
-        ...         outputs = model(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, 
-        ...                        labels=labels, numeric_values=numeric_values, numeric_values_scale=numeric_values_scale, 
-        ...                        float_answer=float_answer)
-        ...         loss = outputs.loss
-        ...         loss.backward()
-        ...         optimizer.step()
-
-Usage: inference
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Here we explain how you can use :class:`~transformers.TapasForQuestionAnswering` for inference (i.e. making predictions
-on new data). For inference, only ``input_ids``, ``attention_mask`` and ``token_type_ids`` (which you can obtain using
-:class:`~transformers.TapasTokenizer`) have to be provided to the model to obtain the logits. Next, you can use the
-handy ``convert_logits_to_predictions`` method of :class:`~transformers.TapasTokenizer` to convert these into predicted
-coordinates and optional aggregation indices.
-
-However, note that inference is **different** depending on whether or not the setup is conversational. In a
-non-conversational set-up, inference can be done in parallel on all table-question pairs of a batch. Here's an example
-of that:
-
-.. code-block::
-
-        >>> from transformers import TapasTokenizer, TapasForQuestionAnswering
-        >>> import pandas as pd 
-
-        >>> model_name = 'google/tapas-base-finetuned-wtq'
-        >>> model = TapasForQuestionAnswering.from_pretrained(model_name)
-        >>> tokenizer = TapasTokenizer.from_pretrained(model_name)
-
-        >>> data = {'Actors': ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"], 'Number of movies': ["87", "53", "69"]}
-        >>> queries = ["What is the name of the first actor?", "How many movies has George Clooney played in?", "What is the total number of movies?"]
-        >>> table = pd.DataFrame.from_dict(data)
-        >>> inputs = tokenizer(table=table, queries=queries, padding='max_length', return_tensors="pt") 
-        >>> outputs = model(**inputs)
-        >>> predicted_answer_coordinates, predicted_aggregation_indices = tokenizer.convert_logits_to_predictions(
-        ...         inputs, 
-        ...         outputs.logits.detach(), 
-        ...         outputs.logits_aggregation.detach()
-        ...)
-
-        >>> # let's print out the results:
-        >>> id2aggregation = {0: "NONE", 1: "SUM", 2: "AVERAGE", 3:"COUNT"}
-        >>> aggregation_predictions_string = [id2aggregation[x] for x in predicted_aggregation_indices]
-
-        >>> answers = []
-        >>> for coordinates in predicted_answer_coordinates:
-        ...   if len(coordinates) == 1:
-        ...     # only a single cell:
-        ...     answers.append(table.iat[coordinates[0]])
-        ...   else:
-        ...     # multiple cells
-        ...     cell_values = []
-        ...     for coordinate in coordinates:
-        ...        cell_values.append(table.iat[coordinate])
-        ...     answers.append(", ".join(cell_values))
-
-        >>> display(table)
-        >>> print("")
-        >>> for query, answer, predicted_agg in zip(queries, answers, aggregation_predictions_string):
-        ...   print(query)
-        ...   if predicted_agg == "NONE":
-        ...     print("Predicted answer: " + answer)
-        ...   else:
-        ...     print("Predicted answer: " + predicted_agg + " > " + answer)    
-        What is the name of the first actor?
-        Predicted answer: Brad Pitt
-        How many movies has George Clooney played in?
-        Predicted answer: COUNT > 69
-        What is the total number of movies?
-        Predicted answer: SUM > 87, 53, 69
-
-In case of a conversational set-up, then each table-question pair must be provided **sequentially** to the model, such
-that the ``prev_labels`` token types can be overwritten by the predicted ``labels`` of the previous table-question
-pair.
-
-
-Tapas specific outputs
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.models.tapas.modeling_tapas.TableQuestionAnsweringOutput
-    :members:
-
-
-TapasConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TapasConfig
-    :members:
-
-
-TapasTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TapasTokenizer
-    :members: __call__, convert_logits_to_predictions, save_vocabulary
-
-
-TapasModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TapasModel
-    :members:
-
-
-TapasForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TapasForMaskedLM
-    :members:
-
-
-TapasForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TapasForSequenceClassification
-    :members: forward
-
-
-TapasForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TapasForQuestionAnswering
-    :members:
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 Transformer XL
 -----------------------------------------------------------------------------------------------------------------------

@@ -87,11 +75,6 @@ TransfoXLLMHeadModel
 .. autoclass:: transformers.TransfoXLLMHeadModel
    :members: forward

-TransfoXLForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TransfoXLForSequenceClassification
-    :members: forward

 TFTransfoXLModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -105,11 +88,3 @@ TFTransfoXLLMHeadModel

 .. autoclass:: transformers.TFTransfoXLLMHeadModel
    :members: call
-
-
-Internal Layers
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AdaptiveEmbedding
-
-.. autoclass:: transformers.TFAdaptiveEmbedding
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 XLM
 -----------------------------------------------------------------------------------------------------------------------

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 XLM-ProphetNet
 -----------------------------------------------------------------------------------------------------------------------

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 XLM-RoBERTa
 -----------------------------------------------------------------------------------------------------------------------

@@ -62,13 +50,6 @@ XLMRobertaTokenizer
        create_token_type_ids_from_sequences, save_vocabulary


-XLMRobertaTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.XLMRobertaTokenizerFast
-    :members:
-
-
 XLMRobertaModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 XLNet
 -----------------------------------------------------------------------------------------------------------------------

@@ -62,13 +50,6 @@ XLNetTokenizer
        create_token_type_ids_from_sequences, save_vocabulary


-XLNetTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.XLNetTokenizerFast
-    :members:
-
-
 XLNet specific outputs
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 Model sharing and uploading
 =======================================================================================================================

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 Summary of the models
 =======================================================================================================================

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 Multi-lingual models
 =======================================================================================================================

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 Perplexity of fixed-length models
 =======================================================================================================================

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 Philosophy
 =======================================================================================================================

@@ -1,19 +1,8 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
-reprocessing data
+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.

@@ -63,7 +52,7 @@ The tokenizer can decode a list of token ids in a proper sentence:
    "[CLS] Hello, I'm a single sentence! [SEP]"

 As you can see, the tokenizer automatically added some special tokens that the model expects. Not all models need
-special tokens; for instance, if we had used `gpt2-medium` instead of `bert-base-cased` to create our tokenizer, we
+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``.

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 Pretrained models
 =======================================================================================================================

@@ -345,12 +333,6 @@ For a list that includes all community-uploaded models, refer to `https://huggin
 |                    | ``facebook/bart-large-cnn``                                | | 24-layer, 1024-hidden, 16-heads, 406M parameters       (same as large)                                                              |
 |                    |                                                            | | bart-large base architecture finetuned on cnn summarization task                                                                    |
 +--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| BARThez            | ``moussaKam/barthez``                                      | | 12-layer,  768-hidden, 12-heads, 216M parameters                                                                                    |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/moussaKam/BARThez>`__)                                                                              |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``moussaKam/mbarthez``                                     | | 24-layer, 1024-hidden, 16-heads, 561M parameters                                                                                    |
-+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 | DialoGPT           | ``DialoGPT-small``                                         | | 12-layer, 768-hidden, 12-heads, 124M parameters                                                                                     |
 |                    |                                                            | | Trained on English text: 147M conversation-like exchanges extracted from Reddit.                                                    |
 |                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 Quick tour
 =======================================================================================================================

@@ -194,7 +182,6 @@ and get tensors back. You can specify all of that to the tokenizer:
    ...     ["We are very happy to show you the 🤗 Transformers library.", "We hope you don't hate it."],
    ...     padding=True,
    ...     truncation=True,
-    ...     max_length=512,
    ...     return_tensors="pt"
    ... )
    >>> ## TENSORFLOW CODE
@@ -202,7 +189,6 @@ and get tensors back. You can specify all of that to the tokenizer:
    ...     ["We are very happy to show you the 🤗 Transformers library.", "We hope you don't hate it."],
    ...     padding=True,
    ...     truncation=True,
-    ...     max_length=512,
    ...     return_tensors="tf"
    ... )

@@ -254,9 +240,7 @@ activations of the model.
           [ 0.08181786, -0.04179301]], dtype=float32)>,)

 The model can return more than just the final activations, which is why the output is a tuple. Here we only asked for
-the final activations, so we get a tuple with one element.
-
-.. note::
+the final activations, so we get a tuple with one element. .. note::

    All 🤗 Transformers models (PyTorch or TensorFlow) return the activations of the model *before* the final activation
    function (like SoftMax) since this final activation function is often fused with the loss.
@@ -1,15 +1,4 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
+***********************************************************************************************************************
 Exporting transformers models
 ***********************************************************************************************************************

@@ -81,8 +70,8 @@ inference.
    optimizations afterwards.

 .. note::
-    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>`_.
+    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
 -----------------------------------------------------------------------------------------------------------------------
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 Summary of the tasks
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 Testing
 =======================================================================================================================

@@ -921,10 +909,9 @@ pipelines), then we should run that test in the non-slow test suite. If it's foc
 such as the documentation or the examples, then we should run these tests in the slow test suite. And then, to refine
 this approach we should have exceptions:

-* All tests that need to download a heavy set of weights or a dataset that is larger than ~50MB (e.g., model or
-  tokenizer integration tests, pipeline integration tests) should be set to slow. If you're adding a new model, you
-  should create and upload to the hub a tiny version of it (with random weights) for integration tests. This is
-  discussed in the following paragraphs.
+* All tests that need to download a heavy set of weights (e.g., model or tokenizer integration tests, pipeline
+  integration tests) should be set to slow. If you're adding a new model, you should create and upload to the hub a
+  tiny version of it (with random weights) for integration tests. This is discussed in the following paragraphs.
 * All tests that need to do a training not specifically optimized to be fast should be set to slow.
 * We can introduce exceptions if some of these should-be-non-slow tests are excruciatingly slow, and set them to
  ``@slow``. Auto-modeling tests, which save and load large files to disk, are a good example of tests that are marked
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 Summary of the tokenizers
 -----------------------------------------------------------------------------------------------------------------------

@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. 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.
-
 Training and fine-tuning
 =======================================================================================================================

@@ -1,73 +1,58 @@
-<!---
-Copyright 2020 The HuggingFace Team. 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.
-->
-
 # Examples

-This folder contains actively maintained examples of use of 🤗 Transformers organized along NLP tasks. If you are looking for an example that used to
-be in this folder, it may have moved to our [research projects](https://github.com/huggingface/transformers/tree/master/examples/research_projects) subfolder (which contains frozen snapshots of research projects).
+Version 2.9 of 🤗 Transformers introduced a new [`Trainer`](https://github.com/huggingface/transformers/blob/master/src/transformers/trainer.py) class for PyTorch, and its equivalent [`TFTrainer`](https://github.com/huggingface/transformers/blob/master/src/transformers/trainer_tf.py) for TF 2.
+Running the examples requires PyTorch 1.3.1+ or TensorFlow 2.2+.
+
+Here is the list of all our examples:
+- **grouped by task** (all official examples work for multiple models)
+- with information on whether they are **built on top of `Trainer`/`TFTrainer`** (if not, they still work, they might
+  just lack some features),
+- whether or not they leverage the [🤗 Datasets](https://github.com/huggingface/datasets) library.
+- links to **Colab notebooks** to walk through the scripts and run them easily,
+- links to **Cloud deployments** to be able to deploy large-scale trainings in the Cloud with little to no setup.
+

 ## Important note

 **Important**

-To make sure you can successfully run the latest versions of the example scripts, you have to **install the library from source** and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:
+To make sure you can successfully run the latest versions of the example scripts, you have to **install the library from source** and install some example-specific requirements.
+Execute the following steps in a new virtual environment:
+
 ```bash
 git clone https://github.com/huggingface/transformers
 cd transformers
 pip install .
-```
-Then cd in the example folder of your choice and run
-```bash
-pip install -r requirements.txt
+pip install -r ./examples/requirements.txt
 ```

-Alternatively, you can run the version of the examples as they were for your current version of Transformers via (for instance with v3.5.1):
+Alternatively, you can run the version of the examples as they were for your current version of Transformers via (for instance with v3.4.0):
 ```bash
-git checkout tags/v3.5.1
+git checkout tags/v3.4.0
 ```

 ## The Big Table of Tasks

-Here is the list of all our examples:
- with information on whether they are **built on top of `Trainer`/`TFTrainer`** (if not, they still work, they might
-  just lack some features),
- whether or not they leverage the [🤗 Datasets](https://github.com/huggingface/datasets) library.
- links to **Colab notebooks** to walk through the scripts and run them easily,
-<!--
-Coming soon!
- links to **Cloud deployments** to be able to deploy large-scale trainings in the Cloud with little to no setup.
-->
-
 | Task | Example datasets | Trainer support | TFTrainer support | 🤗 Datasets | Colab
 |---|---|:---:|:---:|:---:|:---:|
 | [**`language-modeling`**](https://github.com/huggingface/transformers/tree/master/examples/language-modeling)       | Raw text        | ✅ | -  | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/01_how_to_train.ipynb)
-| [**`multiple-choice`**](https://github.com/huggingface/transformers/tree/master/examples/multiple-choice)           | SWAG, RACE, ARC | ✅ | ✅ | - | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ViktorAlm/notebooks/blob/master/MPC_GPU_Demo_for_TF_and_PT.ipynb)
-| [**`question-answering`**](https://github.com/huggingface/transformers/tree/master/examples/question-answering)     | SQuAD           | ✅ | ✅ | ✅ | -
-| [**`summarization`**](https://github.com/huggingface/transformers/tree/master/examples/seq2seq)                     | CNN/Daily Mail  | ✅  | - | - | -
 | [**`text-classification`**](https://github.com/huggingface/transformers/tree/master/examples/text-classification)   | GLUE, XNLI      | ✅ | ✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://github.com/huggingface/notebooks/blob/master/examples/text_classification.ipynb)
-| [**`text-generation`**](https://github.com/huggingface/transformers/tree/master/examples/text-generation)           | -               | n/a | n/a | - | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/02_how_to_generate.ipynb)
 | [**`token-classification`**](https://github.com/huggingface/transformers/tree/master/examples/token-classification) | CoNLL NER       | ✅ | ✅ | ✅ | -
+| [**`multiple-choice`**](https://github.com/huggingface/transformers/tree/master/examples/multiple-choice)           | SWAG, RACE, ARC | ✅ | ✅ | - | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ViktorAlm/notebooks/blob/master/MPC_GPU_Demo_for_TF_and_PT.ipynb)
+| [**`question-answering`**](https://github.com/huggingface/transformers/tree/master/examples/question-answering)     | SQuAD           | ✅ | ✅ | - | -
+| [**`text-generation`**](https://github.com/huggingface/transformers/tree/master/examples/text-generation)           | -               | n/a | n/a | - | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/02_how_to_generate.ipynb)
+| [**`distillation`**](https://github.com/huggingface/transformers/tree/master/examples/distillation)                 | All             | - | -  | - | -
+| [**`summarization`**](https://github.com/huggingface/transformers/tree/master/examples/seq2seq)                     | CNN/Daily Mail  | ✅  | - | - | -
 | [**`translation`**](https://github.com/huggingface/transformers/tree/master/examples/seq2seq)                       | WMT             | ✅  | - | - | -
+| [**`bertology`**](https://github.com/huggingface/transformers/tree/master/examples/bertology)                       | -               | - | - | - | -
+| [**`adversarial`**](https://github.com/huggingface/transformers/tree/master/examples/adversarial)                   | HANS            | ✅ | - | - | -


-<!--
+<br>
+
 ## One-click Deploy to Cloud (wip)

 **Coming soon!**
-->

 ## Running on TPUs

@@ -1,20 +0,0 @@
-tensorboard
-scikit-learn
-seqeval
-psutil
-sacrebleu
-rouge-score
-tensorflow_datasets
-matplotlib
-git-python==1.0.3
-faiss-cpu
-streamlit
-elasticsearch
-nltk
-pandas
-datasets >= 1.1.3
-fire
-pytest
-conllu
-sentencepiece != 0.1.92
-protobuf
Author	SHA1	Message	Date
LysandreJik	c781171dfa	Release: v4.0.0	2020-11-30 11:33:35 -05:00
LysandreJikandLysandre Debut	ab597c84d1	Remove deprecated `evalutate_during_training` (#8852 ) * Remove deprecated `evalutate_during_training` * Update src/transformers/training_args_tf.py Co-authored-by: Lysandre Debut <lysandre@huggingface.co> Co-authored-by: Lysandre Debut <lysandre@huggingface.co>	2020-11-30 11:17:43 -05:00
Sylvain Gugger	e72b4fafeb	Add a direct link to the big table (#8850 )	2020-11-30 10:40:02 -05:00
Fraser Greenlee	dc0dea3e42	Correct docstring. (#8845 ) Related issue: https://github.com/huggingface/transformers/issues/8837	2020-11-30 10:39:52 -05:00
Patrick von Platen	4d8f5d12b3	add xlnet mems and fix merge conflicts	2020-11-30 09:45:12 +01:00
Lysandre DebutandSylvain Gugger	710b0108c9	Migration guide from v3.x to v4.x (#8763 ) * Migration guide from v3.x to v4.x * Better wording * Apply suggestions from code review Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Sylvain's comments * Better wording. Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>	2020-11-29 20:13:31 -05:00
Patrick von Platen	87199dee00	fix mt5 config (#8832 )	2020-11-29 20:12:38 -05:00
Sylvain GuggerandJulien Chaumond	68879472c4	Big model table (#8774 ) * First draft * Styling * With all changes staged * Update docs/source/index.rst Co-authored-by: Julien Chaumond <chaumond@gmail.com> * Styling Co-authored-by: Julien Chaumond <chaumond@gmail.com>	2020-11-29 20:12:24 -05:00
Patrick von Platen	8c5a2b8e36	[Flax test] Add require pytorch to flix flax test (#8816 ) * try flax fix * same for roberta	2020-11-29 20:11:34 -05:00
Kristian Holsheimer	911d8486e8	[FlaxBert] Fix non-broadcastable attention mask for batched forward-passes (#8791 ) * [FlaxBert] Fix non-broadcastable attention mask for batched forward-passes * [FlaxRoberta] Fix non-broadcastable attention mask * Use jax.numpy instead of ordinary numpy (otherwise not jit-able) * Partially revert "Use jax.numpy ..." * Add tests for batched forward passes * Avoid unnecessary OOMs due to preallocation of GPU memory by XLA * Auto-fix style * Re-enable GPU memory preallocation but with mem fraction < 1/paralleism	2020-11-29 20:11:23 -05:00
Lysandre	563efd36ab	Fix dpr<>bart config for RAG (#8808 ) * correct dpr test and bert pos fault * fix dpr bert config problem * fix layoutlm * add config to dpr as well	2020-11-29 20:10:33 -05:00
Lysandre DebutandNicolas Patry	5a63232a8a	Fix QA argument handler (#8765 ) * Fix QA argument handler * Attempt to get a better fix for QA (#8768) Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>	2020-11-29 20:06:10 -05:00
Lysandre Debut	e46890f699	MT5 should have an autotokenizer (#8743 ) * MT5 should have an autotokenizer * Different configurations should be able to point to same tokenizers	2020-11-24 09:51:34 -05:00
Lysandre Debut	df2cdd84f3	Fix slow tests v2 (#8746 ) * Fix BART test * Fix MBART tests * Remove erroneous line from yaml * Update tests/test_modeling_bart.py * Quality	2020-11-24 09:51:28 -05:00
LysandreJik	c6e2876cd4	TF BERT test update	2020-11-23 18:19:54 -05:00
LysandreJik	5580cccd81	Update TF BERT test	2020-11-23 18:19:34 -05:00
Stas Bekman	ccc4f64044	consistent ignore keys + make private (#8737 ) * consistent ignore keys + make private * style * - authorized_missing_keys => _keys_to_ignore_on_load_missing - authorized_unexpected_keys => _keys_to_ignore_on_load_unexpected * move public doc of private attributes to private comment	2020-11-23 17:55:15 -05:00
Sylvain GuggerandLysandre Debut	3408e6ffcd	Change default cache path (#8734 ) * Change default cache path * Document changes * Apply suggestions from code review Co-authored-by: Lysandre Debut <lysandre@huggingface.co> Co-authored-by: Lysandre Debut <lysandre@huggingface.co>	2020-11-23 17:54:45 -05:00
Santiago Castro	a986b02e49	Fix many typos (#8708 )	2020-11-23 17:54:20 -05:00
Sylvain Gugger	b6ec39e41f	Document adam betas TrainingArguments (#8688 )	2020-11-23 17:53:49 -05:00
Sylvain Gugger	f80ea27f80	Add sentencepiece to the CI and fix tests (#8672 ) * Fix the CI and tests * Fix quality * Remove that m form nowhere	2020-11-23 17:53:27 -05:00
				`@@ -1 +0,0 @@`
				`$PYTHON setup.py install # Python command to install the script.`