Remove formatting ?? in odd place.

Making the function private and adding it's doc.
Checking for pair of inputs too.
2020-12-02 11:03:40 +01:00 · 2020-11-26 12:32:31 +01:00 · 2020-11-26 12:27:03 +01:00 · 2020-11-26 12:20:47 +01:00
501 changed files with 2970 additions and 19249 deletions
@@ -3,48 +3,6 @@ orbs:
    gcp-gke: circleci/gcp-gke@1.0.4
    go: circleci/go@1.3.0

-commands:
-  skip-job-on-doc-only-changes:
-    description: "Do not continue this job and exit with success for PRs with only doc changes"
-    steps:
-
-      - run:
-          name: docs-only changes skip check
-          command: |
-            if test -n "$CIRCLE_PR_NUMBER"
-            then
-                echo $CIRCLE_PR_NUMBER
-                resp=$(curl -Ls https://api.github.com/repos/huggingface/transformers/pulls/${CIRCLE_PR_NUMBER})
-                user=$(jq -r .user.login \<<< $resp)   # PR creator username
-                head_ref=$(jq -r .head.ref \<<< $resp) # PR user's branch name
-                echo head_ref=$head_ref, user=$user
-            fi
-            
-            if test -n "$user" && test -n "$head_ref"
-            then
-                git clone https://github.com/$user/transformers user-clone
-                cd user-clone
-                git checkout $head_ref
-                fork_point_sha=$(git merge-base --fork-point master)
-                cd -
-            fi
-
-            if test -n "$fork_point_sha" && test -n "$(git diff --name-only $fork_point_sha)"
-            then
-                git --no-pager diff --name-only $fork_point_sha
-                if git diff --name-only $fork_point_sha | egrep -qv '\.(md|rst)$'
-                then
-                    echo "Non-docs were modified in this PR, proceeding normally"
-                else
-                    echo "Only docs were modified in this PR, quitting this job"
-                    # enable skipping once we get this sorted out
-                    # circleci step halt
-                fi
-            else
-                echo "Not enough data to perform a skipping check - continuing the job"
-            fi
-
-
 # TPU REFERENCES
 references:
    checkout_ml_testing: &checkout_ml_testing
@@ -114,7 +72,6 @@ jobs:
        parallelism: 1
        steps:
            - checkout
-            - skip-job-on-doc-only-changes
            - restore_cache:
                  keys:
                      - v0.4-torch_and_tf-{{ checksum "setup.py" }}
@@ -141,7 +98,6 @@ jobs:
        parallelism: 1
        steps:
            - checkout
-            - skip-job-on-doc-only-changes
            - restore_cache:
                  keys:
                      - v0.4-torch-{{ checksum "setup.py" }}
@@ -168,7 +124,6 @@ jobs:
        parallelism: 1
        steps:
            - checkout
-            - skip-job-on-doc-only-changes
            - restore_cache:
                  keys:
                      - v0.4-tf-{{ checksum "setup.py" }}
@@ -195,7 +150,6 @@ jobs:
        parallelism: 1
        steps:
            - checkout
-            - skip-job-on-doc-only-changes
            - restore_cache:
                keys:
                    - v0.4-flax-{{ checksum "setup.py" }}
@@ -222,7 +176,6 @@ jobs:
        parallelism: 1
        steps:
            - checkout
-            - skip-job-on-doc-only-changes
            - restore_cache:
                  keys:
                      - v0.4-torch-{{ checksum "setup.py" }}
@@ -249,7 +202,6 @@ jobs:
        parallelism: 1
        steps:
            - checkout
-            - skip-job-on-doc-only-changes
            - restore_cache:
                  keys:
                      - v0.4-tf-{{ checksum "setup.py" }}
@@ -274,7 +226,6 @@ jobs:
            RUN_CUSTOM_TOKENIZERS: yes
        steps:
            - checkout
-            - skip-job-on-doc-only-changes
            - restore_cache:
                  keys:
                      - v0.4-custom_tokenizers-{{ checksum "setup.py" }}
@@ -302,7 +253,6 @@ jobs:
        parallelism: 1
        steps:
            - checkout
-            - skip-job-on-doc-only-changes
            - restore_cache:
                  keys:
                      - v0.4-torch_examples-{{ checksum "setup.py" }}
@@ -320,22 +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:
@@ -400,7 +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

@@ -464,7 +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/**"
@@ -57,13 +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:
@@ -129,10 +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:
@@ -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:

@@ -1,4 +1,3 @@
-Copyright 2018- The Hugging Face team. All rights reserved.

                                 Apache License
                           Version 2.0, January 2004
@@ -23,7 +23,6 @@ deps_table_update:

 extra_quality_checks: deps_table_update
 	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
@@ -51,7 +50,6 @@ 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.
@@ -227,10 +194,9 @@ ultilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers/
 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)
-
 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).


@@ -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
 -----------------------------------------------------------------------------------------------------------------------

@@ -20,13 +20,13 @@ 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
 version = u''
 # The full version, including alpha/beta/rc tags
-release = u'4.0.0'
+release = u'3.5.0'


 # -- General 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.
-
 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
 -----------------------------------------------------------------------------------------------------------------------

@@ -88,113 +68,107 @@ 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. :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.
-32. :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.
-33. :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.
-34. :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.
-35. :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:
-
 The table below represents the current support in the library for each of those models, whether they have a Python
 tokenizer (called "slow"). A "fast" tokenizer backed by the 🤗 Tokenizers library, whether they have support in PyTorch,
 TensorFlow and/or Flax.
@@ -243,8 +217,6 @@ TensorFlow and/or Flax.
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |         Longformer          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|            MPNet            |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |           Marian            |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |         MobileBERT          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
@@ -284,6 +256,7 @@ TensorFlow and/or Flax.
 |             mT5             |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+

+
 .. toctree::
    :maxdepth: 2
    :caption: Get started
@@ -349,7 +322,6 @@ 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
@@ -370,7 +342,6 @@ 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
@@ -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.

@@ -53,12 +40,6 @@ generation.
 .. autoclass:: transformers.NoBadWordsLogitsProcessor
    :members: __call__

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

@@ -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
 -----------------------------------------------------------------------------------------------------------------------

@@ -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
 -----------------------------------------------------------------------------------------------------------------------

@@ -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
 -----------------------------------------------------------------------------------------------------------------------

@@ -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
 -----------------------------------------------------------------------------------------------------------------------

@@ -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
 -----------------------------------------------------------------------------------------------------------------------

@@ -1,186 +1,5 @@
-<!---
-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`
-
-A couple of changes were introduced when the switch from version 3 to version 4 was done. Below is a summary of the
-expected changes:
-
-#### 1. AutoTokenizers and pipelines now use fast (rust) tokenizers by default.
-
-The python and rust tokenizers have roughly the same API, but the rust tokenizers have a more complete feature set. 
-
-This introduces two breaking changes:
- The handling of overflowing tokens between the python and rust tokenizers is different.
- The rust tokenizers do not accept integers in the encoding methods.
-
-##### How to obtain the same behavior as v3.x in v4.x
-
- The pipelines now contain additional features out of the box. See the [token-classification pipeline with the `grouped_entities` flag](https://huggingface.co/transformers/main_classes/pipelines.html?highlight=textclassification#tokenclassificationpipeline).
- The auto-tokenizers now return rust tokenizers. In order to obtain the python tokenizers instead, the user may use the `use_fast` flag by setting it to `False`:
-
-In version `v3.x`:
-```py
-from transformers import AutoTokenizer
-
-tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
-```
-to obtain the same in version `v4.x`:
-```py
-from transformers import AutoTokenizer
-
-tokenizer = AutoTokenizer.from_pretrained("bert-base-cased", use_fast=False)
-```
-
-#### 2. SentencePiece is removed from the required dependencies
-
-The requirement on the SentencePiece dependency has been lifted from the `setup.py`. This is done so that we may have a channel on anaconda cloud without relying on `conda-forge`. This means that the tokenizers that depend on the SentencePiece library will not be available with a standard `transformers` installation.
-
-This includes the **slow** versions of:
- `XLNetTokenizer`
- `AlbertTokenizer`
- `CamembertTokenizer`
- `MBartTokenizer`
- `PegasusTokenizer`
- `T5Tokenizer`
- `ReformerTokenizer`
- `XLMRobertaTokenizer`
-
-##### How to obtain the same behavior as v3.x in v4.x
-
-In order to obtain the same behavior as version `v3.x`, you should install `sentencepiece` additionally:
-
-In version `v3.x`:
-```bash
-pip install transformers
-```
-to obtain the same in version `v4.x`:
-```bash
-pip install transformers[sentencepiece]
-```
-or
-```bash
-pip install transformers sentencepiece
-```
-#### 3. The architecture of the repo has been updated so that each model resides in its folder
-
-The past and foreseeable addition of new models means that the number of files in the directory `src/transformers` keeps growing and becomes harder to navigate and understand. We made the choice to put each model and the files accompanying it in their own sub-directories.
-
-This is a breaking change as importing intermediary layers using a model's module directly needs to be done via a different path.
-
-##### How to obtain the same behavior as v3.x in v4.x
-
-In order to obtain the same behavior as version `v3.x`, you should update the path used to access the layers. 
-
-In version `v3.x`:
-```bash
-from transformers.modeling_bert import BertLayer
-```
-to obtain the same in version `v4.x`:
-```bash
-from transformers.models.bert.modeling_bert import BertLayer
-```
-
-#### 4. Switching the `return_dict` argument to `True` by default
-
-The [`return_dict` argument](https://huggingface.co/transformers/main_classes/output.html) enables the return of dict-like python objects containing the model outputs, instead of the standard tuples. This object is self-documented as keys can be used to retrieve values, while also behaving as a tuple as users may retrieve objects by index or by slice.
-
-This is a breaking change as the limitation of that tuple is that it cannot be unpacked: `value0, value1 = outputs` will not work.
-
-##### How to obtain the same behavior as v3.x in v4.x
-
-In order to obtain the same behavior as version `v3.x`, you should specify the `return_dict` argument to `False`, either in the model configuration or during the forward pass.
-
-In version `v3.x`:
-```bash
-model = BertModel.from_pretrained("bert-base-cased")
-outputs = model(**inputs)
-```
-to obtain the same in version `v4.x`:
-```bash
-model = BertModel.from_pretrained("bert-base-cased")
-outputs = model(**inputs, return_dict=False)
-```
-or
-```bash
-model = BertModel.from_pretrained("bert-base-cased", return_dict=False)
-outputs = model(**inputs)
-```
-
-#### 5. Removed some deprecated attributes
-
-Attributes that were deprecated have been removed if they had been deprecated for at least a month. The full list of deprecated attributes can be found in [#8604](https://github.com/huggingface/transformers/pull/8604).
-
-Here is a list of these attributes/methods/arguments and what their replacements should be:
-
-In several models, the labels become consistent with the other models:
- `masked_lm_labels` becomes `labels` in `AlbertForMaskedLM` and `AlbertForPreTraining`.
- `masked_lm_labels` becomes `labels` in `BertForMaskedLM` and `BertForPreTraining`.
- `masked_lm_labels` becomes `labels` in `DistilBertForMaskedLM`.
- `masked_lm_labels` becomes `labels` in `ElectraForMaskedLM`.
- `masked_lm_labels` becomes `labels` in `LongformerForMaskedLM`.
- `masked_lm_labels` becomes `labels` in `MobileBertForMaskedLM`.
- `masked_lm_labels` becomes `labels` in `RobertaForMaskedLM`.
- `lm_labels` becomes `labels` in `BartForConditionalGeneration`.
- `lm_labels` becomes `labels` in `GPT2DoubleHeadsModel`.
- `lm_labels` becomes `labels` in `OpenAIGPTDoubleHeadsModel`.
- `lm_labels` becomes `labels` in `T5ForConditionalGeneration`.
-
-In several models, the caching mechanism becomes consistent with the other models:
- `decoder_cached_states` becomes `past_key_values` in all BART-like, FSMT and T5 models.
- `decoder_past_key_values` becomes `past_key_values` in all BART-like, FSMT and T5 models.
- `past` becomes `past_key_values` in all CTRL models.
- `past` becomes `past_key_values` in all GPT-2 models.
-
-Regarding the tokenizer classes:
- The tokenizer attribute `max_len` becomes `model_max_length`.
- The tokenizer attribute `return_lengths` becomes `return_length`.
- The tokenizer encoding argument `is_pretokenized` becomes `is_split_into_words`.
-
-Regarding the `Trainer` class:
- The `Trainer` argument `tb_writer` is removed in favor of the callback `TensorBoardCallback(tb_writer=...)`.
- The `Trainer` argument `prediction_loss_only` is removed in favor of the class argument `args.prediction_loss_only`.
- The `Trainer` attribute `data_collator` should be a callable.
- The `Trainer` method `_log` is deprecated in favor of `log`.
- The `Trainer` method `_training_step` is deprecated in favor of `training_step`.
- The `Trainer` method `_prediction_loop` is deprecated in favor of `prediction_loop`.
- The `Trainer` method `is_local_master` is deprecated in favor of `is_local_process_zero`.
- The `Trainer` method `is_world_master` is deprecated in favor of `is_world_process_zero`.
-
-Regarding the `TFTrainer` class:
- The `TFTrainer` argument `prediction_loss_only` is removed in favor of the class argument `args.prediction_loss_only`.
- The `Trainer` method `_log` is deprecated in favor of `log`.
- The `TFTrainer` method `_prediction_loop` is deprecated in favor of `prediction_loop`.
- The `TFTrainer` method `_setup_wandb` is deprecated in favor of `setup_wandb`.
- The `TFTrainer` method `_run_model` is deprecated in favor of `run_model`.
-
-Regarding the `TrainerArgument` class:
- The `TrainerArgument` argument `evaluate_during_training` is deprecated in favor of `evaluation_strategy`.
-
-Regarding the Transfo-XL model:
- The Transfo-XL configuration attribute `tie_weight` becomes `tie_words_embeddings`.
- The Transfo-XL modeling method `reset_length` becomes `reset_memory_length`.
-
-Regarding pipelines:
- The `FillMaskPipeline` argument `topk` becomes `top_k`.
-
-
-
 ## Migrating from pytorch-transformers to 🤗 Transformers

 Here is a quick summary of what you should take care of when migrating from `pytorch-transformers` to 🤗 Transformers.
@@ -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
 -----------------------------------------------------------------------------------------------------------------------

@@ -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
 -----------------------------------------------------------------------------------------------------------------------

@@ -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
 -----------------------------------------------------------------------------------------------------------------------

@@ -105,6 +93,8 @@ BartModel
 .. autoclass:: transformers.BartModel
    :members: forward

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

 BartForConditionalGeneration
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -1,53 +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:
-
@@ -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
 -----------------------------------------------------------------------------------------------------------------------

@@ -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
 -----------------------------------------------------------------------------------------------------------------------

@@ -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
 -----------------------------------------------------------------------------------------------------------------------

@@ -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
 -----------------------------------------------------------------------------------------------------------------------

@@ -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
 -----------------------------------------------------------------------------------------------------------------------

@@ -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
 -----------------------------------------------------------------------------------------------------------------------

@@ -1,137 +0,0 @@
-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
 -----------------------------------------------------------------------------------------------------------------------

@@ -51,13 +39,6 @@ MT5ForConditionalGeneration
    :members:


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

@@ -70,10 +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
 -----------------------------------------------------------------------------------------------------------------------

@@ -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
 -----------------------------------------------------------------------------------------------------------------------

@@ -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
 -----------------------------------------------------------------------------------------------------------------------

@@ -120,11 +108,6 @@ T5ForConditionalGeneration
 .. autoclass:: transformers.T5ForConditionalGeneration
    :members: forward, parallelize, deparallelize

-T5EncoderModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.T5EncoderModel
-    :members: forward

 TFT5Model
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -138,9 +121,3 @@ TFT5ForConditionalGeneration

 .. autoclass:: transformers.TFT5ForConditionalGeneration
    :members: call
-
-TFT5EncoderModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFT5EncoderModel
-    :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.
-
 Transformer XL
 -----------------------------------------------------------------------------------------------------------------------

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

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

 TFTransfoXLModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -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
 -----------------------------------------------------------------------------------------------------------------------

@@ -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
 -----------------------------------------------------------------------------------------------------------------------

@@ -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
 =======================================================================================================================

@@ -1,19 +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.
-->
-
 # Examples

 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.
@@ -55,7 +39,7 @@ git checkout tags/v3.4.0
 | [**`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)
 | [**`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           | ✅ | ✅ | ✅ | -
+| [**`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  | ✅  | - | - | -
@@ -102,8 +102,8 @@ class DataTrainingArguments:
        default=None,
        metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
    )
-    block_size: Optional[int] = field(
-        default=None,
+    block_size: int = field(
+        default=-1,
        metadata={
            "help": "Optional input sequence length after tokenization."
            "The training dataset will be truncated in block of this size for training."
@@ -261,14 +261,8 @@ def main():
        load_from_cache_file=not data_args.overwrite_cache,
    )

-    if data_args.block_size is None:
+    if data_args.block_size <= 0:
        block_size = tokenizer.model_max_length
-        if block_size > 1024:
-            logger.warn(
-                f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). "
-                "Picking 1024 instead. You can change that default value by passing --block_size xxx."
-            )
-        block_size = 1024
    else:
        if data_args.block_size > tokenizer.model_max_length:
            logger.warn(
@@ -1,663 +0,0 @@
-# coding=utf-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.
-"""
-Fine-tuning the library models for masked language modeling (BERT, ALBERT, RoBERTa...) with whole word masking on a
-text file or a dataset.
-
-Here is the full list of checkpoints on the hub that can be fine-tuned by this script:
-https://huggingface.co/models?filter=masked-lm
-"""
-import logging
-import os
-import sys
-from dataclasses import dataclass, field
-
-# You can also adapt this script on your own masked language modeling task. Pointers for this are left as comments.
-from pathlib import Path
-from typing import Dict, List, Optional, Tuple
-
-import numpy as np
-from datasets import load_dataset
-from tqdm import tqdm
-
-import jax
-import jax.numpy as jnp
-from flax import jax_utils
-from flax.optim import Adam
-from flax.training import common_utils
-from flax.training.common_utils import get_metrics
-from jax.nn import log_softmax
-from transformers import (
-    CONFIG_MAPPING,
-    MODEL_FOR_MASKED_LM_MAPPING,
-    AutoConfig,
-    AutoTokenizer,
-    FlaxBertForMaskedLM,
-    HfArgumentParser,
-    PreTrainedTokenizerBase,
-    TensorType,
-    TrainingArguments,
-    is_tensorboard_available,
-    set_seed,
-)
-
-# Cache the result
-has_tensorboard = is_tensorboard_available()
-if has_tensorboard:
-    try:
-        from flax.metrics.tensorboard import SummaryWriter
-    except ImportError as ie:
-        has_tensorboard = False
-        print(f"Unable to display metrics through TensorBoard because some package are not installed: {ie}")
-
-else:
-    print(
-        "Unable to display metrics through TensorBoard because the package is not installed: "
-        "Please run pip install tensorboard to enable."
-    )
-
-MODEL_CONFIG_CLASSES = list(MODEL_FOR_MASKED_LM_MAPPING.keys())
-MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
-
-
-@dataclass
-class ModelArguments:
-    """
-    Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
-    """
-
-    model_name_or_path: Optional[str] = field(
-        default=None,
-        metadata={
-            "help": "The model checkpoint for weights initialization."
-                    "Don't set if you want to train a model from scratch."
-        },
-    )
-    model_type: Optional[str] = field(
-        default=None,
-        metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)},
-    )
-    config_name: Optional[str] = field(
-        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
-    )
-    tokenizer_name: Optional[str] = field(
-        default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
-    )
-    cache_dir: Optional[str] = field(
-        default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
-    )
-    use_fast_tokenizer: bool = field(
-        default=True,
-        metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
-    )
-
-
-@dataclass
-class DataTrainingArguments:
-    """
-    Arguments pertaining to what data we are going to input our model for training and eval.
-    """
-
-    dataset_name: Optional[str] = field(
-        default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
-    )
-    dataset_config_name: Optional[str] = field(
-        default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
-    )
-    train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
-    validation_file: Optional[str] = field(
-        default=None,
-        metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
-    )
-    train_ref_file: Optional[str] = field(
-        default=None,
-        metadata={"help": "An optional input train ref data file for whole word masking in Chinese."},
-    )
-    validation_ref_file: Optional[str] = field(
-        default=None,
-        metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."},
-    )
-    overwrite_cache: bool = field(
-        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
-    )
-    max_seq_length: Optional[int] = field(
-        default=None,
-        metadata={
-            "help": "The maximum total input sequence length after tokenization. Sequences longer "
-                    "than this will be truncated. Default to the max input length of the model."
-        },
-    )
-    preprocessing_num_workers: Optional[int] = field(
-        default=None,
-        metadata={"help": "The number of processes to use for the preprocessing."},
-    )
-    mlm_probability: float = field(
-        default=0.15, metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}
-    )
-    pad_to_max_length: bool = field(
-        default=False,
-        metadata={
-            "help": "Whether to pad all samples to `max_seq_length`. "
-                    "If False, will pad the samples dynamically when batching to the maximum length in the batch."
-        },
-    )
-
-    def __post_init__(self):
-        if self.dataset_name is None and self.train_file is None and self.validation_file is None:
-            raise ValueError("Need either a dataset name or a training/validation file.")
-        else:
-            if self.train_file is not None:
-                extension = self.train_file.split(".")[-1]
-                assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
-            if self.validation_file is not None:
-                extension = self.validation_file.split(".")[-1]
-                assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."
-
-
-# Adapted from transformers/data/data_collator.py
-# Letting here for now, let's discuss where it should live
-@dataclass
-class FlaxDataCollatorForLanguageModeling:
-    """
-    Data collator used for language modeling. Inputs are dynamically padded to the maximum length of a batch if they
-    are not all of the same length.
-
-    Args:
-        tokenizer (:class:`~transformers.PreTrainedTokenizer` or :class:`~transformers.PreTrainedTokenizerFast`):
-            The tokenizer used for encoding the data.
-        mlm (:obj:`bool`, `optional`, defaults to :obj:`True`):
-            Whether or not to use masked language modeling. If set to :obj:`False`, the labels are the same as the
-            inputs with the padding tokens ignored (by setting them to -100). Otherwise, the labels are -100 for
-            non-masked tokens and the value to predict for the masked token.
-        mlm_probability (:obj:`float`, `optional`, defaults to 0.15):
-            The probability with which to (randomly) mask tokens in the input, when :obj:`mlm` is set to :obj:`True`.
-
-    .. note::
-
-        For best performance, this data collator should be used with a dataset having items that are dictionaries or
-        BatchEncoding, with the :obj:`"special_tokens_mask"` key, as returned by a
-        :class:`~transformers.PreTrainedTokenizer` or a :class:`~transformers.PreTrainedTokenizerFast` with the
-        argument :obj:`return_special_tokens_mask=True`.
-    """
-
-    tokenizer: PreTrainedTokenizerBase
-    mlm: bool = True
-    mlm_probability: float = 0.15
-
-    def __post_init__(self):
-        if self.mlm and self.tokenizer.mask_token is None:
-            raise ValueError(
-                "This tokenizer does not have a mask token which is necessary for masked language modeling. "
-                "You should pass `mlm=False` to train on causal language modeling instead."
-            )
-
-    def __call__(self, examples: List[Dict[str, np.ndarray]], pad_to_multiple_of: int) -> Dict[str, np.ndarray]:
-        # Handle dict or lists with proper padding and conversion to tensor.
-        batch = self.tokenizer.pad(examples, pad_to_multiple_of=pad_to_multiple_of, return_tensors=TensorType.NUMPY)
-
-        # If special token mask has been preprocessed, pop it from the dict.
-        special_tokens_mask = batch.pop("special_tokens_mask", None)
-        if self.mlm:
-            batch["input_ids"], batch["labels"] = self.mask_tokens(
-                batch["input_ids"], special_tokens_mask=special_tokens_mask
-            )
-        else:
-            labels = batch["input_ids"].copy()
-            if self.tokenizer.pad_token_id is not None:
-                labels[labels == self.tokenizer.pad_token_id] = -100
-            batch["labels"] = labels
-        return batch
-
-    def mask_tokens(
-            self, inputs: np.ndarray, special_tokens_mask: Optional[np.ndarray]
-    ) -> Tuple[jnp.ndarray, jnp.ndarray]:
-        """
-        Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original.
-        """
-        labels = inputs.copy()
-        # We sample a few tokens in each sequence for MLM training (with probability `self.mlm_probability`)
-        probability_matrix = np.full(labels.shape, self.mlm_probability)
-        special_tokens_mask = special_tokens_mask.astype("bool")
-
-        probability_matrix[special_tokens_mask] = 0.0
-        masked_indices = np.random.binomial(1, probability_matrix).astype("bool")
-        labels[~masked_indices] = -100  # We only compute loss on masked tokens
-
-        # 80% of the time, we replace masked input tokens with tokenizer.mask_token ([MASK])
-        indices_replaced = np.random.binomial(1, np.full(labels.shape, 0.8)).astype("bool") & masked_indices
-        inputs[indices_replaced] = self.tokenizer.convert_tokens_to_ids(self.tokenizer.mask_token)
-
-        # 10% of the time, we replace masked input tokens with random word
-        indices_random = np.random.binomial(1, np.full(labels.shape, 0.5)).astype("bool")
-        indices_random &= masked_indices & ~indices_replaced
-
-        random_words = np.random.randint(self.tokenizer.vocab_size, size=labels.shape, dtype="i4")
-        inputs[indices_random] = random_words[indices_random]
-
-        # The rest of the time (10% of the time) we keep the masked input tokens unchanged
-        return inputs, labels
-
-
-def create_learning_rate_scheduler(
-        factors="constant * linear_warmup * rsqrt_decay",
-        base_learning_rate=0.5,
-        warmup_steps=1000,
-        decay_factor=0.5,
-        steps_per_decay=20000,
-        steps_per_cycle=100000,
-):
-    """Creates learning rate schedule.
-    Interprets factors in the factors string which can consist of:
-    * constant: interpreted as the constant value,
-    * linear_warmup: interpreted as linear warmup until warmup_steps,
-    * rsqrt_decay: divide by square root of max(step, warmup_steps)
-    * rsqrt_normalized_decay: divide by square root of max(step/warmup_steps, 1)
-    * decay_every: Every k steps decay the learning rate by decay_factor.
-    * cosine_decay: Cyclic cosine decay, uses steps_per_cycle parameter.
-    Args:
-      factors: string, factors separated by "*" that defines the schedule.
-      base_learning_rate: float, the starting constant for the lr schedule.
-      warmup_steps: int, how many steps to warm up for in the warmup schedule.
-      decay_factor: float, the amount to decay the learning rate by.
-      steps_per_decay: int, how often to decay the learning rate.
-      steps_per_cycle: int, steps per cycle when using cosine decay.
-    Returns:
-      a function learning_rate(step): float -> {"learning_rate": float}, the
-      step-dependent lr.
-    """
-    factors = [n.strip() for n in factors.split("*")]
-
-    def step_fn(step):
-        """Step to learning rate function."""
-        ret = 1.0
-        for name in factors:
-            if name == "constant":
-                ret *= base_learning_rate
-            elif name == "linear_warmup":
-                ret *= jnp.minimum(1.0, step / warmup_steps)
-            elif name == "rsqrt_decay":
-                ret /= jnp.sqrt(jnp.maximum(step, warmup_steps))
-            elif name == "rsqrt_normalized_decay":
-                ret *= jnp.sqrt(warmup_steps)
-                ret /= jnp.sqrt(jnp.maximum(step, warmup_steps))
-            elif name == "decay_every":
-                ret *= decay_factor ** (step // steps_per_decay)
-            elif name == "cosine_decay":
-                progress = jnp.maximum(0.0, (step - warmup_steps) / float(steps_per_cycle))
-                ret *= jnp.maximum(0.0, 0.5 * (1.0 + jnp.cos(jnp.pi * (progress % 1.0))))
-            else:
-                raise ValueError("Unknown factor %s." % name)
-        return jnp.asarray(ret, dtype=jnp.float32)
-
-    return step_fn
-
-
-def compute_metrics(logits, labels, weights, label_smoothing=0.0):
-    """Compute summary metrics."""
-    loss, normalizer = cross_entropy(logits, labels, weights, label_smoothing)
-    acc, _ = accuracy(logits, labels, weights)
-    metrics = {"loss": loss, "accuracy": acc, "normalizer": normalizer}
-    metrics = jax.lax.psum(metrics, axis_name="batch")
-    return metrics
-
-
-def accuracy(logits, targets, weights=None):
-    """Compute weighted accuracy for log probs and targets.
-    Args:
-     logits: [batch, length, num_classes] float array.
-     targets: categorical targets [batch, length] int array.
-     weights: None or array of shape [batch, length]
-    Returns:
-      Tuple of scalar loss and batch normalizing factor.
-    """
-    if logits.ndim != targets.ndim + 1:
-        raise ValueError(
-            "Incorrect shapes. Got shape %s logits and %s targets" % (str(logits.shape), str(targets.shape))
-        )
-
-    loss = jnp.equal(jnp.argmax(logits, axis=-1), targets)
-    loss *= weights
-
-    return loss.sum(), weights.sum()
-
-
-def cross_entropy(logits, targets, weights=None, label_smoothing=0.0):
-    """Compute cross entropy and entropy for log probs and targets.
-    Args:
-     logits: [batch, length, num_classes] float array.
-     targets: categorical targets [batch, length] int array.
-     weights: None or array of shape [batch, length]
-     label_smoothing: label smoothing constant, used to determine the on and off values.
-    Returns:
-      Tuple of scalar loss and batch normalizing factor.
-    """
-    if logits.ndim != targets.ndim + 1:
-        raise ValueError(
-            "Incorrect shapes. Got shape %s logits and %s targets" % (str(logits.shape), str(targets.shape))
-        )
-
-    vocab_size = logits.shape[-1]
-    confidence = 1.0 - label_smoothing
-    low_confidence = (1.0 - confidence) / (vocab_size - 1)
-    normalizing_constant = -(
-            confidence * jnp.log(confidence) + (vocab_size - 1) * low_confidence * jnp.log(low_confidence + 1e-20)
-    )
-    soft_targets = common_utils.onehot(targets, vocab_size, on_value=confidence, off_value=low_confidence)
-
-    loss = -jnp.sum(soft_targets * log_softmax(logits), axis=-1)
-    loss = loss - normalizing_constant
-
-    if weights is not None:
-        loss = loss * weights
-        normalizing_factor = weights.sum()
-    else:
-        normalizing_factor = np.prod(targets.shape)
-
-    return loss.sum(), normalizing_factor
-
-
-def training_step(optimizer, batch, dropout_rng):
-    dropout_rng, new_dropout_rng = jax.random.split(dropout_rng)
-
-    def loss_fn(params):
-        targets = batch.pop("labels")
-
-        # Hide away tokens which doesn't participate in the optimization
-        token_mask = jnp.where(targets > 0, 1.0, 0.0)
-
-        pooled, logits = model(**batch, params=params, dropout_rng=dropout_rng, train=True)
-        loss, weight_sum = cross_entropy(logits, targets, token_mask)
-        return loss / weight_sum
-
-    step = optimizer.state.step
-    lr = lr_scheduler_fn(step)
-    grad_fn = jax.value_and_grad(loss_fn)
-    loss, grad = grad_fn(optimizer.target)
-    grad = jax.lax.pmean(grad, "batch")
-    optimizer = optimizer.apply_gradient(grad, learning_rate=lr)
-
-    return loss, optimizer, new_dropout_rng
-
-
-def eval_step(params, batch):
-    """
-    Calculate evaluation metrics on a batch.
-    """
-    targets = batch.pop("labels")
-
-    # Hide away tokens which doesn't participate in the optimization
-    token_mask = jnp.where(targets > 0, 1.0, 0.0)
-    _, logits = model(**batch, params=params, train=False)
-
-    return compute_metrics(logits, targets, token_mask)
-
-
-def generate_batch_splits(samples_idx: jnp.ndarray, batch_size: int) -> jnp.ndarray:
-    nb_samples = len(samples_idx)
-    samples_to_remove = nb_samples % batch_size
-
-    if samples_to_remove != 0:
-        samples_idx = samples_idx[:-samples_to_remove]
-    sections_split = nb_samples // batch_size
-    batch_idx = np.split(samples_idx, sections_split)
-    return batch_idx
-
-
-if __name__ == "__main__":
-    # See all possible arguments in src/transformers/training_args.py
-    # or by passing the --help flag to this script.
-    # We now keep distinct sets of args, for a cleaner separation of concerns.
-
-    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
-    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
-        # If we pass only one argument to the script and it's the path to a json file,
-        # let's parse it to get our arguments.
-        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
-    else:
-        model_args, data_args, training_args = parser.parse_args_into_dataclasses()
-
-    if (
-            os.path.exists(training_args.output_dir)
-            and os.listdir(training_args.output_dir)
-            and training_args.do_train
-            and not training_args.overwrite_output_dir
-    ):
-        raise ValueError(
-            f"Output directory ({training_args.output_dir}) already exists and is not empty."
-            "Use --overwrite_output_dir to overcome."
-        )
-
-    # Setup logging
-    logging.basicConfig(
-        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
-        level="NOTSET",
-        datefmt="[%X]",
-    )
-
-    # Log on each process the small summary:
-    logger = logging.getLogger(__name__)
-    logger.warning(
-        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
-        + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
-    )
-
-    # Set the verbosity to info of the Transformers logger (on main process only):
-    logger.info("Training/evaluation parameters %s", training_args)
-
-    # Set seed before initializing model.
-    set_seed(training_args.seed)
-
-    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
-    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
-    # (the dataset will be downloaded automatically from the datasets Hub).
-    #
-    # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
-    # 'text' is found. You can easily tweak this behavior (see below).
-    #
-    # In distributed training, the load_dataset function guarantees that only one local process can concurrently
-    # download the dataset.
-    if data_args.dataset_name is not None:
-        # Downloading and loading a dataset from the hub.
-        datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
-        datasets["validation"] = load_dataset(data_args.dataset_name, data_args.dataset_config_name,
-                                              split="train[90%:95%]")
-        datasets["test"] = load_dataset(data_args.dataset_name, data_args.dataset_config_name, split="train[95%:100%]")
-    else:
-        data_files = {}
-        if data_args.train_file is not None:
-            data_files["train"] = data_args.train_file
-        if data_args.validation_file is not None:
-            data_files["validation"] = data_args.validation_file
-        extension = data_args.train_file.split(".")[-1]
-        if extension == "txt":
-            extension = "text"
-        datasets = load_dataset(extension, data_files=data_files)
-    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
-    # https://huggingface.co/docs/datasets/loading_datasets.html.
-
-    # Load pretrained model and tokenizer
-
-    # Distributed training:
-    # The .from_pretrained methods guarantee that only one local process can concurrently
-    # download model & vocab.
-
-    rng = jax.random.PRNGKey(training_args.seed)
-    dropout_rngs = jax.random.split(rng, jax.local_device_count())
-
-    if model_args.model_name_or_path:
-        model = FlaxBertForMaskedLM.from_pretrained(model_args.model_name_or_path, dtype=jnp.float32, dropout_rate=0.1)
-    elif model_args.config_name:
-        config = AutoConfig.from_pretrained(model_args.config_name, cache_dir=model_args.cache_dir)
-        model = FlaxBertForMaskedLM(config)
-        model.init(jax.random.PRNGKey(training_args.seed), (training_args.train_batch_size, model.config.max_length))
-    else:
-        config = CONFIG_MAPPING[model_args.model_type]()
-        logger.warning("You are instantiating a new config instance from scratch.")
-        model = FlaxBertForMaskedLM(config)
-        model.init(jax.random.PRNGKey(training_args.seed), (training_args.train_batch_size, model.config.max_length))
-
-    if model_args.tokenizer_name:
-        tokenizer = AutoTokenizer.from_pretrained(
-            model_args.tokenizer_name, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
-        )
-    elif model_args.model_name_or_path:
-        tokenizer = AutoTokenizer.from_pretrained(
-            model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
-        )
-    else:
-        raise ValueError(
-            "You are instantiating a new tokenizer from scratch. This is not supported by this script."
-            "You can do it from another script, save it, and load it from here, using --tokenizer_name."
-        )
-
-    # Preprocessing the datasets.
-    # First we tokenize all the texts.
-    if training_args.do_train:
-        column_names = datasets["train"].column_names
-    else:
-        column_names = datasets["validation"].column_names
-    text_column_name = "text" if "text" in column_names else column_names[0]
-    columns_to_remove = [column_name for column_name in column_names if column_name != text_column_name]
-
-    padding = "max_length" if data_args.pad_to_max_length else False
-
-
-    def tokenize_function(examples):
-        # Remove empty lines
-        examples = [line for line in examples if len(line) > 0 and not line.isspace()]
-        return tokenizer(
-            examples,
-            return_special_tokens_mask=True,
-            padding=padding,
-            truncation=True,
-            max_length=data_args.max_seq_length,
-        )
-
-
-    tokenized_datasets = datasets.map(
-        tokenize_function,
-        input_columns=[text_column_name],
-        batched=True,
-        num_proc=data_args.preprocessing_num_workers,
-        remove_columns=column_names,
-        load_from_cache_file=not data_args.overwrite_cache,
-    )
-
-    # Enable tensorboard only on the master node
-    if has_tensorboard and jax.host_id() == 0:
-        summary_writer = SummaryWriter(log_dir=Path(training_args.output_dir).joinpath("logs").as_posix())
-
-    # Data collator
-    # This one will take care of randomly masking the tokens.
-    if training_args.tokenizers_parallelism:
-        os.environ["TOKENIZERS_PARALLELISM"] = "true"
-    else:
-        os.environ["TOKENIZERS_PARALLELISM"] = "false"
-    data_collator = FlaxDataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=data_args.mlm_probability)
-
-    # Setup optimizer
-    optimizer = Adam(
-        learning_rate=training_args.learning_rate,
-        weight_decay=training_args.weight_decay,
-        beta1=training_args.adam_beta1,
-        beta2=training_args.adam_beta2,
-    ).create(model.params)
-
-    # Create learning rate scheduler
-    if training_args.warmup_steps == 0:
-        training_args.warmup_steps = 1
-    lr_scheduler_fn = create_learning_rate_scheduler(
-        base_learning_rate=training_args.learning_rate, warmup_steps=training_args.warmup_steps
-    )
-
-    # Create parallel version of the training and evaluation steps
-    p_training_step = jax.pmap(training_step, "batch", donate_argnums=(0,))
-    p_eval_step = jax.pmap(eval_step, "batch", donate_argnums=(0,))
-
-    # Replicate the optimizer on each device
-    optimizer = jax_utils.replicate(optimizer)
-
-    # Store some constant
-    nb_epochs = int(training_args.num_train_epochs)
-    batch_size = int(training_args.train_batch_size)
-    eval_batch_size = int(training_args.eval_batch_size)
-
-
-    def evaluation_routine(epoch=-1):
-
-        nb_eval_samples = len(tokenized_datasets["test"])
-        eval_samples_idx = jnp.arange(nb_eval_samples)
-        eval_batch_idx = generate_batch_splits(eval_samples_idx, eval_batch_size)
-
-        eval_metrics = []
-        for i, batch_idx in enumerate(tqdm(eval_batch_idx, desc="Evaluating ...", position=2)):
-            samples = [tokenized_datasets["test"][int(idx)] for idx in batch_idx]
-            model_inputs = data_collator(samples, pad_to_multiple_of=16)
-
-            # Model forward
-            model_inputs = common_utils.shard(model_inputs.data)
-            metrics = p_eval_step(optimizer.target, model_inputs)
-            eval_metrics.append(metrics)
-
-        eval_metrics_np = get_metrics(eval_metrics)
-        eval_metrics_np = jax.tree_map(jnp.sum, eval_metrics_np)
-        eval_normalizer = eval_metrics_np.pop("normalizer")
-        eval_summary = jax.tree_map(lambda x: x / eval_normalizer, eval_metrics_np)
-
-        # Update progress bar
-        epochs.desc = (
-            f"Epoch... ({epoch + 1}/{nb_epochs} | Loss: {eval_summary['loss']}, Acc: {eval_summary['accuracy']})"
-        )
-        print(f"Epoch... ({epoch + 1}/{nb_epochs} | Loss: {eval_summary['loss']}, Acc: {eval_summary['accuracy']})")
-
-        # Save metrics
-        if has_tensorboard and jax.host_id() == 0:
-            for name, value in eval_summary.items():
-                summary_writer.scalar(name, value, epoch)
-
-
-    epochs = tqdm(range(nb_epochs), desc=f"Epoch ... (1/{nb_epochs})", position=0)
-
-    if training_args.initial_evaluation:
-        evaluation_routine()
-
-    for epoch in epochs:
-
-        # ======================== Training ================================
-        # Create sampling rng
-        rng, training_rng, eval_rng = jax.random.split(rng, 3)
-
-        # Generate an epoch by shuffling sampling indices from the train dataset
-        nb_training_samples = len(tokenized_datasets["train"])
-        training_samples_idx = jax.random.permutation(training_rng, jnp.arange(nb_training_samples))
-        training_batch_idx = generate_batch_splits(training_samples_idx, batch_size)
-
-        # Gather the indexes for creating the batch and do a training step
-        batches = tqdm(training_batch_idx, desc="Training...", position=1)
-        for batch_idx in batches:
-            samples = [tokenized_datasets["train"][int(idx)] for idx in batch_idx]
-            model_inputs = data_collator(samples, pad_to_multiple_of=16)
-
-            # Model forward
-            model_inputs = common_utils.shard(model_inputs.data)
-            loss, optimizer, dropout_rngs = p_training_step(optimizer, model_inputs, dropout_rngs)
-            batches.desc = (
-                f"Loss: {loss})"
-            )
-
-        epochs.write(f"Loss: {loss}")
-
-        # ======================== Evaluating ==============================
-        evaluation_routine(epoch)
@@ -7,17 +7,33 @@ Based on the script [`run_squad.py`](https://github.com/huggingface/transformers
 #### Fine-tuning BERT on SQuAD1.0

 This example code fine-tunes BERT on the SQuAD1.0 dataset. It runs in 24 min (with BERT-base) or 68 min (with BERT-large)
-on a single tesla V100 16GB.
+on a single tesla V100 16GB. The data for SQuAD can be downloaded with the following links and should be saved in a
+$SQUAD_DIR directory.
+
+* [train-v1.1.json](https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json)
+* [dev-v1.1.json](https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json)
+* [evaluate-v1.1.py](https://github.com/allenai/bi-att-flow/blob/master/squad/evaluate-v1.1.py)
+
+And for SQuAD2.0, you need to download:
+
+- [train-v2.0.json](https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v2.0.json)
+- [dev-v2.0.json](https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v2.0.json)
+- [evaluate-v2.0.py](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/)

 ```bash
-python run_qa.py \
+export SQUAD_DIR=/path/to/SQUAD
+
+python run_squad.py \
+  --model_type bert \
  --model_name_or_path bert-base-uncased \
-  --dataset_name squad \
  --do_train \
  --do_eval \
-  --per_device_train_batch_size 12 \
+  --do_lower_case \
+  --train_file $SQUAD_DIR/train-v1.1.json \
+  --predict_file $SQUAD_DIR/dev-v1.1.json \
+  --per_gpu_train_batch_size 12 \
  --learning_rate 3e-5 \
-  --num_train_epochs 2 \
+  --num_train_epochs 2.0 \
  --max_seq_length 384 \
  --doc_stride 128 \
  --output_dir /tmp/debug_squad/
@@ -37,17 +53,20 @@ Here is an example using distributed training on 8 V100 GPUs and Bert Whole Word

 ```bash
 python -m torch.distributed.launch --nproc_per_node=8 ./examples/question-answering/run_squad.py \
+    --model_type bert \
    --model_name_or_path bert-large-uncased-whole-word-masking \
-    --dataset_name squad \
    --do_train \
    --do_eval \
+    --do_lower_case \
+    --train_file $SQUAD_DIR/train-v1.1.json \
+    --predict_file $SQUAD_DIR/dev-v1.1.json \
    --learning_rate 3e-5 \
    --num_train_epochs 2 \
    --max_seq_length 384 \
    --doc_stride 128 \
    --output_dir ./examples/models/wwm_uncased_finetuned_squad/ \
-    --per_device_eval_batch_size=3   \
-    --per_device_train_batch_size=3   \
+    --per_gpu_eval_batch_size=3   \
+    --per_gpu_train_batch_size=3   \
 ```

 Training with the previously defined hyper-parameters yields the following results:
@@ -60,25 +79,29 @@ exact_match = 86.91
 This fine-tuned model is available as a checkpoint under the reference
 [`bert-large-uncased-whole-word-masking-finetuned-squad`](https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad).

-#### Fine-tuning XLNet with beam search on SQuAD
+#### Fine-tuning XLNet on SQuAD

-This example code fine-tunes XLNet on both SQuAD1.0 and SQuAD2.0 dataset.
+This example code fine-tunes XLNet on both SQuAD1.0 and SQuAD2.0 dataset. See above to download the data for SQuAD .

 ##### Command for SQuAD1.0:

 ```bash
-python run_qa_beam_search.py \
+export SQUAD_DIR=/path/to/SQUAD
+
+python run_squad.py \
+    --model_type xlnet \
    --model_name_or_path xlnet-large-cased \
-    --dataset_name squad \
    --do_train \
    --do_eval \
+    --train_file $SQUAD_DIR/train-v1.1.json \
+    --predict_file $SQUAD_DIR/dev-v1.1.json \
    --learning_rate 3e-5 \
    --num_train_epochs 2 \
    --max_seq_length 384 \
    --doc_stride 128 \
    --output_dir ./wwm_cased_finetuned_squad/ \
-    --per_device_eval_batch_size=4  \
-    --per_device_train_batch_size=4   \
+    --per_gpu_eval_batch_size=4  \
+    --per_gpu_train_batch_size=4   \
    --save_steps 5000
 ```

@@ -87,19 +110,21 @@ python run_qa_beam_search.py \
 ```bash
 export SQUAD_DIR=/path/to/SQUAD

-python run_qa_beam_search.py \
+python run_squad.py \
+    --model_type xlnet \
    --model_name_or_path xlnet-large-cased \
-    --dataset_name squad_v2 \
    --do_train \
    --do_eval \
    --version_2_with_negative \
+    --train_file $SQUAD_DIR/train-v2.0.json \
+    --predict_file $SQUAD_DIR/dev-v2.0.json \
    --learning_rate 3e-5 \
    --num_train_epochs 4 \
    --max_seq_length 384 \
    --doc_stride 128 \
    --output_dir ./wwm_cased_finetuned_squad/ \
-    --per_device_eval_batch_size=2  \
-    --per_device_train_batch_size=2   \
+    --per_gpu_eval_batch_size=2  \
+    --per_gpu_train_batch_size=2   \
    --save_steps 5000
 ```

@@ -137,7 +162,7 @@ Larger batch size may improve the performance while costing more memory.
 #### Fine-tuning BERT on SQuAD1.0 with relative position embeddings

 The following examples show how to fine-tune BERT models with different relative position embeddings. The BERT model 
-`bert-base-uncased` was pretrained with default absolute position embeddings. We provide the following pretrained 
+`bert-base-uncased` was pre-trained with default absolute position embeddings. We provide the following pre-trained 
 models which were pre-trained on the same training data (BooksCorpus and English Wikipedia) as in the BERT model 
 training, but with different relative position embeddings. 

@@ -153,19 +178,24 @@ in Huang et al. [Improve Transformer Models with Better Relative Position Embedd
 ##### Base models fine-tuning

 ```bash
+export SQUAD_DIR=/path/to/SQUAD
+output_dir=relative_squad
 export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
 python -m torch.distributed.launch --nproc_per_node=8 ./examples/question-answering/run_squad.py \
+    --model_type bert \
    --model_name_or_path zhiheng-huang/bert-base-uncased-embedding-relative-key-query \
-    --dataset_name squad \
    --do_train \
    --do_eval \
+    --do_lower_case \
+    --train_file $SQUAD_DIR/train-v1.1.json \
+    --predict_file $SQUAD_DIR/dev-v1.1.json \
    --learning_rate 3e-5 \
    --num_train_epochs 2 \
    --max_seq_length 512 \
    --doc_stride 128 \
-    --output_dir relative_squad \
-    --per_device_eval_batch_size=60 \
-    --per_device_train_batch_size=6
+    --output_dir ${output_dir} \
+    --per_gpu_eval_batch_size=60 \
+    --per_gpu_train_batch_size=6
 ```
 Training with the above command leads to the following results. It boosts the BERT default from f1 score of 88.52 to 90.54.

@@ -181,17 +211,22 @@ gpu training leads to the f1 score of 90.71.
 ##### Large models fine-tuning

 ```bash
+export SQUAD_DIR=/path/to/SQUAD
+output_dir=relative_squad
 export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
 python -m torch.distributed.launch --nproc_per_node=8 ./examples/question-answering/run_squad.py \
+    --model_type bert \
    --model_name_or_path zhiheng-huang/bert-large-uncased-whole-word-masking-embedding-relative-key-query \
-    --dataset_name squad \
    --do_train \
    --do_eval \
+    --do_lower_case \
+    --train_file $SQUAD_DIR/train-v1.1.json \
+    --predict_file $SQUAD_DIR/dev-v1.1.json \
    --learning_rate 3e-5 \
    --num_train_epochs 2 \
    --max_seq_length 512 \
    --doc_stride 128 \
-    --output_dir relative_squad \
+    --output_dir ${output_dir} \
    --per_gpu_eval_batch_size=6 \
    --per_gpu_train_batch_size=2 \
    --gradient_accumulation_steps 3
@@ -216,4 +251,5 @@ python run_tf_squad.py \
    --doc_stride 128    
 ```

+
 For the moment evaluation is not available in the Tensorflow Trainer only the training.
@@ -1,469 +0,0 @@
-# coding=utf-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.
-"""
-Fine-tuning the library models for question answering.
-"""
-# You can also adapt this script on your own question answering task. Pointers for this are left as comments.
-
-import logging
-import os
-import sys
-from dataclasses import dataclass, field
-from typing import Optional
-
-from datasets import load_dataset, load_metric
-
-import transformers
-from trainer_qa import QuestionAnsweringTrainer
-from transformers import (
-    AutoConfig,
-    AutoModelForQuestionAnswering,
-    AutoTokenizer,
-    DataCollatorWithPadding,
-    EvalPrediction,
-    HfArgumentParser,
-    PreTrainedTokenizerFast,
-    TrainingArguments,
-    default_data_collator,
-    set_seed,
-)
-from transformers.trainer_utils import is_main_process
-from utils_qa import postprocess_qa_predictions
-
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class ModelArguments:
-    """
-    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
-    """
-
-    model_name_or_path: str = field(
-        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
-    )
-    config_name: Optional[str] = field(
-        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
-    )
-    tokenizer_name: Optional[str] = field(
-        default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
-    )
-    cache_dir: Optional[str] = field(
-        default=None,
-        metadata={"help": "Path to directory to store the pretrained models downloaded from huggingface.co"},
-    )
-
-
-@dataclass
-class DataTrainingArguments:
-    """
-    Arguments pertaining to what data we are going to input our model for training and eval.
-    """
-
-    dataset_name: Optional[str] = field(
-        default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
-    )
-    dataset_config_name: Optional[str] = field(
-        default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
-    )
-    train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
-    validation_file: Optional[str] = field(
-        default=None,
-        metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
-    )
-    overwrite_cache: bool = field(
-        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
-    )
-    preprocessing_num_workers: Optional[int] = field(
-        default=None,
-        metadata={"help": "The number of processes to use for the preprocessing."},
-    )
-    max_seq_length: int = field(
-        default=384,
-        metadata={
-            "help": "The maximum total input sequence length after tokenization. Sequences longer "
-            "than this will be truncated, sequences shorter will be padded."
-        },
-    )
-    pad_to_max_length: bool = field(
-        default=True,
-        metadata={
-            "help": "Whether to pad all samples to `max_seq_length`. "
-            "If False, will pad the samples dynamically when batching to the maximum length in the batch (which can "
-            "be faster on GPU but will be slower on TPU)."
-        },
-    )
-    version_2_with_negative: bool = field(
-        default=False, metadata={"help": "If true, some of the examples do not have an answer."}
-    )
-    null_score_diff_threshold: float = field(
-        default=0.0,
-        metadata={
-            "help": "The threshold used to select the null answer: if the best answer has a score that is less than "
-            "the score of the null answer minus this threshold, the null answer is selected for this example. "
-            "Only useful when `version_2_with_negative=True`."
-        },
-    )
-    doc_stride: int = field(
-        default=128,
-        metadata={"help": "When splitting up a long document into chunks, how much stride to take between chunks."},
-    )
-    n_best_size: int = field(
-        default=20,
-        metadata={"help": "The total number of n-best predictions to generate when looking for an answer."},
-    )
-    max_answer_length: int = field(
-        default=30,
-        metadata={
-            "help": "The maximum length of an answer that can be generated. This is needed because the start "
-            "and end predictions are not conditioned on one another."
-        },
-    )
-
-    def __post_init__(self):
-        if self.dataset_name is None and self.train_file is None and self.validation_file is None:
-            raise ValueError("Need either a dataset name or a training/validation file.")
-        else:
-            if self.train_file is not None:
-                extension = self.train_file.split(".")[-1]
-                assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
-            if self.validation_file is not None:
-                extension = self.validation_file.split(".")[-1]
-                assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
-
-
-def main():
-    # See all possible arguments in src/transformers/training_args.py
-    # or by passing the --help flag to this script.
-    # We now keep distinct sets of args, for a cleaner separation of concerns.
-
-    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
-    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
-        # If we pass only one argument to the script and it's the path to a json file,
-        # let's parse it to get our arguments.
-        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
-    else:
-        model_args, data_args, training_args = parser.parse_args_into_dataclasses()
-
-    if (
-        os.path.exists(training_args.output_dir)
-        and os.listdir(training_args.output_dir)
-        and training_args.do_train
-        and not training_args.overwrite_output_dir
-    ):
-        raise ValueError(
-            f"Output directory ({training_args.output_dir}) already exists and is not empty."
-            "Use --overwrite_output_dir to overcome."
-        )
-
-    # Setup logging
-    logging.basicConfig(
-        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
-        datefmt="%m/%d/%Y %H:%M:%S",
-    )
-    logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN)
-
-    # Log on each process the small summary:
-    logger.warning(
-        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
-        + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
-    )
-    # Set the verbosity to info of the Transformers logger (on main process only):
-    if is_main_process(training_args.local_rank):
-        transformers.utils.logging.set_verbosity_info()
-    logger.info("Training/evaluation parameters %s", training_args)
-
-    # Set seed before initializing model.
-    set_seed(training_args.seed)
-
-    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
-    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
-    # (the dataset will be downloaded automatically from the datasets Hub).
-    #
-    # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
-    # 'text' is found. You can easily tweak this behavior (see below).
-    #
-    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
-    # download the dataset.
-    if data_args.dataset_name is not None:
-        # Downloading and loading a dataset from the hub.
-        datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
-    else:
-        data_files = {}
-        if data_args.train_file is not None:
-            data_files["train"] = data_args.train_file
-        if data_args.validation_file is not None:
-            data_files["validation"] = data_args.validation_file
-        extension = data_args.train_file.split(".")[-1]
-        datasets = load_dataset(extension, data_files=data_files, field="data")
-    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
-    # https://huggingface.co/docs/datasets/loading_datasets.html.
-
-    # Load pretrained model and tokenizer
-    #
-    # Distributed training:
-    # The .from_pretrained methods guarantee that only one local process can concurrently
-    # download model & vocab.
-    config = AutoConfig.from_pretrained(
-        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
-        cache_dir=model_args.cache_dir,
-    )
-    tokenizer = AutoTokenizer.from_pretrained(
-        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
-        cache_dir=model_args.cache_dir,
-        use_fast=True,
-    )
-    model = AutoModelForQuestionAnswering.from_pretrained(
-        model_args.model_name_or_path,
-        from_tf=bool(".ckpt" in model_args.model_name_or_path),
-        config=config,
-        cache_dir=model_args.cache_dir,
-    )
-
-    # Tokenizer check: this script requires a fast tokenizer.
-    if not isinstance(tokenizer, PreTrainedTokenizerFast):
-        raise ValueError(
-            "This example script only works for models that have a fast tokenizer. Checkout the big table of models "
-            "at https://huggingface.co/transformers/index.html#bigtable to find the model types that meet this "
-            "requirement"
-        )
-
-    # Preprocessing the datasets.
-    # Preprocessing is slighlty different for training and evaluation.
-    if training_args.do_train:
-        column_names = datasets["train"].column_names
-    else:
-        column_names = datasets["validation"].column_names
-    question_column_name = "question" if "question" in column_names else column_names[0]
-    context_column_name = "context" if "context" in column_names else column_names[1]
-    answer_column_name = "answers" if "answers" in column_names else column_names[2]
-
-    # Padding side determines if we do (question|context) or (context|question).
-    pad_on_right = tokenizer.padding_side == "right"
-
-    # Training preprocessing
-    def prepare_train_features(examples):
-        # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
-        # in one example possible giving several features when a context is long, each of those features having a
-        # context that overlaps a bit the context of the previous feature.
-        tokenized_examples = tokenizer(
-            examples[question_column_name if pad_on_right else context_column_name],
-            examples[context_column_name if pad_on_right else question_column_name],
-            truncation="only_second" if pad_on_right else "only_first",
-            max_length=data_args.max_seq_length,
-            stride=data_args.doc_stride,
-            return_overflowing_tokens=True,
-            return_offsets_mapping=True,
-            padding="max_length" if data_args.pad_to_max_length else False,
-        )
-
-        # Since one example might give us several features if it has a long context, we need a map from a feature to
-        # its corresponding example. This key gives us just that.
-        sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
-        # The offset mappings will give us a map from token to character position in the original context. This will
-        # help us compute the start_positions and end_positions.
-        offset_mapping = tokenized_examples.pop("offset_mapping")
-
-        # Let's label those examples!
-        tokenized_examples["start_positions"] = []
-        tokenized_examples["end_positions"] = []
-
-        for i, offsets in enumerate(offset_mapping):
-            # We will label impossible answers with the index of the CLS token.
-            input_ids = tokenized_examples["input_ids"][i]
-            cls_index = input_ids.index(tokenizer.cls_token_id)
-
-            # Grab the sequence corresponding to that example (to know what is the context and what is the question).
-            sequence_ids = tokenized_examples.sequence_ids(i)
-
-            # One example can give several spans, this is the index of the example containing this span of text.
-            sample_index = sample_mapping[i]
-            answers = examples[answer_column_name][sample_index]
-            # If no answers are given, set the cls_index as answer.
-            if len(answers["answer_start"]) == 0:
-                tokenized_examples["start_positions"].append(cls_index)
-                tokenized_examples["end_positions"].append(cls_index)
-            else:
-                # Start/end character index of the answer in the text.
-                start_char = answers["answer_start"][0]
-                end_char = start_char + len(answers["text"][0])
-
-                # Start token index of the current span in the text.
-                token_start_index = 0
-                while sequence_ids[token_start_index] != (1 if pad_on_right else 0):
-                    token_start_index += 1
-
-                # End token index of the current span in the text.
-                token_end_index = len(input_ids) - 1
-                while sequence_ids[token_end_index] != (1 if pad_on_right else 0):
-                    token_end_index -= 1
-
-                # Detect if the answer is out of the span (in which case this feature is labeled with the CLS index).
-                if not (offsets[token_start_index][0] <= start_char and offsets[token_end_index][1] >= end_char):
-                    tokenized_examples["start_positions"].append(cls_index)
-                    tokenized_examples["end_positions"].append(cls_index)
-                else:
-                    # Otherwise move the token_start_index and token_end_index to the two ends of the answer.
-                    # Note: we could go after the last offset if the answer is the last word (edge case).
-                    while token_start_index < len(offsets) and offsets[token_start_index][0] <= start_char:
-                        token_start_index += 1
-                    tokenized_examples["start_positions"].append(token_start_index - 1)
-                    while offsets[token_end_index][1] >= end_char:
-                        token_end_index -= 1
-                    tokenized_examples["end_positions"].append(token_end_index + 1)
-
-        return tokenized_examples
-
-    if training_args.do_train:
-        train_dataset = datasets["train"].map(
-            prepare_train_features,
-            batched=True,
-            num_proc=data_args.preprocessing_num_workers,
-            remove_columns=column_names,
-            load_from_cache_file=not data_args.overwrite_cache,
-        )
-
-    # Validation preprocessing
-    def prepare_validation_features(examples):
-        # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
-        # in one example possible giving several features when a context is long, each of those features having a
-        # context that overlaps a bit the context of the previous feature.
-        tokenized_examples = tokenizer(
-            examples[question_column_name if pad_on_right else context_column_name],
-            examples[context_column_name if pad_on_right else question_column_name],
-            truncation="only_second" if pad_on_right else "only_first",
-            max_length=data_args.max_seq_length,
-            stride=data_args.doc_stride,
-            return_overflowing_tokens=True,
-            return_offsets_mapping=True,
-            padding="max_length" if data_args.pad_to_max_length else False,
-        )
-
-        # Since one example might give us several features if it has a long context, we need a map from a feature to
-        # its corresponding example. This key gives us just that.
-        sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
-
-        # For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
-        # corresponding example_id and we will store the offset mappings.
-        tokenized_examples["example_id"] = []
-
-        for i in range(len(tokenized_examples["input_ids"])):
-            # Grab the sequence corresponding to that example (to know what is the context and what is the question).
-            sequence_ids = tokenized_examples.sequence_ids(i)
-            context_index = 1 if pad_on_right else 0
-
-            # One example can give several spans, this is the index of the example containing this span of text.
-            sample_index = sample_mapping[i]
-            tokenized_examples["example_id"].append(examples["id"][sample_index])
-
-            # Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
-            # position is part of the context or not.
-            tokenized_examples["offset_mapping"][i] = [
-                (o if sequence_ids[k] == context_index else None)
-                for k, o in enumerate(tokenized_examples["offset_mapping"][i])
-            ]
-
-        return tokenized_examples
-
-    if training_args.do_eval:
-        validation_dataset = datasets["validation"].map(
-            prepare_validation_features,
-            batched=True,
-            num_proc=data_args.preprocessing_num_workers,
-            remove_columns=column_names,
-            load_from_cache_file=not data_args.overwrite_cache,
-        )
-
-    # Data collator
-    # We have already padded to max length if the corresponding flag is True, otherwise we need to pad in the data
-    # collator.
-    data_collator = default_data_collator if data_args.pad_to_max_length else DataCollatorWithPadding(tokenizer)
-
-    # Post-processing:
-    def post_processing_function(examples, features, predictions):
-        # Post-processing: we match the start logits and end logits to answers in the original context.
-        predictions = postprocess_qa_predictions(
-            examples=examples,
-            features=features,
-            predictions=predictions,
-            version_2_with_negative=data_args.version_2_with_negative,
-            n_best_size=data_args.n_best_size,
-            max_answer_length=data_args.max_answer_length,
-            null_score_diff_threshold=data_args.null_score_diff_threshold,
-            output_dir=training_args.output_dir,
-            is_world_process_zero=trainer.is_world_process_zero(),
-        )
-        # Format the result to the format the metric expects.
-        if data_args.version_2_with_negative:
-            formatted_predictions = [
-                {"id": k, "prediction_text": v, "no_answer_probability": 0.0} for k, v in predictions.items()
-            ]
-        else:
-            formatted_predictions = [{"id": k, "prediction_text": v} for k, v in predictions.items()]
-        references = [{"id": ex["id"], "answers": ex[answer_column_name]} for ex in datasets["validation"]]
-        return EvalPrediction(predictions=formatted_predictions, label_ids=references)
-
-    # TODO: Once the fix lands in a Datasets release, remove the _local here and the squad_v2_local folder.
-    current_dir = os.path.sep.join(os.path.join(__file__).split(os.path.sep)[:-1])
-    metric = load_metric(os.path.join(current_dir, "squad_v2_local") if data_args.version_2_with_negative else "squad")
-
-    def compute_metrics(p: EvalPrediction):
-        return metric.compute(predictions=p.predictions, references=p.label_ids)
-
-    # Initialize our Trainer
-    trainer = QuestionAnsweringTrainer(
-        model=model,
-        args=training_args,
-        train_dataset=train_dataset if training_args.do_train else None,
-        eval_dataset=validation_dataset if training_args.do_eval else None,
-        eval_examples=datasets["validation"] if training_args.do_eval else None,
-        tokenizer=tokenizer,
-        data_collator=data_collator,
-        post_process_function=post_processing_function,
-        compute_metrics=compute_metrics,
-    )
-
-    # Training
-    if training_args.do_train:
-        trainer.train(
-            model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None
-        )
-        trainer.save_model()  # Saves the tokenizer too for easy upload
-
-    # Evaluation
-    results = {}
-    if training_args.do_eval:
-        logger.info("*** Evaluate ***")
-        results = trainer.evaluate()
-
-        output_eval_file = os.path.join(training_args.output_dir, "eval_results.txt")
-        if trainer.is_world_process_zero():
-            with open(output_eval_file, "w") as writer:
-                logger.info("***** Eval results *****")
-                for key, value in results.items():
-                    logger.info(f"  {key} = {value}")
-                    writer.write(f"{key} = {value}\n")
-
-    return results
-
-
-def _mp_fn(index):
-    # For xla_spawn (TPUs)
-    main()
-
-
-if __name__ == "__main__":
-    main()
@@ -1,512 +0,0 @@
-# coding=utf-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.
-"""
-Fine-tuning XLNet for question answering with beam search.
-"""
-# You can also adapt this script on your own question answering task. Pointers for this are left as comments.
-
-import logging
-import os
-import sys
-from dataclasses import dataclass, field
-from typing import Optional
-
-from datasets import load_dataset, load_metric
-
-import transformers
-from trainer_qa import QuestionAnsweringTrainer
-from transformers import (
-    DataCollatorWithPadding,
-    EvalPrediction,
-    HfArgumentParser,
-    TrainingArguments,
-    XLNetConfig,
-    XLNetForQuestionAnswering,
-    XLNetTokenizerFast,
-    default_data_collator,
-    set_seed,
-)
-from transformers.trainer_utils import is_main_process
-from utils_qa import postprocess_qa_predictions_with_beam_search
-
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class ModelArguments:
-    """
-    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
-    """
-
-    model_name_or_path: str = field(
-        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
-    )
-    config_name: Optional[str] = field(
-        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
-    )
-    tokenizer_name: Optional[str] = field(
-        default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
-    )
-    cache_dir: Optional[str] = field(
-        default=None,
-        metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
-    )
-    use_fast_tokenizer: bool = field(
-        default=True,
-        metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
-    )
-
-
-@dataclass
-class DataTrainingArguments:
-    """
-    Arguments pertaining to what data we are going to input our model for training and eval.
-    """
-
-    dataset_name: Optional[str] = field(
-        default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
-    )
-    dataset_config_name: Optional[str] = field(
-        default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
-    )
-    train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
-    validation_file: Optional[str] = field(
-        default=None,
-        metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
-    )
-    overwrite_cache: bool = field(
-        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
-    )
-    preprocessing_num_workers: Optional[int] = field(
-        default=None,
-        metadata={"help": "The number of processes to use for the preprocessing."},
-    )
-    max_seq_length: int = field(
-        default=384,
-        metadata={
-            "help": "The maximum total input sequence length after tokenization. Sequences longer "
-            "than this will be truncated, sequences shorter will be padded."
-        },
-    )
-    pad_to_max_length: bool = field(
-        default=True,
-        metadata={
-            "help": "Whether to pad all samples to `max_seq_length`. "
-            "If False, will pad the samples dynamically when batching to the maximum length in the batch (which can "
-            "be faster on GPU but will be slower on TPU)."
-        },
-    )
-    version_2_with_negative: bool = field(
-        default=False, metadata={"help": "If true, some of the examples do not have an answer."}
-    )
-    null_score_diff_threshold: float = field(
-        default=0.0,
-        metadata={
-            "help": "The threshold used to select the null answer: if the best answer has a score that is less than "
-            "the score of the null answer minus this threshold, the null answer is selected for this example. "
-            "Only useful when `version_2_with_negative=True`."
-        },
-    )
-    doc_stride: int = field(
-        default=128,
-        metadata={"help": "When splitting up a long document into chunks, how much stride to take between chunks."},
-    )
-    n_best_size: int = field(
-        default=20,
-        metadata={"help": "The total number of n-best predictions to generate when looking for an answer."},
-    )
-    max_answer_length: int = field(
-        default=30,
-        metadata={
-            "help": "The maximum length of an answer that can be generated. This is needed because the start "
-            "and end predictions are not conditioned on one another."
-        },
-    )
-
-    def __post_init__(self):
-        if self.dataset_name is None and self.train_file is None and self.validation_file is None:
-            raise ValueError("Need either a dataset name or a training/validation file.")
-        else:
-            if self.train_file is not None:
-                extension = self.train_file.split(".")[-1]
-                assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
-            if self.validation_file is not None:
-                extension = self.validation_file.split(".")[-1]
-                assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
-
-
-def main():
-    # See all possible arguments in src/transformers/training_args.py
-    # or by passing the --help flag to this script.
-    # We now keep distinct sets of args, for a cleaner separation of concerns.
-
-    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
-    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
-        # If we pass only one argument to the script and it's the path to a json file,
-        # let's parse it to get our arguments.
-        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
-    else:
-        model_args, data_args, training_args = parser.parse_args_into_dataclasses()
-
-    if (
-        os.path.exists(training_args.output_dir)
-        and os.listdir(training_args.output_dir)
-        and training_args.do_train
-        and not training_args.overwrite_output_dir
-    ):
-        raise ValueError(
-            f"Output directory ({training_args.output_dir}) already exists and is not empty."
-            "Use --overwrite_output_dir to overcome."
-        )
-
-    # Setup logging
-    logging.basicConfig(
-        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
-        datefmt="%m/%d/%Y %H:%M:%S",
-    )
-    logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN)
-
-    # Log on each process the small summary:
-    logger.warning(
-        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
-        + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
-    )
-    # Set the verbosity to info of the Transformers logger (on main process only):
-    if is_main_process(training_args.local_rank):
-        transformers.utils.logging.set_verbosity_info()
-    logger.info("Training/evaluation parameters %s", training_args)
-
-    # Set seed before initializing model.
-    set_seed(training_args.seed)
-
-    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
-    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
-    # (the dataset will be downloaded automatically from the datasets Hub).
-    #
-    # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
-    # 'text' is found. You can easily tweak this behavior (see below).
-    #
-    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
-    # download the dataset.
-    if data_args.dataset_name is not None:
-        # Downloading and loading a dataset from the hub.
-        datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
-    else:
-        data_files = {}
-        if data_args.train_file is not None:
-            data_files["train"] = data_args.train_file
-        if data_args.validation_file is not None:
-            data_files["validation"] = data_args.validation_file
-        extension = data_args.train_file.split(".")[-1]
-        datasets = load_dataset(extension, data_files=data_files, field="data")
-    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
-    # https://huggingface.co/docs/datasets/loading_datasets.html.
-
-    # Load pretrained model and tokenizer
-    #
-    # Distributed training:
-    # The .from_pretrained methods guarantee that only one local process can concurrently
-    # download model & vocab.
-    config = XLNetConfig.from_pretrained(
-        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
-        cache_dir=model_args.cache_dir,
-    )
-    tokenizer = XLNetTokenizerFast.from_pretrained(
-        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
-        cache_dir=model_args.cache_dir,
-    )
-    model = XLNetForQuestionAnswering.from_pretrained(
-        model_args.model_name_or_path,
-        from_tf=bool(".ckpt" in model_args.model_name_or_path),
-        config=config,
-        cache_dir=model_args.cache_dir,
-    )
-
-    # Preprocessing the datasets.
-    # Preprocessing is slighlty different for training and evaluation.
-    if training_args.do_train:
-        column_names = datasets["train"].column_names
-    else:
-        column_names = datasets["validation"].column_names
-    question_column_name = "question" if "question" in column_names else column_names[0]
-    context_column_name = "context" if "context" in column_names else column_names[1]
-    answer_column_name = "answers" if "answers" in column_names else column_names[2]
-
-    # Padding side determines if we do (question|context) or (context|question).
-    pad_on_right = tokenizer.padding_side == "right"
-
-    # Training preprocessing
-    def prepare_train_features(examples):
-        # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
-        # in one example possible giving several features when a context is long, each of those features having a
-        # context that overlaps a bit the context of the previous feature.
-        tokenized_examples = tokenizer(
-            examples[question_column_name if pad_on_right else context_column_name],
-            examples[context_column_name if pad_on_right else question_column_name],
-            truncation="only_second" if pad_on_right else "only_first",
-            max_length=data_args.max_seq_length,
-            stride=data_args.doc_stride,
-            return_overflowing_tokens=True,
-            return_offsets_mapping=True,
-            return_special_tokens_mask=True,
-            return_token_type_ids=True,
-            padding="max_length",
-        )
-
-        # Since one example might give us several features if it has a long context, we need a map from a feature to
-        # its corresponding example. This key gives us just that.
-        sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
-        # The offset mappings will give us a map from token to character position in the original context. This will
-        # help us compute the start_positions and end_positions.
-        offset_mapping = tokenized_examples.pop("offset_mapping")
-        # The special tokens will help us build the p_mask (which indicates the tokens that can't be in answers).
-        special_tokens = tokenized_examples.pop("special_tokens_mask")
-
-        # Let's label those examples!
-        tokenized_examples["start_positions"] = []
-        tokenized_examples["end_positions"] = []
-        tokenized_examples["is_impossible"] = []
-        tokenized_examples["cls_index"] = []
-        tokenized_examples["p_mask"] = []
-
-        for i, offsets in enumerate(offset_mapping):
-            # We will label impossible answers with the index of the CLS token.
-            input_ids = tokenized_examples["input_ids"][i]
-            cls_index = input_ids.index(tokenizer.cls_token_id)
-            tokenized_examples["cls_index"].append(cls_index)
-
-            # Grab the sequence corresponding to that example (to know what is the context and what is the question).
-            sequence_ids = tokenized_examples["token_type_ids"][i]
-            for k, s in enumerate(special_tokens[i]):
-                if s:
-                    sequence_ids[k] = 3
-            context_idx = 1 if pad_on_right else 0
-
-            # Build the p_mask: non special tokens and context gets 0.0, the others get 1.0.
-            # The cls token gets 1.0 too (for predictions of empty answers).
-            tokenized_examples["p_mask"].append(
-                [
-                    0.0 if (not special_tokens[i][k] and s == context_idx) or k == cls_index else 1.0
-                    for k, s in enumerate(sequence_ids)
-                ]
-            )
-
-            # One example can give several spans, this is the index of the example containing this span of text.
-            sample_index = sample_mapping[i]
-            answers = examples[answer_column_name][sample_index]
-            # If no answers are given, set the cls_index as answer.
-            if len(answers["answer_start"]) == 0:
-                tokenized_examples["start_positions"].append(cls_index)
-                tokenized_examples["end_positions"].append(cls_index)
-                tokenized_examples["is_impossible"].append(1.0)
-            else:
-                # Start/end character index of the answer in the text.
-                start_char = answers["answer_start"][0]
-                end_char = start_char + len(answers["text"][0])
-
-                # Start token index of the current span in the text.
-                token_start_index = 0
-                while sequence_ids[token_start_index] != context_idx:
-                    token_start_index += 1
-
-                # End token index of the current span in the text.
-                token_end_index = len(input_ids) - 1
-                while sequence_ids[token_end_index] != context_idx:
-                    token_end_index -= 1
-                # Detect if the answer is out of the span (in which case this feature is labeled with the CLS index).
-                if not (offsets[token_start_index][0] <= start_char and offsets[token_end_index][1] >= end_char):
-                    tokenized_examples["start_positions"].append(cls_index)
-                    tokenized_examples["end_positions"].append(cls_index)
-                    tokenized_examples["is_impossible"].append(1.0)
-                else:
-                    # Otherwise move the token_start_index and token_end_index to the two ends of the answer.
-                    # Note: we could go after the last offset if the answer is the last word (edge case).
-                    while token_start_index < len(offsets) and offsets[token_start_index][0] <= start_char:
-                        token_start_index += 1
-                    tokenized_examples["start_positions"].append(token_start_index - 1)
-                    while offsets[token_end_index][1] >= end_char:
-                        token_end_index -= 1
-                    tokenized_examples["end_positions"].append(token_end_index + 1)
-                    tokenized_examples["is_impossible"].append(0.0)
-
-        return tokenized_examples
-
-    if training_args.do_train:
-        train_dataset = datasets["train"].map(
-            prepare_train_features,
-            batched=True,
-            num_proc=data_args.preprocessing_num_workers,
-            remove_columns=column_names,
-            load_from_cache_file=not data_args.overwrite_cache,
-        )
-
-    # Validation preprocessing
-    def prepare_validation_features(examples):
-        # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
-        # in one example possible giving several features when a context is long, each of those features having a
-        # context that overlaps a bit the context of the previous feature.
-        tokenized_examples = tokenizer(
-            examples[question_column_name if pad_on_right else context_column_name],
-            examples[context_column_name if pad_on_right else question_column_name],
-            truncation="only_second" if pad_on_right else "only_first",
-            max_length=data_args.max_seq_length,
-            stride=data_args.doc_stride,
-            return_overflowing_tokens=True,
-            return_offsets_mapping=True,
-            return_special_tokens_mask=True,
-            return_token_type_ids=True,
-            padding="max_length",
-        )
-
-        # Since one example might give us several features if it has a long context, we need a map from a feature to
-        # its corresponding example. This key gives us just that.
-        sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
-
-        # The special tokens will help us build the p_mask (which indicates the tokens that can't be in answers).
-        special_tokens = tokenized_examples.pop("special_tokens_mask")
-
-        # For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
-        # corresponding example_id and we will store the offset mappings.
-        tokenized_examples["example_id"] = []
-
-        # We still provide the index of the CLS token and the p_mask to the model, but not the is_impossible label.
-        tokenized_examples["cls_index"] = []
-        tokenized_examples["p_mask"] = []
-
-        for i, input_ids in enumerate(tokenized_examples["input_ids"]):
-            # Find the CLS token in the input ids.
-            cls_index = input_ids.index(tokenizer.cls_token_id)
-            tokenized_examples["cls_index"].append(cls_index)
-
-            # Grab the sequence corresponding to that example (to know what is the context and what is the question).
-            sequence_ids = tokenized_examples["token_type_ids"][i]
-            for k, s in enumerate(special_tokens[i]):
-                if s:
-                    sequence_ids[k] = 3
-            context_idx = 1 if pad_on_right else 0
-
-            # Build the p_mask: non special tokens and context gets 0.0, the others 1.0.
-            tokenized_examples["p_mask"].append(
-                [
-                    0.0 if (not special_tokens[i][k] and s == context_idx) or k == cls_index else 1.0
-                    for k, s in enumerate(sequence_ids)
-                ]
-            )
-
-            # One example can give several spans, this is the index of the example containing this span of text.
-            sample_index = sample_mapping[i]
-            tokenized_examples["example_id"].append(examples["id"][sample_index])
-
-            # Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
-            # position is part of the context or not.
-            tokenized_examples["offset_mapping"][i] = [
-                (o if sequence_ids[k] == context_idx else None)
-                for k, o in enumerate(tokenized_examples["offset_mapping"][i])
-            ]
-
-        return tokenized_examples
-
-    if training_args.do_eval:
-        validation_dataset = datasets["validation"].map(
-            prepare_validation_features,
-            batched=True,
-            num_proc=data_args.preprocessing_num_workers,
-            remove_columns=column_names,
-            load_from_cache_file=not data_args.overwrite_cache,
-        )
-
-    # Data collator
-    # We have already padded to max length if the corresponding flag is True, otherwise we need to pad in the data
-    # collator.
-    data_collator = default_data_collator if data_args.pad_to_max_length else DataCollatorWithPadding(tokenizer)
-
-    # Post-processing:
-    def post_processing_function(examples, features, predictions):
-        # Post-processing: we match the start logits and end logits to answers in the original context.
-        predictions, scores_diff_json = postprocess_qa_predictions_with_beam_search(
-            examples=examples,
-            features=features,
-            predictions=predictions,
-            version_2_with_negative=data_args.version_2_with_negative,
-            n_best_size=data_args.n_best_size,
-            max_answer_length=data_args.max_answer_length,
-            start_n_top=model.config.start_n_top,
-            end_n_top=model.config.end_n_top,
-            output_dir=training_args.output_dir,
-            is_world_process_zero=trainer.is_world_process_zero(),
-        )
-        # Format the result to the format the metric expects.
-        if data_args.version_2_with_negative:
-            formatted_predictions = [
-                {"id": k, "prediction_text": v, "no_answer_probability": scores_diff_json[k]}
-                for k, v in predictions.items()
-            ]
-        else:
-            formatted_predictions = [{"id": k, "prediction_text": v} for k, v in predictions.items()]
-        references = [{"id": ex["id"], "answers": ex[answer_column_name]} for ex in datasets["validation"]]
-        return EvalPrediction(predictions=formatted_predictions, label_ids=references)
-
-    # TODO: Once the fix lands in a Datasets release, remove the _local here and the squad_v2_local folder.
-    current_dir = os.path.sep.join(os.path.join(__file__).split(os.path.sep)[:-1])
-    metric = load_metric(os.path.join(current_dir, "squad_v2_local") if data_args.version_2_with_negative else "squad")
-
-    def compute_metrics(p: EvalPrediction):
-        return metric.compute(predictions=p.predictions, references=p.label_ids)
-
-    # Initialize our Trainer
-    trainer = QuestionAnsweringTrainer(
-        model=model,
-        args=training_args,
-        train_dataset=train_dataset if training_args.do_train else None,
-        eval_dataset=validation_dataset if training_args.do_eval else None,
-        eval_examples=datasets["validation"] if training_args.do_eval else None,
-        tokenizer=tokenizer,
-        data_collator=data_collator,
-        post_process_function=post_processing_function,
-        compute_metrics=compute_metrics,
-    )
-
-    # Training
-    if training_args.do_train:
-        trainer.train(
-            model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None
-        )
-        trainer.save_model()  # Saves the tokenizer too for easy upload
-
-    # Evaluation
-    results = {}
-    if training_args.do_eval:
-        logger.info("*** Evaluate ***")
-        results = trainer.evaluate()
-
-        output_eval_file = os.path.join(training_args.output_dir, "eval_results.txt")
-        if trainer.is_world_process_zero():
-            with open(output_eval_file, "w") as writer:
-                logger.info("***** Eval results *****")
-                for key, value in results.items():
-                    logger.info(f"  {key} = {value}")
-                    writer.write(f"{key} = {value}\n")
-
-    return results
-
-
-def _mp_fn(index):
-    # For xla_spawn (TPUs)
-    main()
-
-
-if __name__ == "__main__":
-    main()
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Nicolas Patry	201db8051d	Remove formatting ?? in odd place.	2020-12-02 11:03:40 +01:00
Nicolas Patry	b8458ada48	Making the function private and adding it's doc.	2020-11-26 12:32:31 +01:00
Nicolas Patry	806d03332f	Checking for pair of inputs too.	2020-11-26 12:27:03 +01:00
Nicolas Patry	c0fe313b61	Warning about too long input for fast tokenizers too If truncation is not set in tokenizers, but the tokenization is too long for the model (`model_max_length`), we used to trigger a warning that The input would probably fail (which it most likely will). This PR re-enables the warning for fast tokenizers too and uses common code for the trigger to make sure it's consistent across.	2020-11-26 12:20:47 +01:00
				`@@ -1 +0,0 @@`
				`$PYTHON setup.py install # Python command to install the script.`