boom boom

debug: log more to json
2020-09-21 20:12:16 -04:00 · 2020-09-21 20:08:10 -04:00 · 2020-09-21 19:51:33 -04:00 · 2020-09-21 15:46:44 -04:00 · 2020-09-21 15:24:54 -04:00 · 2020-09-21 13:26:46 -04:00
1081 changed files with 34132 additions and 102824 deletions
@@ -74,20 +74,21 @@ jobs:
            - checkout
            - restore_cache:
                  keys:
-                      - v0.4-torch_and_tf-{{ checksum "setup.py" }}
-                      - v0.4-{{ checksum "setup.py" }}
+                      - v0.3-torch_and_tf-{{ checksum "setup.py" }}
+                      - v0.3-{{ checksum "setup.py" }}
            - run: pip install --upgrade pip
-            - run: pip install .[sklearn,tf-cpu,torch,testing,sentencepiece]
+            - run: pip install git+https://github.com/huggingface/datasets
+            - run: pip install .[sklearn,tf-cpu,torch,testing]
+            - run: pip install codecov pytest-cov
            - save_cache:
-                key: v0.4-{{ checksum "setup.py" }}
+                key: v0.3-{{ checksum "setup.py" }}
                paths:
                    - '~/.cache/pip'
-            - run: RUN_PT_TF_CROSS_TESTS=1 python -m pytest -n 8 --dist=loadfile -rA -s --make-reports=tests_torch_and_tf ./tests/ -m is_pt_tf_cross_test --durations=0 | tee tests_output.txt
+            - run: python -m pytest -n 8 --dist=loadfile -rA -s ./tests/ --cov  | tee output.txt
+            - run: codecov
            - store_artifacts:
-                  path: ~/transformers/tests_output.txt
-            - store_artifacts:
-                  path: ~/transformers/reports
-
+                  path: ~/transformers/output.txt
+                  destination: test_output.txt
    run_tests_torch:
        working_directory: ~/transformers
        docker:
@@ -100,20 +101,19 @@ jobs:
            - checkout
            - restore_cache:
                  keys:
-                      - v0.4-torch-{{ checksum "setup.py" }}
-                      - v0.4-{{ checksum "setup.py" }}
+                      - v0.3-torch-{{ checksum "setup.py" }}
+                      - v0.3-{{ checksum "setup.py" }}
            - run: pip install --upgrade pip
-            - run: pip install .[sklearn,torch,testing,sentencepiece]
+            - run: pip install git+https://github.com/huggingface/datasets
+            - run: pip install .[sklearn,torch,testing]
            - save_cache:
-                  key: v0.4-torch-{{ checksum "setup.py" }}
+                  key: v0.3-torch-{{ checksum "setup.py" }}
                  paths:
                      - '~/.cache/pip'
-            - run: python -m pytest -n 8 --dist=loadfile -s --make-reports=tests_torch ./tests/ | tee tests_output.txt
+            - run: python -m pytest -n 8 --dist=loadfile -rA -s ./tests/ | tee output.txt
            - store_artifacts:
-                  path: ~/transformers/tests_output.txt
-            - store_artifacts:
-                  path: ~/transformers/reports
-
+                  path: ~/transformers/output.txt
+                  destination: test_output.txt
    run_tests_tf:
        working_directory: ~/transformers
        docker:
@@ -126,98 +126,19 @@ jobs:
            - checkout
            - restore_cache:
                  keys:
-                      - v0.4-tf-{{ checksum "setup.py" }}
-                      - v0.4-{{ checksum "setup.py" }}
+                      - v0.3-tf-{{ checksum "setup.py" }}
+                      - v0.3-{{ checksum "setup.py" }}
            - run: pip install --upgrade pip
-            - run: pip install .[sklearn,tf-cpu,testing,sentencepiece]
+            - run: pip install git+https://github.com/huggingface/datasets
+            - run: pip install .[sklearn,tf-cpu,testing]
            - save_cache:
-                  key: v0.4-tf-{{ checksum "setup.py" }}
+                  key: v0.3-tf-{{ checksum "setup.py" }}
                  paths:
                      - '~/.cache/pip'
-            - run: python -m pytest -n 8 --dist=loadfile -rA -s --make-reports=tests_tf ./tests/ | tee tests_output.txt
+            - run: python -m pytest -n 8 --dist=loadfile -rA -s ./tests/ | tee output.txt
            - store_artifacts:
-                  path: ~/transformers/tests_output.txt
-            - store_artifacts:
-                  path: ~/transformers/reports
-
-    run_tests_flax:
-        working_directory: ~/transformers
-        docker:
-            - image: circleci/python:3.7
-        environment:
-            OMP_NUM_THREADS: 1
-        resource_class: xlarge
-        parallelism: 1
-        steps:
-            - checkout
-            - restore_cache:
-                keys:
-                    - v0.4-flax-{{ checksum "setup.py" }}
-                    - v0.4-{{ checksum "setup.py" }}
-            - run: pip install --upgrade pip
-            - run: sudo pip install .[flax,sklearn,torch,testing,sentencepiece]
-            - save_cache:
-                  key: v0.4-flax-{{ checksum "setup.py" }}
-                  paths:
-                      - '~/.cache/pip'
-            - run: python -m pytest -n 8 --dist=loadfile -rA -s --make-reports=tests_flax ./tests/ | tee tests_output.txt
-            - store_artifacts:
-                  path: ~/transformers/tests_output.txt
-            - store_artifacts:
-                  path: ~/transformers/reports
-
-    run_tests_pipelines_torch:
-        working_directory: ~/transformers
-        docker:
-            - image: circleci/python:3.7
-        environment:
-            OMP_NUM_THREADS: 1
-        resource_class: xlarge
-        parallelism: 1
-        steps:
-            - checkout
-            - restore_cache:
-                  keys:
-                      - v0.4-torch-{{ checksum "setup.py" }}
-                      - v0.4-{{ checksum "setup.py" }}
-            - run: pip install --upgrade pip
-            - run: pip install .[sklearn,torch,testing,sentencepiece]
-            - save_cache:
-                  key: v0.4-torch-{{ checksum "setup.py" }}
-                  paths:
-                      - '~/.cache/pip'
-            - run: RUN_PIPELINE_TESTS=1 python -m pytest -n 8 --dist=loadfile -rA -s --make-reports=tests_pipelines_torch -m is_pipeline_test ./tests/ | tee tests_output.txt
-            - store_artifacts:
-                  path: ~/transformers/tests_output.txt
-            - store_artifacts:
-                  path: ~/transformers/reports
-
-    run_tests_pipelines_tf:
-        working_directory: ~/transformers
-        docker:
-            - image: circleci/python:3.7
-        environment:
-            OMP_NUM_THREADS: 1
-        resource_class: xlarge
-        parallelism: 1
-        steps:
-            - checkout
-            - restore_cache:
-                  keys:
-                      - v0.4-tf-{{ checksum "setup.py" }}
-                      - v0.4-{{ checksum "setup.py" }}
-            - run: pip install --upgrade pip
-            - run: pip install .[sklearn,tf-cpu,testing,sentencepiece]
-            - save_cache:
-                  key: v0.4-tf-{{ checksum "setup.py" }}
-                  paths:
-                      - '~/.cache/pip'
-            - run: RUN_PIPELINE_TESTS=1 python -m pytest -n 8 --dist=loadfile -rA -s --make-reports=tests_pipelines_tf ./tests/ -m is_pipeline_test | tee tests_output.txt
-            - store_artifacts:
-                  path: ~/transformers/tests_output.txt
-            - store_artifacts:
-                  path: ~/transformers/reports
-
+               path: ~/transformers/output.txt
+               destination: test_output.txt
    run_tests_custom_tokenizers:
        working_directory: ~/transformers
        docker:
@@ -228,21 +149,19 @@ jobs:
            - checkout
            - restore_cache:
                  keys:
-                      - v0.4-custom_tokenizers-{{ checksum "setup.py" }}
-                      - v0.4-{{ checksum "setup.py" }}
+                      - v0.3-custom_tokenizers-{{ checksum "setup.py" }}
+                      - v0.3-{{ checksum "setup.py" }}
            - run: pip install --upgrade pip
-            - run: pip install .[ja,testing,sentencepiece]
+            - run: pip install .[ja,testing]
            - run: python -m unidic download
            - save_cache:
-                  key: v0.4-custom_tokenizers-{{ checksum "setup.py" }}
+                  key: v0.3-custom_tokenizers-{{ checksum "setup.py" }}
                  paths:
                      - '~/.cache/pip'
-            - run: python -m pytest -s --make-reports=tests_custom_tokenizers ./tests/test_tokenization_bert_japanese.py | tee tests_output.txt
+            - run: python -m pytest -s ./tests/test_tokenization_bert_japanese.py | tee output.txt
            - store_artifacts:
-                  path: ~/transformers/tests_output.txt
-            - store_artifacts:
-                  path: ~/transformers/reports
-
+                path: ~/transformers/output.txt
+                destination: test_output.txt
    run_examples_torch:
        working_directory: ~/transformers
        docker:
@@ -255,21 +174,19 @@ jobs:
            - checkout
            - restore_cache:
                  keys:
-                      - v0.4-torch_examples-{{ checksum "setup.py" }}
-                      - v0.4-{{ checksum "setup.py" }}
+                      - v0.3-torch_examples-{{ checksum "setup.py" }}
+                      - v0.3-{{ checksum "setup.py" }}
            - run: pip install --upgrade pip
-            - run: pip install .[sklearn,torch,sentencepiece,testing]
+            - run: pip install .[sklearn,torch,testing]
            - run: pip install -r examples/requirements.txt
            - save_cache:
-                  key: v0.4-torch_examples-{{ checksum "setup.py" }}
+                  key: v0.3-torch_examples-{{ checksum "setup.py" }}
                  paths:
                      - '~/.cache/pip'
-            - run: python -m pytest -n 8 --dist=loadfile -s --make-reports=examples_torch ./examples/ | tee examples_output.txt
+            - run: python -m pytest -n 8 --dist=loadfile -rA -s ./examples/ | tee output.txt
            - store_artifacts:
-                  path: ~/transformers/examples_output.txt
-            - store_artifacts:
-                  path: ~/transformers/reports
-
+                  path: ~/transformers/output.txt
+                  destination: test_output.txt
    build_doc:
        working_directory: ~/transformers
        docker:
@@ -278,18 +195,17 @@ jobs:
            - checkout
            - restore_cache:
                  keys:
-                      - v0.4-build_doc-{{ checksum "setup.py" }}
-                      - v0.4-{{ checksum "setup.py" }}
+                      - v0.3-build_doc-{{ checksum "setup.py" }}
+                      - v0.3-{{ checksum "setup.py" }}
            - run: pip install --upgrade pip
-            - run: pip install ."[all, docs]"
+            - run: pip install .[tf,torch,docs]
            - save_cache:
-                  key: v0.4-build_doc-{{ checksum "setup.py" }}
+                  key: v0.3-build_doc-{{ checksum "setup.py" }}
                  paths:
                      - '~/.cache/pip'
            - run: cd docs && make html SPHINXOPTS="-W"
            - store_artifacts:
                path: ./docs/_build
-
    deploy_doc:
        working_directory: ~/transformers
        docker:
@@ -301,15 +217,14 @@ jobs:
            - checkout
            - restore_cache:
                  keys:
-                      - v0.4-deploy_doc-{{ checksum "setup.py" }}
-                      - v0.4-{{ checksum "setup.py" }}
-            - run: pip install ."[all,docs]"
+                      - v0.3-deploy_doc-{{ checksum "setup.py" }}
+                      - v0.3-{{ checksum "setup.py" }}
+            - run: pip install .[tf,torch,docs]
            - save_cache:
-                  key: v0.4-deploy_doc-{{ checksum "setup.py" }}
+                  key: v0.3-deploy_doc-{{ checksum "setup.py" }}
                  paths:
                      - '~/.cache/pip'
            - run: ./.circleci/deploy.sh
-
    check_code_quality:
        working_directory: ~/transformers
        docker:
@@ -320,23 +235,19 @@ jobs:
            - checkout
            - restore_cache:
                  keys:
-                      - v0.4-code_quality-{{ checksum "setup.py" }}
-                      - v0.4-{{ checksum "setup.py" }}
+                      - v0.3-code_quality-{{ checksum "setup.py" }}
+                      - v0.3-{{ checksum "setup.py" }}
            - run: pip install --upgrade pip
            - run: pip install isort
-            - run: pip install .[all,quality]
+            - run: pip install .[tf,torch,quality]
            - save_cache:
-                  key: v0.4-code_quality-{{ checksum "setup.py" }}
+                  key: v0.3-code_quality-{{ checksum "setup.py" }}
                  paths:
                      - '~/.cache/pip'
-            - run: black --check examples tests src utils
-            - run: isort --check-only examples tests src utils
-            - 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_dummies.py
+            - run: black --check --line-length 119 --target-version py35 examples templates tests src utils
+            - run: isort --check-only examples templates tests src utils
+            - run: flake8 examples templates tests src utils
            - run: python utils/check_repo.py
-
    check_repository_consistency:
        working_directory: ~/transformers
        docker:
@@ -367,7 +278,6 @@ jobs:
            - setup_remote_docker
            - *build_push_docker
            - *deploy_cluster
-
    cleanup-gke-jobs:
        docker:
            - image: circleci/python:3.6
@@ -377,7 +287,6 @@ jobs:
                  cluster: $GKE_CLUSTER
                  perform-login: true
            - *delete_gke_jobs
-
 workflow_filters: &workflow_filters
    filters:
        branches:
@@ -394,9 +303,6 @@ workflows:
            - run_tests_torch_and_tf
            - run_tests_torch
            - run_tests_tf
-            - run_tests_flax
-            - run_tests_pipelines_torch
-            - run_tests_pipelines_tf
            - build_doc
            - deploy_doc: *workflow_filters
    tpu_testing_jobs:
@@ -48,9 +48,4 @@ deploy_doc "7cb203f" v2.9.1
 deploy_doc "10d7239" v2.10.0 
 deploy_doc "b42586e" v2.11.0
 deploy_doc "7fb8bdf" v3.0.2
-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 "28d3ccd" # v4.0.1 Latest stable release
+deploy_doc "4b3ee9c" # v3.1.0 Latest stable release
@@ -30,22 +30,20 @@ assignees: ''
 Trainer: @sgugger
 Speed and Memory Benchmarks: @patrickvonplaten
 Model Cards: @julien-c
+ Translation: @sshleifer
+ Summarization: @sshleifer
 TextGeneration: @TevenLeScao 
 examples/distillation: @VictorSanh
 nlp datasets: [different repo](https://github.com/huggingface/nlp)
 rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)
- Text Generation: @patrickvonplaten @TevenLeScao
- Blenderbot: @patrickvonplaten
- Bart: @patrickvonplaten
- Marian: @patrickvonplaten
- Pegasus: @patrickvonplaten
- mBART: @patrickvonplaten
+ Text Generation: @TevenLeScao
+ blenderbot: @mariamabarham
+ Bart: @sshleifer
+ Marian: @sshleifer
 T5: @patrickvonplaten
 Longformer/Reformer: @patrickvonplaten
- TransfoXL/XLNet: @TevenLeScao
- RAG: @patrickvonplaten, @lhoestq
- FSMT: @stas00
- examples/seq2seq: @patil-suraj
+ TransfoXL/XLNet: @TevenLeScao 
+ examples/seq2seq: @sshleifer
 examples/bert-loses-patience: @JetRunner
 tensorflow: @jplu
 examples/token-classification: @stefan-it
@@ -1,6 +1,6 @@
 ---
 name: "❓ Questions & Help"
-about: Post your general questions on the Hugging Face forum: https://discuss.huggingface.co/
+about: Post your general questions on the Hugging Face forum or Stack Overflow tagged huggingface-transformers
 title: ''
 labels: ''
 assignees: ''
@@ -10,17 +10,18 @@ assignees: ''
 # ❓ Questions & Help

 <!-- The GitHub issue tracker is primarly intended for bugs, feature requests,
-     new models, benchmarks, and migration questions. For all other questions,
+     new models and benchmarks, and migration questions. For all other questions,
     we direct you to the Hugging Face forum: https://discuss.huggingface.co/ .
+     You can also try Stack Overflow (SO) where a whole community of PyTorch and
+     Tensorflow enthusiast can help you out. In this case, make sure to tag your
+     question with the right deep learning framework as well as the
+     huggingface-transformers tag: 
+     https://stackoverflow.com/questions/tagged/huggingface-transformers 
     -->

 ## Details
-
 <!-- Description of your issue -->

-<!-- You should first ask your question on the forum, and only if
-     you didn't get an answer after a few days ask it here on GitHub. -->
-
-**A link to original question on the forum**:
-
-<!-- Your issue will be closed if you don't fill this part. -->
+<!-- You should first ask your question on the forum or SO, and only if
+     you didn't get an answer ask it here on GitHub. -->
+**A link to original question on the forum/Stack Overflow**:
@@ -1,62 +1,2 @@
-# What does this PR do?
-
-<!--
-Congratulations! You've made it this far! You're not quite done yet though.
-
-Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution.
-
-Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change.
-
-Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost.
-->
-
-<!-- Remove if not applicable -->
-
-Fixes # (issue)
-
-
-## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md#start-contributing-pull-requests),
-      Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
-      to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
-      [documentation guidelines](https://github.com/huggingface/transformers/tree/master/docs), and
-      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/master/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
-
-
-## Who can review?
-
-Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
-members/contributors which may be interested in your PR.
-
-<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @
-
- If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
- Please tag fewer than 3 people.
-
- albert, bert, XLM: @LysandreJik
- GPT2: @LysandreJik, @patrickvonplaten
- tokenizers: @mfuntowicz
- Trainer: @sgugger
- Benchmarks: @patrickvonplaten
- Model Cards: @julien-c
- examples/distillation: @VictorSanh
- nlp datasets: [different repo](https://github.com/huggingface/nlp)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)
- Text Generation: @patrickvonplaten, @TevenLeScao
- Blenderbot, Bart, Marian, Pegasus: @patrickvonplaten
- T5: @patrickvonplaten
- Rag: @patrickvonplaten, @lhoestq
- EncoderDecoder: @patrickvonplaten
- Longformer, Reformer: @patrickvonplaten
- TransfoXL, XLNet: @TevenLeScao, @patrickvonplaten
- examples/seq2seq: @patil-suraj
- examples/bert-loses-patience: @JetRunner
- tensorflow: @jplu
- examples/token-classification: @stefan-it
- documentation: @sgugger
- FSTM: @stas00
- -->
+<!-- This line specifies which issue to close after the pull request is merged. -->
+Fixes #{issue number}
@@ -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."
@@ -8,9 +8,6 @@ on:
 jobs:
  torch_hub_integration:
    runs-on: ubuntu-latest
-    env:
-      # TODO quickfix but may need more investigation
-      ACTIONS_ALLOW_UNSECURE_COMMANDS: True
    steps:
    # no checkout necessary here.
    - name: Extract branch name
@@ -33,7 +30,7 @@ jobs:
      run: |
        pip install --upgrade pip
        pip install torch
-        pip install numpy filelock protobuf requests tqdm regex sentencepiece sacremoses tokenizers packaging
+        pip install numpy tokenizers filelock requests tqdm regex sentencepiece sacremoses packaging

    - name: Torch hub list
      run: |
@@ -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
@@ -1,273 +1,64 @@
 name: Self-hosted runner (push)

-on:
+on: 
  push:
    branches:
      - master
-      - model-templates
-    paths:
+    paths: 
      - "src/**"
      - "tests/**"
      - ".github/**"
-      - "templates/**"
  # pull_request:
  repository_dispatch:


 jobs:
-  run_tests_torch_gpu:
-    runs-on: [self-hosted, gpu, single-gpu]
+  run_tests_torch_and_tf_gpu:
+    runs-on: self-hosted
    steps:
-      - uses: actions/checkout@v2
-      - name: Python version
-        run: |
-          which python
-          python --version
-          pip --version
+    - uses: actions/checkout@v2
+    - name: Python version
+      run: |
+        which python
+        python --version
+        pip --version
+    - name: Current dir
+      run: pwd
+    - run: nvidia-smi

-      - name: Current dir
-        run: pwd
-      - run: nvidia-smi
+    - name: Loading cache.
+      uses: actions/cache@v2
+      id: cache
+      with:
+        path: .env
+        key: v0-tests_tf_torch_gpu-${{ hashFiles('setup.py') }}

-      - name: Loading cache.
-        uses: actions/cache@v2
-        id: cache
-        with:
-          path: .env
-          key: v1.1-tests_torch_gpu-${{ hashFiles('setup.py') }}
+    - name: Create new python env (on self-hosted runners we have to handle isolation ourselves)
+      run: |
+        python -m venv .env
+        source .env/bin/activate
+        which python
+        python --version
+        pip --version
+    - name: Install dependencies
+      run: |
+        source .env/bin/activate
+        pip install --upgrade pip
+        pip install torch!=1.6.0
+        pip install .[sklearn,testing,onnxruntime]
+        pip install git+https://github.com/huggingface/datasets

-      - name: Create new python env (on self-hosted runners we have to handle isolation ourselves)
-        run: |
-          python -m venv .env
-          source .env/bin/activate
-          which python
-          python --version
-          pip --version
+    - name: Are GPUs recognized by our DL frameworks
+      run: |
+        source .env/bin/activate
+        python -c "import torch; print(torch.cuda.is_available())"

-      - name: Install dependencies
-        run: |
-          source .env/bin/activate
-          pip install --upgrade pip
-          pip install .[torch,sklearn,testing,onnxruntime,sentencepiece]
-          pip install git+https://github.com/huggingface/datasets
-
-      - name: Are GPUs recognized by our DL frameworks
-        run: |
-          source .env/bin/activate
-          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: Run all non-slow tests on GPU
-        env:
-          OMP_NUM_THREADS: 1
-          CUDA_VISIBLE_DEVICES: 0
-        run: |
-          source .env/bin/activate
-          python -m pytest -n 2 --dist=loadfile -s --make-reports=tests_torch_gpu tests
-
-      - name: Failure short reports
-        if: ${{ always() }}
-        run: cat reports/tests_torch_gpu_failures_short.txt
-        
-      - name: Test suite reports artifacts
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v2
-        with:
-          name: run_all_tests_torch_gpu_test_reports
-          path: reports
-                  
-
-  run_tests_tf_gpu:
-    runs-on: [self-hosted, gpu, single-gpu]
-    steps:
-      - uses: actions/checkout@v2
-      - name: Python version
-        run: |
-          which python
-          python --version
-          pip --version
-      - name: Current dir
-        run: pwd
-      - run: nvidia-smi
-
-      - name: Loading cache.
-        uses: actions/cache@v2
-        id: cache
-        with:
-          path: .env
-          key: v1.1-tests_tf_gpu-${{ hashFiles('setup.py') }}
-
-      - name: Create new python env (on self-hosted runners we have to handle isolation ourselves)
-        run: |
-          python -m venv .env
-          source .env/bin/activate
-          which python
-          python --version
-          pip --version
-
-      - name: Install dependencies
-        run: |
-          source .env/bin/activate
-          pip install --upgrade pip
-          pip install .[tf,sklearn,testing,onnxruntime,sentencepiece]
-          pip install git+https://github.com/huggingface/datasets
-
-      - name: Are GPUs recognized by our DL frameworks
-        run: |
-          source .env/bin/activate
-          TF_CPP_MIN_LOG_LEVEL=3 python -c "import tensorflow as tf; print('TF GPUs available:', bool(tf.config.list_physical_devices('GPU')))"
-          TF_CPP_MIN_LOG_LEVEL=3 python -c "import tensorflow as tf; print('Number of TF GPUs available:', len(tf.config.list_physical_devices('GPU')))"
-
-      - 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:
-          OMP_NUM_THREADS: 1
-          CUDA_VISIBLE_DEVICES: 0
-        run: |
-          source .env/bin/activate
-          python -m pytest -n 2 --dist=loadfile -s --make-reports=tests_tf_gpu tests
-
-      - name: Failure short reports
-        if: ${{ always() }}
-        run: cat reports/tests_tf_gpu_failures_short.txt
-        
-      - name: Test suite reports artifacts
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v2
-        with:
-          name: run_all_tests_tf_gpu_test_reports
-          path: reports
-
-  run_tests_torch_multi_gpu:
-    runs-on: [self-hosted, gpu, multi-gpu]
-    steps:
-      - uses: actions/checkout@v2
-      - name: Python version
-        run: |
-          which python
-          python --version
-          pip --version
-
-      - name: Current dir
-        run: pwd
-      - run: nvidia-smi
-
-      - name: Loading cache.
-        uses: actions/cache@v2
-        id: cache
-        with:
-          path: .env
-          key: v1.1-tests_torch_multi_gpu-${{ hashFiles('setup.py') }}
-
-      - name: Create new python env (on self-hosted runners we have to handle isolation ourselves)
-        run: |
-          python -m venv .env
-          source .env/bin/activate
-          which python
-          python --version
-          pip --version
-      - name: Install dependencies
-        run: |
-          source .env/bin/activate
-          pip install --upgrade pip
-          pip install .[torch,sklearn,testing,onnxruntime,sentencepiece]
-          pip install git+https://github.com/huggingface/datasets
-
-      - name: Are GPUs recognized by our DL frameworks
-        run: |
-          source .env/bin/activate
-          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: Run all non-slow tests on GPU
-        env:
-          OMP_NUM_THREADS: 1
-        run: |
-          source .env/bin/activate
-          python -m pytest -n 2 --dist=loadfile -s --make-reports=tests_torch_multi_gpu tests
-
-      - name: Failure short reports
-        if: ${{ always() }}
-        run: cat reports/tests_torch_multi_gpu_failures_short.txt          
-
-      - name: Test suite reports artifacts
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v2
-        with:
-          name: run_all_tests_torch_multi_gpu_test_reports
-          path: reports
-
-  run_tests_tf_multi_gpu:
-    runs-on: [self-hosted, gpu, multi-gpu]
-    steps:
-      - uses: actions/checkout@v2
-      - name: Python version
-        run: |
-          which python
-          python --version
-          pip --version
-
-      - name: Current dir
-        run: pwd
-      - run: nvidia-smi
-
-      - name: Loading cache.
-        uses: actions/cache@v2
-        id: cache
-        with:
-          path: .env
-          key: v1.1-tests_tf_multi_gpu-${{ hashFiles('setup.py') }}
-
-      - name: Create new python env (on self-hosted runners we have to handle isolation ourselves)
-        run: |
-          python -m venv .env
-          source .env/bin/activate
-          which python
-          python --version
-          pip --version
-      - name: Install dependencies
-        run: |
-          source .env/bin/activate
-          pip install --upgrade pip
-          pip install .[tf,sklearn,testing,onnxruntime,sentencepiece]
-          pip install git+https://github.com/huggingface/datasets
-
-      - name: Are GPUs recognized by our DL frameworks
-        run: |
-          source .env/bin/activate
-          TF_CPP_MIN_LOG_LEVEL=3 python -c "import tensorflow as tf; print('TF GPUs available:', bool(tf.config.list_physical_devices('GPU')))"
-          TF_CPP_MIN_LOG_LEVEL=3 python -c "import tensorflow as tf; print('Number of TF GPUs available:', len(tf.config.list_physical_devices('GPU')))"
-
-      - name: Run all non-slow tests on GPU
-        env:
-          OMP_NUM_THREADS: 1
-        run: |
-          source .env/bin/activate
-          python -m pytest -n 2 --dist=loadfile -s --make-reports=tests_tf_multi_gpu tests
-
-      - name: Failure short reports
-        if: ${{ always() }}
-        run: cat reports/tests_tf_multi_gpu_failures_short.txt
-
-      - name: Test suite reports artifacts
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v2
-        with:
-          name: run_all_tests_tf_multi_gpu_test_reports
-          path: reports
-          
+    - name: Run all non-slow tests on GPU
+      env:
+        TF_FORCE_GPU_ALLOW_GROWTH: "true"
+        # TF_GPU_MEMORY_LIMIT: 4096
+        OMP_NUM_THREADS: 1
+        USE_CUDA: yes
+      run: |
+        source .env/bin/activate
+        python -m pytest -n 2 --dist=loadfile -s ./tests/
@@ -1,8 +1,3 @@
-# configuration notes:
-#
-# - `source .env/bin/activate` is currently needed to be run first thing first in each step. Otherwise
-# the step uses the system-wide python interpreter.
-
 name: Self-hosted runner (scheduled)

 on:
@@ -14,346 +9,64 @@ on:
    - cron: "0 0 * * *"

 jobs:
-  run_all_tests_torch_gpu:
-    runs-on: [self-hosted, gpu, single-gpu]
+  run_all_tests_torch_and_tf_gpu:
+    runs-on: self-hosted
    steps:
-      - uses: actions/checkout@v2
+    - uses: actions/checkout@v2

-      - name: Loading cache.
-        uses: actions/cache@v2
-        id: cache
-        with:
-          path: .env
-          key: v  1.1-slow_tests_torch_gpu-${{ hashFiles('setup.py') }}
+    - name: Loading cache.
+      uses: actions/cache@v2
+      id: cache
+      with:
+        path: .env
+        key: v0-slow_tests_tf_torch_gpu-${{ hashFiles('setup.py') }}

-      - name: Python version
-        run: |
-          which python
-          python --version
-          pip --version
+    - name: Python version
+      run: |
+        which python
+        python --version
+        pip --version
+    - name: Current dir
+      run: pwd
+    - run: nvidia-smi
+    - name: Create new python env (on self-hosted runners we have to handle isolation ourselves)
+      if: steps.cache.outputs.cache-hit != 'true'
+      run: |
+        python -m venv .env
+        source .env/bin/activate
+        which python
+        python --version
+        pip --version
+    - name: Install dependencies
+      run: |
+        source .env/bin/activate
+        pip install --upgrade pip
+        pip install torch!=1.6.0
+        pip install .[sklearn,testing,onnxruntime]
+        pip install git+https://github.com/huggingface/datasets

-      - name: Current dir
-        run: pwd
-      - run: nvidia-smi
+    - name: Are GPUs recognized by our DL frameworks
+      run: |
+        source .env/bin/activate
+        python -c "import torch; print(torch.cuda.is_available())"

-      - name: Create new python env (on self-hosted runners we have to handle isolation ourselves)
-        if: steps.cache.outputs.cache-hit != 'true'
-        run: |
-          python -m venv .env
-          source .env/bin/activate
-          which python
-          python --version
-          pip --version
+    - name: Run all tests on GPU
+      env:
+        TF_FORCE_GPU_ALLOW_GROWTH: "true"
+        OMP_NUM_THREADS: 1
+        RUN_SLOW: yes
+        USE_CUDA: yes
+      run: |
+        source .env/bin/activate
+        python -m pytest -n 1 --dist=loadfile -s ./tests/

-      - name: Install dependencies
-        run: |
-          source .env/bin/activate
-          pip install --upgrade pip
-          pip install .[torch,sklearn,testing,onnxruntime,sentencepiece]
-          pip install git+https://github.com/huggingface/datasets
-          pip list
-
-      - name: Are GPUs recognized by our DL frameworks
-        run: |
-          source .env/bin/activate
-          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: Run all tests on GPU
-        env:
-          OMP_NUM_THREADS: 1
-          RUN_SLOW: yes
-        run: |
-          source .env/bin/activate
-          python -m pytest -n 1 --dist=loadfile -s --make-reports=tests_torch_gpu tests
-
-      - name: Failure short reports
-        if: ${{ always() }}
-        run: cat reports/tests_torch_gpu_failures_short.txt
-        
-      - name: Run examples tests on GPU
-        if: ${{ always() }}
-        env:
-          OMP_NUM_THREADS: 1
-          RUN_SLOW: yes
-        run: |
-          source .env/bin/activate
-          pip install -r examples/requirements.txt
-          python -m pytest -n 1 --dist=loadfile -s --make-reports=examples_torch_gpu examples
-
-      - name: Failure short reports
-        if: ${{ always() }}
-        run: cat reports/examples_torch_gpu_failures_short.txt
-
-      - name: Run all pipeline tests on GPU
-        if: ${{ always() }}
-        env:
-          TF_FORCE_GPU_ALLOW_GROWTH: "true"
-          OMP_NUM_THREADS: 1
-          RUN_SLOW: yes
-          RUN_PIPELINE_TESTS: yes
-        run: |
-          source .env/bin/activate
-          python -m pytest -n 1 --dist=loadfile -s -m is_pipeline_test --make-reports=tests_torch_pipeline_gpu tests
-
-      - name: Failure short reports
-        if: ${{ always() }}
-        run: cat reports/tests_torch_pipeline_gpu_failures_short.txt
-
-      - name: Test suite reports artifacts
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v2
-        with:
-          name: run_all_tests_torch_gpu_test_reports
-          path: reports
-
-
-  run_all_tests_tf_gpu:
-    runs-on: [self-hosted, gpu, single-gpu]
-    steps:
-      - uses: actions/checkout@v2
-
-      - name: Loading cache.
-        uses: actions/cache@v2
-        id: cache
-        with:
-          path: .env
-          key: v1.1-slow_tests_tf_gpu-${{ hashFiles('setup.py') }}
-
-      - name: Python version
-        run: |
-          which python
-          python --version
-          pip --version
-
-      - name: Current dir
-        run: pwd
-      - run: nvidia-smi
-
-      - name: Create new python env (on self-hosted runners we have to handle isolation ourselves)
-        if: steps.cache.outputs.cache-hit != 'true'
-        run: |
-          python -m venv .env
-          source .env/bin/activate
-          which python
-          python --version
-          pip --version
-
-      - name: Install dependencies
-        run: |
-          source .env/bin/activate
-          pip install --upgrade pip
-          pip install .[tf,sklearn,testing,onnxruntime,sentencepiece]
-          pip install git+https://github.com/huggingface/datasets
-          pip list
-
-      - name: Are GPUs recognized by our DL frameworks
-        run: |
-          source .env/bin/activate
-          TF_CPP_MIN_LOG_LEVEL=3 python -c "import tensorflow as tf; print('TF GPUs available:', bool(tf.config.list_physical_devices('GPU')))"
-          TF_CPP_MIN_LOG_LEVEL=3 python -c "import tensorflow as tf; print('Number of TF GPUs available:', len(tf.config.list_physical_devices('GPU')))"
-
-      - name: Run all tests on GPU
-        env:
-          OMP_NUM_THREADS: 1
-          RUN_SLOW: yes
-        run: |
-          source .env/bin/activate
-          python -m pytest -n 1 --dist=loadfile -s --make-reports=tests_tf_gpu tests
-          
-      - name: Failure short reports
-        if: ${{ always() }}
-        run: cat reports/tests_tf_gpu_failures_short.txt
-
-      - name: Run all pipeline tests on GPU
-        if: ${{ always() }}
-        env:
-          TF_FORCE_GPU_ALLOW_GROWTH: "true"
-          OMP_NUM_THREADS: 1
-          RUN_SLOW: yes
-          RUN_PIPELINE_TESTS: yes
-        run: |
-          source .env/bin/activate
-          python -m pytest -n 1 --dist=loadfile -s -m is_pipeline_test --make-reports=tests_tf_pipelines_gpu tests
-
-      - name: Failure short reports
-        if: ${{ always() }}
-        run: cat reports/tests_tf_pipelines_gpu_failures_short.txt
-
-      - name: Test suite reports artifacts
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v2
-        with:
-          name: run_all_tests_tf_gpu_test_reports
-          path: reports
-          
-  run_all_tests_torch_multi_gpu:
-    runs-on: [self-hosted, gpu, multi-gpu]
-    steps:
-      - uses: actions/checkout@v2
-
-      - name: Loading cache.
-        uses: actions/cache@v2
-        id: cache
-        with:
-          path: .env
-          key: v1.1-slow_tests_torch_multi_gpu-${{ hashFiles('setup.py') }}
-
-      - name: Python version
-        run: |
-          which python
-          python --version
-          pip --version
-
-      - name: Current dir
-        run: pwd
-      - run: nvidia-smi
-
-      - name: Create new python env (on self-hosted runners we have to handle isolation ourselves)
-        if: steps.cache.outputs.cache-hit != 'true'
-        run: |
-          python -m venv .env
-          source .env/bin/activate
-          which python
-          python --version
-          pip --version
-
-      - name: Install dependencies
-        run: |
-          source .env/bin/activate
-          pip install --upgrade pip
-          pip install .[torch,sklearn,testing,onnxruntime,sentencepiece]
-          pip install git+https://github.com/huggingface/datasets
-          pip list
-
-      - name: Are GPUs recognized by our DL frameworks
-        run: |
-          source .env/bin/activate
-          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: Run all tests on multi-GPU
-        env:
-          OMP_NUM_THREADS: 1
-          RUN_SLOW: yes
-        run: |
-          source .env/bin/activate
-          python -m pytest -n 1 --dist=loadfile -s --make-reports=tests_torch_multi_gpu tests
-
-      - name: Failure short reports
-        if: ${{ always() }}
-        run: cat reports/tests_torch_multi_gpu_failures_short.txt
-
-      - name: Run examples tests on multi-GPU
-        env:
-          OMP_NUM_THREADS: 1
-          RUN_SLOW: yes
-        run: |
-          source .env/bin/activate
-          python -m pytest -n 1 --dist=loadfile -s --make-reports=tests_torch_examples_multi_gpu examples
-
-      - name: Failure short reports
-        if: ${{ always() }}
-        run: cat reports/tests_torch_examples_multi_gpu_failures_short.txt
-
-      - name: Run all pipeline tests on multi-GPU
-        if: ${{ always() }}
-        env:
-          TF_FORCE_GPU_ALLOW_GROWTH: "true"
-          OMP_NUM_THREADS: 1
-          RUN_SLOW: yes
-          RUN_PIPELINE_TESTS: yes
-        run: |
-          source .env/bin/activate
-          python -m pytest -n 1 --dist=loadfile -s -m is_pipeline_test --make-reports=tests_torch_pipeline_multi_gpu tests
-
-      - name: Failure short reports
-        if: ${{ always() }}
-        run: cat reports/tests_torch_pipeline_multi_gpu_failures_short.txt
-
-      - name: Test suite reports artifacts
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v2
-        with:
-          name: run_all_tests_torch_multi_gpu_test_reports
-          path: reports
-
-  run_all_tests_tf_multi_gpu:
-    runs-on: [self-hosted, gpu, multi-gpu]
-    steps:
-      - uses: actions/checkout@v2
-
-      - name: Loading cache.
-        uses: actions/cache@v2
-        id: cache
-        with:
-          path: .env
-          key: v1.1-slow_tests_tf_multi_gpu-${{ hashFiles('setup.py') }}
-
-      - name: Python version
-        run: |
-          which python
-          python --version
-          pip --version
-
-      - name: Current dir
-        run: pwd
-      - run: nvidia-smi
-
-      - name: Create new python env (on self-hosted runners we have to handle isolation ourselves)
-        if: steps.cache.outputs.cache-hit != 'true'
-        run: |
-          python -m venv .env
-          source .env/bin/activate
-          which python
-          python --version
-          pip --version
-
-      - name: Install dependencies
-        run: |
-          source .env/bin/activate
-          pip install --upgrade pip
-          pip install .[tf,sklearn,testing,onnxruntime,sentencepiece]
-          pip install git+https://github.com/huggingface/datasets
-          pip list
-
-      - name: Are GPUs recognized by our DL frameworks
-        run: |
-          source .env/bin/activate
-          TF_CPP_MIN_LOG_LEVEL=3 python -c "import tensorflow as tf; print('TF GPUs available:', bool(tf.config.list_physical_devices('GPU')))"
-          TF_CPP_MIN_LOG_LEVEL=3 python -c "import tensorflow as tf; print('Number of TF GPUs available:', len(tf.config.list_physical_devices('GPU')))"
-
-      - name: Run all tests on multi-GPU
-        env:
-          OMP_NUM_THREADS: 1
-          RUN_SLOW: yes
-        run: |
-          source .env/bin/activate
-          python -m pytest -n 1 --dist=loadfile -s --make-reports=tests_tf_multi_gpu tests
-
-      - name: Failure short reports
-        if: ${{ always() }}
-        run: cat reports/tests_tf_multi_gpu_failures_short.txt
-
-      - name: Run all pipeline tests on multi-GPU
-        if: ${{ always() }}
-        env:
-          TF_FORCE_GPU_ALLOW_GROWTH: "true"
-          OMP_NUM_THREADS: 1
-          RUN_SLOW: yes
-          RUN_PIPELINE_TESTS: yes
-        run: |
-          source .env/bin/activate
-          python -m pytest -n 1 --dist=loadfile -s -m is_pipeline_test --make-reports=tests_tf_pipeline_multi_gpu tests
-          
-      - name: Failure short reports
-        if: ${{ always() }}
-        run: cat reports/tests_tf_pipeline_multi_gpu_failures_short.txt
-
-      - name: Test suite reports artifacts
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v2
-        with:
-          name: run_all_tests_tf_multi_gpu_test_reports
-          path: reports
-          
+    - name: Run examples tests on GPU
+      env:
+        TF_FORCE_GPU_ALLOW_GROWTH: "true"
+        OMP_NUM_THREADS: 1
+        RUN_SLOW: yes
+        USE_CUDA: yes
+      run: |
+        source .env/bin/activate
+        pip install -r examples/requirements.txt
+        python -m pytest -n 1 --dist=loadfile -s examples
@@ -9,11 +9,8 @@ __pycache__/
 *.so

 # tests and logs
-tests/fixtures/*
-!tests/fixtures/sample_text_no_unicode.txt
+tests/fixtures
 logs/
-lightning_logs/
-lang_code_data/

 # Distribution / packaging
 .Python
@@ -133,6 +130,7 @@ dmypy.json
 tensorflow_code

 # Models
+models
 proc_data

 # examples
@@ -141,7 +139,6 @@ runs
 /wandb
 /examples/runs
 /examples/**/*.args
-/examples/rag/sweep

 # data
 /data
@@ -156,6 +153,3 @@ debug.env

 #ctags
 tags
-
-# pre-commit
-.pre-commit*
@@ -1,129 +0,0 @@
-
-# Contributor Covenant Code of Conduct
-
-## Our Pledge
-
-We as members, contributors, and leaders pledge to make participation in our
-community a harassment-free experience for everyone, regardless of age, body
-size, visible or invisible disability, ethnicity, sex characteristics, gender
-identity and expression, level of experience, education, socio-economic status,
-nationality, personal appearance, race, religion, or sexual identity
-and orientation.
-
-We pledge to act and interact in ways that contribute to an open, welcoming,
-diverse, inclusive, and healthy community.
-
-## Our Standards
-
-Examples of behavior that contributes to a positive environment for our
-community include:
-
-* Demonstrating empathy and kindness toward other people
-* Being respectful of differing opinions, viewpoints, and experiences
-* Giving and gracefully accepting constructive feedback
-* Accepting responsibility and apologizing to those affected by our mistakes,
-  and learning from the experience
-* Focusing on what is best not just for us as individuals, but for the
-  overall community
-
-Examples of unacceptable behavior include:
-
-* The use of sexualized language or imagery, and sexual attention or
-  advances of any kind
-* Trolling, insulting or derogatory comments, and personal or political attacks
-* Public or private harassment
-* Publishing others' private information, such as a physical or email
-  address, without their explicit permission
-* Other conduct which could reasonably be considered inappropriate in a
-  professional setting
-
-## Enforcement Responsibilities
-
-Community leaders are responsible for clarifying and enforcing our standards of
-acceptable behavior and will take appropriate and fair corrective action in
-response to any behavior that they deem inappropriate, threatening, offensive,
-or harmful.
-
-Community leaders have the right and responsibility to remove, edit, or reject
-comments, commits, code, wiki edits, issues, and other contributions that are
-not aligned to this Code of Conduct, and will communicate reasons for moderation
-decisions when appropriate.
-
-## Scope
-
-This Code of Conduct applies within all community spaces, and also applies when
-an individual is officially representing the community in public spaces.
-Examples of representing our community include using an official e-mail address,
-posting via an official social media account, or acting as an appointed
-representative at an online or offline event.
-
-## Enforcement
-
-Instances of abusive, harassing, or otherwise unacceptable behavior may be
-reported to the community leaders responsible for enforcement at
-feedback@huggingface.co.
-All complaints will be reviewed and investigated promptly and fairly.
-
-All community leaders are obligated to respect the privacy and security of the
-reporter of any incident.
-
-## Enforcement Guidelines
-
-Community leaders will follow these Community Impact Guidelines in determining
-the consequences for any action they deem in violation of this Code of Conduct:
-
-### 1. Correction
-
-**Community Impact**: Use of inappropriate language or other behavior deemed
-unprofessional or unwelcome in the community.
-
-**Consequence**: A private, written warning from community leaders, providing
-clarity around the nature of the violation and an explanation of why the
-behavior was inappropriate. A public apology may be requested.
-
-### 2. Warning
-
-**Community Impact**: A violation through a single incident or series
-of actions.
-
-**Consequence**: A warning with consequences for continued behavior. No
-interaction with the people involved, including unsolicited interaction with
-those enforcing the Code of Conduct, for a specified period of time. This
-includes avoiding interactions in community spaces as well as external channels
-like social media. Violating these terms may lead to a temporary or
-permanent ban.
-
-### 3. Temporary Ban
-
-**Community Impact**: A serious violation of community standards, including
-sustained inappropriate behavior.
-
-**Consequence**: A temporary ban from any sort of interaction or public
-communication with the community for a specified period of time. No public or
-private interaction with the people involved, including unsolicited interaction
-with those enforcing the Code of Conduct, is allowed during this period.
-Violating these terms may lead to a permanent ban.
-
-### 4. Permanent Ban
-
-**Community Impact**: Demonstrating a pattern of violation of community
-standards, including sustained inappropriate behavior,  harassment of an
-individual, or aggression toward or disparagement of classes of individuals.
-
-**Consequence**: A permanent ban from any sort of public interaction within
-the community.
-
-## Attribution
-
-This Code of Conduct is adapted from the [Contributor Covenant][homepage],
-version 2.0, available at
-https://www.contributor-covenant.org/version/2/0/code_of_conduct.html.
-
-Community Impact Guidelines were inspired by [Mozilla's code of conduct
-enforcement ladder](https://github.com/mozilla/diversity).
-
-[homepage]: https://www.contributor-covenant.org
-
-For answers to common questions about this code of conduct, see the FAQ at
-https://www.contributor-covenant.org/faq. Translations are available at
-https://www.contributor-covenant.org/translations.
@@ -9,9 +9,6 @@ It also helps us if you spread the word: reference the library from blog posts
 on the awesome projects it made possible, shout out on Twitter every time it has
 helped you, or simply star the repo to say "thank you".

-Whichever way you choose to contribute, please be mindful to respect our
-[code of conduct](https://github.com/huggingface/transformers/blob/master/CODE_OF_CONDUCT.md).
-
 ## You can contribute in so many ways!

 There are 4 ways you can contribute to transformers:
@@ -96,7 +93,7 @@ folder.

 ## Start contributing! (Pull Requests)

-Before writing code, we strongly advise you to search through the existing PRs or
+Before writing code, we strongly advise you to search through the exising PRs or
 issues to make sure that nobody is already working on the same thing. If you are
 unsure, it is always a good idea to open an issue to get some feedback.

@@ -173,19 +170,12 @@ Follow these steps to start contributing:
   $ make style
   ```

-   `transformers` also uses `flake8` and a few custom scripts to check for coding mistakes. Quality
+   `transformers` also uses `flake8` to check for coding mistakes. Quality
   control runs in CI, however you can also run the same checks with:

   ```bash
   $ make quality
   ```
-   You can do the automatic style corrections and code verifications that can't be automated in one go:
-
-   ```bash
-   $ make fixup
-   ```
-
-   This target is also optimized to only work with files modified by the PR you're working on.

   If you're modifying documents under `docs/source`, make sure to validate that
   they can still be built. This check also runs in CI. To run a local check
@@ -235,7 +225,7 @@ Follow these steps to start contributing:
 ### Checklist

 1. The title of your pull request should be a summary of its contribution;
-2. If your pull request addresses an issue, please mention the issue number in
+2. If your pull request adresses an issue, please mention the issue number in
   the pull request description to make sure they are linked (and people
   consulting the issue know you are working on it);
 3. To indicate a work in progress please prefix the title with `[WIP]`. These
@@ -308,12 +298,3 @@ Check our [documentation writing guide](https://github.com/huggingface/transform
 for more information.

 #### This guide was heavily inspired by the awesome [scikit-learn guide to contributing](https://github.com/scikit-learn/scikit-learn/blob/master/CONTRIBUTING.md)
-
-
-### Develop on Windows
-
-One way one can run the make command on Window is to pass by MSYS2:
-
-1. [Download MSYS2](https://www.msys2.org/), we assume to have it installed in C:\msys64
-2. Open the command line C:\msys64\msys2.exe (it should be available from the start menu)
-3. Run in the shell: `pacman -Syu` and install make with `pacman -S make`
@@ -1,51 +1,18 @@
-.PHONY: modified_only_fixup extra_quality_checks quality style fixup fix-copies test test-examples docs
-
-
-check_dirs := examples tests src utils
-
-modified_only_fixup:
-	$(eval modified_py_files := $(shell python utils/get_modified_files.py $(check_dirs)))
-	@if test -n "$(modified_py_files)"; then \
-		echo "Checking/fixing $(modified_py_files)"; \
-		black $(modified_py_files); \
-		isort $(modified_py_files); \
-		flake8 $(modified_py_files); \
-	else \
-		echo "No library .py files were modified"; \
-	fi
+.PHONY: quality style test test-examples docs

 # Check that source code meets quality standards

-extra_quality_checks:
-	python utils/check_copies.py
-	python utils/check_dummies.py
-	python utils/check_repo.py
-	python utils/style_doc.py src/transformers docs/source --max_len 119
-
-# this target runs checks on all files
 quality:
-	black --check $(check_dirs)
-	isort --check-only $(check_dirs)
-	flake8 $(check_dirs)
-	python utils/style_doc.py src/transformers docs/source --max_len 119 --check_only
-	${MAKE} extra_quality_checks
+	black --check --line-length 119 --target-version py35 examples templates tests src utils
+	isort --check-only examples templates tests src utils
+	flake8 examples templates tests src utils
+	python utils/check_repo.py

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

 style:
-	black $(check_dirs)
-	isort $(check_dirs)
-	python utils/style_doc.py src/transformers docs/source --max_len 119
-
-# Super fast fix and check target that only works on relevant modified files since the branch was made
-
-fixup: modified_only_fixup extra_quality_checks
-
-# Make marked copies of snippets of codes conform to the original
-
-fix-copies:
-	python utils/check_copies.py --fix_and_overwrite
-	python utils/check_dummies.py --fix_and_overwrite
+	black --line-length 119 --target-version py35 examples templates tests src utils
+	isort examples templates tests src utils

 # Run tests for the library

@@ -16,18 +16,15 @@
    <a href="https://github.com/huggingface/transformers/releases">
        <img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/transformers.svg">
    </a>
-    <a href="https://github.com/huggingface/transformers/blob/master/CODE_OF_CONDUCT.md">
-        <img alt="Contributor Covenant" src="https://img.shields.io/badge/Contributor%20Covenant-v2.0%20adopted-ff69b4.svg">
-    </a>
 </p>

 <h3 align="center">
 <p>State-of-the-art Natural Language Processing for PyTorch and TensorFlow 2.0
 </h3>

-🤗 Transformers provides thousands of pretrained models to perform tasks on texts such as classification, information extraction, question answering, summarization, translation, text generation, etc in 100+ languages. Its aim is to make cutting-edge NLP easier to use for everyone.
+🤗 Transformers provides thousands of pretrained models to perform tasks on texts such as classification, information extraction, question answering, summarization, translation, text generation, etc in 100+ languages. Its aim is to make cutting-edge NLP easier to use for everyone. 

-🤗 Transformers provides APIs to quickly download and use those pretrained models on a given text, fine-tune them on your own datasets then share them with the community on our [model hub](https://huggingface.co/models). At the same time, each python module defining an architecture can be used as a standalone and modified to enable quick research experiments.
+🤗 Transformers provides APIs to quickly download and use those pretrained models on a given text, fine-tune them on your own datasets then share them with the community on our [model hub](https://huggingface.co/models). At the same time, each python module defining an architecture can be used as a standalone and modified to enable quick research experiments. 

 🤗 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.

@@ -38,7 +35,7 @@

 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.

-Here are a few examples:
+Here are a few examples: 
 - [Masked word completion with BERT](https://huggingface.co/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
 - [Name Entity Recognition with Electra](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city)
 - [Text generation with GPT-2](https://huggingface.co/gpt2?text=A+long+time+ago%2C+)
@@ -51,7 +48,7 @@ Here are a few examples:

 ## Quick tour

-To immediately use a model on a given text, we provide the `pipeline` API. Pipelines group together a pretrained model with the preprocessing that was used during that model training. Here is how to quickly use a pipeline to classify positive versus negative texts
+To immediately use a model on a given text, we provide the `pipeline` API. Pipelines group together a pretrained model with the preprocessing that was used during that model training. Here is how to quickly use a pipeline to classify positive versus negative texts 

 ```python
 >>> from transformers import pipeline
@@ -62,7 +59,7 @@ To immediately use a model on a given text, we provide the `pipeline` API. Pipel
 [{'label': 'POSITIVE', 'score': 0.9978193640708923}]
 ```

-The second line of code downloads and caches the pretrained model used by the pipeline, the third line evaluates it on the given text. Here the answer is "positive" with a confidence of 99.8%.
+The second line of code downloads and caches the pretrained model used by the pipeline, the third line evaluates it on the given text. Here the answer is "positive" with a confidence of 99.8%. 

 This is another example of pipeline used for that can extract question answers from some context:

@@ -81,7 +78,7 @@ This is another example of pipeline used for that can extract question answers f

 On top of the answer, the pretrained model used here returned its confidence score, along with the start position and its end position in the tokenized sentence. You can learn more about the tasks supported by the `pipeline` API in [this tutorial](https://huggingface.co/transformers/task_summary.html).

-To download and use any of the pretrained models on your given task, you just need to use those three lines of codes (PyTorch version):
+To download and use any of the pretrained models on your given task, you just need to use those three lines of codes (PyTorch verison):
 ```python
 >>> from transformers import AutoTokenizer, AutoModel

@@ -111,7 +108,7 @@ The model itself is a regular [Pytorch `nn.Module`](https://pytorch.org/docs/sta
 1. Easy-to-use state-of-the-art models:
    - High performance on NLU and NLG tasks.
    - Low barrier to entry for educators and practitioners.
-    - Few user-facing abstractions with just three classes to learn.
+    - Few user-facing abastractions with just three classes to learn.
    - A unified API for using all our pretrained models.

 1. Lower compute costs, smaller carbon footprint:
@@ -127,7 +124,7 @@ The model itself is a regular [Pytorch `nn.Module`](https://pytorch.org/docs/sta
 1. Easily customize a model or an example to your needs:
    - Examples for each architecture to reproduce the results by the official authors of said architecture.
    - Expose the models internal as consistently as possible.
-    - Model files can be used independently of the library for quick experiments.
+    - Model files can be used independently of the library for quick experiments. 

 ## Why shouldn't I use transformers?

@@ -137,16 +134,14 @@ 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:

@@ -156,62 +151,40 @@ 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

 🤗 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. **[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.
-1. **[CamemBERT](https://huggingface.co/transformers/model_doc/camembert.html)** (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.
-1. **[CTRL](https://huggingface.co/transformers/model_doc/ctrl.html)** (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.
-1. **[DeBERTa](https://huggingface.co/transformers/model_doc/deberta.html)** (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.
-1. **[DialoGPT](https://huggingface.co/transformers/model_doc/dialogpt.html)** (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.
-1. **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (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.
-1. **[DPR](https://huggingface.co/transformers/model_doc/dpr.html)** (from Facebook) released with the paper [Dense Passage Retrieval
+2. **[GPT](https://huggingface.co/transformers/model_doc/gpt.html)** (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.
+3. **[GPT-2](https://huggingface.co/transformers/model_doc/gpt2.html)** (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**.
+4. **[Transformer-XL](https://huggingface.co/transformers/model_doc/transformerxl.html)** (from Google/CMU) released with the paper [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
+5. **[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.
+6. **[XLM](https://huggingface.co/transformers/model_doc/xlm.html)** (from Facebook) released together with the paper [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample and Alexis Conneau.
+7. **[RoBERTa](https://huggingface.co/transformers/model_doc/roberta.html)** (from Facebook), released together with the paper a [Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692) by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
+8. **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace), released together with the 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.
+9. **[CTRL](https://huggingface.co/transformers/model_doc/ctrl.html)** (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.
+10. **[CamemBERT](https://huggingface.co/transformers/model_doc/camembert.html)** (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.
+11. **[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.
+12. **[T5](https://huggingface.co/transformers/model_doc/t5.html)** (from Google AI) released with the paper [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
+13. **[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.
+14. **[MMBT](https://github.com/facebookresearch/mmbt/)** (from Facebook), released together with the paper a [Supervised Multimodal Bitransformers for Classifying Images and Text](https://arxiv.org/pdf/1909.02950.pdf) by Douwe Kiela, Suvrat Bhooshan, Hamed Firooz, Davide Testuggine.
+15. **[FlauBERT](https://huggingface.co/transformers/model_doc/flaubert.html)** (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.
+16. **[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.
+17. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (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.
+18. **[DialoGPT](https://huggingface.co/transformers/model_doc/dialogpt.html)** (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.
+19. **[Reformer](https://huggingface.co/transformers/model_doc/reformer.html)** (from Google Research) released with the paper [Reformer: The Efficient Transformer](https://arxiv.org/abs/2001.04451) by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
+20. **[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.
+21. **[Longformer](https://huggingface.co/transformers/model_doc/longformer.html)** (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.
+22. **[DPR](https://github.com/facebookresearch/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.
-1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (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.
-1. **[FlauBERT](https://huggingface.co/transformers/model_doc/flaubert.html)** (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.
-1. **[Funnel Transformer](https://huggingface.co/transformers/model_doc/funnel.html)** (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.
-1. **[GPT](https://huggingface.co/transformers/model_doc/gpt.html)** (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.
-1. **[GPT-2](https://huggingface.co/transformers/model_doc/gpt2.html)** (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**.
-1. **[LayoutLM](https://huggingface.co/transformers/model_doc/layoutlm.html)** (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.
-1. **[Longformer](https://huggingface.co/transformers/model_doc/longformer.html)** (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.
-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. **[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.
-1. **[Reformer](https://huggingface.co/transformers/model_doc/reformer.html)** (from Google Research) released with the paper [Reformer: The Efficient Transformer](https://arxiv.org/abs/2001.04451) by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
-1. **[RoBERTa](https://huggingface.co/transformers/model_doc/roberta.html)** (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.
-1. **[SqueezeBert](https://huggingface.co/transformers/model_doc/squeezebert.html)** released with the paper [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer.
-1. **[T5](https://huggingface.co/transformers/model_doc/t5.html)** (from Google AI) released with the paper [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
-1. **[Transformer-XL](https://huggingface.co/transformers/model_doc/transformerxl.html)** (from Google/CMU) released with the paper [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
-1. **[XLM](https://huggingface.co/transformers/model_doc/xlm.html)** (from Facebook) released together with the paper [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample and Alexis Conneau.
-1. **[XLM-ProphetNet](https://huggingface.co/transformers/model_doc/xlmprophetnet.html)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
-1. **[XLM-RoBERTa](https://huggingface.co/transformers/model_doc/xlmroberta.html)** (from Facebook AI), released together with the paper [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Alexis Conneau*, Kartikay Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov.
-1. **[XLNet](https://huggingface.co/transformers/model_doc/xlnet.html)** (from Google/CMU) released with the paper [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
-1. **[Other community models](https://huggingface.co/models)**, contributed by the [community](https://huggingface.co/users).
-1. Want to contribute a new model? We have added a **detailed guide and templates** to guide you in the process of adding a new model. You can find them in the [`templates`](./templates) folder of the repository. Be sure to check the [contributing guidelines](./CONTRIBUTING.md) and contact the maintainers or open an issue to collect feedbacks before starting your PR.
-
-To cehck if each model has an implementation in PyTorch/TensorFlow/Flax or has an associated tokenizer backed by the 🤗 Tokenizers library, refer to [this table](https://huggingface.co/transformers/index.html#bigtable)
+23. **[Pegasus](https://github.com/google-research/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.
+24. **[MBart](https://github.com/pytorch/fairseq/tree/master/examples/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.  
+25. **[LXMERT](https://github.com/airsplay/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.
+26. **[Funnel Transformer](https://github.com/laiguokun/Funnel-Transformer)** (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.
+27. **[Other community models](https://huggingface.co/models)**, contributed by the [community](https://huggingface.co/users).
+28. 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.

 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).

@@ -0,0 +1,10 @@
+coverage:
+  status:
+    project:
+      default:
+        informational: true
+    patch: off
+comment:
+  require_changes: true    # only comment if there was change in coverage
+  require_head: yes        # don't report if there is no head coverage report
+  require_base: yes        # don't report if there is no base coverage report
@@ -1,4 +1,4 @@
-FROM nvidia/cuda:10.2-cudnn7-devel-ubuntu18.04
+FROM nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04
 LABEL maintainer="Hugging Face"
 LABEL repository="transformers"

@@ -18,14 +18,9 @@ RUN python3 -m pip install --no-cache-dir --upgrade pip && \
    tensorflow \
    torch

-RUN git clone https://github.com/NVIDIA/apex
-RUN cd apex && \
-    python3 setup.py install && \
-    pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
-
 WORKDIR /workspace
 COPY . transformers/
 RUN cd transformers/ && \
    python3 -m pip install --no-cache-dir .

-CMD ["/bin/bash"]
+CMD ["/bin/bash"]
@@ -1,4 +1,4 @@
-FROM nvidia/cuda:10.2-cudnn7-devel-ubuntu18.04
+FROM nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04
 LABEL maintainer="Hugging Face"
 LABEL repository="transformers"

@@ -17,14 +17,9 @@ RUN python3 -m pip install --no-cache-dir --upgrade pip && \
    mkl \
    torch

-RUN git clone https://github.com/NVIDIA/apex
-RUN cd apex && \
-    python3 setup.py install && \
-    pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
-
 WORKDIR /workspace
 COPY . transformers/
 RUN cd transformers/ && \
    python3 -m pip install --no-cache-dir .

-CMD ["/bin/bash"]
+CMD ["/bin/bash"]
@@ -88,25 +88,20 @@ The `huggingface/transformers` documentation follows the
 [Google documentation](https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html) style. It is
 mostly written in ReStructuredText 
 ([Sphinx simple documentation](https://www.sphinx-doc.org/en/master/usage/restructuredtext/index.html), 
-[Sourceforge complete documentation](https://docutils.sourceforge.io/docs/ref/rst/restructuredtext.html)).
+[Sourceforge complete documentation](https://docutils.sourceforge.io/docs/ref/rst/restructuredtext.html))

+### Adding a new section

-### Adding a new tutorial
-
-Adding a new tutorial or section is done in two steps:
+A section is a page held in the `Notes` toc-tree on the documentation. Adding a new section is done in two steps:

 - Add a new file under `./source`. This file can either be ReStructuredText (.rst) or Markdown (.md).
 - Link that file in `./source/index.rst` on the correct toc-tree.

-Make sure to put your new file under the proper section. It's unlikely to go in the first section (*Get Started*), so
-depending on the intended targets (beginners, more advanced users or researchers) it should go in section two, three or
-four.
-
 ### Adding a new model

 When adding a new model:
 
- Create a file `xxx.rst` under `./source/model_doc` (don't hesitate to copy an existing file as template). 
+- Create a file `xxx.rst` under `./source/model_doc`. 
 - Link that file in `./source/index.rst` on the `model_doc` toc-tree.
 - Write a short overview of the model:
    - Overview with paper & authors
@@ -125,18 +120,18 @@ When adding a new model:
 These classes should be added using the RST syntax. Usually as follows:
 ```
 XXXConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.XXXConfig
    :members:
 ```

-This will include every public method of the configuration that is documented. If for some reason you wish for a method
-not to be displayed in the documentation, you can do so by specifying which methods should be in the docs:
+This will include every public method of the configuration. If for some reason you wish for a method not to be
+displayed in the documentation, you can do so by specifying which methods should be in the docs:

 ```
 XXXTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.XXXTokenizer
    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
@@ -147,17 +142,13 @@ XXXTokenizer
 ### Writing source documentation

 Values that should be put in `code` should either be surrounded by double backticks: \`\`like so\`\` or be written as
-an object using the :obj: syntax: :obj:\`like so\`. Note that argument names and objects like True, None or any strings
-should usually be put in `code`.
+an object using the :obj: syntax: :obj:\`like so\`.

 When mentionning a class, it is recommended to use the :class: syntax as the mentioned class will be automatically
-linked by Sphinx: :class:\`~transformers.XXXClass\`
+linked by Sphinx: :class:\`transformers.XXXClass\`

-When mentioning a function, it is recommended to use the :func: syntax as the mentioned function will be automatically
-linked by Sphinx: :func:\`~transformers.function\`.
-
-When mentioning a method, it is recommended to use the :meth: syntax as the mentioned method will be automatically
-linked by Sphinx: :meth:\`~transformers.XXXClass.method\`.
+When mentioning a function, it is recommended to use the :func: syntax as the mentioned method will be automatically
+linked by Sphinx: :func:\`transformers.XXXClass.method\`

 Links should be done as so (note the double underscore at the end): \`text for the link <./local-link-or-global-link#loc>\`__

@@ -174,34 +165,13 @@ Here's an example showcasing everything so far:
        input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

-            Indices can be obtained using :class:`~transformers.AlbertTokenizer`.
-            See :meth:`~transformers.PreTrainedTokenizer.encode` and
-            :meth:`~transformers.PreTrainedTokenizer.__call__` for details.
+            Indices can be obtained using :class:`transformers.AlbertTokenizer`.
+            See :func:`transformers.PreTrainedTokenizer.encode` and
+            :func:`transformers.PreTrainedTokenizer.__call__` for details.

            `What are input IDs? <../glossary.html#input-ids>`__
 ```

-For optional arguments or arguments with defaults we follow the following syntax: imagine we have a function with the
-following signature:
-
-```
-def my_function(x: str = None, a: float = 1):
-```
-
-then its documentation should look like this:
-
-```
-    Args:
-        x (:obj:`str`, `optional`):
-            This argument controls ...
-        a (:obj:`float`, `optional`, defaults to 1):
-            This argument is used to ...
-```
-
-Note that we always omit the "defaults to :obj:\`None\`" when None is the default for any argument. Also note that even
-if the first line describing your argument type and its default gets long, you can't break it on several lines. You can
-however write as many lines as you want in the indented description (see the example above with `input_ids`). 
-
 #### Writing a multi-line code block 

 Multi-line code blocks can be useful for displaying examples. They are done like so:
@@ -216,9 +186,6 @@ Example::

 The `Example` string at the beginning can be replaced by anything as long as there are two semicolons following it.

-We follow the [doctest](https://docs.python.org/3/library/doctest.html) syntax for the examples to automatically test
-the results stay consistent with the library.
-
 #### Writing a return block

 Arguments should be defined with the `Args:` prefix, followed by a line return and an indentation. 
@@ -240,5 +207,5 @@ Here's an example for a single value return:

 ```
    Returns:
-        :obj:`List[int]`: A list of integers in the range [0, 1] --- 1 for a special token, 0 for a sequence token.
+        A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
 ```
@@ -2,15 +2,6 @@

 /* Colab dropdown */

-table.center-aligned-table td {
-    text-align: center;
-}
-
-table.center-aligned-table th {
-    text-align: center;
-    vertical-align: middle;
-}
-
 .colab-dropdown {
    position: relative;
    display: inline-block;
@@ -134,12 +125,6 @@ a.copybtn {
    background-color: #6670FF;
 }

-/* The section headers in the toc tree */
-.wy-menu-vertical p.caption{
-    background-color: #4d59ff;
-    line-height: 40px;
-}
-
 /* The selected items in the toc tree */
 .wy-menu-vertical li.current{
    background-color: #A6B0FF;
@@ -1,16 +1,11 @@
 // These two things need to be updated at each release for the version selector.
 // Last stable version
-const stableVersion = "v4.0.1"
+const stableVersion = "v3.1.0"
 // Dictionary doc folder to label
 const versionMapping = {
    "master": "master",
-    "": "v4.0.0/v4.0.1",
-    "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",
-    "v3.0.2": "v3.0.0/v3.0.1/v3.0.2",
+    "": "v3.1.0 (stable)",
+    "v3.0.2": "v3.0.0/v3.0.1/v3.0.2 (stable)",
    "v2.11.0": "v2.11.0",
    "v2.10.0": "v2.10.0",
    "v2.9.1": "v2.9.0/v2.9.1",
@@ -239,11 +234,9 @@ function platformToggle() {

    const createFrameworkButtons = sample => {
            const pytorchButton = document.createElement("button");
-            pytorchButton.classList.add('pytorch-button')
            pytorchButton.innerText = "PyTorch";

            const tensorflowButton = document.createElement("button");
-            tensorflowButton.classList.add('tensorflow-button')
            tensorflowButton.innerText = "TensorFlow";

            const selectorDiv = document.createElement("div");
@@ -258,36 +251,22 @@ function platformToggle() {
            tensorflowButton.classList.remove("selected");

            pytorchButton.addEventListener("click", () => {
-                for(const codeBlock of updatedCodeBlocks){
-                    codeBlock.element.innerHTML = codeBlock.pytorchSample;
-                }
-                Array.from(document.getElementsByClassName('pytorch-button')).forEach(button => {
-                    button.classList.add("selected");
-                })
-                Array.from(document.getElementsByClassName('tensorflow-button')).forEach(button => {
-                    button.classList.remove("selected");
-                })
+                sample.element.innerHTML = sample.pytorchSample;
+                pytorchButton.classList.add("selected");
+                tensorflowButton.classList.remove("selected");
            });
            tensorflowButton.addEventListener("click", () => {
-                for(const codeBlock of updatedCodeBlocks){
-                    codeBlock.element.innerHTML = codeBlock.tensorflowSample;
-                }
-                Array.from(document.getElementsByClassName('tensorflow-button')).forEach(button => {
-                    button.classList.add("selected");
-                })
-                Array.from(document.getElementsByClassName('pytorch-button')).forEach(button => {
-                    button.classList.remove("selected");
-                })
+               sample.element.innerHTML = sample.tensorflowSample;
+                tensorflowButton.classList.add("selected");
+                pytorchButton.classList.remove("selected");
            });
        };

-    const updatedCodeBlocks = codeBlocks
+    codeBlocks
        .map(element => {return {element: element.firstChild, innerText: element.innerText}})
        .filter(codeBlock => codeBlock.innerText.includes(pytorchIdentifier) && codeBlock.innerText.includes(tensorflowIdentifier))
        .map(getFrameworkSpans)
-
-    updatedCodeBlocks
-        .forEach(createFrameworkButtons)
+        .forEach(createFrameworkButtons);
 }


@@ -1,29 +1,23 @@
 Benchmarks
-=======================================================================================================================
+==========

 Let's take a look at how 🤗 Transformer models can be benchmarked, best practices, and already available benchmarks.

-A notebook explaining in more detail how to benchmark 🤗 Transformer models can be found `here
-<https://github.com/huggingface/transformers/blob/master/notebooks/05-benchmark.ipynb>`__.
+A notebook explaining in more detail how to benchmark 🤗 Transformer models can be found `here <https://github.com/huggingface/transformers/blob/master/notebooks/05-benchmark.ipynb>`__.

 How to benchmark 🤗 Transformer models
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

-The classes :class:`~transformers.PyTorchBenchmark` and :class:`~transformers.TensorFlowBenchmark` allow to flexibly
-benchmark 🤗 Transformer models. The benchmark classes allow us to measure the `peak memory usage` and `required time`
-for both `inference` and `training`.
+The classes :class:`~transformers.PyTorchBenchmark` and :class:`~transformers.TensorFlowBenchmark` allow to flexibly benchmark 🤗 Transformer models.
+The benchmark classes allow us to measure the `peak memory usage` and `required time` for both 
+`inference` and `training`. 

 .. note::

-  Hereby, `inference` is defined by a single forward pass, and `training` is defined by a single forward pass and
-  backward pass.
+  Hereby, `inference` is defined by a single forward pass, and `training` is defined by a single forward pass and backward pass.

-The benchmark classes :class:`~transformers.PyTorchBenchmark` and :class:`~transformers.TensorFlowBenchmark` expect an
-object of type :class:`~transformers.PyTorchBenchmarkArguments` and
-:class:`~transformers.TensorFlowBenchmarkArguments`, respectively, for instantiation.
-:class:`~transformers.PyTorchBenchmarkArguments` and :class:`~transformers.TensorFlowBenchmarkArguments` are data
-classes and contain all relevant configurations for their corresponding benchmark class. In the following example, it
-is shown how a BERT model of type `bert-base-cased` can be benchmarked.
+The benchmark classes :class:`~transformers.PyTorchBenchmark` and :class:`~transformers.TensorFlowBenchmark` expect an object of type :class:`~transformers.PyTorchBenchmarkArguments` and :class:`~transformers.TensorFlowBenchmarkArguments`, respectively, for instantiation. :class:`~transformers.PyTorchBenchmarkArguments` and :class:`~transformers.TensorFlowBenchmarkArguments` are data classes and contain all relevant configurations for their corresponding benchmark class.
+In the following example, it is shown how a BERT model of type `bert-base-cased` can be benchmarked.

 .. code-block::

@@ -40,15 +34,11 @@ is shown how a BERT model of type `bert-base-cased` can be benchmarked.
    >>> benchmark = TensorFlowBenchmark(args)


-Here, three arguments are given to the benchmark argument data classes, namely ``models``, ``batch_sizes``, and
-``sequence_lengths``. The argument ``models`` is required and expects a :obj:`list` of model identifiers from the
-`model hub <https://huggingface.co/models>`__ The :obj:`list` arguments ``batch_sizes`` and ``sequence_lengths`` define
-the size of the ``input_ids`` on which the model is benchmarked. There are many more parameters that can be configured
-via the benchmark argument data classes. For more detail on these one can either directly consult the files
-``src/transformers/benchmark/benchmark_args_utils.py``, ``src/transformers/benchmark/benchmark_args.py`` (for PyTorch)
-and ``src/transformers/benchmark/benchmark_args_tf.py`` (for Tensorflow). Alternatively, running the following shell
-commands from root will print out a descriptive list of all configurable parameters for PyTorch and Tensorflow
-respectively.
+Here, three arguments are given to the benchmark argument data classes, namely ``models``, ``batch_sizes``, and ``sequence_lengths``. The argument ``models`` is required and expects a :obj:`list` of model identifiers from the `model hub <https://huggingface.co/models>`__
+The :obj:`list` arguments ``batch_sizes`` and ``sequence_lengths`` define the size of the ``input_ids`` on which the model is benchmarked. 
+There are many more parameters that can be configured via the benchmark argument data classes. For more detail on these one can either directly consult the files 
+``src/transformers/benchmark/benchmark_args_utils.py``, ``src/transformers/benchmark/benchmark_args.py`` (for PyTorch) and ``src/transformers/benchmark/benchmark_args_tf.py`` (for Tensorflow). 
+Alternatively, running the following shell commands from root will print out a descriptive list of all configurable parameters for PyTorch and Tensorflow respectively.

 .. code-block:: bash

@@ -75,7 +65,7 @@ An instantiated benchmark object can then simply be run by calling ``benchmark.r
    bert-base-uncased          8              128            0.018     
    bert-base-uncased          8              512            0.088     
    --------------------------------------------------------------------------------
-
+    
    ====================      INFERENCE - MEMORY - RESULT       ====================
    --------------------------------------------------------------------------------
    Model Name             Batch Size     Seq Length    Memory in MB 
@@ -85,7 +75,7 @@ An instantiated benchmark object can then simply be run by calling ``benchmark.r
    bert-base-uncased          8              128            1307
    bert-base-uncased          8              512            1539
    --------------------------------------------------------------------------------
-
+    
    ====================        ENVIRONMENT INFORMATION         ====================
    - transformers_version: 2.11.0
    - framework: PyTorch
@@ -108,7 +98,7 @@ An instantiated benchmark object can then simply be run by calling ``benchmark.r
    - gpu_power_watts: 280.0
    - gpu_performance_state: 2
    - use_tpu: False
-
+    
    >>> ## TENSORFLOW CODE
    >>> results = benchmark.run()
    >>> print(results)
@@ -121,7 +111,7 @@ An instantiated benchmark object can then simply be run by calling ``benchmark.r
    bert-base-uncased          8              128            0.022
    bert-base-uncased          8              512            0.105
    --------------------------------------------------------------------------------
-
+    
    ====================      INFERENCE - MEMORY - RESULT       ====================
    --------------------------------------------------------------------------------
    Model Name             Batch Size     Seq Length    Memory in MB 
@@ -131,7 +121,7 @@ An instantiated benchmark object can then simply be run by calling ``benchmark.r
    bert-base-uncased          8              128            1330
    bert-base-uncased          8              512            1770
    --------------------------------------------------------------------------------
-
+    
    ====================        ENVIRONMENT INFORMATION         ====================
    - transformers_version: 2.11.0
    - framework: Tensorflow
@@ -155,17 +145,14 @@ An instantiated benchmark object can then simply be run by calling ``benchmark.r
    - gpu_performance_state: 2
    - use_tpu: False

-By default, the `time` and the `required memory` for `inference` are benchmarked. In the example output above the first
-two sections show the result corresponding to `inference time` and `inference memory`. In addition, all relevant
-information about the computing environment, `e.g.` the GPU type, the system, the library versions, etc... are printed
-out in the third section under `ENVIRONMENT INFORMATION`. This information can optionally be saved in a `.csv` file
-when adding the argument :obj:`save_to_csv=True` to :class:`~transformers.PyTorchBenchmarkArguments` and
-:class:`~transformers.TensorFlowBenchmarkArguments` respectively. In this case, every section is saved in a separate
-`.csv` file. The path to each `.csv` file can optionally be defined via the argument data classes.
+By default, the `time` and the `required memory` for `inference` are benchmarked. 
+In the example output above the first two sections show the result corresponding to `inference time` and `inference memory`. 
+In addition, all relevant information about the computing environment, `e.g.` the GPU type, the system, the library versions, etc... are printed out in the third section under `ENVIRONMENT INFORMATION`.
+This information can optionally be saved in a `.csv` file when adding the argument :obj:`save_to_csv=True` to :class:`~transformers.PyTorchBenchmarkArguments` and :class:`~transformers.TensorFlowBenchmarkArguments` respectively.
+In this case, every section is saved in a separate `.csv` file. The path to each `.csv` file can optionally be defined via the argument data classes.

-Instead of benchmarking pre-trained models via their model identifier, `e.g.` `bert-base-uncased`, the user can
-alternatively benchmark an arbitrary configuration of any available model class. In this case, a :obj:`list` of
-configurations must be inserted with the benchmark args as follows.
+Instead of benchmarking pre-trained models via their model identifier, `e.g.` `bert-base-uncased`, the user can alternatively benchmark an arbitrary configuration of any available model class. 
+In this case, a :obj:`list` of configurations must be inserted with the benchmark args as follows.

 .. code-block::

@@ -196,7 +183,7 @@ configurations must be inserted with the benchmark args as follows.
    bert-6-lay                 8              128            0.009     
    bert-6-lay                 8              512            0.044
    --------------------------------------------------------------------------------
-
+    
    ====================      INFERENCE - MEMORY - RESULT       ====================
    --------------------------------------------------------------------------------
    Model Name             Batch Size     Seq Length      Memory in MB 
@@ -214,7 +201,7 @@ configurations must be inserted with the benchmark args as follows.
    bert-6-lay                 8              128            1127     
    bert-6-lay                 8              512            1359
    --------------------------------------------------------------------------------
-
+    
    ====================        ENVIRONMENT INFORMATION         ====================
    - transformers_version: 2.11.0
    - framework: PyTorch
@@ -265,7 +252,7 @@ configurations must be inserted with the benchmark args as follows.
    bert-6-lay                 8              128            0.0011
    bert-6-lay                 8              512            0.074
    --------------------------------------------------------------------------------
-
+    
    ====================      INFERENCE - MEMORY - RESULT       ====================
    --------------------------------------------------------------------------------
    Model Name             Batch Size     Seq Length      Memory in MB 
@@ -283,7 +270,7 @@ configurations must be inserted with the benchmark args as follows.
    bert-6-lay                 8              128            1330
    bert-6-lay                 8              512            1540
    --------------------------------------------------------------------------------
-
+    
    ====================        ENVIRONMENT INFORMATION         ====================
    - transformers_version: 2.11.0
    - framework: Tensorflow
@@ -308,38 +295,28 @@ configurations must be inserted with the benchmark args as follows.
    - use_tpu: False


-Again, `inference time` and `required memory` for `inference` are measured, but this time for customized configurations
-of the :obj:`BertModel` class. This feature can especially be helpful when deciding for which configuration the model
-should be trained.
+Again, `inference time` and `required memory` for `inference` are measured, but this time for customized configurations of the :obj:`BertModel` class. This feature can especially be helpful when 
+deciding for which configuration the model should be trained.


 Benchmark best practices
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~

 This section lists a couple of best practices one should be aware of when benchmarking a model.

- Currently, only single device benchmarking is supported. When benchmarking on GPU, it is recommended that the user
-  specifies on which device the code should be run by setting the ``CUDA_VISIBLE_DEVICES`` environment variable in the
-  shell, `e.g.` ``export CUDA_VISIBLE_DEVICES=0`` before running the code.
- The option :obj:`no_multi_processing` should only be set to :obj:`True` for testing and debugging. To ensure accurate
-  memory measurement it is recommended to run each memory benchmark in a separate process by making sure
-  :obj:`no_multi_processing` is set to :obj:`True`.
- One should always state the environment information when sharing the results of a model benchmark. Results can vary
-  heavily between different GPU devices, library versions, etc., so that benchmark results on their own are not very
-  useful for the community.
+- Currently, only single device benchmarking is supported. When benchmarking on GPU, it is recommended that the user 
+  specifies on which device the code should be run by setting the ``CUDA_VISIBLE_DEVICES`` environment variable in the shell, `e.g.` ``export CUDA_VISIBLE_DEVICES=0`` before running the code.
+- The option :obj:`no_multi_processing` should only be set to :obj:`True` for testing and debugging. To ensure accurate memory measurement it is recommended to run each memory benchmark in a separate process by making sure :obj:`no_multi_processing` is set to :obj:`True`.
+- One should always state the environment information when sharing the results of a model benchmark. Results can vary heavily between different GPU devices, library versions, etc., so that benchmark results on their own are not very useful for the community.


 Sharing your benchmark
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~

-Previously all available core models (10 at the time) have been benchmarked for `inference time`, across many different
-settings: using PyTorch, with and without TorchScript, using TensorFlow, with and without XLA. All of those tests were
-done across CPUs (except for TensorFlow XLA) and GPUs.
+Previously all available core models (10 at the time) have been benchmarked for `inference time`, across many different settings: using PyTorch, with
+and without TorchScript, using TensorFlow, with and without XLA. All of those tests were done across CPUs (except for
+TensorFlow XLA) and GPUs.

-The approach is detailed in the `following blogpost
-<https://medium.com/huggingface/benchmarking-transformers-pytorch-and-tensorflow-e2917fb891c2>`__ and the results are
-available `here
-<https://docs.google.com/spreadsheets/d/1sryqufw2D0XlUH4sq3e9Wnxu5EAQkaohzrJbd5HdQ_w/edit?usp=sharing>`__.
+The approach is detailed in the `following blogpost <https://medium.com/huggingface/benchmarking-transformers-pytorch-and-tensorflow-e2917fb891c2>`__ and the results are available `here <https://docs.google.com/spreadsheets/d/1sryqufw2D0XlUH4sq3e9Wnxu5EAQkaohzrJbd5HdQ_w/edit?usp=sharing>`__.

-With the new `benchmark` tools, it is easier than ever to share your benchmark results with the community `here
-<https://github.com/huggingface/transformers/blob/master/examples/benchmarking/README.md>`__.
+With the new `benchmark` tools, it is easier than ever to share your benchmark results with the community `here <https://github.com/huggingface/transformers/blob/master/examples/benchmarking/README.md>`__.
@@ -1,26 +1,18 @@
 BERTology
-----------------------------------------------------------------------------------------------------------------------
+---------

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


-* BERT Rediscovers the Classical NLP Pipeline by Ian Tenney, Dipanjan Das, Ellie Pavlick:
-  https://arxiv.org/abs/1905.05950
+* BERT Rediscovers the Classical NLP Pipeline by Ian Tenney, Dipanjan Das, Ellie Pavlick: https://arxiv.org/abs/1905.05950
 * Are Sixteen Heads Really Better than One? by Paul Michel, Omer Levy, Graham Neubig: https://arxiv.org/abs/1905.10650
-* What Does BERT Look At? An Analysis of BERT's Attention by Kevin Clark, Urvashi Khandelwal, Omer Levy, Christopher D.
-  Manning: https://arxiv.org/abs/1906.04341
+* What Does BERT Look At? An Analysis of BERT's Attention by Kevin Clark, Urvashi Khandelwal, Omer Levy, Christopher D. Manning: https://arxiv.org/abs/1906.04341

-In order to help this new field develop, we have included a few additional features in the BERT/GPT/GPT-2 models to
-help people access the inner representations, mainly adapted from the great work of Paul Michel
-(https://arxiv.org/abs/1905.10650):
+In order to help this new field develop, we have included a few additional features in the BERT/GPT/GPT-2 models to help people access the inner representations, mainly adapted from the great work of Paul Michel (https://arxiv.org/abs/1905.10650):


 * accessing all the hidden-states of BERT/GPT/GPT-2,
 * accessing all the attention weights for each head of BERT/GPT/GPT-2,
-* retrieving heads output values and gradients to be able to compute head importance score and prune head as explained
-  in https://arxiv.org/abs/1905.10650.
+* retrieving heads output values and gradients to be able to compute head importance score and prune head as explained in https://arxiv.org/abs/1905.10650.

-To help you understand and use these features, we have added a specific example script: `bertology.py
-<https://github.com/huggingface/transformers/blob/master/examples/bertology/run_bertology.py>`_ while extract
-information and prune a model pre-trained on GLUE.
+To help you understand and use these features, we have added a specific example script: `bertology.py <https://github.com/huggingface/transformers/blob/master/examples/bertology/run_bertology.py>`_ while extract information and prune a model pre-trained on GLUE.
@@ -26,7 +26,7 @@ author = u'huggingface'
 # The short X.Y version
 version = u''
 # The full version, including alpha/beta/rc tags
-release = u'4.0.1'
+release = u'3.1.0'


 # -- General configuration ---------------------------------------------------
@@ -1,40 +1,24 @@
 Converting Tensorflow Checkpoints
-=======================================================================================================================
+================================================

-A command-line interface is provided to convert original Bert/GPT/GPT-2/Transformer-XL/XLNet/XLM checkpoints in models
-than be loaded using the ``from_pretrained`` methods of the library.
+A command-line interface is provided to convert original Bert/GPT/GPT-2/Transformer-XL/XLNet/XLM checkpoints in models than be loaded using the ``from_pretrained`` methods of the library.

 .. note::
-    Since 2.3.0 the conversion script is now part of the transformers CLI (**transformers-cli**) available in any
-    transformers >= 2.3.0 installation.
+    Since 2.3.0 the conversion script is now part of the transformers CLI (**transformers-cli**)
+    available in any transformers >= 2.3.0 installation.

    The documentation below reflects the **transformers-cli convert** command format.

 BERT
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^

-You can convert any TensorFlow checkpoint for BERT (in particular `the pre-trained models released by Google
-<https://github.com/google-research/bert#pre-trained-models>`_\ ) in a PyTorch save file by using the
-`convert_bert_original_tf_checkpoint_to_pytorch.py
-<https://github.com/huggingface/transformers/blob/master/src/transformers/convert_bert_original_tf_checkpoint_to_pytorch.py>`_
-script.
+You can convert any TensorFlow checkpoint for BERT (in particular `the pre-trained models released by Google <https://github.com/google-research/bert#pre-trained-models>`_\ ) in a PyTorch save file by using the `convert_bert_original_tf_checkpoint_to_pytorch.py <https://github.com/huggingface/transformers/blob/master/src/transformers/convert_bert_original_tf_checkpoint_to_pytorch.py>`_ script.

-This CLI takes as input a TensorFlow checkpoint (three files starting with ``bert_model.ckpt``\ ) and the associated
-configuration file (\ ``bert_config.json``\ ), and creates a PyTorch model for this configuration, loads the weights
-from the TensorFlow checkpoint in the PyTorch model and saves the resulting model in a standard PyTorch save file that
-can be imported using ``torch.load()`` (see examples in `run_bert_extract_features.py
-<https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/examples/run_bert_extract_features.py>`_\ ,
-`run_bert_classifier.py
-<https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/examples/run_bert_classifier.py>`_ and
-`run_bert_squad.py <https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/examples/run_bert_squad.py>`_\
-).
+This CLI takes as input a TensorFlow checkpoint (three files starting with ``bert_model.ckpt``\ ) and the associated configuration file (\ ``bert_config.json``\ ), and creates a PyTorch model for this configuration, loads the weights from the TensorFlow checkpoint in the PyTorch model and saves the resulting model in a standard PyTorch save file that can be imported using ``torch.load()`` (see examples in `run_bert_extract_features.py <https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/examples/run_bert_extract_features.py>`_\ , `run_bert_classifier.py <https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/examples/run_bert_classifier.py>`_ and `run_bert_squad.py <https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/examples/run_bert_squad.py>`_\ ).

-You only need to run this conversion script **once** to get a PyTorch model. You can then disregard the TensorFlow
-checkpoint (the three files starting with ``bert_model.ckpt``\ ) but be sure to keep the configuration file (\
-``bert_config.json``\ ) and the vocabulary file (\ ``vocab.txt``\ ) as these are needed for the PyTorch model too.
+You only need to run this conversion script **once** to get a PyTorch model. You can then disregard the TensorFlow checkpoint (the three files starting with ``bert_model.ckpt``\ ) but be sure to keep the configuration file (\ ``bert_config.json``\ ) and the vocabulary file (\ ``vocab.txt``\ ) as these are needed for the PyTorch model too.

-To run this specific conversion script you will need to have TensorFlow and PyTorch installed (\ ``pip install
-tensorflow``\ ). The rest of the repository only requires PyTorch.
+To run this specific conversion script you will need to have TensorFlow and PyTorch installed (\ ``pip install tensorflow``\ ). The rest of the repository only requires PyTorch.

 Here is an example of the conversion process for a pre-trained ``BERT-Base Uncased`` model:

@@ -47,20 +31,14 @@ Here is an example of the conversion process for a pre-trained ``BERT-Base Uncas
     --config $BERT_BASE_DIR/bert_config.json \
     --pytorch_dump_output $BERT_BASE_DIR/pytorch_model.bin

-You can download Google's pre-trained models for the conversion `here
-<https://github.com/google-research/bert#pre-trained-models>`__.
+You can download Google's pre-trained models for the conversion `here <https://github.com/google-research/bert#pre-trained-models>`__.

 ALBERT
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^

-Convert TensorFlow model checkpoints of ALBERT to PyTorch using the
-`convert_albert_original_tf_checkpoint_to_pytorch.py
-<https://github.com/huggingface/transformers/blob/master/src/transformers/convert_bert_original_tf_checkpoint_to_pytorch.py>`_
-script.
+Convert TensorFlow model checkpoints of ALBERT to PyTorch using the `convert_albert_original_tf_checkpoint_to_pytorch.py <https://github.com/huggingface/transformers/blob/master/src/transformers/convert_bert_original_tf_checkpoint_to_pytorch.py>`_ script.

-The CLI takes as input a TensorFlow checkpoint (three files starting with ``model.ckpt-best``\ ) and the accompanying
-configuration file (\ ``albert_config.json``\ ), then creates and saves a PyTorch model. To run this conversion you
-will need to have TensorFlow and PyTorch installed.
+The CLI takes as input a TensorFlow checkpoint (three files starting with ``model.ckpt-best``\ ) and the accompanying configuration file (\ ``albert_config.json``\ ), then creates and saves a PyTorch model. To run this conversion you will need to have TensorFlow and PyTorch installed.

 Here is an example of the conversion process for the pre-trained ``ALBERT Base`` model:

@@ -73,15 +51,12 @@ Here is an example of the conversion process for the pre-trained ``ALBERT Base``
     --config $ALBERT_BASE_DIR/albert_config.json \
     --pytorch_dump_output $ALBERT_BASE_DIR/pytorch_model.bin

-You can download Google's pre-trained models for the conversion `here
-<https://github.com/google-research/albert#pre-trained-models>`__.
+You can download Google's pre-trained models for the conversion `here <https://github.com/google-research/albert#pre-trained-models>`__.

 OpenAI GPT
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^

-Here is an example of the conversion process for a pre-trained OpenAI GPT model, assuming that your NumPy checkpoint
-save as the same format than OpenAI pretrained model (see `here <https://github.com/openai/finetune-transformer-lm>`__\
-)
+Here is an example of the conversion process for a pre-trained OpenAI GPT model, assuming that your NumPy checkpoint save as the same format than OpenAI pretrained model (see `here <https://github.com/openai/finetune-transformer-lm>`__\ )

 .. code-block:: shell

@@ -95,10 +70,9 @@ save as the same format than OpenAI pretrained model (see `here <https://github.


 OpenAI GPT-2
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^

-Here is an example of the conversion process for a pre-trained OpenAI GPT-2 model (see `here
-<https://github.com/openai/gpt-2>`__\ )
+Here is an example of the conversion process for a pre-trained OpenAI GPT-2 model (see `here <https://github.com/openai/gpt-2>`__\ )

 .. code-block:: shell

@@ -111,10 +85,9 @@ Here is an example of the conversion process for a pre-trained OpenAI GPT-2 mode
     [--finetuning_task_name OPENAI_GPT2_FINETUNED_TASK]

 Transformer-XL
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^

-Here is an example of the conversion process for a pre-trained Transformer-XL model (see `here
-<https://github.com/kimiyoung/transformer-xl/tree/master/tf#obtain-and-evaluate-pretrained-sota-models>`__\ )
+Here is an example of the conversion process for a pre-trained Transformer-XL model (see `here <https://github.com/kimiyoung/transformer-xl/tree/master/tf#obtain-and-evaluate-pretrained-sota-models>`__\ )

 .. code-block:: shell

@@ -128,7 +101,7 @@ Here is an example of the conversion process for a pre-trained Transformer-XL mo


 XLNet
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^

 Here is an example of the conversion process for a pre-trained XLNet model:

@@ -145,7 +118,7 @@ Here is an example of the conversion process for a pre-trained XLNet model:


 XLM
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^

 Here is an example of the conversion process for a pre-trained XLM model:

@@ -157,4 +130,4 @@ Here is an example of the conversion process for a pre-trained XLM model:
     --tf_checkpoint $XLM_CHECKPOINT_PATH \
     --pytorch_dump_output $PYTORCH_DUMP_OUTPUT
    [--config XML_CONFIG] \
-    [--finetuning_task_name XML_FINETUNED_TASK]
+    [--finetuning_task_name XML_FINETUNED_TASK]
@@ -1,17 +1,17 @@
 Fine-tuning with custom datasets
-=======================================================================================================================
+================================

 .. note::

-    The datasets used in this tutorial are available and can be more easily accessed using the `🤗 NLP library
-    <https://github.com/huggingface/nlp>`_. We do not use this library to access the datasets here since this tutorial
-    meant to illustrate how to work with your own data. A brief of introduction can be found at the end of the tutorial
-    in the section ":ref:`nlplib`".
+    The datasets used in this tutorial are available and can be more easily accessed using the
+    `🤗 NLP library <https://github.com/huggingface/nlp>`_. We do not use this library to access the datasets here
+    since this tutorial meant to illustrate how to work with your own data. A brief of introduction can be found
+    at the end of the tutorial in the section ":ref:`nlplib`".

-This tutorial will take you through several examples of using 🤗 Transformers models with your own datasets. The guide
-shows one of many valid workflows for using these models and is meant to be illustrative rather than definitive. We
-show examples of reading in several data formats, preprocessing the data for several types of tasks, and then preparing
-the data into PyTorch/TensorFlow ``Dataset`` objects which can easily be used either with
+This tutorial will take you through several examples of using 🤗 Transformers models with your own datasets. The
+guide shows one of many valid workflows for using these models and is meant to be illustrative rather than
+definitive. We show examples of reading in several data formats, preprocessing the data for several types of tasks,
+and then preparing the data into PyTorch/TensorFlow ``Dataset`` objects which can easily be used either with
 :class:`~transformers.Trainer`/:class:`~transformers.TFTrainer` or with native PyTorch/TensorFlow.

 We include several examples, each of which demonstrates a different type of common downstream task:
@@ -24,17 +24,17 @@ We include several examples, each of which demonstrates a different type of comm
 .. _seq_imdb:

 Sequence Classification with IMDb Reviews
-----------------------------------------------------------------------------------------------------------------------
+-----------------------------------------

 .. note::

-    This dataset can be explored in the Hugging Face model hub (`IMDb <https://huggingface.co/datasets/imdb>`_), and
-    can be alternatively downloaded with the 🤗 NLP library with ``load_dataset("imdb")``.
+    This dataset can be explored in the Hugging Face model hub (`IMDb <https://huggingface.co/datasets/imdb>`_), and can
+    be alternatively downloaded with the 🤗 NLP library with ``load_dataset("imdb")``.

-In this example, we'll show how to download, tokenize, and train a model on the IMDb reviews dataset. This task takes
-the text of a review and requires the model to predict whether the sentiment of the review is positive or negative.
-Let's start by downloading the dataset from the `Large Movie Review Dataset
-<http://ai.stanford.edu/~amaas/data/sentiment/>`_ webpage.
+In this example, we'll show how to download, tokenize, and train a model on the IMDb reviews dataset. This task
+takes the text of a review and requires the model to predict whether the sentiment of the review is positive or
+negative. Let's start by downloading the dataset from the
+`Large Movie Review Dataset <http://ai.stanford.edu/~amaas/data/sentiment/>`_ webpage.

 .. code-block:: bash

@@ -62,8 +62,9 @@ read this in.
    train_texts, train_labels = read_imdb_split('aclImdb/train')
    test_texts, test_labels = read_imdb_split('aclImdb/test')

-We now have a train and test dataset, but let's also also create a validation set which we can use for for evaluation
-and tuning without training our test set results. Sklearn has a convenient utility for creating such splits:
+We now have a train and test dataset, but let's also also create a validation set which we can use for for
+evaluation and tuning without training our test set results. Sklearn has a convenient utility for creating such
+splits:

 .. code-block:: python

@@ -79,8 +80,8 @@ pre-trained DistilBert, so let's use the DistilBert tokenizer.
    tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-uncased')

 Now we can simply pass our texts to the tokenizer. We'll pass ``truncation=True`` and ``padding=True``, which will
-ensure that all of our sequences are padded to the same length and are truncated to be no longer model's maximum input
-length. This will allow us to feed batches of sequences into the model at the same time.
+ensure that all of our sequences are padded to the same length and are truncated to be no longer model's maximum
+input length. This will allow us to feed batches of sequences into the model at the same time.

 .. code-block:: python

@@ -89,9 +90,9 @@ length. This will allow us to feed batches of sequences into the model at the sa
    test_encodings = tokenizer(test_texts, truncation=True, padding=True)

 Now, let's turn our labels and encodings into a Dataset object. In PyTorch, this is done by subclassing a
-``torch.utils.data.Dataset`` object and implementing ``__len__`` and ``__getitem__``. In TensorFlow, we pass our input
-encodings and labels to the ``from_tensor_slices`` constructor method. We put the data in this format so that the data
-can be easily batched such that each key in the batch encoding corresponds to a named parameter of the
+``torch.utils.data.Dataset`` object and implementing ``__len__`` and ``__getitem__``. In TensorFlow, we pass our input encodings and
+labels to the ``from_tensor_slices`` constructor method. We put the data in this format so that the data can be
+easily batched such that each key in the batch encoding corresponds to a named parameter of the
 :meth:`~transformers.DistilBertForSequenceClassification.forward` method of the model we will train.

 .. code-block:: python
@@ -132,17 +133,17 @@ can be easily batched such that each key in the batch encoding corresponds to a
    ))

 Now that our datasets our ready, we can fine-tune a model either with the 🤗
-:class:`~transformers.Trainer`/:class:`~transformers.TFTrainer` or with native PyTorch/TensorFlow. See :doc:`training
-<training>`.
+:class:`~transformers.Trainer`/:class:`~transformers.TFTrainer` or with native PyTorch/TensorFlow. See
+:doc:`training <training>`.

 .. _ft_trainer:

 Fine-tuning with Trainer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~

-The steps above prepared the datasets in the way that the trainer is expected. Now all we need to do is create a model
-to fine-tune, define the :class:`~transformers.TrainingArguments`/:class:`~transformers.TFTrainingArguments` and
-instantiate a :class:`~transformers.Trainer`/:class:`~transformers.TFTrainer`.
+The steps above prepared the datasets in the way that the trainer is expected. Now all we need to do is create a
+model to fine-tune, define the :class:`~transformers.TrainingArguments`/:class:`~transformers.TFTrainingArguments`
+and instantiate a :class:`~transformers.Trainer`/:class:`~transformers.TFTrainer`.

 .. code-block:: python

@@ -199,7 +200,7 @@ instantiate a :class:`~transformers.Trainer`/:class:`~transformers.TFTrainer`.
 .. _ft_native:

 Fine-tuning with native PyTorch/TensorFlow
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 We can also train use native PyTorch or TensorFlow:

@@ -243,19 +244,19 @@ We can also train use native PyTorch or TensorFlow:
 .. _tok_ner:

 Token Classification with W-NUT Emerging Entities
-----------------------------------------------------------------------------------------------------------------------
+-------------------------------------------------

 .. note::

-    This dataset can be explored in the Hugging Face model hub (`WNUT-17 <https://huggingface.co/datasets/wnut_17>`_),
-    and can be alternatively downloaded with the 🤗 NLP library with ``load_dataset("wnut_17")``.
+    This dataset can be explored in the Hugging Face model hub (`WNUT-17 <https://huggingface.co/datasets/wnut_17>`_), and can
+    be alternatively downloaded with the 🤗 NLP library with ``load_dataset("wnut_17")``.

 Next we will look at token classification. Rather than classifying an entire sequence, this task classifies token by
-token. We'll demonstrate how to do this with `Named Entity Recognition
-<http://nlpprogress.com/english/named_entity_recognition.html>`_, which involves identifying tokens which correspond to
-a predefined set of "entities". Specifically, we'll use the `W-NUT Emerging and Rare entities
-<http://noisy-text.github.io/2017/emerging-rare-entities.html>`_ corpus. The data is given as a collection of
-pre-tokenized documents where each token is assigned a tag.
+token. We'll demonstrate how to do this with 
+`Named Entity Recognition <http://nlpprogress.com/english/named_entity_recognition.html>`_, which involves
+identifying tokens which correspond to a predefined set of "entities". Specifically, we'll use the
+`W-NUT Emerging and Rare entities <http://noisy-text.github.io/2017/emerging-rare-entities.html>`_ corpus. The data
+is given as a collection of pre-tokenized documents where each token is assigned a tag.

 Let's start by downloading the data.

@@ -263,10 +264,10 @@ Let's start by downloading the data.

    wget http://noisy-text.github.io/2017/files/wnut17train.conll

-In this case, we'll just download the train set, which is a single text file. Each line of the file contains either (1)
-a word and tag separated by a tab, or (2) a blank line indicating the end of a document. Let's write a function to read
-this in. We'll take in the file path and return ``token_docs`` which is a list of lists of token strings, and
-``token_tags`` which is a list of lists of tag strings.
+In this case, we'll just download the train set, which is a single text file. Each line of the file contains either
+(1) a word and tag separated by a tab, or (2) a blank line indicating the end of a document. Let's write a
+function to read this in. We'll take in the file path and return ``token_docs`` which is a list of lists of token
+strings, and ``token_tags`` which is a list of lists of tag strings.

 .. code-block:: python

@@ -289,11 +290,11 @@ this in. We'll take in the file path and return ``token_docs`` which is a list o
                tags.append(tag)
            token_docs.append(tokens)
            tag_docs.append(tags)
-
+        
        return token_docs, tag_docs
-
+    
    texts, tags = read_wnut('wnut17train.conll')
-
+    
 Just to see what this data looks like, let's take a look at a segment of the first document.

 .. code-block:: python
@@ -302,8 +303,8 @@ Just to see what this data looks like, let's take a look at a segment of the fir
    ['for', 'two', 'weeks', '.', 'Empire', 'State', 'Building']
    ['O', 'O', 'O', 'O', 'B-location', 'I-location', 'I-location']

-``location`` is an entity type, ``B-`` indicates the beginning of an entity, and ``I-`` indicates consecutive positions
-of the same entity ("Empire State Building" is considered one entity). ``O`` indicates the token does not correspond to
+``location`` is an entity type, ``B-`` indicates the beginning of an entity, and ``I-`` indicates consecutive positions of
+the same entity ("Empire State Building" is considered one entity). ``O`` indicates the token does not correspond to
 any entity.

 Now that we've read the data in, let's create a train/validation split:
@@ -313,8 +314,8 @@ Now that we've read the data in, let's create a train/validation split:
    from sklearn.model_selection import train_test_split
    train_texts, val_texts, train_tags, val_tags = train_test_split(texts, tags, test_size=.2)

-Next, let's create encodings for our tokens and tags. For the tags, we can start by just create a simple mapping which
-we'll use in a moment:
+Next, let's create encodings for our tokens and tags. For the tags, we can start by just create a simple mapping
+which we'll use in a moment:

 .. code-block:: python

@@ -322,42 +323,42 @@ we'll use in a moment:
    tag2id = {tag: id for id, tag in enumerate(unique_tags)}
    id2tag = {id: tag for tag, id in tag2id.items()}

-To encode the tokens, we'll use a pre-trained DistilBert tokenizer. We can tell the tokenizer that we're dealing with
-ready-split tokens rather than full sentence strings by passing ``is_split_into_words=True``. We'll also pass
-``padding=True`` and ``truncation=True`` to pad the sequences to be the same length. Lastly, we can tell the model to
-return information about the tokens which are split by the wordpiece tokenization process, which we will need in a
-moment.
+To encode the tokens, we'll use a pre-trained DistilBert tokenizer. We can tell the tokenizer that we're dealing
+with ready-split tokens rather than full sentence strings by passing ``is_pretokenized=True``. We'll also pass
+``padding=True`` and ``truncation=True`` to pad the sequences to be the same length. Lastly, we can tell the model
+to return information about the tokens which are split by the wordpiece tokenization process, which we will need in
+a moment.

 .. code-block:: python

    from transformers import DistilBertTokenizerFast
    tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-cased')
-    train_encodings = tokenizer(train_texts, is_split_into_words=True, return_offsets_mapping=True, padding=True, truncation=True)
-    val_encodings = tokenizer(val_texts, is_split_into_words=True, return_offsets_mapping=True, padding=True, truncation=True)
+    train_encodings = tokenizer(train_texts, is_pretokenized=True, return_offsets_mapping=True, padding=True, truncation=True)
+    val_encodings = tokenizer(val_texts, is_pretokenized=True, return_offsets_mapping=True, padding=True, truncation=True)

 Great, so now our tokens are nicely encoded in the format that they need to be in to feed them into our DistilBert
 model below.

-Now we arrive at a common obstacle with using pre-trained models for token-level classification: many of the tokens in
-the W-NUT corpus are not in DistilBert's vocabulary. Bert and many models like it use a method called WordPiece
-Tokenization, meaning that single words are split into multiple tokens such that each token is likely to be in the
-vocabulary. For example, DistilBert's tokenizer would split the Twitter handle ``@huggingface`` into the tokens ``['@',
-'hugging', '##face']``. This is a problem for us because we have exactly one tag per token. If the tokenizer splits a
-token into multiple sub-tokens, then we will end up with a mismatch between our tokens and our labels.
+Now we arrive at a common obstacle with using pre-trained models for token-level classification: many of the tokens
+in the W-NUT corpus are not in DistilBert's vocabulary. Bert and many models like it use a method called WordPiece
+Tokenization, meaning that single words are split into multiple tokens such that each token is likely to be in
+the vocabulary. For example, DistilBert's tokenizer would split the Twitter handle ``@huggingface`` into the tokens
+``['@', 'hugging', '##face']``. This is a problem for us because we have exactly one tag per token. If the tokenizer
+splits a token into multiple sub-tokens, then we will end up with a mismatch between our tokens and our labels.

-One way to handle this is to only train on the tag labels for the first subtoken of a split token. We can do this in 🤗
-Transformers by setting the labels we wish to ignore to ``-100``. In the example above, if the label for
+One way to handle this is to only train on the tag labels for the first subtoken of a split token. We can do this in
+🤗 Transformers by setting the labels we wish to ignore to ``-100``. In the example above, if the label for
 ``@HuggingFace`` is ``3`` (indexing ``B-corporation``), we would set the labels of ``['@', 'hugging', '##face']`` to
 ``[3, -100, -100]``.

 Let's write a function to do this. This is where we will use the ``offset_mapping`` from the tokenizer as mentioned
 above. For each sub-token returned by the tokenizer, the offset mapping gives us a tuple indicating the sub-token's
-start position and end position relative to the original token it was split from. That means that if the first position
-in the tuple is anything other than ``0``, we will set its corresponding label to ``-100``. While we're at it, we can
-also set labels to ``-100`` if the second position of the offset mapping is ``0``, since this means it must be a
-special token like ``[PAD]`` or ``[CLS]``.
+start position and end position relative to the original token it was split from. That means that if the first
+position in the tuple is anything other than ``0``, we will set its corresponding label to ``-100``. While we're at
+it, we can also set labels to ``-100`` if the second position of the offset mapping is ``0``, since this means it must
+be a special token like ``[PAD]`` or ``[CLS]``.

-.. note::
+.. note:: 

    Due to a recently fixed bug, -1 must be used instead of -100 when using TensorFlow in 🤗 Transformers <= 3.02.

@@ -378,7 +379,7 @@ special token like ``[PAD]`` or ``[CLS]``.
            encoded_labels.append(doc_enc_labels.tolist())

        return encoded_labels
-
+    
    train_labels = encode_tags(train_tags, train_encodings)
    val_labels = encode_tags(val_tags, val_encodings)

@@ -442,13 +443,12 @@ sequence classification example above.
 .. _qa_squad:

 Question Answering with SQuAD 2.0
-----------------------------------------------------------------------------------------------------------------------
+---------------------------------

 .. note::

-    This dataset can be explored in the Hugging Face model hub (`SQuAD V2
-    <https://huggingface.co/datasets/squad_v2>`_), and can be alternatively downloaded with the 🤗 NLP library with
-    ``load_dataset("squad_v2")``.
+    This dataset can be explored in the Hugging Face model hub (`SQuAD V2 <https://huggingface.co/datasets/squad_v2>`_), and can
+    be alternatively downloaded with the 🤗 NLP library with ``load_dataset("squad_v2")``.

 Question answering comes in many forms. In this example, we'll look at the particular type of extractive QA that
 involves answering a question about a passage by highlighting the segment of the passage that answers the question.
@@ -464,8 +464,8 @@ We will start by downloading the data:
    wget https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v2.0.json -O squad/dev-v2.0.json

 Each split is in a structured json file with a number of questions and answers for each passage (or context). We'll
-take this apart into parallel lists of contexts, questions, and answers (note that the contexts here are repeated since
-there are multiple questions per context):
+take this apart into parallel lists of contexts, questions, and answers (note that the contexts here are repeated
+since there are multiple questions per context):

 .. code-block:: python

@@ -491,17 +491,17 @@ there are multiple questions per context):
                        answers.append(answer)

        return contexts, questions, answers
-
+    
    train_contexts, train_questions, train_answers = read_squad('squad/train-v2.0.json')
    val_contexts, val_questions, val_answers = read_squad('squad/dev-v2.0.json')

-The contexts and questions are just strings. The answers are dicts containing the subsequence of the passage with the
-correct answer as well as an integer indicating the character at which the answer begins. In order to train a model on
-this data we need (1) the tokenized context/question pairs, and (2) integers indicating at which *token* positions the
-answer begins and ends.
+The contexts and questions are just strings. The answers are dicts containing the subsequence of the passage with
+the correct answer as well as an integer indicating the character at which the answer begins. In order to train a
+model on this data we need (1) the tokenized context/question pairs, and (2) integers indicating at which *token*
+positions the answer begins and ends.

-First, let's get the *character* position at which the answer ends in the passage (we are given the starting position).
-Sometimes SQuAD answers are off by one or two characters, so we will also adjust for that.
+First, let's get the *character* position at which the answer ends in the passage (we are given the starting
+position). Sometimes SQuAD answers are off by one or two characters, so we will also adjust for that.

 .. code-block:: python

@@ -510,7 +510,7 @@ Sometimes SQuAD answers are off by one or two characters, so we will also adjust
            gold_text = answer['text']
            start_idx = answer['answer_start']
            end_idx = start_idx + len(gold_text)
-
+            
            # sometimes squad answers are off by a character or two – fix this
            if context[start_idx:end_idx] == gold_text:
                answer['answer_end'] = end_idx
@@ -524,9 +524,9 @@ Sometimes SQuAD answers are off by one or two characters, so we will also adjust
    add_end_idx(train_answers, train_contexts)
    add_end_idx(val_answers, val_contexts)

-Now ``train_answers`` and ``val_answers`` include the character end positions and the corrected start positions. Next,
-let's tokenize our context/question pairs. 🤗 Tokenizers can accept parallel lists of sequences and encode them together
-as sequence pairs.
+Now ``train_answers`` and ``val_answers`` include the character end positions and the corrected start positions.
+Next, let's tokenize our context/question pairs. 🤗 Tokenizers can accept parallel lists of sequences and encode
+them together as sequence pairs.

 .. code-block:: python

@@ -536,8 +536,8 @@ as sequence pairs.
    train_encodings = tokenizer(train_contexts, train_questions, truncation=True, padding=True)
    val_encodings = tokenizer(val_contexts, val_questions, truncation=True, padding=True)

-Next we need to convert our character start/end positions to token start/end positions. When using 🤗 Fast Tokenizers,
-we can use the built in :func:`~transformers.BatchEncoding.char_to_token` method.
+Next we need to convert our character start/end positions to token start/end positions. When using 🤗 Fast
+Tokenizers, we can use the built in :func:`~transformers.BatchEncoding.char_to_token` method.

 .. code-block:: python

@@ -557,9 +557,9 @@ we can use the built in :func:`~transformers.BatchEncoding.char_to_token` method
    add_token_positions(train_encodings, train_answers)
    add_token_positions(val_encodings, val_answers)

-Our data is ready. Let's just put it in a PyTorch/TensorFlow dataset so that we can easily use it for training. In
-PyTorch, we define a custom ``Dataset`` class. In TensorFlow, we pass a tuple of ``(inputs_dict, labels_dict)`` to the
-``from_tensor_slices`` method.
+Our data is ready. Let's just put it in a PyTorch/TensorFlow dataset so that we can easily use it for
+training. In PyTorch, we define a custom ``Dataset`` class. In TensorFlow, we pass a tuple of
+``(inputs_dict, labels_dict)`` to the ``from_tensor_slices`` method.

 .. code-block:: python

@@ -575,7 +575,7 @@ PyTorch, we define a custom ``Dataset`` class. In TensorFlow, we pass a tuple of

        def __len__(self):
            return len(self.encodings.input_ids)
-
+        
    train_dataset = SquadDataset(train_encodings)
    val_dataset = SquadDataset(val_encodings)
    ## TENSORFLOW CODE
@@ -655,7 +655,7 @@ multiple model outputs.
 .. _resources:

 Additional Resources
-----------------------------------------------------------------------------------------------------------------------
+--------------------

  - `How to train a new language model from scratch using Transformers and Tokenizers
    <https://huggingface.co/blog/how-to-train>`_. Blog post showing the steps to load in Esperanto data and train a
@@ -666,13 +666,14 @@ Additional Resources
 .. _nlplib:

 Using the 🤗 NLP Datasets & Metrics library
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

-This tutorial demonstrates how to read in datasets from various raw text formats and prepare them for training with 🤗
-Transformers so that you can do the same thing with your own custom datasets. However, we recommend users use the `🤗
-NLP library <https://github.com/huggingface/nlp>`_ for working with the 150+ datasets included in the `hub
-<https://huggingface.co/datasets>`_, including the three datasets used in this tutorial. As a very brief overview, we
-will show how to use the NLP library to download and prepare the IMDb dataset from the first example, :ref:`seq_imdb`.
+This tutorial demonstrates how to read in datasets from various raw text formats and prepare them for training with
+🤗 Transformers so that you can do the same thing with your own custom datasets. However, we recommend users use the
+`🤗 NLP library <https://github.com/huggingface/nlp>`_ for working with the 150+ datasets included in the
+`hub <https://huggingface.co/datasets>`_, including the three datasets used in this tutorial. As a very brief overview,
+we will show how to use the NLP library to download and prepare the IMDb dataset from the first example,
+:ref:`seq_imdb`.

 Start by downloading the dataset:

@@ -688,8 +689,8 @@ Each dataset has multiple columns corresponding to different features. Let's see
    >>> print(train.column_names)
    ['label', 'text']

-Great. Now let's tokenize the text. We can do this using the ``map`` method. We'll also rename the ``label`` column to
-``labels`` to match the model's input arguments.
+Great. Now let's tokenize the text. We can do this using the ``map`` method. We'll also rename the ``label`` column
+to ``labels`` to match the model's input arguments.

 .. code-block:: python

@@ -710,5 +711,5 @@ dataset elements.
    >>> {key: val.shape for key, val in train[0].items()})
    {'labels': TensorShape([]), 'input_ids': TensorShape([512]), 'attention_mask': TensorShape([512])}

-We now have a fully-prepared dataset. Check out `the 🤗 NLP docs <https://huggingface.co/nlp/processing.html>`_ for a
-more thorough introduction.
+We now have a fully-prepared dataset. Check out `the 🤗 NLP docs <https://huggingface.co/nlp/processing.html>`_ for
+a more thorough introduction.
@@ -1,8 +1,8 @@
 Glossary
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^

 General terms
-----------------------------------------------------------------------------------------------------------------------
+-------------

 - autoencoding models: see MLM
 - autoregressive models: see CLM
@@ -27,7 +27,7 @@ General terms
  or a punctuation symbol.

 Model inputs
-----------------------------------------------------------------------------------------------------------------------
+------------

 Every model is different yet bears similarities with the others. Therefore most models use the same inputs, which are
 detailed here alongside usage examples.
@@ -35,7 +35,7 @@ detailed here alongside usage examples.
 .. _input-ids:

 Input IDs
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~

 The input ids are often the only required parameters to be passed to the model as input. *They are token indices,
 numerical representations of tokens building the sequences that will be used as input by the model*.
@@ -43,7 +43,7 @@ numerical representations of tokens building the sequences that will be used as
 Each tokenizer works differently but the underlying mechanism remains the same. Here's an example using the BERT
 tokenizer, which is a `WordPiece <https://arxiv.org/pdf/1609.08144.pdf>`__ tokenizer:

-.. code-block::
+::

    >>> from transformers import BertTokenizer
    >>> tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
@@ -52,31 +52,31 @@ tokenizer, which is a `WordPiece <https://arxiv.org/pdf/1609.08144.pdf>`__ token

 The tokenizer takes care of splitting the sequence into tokens available in the tokenizer vocabulary.

-.. code-block::
+::

    >>> tokenized_sequence = tokenizer.tokenize(sequence)

 The tokens are either words or subwords. Here for instance, "VRAM" wasn't in the model vocabulary, so it's been split
-in "V", "RA" and "M". To indicate those tokens are not separate words but parts of the same word, a double-hash prefix
-is added for "RA" and "M":
+in "V", "RA" and "M". To indicate those tokens are not separate words but parts of the same word, a double-hash prefix is
+added for "RA" and "M":

-.. code-block::
+::

    >>> print(tokenized_sequence)
    ['A', 'Titan', 'R', '##T', '##X', 'has', '24', '##GB', 'of', 'V', '##RA', '##M']

 These tokens can then be converted into IDs which are understandable by the model. This can be done by directly feeding
-the sentence to the tokenizer, which leverages the Rust implementation of `huggingface/tokenizers
-<https://github.com/huggingface/tokenizers>`__ for peak performance.
+the sentence to the tokenizer, which leverages the Rust implementation of
+`huggingface/tokenizers <https://github.com/huggingface/tokenizers>`__ for peak performance.

-.. code-block::
+::

    >>> inputs = tokenizer(sequence)

 The tokenizer returns a dictionary with all the arguments necessary for its corresponding model to work properly. The
 token indices are under the key "input_ids":

-.. code-block::
+::

    >>> encoded_sequence = inputs["input_ids"]
    >>> print(encoded_sequence)
@@ -87,13 +87,13 @@ IDs the model sometimes uses.

 If we decode the previous sequence of ids,

-.. code-block::
+::

    >>> decoded_sequence = tokenizer.decode(encoded_sequence)

 we will see

-.. code-block::
+::

    >>> print(decoded_sequence)
    [CLS] A Titan RTX has 24GB of VRAM [SEP]
@@ -103,14 +103,14 @@ because this is the way a :class:`~transformers.BertModel` is going to expect it
 .. _attention-mask:

 Attention mask
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~

-The attention mask is an optional argument used when batching sequences together. This argument indicates to the model
-which tokens should be attended to, and which should not.
+The attention mask is an optional argument used when batching sequences together. This argument indicates to the
+model which tokens should be attended to, and which should not.

 For example, consider these two sequences:

-.. code-block::
+::

    >>> from transformers import BertTokenizer
    >>> tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
@@ -123,7 +123,7 @@ For example, consider these two sequences:

 The encoded versions have different lengths:

-.. code-block::
+::

    >>> len(encoded_sequence_a), len(encoded_sequence_b)
    (8, 19)
@@ -134,23 +134,23 @@ of the second one, or the second one needs to be truncated down to the length of
 In the first case, the list of IDs will be extended by the padding indices. We can pass a list to the tokenizer and ask
 it to pad like this:

-.. code-block::
+::

    >>> padded_sequences = tokenizer([sequence_a, sequence_b], padding=True)

 We can see that 0s have been added on the right of the first sentence to make it the same length as the second one:

-.. code-block::
+::

    >>> padded_sequences["input_ids"]
    [[101, 1188, 1110, 170, 1603, 4954, 119, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [101, 1188, 1110, 170, 1897, 1263, 4954, 119, 1135, 1110, 1120, 1655, 2039, 1190, 1103, 4954, 138, 119, 102]]

-This can then be converted into a tensor in PyTorch or TensorFlow. The attention mask is a binary tensor indicating the
-position of the padded indices so that the model does not attend to them. For the :class:`~transformers.BertTokenizer`,
-:obj:`1` indicates a value that should be attended to, while :obj:`0` indicates a padded value. This attention mask is
-in the dictionary returned by the tokenizer under the key "attention_mask":
+This can then be converted into a tensor in PyTorch or TensorFlow. The attention mask is a binary tensor indicating
+the position of the padded indices so that the model does not attend to them. For the
+:class:`~transformers.BertTokenizer`, :obj:`1` indicates a value that should be attended to, while :obj:`0` indicates
+a padded value. This attention mask is in the dictionary returned by the tokenizer under the key "attention_mask":

-.. code-block::
+::

    >>> padded_sequences["attention_mask"]
    [[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
@@ -158,21 +158,20 @@ in the dictionary returned by the tokenizer under the key "attention_mask":
 .. _token-type-ids:

 Token Type IDs
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~

 Some models' purpose is to do sequence classification or question answering. These require two different sequences to
-be joined in a single "input_ids" entry, which usually is performed with the help of special tokens, such as the
-classifier (``[CLS]``) and separator (``[SEP]``) tokens. For example, the BERT model builds its two sequence input as
-such:
+be joined in a single "input_ids" entry, which usually is performed with the help of special tokens, such as the classifier (``[CLS]``) and separator (``[SEP]``)
+tokens. For example, the BERT model builds its two sequence input as such:

-.. code-block::
+::

   >>> # [CLS] SEQUENCE_A [SEP] SEQUENCE_B [SEP]

-We can use our tokenizer to automatically generate such a sentence by passing the two sequences to ``tokenizer`` as two
-arguments (and not a list, like before) like this:
+We can use our tokenizer to automatically generate such a sentence by passing the two sequences to ``tokenizer`` as two arguments (and
+not a list, like before) like this:

-.. code-block::
+::

    >>> from transformers import BertTokenizer
    >>> tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
@@ -184,18 +183,18 @@ arguments (and not a list, like before) like this:

 which will return:

-.. code-block::
+::

    >>> print(decoded)
    [CLS] HuggingFace is based in NYC [SEP] Where is HuggingFace based? [SEP]

 This is enough for some models to understand where one sequence ends and where another begins. However, other models,
-such as BERT, also deploy token type IDs (also called segment IDs). They are represented as a binary mask identifying
-the two types of sequence in the model.
+such as BERT, also deploy token type IDs (also called segment IDs). They are represented as a binary
+mask identifying the two types of sequence in the model.

 The tokenizer returns this mask as the "token_type_ids" entry:

-.. code-block::
+::

    >>> encoded_dict['token_type_ids']
    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]
@@ -208,80 +207,35 @@ Some models, like :class:`~transformers.XLNetModel` use an additional token repr
 .. _position-ids:

 Position IDs
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~

-Contrary to RNNs that have the position of each token embedded within them, transformers are unaware of the position of
-each token. Therefore, the position IDs (``position_ids``) are used by the model to identify each token's position in
-the list of tokens.
+Contrary to RNNs that have the position of each token embedded within them,
+transformers are unaware of the position of each token. Therefore, the position IDs (``position_ids``) are used by the model to identify each token's position in the list of tokens.

-They are an optional parameter. If no ``position_ids`` is passed to the model, the IDs are automatically created as
-absolute positional embeddings.
+They are an optional parameter. If no ``position_ids`` is passed to the model, the IDs are automatically created as absolute
+positional embeddings.

-Absolute positional embeddings are selected in the range ``[0, config.max_position_embeddings - 1]``. Some models use
-other types of positional embeddings, such as sinusoidal position embeddings or relative position embeddings.
-
-.. _labels:
-
-Labels
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The labels are an optional argument which can be passed in order for the model to compute the loss itself. These labels
-should be the expected prediction of the model: it will use the standard loss in order to compute the loss between its
-predictions and the expected value (the label).
-
-These labels are different according to the model head, for example:
-
- For sequence classification models (e.g., :class:`~transformers.BertForSequenceClassification`), the model expects a
-  tensor of dimension :obj:`(batch_size)` with each value of the batch corresponding to the expected label of the
-  entire sequence.
- For token classification models (e.g., :class:`~transformers.BertForTokenClassification`), the model expects a tensor
-  of dimension :obj:`(batch_size, seq_length)` with each value corresponding to the expected label of each individual
-  token.
- For masked language modeling (e.g., :class:`~transformers.BertForMaskedLM`), the model expects a tensor of dimension
-  :obj:`(batch_size, seq_length)` with each value corresponding to the expected label of each individual token: the
-  labels being the token ID for the masked token, and values to be ignored for the rest (usually -100).
- For sequence to sequence tasks,(e.g., :class:`~transformers.BartForConditionalGeneration`,
-  :class:`~transformers.MBartForConditionalGeneration`), the model expects a tensor of dimension :obj:`(batch_size,
-  tgt_seq_length)` with each value corresponding to the target sequences associated with each input sequence. During
-  training, both `BART` and `T5` will make the appropriate `decoder_input_ids` and decoder attention masks internally.
-  They usually do not need to be supplied. This does not apply to models leveraging the Encoder-Decoder framework. See
-  the documentation of each model for more information on each specific model's labels.
-
-The base models (e.g., :class:`~transformers.BertModel`) do not accept labels, as these are the base transformer
-models, simply outputting features.
-
-.. _decoder-input-ids:
-
-Decoder input IDs
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-This input is specific to encoder-decoder models, and contains the input IDs that will be fed to the decoder. These
-inputs should be used for sequence to sequence tasks, such as translation or summarization, and are usually built in a
-way specific to each model.
-
-Most encoder-decoder models (BART, T5) create their :obj:`decoder_input_ids` on their own from the :obj:`labels`. In
-such models, passing the :obj:`labels` is the preferred way to handle training.
-
-Please check each model's docs to see how they handle these input IDs for sequence to sequence training.
+Absolute positional embeddings are selected in the range ``[0, config.max_position_embeddings - 1]``. Some models
+use other types of positional embeddings, such as sinusoidal position embeddings or relative position embeddings.

 .. _feed-forward-chunking:

 Feed Forward Chunking
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

 In each residual attention block in transformers the self-attention layer is usually followed by 2 feed forward layers.
-The intermediate embedding size of the feed forward layers is often bigger than the hidden size of the model (e.g., for
-``bert-base-uncased``).
+The intermediate embedding size of the feed forward layers is often bigger than the hidden size of the model (e.g.,
+for ``bert-base-uncased``).

 For an input of size ``[batch_size, sequence_length]``, the memory required to store the intermediate feed forward
 embeddings ``[batch_size, sequence_length, config.intermediate_size]`` can account for a large fraction of the memory
 use. The authors of `Reformer: The Efficient Transformer <https://arxiv.org/abs/2001.04451>`_ noticed that since the
 computation is independent of the ``sequence_length`` dimension, it is mathematically equivalent to compute the output
 embeddings of both feed forward layers ``[batch_size, config.hidden_size]_0, ..., [batch_size, config.hidden_size]_n``
-individually and concat them afterward to ``[batch_size, sequence_length, config.hidden_size]`` with ``n =
-sequence_length``, which trades increased computation time against reduced memory use, but yields a mathematically
-**equivalent** result.
+individually and concat them afterward to ``[batch_size, sequence_length, config.hidden_size]`` with
+``n = sequence_length``, which trades increased computation time against reduced memory use, but yields a
+mathematically **equivalent** result.

 For models employing the function :func:`~.transformers.apply_chunking_to_forward`, the ``chunk_size`` defines the
 number of output embeddings that are computed in parallel and thus defines the trade-off between memory and time
-complexity. If ``chunk_size`` is set to 0, no feed forward chunking is done.
+complexity.  If ``chunk_size`` is set to 0, no feed forward chunking is done.
@@ -1,5 +1,5 @@
 Transformers
-=======================================================================================================================
+================================================================================================================================================

 State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0.

@@ -11,7 +11,7 @@ TensorFlow 2.0 and PyTorch.
 This is the documentation of our repository `transformers <https://github.com/huggingface/transformers>`_.

 Features
-----------------------------------------------------------------------------------------------------------------------
+---------------------------------------------------

 - High performance on NLU and NLG tasks
 - Low barrier to entry for educators and practitioners
@@ -35,10 +35,8 @@ Choose the right framework for every part of a model's lifetime:
 - Move a single model between TF2.0/PyTorch frameworks at will
 - Seamlessly pick the right framework for training, evaluation, production

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

 The documentation is organized in five parts:

@@ -46,218 +44,101 @@ The documentation is organized in five parts:
  and a glossary.
 - **USING 🤗 TRANSFORMERS** contains general tutorials on how to use the library.
 - **ADVANCED GUIDES** contains more advanced guides that are more specific to a given script or part of the library.
- **RESEARCH** focuses on tutorials that have less to do with how to use the library but more about general research in
+- **RESEARCH** focuses on tutorials that have less to do with how to use the library but more about general resarch in
  transformers model
- The three last section contain the documentation of each public class and function, grouped in:
+- **PACKAGE REFERENCE** contains the documentation of each public class and function.

-    - **MAIN CLASSES** for the main classes exposing the important APIs of the library.
-    - **MODELS** for the classes and functions related to each model implemented in the library.
-    - **INTERNAL HELPERS** for the classes and functions we use internally.
-
-The library currently contains PyTorch, Tensorflow and Flax implementations, pretrained model weights, usage scripts
-and conversion utilities for the following models:
-
-..
-    This list is updated automatically from the README with `make fix-copies`. Do not update manually!
-
-1. :doc:`ALBERT <model_doc/albert>` (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.
-2. :doc:`BART <model_doc/bart>` (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.
-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.
-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.
-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.
-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.
-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.
-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.
-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.
-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.
-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.
-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.
-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.
-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.
-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**.
-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.
-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.
-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.
-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.
-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.
-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.
-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.
-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.
-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.
-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.
-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.
-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.
-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.
-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.
-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.
-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.
-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.
-
-..
-    This table is updated automatically from the auto modules with `make fix-copies`. Do not update manually!
-
-.. rst-class:: center-aligned-table
-
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|            Model            | Tokenizer slow | Tokenizer fast | PyTorch support | TensorFlow support | Flax Support |
-+=============================+================+================+=================+====================+==============+
-|           ALBERT            |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|            BART             |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|            BERT             |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|       Bert Generation       |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|         Blenderbot          |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|            CTRL             |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|          CamemBERT          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|             DPR             |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|           DeBERTa           |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|         DistilBERT          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|           ELECTRA           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|       Encoder decoder       |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-| FairSeq Machine-Translation |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|          FlauBERT           |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|     Funnel Transformer      |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|           LXMERT            |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|          LayoutLM           |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|         Longformer          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|           Marian            |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|         MobileBERT          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|         OpenAI GPT          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|        OpenAI GPT-2         |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|           Pegasus           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|         ProphetNet          |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|             RAG             |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|          Reformer           |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|          RetriBERT          |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|           RoBERTa           |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|         SqueezeBERT         |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|             T5              |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|       Transformer-XL        |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|             XLM             |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|         XLM-RoBERTa         |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|        XLMProphetNet        |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|            XLNet            |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|            mBART            |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|             mT5             |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+The library currently contains PyTorch and Tensorflow implementations, pre-trained model weights, usage scripts and
+conversion utilities for the following models:

+1. `BERT <https://github.com/google-research/bert>`_ (from Google) released with the paper `BERT: Pre-training of Deep
+   Bidirectional Transformers for Language Understanding <https://arxiv.org/abs/1810.04805>`_ by Jacob Devlin, Ming-Wei
+   Chang, Kenton Lee, and Kristina Toutanova.
+2. `GPT <https://github.com/openai/finetune-transformer-lm>`_ (from OpenAI) released with the paper `Improving Language
+   Understanding by Generative Pre-Training <https://blog.openai.com/language-unsupervised>`_ by Alec Radford, Karthik
+   Narasimhan, Tim Salimans, and Ilya Sutskever.
+3. `GPT-2 <https://blog.openai.com/better-language-models>`_ (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.
+4. `Transformer-XL <https://github.com/kimiyoung/transformer-xl>`_ (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, and Ruslan Salakhutdinov.
+5. `XLNet <https://github.com/zihangdai/xlnet>`_ (from Google/CMU) released with the paper `XLNet: Generalized
+   Autoregressive Pretraining for Language Understanding <https://arxiv.org/abs/1906.08237>`_ by Zhilin Yang, Zihang
+   Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, and Quoc V. Le.
+6. `XLM <https://github.com/facebookresearch/XLM>`_ (from Facebook) released together with the paper `Cross-lingual
+   Language Model Pretraining <https://arxiv.org/abs/1901.07291>`_ by Guillaume Lample and Alexis Conneau.
+7. `RoBERTa <https://github.com/pytorch/fairseq/tree/master/examples/roberta>`_ (from Facebook), released together with
+   the paper a `Robustly Optimized BERT Pretraining Approach <https://arxiv.org/abs/1907.11692>`_ by Yinhan Liu, Myle
+   Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin
+   Stoyanov.
+8. `DistilBERT <https://huggingface.co/transformers/model_doc/distilbert.html>`_ (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>`_.
+9. `CTRL <https://github.com/pytorch/fairseq/tree/master/examples/ctrl>`_ (from Salesforce), released together with the
+   paper `CTRL: A Conditional Transformer Language Model for Controllable Generation
+   <https://www.github.com/salesforce/ctrl>`_ by Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, Caiming Xiong,
+   and Richard Socher.
+10. `CamemBERT <https://huggingface.co/transformers/model_doc/camembert.html>`_ (from FAIR, Inria, Sorbonne Université)
+    released together with the paper `CamemBERT: a Tasty French Language Model <https://arxiv.org/abs/1911.03894>`_ by
+    Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suarez, Yoann Dupont, Laurent Romary, Eric Villemonte de la
+    Clergerie, Djame Seddah, and Benoît Sagot.
+11. `ALBERT <https://github.com/google-research/ALBERT>`_ (from Google Research), released together 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, and Radu Soricut.
+12. `T5 <https://github.com/google-research/text-to-text-transfer-transformer>`_ (from Google) 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, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang,
+    Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu.
+13. `XLM-RoBERTa <https://github.com/pytorch/fairseq/tree/master/examples/xlmr>`_ (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.
+14. `MMBT <https://github.com/facebookresearch/mmbt/>`_ (from Facebook), released together with the paper a `Supervised
+    Multimodal Bitransformers for Classifying Images and Text <https://arxiv.org/pdf/1909.02950.pdf>`_ by Douwe Kiela,
+    Suvrat Bhooshan, Hamed Firooz, and Davide Testuggine.
+15. `FlauBERT <https://github.com/getalp/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, and
+    Didier Schwab.
+16. `BART <https://github.com/pytorch/fairseq/tree/master/examples/bart>`_ (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.
+17. `ELECTRA <https://github.com/google-research/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, and Christopher D. Manning.
+18. `DialoGPT <https://github.com/microsoft/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,
+    and Bill Dolan.
+19. `Reformer <https://github.com/google/trax/tree/master/trax/models/reformer>`_ (from Google Research) released with
+    the paper `Reformer: The Efficient Transformer <https://arxiv.org/abs/2001.04451>`_ by Nikita Kitaev, Łukasz
+    Kaiser, and Anselm Levskaya.
+20. `MarianMT <https://marian-nmt.github.io/>`_ (developed by the Microsoft Translator Team) machine translation models
+    trained using `OPUS <http://opus.nlpl.eu/>`_ pretrained_models data by Jörg Tiedemann.
+21. `Longformer <https://github.com/allenai/longformer>`_ (from AllenAI) released with the paper `Longformer: The
+    Long-Document Transformer <https://arxiv.org/abs/2004.05150>`_ by Iz Beltagy, Matthew E. Peters, and Arman Cohan.
+22. `DPR <https://github.com/facebookresearch/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.
+23. `Pegasus <https://github.com/google-research/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.
+24. `MBart <https://github.com/pytorch/fairseq/tree/master/examples/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.
+25. `LXMERT <https://github.com/airsplay/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.
+26. `Funnel Transformer <https://github.com/laiguokun/Funnel-Transformer>`_ (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.
+27. `Bert For Sequence Generation <https://tfhub.dev/s?module-type=text-generation&subtype=module,placeholder>`_ (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.
+28. `Other community models <https://huggingface.co/models>`_, contributed by the `community
+    <https://huggingface.co/users>`_.

 .. toctree::
    :maxdepth: 2
@@ -304,69 +185,48 @@ TensorFlow and/or Flax.

 .. toctree::
    :maxdepth: 2
-    :caption: Main Classes
+    :caption: Package Reference

-    main_classes/callback
    main_classes/configuration
-    main_classes/logging
-    main_classes/model
-    main_classes/optimizer_schedules
    main_classes/output
-    main_classes/pipelines
-    main_classes/processors
+    main_classes/model
    main_classes/tokenizer
+    main_classes/pipelines
    main_classes/trainer
-
-.. toctree::
-    :maxdepth: 2
-    :caption: Models
-
-    model_doc/albert
+    main_classes/optimizer_schedules
+    main_classes/processors
+    main_classes/logging
    model_doc/auto
-    model_doc/bart
-    model_doc/bert
-    model_doc/bertgeneration
-    model_doc/blenderbot
-    model_doc/camembert
-    model_doc/ctrl
-    model_doc/deberta
-    model_doc/dialogpt
-    model_doc/distilbert
-    model_doc/dpr
-    model_doc/electra
    model_doc/encoderdecoder
+    model_doc/bert
+    model_doc/gpt
+    model_doc/transformerxl
+    model_doc/gpt2
+    model_doc/xlm
+    model_doc/xlnet
+    model_doc/roberta
+    model_doc/distilbert
+    model_doc/ctrl
+    model_doc/camembert
+    model_doc/albert
+    model_doc/xlmroberta
    model_doc/flaubert
+    model_doc/bart
+    model_doc/t5
+    model_doc/electra
+    model_doc/dialogpt
+    model_doc/reformer
+    model_doc/marian
+    model_doc/longformer
+    model_doc/retribert
+    model_doc/mobilebert
+    model_doc/dpr
+    model_doc/pegasus
+    model_doc/mbart
    model_doc/fsmt
    model_doc/funnel
-    model_doc/layoutlm
-    model_doc/longformer
    model_doc/lxmert
-    model_doc/marian
-    model_doc/mbart
-    model_doc/mobilebert
-    model_doc/mt5
-    model_doc/gpt
-    model_doc/gpt2
-    model_doc/pegasus
-    model_doc/prophetnet
-    model_doc/rag
-    model_doc/reformer
-    model_doc/retribert
-    model_doc/roberta
-    model_doc/squeezebert
-    model_doc/t5
-    model_doc/transformerxl
-    model_doc/xlm
-    model_doc/xlmprophetnet
-    model_doc/xlmroberta
-    model_doc/xlnet
-
-.. toctree::
-    :maxdepth: 2
-    :caption: Internal Helpers
-
+    model_doc/bertgeneration
    internal/modeling_utils
-    internal/pipelines_utils
    internal/tokenization_utils
-    internal/trainer_utils
-    internal/generation_utils
+    internal/pipelines_utils
@@ -12,10 +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:

@@ -35,22 +34,16 @@ 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
-python -c "from transformers import pipeline; print(pipeline('sentiment-analysis')('we love you'))"
+python -c "from transformers import pipeline; print(pipeline('sentiment-analysis')('I hate you'))"
 ```

 It should download a pretrained model then print something like

 ```bash
-[{'label': 'POSITIVE', 'score': 0.9998704791069031}]
+[{'label': 'NEGATIVE', 'score': 0.9991129040718079}]
 ```

 (Note that TensorFlow will print additional stuff before that last statement.)
@@ -73,36 +66,23 @@ 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
 `cache_dir=...` when you use methods like `from_pretrained`, these models will automatically be downloaded in the
-folder given by the shell environment variable ``TRANSFORMERS_CACHE``. The default value for it will be the Hugging
-Face cache home followed by ``/transformers/``. This is (by order of priority):
+folder given by the shell environment variable ``TRANSFORMERS_CACHE``. The default value for it will be the PyTorch
+cache home followed by ``/transformers/`` (even if you don't have PyTorch installed). This is (by order of priority):

-  * shell environment variable ``HF_HOME`` 
-  * shell environment variable ``XDG_CACHE_HOME`` + ``/huggingface/``
-  * default: ``~/.cache/huggingface/``
+  * shell environment variable ``TORCH_HOME``
+  * shell environment variable ``XDG_CACHE_HOME`` + ``/torch/``
+  * default: ``~/.cache/torch/``

 So if you don't have any specific environment variable set, the cache directory will be at
-``~/.cache/huggingface/transformers/``.
+``~/.cache/torch/transformers/``.

-**Note:** If you have set a shell environment variable for one of the predecessors of this library
+**Note:** If you have set a shell enviromnent variable for one of the predecessors of this library
 (``PYTORCH_TRANSFORMERS_CACHE`` or ``PYTORCH_PRETRAINED_BERT_CACHE``), those will be used if there is no shell
-environment variable for ``TRANSFORMERS_CACHE``.
+enviromnent variable for ``TRANSFORMERS_CACHE``.

 ### Note on model downloads (Continuous Integration or large-scale deployments)

@@ -117,6 +97,6 @@ You should check out our [swift-coreml-transformers](https://github.com/huggingf
 It contains a set of tools to convert PyTorch or TensorFlow 2.0 trained Transformer models (currently contains `GPT-2`, 
 `DistilGPT-2`, `BERT`, and `DistilBERT`) to CoreML models that run on iOS devices.

-At some point in the future, you'll be able to seamlessly move from pretraining or fine-tuning models in PyTorch or
+At some point in the future, you'll be able to seamlessly move from pre-training or fine-tuning models in PyTorch or
 TensorFlow 2.0 to productizing them in CoreML, or prototype a model or an app in CoreML then research its
 hyperparameters or architecture from PyTorch or TensorFlow 2.0. Super exciting!
@@ -1,50 +0,0 @@
-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`, 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.
-
-LogitsProcessor
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-A :class:`~transformers.LogitsProcessor` can be used to modify the prediction scores of a language model head for
-generation.
-
-.. autoclass:: transformers.LogitsProcessor
-    :members: __call__
-
-.. autoclass:: transformers.LogitsProcessorList
-    :members: __call__
-
-.. autoclass:: transformers.MinLengthLogitsProcessor
-    :members: __call__
-
-.. autoclass:: transformers.TemperatureLogitsWarper
-    :members: __call__
-
-.. autoclass:: transformers.RepetitionPenaltyLogitsProcessor
-    :members: __call__
-
-.. autoclass:: transformers.TopPLogitsWarper
-    :members: __call__
-
-.. autoclass:: transformers.TopKLogitsWarper
-    :members: __call__
-
-.. autoclass:: transformers.NoRepeatNGramLogitsProcessor
-    :members: __call__
-
-.. autoclass:: transformers.NoBadWordsLogitsProcessor
-    :members: __call__
-
-BeamSearch
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BeamScorer
-    :members: process, finalize
-
-.. autoclass:: transformers.BeamSearchScorer
-    :members: process, finalize
@@ -1,13 +1,13 @@
 Custom Layers and Utilities
-----------------------------------------------------------------------------------------------------------------------
+---------------------------

 This page lists all the custom layers used by the library, as well as the utility functions it provides for modeling.

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


-Pytorch custom modules
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+``Pytorch custom modules``
+~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.modeling_utils.Conv1D

@@ -29,8 +29,8 @@ Pytorch custom modules
    :members: forward


-PyTorch Helper Functions
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+``PyTorch Helper Functions``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autofunction:: transformers.apply_chunking_to_forward

@@ -42,8 +42,8 @@ PyTorch Helper Functions

 .. autofunction:: transformers.modeling_utils.prune_linear_layer

-TensorFlow custom layers
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+``TensorFlow custom layers``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.modeling_tf_utils.TFConv1D

@@ -54,8 +54,8 @@ TensorFlow custom layers
    :members: call


-TensorFlow loss functions
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+``TensorFlow loss functions``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.modeling_tf_utils.TFCausalLanguageModelingLoss
    :members:
@@ -76,8 +76,8 @@ TensorFlow loss functions
    :members:


-TensorFlow Helper Functions
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+``TensorFlow Helper Functions``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autofunction:: transformers.modeling_tf_utils.cast_bool_to_primitive

@@ -85,4 +85,4 @@ TensorFlow Helper Functions

 .. autofunction:: transformers.modeling_tf_utils.keras_serializable

-.. autofunction:: transformers.modeling_tf_utils.shape_list
+.. autofunction:: transformers.modeling_tf_utils.shape_list
@@ -1,40 +1,40 @@
-Utilities for pipelines
-----------------------------------------------------------------------------------------------------------------------
-
-This page lists all the utility functions the library provides for pipelines.
-
-Most of those are only useful if you are studying the code of the models in the library.
-
-
-Argument handling
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.pipelines.ArgumentHandler
-
-.. autoclass:: transformers.pipelines.ZeroShotClassificationArgumentHandler
-
-.. autoclass:: transformers.pipelines.QuestionAnsweringArgumentHandler
-
-
-Data format
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.pipelines.PipelineDataFormat
-    :members:
-
-.. autoclass:: transformers.pipelines.CsvPipelineDataFormat
-    :members:
-
-.. autoclass:: transformers.pipelines.JsonPipelineDataFormat
-    :members:
-
-.. autoclass:: transformers.pipelines.PipedPipelineDataFormat
-    :members:
-
-
-Utilities
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autofunction:: transformers.pipelines.get_framework
-
-.. autoclass:: transformers.pipelines.PipelineException
+Utilities for pipelines
+-----------------------
+
+This page lists all the utility functions the library provides for pipelines.
+
+Most of those are only useful if you are studying the code of the models in the library.
+
+
+Argument handling
+~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.pipelines.ArgumentHandler
+
+.. autoclass:: transformers.pipelines.ZeroShotClassificationArgumentHandler
+
+.. autoclass:: transformers.pipelines.QuestionAnsweringArgumentHandler
+
+
+Data format
+~~~~~~~~~~~
+
+.. autoclass:: transformers.pipelines.PipelineDataFormat
+    :members:
+
+.. autoclass:: transformers.pipelines.CsvPipelineDataFormat
+    :members:
+
+.. autoclass:: transformers.pipelines.JsonPipelineDataFormat
+    :members:
+
+.. autoclass:: transformers.pipelines.PipedPipelineDataFormat
+    :members:
+
+
+Utilities
+~~~~~~~~~
+
+.. autofunction:: transformers.pipelines.get_framework
+
+.. autoclass:: transformers.pipelines.PipelineException
@@ -1,39 +1,38 @@
-Utilities for Tokenizers
-----------------------------------------------------------------------------------------------------------------------
-
-This page lists all the utility functions used by the tokenizers, mainly the class
-:class:`~transformers.tokenization_utils_base.PreTrainedTokenizerBase` that implements the common methods between
-:class:`~transformers.PreTrainedTokenizer` and :class:`~transformers.PreTrainedTokenizerFast` and the mixin
-:class:`~transformers.tokenization_utils_base.SpecialTokensMixin`.
-
-Most of those are only useful if you are studying the code of the tokenizers in the library.
-
-PreTrainedTokenizerBase
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.tokenization_utils_base.PreTrainedTokenizerBase
-    :special-members: __call__
-    :members:
-
-
-SpecialTokensMixin
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.tokenization_utils_base.SpecialTokensMixin
-    :members:
-
-
-Enums and namedtuples
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.tokenization_utils_base.ExplicitEnum
-
-.. autoclass:: transformers.tokenization_utils_base.PaddingStrategy
-
-.. autoclass:: transformers.tokenization_utils_base.TensorType
-
-.. autoclass:: transformers.tokenization_utils_base.TruncationStrategy
-
-.. autoclass:: transformers.tokenization_utils_base.CharSpan
-
-.. autoclass:: transformers.tokenization_utils_base.TokenSpan
+Utilities for Tokenizers
+------------------------
+
+This page lists all the utility functions used by the tokenizers, mainly the class
+:class:`~transformers.tokenization_utils_base.PreTrainedTokenizerBase` that implements the common methods between
+:class:`~transformers.PreTrainedTokenizer` and :class:`~transformers.PreTrainedTokenizerFast` and the mixin
+:class:`~transformers.tokenization_utils_base.SpecialTokensMixin`.
+
+Most of those are only useful if you are studying the code of the tokenizers in the library.
+
+``PreTrainedTokenizerBase``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.tokenization_utils_base.PreTrainedTokenizerBase
+    :special-members: __call__
+    :members:
+
+
+``SpecialTokensMixin``
+~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.tokenization_utils_base.SpecialTokensMixin
+    :members:
+
+
+Enums and namedtuples
+~~~~~~~~~~~~~~~~~~~~~
+.. autoclass:: transformers.tokenization_utils_base.ExplicitEnum
+
+.. autoclass:: transformers.tokenization_utils_base.PaddingStrategy
+
+.. autoclass:: transformers.tokenization_utils_base.TensorType
+
+.. autoclass:: transformers.tokenization_utils_base.TruncationStrategy
+
+.. autoclass:: transformers.tokenization_utils_base.CharSpan
+
+.. autoclass:: transformers.tokenization_utils_base.TokenSpan
@@ -1,27 +0,0 @@
-Utilities for Trainer
-----------------------------------------------------------------------------------------------------------------------
-
-This page lists all the utility functions used by :class:`~transformers.Trainer`.
-
-Most of those are only useful if you are studying the code of the Trainer in the library.
-
-Utilities
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.EvalPrediction
-
-.. autofunction:: transformers.set_seed
-
-.. autofunction:: transformers.torch_distributed_zero_first
-
-
-Callbacks internals
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.trainer_callback.CallbackHandler
-
-Distributed Evaluation
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.trainer_pt_utils.DistributedTensorGatherer
-    :members:
@@ -1,75 +0,0 @@
-Callbacks
-----------------------------------------------------------------------------------------------------------------------
-
-Callbacks are objects that can customize the behavior of the training loop in the PyTorch
-:class:`~transformers.Trainer` (this feature is not yet implemented in TensorFlow) that can inspect the training loop
-state (for progress reporting, logging on TensorBoard or other ML platforms...) and take decisions (like early
-stopping).
-
-Callbacks are "read only" pieces of code, apart from the :class:`~transformers.TrainerControl` object they return, they
-cannot change anything in the training loop. For customizations that require changes in the training loop, you should
-subclass :class:`~transformers.Trainer` and override the methods you need (see :doc:`trainer` for examples).
-
-By default a :class:`~transformers.Trainer` will use the following callbacks:
-
- :class:`~transformers.DefaultFlowCallback` which handles the default behavior for logging, saving and evaluation.
- :class:`~transformers.PrinterCallback` or :class:`~transformers.ProgressCallback` to display progress and print the
-  logs (the first one is used if you deactivate tqdm through the :class:`~transformers.TrainingArguments`, otherwise
-  it's the second one).
- :class:`~transformers.integrations.TensorBoardCallback` if tensorboard is accessible (either through PyTorch >= 1.4
-  or tensorboardX).
- :class:`~transformers.integrations.WandbCallback` if `wandb <https://www.wandb.com/>`__ is installed.
- :class:`~transformers.integrations.CometCallback` if `comet_ml <https://www.comet.ml/site/>`__ is installed.
- :class:`~transformers.integrations.MLflowCallback` if `mlflow <https://www.mlflow.org/>`__ is installed.
- :class:`~transformers.integrations.AzureMLCallback` if `azureml-sdk <https://pypi.org/project/azureml-sdk/>`__ is
-  installed.
-
-The main class that implements callbacks is :class:`~transformers.TrainerCallback`. It gets the
-:class:`~transformers.TrainingArguments` used to instantiate the :class:`~transformers.Trainer`, can access that
-Trainer's internal state via :class:`~transformers.TrainerState`, and can take some actions on the training loop via
-:class:`~transformers.TrainerControl`.
-
-
-Available Callbacks
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Here is the list of the available :class:`~transformers.TrainerCallback` in the library:
-
-.. autoclass:: transformers.integrations.CometCallback
-    :members: setup
-
-.. autoclass:: transformers.DefaultFlowCallback
-
-.. autoclass:: transformers.PrinterCallback
-
-.. autoclass:: transformers.ProgressCallback
-
-.. autoclass:: transformers.integrations.TensorBoardCallback
-
-.. autoclass:: transformers.integrations.WandbCallback
-    :members: setup
-
-.. autoclass:: transformers.integrations.MLflowCallback
-    :members: setup
-
-.. autoclass:: transformers.integrations.AzureMLCallback
-
-TrainerCallback
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TrainerCallback
-    :members:
-
-
-TrainerState
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TrainerState
-    :members:
-
-
-TrainerControl
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TrainerControl
-    :members:
@@ -1,5 +1,5 @@
 Configuration
-----------------------------------------------------------------------------------------------------------------------
+----------------------------------------------------

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


 PretrainedConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.PretrainedConfig
    :members:
@@ -1,23 +1,21 @@
 Logging
-----------------------------------------------------------------------------------------------------------------------
+-------

 🤗 Transformers has a centralized logging system, so that you can setup the verbosity of the library easily.

 Currently the default verbosity of the library is ``WARNING``.

-To change the level of verbosity, just use one of the direct setters. For instance, here is how to change the verbosity
-to the INFO level.
+To change the level of verbosity, just use one of the direct setters. For instance, here is how to change the verbosity to the INFO level.

 .. code-block:: python

    import transformers
    transformers.logging.set_verbosity_info()

-You can also use the environment variable ``TRANSFORMERS_VERBOSITY`` to override the default verbosity. You can set it
-to one of the following: ``debug``, ``info``, ``warning``, ``error``, ``critical``. For example:
+You can also use the environment variable ``TRANSFORMERS_VERBOSITY`` to override the default verbosity. You can set it to one of the following: ``debug``, ``info``, ``warning``, ``error``, ``critical``. For example:

 .. code-block:: bash
-
+               
    TRANSFORMERS_VERBOSITY=error ./myprogram.py

 All the methods of this logging module are documented below, the main ones are
@@ -34,7 +32,7 @@ verbose to the most verbose), those levels (with their corresponding int values
 - :obj:`transformers.logging.DEBUG` (int value, 10): report all information.

 Base setters
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~

 .. autofunction:: transformers.logging.set_verbosity_error

@@ -45,14 +43,10 @@ Base setters
 .. autofunction:: transformers.logging.set_verbosity_debug

 Other functions
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~

 .. autofunction:: transformers.logging.get_verbosity

 .. autofunction:: transformers.logging.set_verbosity

 .. autofunction:: transformers.logging.get_logger
-
-.. autofunction:: transformers.logging.enable_explicit_format
-
-.. autofunction:: transformers.logging.reset_format
@@ -1,5 +1,5 @@
 Models
-----------------------------------------------------------------------------------------------------------------------
+----------------------------------------------------

 The base classes :class:`~transformers.PreTrainedModel` and :class:`~transformers.TFPreTrainedModel` implement the
 common methods for loading/saving a model either from a local file or directory, or from a pretrained model
@@ -17,39 +17,39 @@ for text generation, :class:`~transformers.generation_utils.GenerationMixin` (fo
 :class:`~transformers.generation_tf_utils.TFGenerationMixin` (for the TensorFlow models)


-PreTrainedModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+``PreTrainedModel``
+~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.PreTrainedModel
    :members:


-ModuleUtilsMixin
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+``ModuleUtilsMixin``
+~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.modeling_utils.ModuleUtilsMixin
    :members:


-TFPreTrainedModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+``TFPreTrainedModel``
+~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFPreTrainedModel
    :members:


-TFModelUtilsMixin
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+``TFModelUtilsMixin``
+~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.modeling_tf_utils.TFModelUtilsMixin
    :members:


-Generation
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Generative models
+~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.generation_utils.GenerationMixin
    :members:

 .. autoclass:: transformers.generation_tf_utils.TFGenerationMixin
-    :members:
+    :members:
@@ -1,5 +1,5 @@
 Optimization
-----------------------------------------------------------------------------------------------------------------------
+----------------------------------------------------

 The ``.optimization`` module provides:

@@ -7,29 +7,29 @@ The ``.optimization`` module provides:
 - several schedules in the form of schedule objects that inherit from ``_LRSchedule``:
 - a gradient accumulation class to accumulate the gradients of multiple batches

-AdamW (PyTorch)
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+``AdamW`` (PyTorch)
+~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.AdamW
    :members:

-AdaFactor (PyTorch)
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+``AdaFactor`` (PyTorch)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.Adafactor

-AdamWeightDecay (TensorFlow)
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+``AdamWeightDecay`` (TensorFlow)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.AdamWeightDecay

 .. autofunction:: transformers.create_optimizer

 Schedules
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~

 Learning Rate Schedules (Pytorch)
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

 .. autofunction:: transformers.get_constant_schedule

@@ -62,16 +62,16 @@ Learning Rate Schedules (Pytorch)
    :target: /imgs/warmup_linear_schedule.png
    :alt:

-Warmup (TensorFlow)
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+``Warmup`` (TensorFlow)
+^^^^^^^^^^^^^^^^^^^^^^^

 .. autoclass:: transformers.WarmUp
    :members:

 Gradient Strategies
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~

-GradientAccumulator (TensorFlow)
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+``GradientAccumulator`` (TensorFlow)
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

 .. autoclass:: transformers.GradientAccumulator
@@ -1,5 +1,5 @@
 Model outputs
-----------------------------------------------------------------------------------------------------------------------
+-------------

 PyTorch models have outputs that are instances of subclasses of :class:`~transformers.file_utils.ModelOutput`. Those
 are data structures containing all the information returned by the model, but that can also be used as tuples or
@@ -44,253 +44,98 @@ values. Here for instance, it has two keys that are ``loss`` and ``logits``.
 We document here the generic model outputs that are used by more than one model type. Specific output types are
 documented on their corresponding model page.

-ModelOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+``ModelOutput``
+~~~~~~~~~~~~~~~

 .. autoclass:: transformers.file_utils.ModelOutput
    :members:

-
-BaseModelOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+``BaseModelOutput``
+~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.modeling_outputs.BaseModelOutput
    :members:

-
-BaseModelOutputWithPooling
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+``BaseModelOutputWithPooling``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.modeling_outputs.BaseModelOutputWithPooling
    :members:

-
-BaseModelOutputWithCrossAttentions
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_outputs.BaseModelOutputWithCrossAttentions
-    :members:
-
-
-BaseModelOutputWithPoolingAndCrossAttentions
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions
-    :members:
-
-
-BaseModelOutputWithPast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+``BaseModelOutputWithPast``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.modeling_outputs.BaseModelOutputWithPast
    :members:

-
-BaseModelOutputWithPastAndCrossAttentions
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions
-    :members:
-
-
-Seq2SeqModelOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+``Seq2SeqModelOutput``
+~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.modeling_outputs.Seq2SeqModelOutput
    :members:

-
-CausalLMOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+``CausalLMOutput``
+~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.modeling_outputs.CausalLMOutput
    :members:

-
-CausalLMOutputWithCrossAttentions
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_outputs.CausalLMOutputWithCrossAttentions
-    :members:
-
-
-CausalLMOutputWithPastAndCrossAttentions
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_outputs.CausalLMOutputWithPastAndCrossAttentions
-    :members:
-
-
-CausalLMOutputWithPast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+``CausalLMOutputWithPast``
+~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.modeling_outputs.CausalLMOutputWithPast
    :members:

-
-MaskedLMOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+``MaskedLMOutput``
+~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.modeling_outputs.MaskedLMOutput
    :members:

-
-Seq2SeqLMOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+``Seq2SeqLMOutput``
+~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.modeling_outputs.Seq2SeqLMOutput
    :members:

-
-NextSentencePredictorOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+``NextSentencePredictorOutput``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.modeling_outputs.NextSentencePredictorOutput
    :members:

-
-SequenceClassifierOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+``SequenceClassifierOutput``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.modeling_outputs.SequenceClassifierOutput
    :members:

-
-Seq2SeqSequenceClassifierOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+``Seq2SeqSequenceClassifierOutput``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput
    :members:

-
-MultipleChoiceModelOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+``MultipleChoiceModelOutput``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.modeling_outputs.MultipleChoiceModelOutput
    :members:

-
-TokenClassifierOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+``TokenClassifierOutput``
+~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.modeling_outputs.TokenClassifierOutput
    :members:

-
-QuestionAnsweringModelOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+``QuestionAnsweringModelOutput``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.modeling_outputs.QuestionAnsweringModelOutput
    :members:

-
-Seq2SeqQuestionAnsweringModelOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+``Seq2SeqQuestionAnsweringModelOutput``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput
    :members:
-
-
-TFBaseModelOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_tf_outputs.TFBaseModelOutput
-    :members:
-
-
-TFBaseModelOutputWithPooling
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling
-    :members:
-
-
-TFBaseModelOutputWithPast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_tf_outputs.TFBaseModelOutputWithPast
-    :members:
-
-
-TFSeq2SeqModelOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_tf_outputs.TFSeq2SeqModelOutput
-    :members:
-
-
-TFCausalLMOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_tf_outputs.TFCausalLMOutput
-    :members:
-
-
-TFCausalLMOutputWithPast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_tf_outputs.TFCausalLMOutputWithPast
-    :members:
-
-
-TFMaskedLMOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_tf_outputs.TFMaskedLMOutput
-    :members:
-
-
-TFSeq2SeqLMOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_tf_outputs.TFSeq2SeqLMOutput
-    :members:
-
-
-TFNextSentencePredictorOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_tf_outputs.TFNextSentencePredictorOutput
-    :members:
-
-
-TFSequenceClassifierOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_tf_outputs.TFSequenceClassifierOutput
-    :members:
-
-
-TFSeq2SeqSequenceClassifierOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_tf_outputs.TFSeq2SeqSequenceClassifierOutput
-    :members:
-
-
-TFMultipleChoiceModelOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput
-    :members:
-
-
-TFTokenClassifierOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_tf_outputs.TFTokenClassifierOutput
-    :members:
-
-
-TFQuestionAnsweringModelOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput
-    :members:
-
-
-TFSeq2SeqQuestionAnsweringModelOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_tf_outputs.TFSeq2SeqQuestionAnsweringModelOutput
-    :members:
@@ -1,8 +1,8 @@
 Pipelines
-----------------------------------------------------------------------------------------------------------------------
+----------------------------------------------------

-The pipelines are a great and easy way to use models for inference. These pipelines are objects that abstract most of
-the complex code from the library, offering a simple API dedicated to several tasks, including Named Entity
+The pipelines are a great and easy way to use models for inference. These pipelines are objects that abstract most
+of the complex code from the library, offering a simple API dedicated to several tasks, including Named Entity
 Recognition, Masked Language Modeling, Sentiment Analysis, Feature Extraction and Question Answering. See the
 :doc:`task summary <../task_summary>` for examples of use.

@@ -24,19 +24,19 @@ There are two categories of pipeline abstractions to be aware about:
    - :class:`~transformers.Text2TextGenerationPipeline`

 The pipeline abstraction
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

-The `pipeline` abstraction is a wrapper around all the other available pipelines. It is instantiated as any other
-pipeline but requires an additional argument which is the `task`.
+The `pipeline` abstraction is a wrapper around all the other available pipelines. It is instantiated as any
+other pipeline but requires an additional argument which is the `task`.

 .. autofunction:: transformers.pipeline


 The task specific pipelines
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 ConversationalPipeline
-=======================================================================================================================
+==========================================

 .. autoclass:: transformers.Conversation

@@ -45,76 +45,76 @@ ConversationalPipeline
    :members:

 FeatureExtractionPipeline
-=======================================================================================================================
+==========================================

 .. autoclass:: transformers.FeatureExtractionPipeline
    :special-members: __call__
    :members:

 FillMaskPipeline
-=======================================================================================================================
+==========================================

 .. autoclass:: transformers.FillMaskPipeline
    :special-members: __call__
    :members:

 NerPipeline
-=======================================================================================================================
+==========================================

 This class is an alias of the :class:`~transformers.TokenClassificationPipeline` defined below. Please refer to that
 pipeline for documentation and usage examples.

 QuestionAnsweringPipeline
-=======================================================================================================================
+==========================================

 .. autoclass:: transformers.QuestionAnsweringPipeline
    :special-members: __call__
    :members:

 SummarizationPipeline
-=======================================================================================================================
+==========================================

 .. autoclass:: transformers.SummarizationPipeline
    :special-members: __call__
    :members:

 TextClassificationPipeline
-=======================================================================================================================
+==========================================

 .. autoclass:: transformers.TextClassificationPipeline
    :special-members: __call__
    :members:

 TextGenerationPipeline
-=======================================================================================================================
+==========================================

 .. autoclass:: transformers.TextGenerationPipeline
    :special-members: __call__
    :members:

 Text2TextGenerationPipeline
-=======================================================================================================================
+==========================================

 .. autoclass:: transformers.Text2TextGenerationPipeline
    :special-members: __call__
    :members:

 TokenClassificationPipeline
-=======================================================================================================================
+==========================================

 .. autoclass:: transformers.TokenClassificationPipeline
    :special-members: __call__
    :members:

 ZeroShotClassificationPipeline
-=======================================================================================================================
+==========================================

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

 Parent class: :obj:`Pipeline`
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.Pipeline
    :members:
@@ -1,15 +1,15 @@
 Processors
-----------------------------------------------------------------------------------------------------------------------
+----------------------------------------------------

 This library includes processors for several traditional tasks. These processors can be used to process a dataset into
 examples that can be fed to a model.

 Processors
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

 All processors follow the same architecture which is that of the
-:class:`~transformers.data.processors.utils.DataProcessor`. The processor returns a list of
-:class:`~transformers.data.processors.utils.InputExample`. These
+:class:`~transformers.data.processors.utils.DataProcessor`. The processor returns a list
+of :class:`~transformers.data.processors.utils.InputExample`. These
 :class:`~transformers.data.processors.utils.InputExample` can be converted to
 :class:`~transformers.data.processors.utils.InputFeatures` in order to be fed to the model.

@@ -26,18 +26,16 @@ All processors follow the same architecture which is that of the


 GLUE
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

-`General Language Understanding Evaluation (GLUE) <https://gluebenchmark.com/>`__ is a benchmark that evaluates the
-performance of models across a diverse set of existing NLU tasks. It was released together with the paper `GLUE: A
-multi-task benchmark and analysis platform for natural language understanding
-<https://openreview.net/pdf?id=rJ4km2R5t7>`__
+`General Language Understanding Evaluation (GLUE) <https://gluebenchmark.com/>`__ is a benchmark that evaluates
+the performance of models across a diverse set of existing NLU tasks. It was released together with the paper
+`GLUE: A multi-task benchmark and analysis platform for natural language understanding <https://openreview.net/pdf?id=rJ4km2R5t7>`__

-This library hosts a total of 10 processors for the following tasks: MRPC, MNLI, MNLI (mismatched), CoLA, SST2, STSB,
-QQP, QNLI, RTE and WNLI.
+This library hosts a total of 10 processors for the following tasks: MRPC, MNLI, MNLI (mismatched),
+CoLA, SST2, STSB, QQP, QNLI, RTE and WNLI.

 Those processors are:
-
    - :class:`~transformers.data.processors.utils.MrpcProcessor`
    - :class:`~transformers.data.processors.utils.MnliProcessor`
    - :class:`~transformers.data.processors.utils.MnliMismatchedProcessor`
@@ -48,55 +46,51 @@ Those processors are:
    - :class:`~transformers.data.processors.utils.RteProcessor`
    - :class:`~transformers.data.processors.utils.WnliProcessor`

-Additionally, the following method can be used to load values from a data file and convert them to a list of
+Additionally, the following method  can be used to load values from a data file and convert them to a list of
 :class:`~transformers.data.processors.utils.InputExample`.

 .. automethod:: transformers.data.processors.glue.glue_convert_examples_to_features

 Example usage
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^

-An example using these processors is given in the `run_glue.py
-<https://github.com/huggingface/pytorch-transformers/blob/master/examples/text-classification/run_glue.py>`__ script.
+An example using these processors is given in the `run_glue.py <https://github.com/huggingface/pytorch-transformers/blob/master/examples/text-classification/run_glue.py>`__ script.


 XNLI
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

-`The Cross-Lingual NLI Corpus (XNLI) <https://www.nyu.edu/projects/bowman/xnli/>`__ is a benchmark that evaluates the
-quality of cross-lingual text representations. XNLI is crowd-sourced dataset based on `MultiNLI
-<http://www.nyu.edu/projects/bowman/multinli/>`: pairs of text are labeled with textual entailment annotations for 15
-different languages (including both high-resource language such as English and low-resource languages such as Swahili).
+`The Cross-Lingual NLI Corpus (XNLI) <https://www.nyu.edu/projects/bowman/xnli/>`__ is a benchmark that evaluates
+the quality of cross-lingual text representations. 
+XNLI is crowd-sourced dataset based on `MultiNLI <http://www.nyu.edu/projects/bowman/multinli/>`: pairs of text are labeled with textual entailment 
+annotations for 15 different languages (including both high-resource language such as English and low-resource languages such as Swahili).

-It was released together with the paper `XNLI: Evaluating Cross-lingual Sentence Representations
-<https://arxiv.org/abs/1809.05053>`__
+It was released together with the paper
+`XNLI: Evaluating Cross-lingual Sentence Representations <https://arxiv.org/abs/1809.05053>`__

 This library hosts the processor to load the XNLI data:
-
    - :class:`~transformers.data.processors.utils.XnliProcessor`

 Please note that since the gold labels are available on the test set, evaluation is performed on the test set.

-An example using these processors is given in the `run_xnli.py
-<https://github.com/huggingface/pytorch-transformers/blob/master/examples/text-classification/run_xnli.py>`__ script.
+An example using these processors is given in the
+`run_xnli.py <https://github.com/huggingface/pytorch-transformers/blob/master/examples/text-classification/run_xnli.py>`__ script.


 SQuAD
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

-`The Stanford Question Answering Dataset (SQuAD) <https://rajpurkar.github.io/SQuAD-explorer//>`__ is a benchmark that
-evaluates the performance of models on question answering. Two versions are available, v1.1 and v2.0. The first version
-(v1.1) was released together with the paper `SQuAD: 100,000+ Questions for Machine Comprehension of Text
-<https://arxiv.org/abs/1606.05250>`__. The second version (v2.0) was released alongside the paper `Know What You Don't
-Know: Unanswerable Questions for SQuAD <https://arxiv.org/abs/1806.03822>`__.
+`The Stanford Question Answering Dataset (SQuAD) <https://rajpurkar.github.io/SQuAD-explorer//>`__ is a benchmark that evaluates
+the performance of models on question answering. Two versions are available, v1.1 and v2.0. The first version (v1.1) was released together with the paper
+`SQuAD: 100,000+ Questions for Machine Comprehension of Text <https://arxiv.org/abs/1606.05250>`__. The second version (v2.0) was released alongside 
+the paper `Know What You Don't Know: Unanswerable Questions for SQuAD <https://arxiv.org/abs/1806.03822>`__.

 This library hosts a processor for each of the two versions:

 Processors
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^

 Those processors are:
-
    - :class:`~transformers.data.processors.utils.SquadV1Processor`
    - :class:`~transformers.data.processors.utils.SquadV2Processor`

@@ -105,21 +99,20 @@ They both inherit from the abstract class :class:`~transformers.data.processors.
 .. autoclass:: transformers.data.processors.squad.SquadProcessor
    :members:

-Additionally, the following method can be used to convert SQuAD examples into
-:class:`~transformers.data.processors.utils.SquadFeatures` that can be used as model inputs.
+Additionally, the following method can be used to convert SQuAD examples into :class:`~transformers.data.processors.utils.SquadFeatures`
+that can be used as model inputs.

 .. automethod:: transformers.data.processors.squad.squad_convert_examples_to_features

-These processors as well as the aforementionned method can be used with files containing the data as well as with the
-`tensorflow_datasets` package. Examples are given below.
+These processors as well as the aforementionned method can be used with files containing the data as well as with the `tensorflow_datasets` package.
+Examples are given below.


 Example usage
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
+^^^^^^^^^^^^^^^^^^^^^^^^^
 Here is an example using the processors as well as the conversion method using data files:

-.. code-block::
+Example::

    # Loading a V2 processor
    processor = SquadV2Processor()
@@ -140,7 +133,7 @@ Here is an example using the processors as well as the conversion method using d

 Using `tensorflow_datasets` is as easy as using a data file:

-.. code-block::
+Example::

    # tensorflow_datasets only handle Squad V1.
    tfds_examples = tfds.load("squad")
@@ -156,5 +149,5 @@ Using `tensorflow_datasets` is as easy as using a data file:
    )


-Another example using these processors is given in the `run_squad.py
-<https://github.com/huggingface/transformers/blob/master/examples/question-answering/run_squad.py>`__ script.
+Another example using these processors is given in the
+`run_squad.py <https://github.com/huggingface/transformers/blob/master/examples/question-answering/run_squad.py>`__ script.
@@ -1,5 +1,5 @@
 Tokenizer
-----------------------------------------------------------------------------------------------------------------------
+----------------------------------------------------

 A tokenizer is in charge of preparing the inputs for a model. The library contains tokenizers for all the models. Most
 of the tokenizers are available in two flavors: a full python implementation and a "Fast" implementation based on the
@@ -29,32 +29,31 @@ methods for using all the tokenizers:

 :class:`~transformers.BatchEncoding` holds the output of the tokenizer's encoding methods (``__call__``,
 ``encode_plus`` and ``batch_encode_plus``) and is derived from a Python dictionary. When the tokenizer is a pure python
-tokenizer, this class behaves just like a standard python dictionary and holds the various model inputs computed by
-these methods (``input_ids``, ``attention_mask``...). When the tokenizer is a "Fast" tokenizer (i.e., backed by
-HuggingFace `tokenizers library <https://github.com/huggingface/tokenizers>`__), this class provides in addition
-several advanced alignment methods which can be used to map between the original string (character and words) and the
-token space (e.g., getting the index of the token comprising a given character or the span of characters corresponding
-to a given token).
+tokenizer, this class behaves just like a standard python dictionary and holds the various model inputs computed by these
+methods (``input_ids``, ``attention_mask``...). When the tokenizer is a "Fast" tokenizer (i.e., backed by HuggingFace
+`tokenizers library <https://github.com/huggingface/tokenizers>`__), this class provides in addition several advanced
+alignment methods which can be used to map between the original string (character and words) and the token space (e.g.,
+getting the index of the token comprising a given character or the span of characters corresponding to a given token).


-PreTrainedTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+``PreTrainedTokenizer``
+~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.PreTrainedTokenizer
    :special-members: __call__
    :members:


-PreTrainedTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+``PreTrainedTokenizerFast``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.PreTrainedTokenizerFast
    :special-members: __call__
    :members:


-BatchEncoding
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+``BatchEncoding``
+~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.BatchEncoding
    :members:
@@ -1,72 +1,75 @@
-Trainer
-----------------------------------------------------------------------------------------------------------------------
-
-The :class:`~transformers.Trainer` and :class:`~transformers.TFTrainer` classes provide an API for feature-complete
-training in most standard use cases. It's used in most of the :doc:`example scripts <../examples>`.
-
-Before instantiating your :class:`~transformers.Trainer`/:class:`~transformers.TFTrainer`, create a
-:class:`~transformers.TrainingArguments`/:class:`~transformers.TFTrainingArguments` to access all the points of
-customization during training.
-
-The API supports distributed training on multiple GPUs/TPUs, mixed precision through `NVIDIA Apex
-<https://github.com/NVIDIA/apex>`__ for PyTorch and :obj:`tf.keras.mixed_precision` for TensorFlow.
-
-Both :class:`~transformers.Trainer` and :class:`~transformers.TFTrainer` contain the basic training loop supporting the
-previous features. To inject custom behavior you can subclass them and override the following methods:
-
- **get_train_dataloader**/**get_train_tfdataset** -- Creates the training DataLoader (PyTorch) or TF Dataset.
- **get_eval_dataloader**/**get_eval_tfdataset** -- Creates the evaluation DataLoader (PyTorch) or TF Dataset.
- **get_test_dataloader**/**get_test_tfdataset** -- Creates the test DataLoader (PyTorch) or TF Dataset.
- **log** -- Logs information on the various objects watching training.
- **create_optimizer_and_scheduler** -- Setups the optimizer and learning rate scheduler if they were not passed at
-  init.
- **compute_loss** - Computes the loss on a batch of training inputs.
- **training_step** -- Performs a training step.
- **prediction_step** -- Performs an evaluation/test step.
- **run_model** (TensorFlow only) -- Basic pass through the model.
- **evaluate** -- Runs an evaluation loop and returns metrics.
- **predict** -- Returns predictions (with metrics if labels are available) on a test set.
-
-Here is an example of how to customize :class:`~transformers.Trainer` using a custom loss function:
-
-.. code-block:: python
-
-    from transformers import Trainer
-    class MyTrainer(Trainer):
-        def compute_loss(self, model, inputs):
-            labels = inputs.pop("labels")
-            outputs = model(**inputs)
-            logits = outputs[0]
-            return my_custom_loss(logits, labels)
-
-Another way to customize the training loop behavior for the PyTorch :class:`~transformers.Trainer` is to use
-:doc:`callbacks <callback>` that can inspect the training loop state (for progress reporting, logging on TensorBoard or
-other ML platforms...) and take decisions (like early stopping).
-
-
-Trainer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.Trainer
-    :members:
-
-
-TFTrainer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFTrainer
-    :members:
-
-
-TrainingArguments
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TrainingArguments
-    :members:
-
-
-TFTrainingArguments
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFTrainingArguments
-    :members:
+Trainer
+----------
+
+The :class:`~transformers.Trainer` and :class:`~transformers.TFTrainer` classes provide an API for feature-complete
+training in most standard use cases. It's used in most of the :doc:`example scripts <../examples>`.
+
+Before instantiating your :class:`~transformers.Trainer`/:class:`~transformers.TFTrainer`, create a 
+:class:`~transformers.TrainingArguments`/:class:`~transformers.TFTrainingArguments` to access all the points of
+customization during training.
+
+The API supports distributed training on multiple GPUs/TPUs, mixed precision through `NVIDIA Apex
+<https://github.com/NVIDIA/apex>`__ for PyTorch and :obj:`tf.keras.mixed_precision` for TensorFlow.
+
+Both :class:`~transformers.Trainer` and :class:`~transformers.TFTrainer` contain the basic training loop supporting the
+previous features. To inject custom behavior you can subclass them and override the following methods:
+
+- **get_train_dataloader**/**get_train_tfdataset** -- Creates the training DataLoader (PyTorch) or TF Dataset.
+- **get_eval_dataloader**/**get_eval_tfdataset** -- Creates the evaulation DataLoader (PyTorch) or TF Dataset.
+- **get_test_dataloader**/**get_test_tfdataset** -- Creates the test DataLoader (PyTorch) or TF Dataset.
+- **log** -- Logs information on the various objects watching training.
+- **setup_wandb** -- Setups wandb (see `here <https://docs.wandb.com/huggingface>`__ for more information).
+- **create_optimizer_and_scheduler** -- Setups the optimizer and learning rate scheduler if they were not passed at
+  init.
+- **compute_loss** - Computes the loss on a batch of training inputs.
+- **training_step** -- Performs a training step.
+- **prediction_step** -- Performs an evaluation/test step.
+- **run_model** (TensorFlow only) -- Basic pass through the model.
+- **evaluate** -- Runs an evaluation loop and returns metrics.
+- **predict** -- Returns predictions (with metrics if labels are available) on a test set.
+
+Here is an example of how to customize :class:`~transformers.Trainer` using a custom loss function:
+
+.. code-block:: python
+
+    from transformers import Trainer
+    class MyTrainer(Trainer):
+        def compute_loss(self, model, inputs):
+            labels = inputs.pop("labels")
+            outputs = models(**inputs)
+            logits = outputs[0]
+            return my_custom_loss(logits, labels)
+
+
+``Trainer`` 
+~~~~~~~~~~~
+
+.. autoclass:: transformers.Trainer
+    :members:
+
+``TFTrainer`` 
+~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFTrainer
+    :members:
+
+``TrainingArguments``
+~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TrainingArguments
+    :members:
+
+``TFTrainingArguments``
+~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFTrainingArguments
+    :members:
+
+Utilities
+~~~~~~~~~
+
+.. autoclass:: transformers.EvalPrediction
+
+.. autofunction:: transformers.set_seed
+
+.. autofunction:: transformers.torch_distributed_zero_first
@@ -1,170 +1,5 @@
 # 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.
@@ -185,7 +20,7 @@ Here is a quick summary of what you should take care of when migrating from `pyt

 The main breaking change when migrating from `pytorch-pretrained-bert` to 🤗 Transformers is that the models forward method always outputs a `tuple` with various elements depending on the model and the configuration parameters.

-The exact content of the tuples for each model are detailed in the models' docstrings and the [documentation](https://huggingface.co/transformers/).
+The exact content of the tuples for each model are detailled in the models' docstrings and the [documentation](https://huggingface.co/transformers/).

 In pretty much every case, you will be fine by taking the first element of the output as the output you previously used in `pytorch-pretrained-bert`.

@@ -274,7 +109,7 @@ for batch in train_data:
    loss.backward()
    optimizer.step()

-### In 🤗 Transformers, optimizer and schedules are split and instantiated like this:
+### In 🤗 Transformers, optimizer and schedules are splitted and instantiated like this:
 optimizer = AdamW(model.parameters(), lr=lr, correct_bias=False)  # To reproduce BertAdam specific behavior set correct_bias=False
 scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps)  # PyTorch scheduler
 ### and used like this:
@@ -1,16 +1,15 @@
 ALBERT
-----------------------------------------------------------------------------------------------------------------------
+----------------------------------------------------

 Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

-The ALBERT model was proposed in `ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
-<https://arxiv.org/abs/1909.11942>`__ by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma,
-Radu Soricut. It presents two parameter-reduction techniques to lower memory consumption and increase the training
-speed of BERT:
+The ALBERT model was proposed in `ALBERT: A Lite BERT for Self-supervised Learning of Language Representations <https://arxiv.org/abs/1909.11942>`_
+by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut. It presents
+two parameter-reduction techniques to lower memory consumption and increase the training speed of BERT:

- Splitting the embedding matrix into two smaller matrices.
- Using repeating layers split among groups.
+- Splitting the embedding matrix into two smaller matrices
+- Using repeating layers split among groups

 The abstract from the paper is the following:

@@ -19,29 +18,29 @@ downstream tasks. However, at some point further model increases become harder d
 longer training times, and unexpected model degradation. To address these problems, we present two parameter-reduction
 techniques to lower memory consumption and increase the training speed of BERT. Comprehensive empirical evidence shows
 that our proposed methods lead to models that scale much better compared to the original BERT. We also use a
-self-supervised loss that focuses on modeling inter-sentence coherence, and show it consistently helps downstream tasks
-with multi-sentence inputs. As a result, our best model establishes new state-of-the-art results on the GLUE, RACE, and
-SQuAD benchmarks while having fewer parameters compared to BERT-large.*
+self-supervised loss that focuses on modeling inter-sentence coherence, and show it consistently helps downstream
+tasks with multi-sentence inputs. As a result, our best model establishes new state-of-the-art results on the GLUE,
+RACE, and SQuAD benchmarks while having fewer parameters compared to BERT-large.*

 Tips:

- ALBERT is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather
-  than the left.
+- ALBERT is a model with absolute position embeddings so it's usually advised to pad the inputs on
+  the right rather than the left.
 - ALBERT uses repeating layers which results in a small memory footprint, however the computational cost remains
  similar to a BERT-like architecture with the same number of hidden layers as it has to iterate through the same
  number of (repeating) layers.

-The original code can be found `here <https://github.com/google-research/ALBERT>`__.
+The original code can be found `here <https://github.com/google-research/ALBERT>`_.

 AlbertConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.AlbertConfig
    :members:


 AlbertTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.AlbertTokenizer
    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
@@ -49,108 +48,108 @@ AlbertTokenizer


 Albert specific outputs
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~

-.. autoclass:: transformers.models.albert.modeling_albert.AlbertForPreTrainingOutput
+.. autoclass:: transformers.modeling_albert.AlbertForPreTrainingOutput
    :members:

-.. autoclass:: transformers.models.albert.modeling_tf_albert.TFAlbertForPreTrainingOutput
+.. autoclass:: transformers.modeling_tf_albert.TFAlbertForPreTrainingOutput
    :members:


 AlbertModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.AlbertModel
-    :members: forward
+    :members:


 AlbertForPreTraining
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.AlbertForPreTraining
-    :members: forward
+    :members:


 AlbertForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.AlbertForMaskedLM
-    :members: forward
+    :members:


 AlbertForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.AlbertForSequenceClassification
-    :members: forward
+    :members:


 AlbertForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.AlbertForMultipleChoice
    :members:


 AlbertForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.AlbertForTokenClassification
-    :members: forward
+    :members:


 AlbertForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.AlbertForQuestionAnswering
-    :members: forward
+    :members:


 TFAlbertModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFAlbertModel
-    :members: call
+    :members:


 TFAlbertForPreTraining
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFAlbertForPreTraining
-    :members: call
+    :members:


 TFAlbertForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFAlbertForMaskedLM
-    :members: call
+    :members:


 TFAlbertForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFAlbertForSequenceClassification
-    :members: call
+    :members:


 TFAlbertForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFAlbertForMultipleChoice
-    :members: call
+    :members:


 TFAlbertForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFAlbertForTokenClassification
-    :members: call
+    :members:


 TFAlbertForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFAlbertForQuestionAnswering
-    :members: call
+    :members:
@@ -1,9 +1,10 @@
-Auto Classes
-----------------------------------------------------------------------------------------------------------------------
+AutoClasses
+-----------

 In many cases, the architecture you want to use can be guessed from the name or the path of the pretrained model you
-are supplying to the :obj:`from_pretrained()` method. AutoClasses are here to do this job for you so that you
-automatically retrieve the relevant model given the name/path to the pretrained weights/config/vocabulary.
+are supplying to the :obj:`from_pretrained()` method.
+AutoClasses are here to do this job for you so that you automatically retrieve the relevant model given the name/path
+to the pretrained weights/config/vocabulary.

 Instantiating one of :class:`~transformers.AutoConfig`, :class:`~transformers.AutoModel`, and
 :class:`~transformers.AutoTokenizer` will directly create a class of the relevant architecture. For instance
@@ -19,147 +20,112 @@ There is one class of :obj:`AutoModel` for each task, and for each backend (PyTo


 AutoConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~

 .. autoclass:: transformers.AutoConfig
    :members:


 AutoTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~

 .. autoclass:: transformers.AutoTokenizer
    :members:


 AutoModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~

 .. autoclass:: transformers.AutoModel
    :members:


 AutoModelForPreTraining
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.AutoModelForPreTraining
    :members:


-AutoModelForCausalLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+AutoModelWithLMHead
+~~~~~~~~~~~~~~~~~~~

-.. autoclass:: transformers.AutoModelForCausalLM
-    :members:
-
-
-AutoModelForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AutoModelForMaskedLM
-    :members:
-
-
-AutoModelForSeq2SeqLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AutoModelForSeq2SeqLM
+.. autoclass:: transformers.AutoModelWithLMHead
    :members:


 AutoModelForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.AutoModelForSequenceClassification
    :members:


 AutoModelForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.AutoModelForMultipleChoice
    :members:


-AutoModelForNextSentencePrediction
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AutoModelForNextSentencePrediction
-    :members:
-
-
 AutoModelForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.AutoModelForTokenClassification
    :members:


 AutoModelForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.AutoModelForQuestionAnswering
    :members:


 TFAutoModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~

 .. autoclass:: transformers.TFAutoModel
    :members:


 TFAutoModelForPreTraining
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFAutoModelForPreTraining
    :members:


-TFAutoModelForCausalLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+TFAutoModelWithLMHead
+~~~~~~~~~~~~~~~~~~~~~

-.. autoclass:: transformers.TFAutoModelForCausalLM
-    :members:
-
-
-TFAutoModelForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFAutoModelForMaskedLM
-    :members:
-
-
-TFAutoModelForSeq2SeqLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFAutoModelForSeq2SeqLM
+.. autoclass:: transformers.TFAutoModelWithLMHead
    :members:


 TFAutoModelForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFAutoModelForSequenceClassification
    :members:


 TFAutoModelForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFAutoModelForMultipleChoice
    :members:


 TFAutoModelForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFAutoModelForTokenClassification
    :members:


 TFAutoModelForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFAutoModelForQuestionAnswering
    :members:
@@ -1,86 +1,49 @@
-BART
-----------------------------------------------------------------------------------------------------------------------
-
-**DISCLAIMER:** If you see something strange, file a `Github Issue
-<https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title>`__ and assign
-@patrickvonplaten
+Bart
+----------------------------------------------------
+**DISCLAIMER:** If you see something strange,
+file a `Github Issue <https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title>`__ and assign
+@sshleifer

 Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The Bart model was proposed in `BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation,
-Translation, and Comprehension <https://arxiv.org/abs/1910.13461>`__ by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan
-Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer on 29 Oct, 2019.
+~~~~~~~~~~~~~~~~~~~~~

+The Bart model was `proposed <https://arxiv.org/abs/1910.13461>`_ by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer on 29 Oct, 2019.
 According to the abstract,

- Bart uses a standard seq2seq/machine translation architecture with a bidirectional encoder (like BERT) and a
-  left-to-right decoder (like GPT).
- The pretraining task involves randomly shuffling the order of the original sentences and a novel in-filling scheme,
-  where spans of text are replaced with a single mask token.
- BART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. It
-  matches the performance of RoBERTa with comparable training resources on GLUE and SQuAD, achieves new
-  state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains
-  of up to 6 ROUGE.
+- Bart uses a standard seq2seq/machine translation architecture with a bidirectional encoder (like BERT) and a left-to-right decoder (like GPT).
+- The pretraining task involves randomly shuffling the order of the original sentences and a novel in-filling scheme, where spans of text are replaced with a single mask token.
+- BART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. It matches the performance of RoBERTa with comparable training resources on GLUE and SQuAD, achieves new state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains of up to 6 ROUGE.

-The Authors' code can be found `here <https://github.com/pytorch/fairseq/tree/master/examples/bart>`__.
-
-
-Examples
-_______________________________________________________________________________________________________________________
-
- Examples and scripts for fine-tuning BART and other models for sequence to sequence tasks can be found in
-  `examples/seq2seq/ <https://github.com/huggingface/transformers/blob/master/examples/seq2seq/README.md>`__.
- An example of how to train :class:`~transformers.BartForConditionalGeneration` with a Hugging Face :obj:`datasets`
-  object can be found in this `forum discussion
-  <https://discuss.huggingface.co/t/train-bart-for-conditional-generation-e-g-summarization/1904>`__.
- `Distilled checkpoints <https://huggingface.co/models?search=distilbart>`__ are described in this `paper
-  <https://arxiv.org/abs/2010.13002>`__.
+The Authors' code can be found `here <https://github.com/pytorch/fairseq/tree/master/examples/bart>`_


 Implementation Notes
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~

- Bart doesn't use :obj:`token_type_ids` for sequence classification. Use :class:`~transformers.BartTokenizer` or
-  :meth:`~transformers.BartTokenizer.encode` to get the proper splitting.
- The forward pass of :class:`~transformers.BartModel` will create decoder inputs (using the helper function
-  :func:`transformers.models.bart.modeling_bart._prepare_bart_decoder_inputs`) if they are not passed. This is
-  different than some other modeling APIs.
- Model predictions are intended to be identical to the original implementation when
-  :obj:`force_bos_token_to_be_generated=True`. This only works, however, if the string you pass to
-  :func:`fairseq.encode` starts with a space.
- :meth:`~transformers.BartForConditionalGeneration.generate` should be used for conditional generation tasks like
-  summarization, see the example in that docstrings.
- Models that load the `facebook/bart-large-cnn` weights will not have a :obj:`mask_token_id`, or be able to perform
-  mask-filling tasks.
- For training/forward passes that don't involve beam search, pass :obj:`use_cache=False`.
+- Bart doesn't use :obj:`token_type_ids` for sequence classification. Use BartTokenizer.encode to get the proper splitting.
+- The forward pass of ``BartModel`` will create decoder inputs (using the helper function ``transformers.modeling_bart._prepare_bart_decoder_inputs``)  if they are not passed. This is different than some other modeling APIs.
+- Model predictions are intended to be identical to the original implementation. This only works, however, if the string you pass to ``fairseq.encode`` starts with a space.
+- ``BartForConditionalGeneration.generate`` should be used for conditional generation tasks like summarization, see the example in that docstrings
+- Models that load the ``"facebook/bart-large-cnn"`` weights will not have a ``mask_token_id``, or be able to perform mask filling tasks.
+- for training/forward passes that don't involve beam search, pass ``use_cache=False``

-Mask Filling
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

-The :obj:`facebook/bart-base` and :obj:`facebook/bart-large` checkpoints can be used to fill multi-token masks.
-
-.. code-block::
-
-    from transformers import BartForConditionalGeneration, BartTokenizer
-    model = BartForConditionalGeneration.from_pretrained("facebook/bart-large", force_bos_token_to_be_generated=True)
-    tok = BartTokenizer.from_pretrained("facebook/bart-large")
-    example_english_phrase = "UN Chief Says There Is No <mask> in Syria"
-    batch = tok(example_english_phrase, return_tensors='pt')
-    generated_ids = model.generate(batch['input_ids'])
-    assert tok.batch_decode(generated_ids, skip_special_tokens=True) == ['UN Chief Says There Is No Plan to Stop Chemical Weapons in Syria']
+BartForConditionalGeneration
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

+.. autoclass:: transformers.BartForConditionalGeneration
+    :members: forward


 BartConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.BartConfig
    :members:


 BartTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.BartTokenizer
    :members:
@@ -88,45 +51,25 @@ BartTokenizer


 BartModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~

 .. autoclass:: transformers.BartModel
    :members: forward

-.. autofunction:: transformers.models.bart.modeling_bart._prepare_bart_decoder_inputs
-
-
-BartForConditionalGeneration
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BartForConditionalGeneration
-    :members: forward
+.. autofunction:: transformers.modeling_bart._prepare_bart_decoder_inputs


 BartForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.BartForSequenceClassification
    :members: forward


 BartForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.BartForQuestionAnswering
    :members: forward


-
-TFBartModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFBartModel
-    :members: call
-
-
-TFBartForConditionalGeneration
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFBartForConditionalGeneration
-    :members: call
@@ -1,13 +1,13 @@
 BERT
-----------------------------------------------------------------------------------------------------------------------
+----------------------------------------------------

 Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

-The BERT model was proposed in `BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
-<https://arxiv.org/abs/1810.04805>`__ by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova. It's a
-bidirectional transformer pretrained using a combination of masked language modeling objective and next sentence
-prediction on a large corpus comprising the Toronto Book Corpus and Wikipedia.
+The BERT model was proposed in `BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding <https://arxiv.org/abs/1810.04805>`__
+by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova. It's a bidirectional transformer
+pre-trained using a combination of masked language modeling objective and next sentence prediction
+on a large corpus comprising the Toronto Book Corpus and Wikipedia.

 The abstract from the paper is the following:

@@ -25,22 +25,22 @@ improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).*

 Tips:

- BERT is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than
-  the left.
- BERT was trained with the masked language modeling (MLM) and next sentence prediction (NSP) objectives. It is
-  efficient at predicting masked tokens and at NLU in general, but is not optimal for text generation.
+- BERT is a model with absolute position embeddings so it's usually advised to pad the inputs on
+  the right rather than the left.
+- BERT was trained with the masked language modeling (MLM) and next sentence prediction (NSP) objectives. It is efficient at predicting masked
+  tokens and at NLU in general, but is not optimal for text generation.

-The original code can be found `here <https://github.com/google-research/bert>`__.
+The original code can be found `here <https://github.com/google-research/bert>`_.

 BertConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.BertConfig
    :members:


 BertTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.BertTokenizer
    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
@@ -48,150 +48,144 @@ BertTokenizer


 BertTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.BertTokenizerFast
    :members:


 Bert specific outputs
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

-.. autoclass:: transformers.models.bert.modeling_bert.BertForPreTrainingOutput
+.. autoclass:: transformers.modeling_bert.BertForPreTrainingOutput
    :members:

-.. autoclass:: transformers.models.bert.modeling_tf_bert.TFBertForPreTrainingOutput
+.. autoclass:: transformers.modeling_tf_bert.TFBertForPreTrainingOutput
    :members:


 BertModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.BertModel
-    :members: forward
+    :members:


 BertForPreTraining
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.BertForPreTraining
-    :members: forward
+    :members:


 BertModelLMHeadModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.BertLMHeadModel
-    :members: forward
+    :members:


 BertForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.BertForMaskedLM
-    :members: forward
+    :members:


 BertForNextSentencePrediction
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.BertForNextSentencePrediction
-    :members: forward
+    :members:


 BertForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.BertForSequenceClassification
-    :members: forward
+    :members:


 BertForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.BertForMultipleChoice
-    :members: forward
+    :members:


 BertForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.BertForTokenClassification
-    :members: forward
+    :members:


 BertForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.BertForQuestionAnswering
-    :members: forward
+    :members:


 TFBertModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFBertModel
-    :members: call
+    :members:


 TFBertForPreTraining
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFBertForPreTraining
-    :members: call
+    :members:


 TFBertModelLMHeadModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFBertLMHeadModel
-    :members: call
+    :members:


 TFBertForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFBertForMaskedLM
-    :members: call
+    :members:


 TFBertForNextSentencePrediction
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFBertForNextSentencePrediction
-    :members: call
+    :members:


 TFBertForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFBertForSequenceClassification
-    :members: call
+    :members:


 TFBertForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFBertForMultipleChoice
-    :members: call
+    :members:


 TFBertForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFBertForTokenClassification
-    :members: call
+    :members:


 TFBertForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFBertForQuestionAnswering
-    :members: call
+    :members:

-
-FlaxBertModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxBertModel
-    :members: __call__
@@ -1,38 +1,26 @@
 BertGeneration
-----------------------------------------------------------------------------------------------------------------------
+----------------------------------------------------

 Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

-The BertGeneration model is a BERT model that can be leveraged for sequence-to-sequence tasks using
-:class:`~transformers.EncoderDecoderModel` as proposed in `Leveraging Pre-trained Checkpoints for Sequence Generation
-Tasks <https://arxiv.org/abs/1907.12461>`__ by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
+The BertGeneration model is a BERT model that can be leveraged for sequence-to-sequence tasks using :class:`~transformers.EncoderDecoderModel` as proposed in `Leveraging Pre-trained Checkpoints for Sequence Generation Tasks <https://arxiv.org/abs/1907.12461>`__ by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.

 The abstract from the paper is the following:

-*Unsupervised pretraining of large neural models has recently revolutionized Natural Language Processing. By
-warm-starting from the publicly released checkpoints, NLP practitioners have pushed the state-of-the-art on multiple
-benchmarks while saving significant amounts of compute time. So far the focus has been mainly on the Natural Language
-Understanding tasks. In this paper, we demonstrate the efficacy of pre-trained checkpoints for Sequence Generation. We
-developed a Transformer-based sequence-to-sequence model that is compatible with publicly available pre-trained BERT,
-GPT-2 and RoBERTa checkpoints and conducted an extensive empirical study on the utility of initializing our model, both
-encoder and decoder, with these checkpoints. Our models result in new state-of-the-art results on Machine Translation,
-Text Summarization, Sentence Splitting, and Sentence Fusion.*
+*Unsupervised pre-training of large neural models has recently revolutionized Natural Language Processing. By warm-starting from the publicly released checkpoints, NLP practitioners have pushed the state-of-the-art on multiple benchmarks while saving significant amounts of compute time. So far the focus has been mainly on the Natural Language Understanding tasks. In this paper, we demonstrate the efficacy of pre-trained checkpoints for Sequence Generation. We developed a Transformer-based sequence-to-sequence model that is compatible with publicly available pre-trained BERT, GPT-2 and RoBERTa checkpoints and conducted an extensive empirical study on the utility of initializing our model, both encoder and decoder, with these checkpoints. Our models result in new state-of-the-art results on Machine Translation, Text Summarization, Sentence Splitting, and Sentence Fusion.*

 Usage:

- The model can be used in combination with the :class:`~transformers.EncoderDecoderModel` to leverage two pretrained
-  BERT checkpoints for subsequent fine-tuning.
-
-.. code-block::
+- The model can be used in combination with the :class:`~transformers.EncoderDecoderModel` to leverage two bert pretrained bert checkpoints for subsequent fine-tuning.

+::
+  
  # leverage checkpoints for Bert2Bert model...
-  # use BERT's cls token as BOS token and sep token as EOS token
-  encoder = BertGenerationEncoder.from_pretrained("bert-large-uncased", bos_token_id=101, eos_token_id=102)
-  # add cross attention layers and use BERT's cls token as BOS token and sep token as EOS token
-  decoder = BertGenerationDecoder.from_pretrained("bert-large-uncased", add_cross_attention=True, is_decoder=True, bos_token_id=101, eos_token_id=102)
+  encoder = BertGenerationEncoder.from_pretrained("bert-large-uncased", bos_token_id=101, eos_token_id=102)  # use BERT's cls token as BOS token and sep token as EOS token
+  decoder = BertGenerationDecoder.from_pretrained("bert-large-uncased", add_cross_attention=True, is_decoder=True, bos_token_id=101, eos_token_id=102)  # add cross attention layers and use BERT's cls token as BOS token and sep token as EOS token
  bert2bert = EncoderDecoderModel(encoder=encoder, decoder=decoder)
-
+  
  # create tokenizer...
  tokenizer = BertTokenizer.from_pretrained("bert-large-uncased")

@@ -40,14 +28,14 @@ Usage:
  labels = tokenizer('This is a short summary', return_tensors="pt").input_ids

  # train...
-  loss = bert2bert(input_ids=input_ids, decoder_input_ids=labels, labels=labels).loss
+  loss = bert2bert(input_ids=input_ids, decoder_input_ids=labels, labels=labels, return_dict=True).loss
  loss.backward()


- Pretrained :class:`~transformers.EncoderDecoderModel` are also directly available in the model hub, e.g.,
+- Pretrained :class:`~transformers.EncoderDecoderModel` are also directly available in the model hub, *e.g.*:


-.. code-block::
+::

  # instantiate sentence fusion model
  sentence_fuser = EncoderDecoderModel.from_pretrained("google/roberta2roberta_L-24_discofuse")
@@ -62,35 +50,33 @@ Usage:

 Tips:

- :class:`~transformers.BertGenerationEncoder` and :class:`~transformers.BertGenerationDecoder` should be used in
-  combination with :class:`~transformers.EncoderDecoder`.
- For summarization, sentence splitting, sentence fusion and translation, no special tokens are required for the input.
-  Therefore, no EOS token should be added to the end of the input.
+- :class:`~transformers.BertGenerationEncoder` and :class:`~transformers.BertGenerationDecoder`  should be used in combination with :class:`~transformers.EncoderDecoder`.
+- For summarization, sentence splitting, sentence fusion and translation, no special tokens are required for the input. Therefore, no EOS token should be added to the end of the input.

 The original code can be found `here <https://tfhub.dev/s?module-type=text-generation&subtype=module,placeholder>`__.

 BertGenerationConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.BertGenerationConfig
    :members:


 BertGenerationTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.BertGenerationTokenizer
-    :members: save_vocabulary
+    :members: 

 BertGenerationEncoder
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.BertGenerationEncoder
-    :members: forward
+    :members:


 BertGenerationDecoder
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.BertGenerationDecoder
-    :members: forward
+    :members:
@@ -1,106 +0,0 @@
-Blenderbot
-----------------------------------------------------------------------------------------------------------------------
-
-**DISCLAIMER:** If you see something strange, file a `Github Issue
-<https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title>`__ .
-
-Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The Blender chatbot model was proposed in `Recipes for building an open-domain chatbot
-<https://arxiv.org/pdf/2004.13637.pdf>`__ 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 on 30 Apr 2020.
-
-The abstract of the paper is the following:
-
-*Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that
-scaling neural models in the number of parameters and the size of the data they are trained on gives improved results,
-we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of
-skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to
-their partners, and displaying knowledge, empathy and personality appropriately, while maintaining a consistent
-persona. We show that large scale models can learn these skills when given appropriate training data and choice of
-generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter models, and make our models
-and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn
-dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing
-failure cases of our models.*
-
-The authors' code can be found `here <https://github.com/facebookresearch/ParlAI>`__ .
-
-
-Implementation Notes
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
- Blenderbot uses a standard `seq2seq model transformer <https://arxiv.org/pdf/1706.03762.pdf>`__ based architecture.
- It inherits completely from :class:`~transformers.BartForConditionalGeneration`
- Even though blenderbot is one model, it uses two tokenizers :class:`~transformers.BlenderbotSmallTokenizer` for 90M
-  checkpoint and :class:`~transformers.BlenderbotTokenizer` for all other checkpoints.
- :class:`~transformers.BlenderbotSmallTokenizer` will always return :class:`~transformers.BlenderbotSmallTokenizer`,
-  regardless of checkpoint. To use the 3B parameter checkpoint, you must call
-  :class:`~transformers.BlenderbotTokenizer` directly.
- Available checkpoints can be found in the `model hub <https://huggingface.co/models?search=blenderbot>`__.
-
-
-Usage
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Here is an example of model usage:
-
-.. code-block::
-
-        >>> from transformers import BlenderbotSmallTokenizer, BlenderbotForConditionalGeneration
-        >>> mname = 'facebook/blenderbot-90M'
-        >>> model = BlenderbotForConditionalGeneration.from_pretrained(mname)
-        >>> tokenizer = BlenderbotSmallTokenizer.from_pretrained(mname)
-        >>> UTTERANCE = "My friends are cool but they eat too many carbs."
-        >>> inputs = tokenizer([UTTERANCE], return_tensors='pt')
-        >>> reply_ids = model.generate(**inputs)
-        >>> print([tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in reply_ids])
-
-
-Here is how you can check out config values:
-
-.. code-block::
-
-
-        >>> from transformers import BlenderbotConfig
-        >>> config_90 = BlenderbotConfig.from_pretrained("facebook/blenderbot-90M")
-        >>> config_90.to_diff_dict()  # show interesting Values.
-        >>> configuration_3B = BlenderbotConfig("facebook/blenderbot-3B")
-        >>> configuration_3B.to_diff_dict()
-
-
-BlenderbotConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BlenderbotConfig
-    :members:
-
-BlenderbotTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BlenderbotTokenizer
-    :members: build_inputs_with_special_tokens
-
-BlenderbotSmallTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BlenderbotSmallTokenizer
-    :members:
-
-
-BlenderbotForConditionalGeneration
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-See :obj:`transformers.BartForConditionalGeneration` for arguments to `forward` and `generate`
-
-.. autoclass:: transformers.BlenderbotForConditionalGeneration
-    :members:
-
-
-TFBlenderbotForConditionalGeneration
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-See :obj:`transformers.TFBartForConditionalGeneration` for arguments to `forward` and `generate`
-
-.. autoclass:: transformers.TFBlenderbotForConditionalGeneration
-    :members:
@@ -1,41 +1,41 @@
 CamemBERT
-----------------------------------------------------------------------------------------------------------------------
+----------------------------------------------------

 Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

-The CamemBERT model was proposed in `CamemBERT: a Tasty French Language Model <https://arxiv.org/abs/1911.03894>`__ by
-Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suárez, Yoann Dupont, Laurent Romary, Éric Villemonte de la
+The CamemBERT model was proposed in `CamemBERT: a Tasty French Language Model <https://arxiv.org/abs/1911.03894>`__
+by Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suárez, Yoann Dupont, Laurent Romary, Éric Villemonte de la
 Clergerie, Djamé Seddah, and Benoît Sagot. It is based on Facebook's RoBERTa model released in 2019. It is a model
 trained on 138GB of French text.

 The abstract from the paper is the following:

-*Pretrained language models are now ubiquitous in Natural Language Processing. Despite their success, most available
-models have either been trained on English data or on the concatenation of data in multiple languages. This makes
-practical use of such models --in all languages except English-- very limited. Aiming to address this issue for French,
-we release CamemBERT, a French version of the Bi-directional Encoders for Transformers (BERT). We measure the
-performance of CamemBERT compared to multilingual models in multiple downstream tasks, namely part-of-speech tagging,
-dependency parsing, named-entity recognition, and natural language inference. CamemBERT improves the state of the art
-for most of the tasks considered. We release the pretrained model for CamemBERT hoping to foster research and
-downstream applications for French NLP.*
+*Pretrained language models are now ubiquitous in Natural Language Processing. Despite their success,
+most available models have either been trained on English data or on the concatenation of data in multiple
+languages. This makes practical use of such models --in all languages except English-- very limited. Aiming
+to address this issue for French, we release CamemBERT, a French version of the Bi-directional Encoders for
+Transformers (BERT). We measure the performance of CamemBERT compared to multilingual models in multiple
+downstream tasks, namely part-of-speech tagging, dependency parsing, named-entity recognition, and natural
+language inference. CamemBERT improves the state of the art for most of the tasks considered. We release the
+pretrained model for CamemBERT hoping to foster research and downstream applications for French NLP.*

 Tips:

- This implementation is the same as RoBERTa. Refer to the :doc:`documentation of RoBERTa <roberta>` for usage examples
-  as well as the information relative to the inputs and outputs.
+- This implementation is the same as RoBERTa. Refer to the `documentation of RoBERTa <./roberta.html>`__ for usage
+  examples as well as the information relative to the inputs and outputs.

-The original code can be found `here <https://camembert-model.fr/>`__.
+The original code can be found `here <https://camembert-model.fr/>`_.

 CamembertConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.CamembertConfig
    :members:


 CamembertTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.CamembertTokenizer
    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
@@ -43,91 +43,91 @@ CamembertTokenizer


 CamembertModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.CamembertModel
    :members:


 CamembertForCausalLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.CamembertForCausalLM
    :members:


 CamembertForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.CamembertForMaskedLM
    :members:


 CamembertForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.CamembertForSequenceClassification
    :members:


 CamembertForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.CamembertForMultipleChoice
    :members:


 CamembertForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.CamembertForTokenClassification
    :members:


 CamembertForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.CamembertForQuestionAnswering
    :members:


 TFCamembertModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFCamembertModel
    :members:


 TFCamembertForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFCamembertForMaskedLM
    :members:


 TFCamembertForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFCamembertForSequenceClassification
    :members:


 TFCamembertForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFCamembertForMultipleChoice
    :members:


 TFCamembertForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFCamembertForTokenClassification
    :members:


 TFCamembertForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFCamembertForQuestionAnswering
-    :members:
+    :members:
@@ -1,80 +1,80 @@
 CTRL
-----------------------------------------------------------------------------------------------------------------------
+----------------------------------------------------

 Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

-CTRL model was proposed in `CTRL: A Conditional Transformer Language Model for Controllable Generation
-<https://arxiv.org/abs/1909.05858>`_ by Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and
-Richard Socher. It's a causal (unidirectional) transformer pre-trained using language modeling on a very large corpus
-of ~140 GB of text data with the first token reserved as a control code (such as Links, Books, Wikipedia etc.).
+CTRL model was proposed in `CTRL: A Conditional Transformer Language Model for Controllable Generation <https://arxiv.org/abs/1909.05858>`_
+by Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and Richard Socher.
+It's a causal (unidirectional) transformer pre-trained using language modeling on a very large
+corpus of ~140 GB of text data with the first token reserved as a control code (such as Links, Books, Wikipedia etc.).

 The abstract from the paper is the following:

 *Large-scale language models show promising text generation capabilities, but users cannot easily control particular
 aspects of the generated text. We release CTRL, a 1.63 billion-parameter conditional transformer language model,
 trained to condition on control codes that govern style, content, and task-specific behavior. Control codes were
-derived from structure that naturally co-occurs with raw text, preserving the advantages of unsupervised learning while
-providing more explicit control over text generation. These codes also allow CTRL to predict which parts of the
-training data are most likely given a sequence. This provides a potential method for analyzing large amounts of data
-via model-based source attribution.*
+derived from structure that naturally co-occurs with raw text, preserving the advantages of unsupervised learning
+while providing more explicit control over text generation. These codes also allow CTRL to predict which parts of
+the training data are most likely given a sequence. This provides a potential method for analyzing large amounts
+of data via model-based source attribution.*

 Tips:

 - CTRL makes use of control codes to generate text: it requires generations to be started by certain words, sentences
-  or links to generate coherent text. Refer to the `original implementation <https://github.com/salesforce/ctrl>`__ for
-  more information.
- CTRL is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than
-  the left.
+  or links to generate coherent text. Refer to the `original implementation <https://github.com/salesforce/ctrl>`__
+  for more information.
+- CTRL is a model with absolute position embeddings so it's usually advised to pad the inputs on
+  the right rather than the left.
 - CTRL was trained with a causal language modeling (CLM) objective and is therefore powerful at predicting the next
-  token in a sequence. Leveraging this feature allows CTRL to generate syntactically coherent text as it can be
-  observed in the `run_generation.py` example script.
+  token in a sequence. Leveraging this feature allows CTRL to generate syntactically coherent text as
+  it can be observed in the `run_generation.py` example script.
 - The PyTorch models can take the `past` as input, which is the previously computed key/value attention pairs. Using
-  this `past` value prevents the model from re-computing pre-computed values in the context of text generation. See
-  `reusing the past in generative models <../quickstart.html#using-the-past>`__ for more information on the usage of
-  this argument.
+  this `past` value prevents the model from re-computing pre-computed values in the context of text generation.
+  See `reusing the past in generative models <../quickstart.html#using-the-past>`_ for more information on the usage
+  of this argument.

-The original code can be found `here <https://github.com/salesforce/ctrl>`__.
+The original code can be found `here <https://github.com/salesforce/ctrl>`_.


 CTRLConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.CTRLConfig
    :members:


 CTRLTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.CTRLTokenizer
    :members: save_vocabulary


 CTRLModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.CTRLModel
-    :members: forward
+    :members:


 CTRLLMHeadModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.CTRLLMHeadModel
-    :members: forward
+    :members:


 TFCTRLModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFCTRLModel
-    :members: call
+    :members:


 TFCTRLLMHeadModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFCTRLLMHeadModel
-    :members: call
+    :members:

@@ -1,65 +0,0 @@
-DeBERTa
-----------------------------------------------------------------------------------------------------------------------
-
-Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The DeBERTa model was proposed in `DeBERTa: Decoding-enhanced BERT with Disentangled Attention
-<https://arxiv.org/abs/2006.03654>`__ by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen It is based on Google's
-BERT model released in 2018 and Facebook's RoBERTa model released in 2019.
-
-It builds on RoBERTa with disentangled attention and enhanced mask decoder training with half of the data used in
-RoBERTa.
-
-The abstract from the paper is the following:
-
-*Recent progress in pre-trained neural language models has significantly improved the performance of many natural
-language processing (NLP) tasks. In this paper we propose a new model architecture DeBERTa (Decoding-enhanced BERT with
-disentangled attention) that improves the BERT and RoBERTa models using two novel techniques. The first is the
-disentangled attention mechanism, where each word is represented using two vectors that encode its content and
-position, respectively, and the attention weights among words are computed using disentangled matrices on their
-contents and relative positions. Second, an enhanced mask decoder is used to replace the output softmax layer to
-predict the masked tokens for model pretraining. We show that these two techniques significantly improve the efficiency
-of model pretraining and performance of downstream tasks. Compared to RoBERTa-Large, a DeBERTa model trained on half of
-the training data performs consistently better on a wide range of NLP tasks, achieving improvements on MNLI by +0.9%
-(90.2% vs. 91.1%), on SQuAD v2.0 by +2.3% (88.4% vs. 90.7%) and RACE by +3.6% (83.2% vs. 86.8%). The DeBERTa code and
-pre-trained models will be made publicly available at https://github.com/microsoft/DeBERTa.*
-
-
-The original code can be found `here <https://github.com/microsoft/DeBERTa>`__.
-
-
-DebertaConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.DebertaConfig
-    :members:
-
-
-DebertaTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.DebertaTokenizer
-    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
-        create_token_type_ids_from_sequences, save_vocabulary
-
-
-DebertaModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.DebertaModel
-    :members:
-
-
-DebertaPreTrainedModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.DebertaPreTrainedModel
-    :members:
-
-
-DebertaForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.DebertaForSequenceClassification
-    :members:
@@ -1,42 +1,39 @@
 DialoGPT
-----------------------------------------------------------------------------------------------------------------------
+----------------------------------------------------

 Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

-DialoGPT was proposed in `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. It's a GPT2 Model trained on 147M conversation-like exchanges extracted from
-Reddit.
+DialoGPT was proposed in
+`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.
+It's a GPT2 Model trained on 147M conversation-like exchanges extracted from Reddit.

 The abstract from the paper is the following:

-*We present a large, tunable neural conversational response generation model, DialoGPT (dialogue generative pre-trained
-transformer). Trained on 147M conversation-like exchanges extracted from Reddit comment chains over a period spanning
-from 2005 through 2017, DialoGPT extends the Hugging Face PyTorch transformer to attain a performance close to human
-both in terms of automatic and human evaluation in single-turn dialogue settings. We show that conversational systems
-that leverage DialoGPT generate more relevant, contentful and context-consistent responses than strong baseline
-systems. The pre-trained model and training pipeline are publicly released to facilitate research into neural response
-generation and the development of more intelligent open-domain dialogue systems.*
+*We present a large, tunable neural conversational response generation model, DialoGPT (dialogue generative pre-trained transformer). 
+Trained on 147M conversation-like exchanges extracted from Reddit comment chains over a period spanning from 2005 through 2017, DialoGPT extends the Hugging Face PyTorch transformer to attain a performance close to human both in terms of automatic and human evaluation in single-turn dialogue settings.
+We show that conversational systems that leverage DialoGPT generate more relevant, contentful and context-consistent responses than strong baseline systems.
+The pre-trained model and training pipeline are publicly released to facilitate research into neural response generation and the development of more intelligent open-domain dialogue systems.*

 Tips:

- DialoGPT is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather
-  than the left.
- DialoGPT was trained with a causal language modeling (CLM) objective on conversational data and is therefore powerful
-  at response generation in open-domain dialogue systems.
- DialoGPT enables the user to create a chat bot in just 10 lines of code as shown on `DialoGPT's model card
-  <https://huggingface.co/microsoft/DialoGPT-medium>`_.
+- DialoGPT is a model with absolute position embeddings so it's usually advised to pad the inputs on
+  the right rather than the left.
+- DialoGPT was trained with a causal language modeling (CLM) objective on conversational data and is therefore powerful at response generation in open-domain dialogue systems.
+- DialoGPT enables the user to create a chat bot in just 10 lines of code as shown on `DialoGPT's model card <https://huggingface.co/microsoft/DialoGPT-medium>`_.

 Training:

-In order to train or fine-tune DialoGPT, one can use causal language modeling training. To cite the official paper: *We
-follow the OpenAI GPT-2 to model a multiturn dialogue session as a long text and frame the generation task as language
-modeling. We first concatenate all dialog turns within a dialogue session into a long text x_1,..., x_N (N is the
-sequence length), ended by the end-of-text token.* For more information please confer to the original paper.
+In order to train or fine-tune DialoGPT, one can use causal language modeling training. 
+To cite the official paper: 
+*We follow the OpenAI GPT-2 to model a multiturn dialogue session 
+as a long text and frame the generation task as language modeling. We first
+concatenate all dialog turns within a dialogue session into a long text 
+x_1,..., x_N (N is the sequence length), ended by the end-of-text token.* 
+For more information please confer to the original paper.
+    

-
-DialoGPT's architecture is based on the GPT2 model, so one can refer to GPT2's `docstring
-<https://huggingface.co/transformers/model_doc/gpt2.html>`_.
+DialoGPT's architecture is based on the GPT2 model, so one can refer to GPT2's `docstring <https://huggingface.co/transformers/model_doc/gpt2.html>`_.

 The original code can be found `here <https://github.com/microsoft/DialoGPT>`_.
@@ -1,15 +1,15 @@
 DistilBERT
-----------------------------------------------------------------------------------------------------------------------
+----------------------------------------------------

 Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

-The DistilBERT model was proposed in the blog post `Smaller, faster, cheaper, lighter: Introducing DistilBERT, a
-distilled version of BERT <https://medium.com/huggingface/distilbert-8cf3380435b5>`__, and the paper `DistilBERT, a
-distilled version of BERT: smaller, faster, cheaper and lighter <https://arxiv.org/abs/1910.01108>`__. DistilBERT is a
-small, fast, cheap and light Transformer model trained by distilling BERT base. It has 40% less parameters than
-`bert-base-uncased`, runs 60% faster while preserving over 95% of BERT's performances as measured on the GLUE language
-understanding benchmark.
+The DistilBERT model was proposed in the blog post
+`Smaller, faster, cheaper, lighter: Introducing DistilBERT, a distilled version of BERT <https://medium.com/huggingface/distilbert-8cf3380435b5>`__,
+and the paper `DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter <https://arxiv.org/abs/1910.01108>`__.
+DistilBERT is a small, fast, cheap and light Transformer model trained by distilling Bert base. It has 40% less
+parameters than `bert-base-uncased`, runs 60% faster while preserving over 95% of Bert's performances as measured on
+the GLUE language understanding benchmark.

 The abstract from the paper is the following:

@@ -17,126 +17,123 @@ The abstract from the paper is the following:
 operating these large models in on-the-edge and/or under constrained computational training or inference budgets
 remains challenging. In this work, we propose a method to pre-train a smaller general-purpose language representation
 model, called DistilBERT, which can then be fine-tuned with good performances on a wide range of tasks like its larger
-counterparts. While most prior work investigated the use of distillation for building task-specific models, we leverage
-knowledge distillation during the pretraining phase and show that it is possible to reduce the size of a BERT model by
-40%, while retaining 97% of its language understanding capabilities and being 60% faster. To leverage the inductive
-biases learned by larger models during pretraining, we introduce a triple loss combining language modeling,
-distillation and cosine-distance losses. Our smaller, faster and lighter model is cheaper to pre-train and we
-demonstrate its capabilities for on-device computations in a proof-of-concept experiment and a comparative on-device
-study.*
+counterparts. While most prior work investigated the use of distillation for building task-specific models, we
+leverage knowledge distillation during the pre-training phase and show that it is possible to reduce the size of a
+BERT model by 40%, while retaining 97% of its language understanding capabilities and being 60% faster. To leverage
+the inductive biases learned by larger models during pre-training, we introduce a triple loss combining language
+modeling, distillation and cosine-distance losses. Our smaller, faster and lighter model is cheaper to pre-train
+and we demonstrate its capabilities for on-device computations in a proof-of-concept experiment and a comparative
+on-device study.*

 Tips:

- DistilBERT doesn't have :obj:`token_type_ids`, you don't need to indicate which token belongs to which segment. Just
-  separate your segments with the separation token :obj:`tokenizer.sep_token` (or :obj:`[SEP]`).
- DistilBERT doesn't have options to select the input positions (:obj:`position_ids` input). This could be added if
-  necessary though, just let us know if you need this option.
+- DistilBert doesn't have `token_type_ids`, you don't need to indicate which token belongs to which segment. Just separate your segments with the separation token `tokenizer.sep_token` (or `[SEP]`)
+- DistilBert doesn't have options to select the input positions (`position_ids` input). This could be added if necessary though, just let's us know if you need this option.

-The original code can be found `here
-<https://github.com/huggingface/transformers/tree/master/examples/distillation>`__.
+The original code can be found `here <https://github.com/huggingface/transformers/tree/master/examples/distillation>`_.


 DistilBertConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.DistilBertConfig
    :members:


 DistilBertTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.DistilBertTokenizer
    :members:


 DistilBertTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.DistilBertTokenizerFast
    :members:


 DistilBertModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.DistilBertModel
-    :members: forward
+    :members:


 DistilBertForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.DistilBertForMaskedLM
-    :members: forward
+    :members:


 DistilBertForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.DistilBertForSequenceClassification
-    :members: forward
+    :members:


 DistilBertForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.DistilBertForMultipleChoice
-    :members: forward
+    :members:


 DistilBertForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.DistilBertForTokenClassification
-    :members: forward
+    :members:


 DistilBertForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.DistilBertForQuestionAnswering
-    :members: forward
+    :members:

 TFDistilBertModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFDistilBertModel
-    :members: call
+    :members:


 TFDistilBertForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFDistilBertForMaskedLM
-    :members: call
+    :members:


 TFDistilBertForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFDistilBertForSequenceClassification
-    :members: call
+    :members:



 TFDistilBertForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFDistilBertForMultipleChoice
-    :members: call
+    :members:



 TFDistilBertForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFDistilBertForTokenClassification
-    :members: call
+    :members:


 TFDistilBertForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFDistilBertForQuestionAnswering
-    :members: call
+    :members:
@@ -1,12 +1,13 @@
 DPR
-----------------------------------------------------------------------------------------------------------------------
+----------------------------------------------------

 Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

-Dense Passage Retrieval (DPR) is a set of tools and models for state-of-the-art open-domain Q&A research. It was
-introduced in `Dense Passage Retrieval for Open-Domain Question Answering <https://arxiv.org/abs/2004.04906>`__ by
-Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih.
+Dense Passage Retrieval (DPR) - is a set of tools and models for state-of-the-art open-domain Q&A research.
+It is based on the following paper:
+
+Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih, Dense Passage Retrieval for Open-Domain Question Answering.

 The abstract from the paper is the following:

@@ -18,103 +19,84 @@ our dense retriever outperforms a strong Lucene-BM25 system largely by 9%-19% ab
 retrieval accuracy, and helps our end-to-end QA system establish new state-of-the-art on multiple open-domain QA
 benchmarks.*

-The original code can be found `here <https://github.com/facebookresearch/DPR>`__.
+The original code can be found `here <https://github.com/facebookresearch/DPR>`_.


 DPRConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.DPRConfig
    :members:


 DPRContextEncoderTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.DPRContextEncoderTokenizer
    :members:


 DPRContextEncoderTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.DPRContextEncoderTokenizerFast
    :members:

 DPRQuestionEncoderTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.DPRQuestionEncoderTokenizer
    :members:


 DPRQuestionEncoderTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.DPRQuestionEncoderTokenizerFast
    :members:

 DPRReaderTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.DPRReaderTokenizer
    :members:


 DPRReaderTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.DPRReaderTokenizerFast
    :members:


 DPR specific outputs
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~

-.. autoclass:: transformers.models.dpr.modeling_dpr.DPRContextEncoderOutput
+.. autoclass:: transformers.modeling_dpr.DPRContextEncoderOutput
    :members:

-.. autoclass:: transformers.models.dpr.modeling_dpr.DPRQuestionEncoderOutput
+.. autoclass:: transformers.modeling_dpr.DPRQuestionEncoderOutput
    :members:

-.. autoclass:: transformers.models.dpr.modeling_dpr.DPRReaderOutput
+.. autoclass:: transformers.modeling_dpr.DPRReaderOutput
    :members:


 DPRContextEncoder
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.DPRContextEncoder
-    :members: forward
+    :members:

 DPRQuestionEncoder
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.DPRQuestionEncoder
-    :members: forward
+    :members:


 DPRReader
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.DPRReader
-    :members: forward
-
-TFDPRContextEncoder
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFDPRContextEncoder
-    :members: call
-
-TFDPRQuestionEncoder
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFDPRQuestionEncoder
-    :members: call
-
-
-TFDPRReader
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFDPRReader
-    :members: call
+    :members:
@@ -1,174 +1,179 @@
 ELECTRA
-----------------------------------------------------------------------------------------------------------------------
+----------------------------------------------------

 Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

-The ELECTRA model was proposed in the paper `ELECTRA: Pre-training Text Encoders as Discriminators Rather Than
-Generators <https://openreview.net/pdf?id=r1xMH1BtvB>`__. ELECTRA is a new pretraining approach which trains two
-transformer models: the generator and the discriminator. The generator's role is to replace tokens in a sequence, and
-is therefore trained as a masked language model. The discriminator, which is the model we're interested in, tries to
-identify which tokens were replaced by the generator in the sequence.
+The ELECTRA model was proposed in the paper.
+`ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators <https://openreview.net/pdf?id=r1xMH1BtvB>`__.
+ELECTRA is a new pre-training approach which trains two transformer models: the generator and the discriminator. The
+generator's role is to replace tokens in a sequence, and is therefore trained as a masked language model. The discriminator,
+which is the model we're interested in, tries to identify which tokens were replaced by the generator in the sequence.

 The abstract from the paper is the following:

-*Masked language modeling (MLM) pretraining methods such as BERT corrupt the input by replacing some tokens with [MASK]
-and then train a model to reconstruct the original tokens. While they produce good results when transferred to
-downstream NLP tasks, they generally require large amounts of compute to be effective. As an alternative, we propose a
-more sample-efficient pretraining task called replaced token detection. Instead of masking the input, our approach
-corrupts it by replacing some tokens with plausible alternatives sampled from a small generator network. Then, instead
-of training a model that predicts the original identities of the corrupted tokens, we train a discriminative model that
-predicts whether each token in the corrupted input was replaced by a generator sample or not. Thorough experiments
-demonstrate this new pretraining task is more efficient than MLM because the task is defined over all input tokens
-rather than just the small subset that was masked out. As a result, the contextual representations learned by our
-approach substantially outperform the ones learned by BERT given the same model size, data, and compute. The gains are
-particularly strong for small models; for example, we train a model on one GPU for 4 days that outperforms GPT (trained
-using 30x more compute) on the GLUE natural language understanding benchmark. Our approach also works well at scale,
-where it performs comparably to RoBERTa and XLNet while using less than 1/4 of their compute and outperforms them when
-using the same amount of compute.*
+*Masked language modeling (MLM) pre-training methods such as BERT corrupt
+the input by replacing some tokens with [MASK] and then train a model to
+reconstruct the original tokens. While they produce good results when transferred
+to downstream NLP tasks, they generally require large amounts of compute to be
+effective. As an alternative, we propose a more sample-efficient pre-training task
+called replaced token detection. Instead of masking the input, our approach
+corrupts it by replacing some tokens with plausible alternatives sampled from a small
+generator network. Then, instead of training a model that predicts the original
+identities of the corrupted tokens, we train a discriminative model that predicts
+whether each token in the corrupted input was replaced by a generator sample
+or not. Thorough experiments demonstrate this new pre-training task is more
+efficient than MLM because the task is defined over all input tokens rather than
+just the small subset that was masked out. As a result, the contextual representations
+learned by our approach substantially outperform the ones learned by BERT
+given the same model size, data, and compute. The gains are particularly strong
+for small models; for example, we train a model on one GPU for 4 days that
+outperforms GPT (trained using 30x more compute) on the GLUE natural language
+understanding benchmark. Our approach also works well at scale, where it
+performs comparably to RoBERTa and XLNet while using less than 1/4 of their
+compute and outperforms them when using the same amount of compute.*

 Tips:

- ELECTRA is the pretraining approach, therefore there is nearly no changes done to the underlying model: BERT. The
-  only change is the separation of the embedding size and the hidden size: the embedding size is generally smaller,
-  while the hidden size is larger. An additional projection layer (linear) is used to project the embeddings from their
-  embedding size to the hidden size. In the case where the embedding size is the same as the hidden size, no projection
-  layer is used.
+- ELECTRA is the pre-training approach, therefore there is nearly no changes done to the underlying model: BERT. The
+  only change is the separation of the embedding size and the hidden size -> The embedding size is generally smaller,
+  while the hidden size is larger. An additional projection layer (linear) is used to project the embeddings from
+  their embedding size to the hidden size. In the case where the embedding size is the same as the hidden size, no
+  projection layer is used.
 - The ELECTRA checkpoints saved using `Google Research's implementation <https://github.com/google-research/electra>`__
  contain both the generator and discriminator. The conversion script requires the user to name which model to export
  into the correct architecture. Once converted to the HuggingFace format, these checkpoints may be loaded into all
-  available ELECTRA models, however. This means that the discriminator may be loaded in the
-  :class:`~transformers.ElectraForMaskedLM` model, and the generator may be loaded in the
-  :class:`~transformers.ElectraForPreTraining` model (the classification head will be randomly initialized as it
-  doesn't exist in the generator).
+  available ELECTRA models, however. This means that the discriminator may be loaded in the `ElectraForMaskedLM` model,
+  and the generator may be loaded in the `ElectraForPreTraining` model (the classification head will be randomly
+  initialized as it doesn't exist in the generator).

-The original code can be found `here <https://github.com/google-research/electra>`__.
+The original code can be found `here <https://github.com/google-research/electra>`_.


 ElectraConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.ElectraConfig
    :members:


 ElectraTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.ElectraTokenizer
    :members:


 ElectraTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.ElectraTokenizerFast
    :members:


 Electra specific outputs
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~

-.. autoclass:: transformers.models.electra.modeling_electra.ElectraForPreTrainingOutput
+.. autoclass:: transformers.modeling_electra.ElectraForPreTrainingOutput
    :members:

-.. autoclass:: transformers.models.electra.modeling_tf_electra.TFElectraForPreTrainingOutput
+.. autoclass:: transformers.modeling_tf_electra.TFElectraForPreTrainingOutput
    :members:


 ElectraModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.ElectraModel
-    :members: forward
+    :members:


 ElectraForPreTraining
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.ElectraForPreTraining
-    :members: forward
+    :members:


 ElectraForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.ElectraForMaskedLM
-    :members: forward
+    :members:


 ElectraForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.ElectraForSequenceClassification
-    :members: forward
+    :members:


 ElectraForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.ElectraForMultipleChoice
-    :members: forward
+    :members:


 ElectraForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.ElectraForTokenClassification
-    :members: forward
+    :members:


 ElectraForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.ElectraForQuestionAnswering
-    :members: forward
+    :members:


 TFElectraModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFElectraModel
-    :members: call
+    :members:


 TFElectraForPreTraining
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFElectraForPreTraining
-    :members: call
+    :members:


 TFElectraForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFElectraForMaskedLM
-    :members: call
+    :members:


 TFElectraForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFElectraForSequenceClassification
-    :members: call
+    :members:


 TFElectraForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFElectraForMultipleChoice
-    :members: call
+    :members:


 TFElectraForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFElectraForTokenClassification
-    :members: call
+    :members:


 TFElectraForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFElectraForQuestionAnswering
-    :members: call
+    :members:
@@ -1,30 +1,24 @@
 Encoder Decoder Models
-----------------------------------------------------------------------------------------------------------------------
+------------------------

-The :class:`~transformers.EncoderDecoderModel` can be used to initialize a sequence-to-sequence model with any
-pretrained autoencoding model as the encoder and any pretrained autoregressive model as the decoder.
+The :class:`~transformers.EncoderDecoderModel` can be used to initialize a sequence-to-sequence model with any pre-trained autoencoding model as the encoder and any pre-trained autoregressive model as the decoder.

-The effectiveness of initializing sequence-to-sequence models with pretrained checkpoints for sequence generation tasks
-was shown in `Leveraging Pre-trained Checkpoints for Sequence Generation Tasks <https://arxiv.org/abs/1907.12461>`__ by
-Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
+The effectiveness of initializing sequence-to-sequence models with pre-trained checkpoints for sequence generation tasks was shown in `Leveraging Pre-trained Checkpoints for Sequence Generation Tasks <https://arxiv.org/abs/1907.12461>`__ by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.

-After such an :class:`~transformers.EncoderDecoderModel` has been trained/fine-tuned, it can be saved/loaded just like
-any other models (see the examples for more information).
+After such an :class:`~transformers.EncoderDecoderModel` has been trained / fine-tuned, it can be saved / loaded just like any other models (see Examples for more information).

-An application of this architecture could be to leverage two pretrained :class:`~transformers.BertModel` as the encoder
-and decoder for a summarization model as was shown in: `Text Summarization with Pretrained Encoders
-<https://arxiv.org/abs/1908.08345>`__ by Yang Liu and Mirella Lapata.
+An application of this architecture could be to leverage two pre-trained :obj:`transformers.BertModel` models as the encoder and decoder for a summarization model as was shown in: `Text Summarization with Pretrained Encoders <https://arxiv.org/abs/1908.08345>`_ by Yang Liu and Mirella Lapata. 


-EncoderDecoderConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+``EncoderDecoderConfig``
+~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.EncoderDecoderConfig
    :members:


-EncoderDecoderModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+``EncoderDecoderModel``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.EncoderDecoderModel
-    :members: forward, from_encoder_decoder_pretrained
+    :members:
@@ -1,131 +1,131 @@
 FlauBERT
-----------------------------------------------------------------------------------------------------------------------
+----------------------------------------------------

 Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

-The FlauBERT model was proposed in the paper `FlauBERT: Unsupervised Language Model Pre-training for French
-<https://arxiv.org/abs/1912.05372>`__ by Hang Le et al. It's a transformer model pretrained using a masked language
-modeling (MLM) objective (like BERT).
+The FlauBERT model was proposed in the paper
+`FlauBERT: Unsupervised Language Model Pre-training for French <https://arxiv.org/abs/1912.05372>`__ by Hang Le et al.
+It's a transformer pre-trained using a masked language modeling (MLM) objective (BERT-like).

 The abstract from the paper is the following:

 *Language models have become a key step to achieve state-of-the art results in many different Natural Language
-Processing (NLP) tasks. Leveraging the huge amount of unlabeled texts nowadays available, they provide an efficient way
-to pre-train continuous word representations that can be fine-tuned for a downstream task, along with their
+Processing (NLP) tasks. Leveraging the huge amount of unlabeled texts nowadays available, they provide an efficient
+way to pre-train continuous word representations that can be fine-tuned for a downstream task, along with their
 contextualization at the sentence level. This has been widely demonstrated for English using contextualized
-representations (Dai and Le, 2015; Peters et al., 2018; Howard and Ruder, 2018; Radford et al., 2018; Devlin et al.,
-2019; Yang et al., 2019b). In this paper, we introduce and share FlauBERT, a model learned on a very large and
-heterogeneous French corpus. Models of different sizes are trained using the new CNRS (French National Centre for
-Scientific Research) Jean Zay supercomputer. We apply our French language models to diverse NLP tasks (text
-classification, paraphrasing, natural language inference, parsing, word sense disambiguation) and show that most of the
-time they outperform other pretraining approaches. Different versions of FlauBERT as well as a unified evaluation
-protocol for the downstream tasks, called FLUE (French Language Understanding Evaluation), are shared to the research
-community for further reproducible experiments in French NLP.*
+representations (Dai and Le, 2015; Peters et al., 2018; Howard and Ruder, 2018; Radford et al., 2018; Devlin et
+al., 2019; Yang et al., 2019b). In this paper, we introduce and share FlauBERT, a model learned on a very large
+and heterogeneous French corpus. Models of different sizes are trained using the new CNRS (French National Centre
+for Scientific Research) Jean Zay supercomputer. We apply our French language models to diverse NLP tasks (text
+classification, paraphrasing, natural language inference, parsing, word sense disambiguation) and show that most
+of the time they outperform other pre-training approaches. Different versions of FlauBERT as well as a unified
+evaluation protocol for the downstream tasks, called FLUE (French Language Understanding Evaluation), are shared
+to the research community for further reproducible experiments in French NLP.*

-The original code can be found `here <https://github.com/getalp/Flaubert>`__.
+The original code can be found `here <https://github.com/getalp/Flaubert>`_.


 FlaubertConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.FlaubertConfig
    :members:


 FlaubertTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.FlaubertTokenizer
    :members:


 FlaubertModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.FlaubertModel
-    :members: forward
+    :members:


 FlaubertWithLMHeadModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.FlaubertWithLMHeadModel
-    :members: forward
+    :members:


 FlaubertForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.FlaubertForSequenceClassification
-    :members: forward
+    :members:


 FlaubertForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.FlaubertForMultipleChoice
-    :members: forward
+    :members:


 FlaubertForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.FlaubertForTokenClassification
-    :members: forward
+    :members:


 FlaubertForQuestionAnsweringSimple
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.FlaubertForQuestionAnsweringSimple
-    :members: forward
+    :members:


 FlaubertForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.FlaubertForQuestionAnswering
-    :members: forward
+    :members:


 TFFlaubertModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFFlaubertModel
-    :members: call
+    :members:


 TFFlaubertWithLMHeadModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFFlaubertWithLMHeadModel
-    :members: call
+    :members:


 TFFlaubertForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFFlaubertForSequenceClassification
-    :members: call
+    :members:


 TFFlaubertForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFFlaubertForMultipleChoice
-    :members: call
+    :members:


 TFFlaubertForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFFlaubertForTokenClassification
-    :members: call
+    :members:


 TFFlaubertForQuestionAnsweringSimple
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFFlaubertForQuestionAnsweringSimple
-    :members: call
+    :members:
@@ -1,61 +1,49 @@
 FSMT
-----------------------------------------------------------------------------------------------------------------------
-
-**DISCLAIMER:** If you see something strange, file a `Github Issue
-<https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title>`__ and assign
+----------------------------------------------------
+**DISCLAIMER:** If you see something strange,
+file a `Github Issue <https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title>`__ and assign
@stas00.

 Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

-FSMT (FairSeq MachineTranslation) models were introduced in `Facebook FAIR's WMT19 News Translation Task Submission
-<https://arxiv.org/abs/1907.06616>`__ by Nathan Ng, Kyra Yee, Alexei Baevski, Myle Ott, Michael Auli, Sergey Edunov.
+FSMT (FairSeq MachineTranslation) models were introduced in "Facebook FAIR's WMT19 News Translation Task Submission" <this paper <https://arxiv.org/abs/1907.06616>__ by Nathan Ng, Kyra Yee, Alexei Baevski, Myle Ott, Michael Auli, Sergey Edunov.

 The abstract of the paper is the following:

-*This paper describes Facebook FAIR's submission to the WMT19 shared news translation task. We participate in two
-language pairs and four language directions, English <-> German and English <-> Russian. Following our submission from
-last year, our baseline systems are large BPE-based transformer models trained with the Fairseq sequence modeling
-toolkit which rely on sampled back-translations. This year we experiment with different bitext data filtering schemes,
-as well as with adding filtered back-translated data. We also ensemble and fine-tune our models on domain-specific
-data, then decode using noisy channel model reranking. Our submissions are ranked first in all four directions of the
-human evaluation campaign. On En->De, our system significantly outperforms other systems as well as human translations.
-This system improves upon our WMT'18 submission by 4.5 BLEU points.*
+    This paper describes Facebook FAIR's submission to the WMT19 shared news translation task. We participate in two language pairs and four language directions, English <-> German and English <-> Russian. Following our submission from last year, our baseline systems are large BPE-based transformer models trained with the Fairseq sequence modeling toolkit which rely on sampled back-translations. This year we experiment with different bitext data filtering schemes, as well as with adding filtered back-translated data. We also ensemble and fine-tune our models on domain-specific data, then decode using noisy channel model reranking. Our submissions are ranked first in all four directions of the human evaluation campaign. On En->De, our system significantly outperforms other systems as well as human translations. This system improves upon our WMT'18 submission by 4.5 BLEU points.

 The original code can be found here <https://github.com/pytorch/fairseq/tree/master/examples/wmt19>__.

 Implementation Notes
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~

- FSMT uses source and target vocabulary pairs that aren't combined into one. It doesn't share embeddings tokens
-  either. Its tokenizer is very similar to :class:`~transformers.XLMTokenizer` and the main model is derived from
-  :class:`~transformers.BartModel`.
+- FSMT uses source and target vocab pair, that aren't combined into one. It doesn't share embed tokens either. Its tokenizer is very similar to `XLMTokenizer` and the main model is derived from `BartModel`.
+
+
+FSMTForConditionalGeneration
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FSMTForConditionalGeneration
+    :members: forward


 FSMTConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.FSMTConfig
    :members:


 FSMTTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.FSMTTokenizer
-    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
-        create_token_type_ids_from_sequences, prepare_seq2seq_batch, save_vocabulary
+    :members:


 FSMTModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~

 .. autoclass:: transformers.FSMTModel
    :members: forward
-
-
-FSMTForConditionalGeneration
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FSMTForConditionalGeneration
-    :members: forward
@@ -1,13 +1,14 @@
 Funnel Transformer
-----------------------------------------------------------------------------------------------------------------------
+------------------

 Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

-The Funnel Transformer model was proposed in the paper `Funnel-Transformer: Filtering out Sequential Redundancy for
-Efficient Language Processing <https://arxiv.org/abs/2006.03236>`__. It is a bidirectional transformer model, like
-BERT, but with a pooling operation after each block of layers, a bit like in traditional convolutional neural networks
-(CNN) in computer vision.
+The Funnel Transformer model was proposed in the paper
+`Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing
+<https://arxiv.org/abs/2006.03236>`__.
+It is a bidirectional transformer model, like BERT, but with a pooling operation after each block of layers, a bit
+like in traditional convolutional neural networks (CNN) in computer vision.

 The abstract from the paper is the following:

@@ -30,25 +31,25 @@ Tips:
  directly for tasks that just require a sentence summary (like sequence classification or multiple choice). For other
  tasks, the full model is used; this full model has a decoder that upsamples the final hidden states to the same
  sequence length as the input.
- The Funnel Transformer checkpoints are all available with a full version and a base version. The first ones should be
-  used for :class:`~transformers.FunnelModel`, :class:`~transformers.FunnelForPreTraining`,
+- The Funnel Transformer checkpoints are all available with a full version and a base version. The first ones should
+  be used for :class:`~transformers.FunnelModel`, :class:`~transformers.FunnelForPreTraining`,
  :class:`~transformers.FunnelForMaskedLM`, :class:`~transformers.FunnelForTokenClassification` and
  class:`~transformers.FunnelForQuestionAnswering`. The second ones should be used for
  :class:`~transformers.FunnelBaseModel`, :class:`~transformers.FunnelForSequenceClassification` and
  :class:`~transformers.FunnelForMultipleChoice`.

-The original code can be found `here <https://github.com/laiguokun/Funnel-Transformer>`__.
+The original code can be found `here <https://github.com/laiguokun/Funnel-Transformer>`_.


 FunnelConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~

 .. autoclass:: transformers.FunnelConfig
    :members:


 FunnelTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~

 .. autoclass:: transformers.FunnelTokenizer
    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
@@ -56,129 +57,129 @@ FunnelTokenizer


 FunnelTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.FunnelTokenizerFast
    :members:


 Funnel specific outputs
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~

-.. autoclass:: transformers.models.funnel.modeling_funnel.FunnelForPreTrainingOutput
+.. autoclass:: transformers.modeling_funnel.FunnelForPreTrainingOutput
    :members:

-.. autoclass:: transformers.models.funnel.modeling_tf_funnel.TFFunnelForPreTrainingOutput
+.. autoclass:: transformers.modeling_tf_funnel.TFFunnelForPreTrainingOutput
    :members:


 FunnelBaseModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~

 .. autoclass:: transformers.FunnelBaseModel
-    :members: forward
+    :members:


 FunnelModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~

 .. autoclass:: transformers.FunnelModel
-    :members: forward
+    :members:


 FunnelModelForPreTraining
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.FunnelForPreTraining
-    :members: forward
+    :members:


 FunnelForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.FunnelForMaskedLM
-    :members: forward
+    :members:


 FunnelForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.FunnelForSequenceClassification
-    :members: forward
+    :members:


 FunnelForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.FunnelForMultipleChoice
-    :members: forward
+    :members:


 FunnelForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.FunnelForTokenClassification
-    :members: forward
+    :members:


 FunnelForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.FunnelForQuestionAnswering
-    :members: forward
+    :members:


 TFFunnelBaseModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFFunnelBaseModel
-    :members: call
+    :members:


 TFFunnelModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~

 .. autoclass:: transformers.TFFunnelModel
-    :members: call
+    :members:


 TFFunnelModelForPreTraining
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFFunnelForPreTraining
-    :members: call
+    :members:


 TFFunnelForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFFunnelForMaskedLM
-    :members: call
+    :members:


 TFFunnelForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFFunnelForSequenceClassification
-    :members: call
+    :members:


 TFFunnelForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFFunnelForMultipleChoice
-    :members: call
+    :members:


 TFFunnelForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFFunnelForTokenClassification
-    :members: call
+    :members:


 TFFunnelForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFFunnelForQuestionAnswering
-    :members: call
+    :members:
@@ -1,128 +1,123 @@
 OpenAI GPT
-----------------------------------------------------------------------------------------------------------------------
+----------------------------------------------------

 Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

-OpenAI GPT model was proposed in `Improving Language Understanding by Generative Pre-Training
-<https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf>`__
-by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever. It's a causal (unidirectional) transformer
-pre-trained using language modeling on a large corpus will long range dependencies, the Toronto Book Corpus.
+OpenAI GPT model was proposed in `Improving Language Understanding by Generative Pre-Training <https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf>`__
+by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever. It's a causal (unidirectional)
+transformer pre-trained using language modeling on a large corpus will long range dependencies, the Toronto Book Corpus.

 The abstract from the paper is the following:

-*Natural language understanding comprises a wide range of diverse tasks such as textual entailment, question answering,
-semantic similarity assessment, and document classification. Although large unlabeled text corpora are abundant,
-labeled data for learning these specific tasks is scarce, making it challenging for discriminatively trained models to
-perform adequately. We demonstrate that large gains on these tasks can be realized by generative pretraining of a
-language model on a diverse corpus of unlabeled text, followed by discriminative fine-tuning on each specific task. In
-contrast to previous approaches, we make use of task-aware input transformations during fine-tuning to achieve
-effective transfer while requiring minimal changes to the model architecture. We demonstrate the effectiveness of our
-approach on a wide range of benchmarks for natural language understanding. Our general task-agnostic model outperforms
-discriminatively trained models that use architectures specifically crafted for each task, significantly improving upon
-the state of the art in 9 out of the 12 tasks studied.*
+*Natural language understanding comprises a wide range of diverse tasks such
+as textual entailment, question answering, semantic similarity assessment, and
+document classification. Although large unlabeled text corpora are abundant,
+labeled data for learning these specific tasks is scarce, making it challenging for
+discriminatively trained models to perform adequately. We demonstrate that large
+gains on these tasks can be realized by generative pre-training of a language model
+on a diverse corpus of unlabeled text, followed by discriminative fine-tuning on each
+specific task. In contrast to previous approaches, we make use of task-aware input
+transformations during fine-tuning to achieve effective transfer while requiring
+minimal changes to the model architecture. We demonstrate the effectiveness of
+our approach on a wide range of benchmarks for natural language understanding.
+Our general task-agnostic model outperforms discriminatively trained models that
+use architectures specifically crafted for each task, significantly improving upon the
+state of the art in 9 out of the 12 tasks studied.*

 Tips:

- GPT is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than
-  the left.
+- GPT is a model with absolute position embeddings so it's usually advised to pad the inputs on
+  the right rather than the left.
 - GPT was trained with a causal language modeling (CLM) objective and is therefore powerful at predicting the next
-  token in a sequence. Leveraging this feature allows GPT-2 to generate syntactically coherent text as it can be
-  observed in the `run_generation.py` example script.
+  token in a sequence. Leveraging this feature allows GPT-2 to generate syntactically coherent text as
+  it can be observed in the `run_generation.py` example script.

-`Write With Transformer <https://transformer.huggingface.co/doc/gpt>`__ is a webapp created and hosted by Hugging Face
-showcasing the generative capabilities of several models. GPT is one of them.
+`Write With Transformer <https://transformer.huggingface.co/doc/gpt>`__ is a webapp created and hosted by
+Hugging Face showcasing the generative capabilities of several models. GPT is one of them.

-The original code can be found `here <https://github.com/openai/finetune-transformer-lm>`__.
+The original code can be found `here <https://github.com/openai/finetune-transformer-lm>`_.

 Note:

-If you want to reproduce the original tokenization process of the `OpenAI GPT` paper, you will need to install ``ftfy``
-and ``SpaCy``::
-
-.. code-block:: bash
+If you want to reproduce the original tokenization process of the `OpenAI GPT` paper, you will need to install 
+``ftfy`` and ``SpaCy``::

    pip install spacy ftfy==4.4.3
    python -m spacy download en

-If you don't install ``ftfy`` and ``SpaCy``, the :class:`~transformers.OpenAIGPTTokenizer` will default to tokenize
-using BERT's :obj:`BasicTokenizer` followed by Byte-Pair Encoding (which should be fine for most usage, don't worry).
+If you don't install ``ftfy`` and ``SpaCy``, the :class:`transformers.OpenAIGPTTokenizer` will default to tokenize using 
+BERT's :obj:`BasicTokenizer` followed by Byte-Pair Encoding (which should be fine for most usage, don't 
+worry).

 OpenAIGPTConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.OpenAIGPTConfig
    :members:


 OpenAIGPTTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.OpenAIGPTTokenizer
    :members: save_vocabulary


 OpenAIGPTTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.OpenAIGPTTokenizerFast
    :members:


 OpenAI specific outputs
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~

-.. autoclass:: transformers.models.openai.modeling_openai.OpenAIGPTDoubleHeadsModelOutput
+.. autoclass:: transformers.modeling_openai.OpenAIGPTDoubleHeadsModelOutput
    :members:

-.. autoclass:: transformers.models.openai.modeling_tf_openai.TFOpenAIGPTDoubleHeadsModelOutput
+.. autoclass:: transformers.modeling_tf_openai.TFOpenAIGPTDoubleHeadsModelOutput
    :members:


 OpenAIGPTModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.OpenAIGPTModel
-    :members: forward
+    :members:


 OpenAIGPTLMHeadModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.OpenAIGPTLMHeadModel
-    :members: forward
+    :members:


 OpenAIGPTDoubleHeadsModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.OpenAIGPTDoubleHeadsModel
-    :members: forward
-
-
-OpenAIGPTForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.OpenAIGPTForSequenceClassification
-    :members: forward
+    :members:


 TFOpenAIGPTModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFOpenAIGPTModel
-    :members: call
+    :members:


 TFOpenAIGPTLMHeadModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFOpenAIGPTLMHeadModel
-    :members: call
+    :members:


 TFOpenAIGPTDoubleHeadsModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFOpenAIGPTDoubleHeadsModel
-    :members: call
+    :members:
@@ -1,116 +1,110 @@
 OpenAI GPT2
-----------------------------------------------------------------------------------------------------------------------
+----------------------------------------------------

 Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

-OpenAI GPT-2 model was proposed in `Language Models are Unsupervised Multitask Learners
-<https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf>`_ by Alec
-Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei and Ilya Sutskever. It's a causal (unidirectional)
-transformer pretrained using language modeling on a very large corpus of ~40 GB of text data.
+OpenAI GPT-2 model was proposed in
+`Language Models are Unsupervised Multitask Learners <https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf>`_
+by Alec Radford*, Jeffrey Wu*, Rewon Child, David Luan, Dario Amodei** and Ilya Sutskever**.
+It's a causal (unidirectional) transformer pre-trained using  language modeling on a very large
+corpus of ~40 GB of text data.

 The abstract from the paper is the following:

-*GPT-2 is a large transformer-based language model with 1.5 billion parameters, trained on a dataset[1] of 8 million
-web pages. GPT-2 is trained with a simple objective: predict the next word, given all of the previous words within some
-text. The diversity of the dataset causes this simple goal to contain naturally occurring demonstrations of many tasks
-across diverse domains. GPT-2 is a direct scale-up of GPT, with more than 10X the parameters and trained on more than
-10X the amount of data.*
+*GPT-2 is a large transformer-based language model with 1.5 billion parameters, trained on a dataset[1]
+of 8 million web pages. GPT-2 is trained with a simple objective: predict the next word, given all of the previous
+words within some text. The diversity of the dataset causes this simple goal to contain naturally occurring
+demonstrations of many tasks across diverse domains. GPT-2 is a direct scale-up of GPT, with more than 10X
+the parameters and trained on more than 10X the amount of data.*

 Tips:

- GPT-2 is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than
-  the left.
+- GPT-2 is a model with absolute position embeddings so it's usually advised to pad the inputs on
+  the right rather than the left.
 - GPT-2 was trained with a causal language modeling (CLM) objective and is therefore powerful at predicting the next
-  token in a sequence. Leveraging this feature allows GPT-2 to generate syntactically coherent text as it can be
-  observed in the `run_generation.py` example script.
+  token in a sequence. Leveraging this feature allows GPT-2 to generate syntactically coherent text as
+  it can be observed in the `run_generation.py` example script.
 - The PyTorch models can take the `past` as input, which is the previously computed key/value attention pairs. Using
-  this `past` value prevents the model from re-computing pre-computed values in the context of text generation. See
-  `reusing the past in generative models <../quickstart.html#using-the-past>`__ for more information on the usage of
-  this argument.
+  this `past` value prevents the model from re-computing pre-computed values in the context of text generation.
+  See `reusing the past in generative models <../quickstart.html#using-the-past>`_ for more information on the usage
+  of this argument.

 `Write With Transformer <https://transformer.huggingface.co/doc/gpt2-large>`__ is a webapp created and hosted by
 Hugging Face showcasing the generative capabilities of several models. GPT-2 is one of them and is available in five
-different sizes: small, medium, large, xl and a distilled version of the small checkpoint: `distilgpt-2`.
+different sizes: small, medium, large, xl and a distilled version of the small checkpoint: distilgpt-2.

-The original code can be found `here <https://openai.com/blog/better-language-models/>`__.
+The original code can be found `here <https://openai.com/blog/better-language-models/>`_.


 GPT2Config
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.GPT2Config
    :members:


 GPT2Tokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.GPT2Tokenizer
    :members: save_vocabulary


 GPT2TokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.GPT2TokenizerFast
    :members:


 GPT2 specific outputs
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

-.. autoclass:: transformers.models.gpt2.modeling_gpt2.GPT2DoubleHeadsModelOutput
+.. autoclass:: transformers.modeling_gpt2.GPT2DoubleHeadsModelOutput
    :members:

-.. autoclass:: transformers.models.gpt2.modeling_tf_gpt2.TFGPT2DoubleHeadsModelOutput
+.. autoclass:: transformers.modeling_tf_gpt2.TFGPT2DoubleHeadsModelOutput
    :members:


 GPT2Model
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.GPT2Model
-    :members: forward
+    :members:


 GPT2LMHeadModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.GPT2LMHeadModel
-    :members: forward
+    :members:


 GPT2DoubleHeadsModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.GPT2DoubleHeadsModel
-    :members: forward
-
-
-GPT2ForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.GPT2ForSequenceClassification
-    :members: forward
+    :members:


 TFGPT2Model
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFGPT2Model
-    :members: call
+    :members:


 TFGPT2LMHeadModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFGPT2LMHeadModel
-    :members: call
+    :members:


 TFGPT2DoubleHeadsModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFGPT2DoubleHeadsModel
-    :members: call
+    :members:
@@ -1,66 +0,0 @@
-LayoutLM
-----------------------------------------------------------------------------------------------------------------------
-
-Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The LayoutLM model was proposed in 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, and
-Ming Zhou. It's a simple but effective pretraining method of text and layout for document image understanding and
-information extraction tasks, such as form understanding and receipt understanding.
-
-The abstract from the paper is the following:
-
-*Pre-training techniques have been verified successfully in a variety of NLP tasks in recent years. Despite the
-widespread use of pretraining models for NLP applications, they almost exclusively focus on text-level manipulation,
-while neglecting layout and style information that is vital for document image understanding. In this paper, we propose
-the \textbf{LayoutLM} to jointly model interactions between text and layout information across scanned document images,
-which is beneficial for a great number of real-world document image understanding tasks such as information extraction
-from scanned documents. Furthermore, we also leverage image features to incorporate words' visual information into
-LayoutLM. To the best of our knowledge, this is the first time that text and layout are jointly learned in a single
-framework for document-level pretraining. It achieves new state-of-the-art results in several downstream tasks,
-including form understanding (from 70.72 to 79.27), receipt understanding (from 94.02 to 95.24) and document image
-classification (from 93.07 to 94.42).*
-
-Tips:
-
- LayoutLM has an extra input called :obj:`bbox`, which is the bounding boxes of the input tokens.
- The :obj:`bbox` requires the data that on 0-1000 scale, which means you should normalize the bounding box before
-  passing them into model.
-
-The original code can be found `here <https://github.com/microsoft/unilm/tree/master/layoutlm>`_.
-
-
-LayoutLMConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.LayoutLMConfig
-    :members:
-
-
-LayoutLMTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.LayoutLMTokenizer
-    :members:
-
-
-LayoutLMModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.LayoutLMModel
-    :members:
-
-
-LayoutLMForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.LayoutLMForMaskedLM
-    :members:
-
-
-LayoutLMForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.LayoutLMForTokenClassification
-    :members:
@@ -1,220 +1,126 @@
 Longformer
-----------------------------------------------------------------------------------------------------------------------
-
-**DISCLAIMER:** This model is still a work in progress, if you see something strange, file a `Github Issue
-<https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title>`__.
+----------------------------------------------------
+**DISCLAIMER:** This model is still a work in progress, if you see something strange,
+file a `Github Issue <https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title>`_

 Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~
+The Longformer model was presented in `Longformer: The Long-Document Transformer <https://arxiv.org/pdf/2004.05150.pdf>`_ by Iz Beltagy, Matthew E. Peters, Arman Cohan.
+Here the abstract: 

-The Longformer model was presented in `Longformer: The Long-Document Transformer
-<https://arxiv.org/pdf/2004.05150.pdf>`__ by Iz Beltagy, Matthew E. Peters, Arman Cohan.
+*Transformer-based models are unable to process long sequences due to their self-attention operation, which scales quadratically with the sequence length. To address this limitation, we introduce the Longformer with an attention mechanism that scales linearly with sequence length, making it easy to process documents of thousands of tokens or longer. Longformer's attention mechanism is a drop-in replacement for the standard self-attention and combines a local windowed attention with a task motivated global attention. Following prior work on long-sequence transformers, we evaluate Longformer on character-level language modeling and achieve state-of-the-art results on text8 and enwik8. In contrast to most prior work, we also pretrain Longformer and finetune it on a variety of downstream tasks. Our pretrained Longformer consistently outperforms RoBERTa on long document tasks and sets new state-of-the-art results on WikiHop and TriviaQA.*

-The abstract from the paper is the following:
-
-*Transformer-based models are unable to process long sequences due to their self-attention operation, which scales
-quadratically with the sequence length. To address this limitation, we introduce the Longformer with an attention
-mechanism that scales linearly with sequence length, making it easy to process documents of thousands of tokens or
-longer. Longformer's attention mechanism is a drop-in replacement for the standard self-attention and combines a local
-windowed attention with a task motivated global attention. Following prior work on long-sequence transformers, we
-evaluate Longformer on character-level language modeling and achieve state-of-the-art results on text8 and enwik8. In
-contrast to most prior work, we also pretrain Longformer and finetune it on a variety of downstream tasks. Our
-pretrained Longformer consistently outperforms RoBERTa on long document tasks and sets new state-of-the-art results on
-WikiHop and TriviaQA.*
-
-The Authors' code can be found `here <https://github.com/allenai/longformer>`__.
+The Authors' code can be found `here <https://github.com/allenai/longformer>`_ .

 Longformer Self Attention
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+Longformer self attention employs self attention on both a "local" context and a "global" context.
+Most tokens only attend "locally" to each other meaning that each token attends to its :math:`\frac{1}{2} w` previous tokens and :math:`\frac{1}{2} w` succeding tokens with :math:`w` being the window length as defined in `config.attention_window`. Note that `config.attention_window` can be of type ``list`` to define a different :math:`w` for each layer. 
+A selected few tokens attend "globally" to all other tokens, as it is conventionally done for all tokens in *e.g.* `BertSelfAttention`.

-Longformer self attention employs self attention on both a "local" context and a "global" context. Most tokens only
-attend "locally" to each other meaning that each token attends to its :math:`\frac{1}{2} w` previous tokens and
-:math:`\frac{1}{2} w` succeding tokens with :math:`w` being the window length as defined in
-:obj:`config.attention_window`. Note that :obj:`config.attention_window` can be of type :obj:`List` to define a
-different :math:`w` for each layer. A selected few tokens attend "globally" to all other tokens, as it is
-conventionally done for all tokens in :obj:`BertSelfAttention`.
+Note that "locally" and "globally" attending tokens are projected by different query, key and value matrices.
+Also note that every "locally" attending token not only attends to tokens within its window :math:`w`, but also to all "globally" attending tokens so that global attention is *symmetric*.

-Note that "locally" and "globally" attending tokens are projected by different query, key and value matrices. Also note
-that every "locally" attending token not only attends to tokens within its window :math:`w`, but also to all "globally"
-attending tokens so that global attention is *symmetric*.
+The user can define which tokens attend "locally" and which tokens attend "globally" by setting the tensor `global_attention_mask` at run-time appropriately. `Longformer` employs the following logic for `global_attention_mask`: `0` - the token attends "locally", `1` - token attends "globally". For more information please also refer to :func:`~transformers.LongformerModel.forward` method.

-The user can define which tokens attend "locally" and which tokens attend "globally" by setting the tensor
-:obj:`global_attention_mask` at run-time appropriately. All Longformer models employ the following logic for
-:obj:`global_attention_mask`:
+Using Longformer self attention, the memory and time complexity of the query-key matmul operation, which usually represents the memory and time bottleneck, can be reduced from :math:`\mathcal{O}(n_s \times n_s)` to :math:`\mathcal{O}(n_s \times w)`, with :math:`n_s` being the sequence length and :math:`w` being the average window size. It is assumed that the number of "globally" attending tokens is insignificant as compared to the number of "locally" attending tokens.

- 0: the token attends "locally",
- 1: the token attends "globally".
-
-For more information please also refer to :meth:`~transformers.LongformerModel.forward` method.
-
-Using Longformer self attention, the memory and time complexity of the query-key matmul operation, which usually
-represents the memory and time bottleneck, can be reduced from :math:`\mathcal{O}(n_s \times n_s)` to
-:math:`\mathcal{O}(n_s \times w)`, with :math:`n_s` being the sequence length and :math:`w` being the average window
-size. It is assumed that the number of "globally" attending tokens is insignificant as compared to the number of
-"locally" attending tokens.
-
-For more information, please refer to the official `paper <https://arxiv.org/pdf/2004.05150.pdf>`__.
+For more information, please refer to the official `paper <https://arxiv.org/pdf/2004.05150.pdf>`_ .


 Training
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~
+``LongformerForMaskedLM`` is trained the exact same way, ``RobertaForMaskedLM`` is trained and 
+should be used as follows:

-:class:`~transformers.LongformerForMaskedLM` is trained the exact same way :class:`~transformers.RobertaForMaskedLM` is
-trained and should be used as follows:
+::

-.. code-block::
+  input_ids = tokenizer.encode('This is a sentence from [MASK] training data', return_tensors='pt')
+  mlm_labels = tokenizer.encode('This is a sentence from the training data', return_tensors='pt')

-    input_ids = tokenizer.encode('This is a sentence from [MASK] training data', return_tensors='pt')
-    mlm_labels = tokenizer.encode('This is a sentence from the training data', return_tensors='pt')
-
-    loss = model(input_ids, labels=input_ids, masked_lm_labels=mlm_labels)[0]
+  loss = model(input_ids, labels=input_ids, masked_lm_labels=mlm_labels)[0]


 LongformerConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.LongformerConfig
    :members:


 LongformerTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.LongformerTokenizer
    :members: 


 LongformerTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.LongformerTokenizerFast
    :members: 

-Longformer specific outputs
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.models.longformer.modeling_longformer.LongformerBaseModelOutput
-    :members: 
-
-.. autoclass:: transformers.models.longformer.modeling_longformer.LongformerBaseModelOutputWithPooling
-    :members: 
-
-.. autoclass:: transformers.models.longformer.modeling_longformer.LongformerMaskedLMOutput
-    :members: 
-
-.. autoclass:: transformers.models.longformer.modeling_longformer.LongformerQuestionAnsweringModelOutput
-    :members: 
-
-.. autoclass:: transformers.models.longformer.modeling_longformer.LongformerSequenceClassifierOutput
-    :members: 
-
-.. autoclass:: transformers.models.longformer.modeling_longformer.LongformerMultipleChoiceModelOutput
-    :members: 
-
-.. autoclass:: transformers.models.longformer.modeling_longformer.LongformerTokenClassifierOutput
-    :members: 
-
-.. autoclass:: transformers.models.longformer.modeling_tf_longformer.TFLongformerBaseModelOutput
-    :members: 
-
-.. autoclass:: transformers.models.longformer.modeling_tf_longformer.TFLongformerBaseModelOutputWithPooling
-    :members: 
-
-.. autoclass:: transformers.models.longformer.modeling_tf_longformer.TFLongformerMaskedLMOutput
-    :members: 
-
-.. autoclass:: transformers.models.longformer.modeling_tf_longformer.TFLongformerQuestionAnsweringModelOutput
-    :members: 
-
-.. autoclass:: transformers.models.longformer.modeling_tf_longformer.TFLongformerSequenceClassifierOutput
-    :members: 
-
-.. autoclass:: transformers.models.longformer.modeling_tf_longformer.TFLongformerMultipleChoiceModelOutput
-    :members: 
-
-.. autoclass:: transformers.models.longformer.modeling_tf_longformer.TFLongformerTokenClassifierOutput
-    :members: 

 LongformerModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.LongformerModel
-    :members: forward
+    :members:


 LongformerForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.LongformerForMaskedLM
-    :members: forward
+    :members:


 LongformerForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.LongformerForSequenceClassification
-    :members: forward
+    :members:


 LongformerForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.LongformerForMultipleChoice
-    :members: forward
+    :members:


 LongformerForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.LongformerForTokenClassification
-    :members: forward
+    :members:


 LongformerForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.LongformerForQuestionAnswering
-    :members: forward
+    :members:


 TFLongformerModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFLongformerModel
-    :members: call
+    :members:


 TFLongformerForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFLongformerForMaskedLM
-    :members: call
+    :members:


 TFLongformerForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFLongformerForQuestionAnswering
-    :members: call
-
-
-TFLongformerForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFLongformerForSequenceClassification
-    :members: call
-
-
-TFLongformerForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFLongformerForTokenClassification
-    :members: call
-
-
-TFLongformerForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFLongformerForMultipleChoice
-    :members: call
+    :members:

@@ -1,115 +1,109 @@
 LXMERT
-----------------------------------------------------------------------------------------------------------------------
+----------------------------------------------------

 Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

-The LXMERT model was proposed in `LXMERT: Learning Cross-Modality Encoder Representations from Transformers
-<https://arxiv.org/abs/1908.07490>`__ by Hao Tan & Mohit Bansal. It is a series of bidirectional transformer encoders
-(one for the vision modality, one for the language modality, and then one to fuse both modalities) pretrained using a
-combination of masked language modeling, visual-language text alignment, ROI-feature regression, masked
-visual-attribute modeling, masked visual-object modeling, and visual-question answering objectives. The pretraining
-consists of multiple multi-modal datasets: MSCOCO, Visual-Genome + Visual-Genome Question Answering, VQA 2.0, and GQA.
+The LXMERT model was proposed in `LXMERT: Learning Cross-Modality Encoder Representations from Transformers <https://arxiv.org/abs/1908.07490>`__
+by Hao Tan & Mohit Bansal. It is a series of bidirectional transformer encoders (one for the vision modality, one for the language modality, and then one to fuse both modalities)
+pre-trained using a combination of masked language modeling, visual-language text alignment, ROI-feature regression, masked visual-attribute modeling, masked visual-object modeling, and visual-question answering objectives.
+The pretraining consists of multiple multi-modal datasets: MSCOCO, Visual-Genome + Visual-Genome Question Answering, VQA 2.0, and GQA.

 The abstract from the paper is the following:

-*Vision-and-language reasoning requires an understanding of visual concepts, language semantics, and, most importantly,
-the alignment and relationships between these two modalities. We thus propose the LXMERT (Learning Cross-Modality
-Encoder Representations from Transformers) framework to learn these vision-and-language connections. In LXMERT, we
-build a large-scale Transformer model that consists of three encoders: an object relationship encoder, a language
-encoder, and a cross-modality encoder. Next, to endow our model with the capability of connecting vision and language
-semantics, we pre-train the model with large amounts of image-and-sentence pairs, via five diverse representative
-pretraining tasks: masked language modeling, masked object prediction (feature regression and label classification),
-cross-modality matching, and image question answering. These tasks help in learning both intra-modality and
-cross-modality relationships. After fine-tuning from our pretrained parameters, our model achieves the state-of-the-art
-results on two visual question answering datasets (i.e., VQA and GQA). We also show the generalizability of our
-pretrained cross-modality model by adapting it to a challenging visual-reasoning task, NLVR, and improve the previous
-best result by 22% absolute (54% to 76%). Lastly, we demonstrate detailed ablation studies to prove that both our novel
-model components and pretraining strategies significantly contribute to our strong results; and also present several
+*Vision-and-language reasoning requires an understanding of visual concepts, language semantics, and, most importantly, the alignment and relationships between these two
+modalities. We thus propose the LXMERT
+(Learning Cross-Modality Encoder Representations from Transformers) framework to learn
+these vision-and-language connections. In
+LXMERT, we build a large-scale Transformer
+model that consists of three encoders: an object relationship encoder, a language encoder,
+and a cross-modality encoder. Next, to endow our model with the capability of connecting vision and language semantics, we
+pre-train the model with large amounts of
+image-and-sentence pairs, via five diverse representative pre-training tasks: masked language modeling, masked object prediction
+(feature regression and label classification),
+cross-modality matching, and image question answering. These tasks help in learning both intra-modality and cross-modality relationships. After fine-tuning from our pretrained parameters, our model achieves the
+state-of-the-art results on two visual question answering datasets (i.e., VQA and GQA).
+We also show the generalizability of our pretrained cross-modality model by adapting it to
+a challenging visual-reasoning task, NLVR
+,
+and improve the previous best result by 22%
+absolute (54% to 76%). Lastly, we demonstrate detailed ablation studies to prove that
+both our novel model components and pretraining strategies significantly contribute to
+our strong results; and also present several
 attention visualizations for the different encoders*

 Tips:

- Bounding boxes are not necessary to be used in the visual feature embeddings, any kind of visual-spacial features
-  will work.
- Both the language hidden states and the visual hidden states that LXMERT outputs are passed through the
-  cross-modality layer, so they contain information from both modalities. To access a modality that only attends to
-  itself, select the vision/language hidden states from the first input in the tuple.
- The bidirectional cross-modality encoder attention only returns attention values when the language modality is used
-  as the input and the vision modality is used as the context vector. Further, while the cross-modality encoder
-  contains self-attention for each respective modality and cross-attention, only the cross attention is returned and
-  both self attention outputs are disregarded.
+- Bounding boxes are not necessary to be used in the visual feature embeddings, any kind of visual-spacial features will work.
+- Both the language hidden states and the visual hidden states that LXMERT outputs are passed through the cross-modality layer, so they
+  contain information from both modalities. To access a modality that only attends to itself, select the vision/language hidden states from the first input in the tuple.
+- The bi-directional cross-modality encoder attention only returns attention values when the language modality is used as the input and the vision modality is used as the context vector. Further,
+  while the cross-modality encoder contains self-attention for each respective modality and cross-attention, only the cross attention is returned and both self attention outputs are disregarded.

-The original code can be found `here <https://github.com/airsplay/lxmert>`__.
+The code can be found `here <https://github.com/airsplay/lxmert>`__


 LxmertConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.LxmertConfig
    :members:


 LxmertTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.LxmertTokenizer
-    :members:
-
-
-LxmertTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.LxmertTokenizerFast
-    :members:
+    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
+        create_token_type_ids_from_sequences, save_vocabulary


 Lxmert specific outputs
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

-.. autoclass:: transformers.models.lxmert.modeling_lxmert.LxmertModelOutput
+.. autoclass:: transformers.modeling_lxmert.LxmertModelOutput
    :members:

-.. autoclass:: transformers.models.lxmert.modeling_lxmert.LxmertForPreTrainingOutput
+.. autoclass:: transformers.modeling_lxmert.LxmertForPreTrainingOutput
    :members:

-.. autoclass:: transformers.models.lxmert.modeling_lxmert.LxmertForQuestionAnsweringOutput
+.. autoclass:: transformers.modeling_lxmert.LxmertForQuestionAnsweringOutput
    :members:

-.. autoclass:: transformers.models.lxmert.modeling_tf_lxmert.TFLxmertModelOutput
+.. autoclass:: transformers.modeling_tf_lxmert.TFLxmertModelOutput
    :members:

-.. autoclass:: transformers.models.lxmert.modeling_tf_lxmert.TFLxmertForPreTrainingOutput
+.. autoclass:: transformers.modeling_tf_lxmert.TFLxmertForPreTrainingOutput
    :members:


 LxmertModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.LxmertModel
-    :members: forward
+    :members:

 LxmertForPreTraining
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.LxmertForPreTraining
-    :members: forward
+    :members:

 LxmertForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.LxmertForQuestionAnswering
-    :members: forward
+    :members:


 TFLxmertModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFLxmertModel
-    :members: call
+    :members:

 TFLxmertForPreTraining
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFLxmertForPreTraining
-    :members: call
+    :members:
@@ -1,143 +1,42 @@
 MarianMT
-----------------------------------------------------------------------------------------------------------------------
-
-**Bugs:** If you see something strange, file a `Github Issue
-<https://github.com/huggingface/transformers/issues/new?assignees=sshleifer&labels=&template=bug-report.md&title>`__
-and assign @patrickvonplaten.
-
-Translations should be similar, but not identical to output in the test set linked to in each model card.
+----------------------------------------------------
+**Bugs:** If you see something strange,
+file a `Github Issue <https://github.com/huggingface/transformers/issues/new?assignees=sshleifer&labels=&template=bug-report.md&title>`__ and assign
+@sshleifer. Translations should be similar, but not identical to, output in the test set linked to in each model card.

 Implementation Notes
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
- Each model is about 298 MB on disk, there are more than 1,000 models.
+~~~~~~~~~~~~~~~~~~~~
+- Each model is about 298 MB on disk, there are 1,000+ models.
 - The list of supported language pairs can be found `here <https://huggingface.co/Helsinki-NLP>`__.
- Models were originally trained by `Jörg Tiedemann
-  <https://researchportal.helsinki.fi/en/persons/j%C3%B6rg-tiedemann>`__ using the `Marian
-  <https://marian-nmt.github.io/>`__ C++ library, which supports fast training and translation.
- All models are transformer encoder-decoders with 6 layers in each component. Each model's performance is documented
-  in a model card.
+- models were originally trained by `Jörg Tiedemann <https://researchportal.helsinki.fi/en/persons/j%C3%B6rg-tiedemann>`__ using the `Marian <https://marian-nmt.github.io/>`_ C++ library, which supports fast training and translation.
+- All models are transformer encoder-decoders with 6 layers in each component. Each model's performance is documented in a model card.
 - The 80 opus models that require BPE preprocessing are not supported.
- The modeling code is the same as :class:`~transformers.BartForConditionalGeneration` with a few minor modifications:
-
-    - static (sinusoid) positional embeddings (:obj:`MarianConfig.static_position_embeddings=True`)
-    - a new final_logits_bias (:obj:`MarianConfig.add_bias_logits=True`)
-    - no layernorm_embedding (:obj:`MarianConfig.normalize_embedding=False`)
-    - the model starts generating with :obj:`pad_token_id` (which has 0 as a token_embedding) as the prefix (Bart uses
-      :obj:`<s/>`),
- Code to bulk convert models can be found in ``convert_marian_to_pytorch.py``.
+- The modeling code is the same as ``BartForConditionalGeneration`` with a few minor modifications:
+    - static (sinusoid) positional embeddings (``MarianConfig.static_position_embeddings=True``)
+    - a new final_logits_bias (``MarianConfig.add_bias_logits=True``)
+    - no layernorm_embedding (``MarianConfig.normalize_embedding=False``)
+    - the model starts generating with pad_token_id (which has 0 token_embedding) as the prefix. (Bart uses <s/>)
+- Code to bulk convert models can be found in ``convert_marian_to_pytorch.py``

 Naming
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~
+- All  model names use the following format: ``Helsinki-NLP/opus-mt-{src}-{tgt}``
+- The language codes used to name models are inconsistent. Two digit codes can usually be found `here <https://developers.google.com/admin-sdk/directory/v1/languages>`_, three digit codes require googling "language code {code}".
+- Codes formatted like ``es_AR`` are usually ``code_{region}``. That one is spanish documents from Argentina.

- All model names use the following format: :obj:`Helsinki-NLP/opus-mt-{src}-{tgt}`
- The language codes used to name models are inconsistent. Two digit codes can usually be found `here
-  <https://developers.google.com/admin-sdk/directory/v1/languages>`__, three digit codes require googling "language
-  code {code}".
- Codes formatted like :obj:`es_AR` are usually :obj:`code_{region}`. That one is Spanish from Argentina.
- The models were converted in two stages. The first 1000 models use ISO-639-2 codes to identify languages, the second
-  group use a combination of ISO-639-5 codes and ISO-639-2 codes.
-
-
-Examples
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
- Since Marian models are smaller than many other translation models available in the library, they can be useful for
-  fine-tuning experiments and integration tests.
- `Fine-tune on TPU
-  <https://github.com/huggingface/transformers/blob/master/examples/seq2seq/builtin_trainer/train_distil_marian_enro_tpu.sh>`__
- `Fine-tune on GPU
-  <https://github.com/huggingface/transformers/blob/master/examples/seq2seq/builtin_trainer/train_distil_marian_enro.sh>`__
- `Fine-tune on GPU with pytorch-lightning
-  <https://github.com/huggingface/transformers/blob/master/examples/seq2seq/distil_marian_no_teacher.sh>`__

 Multilingual Models
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~

- All model names use the following format: :obj:`Helsinki-NLP/opus-mt-{src}-{tgt}`:
- If a model can output multiple languages, and you should specify a language code by prepending the desired output
-  language to the :obj:`src_text`.
- You can see a models's supported language codes in its model card, under target constituents, like in `opus-mt-en-roa
-  <https://huggingface.co/Helsinki-NLP/opus-mt-en-roa>`__.
- Note that if a model is only multilingual on the source side, like :obj:`Helsinki-NLP/opus-mt-roa-en`, no language
-  codes are required.
+All  model names use the following format: ``Helsinki-NLP/opus-mt-{src}-{tgt}``:
+    - if ``src`` is in all caps, the model supports multiple input languages, you can figure out which ones by looking at the model card, or the Group Members `mapping <https://gist.github.com/sshleifer/6d20e7761931b08e73c3219027b97b8a>`_ .
+    - if ``tgt`` is in all caps, the model can output multiple languages, and you should specify a language code by prepending the desired output language to the src_text
+    - You can see a tokenizer's supported language codes in ``tokenizer.supported_language_codes``

-New multi-lingual models from the `Tatoeba-Challenge repo <https://github.com/Helsinki-NLP/Tatoeba-Challenge>`__
-require 3 character language codes:
+Example of translating english to many romance languages, using language codes:

 .. code-block:: python

-    from transformers import MarianMTModel, MarianTokenizer
-    src_text = [
-        '>>fra<< this is a sentence in english that we want to translate to french',
-        '>>por<< This should go to portuguese',
-        '>>esp<< And this to Spanish'
-    ]
-
-    model_name = 'Helsinki-NLP/opus-mt-en-roa'
-    tokenizer = MarianTokenizer.from_pretrained(model_name)
-    print(tokenizer.supported_language_codes)
-    model = MarianMTModel.from_pretrained(model_name)
-    translated = model.generate(**tokenizer.prepare_seq2seq_batch(src_text, return_tensors="pt"))
-    tgt_text = [tokenizer.decode(t, skip_special_tokens=True) for t in translated]
-    # ["c'est une phrase en anglais que nous voulons traduire en français",
-    # 'Isto deve ir para o português.',
-    # 'Y esto al español']
-
-
-
-
-Code to see available pretrained models:
-
-.. code-block:: python
-
-    from transformers.hf_api import HfApi
-    model_list = HfApi().model_list()
-    org = "Helsinki-NLP"
-    model_ids = [x.modelId for x in model_list if x.modelId.startswith(org)]
-    suffix = [x.split('/')[1] for x in model_ids]
-    old_style_multi_models = [f'{org}/{s}' for s in suffix if s != s.lower()]
-
-
-
-Old Style Multi-Lingual Models
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-These are the old style multi-lingual models ported from the OPUS-MT-Train repo: and the members of each language
-group:
-
-.. code-block:: python
-
-    ['Helsinki-NLP/opus-mt-NORTH_EU-NORTH_EU',
-     'Helsinki-NLP/opus-mt-ROMANCE-en',
-     'Helsinki-NLP/opus-mt-SCANDINAVIA-SCANDINAVIA',
-     'Helsinki-NLP/opus-mt-de-ZH',
-     'Helsinki-NLP/opus-mt-en-CELTIC',
-     'Helsinki-NLP/opus-mt-en-ROMANCE',
-     'Helsinki-NLP/opus-mt-es-NORWAY',
-     'Helsinki-NLP/opus-mt-fi-NORWAY',
-     'Helsinki-NLP/opus-mt-fi-ZH',
-     'Helsinki-NLP/opus-mt-fi_nb_no_nn_ru_sv_en-SAMI',
-     'Helsinki-NLP/opus-mt-sv-NORWAY',
-     'Helsinki-NLP/opus-mt-sv-ZH']
-    GROUP_MEMBERS = {
-     'ZH': ['cmn', 'cn', 'yue', 'ze_zh', 'zh_cn', 'zh_CN', 'zh_HK', 'zh_tw', 'zh_TW', 'zh_yue', 'zhs', 'zht', 'zh'],
-     'ROMANCE': ['fr', 'fr_BE', 'fr_CA', 'fr_FR', 'wa', 'frp', 'oc', 'ca', 'rm', 'lld', 'fur', 'lij', 'lmo', 'es', 'es_AR', 'es_CL', 'es_CO', 'es_CR', 'es_DO', 'es_EC', 'es_ES', 'es_GT', 'es_HN', 'es_MX', 'es_NI', 'es_PA', 'es_PE', 'es_PR', 'es_SV', 'es_UY', 'es_VE', 'pt', 'pt_br', 'pt_BR', 'pt_PT', 'gl', 'lad', 'an', 'mwl', 'it', 'it_IT', 'co', 'nap', 'scn', 'vec', 'sc', 'ro', 'la'],
-     'NORTH_EU': ['de', 'nl', 'fy', 'af', 'da', 'fo', 'is', 'no', 'nb', 'nn', 'sv'],
-     'SCANDINAVIA': ['da', 'fo', 'is', 'no', 'nb', 'nn', 'sv'],
-     'SAMI': ['se', 'sma', 'smj', 'smn', 'sms'],
-     'NORWAY': ['nb_NO', 'nb', 'nn_NO', 'nn', 'nog', 'no_nb', 'no'],
-     'CELTIC': ['ga', 'cy', 'br', 'gd', 'kw', 'gv']
-    }
-
-
-
-
-Example of translating english to many romance languages, using old-style 2 character language codes
-
-
-.. code-block::python
-
    from transformers import MarianMTModel, MarianTokenizer
    src_text = [
        '>>fr<< this is a sentence in english that we want to translate to french',
@@ -148,35 +47,65 @@ Example of translating english to many romance languages, using old-style 2 char
    model_name = 'Helsinki-NLP/opus-mt-en-ROMANCE'
    tokenizer = MarianTokenizer.from_pretrained(model_name)
    print(tokenizer.supported_language_codes)
-
    model = MarianMTModel.from_pretrained(model_name)
-    translated = model.generate(**tokenizer.prepare_seq2seq_batch(src_text, return_tensors="pt"))
+    translated = model.generate(**tokenizer.prepare_seq2seq_batch(src_text))
    tgt_text = [tokenizer.decode(t, skip_special_tokens=True) for t in translated]
-    # ["c'est une phrase en anglais que nous voulons traduire en français", 'Isto deve ir para o português.',  'Y esto al español']
+    # ["c'est une phrase en anglais que nous voulons traduire en français",
+    # 'Isto deve ir para o português.',
+    # 'Y esto al español']
+
+Sometimes, models were trained on collections of languages that do not resolve to a group. In this case, _ is used as a separator for src or tgt, as in ``'Helsinki-NLP/opus-mt-en_el_es_fi-en_el_es_fi'``. These still require language codes.
+There are many supported regional language codes, like ``>>es_ES<<`` (Spain) and ``>>es_AR<<`` (Argentina), that do not seem to change translations. I have not found these to provide different results than just using ``>>es<<``.
+
+For Example:
+    - ``Helsinki-NLP/opus-mt-NORTH_EU-NORTH_EU``: translates from all NORTH_EU languages (see `mapping <https://gist.github.com/sshleifer/6d20e7761931b08e73c3219027b97b8a>`_) to all NORTH_EU languages. Use a special language code like ``>>de<<`` to specify output language.
+    - ``Helsinki-NLP/opus-mt-ROMANCE-en``: translates from many romance languages to english, no codes needed since there is only 1 tgt language.



+.. code-block:: python
+
+    GROUP_MEMBERS = {
+     'ZH': ['cmn', 'cn', 'yue', 'ze_zh', 'zh_cn', 'zh_CN', 'zh_HK', 'zh_tw', 'zh_TW', 'zh_yue', 'zhs', 'zht', 'zh'],
+     'ROMANCE': ['fr', 'fr_BE', 'fr_CA', 'fr_FR', 'wa', 'frp', 'oc', 'ca', 'rm', 'lld', 'fur', 'lij', 'lmo', 'es', 'es_AR', 'es_CL', 'es_CO', 'es_CR', 'es_DO', 'es_EC', 'es_ES', 'es_GT', 'es_HN', 'es_MX', 'es_NI', 'es_PA', 'es_PE', 'es_PR', 'es_SV', 'es_UY', 'es_VE', 'pt', 'pt_br', 'pt_BR', 'pt_PT', 'gl', 'lad', 'an', 'mwl', 'it', 'it_IT', 'co', 'nap', 'scn', 'vec', 'sc', 'ro', 'la'],
+     'NORTH_EU': ['de', 'nl', 'fy', 'af', 'da', 'fo', 'is', 'no', 'nb', 'nn', 'sv'],
+     'SCANDINAVIA': ['da', 'fo', 'is', 'no', 'nb', 'nn', 'sv'],
+     'SAMI': ['se', 'sma', 'smj', 'smn', 'sms'],
+     'NORWAY': ['nb_NO', 'nb', 'nn_NO', 'nn', 'nog', 'no_nb', 'no'],
+     'CELTIC': ['ga', 'cy', 'br', 'gd', 'kw', 'gv']
+    }
+
+Code to see available pretrained models:
+
+.. code-block:: python
+
+    from transformers.hf_api import HfApi
+    model_list = HfApi().model_list()
+    org = "Helsinki-NLP"
+    model_ids = [x.modelId for x in model_list if x.modelId.startswith(org)]
+    suffix = [x.split('/')[1] for x in model_ids]
+    multi_models = [f'{org}/{s}' for s in suffix if s != s.lower()]
+
+MarianMTModel
+~~~~~~~~~~~~~
+
+Pytorch version of marian-nmt's transformer.h (c++). Designed for the OPUS-NMT translation checkpoints.
+Model API is identical to BartForConditionalGeneration.
+Available models are listed at `Model List <https://huggingface.co/models?search=Helsinki-NLP>`__
+This class inherits nearly all functionality from ``BartForConditionalGeneration``, see that page for method signatures.
+
 MarianConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
+~~~~~~~~~~~~~~~~~~~
 .. autoclass:: transformers.MarianConfig
    :members:


 MarianTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.MarianTokenizer
    :members: prepare_seq2seq_batch


-MarianMTModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MarianMTModel


-TFMarianMTModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFMarianMTModel
@@ -1,92 +1,76 @@
 MBart
-----------------------------------------------------------------------------------------------------------------------
-
-**DISCLAIMER:** If you see something strange, file a `Github Issue
-<https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title>`__ and assign
-@patrickvonplaten
+----------------------------------------------------
+**DISCLAIMER:** If you see something strange,
+file a `Github Issue <https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title>`__ and assign
+@sshleifer

 Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~
+The MBart model was presented in `Multilingual Denoising Pre-training for Neural Machine Translation <https://arxiv.org/abs/2001.08210>`_ by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov
+Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer. According to the abstract,

-The MBart model was presented in `Multilingual Denoising Pre-training for Neural Machine Translation
-<https://arxiv.org/abs/2001.08210>`_ by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov Marjan
-Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
-
-According to the abstract, MBART is a sequence-to-sequence denoising auto-encoder pretrained on large-scale monolingual
-corpora in many languages using the BART objective. mBART is one of the first methods for pretraining a complete
-sequence-to-sequence model by denoising full texts in multiple languages, while previous approaches have focused only
-on the encoder, decoder, or reconstructing parts of the text.
+MBART is a sequence-to-sequence denoising auto-encoder pre-trained on large-scale monolingual corpora in many languages using the BART objective. mBART is one of the first methods for pre-training a complete sequence-to-sequence model by denoising full texts in multiple languages, while previous approaches have focused only on the encoder, decoder, or reconstructing parts of the text.

 The Authors' code can be found `here <https://github.com/pytorch/fairseq/tree/master/examples/mbart>`__

-Examples
-_______________________________________________________________________________________________________________________
-
- Examples and scripts for fine-tuning mBART and other models for sequence to sequence tasks can be found in
-  `examples/seq2seq/ <https://github.com/huggingface/transformers/blob/master/examples/seq2seq/README.md>`__.
- Given the large embeddings table, mBART consumes a large amount of GPU RAM, especially for fine-tuning.
-  :class:`MarianMTModel` is usually a better choice for bilingual machine translation.

 Training
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-MBart is a multilingual encoder-decoder (seq-to-seq) model primarily intended for translation task. As the model is
-multilingual it expects the sequences in a different format. A special language id token is added in both the source
-and target text. The source text format is :obj:`X [eos, src_lang_code]` where :obj:`X` is the source text. The target
-text format is :obj:`[tgt_lang_code] X [eos]`. :obj:`bos` is never used.
-
-The :meth:`~transformers.MBartTokenizer.prepare_seq2seq_batch` handles this automatically and should be used to encode
-the sequences for sequence-to-sequence fine-tuning.
+~~~~~~~~~~~~~~~~~~~~~
+MBart is a multilingual encoder-decoder (seq-to-seq) model primarily intended for translation task. 
+As the model is multilingual it expects the sequences in a different format. A special language id token 
+is added in both the source and target text. The source text format is ``X [eos, src_lang_code]`` 
+where ``X`` is the source text. The target text format is ```[tgt_lang_code] X [eos]```. ```bos``` is never used.
+The ```MBartTokenizer.prepare_seq2seq_batch``` handles this automatically and should be used to encode 
+the sequences for seq-2-seq fine-tuning.

 - Supervised training

-.. code-block::
+::

    example_english_phrase = "UN Chief Says There Is No Military Solution in Syria"
    expected_translation_romanian = "Şeful ONU declară că nu există o soluţie militară în Siria"
-    batch = tokenizer.prepare_seq2seq_batch(example_english_phrase, src_lang="en_XX", tgt_lang="ro_RO", tgt_texts=expected_translation_romanian, return_tensors="pt")
-    model(input_ids=batch['input_ids'], labels=batch['labels']) # forward pass
+    batch = tokenizer.prepare_seq2seq_batch(example_english_phrase, src_lang="en_XX", tgt_lang="ro_RO", tgt_texts=expected_translation_romanian)
+    input_ids = batch["input_ids"]
+    target_ids = batch["decoder_input_ids"]
+    decoder_input_ids = target_ids[:, :-1].contiguous()
+    labels = target_ids[:, 1:].clone()
+    model(input_ids=input_ids, decoder_input_ids=decoder_input_ids, labels=labels) #forward

 - Generation

-    While generating the target text set the :obj:`decoder_start_token_id` to the target language id. The following
-    example shows how to translate English to Romanian using the `facebook/mbart-large-en-ro` model.
+    While generating the target text set the `decoder_start_token_id` to the target language id. 
+    The following example shows how to translate English to Romanian using the ```facebook/mbart-large-en-ro``` model.

-.. code-block::
+::

    from transformers import MBartForConditionalGeneration, MBartTokenizer
    model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-en-ro")
    tokenizer = MBartTokenizer.from_pretrained("facebook/mbart-large-en-ro")
    article = "UN Chief Says There Is No Military Solution in Syria"
-    batch = tokenizer.prepare_seq2seq_batch(src_texts=[article], src_lang="en_XX", return_tensors="pt")
+    batch = tokenizer.prepare_seq2seq_batch(src_texts=[article], src_lang="en_XX")
    translated_tokens = model.generate(**batch, decoder_start_token_id=tokenizer.lang_code_to_id["ro_RO"])
    translation = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
    assert translation == "Şeful ONU declară că nu există o soluţie militară în Siria"


 MBartConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.MBartConfig
    :members:


 MBartTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.MBartTokenizer
    :members: build_inputs_with_special_tokens, prepare_seq2seq_batch


 MBartForConditionalGeneration
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.MBartForConditionalGeneration
-    :members:
+    :members: generate, forward


-TFMBartForConditionalGeneration
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFMBartForConditionalGeneration
-    :members:
@@ -1,177 +1,179 @@
 MobileBERT
-----------------------------------------------------------------------------------------------------------------------
+----------------------------------------------------

 Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

-The MobileBERT model was proposed in `MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices
-<https://arxiv.org/abs/2004.02984>`__ by Zhiqing Sun, Hongkun Yu, Xiaodan Song, Renjie Liu, Yiming Yang, and Denny
-Zhou. It's a bidirectional transformer based on the BERT model, which is compressed and accelerated using several
-approaches.
+The MobileBERT model was proposed in `MobileBERT: a Compact Task-Agnostic BERT
+for Resource-Limited Devices <https://arxiv.org/abs/2004.02984>`__
+by Zhiqing Sun, Hongkun Yu, Xiaodan Song, Renjie Liu, Yiming Yang, and Denny Zhou. It's a bidirectional transformer
+based on the BERT model, which is compressed and accelerated using several approaches.

 The abstract from the paper is the following:

 *Natural Language Processing (NLP) has recently achieved great success by using huge pre-trained models with hundreds
 of millions of parameters. However, these models suffer from heavy model sizes and high latency such that they cannot
 be deployed to resource-limited mobile devices. In this paper, we propose MobileBERT for compressing and accelerating
-the popular BERT model. Like the original BERT, MobileBERT is task-agnostic, that is, it can be generically applied to
-various downstream NLP tasks via simple fine-tuning. Basically, MobileBERT is a thin version of BERT_LARGE, while
-equipped with bottleneck structures and a carefully designed balance between self-attentions and feed-forward networks.
-To train MobileBERT, we first train a specially designed teacher model, an inverted-bottleneck incorporated BERT_LARGE
-model. Then, we conduct knowledge transfer from this teacher to MobileBERT. Empirical studies show that MobileBERT is
-4.3x smaller and 5.5x faster than BERT_BASE while achieving competitive results on well-known benchmarks. On the
-natural language inference tasks of GLUE, MobileBERT achieves a GLUEscore o 77.7 (0.6 lower than BERT_BASE), and 62 ms
-latency on a Pixel 4 phone. On the SQuAD v1.1/v2.0 question answering task, MobileBERT achieves a dev F1 score of
-90.0/79.2 (1.5/2.1 higher than BERT_BASE).*
+the popular BERT model. Like the original BERT, MobileBERT is task-agnostic, that is, it can be generically applied
+to various downstream NLP tasks via simple fine-tuning. Basically, MobileBERT is a thin version of BERT_LARGE, while
+equipped with bottleneck structures and a carefully designed balance between self-attentions and feed-forward
+networks. To train MobileBERT, we first train a specially designed teacher model, an inverted-bottleneck incorporated
+BERT_LARGE model. Then, we conduct knowledge transfer from this teacher to MobileBERT. Empirical studies show that
+MobileBERT is 4.3x smaller and 5.5x faster than BERT_BASE while achieving competitive results on well-known
+benchmarks. On the natural language inference tasks of GLUE, MobileBERT achieves a GLUEscore o 77.7
+(0.6 lower than BERT_BASE), and 62 ms latency on a Pixel 4 phone. On the SQuAD v1.1/v2.0 question answering task,
+MobileBERT achieves a dev F1 score of 90.0/79.2 (1.5/2.1 higher than BERT_BASE).*

 Tips:

- MobileBERT is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather
-  than the left.
- MobileBERT is similar to BERT and therefore relies on the masked language modeling (MLM) objective. It is therefore
-  efficient at predicting masked tokens and at NLU in general, but is not optimal for text generation. Models trained
-  with a causal language modeling (CLM) objective are better in that regard.
+- MobileBERT is a model with absolute position embeddings so it's usually advised to pad the inputs on
+  the right rather than the left.
+- MobileBERT is similar to BERT and therefore relies on the masked language modeling (MLM) objective.
+  It is therefore efficient at predicting masked tokens and at NLU in general, but is not optimal for
+  text generation. Models trained with a causal language modeling (CLM) objective are better in that regard.

-The original code can be found `here <https://github.com/google-research/mobilebert>`__.
+The original code can be found `here <https://github.com/google-research/mobilebert>`_.

 MobileBertConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.MobileBertConfig
    :members:


 MobileBertTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.MobileBertTokenizer
-    :members:
+    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
+        create_token_type_ids_from_sequences, save_vocabulary


 MobileBertTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.MobileBertTokenizerFast
    :members:


 MobileBert specific outputs
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~

-.. autoclass:: transformers.models.mobilebert.modeling_mobilebert.MobileBertForPreTrainingOutput
+.. autoclass:: transformers.modeling_mobilebert.MobileBertForPreTrainingOutput
    :members:

-.. autoclass:: transformers.models.mobilebert.modeling_tf_mobilebert.TFMobileBertForPreTrainingOutput
+.. autoclass:: transformers.modeling_tf_mobilebert.TFMobileBertForPreTrainingOutput
    :members:


 MobileBertModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.MobileBertModel
-    :members: forward
+    :members:


 MobileBertForPreTraining
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.MobileBertForPreTraining
-    :members: forward
+    :members:


 MobileBertForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.MobileBertForMaskedLM
-    :members: forward
+    :members:


 MobileBertForNextSentencePrediction
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.MobileBertForNextSentencePrediction
-    :members: forward
+    :members:


 MobileBertForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.MobileBertForSequenceClassification
-    :members: forward
+    :members:


 MobileBertForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.MobileBertForMultipleChoice
-    :members: forward
+    :members:


 MobileBertForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.MobileBertForTokenClassification
-    :members: forward
+    :members:


 MobileBertForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.MobileBertForQuestionAnswering
-    :members: forward
+    :members:


 TFMobileBertModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFMobileBertModel
-    :members: call
+    :members:


 TFMobileBertForPreTraining
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFMobileBertForPreTraining
-    :members: call
+    :members:


 TFMobileBertForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFMobileBertForMaskedLM
-    :members: call
+    :members:


 TFMobileBertForNextSentencePrediction
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFMobileBertForNextSentencePrediction
-    :members: call
+    :members:


 TFMobileBertForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFMobileBertForSequenceClassification
-    :members: call
+    :members:


 TFMobileBertForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFMobileBertForMultipleChoice
-    :members: call
+    :members:


 TFMobileBertForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFMobileBertForTokenClassification
-    :members: call
+    :members:


 TFMobileBertForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFMobileBertForQuestionAnswering
-    :members: call
+    :members:
+
@@ -1,53 +0,0 @@
-MT5
-----------------------------------------------------------------------------------------------------------------------
-
-Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The mT5 model was presented in `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.
-
-The abstract from the paper is the following:
-
-*The recent "Text-to-Text Transfer Transformer" (T5) leveraged a unified text-to-text format and scale to attain
-state-of-the-art results on a wide variety of English-language NLP tasks. In this paper, we introduce mT5, a
-multilingual variant of T5 that was pre-trained on a new Common Crawl-based dataset covering 101 languages. We describe
-the design and modified training of mT5 and demonstrate its state-of-the-art performance on many multilingual
-benchmarks. All of the code and model checkpoints*
-
-The original code can be found `here <https://github.com/google-research/multilingual-t5>`__.
-
-MT5Config
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MT5Config
-    :members:
-
-
-MT5Model
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MT5Model
-    :members:
-
-
-MT5ForConditionalGeneration
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MT5ForConditionalGeneration
-    :members:
-
-
-TFMT5Model
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFMT5Model
-    :members:
-
-
-TFMT5ForConditionalGeneration
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFMT5ForConditionalGeneration
-    :members:
@@ -1,70 +1,49 @@
 Pegasus
-----------------------------------------------------------------------------------------------------------------------
-
-**DISCLAIMER:** If you see something strange, file a `Github Issue
-<https://github.com/huggingface/transformers/issues/new?assignees=sshleifer&labels=&template=bug-report.md&title>`__
-and assign @patrickvonplaten.
+----------------------------------------------------
+**DISCLAIMER:** If you see something strange,
+file a `Github Issue <https://github.com/huggingface/transformers/issues/new?assignees=sshleifer&labels=&template=bug-report.md&title>`__ and assign
+@sshleifer.


 Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The Pegasus model was proposed in `PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
-<https://arxiv.org/pdf/1912.08777.pdf>`__ by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019.
+~~~~~~~~~~~~~~~~~~~~~

+The Pegasus model was proposed in `PEGASUS: Pre-training with Extracted Gap-sentences for
+Abstractive Summarization <https://arxiv.org/pdf/1912.08777.pdf>`_ by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019.
 According to the abstract,

- Pegasus' pretraining task is intentionally similar to summarization: important sentences are removed/masked from an
-  input document and are generated together as one output sequence from the remaining sentences, similar to an
-  extractive summary.
+- Pegasus' pretraining task is intentionally similar to summarization: important sentences are removed/masked from an input document and are generated together as one output sequence from the remaining sentences, similar to an extractive summary.
 - Pegasus achieves SOTA summarization performance on all 12 downstream tasks, as measured by ROUGE and human eval.

-The Authors' code can be found `here <https://github.com/google-research/pegasus>`__.
+The Authors' code can be found `here <https://github.com/google-research/pegasus>`_.


 Checkpoints
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-All the `checkpoints <https://huggingface.co/models?search=pegasus>`__ are fine-tuned for summarization, besides
-`pegasus-large`, whence the other checkpoints are fine-tuned:
-
+~~~~~~~~~~~
+All the `checkpoints <https://huggingface.co/models?search=pegasus>`_ are finetuned for summarization, besides ``pegasus-large``, whence the other checkpoints are finetuned.
 - Each checkpoint is 2.2 GB on disk and 568M parameters.
 - FP16 is not supported (help/ideas on this appreciated!).
 - Summarizing xsum in fp32 takes about 400ms/sample, with default parameters on a v100 GPU.
- Full replication results and correctly pre-processed data can be found in this `Issue
-  <https://github.com/huggingface/transformers/issues/6844#issue-689259666>`__.
- `Distilled checkpoints <https://huggingface.co/models?search=distill-pegasus>`__ are described in this `paper
-  <https://arxiv.org/abs/2010.13002>`__.
-
-Examples
-_______________________________________________________________________________________________________________________
-
- `Script <https://github.com/huggingface/transformers/blob/master/examples/seq2seq/finetune_pegasus_xsum.sh>`__ to
-  fine-tune pegasus on the XSUM dataset. Data download instructions at `examples/seq2seq/
-  <https://github.com/huggingface/transformers/blob/master/examples/seq2seq/README.md>`__.
- FP16 is not supported (help/ideas on this appreciated!).
- The adafactor optimizer is recommended for pegasus fine-tuning.
+- For XSUM, The paper reports rouge1,rouge2, rougeL of paper: 47.21/24.56/39.25. As of Aug 9, this port scores 46.91/24.34/39.1.
+The gap is likely because of different alpha/length_penalty implementations in beam search.


 Implementation Notes
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~

 - All models are transformer encoder-decoders with 16 layers in each component.
- The implementation is completely inherited from :class:`~transformers.BartForConditionalGeneration`
+- The implementation is completely inherited from ``BartForConditionalGeneration``
 - Some key configuration differences:
-
    - static, sinusoidal position embeddings
-    - no :obj:`layernorm_embedding` (:obj:`PegasusConfig.normalize_embedding=False`)
+    - no ``layernorm_embedding`` (``PegasusConfig.normalize_embedding=False``)
    - the model starts generating with pad_token_id (which has 0 token_embedding) as the prefix.
-    - more beams are used (:obj:`num_beams=8`)
- All pretrained pegasus checkpoints are the same besides three attributes: :obj:`tokenizer.model_max_length` (maximum
-  input size), :obj:`max_length` (the maximum number of tokens to generate) and :obj:`length_penalty`.
- The code to convert checkpoints trained in the author's `repo <https://github.com/google-research/pegasus>`_ can be
-  found in ``convert_pegasus_tf_to_pytorch.py``.
+    - ``num_beams=8``
+- All pretrained pegasus checkpoints are the same besides three attributes: ``tokenizer.model_max_length`` (max input size),  ``max_length`` (max num tokens to generate) and ``length_penalty``
+- Code to convert checkpoints trained in the author's `repo <https://github.com/google-research/pegasus>`_ can be found in ``convert_pegasus_tf_to_pytorch.py``


 Usage Example
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~

 .. code-block:: python

@@ -78,35 +57,61 @@ Usage Example
    torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'
    tokenizer = PegasusTokenizer.from_pretrained(model_name)
    model = PegasusForConditionalGeneration.from_pretrained(model_name).to(torch_device)
-    batch = tokenizer.prepare_seq2seq_batch(src_text, truncation=True, padding='longest', return_tensors="pt").to(torch_device)
+    batch = tokenizer.prepare_seq2seq_batch(src_text, truncation=True, padding='longest').to(torch_device)
    translated = model.generate(**batch)
    tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True)
    assert tgt_text[0] == "California's largest electricity provider has turned off power to hundreds of thousands of customers."

+PegasusForConditionalGeneration
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+This class inherits all functionality from ``BartForConditionalGeneration``, see that page for method signatures.
+Available models are listed at `Model List <https://huggingface.co/models?search=pegasus>`__
+
+.. autoclass:: transformers.PegasusForConditionalGeneration
+    :members:


 PegasusConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~
+This config fully inherits from ``BartConfig``, but pegasus uses different default values:
+Up to date parameter values can be seen in `S3 <https://s3.amazonaws.com/models.huggingface.co/bert/google/pegasus-xsum/config.json>`_.
+As of Aug 10, 2020, they are:

-.. autoclass:: transformers.PegasusConfig
+.. code-block:: python
+
+    dict(
+    vocab_size=96103,
+    max_position_embeddings=512,
+    d_model=1024,
+    encoder_ffn_dim=4096,
+    decoder_ffn_dim=4096,
+    encoder_attention_heads=16,
+    decoder_attention_heads=16,
+    encoder_layers=16,
+    decoder_layers=16,
+    dropout=0.1,
+    attention_dropout=0.1,
+    activation_dropout=0.1,
+    pad_token_id=0,
+    eos_token_id=1,
+    is_encoder_decoder=True,
+    normalize_before=True,
+    scale_embedding=True,
+    normalize_embedding=False,
+    add_final_layer_norm=True,
+    static_position_embeddings=True,
+    num_beams=8,
+    activation_function="relu",
+    )


 PegasusTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 warning: ``add_tokens`` does not work at the moment.

 .. autoclass:: transformers.PegasusTokenizer
    :members: __call__, prepare_seq2seq_batch


-PegasusForConditionalGeneration
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

-.. autoclass:: transformers.PegasusForConditionalGeneration
-
-
-TFPegasusForConditionalGeneration
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFPegasusForConditionalGeneration
@@ -1,94 +0,0 @@
-ProphetNet
-----------------------------------------------------------------------------------------------------------------------
-
-**DISCLAIMER:** If you see something strange, file a `Github Issue
-<https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title>`__ and assign
-@patrickvonplaten
-
-Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The ProphetNet model was proposed in `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, Ming Zhou on 13 Jan, 2020.
-
-ProphetNet is an encoder-decoder model and can predict n-future tokens for "ngram" language modeling instead of just
-the next token.
-
-The abstract from the paper is the following:
-
-*In this paper, we present a new sequence-to-sequence pretraining model called ProphetNet, which introduces a novel
-self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism. Instead of
-the optimization of one-step ahead prediction in traditional sequence-to-sequence model, the ProphetNet is optimized by
-n-step ahead prediction which predicts the next n tokens simultaneously based on previous context tokens at each time
-step. The future n-gram prediction explicitly encourages the model to plan for the future tokens and prevent
-overfitting on strong local correlations. We pre-train ProphetNet using a base scale dataset (16GB) and a large scale
-dataset (160GB) respectively. Then we conduct experiments on CNN/DailyMail, Gigaword, and SQuAD 1.1 benchmarks for
-abstractive summarization and question generation tasks. Experimental results show that ProphetNet achieves new
-state-of-the-art results on all these datasets compared to the models using the same scale pretraining corpus.*
-
-The Authors' code can be found `here <https://github.com/microsoft/ProphetNet>`__.
-
-
-ProphetNetConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.ProphetNetConfig
-    :members:
-
-
-ProphetNetTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.ProphetNetTokenizer
-    :members:
-
-
-ProphetNet specific outputs
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqLMOutput
-    :members:
-
-.. autoclass:: transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqModelOutput
-    :members:
-
-.. autoclass:: transformers.models.prophetnet.modeling_prophetnet.ProphetNetDecoderModelOutput
-    :members:
-
-.. autoclass:: transformers.models.prophetnet.modeling_prophetnet.ProphetNetDecoderLMOutput
-    :members:
-
-ProphetNetModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.ProphetNetModel
-    :members: forward
-
-
-ProphetNetEncoder
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.ProphetNetEncoder
-    :members: forward
-
-
-ProphetNetDecoder
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.ProphetNetDecoder
-    :members: forward
-
-
-ProphetNetForConditionalGeneration
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.ProphetNetForConditionalGeneration
-    :members: forward
-
-
-ProphetNetForCausalLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.ProphetNetForCausalLM
-    :members: forward
@@ -1,84 +0,0 @@
-RAG
-----------------------------------------------------------------------------------------------------------------------
-
-Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Retrieval-augmented generation ("RAG") models combine the powers of pretrained dense retrieval (DPR) and
-sequence-to-sequence models. RAG models retrieve documents, pass them to a seq2seq model, then marginalize to generate
-outputs. The retriever and seq2seq modules are initialized from pretrained models, and fine-tuned jointly, allowing
-both retrieval and generation to adapt to downstream tasks.
-
-It is based on the paper `Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
-<https://arxiv.org/abs/2005.11401>`__ by Patrick Lewis, Ethan Perez, Aleksandara Piktus, Fabio Petroni, Vladimir
-Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela.
-
-The abstract from the paper is the following:
-
-*Large pre-trained language models have been shown to store factual knowledge in their parameters, and achieve
-state-of-the-art results when fine-tuned on downstream NLP tasks. However, their ability to access and precisely
-manipulate knowledge is still limited, and hence on knowledge-intensive tasks, their performance lags behind
-task-specific architectures. Additionally, providing provenance for their decisions and updating their world knowledge
-remain open research problems. Pre-trained models with a differentiable access mechanism to explicit nonparametric
-memory can overcome this issue, but have so far been only investigated for extractive downstream tasks. We explore a
-general-purpose fine-tuning recipe for retrieval-augmented generation (RAG) — models which combine pre-trained
-parametric and non-parametric memory for language generation. We introduce RAG models where the parametric memory is a
-pre-trained seq2seq model and the non-parametric memory is a dense vector index of Wikipedia, accessed with a
-pre-trained neural retriever. We compare two RAG formulations, one which conditions on the same retrieved passages
-across the whole generated sequence, the other can use different passages per token. We fine-tune and evaluate our
-models on a wide range of knowledge-intensive NLP tasks and set the state-of-the-art on three open domain QA tasks,
-outperforming parametric seq2seq models and task-specific retrieve-and-extract architectures. For language generation
-tasks, we find that RAG models generate more specific, diverse and factual language than a state-of-the-art
-parametric-only seq2seq baseline.*
-
-
-
-RagConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.RagConfig
-    :members:
-
-
-RagTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.RagTokenizer
-    :members: prepare_seq2seq_batch
-
-
-Rag specific outputs
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.models.rag.modeling_rag.RetrievAugLMMarginOutput
-    :members:
-
-.. autoclass:: transformers.models.rag.modeling_rag.RetrievAugLMOutput
-    :members:
-
-RagRetriever
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.RagRetriever
-    :members:
-
-
-RagModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.RagModel
-    :members: forward
-
-
-RagSequenceForGeneration
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.RagSequenceForGeneration
-    :members: forward, generate
-
-
-RagTokenForGeneration
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.RagTokenForGeneration
-    :members: forward, generate
@@ -1,47 +1,30 @@
 Reformer
-----------------------------------------------------------------------------------------------------------------------
-
-**DISCLAIMER:** This model is still a work in progress, if you see something strange, file a `Github Issue
-<https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title>`__.
+----------------------------------------------------
+**DISCLAIMER:** This model is still a work in progress, if you see something strange,
+file a `Github Issue <https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title>`_

 Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~
+The Reformer model was presented in `Reformer: The Efficient Transformer <https://arxiv.org/abs/2001.04451.pdf>`_ by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
+Here the abstract: 

-The Reformer model was proposed in the paper `Reformer: The Efficient Transformer
-<https://arxiv.org/abs/2001.04451.pdf>`__ by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
+*Large Transformer models routinely achieve state-of-the-art results on a number of tasks but training these models can be prohibitively costly, especially on long sequences. We introduce two techniques to improve the efficiency of Transformers. For one, we replace dot-product attention by one that uses locality-sensitive hashing, changing its complexity from O(L^2) to O(Llog(L)), where L is the length of the sequence. Furthermore, we use reversible residual layers instead of the standard residuals, which allows storing activations only once in the training process instead of N times, where N is the number of layers. The resulting model, the Reformer, performs on par with Transformer models while being much more memory-efficient and much faster on long sequences.*

-The abstract from the paper is the following:
-
-*Large Transformer models routinely achieve state-of-the-art results on a number of tasks but training these models can
-be prohibitively costly, especially on long sequences. We introduce two techniques to improve the efficiency of
-Transformers. For one, we replace dot-product attention by one that uses locality-sensitive hashing, changing its
-complexity from O(L^2) to O(Llog(L)), where L is the length of the sequence. Furthermore, we use reversible residual
-layers instead of the standard residuals, which allows storing activations only once in the training process instead of
-N times, where N is the number of layers. The resulting model, the Reformer, performs on par with Transformer models
-while being much more memory-efficient and much faster on long sequences.*
-
-The Authors' code can be found `here <https://github.com/google/trax/tree/master/trax/models/reformer>`__.
+The Authors' code can be found `here <https://github.com/google/trax/tree/master/trax/models/reformer>`_ .

 Axial Positional Encodings
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Axial Positional Encodings were first implemented in Google's `trax library
-<https://github.com/google/trax/blob/4d99ad4965bab1deba227539758d59f0df0fef48/trax/layers/research/position_encodings.py#L29>`__
-and developed by the authors of this model's paper. In models that are treating very long input sequences, the
-conventional position id encodings store an embedings vector of size :math:`d` being the :obj:`config.hidden_size` for
-every position :math:`i, \ldots, n_s`, with :math:`n_s` being :obj:`config.max_embedding_size`. This means that having
-a sequence length of :math:`n_s = 2^{19} \approx 0.5M` and a ``config.hidden_size`` of :math:`d = 2^{10} \approx 1000`
-would result in a position encoding matrix:
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Axial Positional Encodings were first implemented in Google's `trax library <https://github.com/google/trax/blob/4d99ad4965bab1deba227539758d59f0df0fef48/trax/layers/research/position_encodings.py#L29>`_ and developed by the authors of this model's paper. In models that are treating very long input sequences, the conventional position id encodings store an embedings vector of size :math:`d` being the ``config.hidden_size`` for every position :math:`i, \ldots, n_s`, with :math:`n_s` being ``config.max_embedding_size``. *E.g.*, having a sequence length of :math:`n_s = 2^{19} \approx 0.5M` and a ``config.hidden_size`` of :math:`d = 2^{10} \approx 1000` would result in a position encoding matrix:

 .. math::
    X_{i,j}, \text{ with } i \in \left[1,\ldots, d\right] \text{ and } j \in \left[1,\ldots, n_s\right] 

-which alone has over 500M parameters to store. Axial positional encodings factorize :math:`X_{i,j}` into two matrices:
+which alone has over 500M parameters to store. Axial positional encodings factorize :math:`X_{i,j}` into two matrices: 

 .. math::
    X^{1}_{i,j}, \text{ with } i \in \left[1,\ldots, d^1\right] \text{ and } j \in \left[1,\ldots, n_s^1\right] 

-and
+and 

 .. math::
    X^{2}_{i,j}, \text{ with } i \in \left[1,\ldots, d^2\right] \text{ and } j \in \left[1,\ldots, n_s^2\right] 
@@ -59,128 +42,94 @@ Therefore the following holds:
                X^{2}_{i - d^1, l}, & \text{if } i \ge d^1 \text{ with } l = \lfloor\frac{j}{n_s^1}\rfloor
              \end{cases}

-Intuitively, this means that a position embedding vector :math:`x_j \in \mathbb{R}^{d}` is now the composition of two
-factorized embedding vectors: :math:`x^1_{k, l} + x^2_{l, k}`, where as the :obj:`config.max_embedding_size` dimension
-:math:`j` is factorized into :math:`k \text{ and } l`. This design ensures that each position embedding vector
-:math:`x_j` is unique.
+Intuitively, this means that a position embedding vector :math:`x_j \in \mathbb{R}^{d}` is now the composition of two factorized embedding vectors: :math:`x^1_{k, l} + x^2_{l, k}`, where as the ``config.max_embedding_size`` dimension :math:`j` is factorized into :math:`k \text{ and } l`.
+This design ensures that each position embedding vector :math:`x_j` is unique.

-Using the above example again, axial position encoding with :math:`d^1 = 2^5, d^2 = 2^5, n_s^1 = 2^9, n_s^2 = 2^{10}`
-can drastically reduced the number of parameters to :math:`2^{14} + 2^{15} \approx 49000` parameters.
+Using the above example again, axial position encoding with :math:`d^1 = 2^5, d^2 = 2^5, n_s^1 = 2^9, n_s^2 = 2^{10}` can drastically reduced the number of parameters to :math:`2^{14} + 2^{15} \approx 49000` parameters.
+
+In practice, the parameter ``config.axial_pos_embds_dim`` is set to ``list``:math:`(d^1, d^2)` which sum has to be equal to ``config.hidden_size`` and ``config.axial_pos_shape`` is set to ``list``:math:`(n_s^1, n_s^2)` and which product has to be equal to ``config.max_embedding_size`` which during training has to be equal to the ``sequence length`` of the ``input_ids``.

-In practice, the parameter :obj:`config.axial_pos_embds_dim` is set to a tuple :math:`(d^1, d^2)` which sum has to be
-equal to :obj:`config.hidden_size` and :obj:`config.axial_pos_shape` is set to a tuple :math:`(n_s^1, n_s^2)` which
-product has to be equal to :obj:`config.max_embedding_size`, which during training has to be equal to the `sequence
-length` of the :obj:`input_ids`.


 LSH Self Attention
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~
+In Locality sensitive hashing (LSH) self attention the key and query projection weights are tied. Therefore, the key query embedding vectors are also tied.
+LSH self attention uses the locality sensitive 
+hashing mechanism proposed in `Practical and Optimal LSH for Angular Distance <https://arxiv.org/abs/1509.02897>`_ to assign each of the tied key query embedding vectors to one of ``config.num_buckets`` possible buckets. The premise is that the more "similar" key query embedding vectors (in terms of *cosine similarity*) are to each other, the more likely they are assigned to the same bucket. 
+The accuracy of the LSH mechanism can be improved by increasing ``config.num_hashes`` or directly the argument ``num_hashes`` of the forward function so that the output of the LSH self attention better approximates the output of the "normal" full self attention.
+The buckets are then sorted and chunked into query key embedding vector chunks each of length ``config.lsh_chunk_length``. For each chunk, the query embedding vectors attend to its key vectors (which are tied to themselves) and to the key embedding vectors of ``config.lsh_num_chunks_before`` previous neighboring chunks and ``config.lsh_num_chunks_after`` following neighboring chunks.
+For more information, see the `original Paper <https://arxiv.org/abs/2001.04451>`_ or this great `blog post <https://www.pragmatic.ml/reformer-deep-dive/>`_.

-In Locality sensitive hashing (LSH) self attention the key and query projection weights are tied. Therefore, the key
-query embedding vectors are also tied. LSH self attention uses the locality sensitive hashing mechanism proposed in
-`Practical and Optimal LSH for Angular Distance <https://arxiv.org/abs/1509.02897>`__ to assign each of the tied key
-query embedding vectors to one of :obj:`config.num_buckets` possible buckets. The premise is that the more "similar"
-key query embedding vectors (in terms of *cosine similarity*) are to each other, the more likely they are assigned to
-the same bucket.
+Note that ``config.num_buckets`` can also be factorized into a ``list``:math:`(n_{\text{buckets}}^1, n_{\text{buckets}}^2)`. This way instead of assigning the query key embedding vectors to one of :math:`(1,\ldots, n_{\text{buckets}})` they are assigned to one of :math:`(1-1,\ldots, n_{\text{buckets}}^1-1, \ldots, 1-n_{\text{buckets}}^2, \ldots, n_{\text{buckets}}^1-n_{\text{buckets}}^2)`. This is crucial for very long sequences to save memory.

-The accuracy of the LSH mechanism can be improved by increasing :obj:`config.num_hashes` or directly the argument
-:obj:`num_hashes` of the forward function so that the output of the LSH self attention better approximates the output
-of the "normal" full self attention. The buckets are then sorted and chunked into query key embedding vector chunks
-each of length :obj:`config.lsh_chunk_length`. For each chunk, the query embedding vectors attend to its key vectors
-(which are tied to themselves) and to the key embedding vectors of :obj:`config.lsh_num_chunks_before` previous
-neighboring chunks and :obj:`config.lsh_num_chunks_after` following neighboring chunks.
+When training a model from scratch, it is recommended to leave ``config.num_buckets=None``, so that depending on the sequence length a good value for ``num_buckets`` is calculated on the fly. This value will then automatically be saved in the config and should be reused for inference.

-For more information, see the `original Paper <https://arxiv.org/abs/2001.04451>`__ or this great `blog post
-<https://www.pragmatic.ml/reformer-deep-dive/>`__.
-
-Note that :obj:`config.num_buckets` can also be factorized into a list :math:`(n_{\text{buckets}}^1,
-n_{\text{buckets}}^2)`. This way instead of assigning the query key embedding vectors to one of :math:`(1,\ldots,
-n_{\text{buckets}})` they are assigned to one of :math:`(1-1,\ldots, n_{\text{buckets}}^1-1, \ldots,
-1-n_{\text{buckets}}^2, \ldots, n_{\text{buckets}}^1-n_{\text{buckets}}^2)`. This is crucial for very long sequences to
-save memory.
-
-When training a model from scratch, it is recommended to leave :obj:`config.num_buckets=None`, so that depending on the
-sequence length a good value for :obj:`num_buckets` is calculated on the fly. This value will then automatically be
-saved in the config and should be reused for inference.
-
-Using LSH self attention, the memory and time complexity of the query-key matmul operation can be reduced from
-:math:`\mathcal{O}(n_s \times n_s)` to :math:`\mathcal{O}(n_s \times \log(n_s))`, which usually represents the memory
-and time bottleneck in a transformer model, with :math:`n_s` being the sequence length.
+Using LSH self attention, the memory and time complexity of the query-key matmul operation can be reduced from :math:`\mathcal{O}(n_s \times n_s)` to :math:`\mathcal{O}(n_s \times \log(n_s))`, which usually represents the memory and time bottleneck in a transformer model, with :math:`n_s` being the sequence length.


 Local Self Attention
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~
+Local self attention is essentially a "normal" self attention layer with 
+key, query and value projections, but is chunked so that in each chunk of length ``config.local_chunk_length`` the query embedding vectors only attends to the key embedding vectors in its chunk and to the key embedding vectors of ``config.local_num_chunks_before`` previous neighboring chunks and ``config.local_num_chunks_after`` following neighboring chunks.

-Local self attention is essentially a "normal" self attention layer with key, query and value projections, but is
-chunked so that in each chunk of length :obj:`config.local_chunk_length` the query embedding vectors only attends to
-the key embedding vectors in its chunk and to the key embedding vectors of :obj:`config.local_num_chunks_before`
-previous neighboring chunks and :obj:`config.local_num_chunks_after` following neighboring chunks.
-
-Using Local self attention, the memory and time complexity of the query-key matmul operation can be reduced from
-:math:`\mathcal{O}(n_s \times n_s)` to :math:`\mathcal{O}(n_s \times \log(n_s))`, which usually represents the memory
-and time bottleneck in a transformer model, with :math:`n_s` being the sequence length.
+Using Local self attention, the memory and time complexity of the query-key matmul operation can be reduced from :math:`\mathcal{O}(n_s \times n_s)` to :math:`\mathcal{O}(n_s \times \log(n_s))`, which usually represents the memory and time bottleneck in a transformer model, with :math:`n_s` being the sequence length.


 Training
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~
+During training, we must ensure that the sequence length is set to a value that can be divided by the least common multiple of ``config.lsh_chunk_length`` and ``config.local_chunk_length`` and that the parameters of the Axial Positional Encodings are correctly set as described above. Reformer is very memory efficient so that the model can easily be trained on sequences as long as 64000 tokens.
+For training, the ``ReformerModelWithLMHead`` should be used as follows: 

-During training, we must ensure that the sequence length is set to a value that can be divided by the least common
-multiple of :obj:`config.lsh_chunk_length` and :obj:`config.local_chunk_length` and that the parameters of the Axial
-Positional Encodings are correctly set as described above. Reformer is very memory efficient so that the model can
-easily be trained on sequences as long as 64000 tokens.
-
-For training, the :class:`~transformers.ReformerModelWithLMHead` should be used as follows:
-
-.. code-block::
+::

  input_ids = tokenizer.encode('This is a sentence from the training data', return_tensors='pt')
  loss = model(input_ids, labels=input_ids)[0]


 ReformerConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.ReformerConfig
    :members:


 ReformerTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.ReformerTokenizer
-    :members: save_vocabulary
+    :members: 


 ReformerModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.ReformerModel
-    :members: forward
+    :members:


 ReformerModelWithLMHead
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.ReformerModelWithLMHead
-    :members: forward
+    :members:


 ReformerForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.ReformerForMaskedLM
-    :members: forward
+    :members:


 ReformerForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.ReformerForSequenceClassification
-    :members: forward
+    :members:


 ReformerForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.ReformerForQuestionAnswering
-    :members: forward
+    :members:
@@ -1,40 +1,39 @@
 RetriBERT
-----------------------------------------------------------------------------------------------------------------------
+----------------------------------------------------

 Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

-The RetriBERT model was proposed in the blog post `Explain Anything Like I'm Five: A Model for Open Domain Long Form
-Question Answering <https://yjernite.github.io/lfqa.html>`__. RetriBERT is a small model that uses either a single or
-pair of BERT encoders with lower-dimension projection for dense semantic indexing of text.
+The RetriBERT model was proposed in the blog post
+`Explain Anything Like I'm Five: A Model for Open Domain Long Form Question Answering <https://yjernite.github.io/lfqa.html>`__,
+RetriBERT is a small model that uses either a single or pair of Bert encoders with lower-dimension projection for dense semantic indexing of text.

-Code to train and use the model can be found `here
-<https://github.com/huggingface/transformers/tree/master/examples/distillation>`__.
+Code to train and use the model can be found `here <https://github.com/huggingface/transformers/tree/master/examples/distillation>`_.


 RetriBertConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.RetriBertConfig
    :members:


 RetriBertTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.RetriBertTokenizer
    :members:


 RetriBertTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.RetriBertTokenizerFast
    :members:


 RetriBertModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.RetriBertModel
-    :members: forward
+    :members:
@@ -1,15 +1,15 @@
 RoBERTa
-----------------------------------------------------------------------------------------------------------------------
+----------------------------------------------------

 Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

-The RoBERTa model was proposed in `RoBERTa: A Robustly Optimized BERT Pretraining Approach
-<https://arxiv.org/abs/1907.11692>`_ by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer
-Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov. It is based on Google's BERT model released in 2018.
+The RoBERTa model was proposed in `RoBERTa: A Robustly Optimized BERT Pretraining Approach <https://arxiv.org/abs/1907.11692>`_
+by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer,
+Veselin Stoyanov. It is based on Google's BERT model released in 2018.

-It builds on BERT and modifies key hyperparameters, removing the next-sentence pretraining objective and training with
-much larger mini-batches and learning rates.
+It builds on BERT and modifies key hyperparameters, removing the next-sentence pretraining
+objective and training with much larger mini-batches and learning rates.

 The abstract from the paper is the following:

@@ -17,33 +17,32 @@ The abstract from the paper is the following:
 approaches is challenging. Training is computationally expensive, often done on private datasets of different sizes,
 and, as we will show, hyperparameter choices have significant impact on the final results. We present a replication
 study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and
-training data size. We find that BERT was significantly undertrained, and can match or exceed the performance of every
-model published after it. Our best model achieves state-of-the-art results on GLUE, RACE and SQuAD. These results
-highlight the importance of previously overlooked design choices, and raise questions about the source of recently
-reported improvements. We release our models and code.*
+training data size. We find that BERT was significantly undertrained, and can match or exceed the performance of
+every model published after it. Our best model achieves state-of-the-art results on GLUE, RACE and SQuAD. These
+results highlight the importance of previously overlooked design choices, and raise questions about the source
+of recently reported improvements. We release our models and code.*

 Tips:

- This implementation is the same as :class:`~transformers.BertModel` with a tiny embeddings tweak as well as a setup
-  for Roberta pretrained models.
+- This implementation is the same as :class:`~transformers.BertModel` with a tiny embeddings tweak as well as a
+  setup for Roberta pretrained models.
 - RoBERTa has the same architecture as BERT, but uses a byte-level BPE as a tokenizer (same as GPT-2) and uses a
-  different pretraining scheme.
- RoBERTa doesn't have :obj:`token_type_ids`, you don't need to indicate which token belongs to which segment. Just
-  separate your segments with the separation token :obj:`tokenizer.sep_token` (or :obj:`</s>`)
- :doc:`CamemBERT <camembert>` is a wrapper around RoBERTa. Refer to this page for usage examples.
+  different pre-training scheme.
+- RoBERTa doesn't have `token_type_ids`, you don't need to indicate which token belongs to which segment. Just separate your segments with the separation token `tokenizer.sep_token` (or `</s>`)
+- `Camembert <./camembert.html>`__ is a wrapper around RoBERTa. Refer to this page for usage examples.

 The original code can be found `here <https://github.com/pytorch/fairseq/tree/master/examples/roberta>`_.


 RobertaConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.RobertaConfig
    :members:


 RobertaTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.RobertaTokenizer
    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
@@ -51,105 +50,98 @@ RobertaTokenizer


 RobertaTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.RobertaTokenizerFast
    :members: build_inputs_with_special_tokens


 RobertaModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.RobertaModel
-    :members: forward
+    :members:


 RobertaForCausalLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.RobertaForCausalLM
-    :members: forward
+    :members:


 RobertaForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.RobertaForMaskedLM
-    :members: forward
+    :members:


 RobertaForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.RobertaForSequenceClassification
-    :members: forward
+    :members:


 RobertaForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.RobertaForMultipleChoice
-    :members: forward
+    :members:


 RobertaForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.RobertaForTokenClassification
-    :members: forward
+    :members:


 RobertaForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.RobertaForQuestionAnswering
-    :members: forward
+    :members:


 TFRobertaModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFRobertaModel
-    :members: call
+    :members:


 TFRobertaForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFRobertaForMaskedLM
-    :members: call
+    :members:


 TFRobertaForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFRobertaForSequenceClassification
-    :members: call
+    :members:


 TFRobertaForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFRobertaForMultipleChoice
-    :members: call
+    :members:


 TFRobertaForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFRobertaForTokenClassification
-    :members: call
+    :members:


 TFRobertaForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFRobertaForQuestionAnswering
-    :members: call
-
-
-FlaxRobertaModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxRobertaModel
-    :members: __call__
+    :members:
@@ -1,99 +0,0 @@
-SqueezeBERT
-----------------------------------------------------------------------------------------------------------------------
-
-Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The SqueezeBERT model was proposed in `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, Kurt W. Keutzer. It's a
-bidirectional transformer similar to the BERT model. The key difference between the BERT architecture and the
-SqueezeBERT architecture is that SqueezeBERT uses `grouped convolutions <https://blog.yani.io/filter-group-tutorial>`__
-instead of fully-connected layers for the Q, K, V and FFN layers.
-
-The abstract from the paper is the following:
-
-*Humans read and write hundreds of billions of messages every day. Further, due to the availability of large datasets,
-large computing systems, and better neural network models, natural language processing (NLP) technology has made
-significant strides in understanding, proofreading, and organizing these messages. Thus, there is a significant
-opportunity to deploy NLP in myriad applications to help web users, social networks, and businesses. In particular, we
-consider smartphones and other mobile devices as crucial platforms for deploying NLP models at scale. However, today's
-highly-accurate NLP neural network models such as BERT and RoBERTa are extremely computationally expensive, with
-BERT-base taking 1.7 seconds to classify a text snippet on a Pixel 3 smartphone. In this work, we observe that methods
-such as grouped convolutions have yielded significant speedups for computer vision networks, but many of these
-techniques have not been adopted by NLP neural network designers. We demonstrate how to replace several operations in
-self-attention layers with grouped convolutions, and we use this technique in a novel network architecture called
-SqueezeBERT, which runs 4.3x faster than BERT-base on the Pixel 3 while achieving competitive accuracy on the GLUE test
-set. The SqueezeBERT code will be released.*
-
-Tips:
-
- SqueezeBERT is a model with absolute position embeddings so it's usually advised to pad the inputs on the right
-  rather than the left.
- SqueezeBERT is similar to BERT and therefore relies on the masked language modeling (MLM) objective. It is therefore
-  efficient at predicting masked tokens and at NLU in general, but is not optimal for text generation. Models trained
-  with a causal language modeling (CLM) objective are better in that regard.
- For best results when finetuning on sequence classification tasks, it is recommended to start with the
-  `squeezebert/squeezebert-mnli-headless` checkpoint.
-
-SqueezeBertConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.SqueezeBertConfig
-    :members:
-
-
-SqueezeBertTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.SqueezeBertTokenizer
-    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
-        create_token_type_ids_from_sequences, save_vocabulary
-
-
-SqueezeBertTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.SqueezeBertTokenizerFast
-    :members:
-
-
-SqueezeBertModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.SqueezeBertModel
-    :members:
-
-
-SqueezeBertForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.SqueezeBertForMaskedLM
-    :members:
-
-
-SqueezeBertForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.SqueezeBertForSequenceClassification
-    :members:
-
-
-SqueezeBertForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.SqueezeBertForMultipleChoice
-    :members:
-
-
-SqueezeBertForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.SqueezeBertForTokenClassification
-    :members:
-
-
-SqueezeBertForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.SqueezeBertForQuestionAnswering
-    :members:
@@ -1,123 +1,105 @@
 T5
-----------------------------------------------------------------------------------------------------------------------
-
-**DISCLAIMER:** This model is still a work in progress, if you see something strange, file a `Github Issue
-<https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title>`__.
+----------------------------------------------------
+**DISCLAIMER:** This model is still a work in progress, if you see something strange,
+file a `Github Issue <https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title>`_

 Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

-The T5 model was presented in `Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
-<https://arxiv.org/pdf/1910.10683.pdf>`_ by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang,
-Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu.
+The T5 model was presented in `Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer <https://arxiv.org/pdf/1910.10683.pdf>`_ by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu in 
+Here the abstract: 

-The abstract from the paper is the following:
-
-*Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream
-task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning
-has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of
-transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a
-text-to-text format. Our systematic study compares pretraining objectives, architectures, unlabeled datasets, transfer
-approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration
-with scale and our new "Colossal Clean Crawled Corpus", we achieve state-of-the-art results on many benchmarks covering
-summarization, question answering, text classification, and more. To facilitate future work on transfer learning for
-NLP, we release our dataset, pre-trained models, and code.*
+*Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. 
+In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. 
+Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. 
+By combining the insights from our exploration with scale and our new "Colossal Clean Crawled Corpus", we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. 
+To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code.*

 Tips:

- T5 is an encoder-decoder model pre-trained on a multi-task mixture of unsupervised and supervised tasks and for which
-  each task is converted into a text-to-text format. T5 works well on a variety of tasks out-of-the-box by prepending a
-  different prefix to the input corresponding to each task, e.g., for translation: *translate English to German: ...*,
-  for summarization: *summarize: ...*.
+- T5 is an encoder-decoder model pre-trained on a multi-task mixture of unsupervised 
+  and supervised tasks and for which each task is converted into a text-to-text format.
+  T5 works well on a variety of tasks out-of-the-box by prepending a different prefix to the input corresponding to each task, e.g.: for translation: *translate English to German: ..., summarize: ...*.
+  For more information about which prefix to use, it is easiest to look into Appendix D of the `paper <https://arxiv.org/pdf/1910.10683.pdf>`_ .
+- For sequence to sequence generation, it is recommended to use ``T5ForConditionalGeneration.generate()``. The method takes care of feeding the encoded input via cross-attention layers to the decoder and auto-regressively generates the decoder output.
+- T5 uses relative scalar embeddings. Encoder input padding can be done on the left and on the right.

-  For more information about which prefix to use, it is easiest to look into Appendix D of the `paper
-  <https://arxiv.org/pdf/1910.10683.pdf>`__. - For sequence-to-sequence generation, it is recommended to use
-  :obj:`T5ForConditionalGeneration.generate()``. This method takes care of feeding the encoded input via
-  cross-attention layers to the decoder and auto-regressively generates the decoder output. - T5 uses relative scalar
-  embeddings. Encoder input padding can be done on the left and on the right.
-
-The original code can be found `here <https://github.com/google-research/text-to-text-transfer-transformer>`__.
+The original code can be found `here <https://github.com/google-research/text-to-text-transfer-transformer>`_.

 Training
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

-T5 is an encoder-decoder model and converts all NLP problems into a text-to-text format. It is trained using teacher
-forcing. This means that for training we always need an input sequence and a target sequence. The input sequence is fed
-to the model using :obj:`input_ids``. The target sequence is shifted to the right, i.e., prepended by a start-sequence
-token and fed to the decoder using the :obj:`decoder_input_ids`. In teacher-forcing style, the target sequence is then
-appended by the EOS token and corresponds to the :obj:`labels`. The PAD token is hereby used as the start-sequence
-token. T5 can be trained / fine-tuned both in a supervised and unsupervised fashion.
+T5 is an encoder-decoder model and converts all NLP problems into a text-to-text format. It is trained using teacher forcing.
+This means that for training we always need an input sequence and a target sequence. 
+The input sequence is fed to the model using ``input_ids``. The target sequence is shifted to the right, *i.e.* prepended by a start-sequence token and fed to the decoder using the `decoder_input_ids`. In teacher-forcing style, the target sequence is then appended by the EOS token and corresponds to the ``labels``. The PAD token is hereby used as the start-sequence token.
+T5 can be trained / fine-tuned both in a supervised and unsupervised fashion.

 - Unsupervised denoising training

-  In this setup spans of the input sequence are masked by so-called sentinel tokens (*a.k.a* unique mask tokens) and
-  the output sequence is formed as a concatenation of the same sentinel tokens and the *real* masked tokens. Each
-  sentinel token represents a unique mask token for this sentence and should start with :obj:`<extra_id_0>`,
-  :obj:`<extra_id_1>`, ... up to :obj:`<extra_id_99>`. As a default, 100 sentinel tokens are available in
-  :class:`~transformers.T5Tokenizer`.
+  In this setup spans of the input sequence are masked by so-called sentinel tokens (*a.k.a* unique mask tokens) 
+  and the output sequence is formed as a concatenation of the same sentinel tokens and the *real* masked tokens. 
+  Each sentinel token represents a unique mask token for this sentence and should start with ``<extra_id_0>``, ``<extra_id_1>``, ... up to ``<extra_id_99>``. As a default 100 sentinel tokens are available in ``T5Tokenizer``.
+  *E.g.* the sentence "The cute dog walks in the park" with the masks put on "cute dog" and "the" should be processed as follows: 

-  For instance, the sentence "The cute dog walks in the park" with the masks put on "cute dog" and "the" should be
-  processed as follows:
+::

-.. code-block::
-
-  input_ids = tokenizer('The <extra_id_0> walks in <extra_id_1> park', return_tensors='pt').input_ids
-  labels = tokenizer('<extra_id_0> cute dog <extra_id_1> the <extra_id_2>', return_tensors='pt').input_ids
+  input_ids = tokenizer.encode('The <extra_id_0> walks in <extra_id_1> park', return_tensors='pt')
+  labels = tokenizer.encode('<extra_id_0> cute dog <extra_id_1> the <extra_id_2> </s>', return_tensors='pt')
  # the forward function automatically creates the correct decoder_input_ids
-  loss = model(input_ids=input_ids, labels=labels).loss
+  model(input_ids=input_ids, labels=labels)

 - Supervised training

-  In this setup the input sequence and output sequence are standard sequence-to-sequence input output mapping. In
-  translation, for instance with the input sequence "The house is wonderful." and output sequence "Das Haus ist
-  wunderbar.", the sentences should be processed as follows:
+  In this setup the input sequence and output sequence are standard sequence to sequence input output mapping.
+  In translation, *e.g.* the input sequence "The house is wonderful." and output sequence "Das Haus ist wunderbar." should 
+  be processed as follows:
+  
+::

-.. code-block::
-
-  input_ids = tokenizer('translate English to German: The house is wonderful.', return_tensors='pt').input_ids
-  labels = tokenizer('Das Haus ist wunderbar.', return_tensors='pt').input_ids
+  input_ids = tokenizer.encode('translate English to German: The house is wonderful. </s>', return_tensors='pt')
+  labels = tokenizer.encode('Das Haus ist wunderbar. </s>', return_tensors='pt')
  # the forward function automatically creates the correct decoder_input_ids
-  loss = model(input_ids=input_ids, labels=labels).loss
+  model(input_ids=input_ids, labels=labels)


 T5Config
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.T5Config
    :members:


 T5Tokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.T5Tokenizer
    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
-        create_token_type_ids_from_sequences, prepare_seq2seq_batch, save_vocabulary
+        create_token_type_ids_from_sequences, save_vocabulary


 T5Model
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.T5Model
-    :members: forward
+    :members:


 T5ForConditionalGeneration
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.T5ForConditionalGeneration
-    :members: forward
+    :members:


 TFT5Model
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFT5Model
-    :members: call
+    :members:


 TFT5ForConditionalGeneration
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFT5ForConditionalGeneration
-    :members: call
+    :members:
@@ -1,90 +1,98 @@
 Transformer XL
-----------------------------------------------------------------------------------------------------------------------
+----------------------------------------------------

 Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

-The Transformer-XL model was proposed in `Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context
-<https://arxiv.org/abs/1901.02860>`__ by Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan
-Salakhutdinov. It's a causal (uni-directional) transformer with relative positioning (sinusoïdal) embeddings which can
-reuse previously computed hidden-states to attend to longer context (memory). This model also uses adaptive softmax
-inputs and outputs (tied).
+The Transformer-XL model was proposed in
+`Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context <https://arxiv.org/abs/1901.02860>`__
+by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
+It's a causal (uni-directional) transformer with relative positioning (sinusoïdal) embeddings which can reuse
+previously computed hidden-states to attend to longer context (memory).
+This model also uses adaptive softmax inputs and outputs (tied).

 The abstract from the paper is the following:

 *Transformers have a potential of learning longer-term dependency, but are limited by a fixed-length context in the
 setting of language modeling. We propose a novel neural architecture Transformer-XL that enables learning dependency
-beyond a fixed length without disrupting temporal coherence. It consists of a segment-level recurrence mechanism and a
-novel positional encoding scheme. Our method not only enables capturing longer-term dependency, but also resolves the
-context fragmentation problem. As a result, Transformer-XL learns dependency that is 80% longer than RNNs and 450%
-longer than vanilla Transformers, achieves better performance on both short and long sequences, and is up to 1,800+
-times faster than vanilla Transformers during evaluation. Notably, we improve the state-of-the-art results of
-bpc/perplexity to 0.99 on enwiki8, 1.08 on text8, 18.3 on WikiText-103, 21.8 on One Billion Word, and 54.5 on Penn
-Treebank (without finetuning). When trained only on WikiText-103, Transformer-XL manages to generate reasonably
+beyond a fixed length without disrupting temporal coherence. It consists of a segment-level recurrence mechanism and
+a novel positional encoding scheme. Our method not only enables capturing longer-term dependency, but also resolves
+the context fragmentation problem. As a result, Transformer-XL learns dependency that is 80% longer than RNNs and
+450% longer than vanilla Transformers, achieves better performance on both short and long sequences, and is up
+to 1,800+ times faster than vanilla Transformers during evaluation. Notably, we improve the state-of-the-art results
+of bpc/perplexity to 0.99 on enwiki8, 1.08 on text8, 18.3 on WikiText-103, 21.8 on One Billion Word, and 54.5 on
+Penn Treebank (without finetuning). When trained only on WikiText-103, Transformer-XL manages to generate reasonably
 coherent, novel text articles with thousands of tokens.*

 Tips:

- Transformer-XL uses relative sinusoidal positional embeddings. Padding can be done on the left or on the right. The
-  original implementation trains on SQuAD with padding on the left, therefore the padding defaults are set to left.
+- Transformer-XL uses relative sinusoidal positional embeddings. Padding can be done on the left or on the right.
+  The original implementation trains on SQuAD with padding on the left, therefore the padding defaults are set to left.
 - Transformer-XL is one of the few models that has no sequence length limit.

-The original code can be found `here <https://github.com/kimiyoung/transformer-xl>`__.
+The original code can be found `here <https://github.com/kimiyoung/transformer-xl>`_.


 TransfoXLConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TransfoXLConfig
    :members:


 TransfoXLTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TransfoXLTokenizer
    :members: save_vocabulary


+TransfoXLTokenizerFast
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TransfoXLTokenizerFast
+    :members:
+
+
 TransfoXL specific outputs
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~

-.. autoclass:: transformers.models.transfo_xl.modeling_transfo_xl.TransfoXLModelOutput
+.. autoclass:: transformers.modeling_transfo_xl.TransfoXLModelOutput
    :members:

-.. autoclass:: transformers.models.transfo_xl.modeling_transfo_xl.TransfoXLLMHeadModelOutput
+.. autoclass:: transformers.modeling_transfo_xl.TransfoXLLMHeadModelOutput
    :members:

-.. autoclass:: transformers.models.transfo_xl.modeling_tf_transfo_xl.TFTransfoXLModelOutput
+.. autoclass:: transformers.modeling_tf_transfo_xl.TFTransfoXLModelOutput
    :members:

-.. autoclass:: transformers.models.transfo_xl.modeling_tf_transfo_xl.TFTransfoXLLMHeadModelOutput
+.. autoclass:: transformers.modeling_tf_transfo_xl.TFTransfoXLLMHeadModelOutput
    :members:


 TransfoXLModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TransfoXLModel
-    :members: forward
+    :members:


 TransfoXLLMHeadModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TransfoXLLMHeadModel
-    :members: forward
+    :members:


 TFTransfoXLModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFTransfoXLModel
-    :members: call
+    :members:


 TFTransfoXLLMHeadModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFTransfoXLLMHeadModel
-    :members: call
+    :members:
@@ -1,46 +1,46 @@
 XLM
-----------------------------------------------------------------------------------------------------------------------
+----------------------------------------------------

 Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

-The XLM model was proposed in `Cross-lingual Language Model Pretraining <https://arxiv.org/abs/1901.07291>`__ by
-Guillaume Lample, Alexis Conneau. It's a transformer pretrained using one of the following objectives:
+The XLM model was proposed in `Cross-lingual Language Model Pretraining <https://arxiv.org/abs/1901.07291>`_
+by Guillaume Lample*, Alexis Conneau*. It's a transformer pre-trained using one of the following objectives:

 - a causal language modeling (CLM) objective (next token prediction),
- a masked language modeling (MLM) objective (BERT-like), or
- a Translation Language Modeling (TLM) object (extension of BERT's MLM to multiple language inputs)
+- a masked language modeling (MLM) objective (Bert-like), or
+- a Translation Language Modeling (TLM) object (extension of Bert's MLM to multiple language inputs)

 The abstract from the paper is the following:

 *Recent studies have demonstrated the efficiency of generative pretraining for English natural language understanding.
-In this work, we extend this approach to multiple languages and show the effectiveness of cross-lingual pretraining. We
-propose two methods to learn cross-lingual language models (XLMs): one unsupervised that only relies on monolingual
+In this work, we extend this approach to multiple languages and show the effectiveness of cross-lingual pretraining.
+We propose two methods to learn cross-lingual language models (XLMs): one unsupervised that only relies on monolingual
 data, and one supervised that leverages parallel data with a new cross-lingual language model objective. We obtain
-state-of-the-art results on cross-lingual classification, unsupervised and supervised machine translation. On XNLI, our
-approach pushes the state of the art by an absolute gain of 4.9% accuracy. On unsupervised machine translation, we
-obtain 34.3 BLEU on WMT'16 German-English, improving the previous state of the art by more than 9 BLEU. On supervised
-machine translation, we obtain a new state of the art of 38.5 BLEU on WMT'16 Romanian-English, outperforming the
-previous best approach by more than 4 BLEU. Our code and pretrained models will be made publicly available.*
+state-of-the-art results on cross-lingual classification, unsupervised and supervised machine translation. On XNLI,
+our approach pushes the state of the art by an absolute gain of 4.9% accuracy. On unsupervised machine translation,
+we obtain 34.3 BLEU on WMT'16 German-English, improving the previous state of the art by more than 9 BLEU. On
+supervised machine translation, we obtain a new state of the art of 38.5 BLEU on WMT'16 Romanian-English, outperforming
+the previous best approach by more than 4 BLEU. Our code and pretrained models will be made publicly available.*

 Tips:

 - XLM has many different checkpoints, which were trained using different objectives: CLM, MLM or TLM. Make sure to
  select the correct objective for your task (e.g. MLM checkpoints are not suitable for generation).
- XLM has multilingual checkpoints which leverage a specific :obj:`lang` parameter. Check out the :doc:`multi-lingual
-  <../multilingual>` page for more information.
+- XLM has multilingual checkpoints which leverage a specific `lang` parameter. Check out the
+  `multi-lingual <../multilingual.html>`__ page for more information.

-The original code can be found `here <https://github.com/facebookresearch/XLM/>`__.
+The original code can be found `here <https://github.com/facebookresearch/XLM/>`_.


 XLMConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.XLMConfig
    :members:

 XLMTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.XLMTokenizer
    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
@@ -48,99 +48,99 @@ XLMTokenizer


 XLM specific outputs
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~

-.. autoclass:: transformers.models.xlm.modeling_xlm.XLMForQuestionAnsweringOutput
+.. autoclass:: transformers.modeling_xlm.XLMForQuestionAnsweringOutput
    :members:


 XLMModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.XLMModel
-    :members: forward
+    :members:


 XLMWithLMHeadModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.XLMWithLMHeadModel
-    :members: forward
+    :members:


 XLMForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.XLMForSequenceClassification
-    :members: forward
+    :members:


 XLMForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.XLMForMultipleChoice
-    :members: forward
+    :members:


 XLMForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.XLMForTokenClassification
-    :members: forward
+    :members:


 XLMForQuestionAnsweringSimple
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.XLMForQuestionAnsweringSimple
-    :members: forward
+    :members:


 XLMForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.XLMForQuestionAnswering
-    :members: forward
+    :members:


 TFXLMModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFXLMModel
-    :members: call
+    :members:


 TFXLMWithLMHeadModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFXLMWithLMHeadModel
-    :members: call
+    :members:


 TFXLMForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFXLMForSequenceClassification
-    :members: call
+    :members:


 TFXLMForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFXLMForMultipleChoice
-    :members: call
+    :members:


 TFXLMForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFXLMForTokenClassification
-    :members: call
+    :members:



 TFXLMForQuestionAnsweringSimple
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFXLMForQuestionAnsweringSimple
-    :members: call
+    :members:
@@ -1,75 +0,0 @@
-XLM-ProphetNet
-----------------------------------------------------------------------------------------------------------------------
-
-**DISCLAIMER:** If you see something strange, file a `Github Issue
-<https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title>`__ and assign
-@patrickvonplaten
-
-
-Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The XLM-ProphetNet model was proposed in `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, Ming Zhou on 13 Jan, 2020.
-
-XLM-ProphetNet is an encoder-decoder model and can predict n-future tokens for "ngram" language modeling instead of
-just the next token. Its architecture is identical to ProhpetNet, but the model was trained on the multi-lingual
-"wiki100" Wikipedia dump.
-
-The abstract from the paper is the following:
-
-*In this paper, we present a new sequence-to-sequence pretraining model called ProphetNet, which introduces a novel
-self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism. Instead of
-the optimization of one-step ahead prediction in traditional sequence-to-sequence model, the ProphetNet is optimized by
-n-step ahead prediction which predicts the next n tokens simultaneously based on previous context tokens at each time
-step. The future n-gram prediction explicitly encourages the model to plan for the future tokens and prevent
-overfitting on strong local correlations. We pre-train ProphetNet using a base scale dataset (16GB) and a large scale
-dataset (160GB) respectively. Then we conduct experiments on CNN/DailyMail, Gigaword, and SQuAD 1.1 benchmarks for
-abstractive summarization and question generation tasks. Experimental results show that ProphetNet achieves new
-state-of-the-art results on all these datasets compared to the models using the same scale pretraining corpus.*
-
-The Authors' code can be found `here <https://github.com/microsoft/ProphetNet>`__.
-
-XLMProphetNetConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.XLMProphetNetConfig
-    :members:
-
-
-XLMProphetNetTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.XLMProphetNetTokenizer
-    :members:
-
-
-XLMProphetNetModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.XLMProphetNetModel
-
-
-XLMProphetNetEncoder
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.XLMProphetNetEncoder
-
-
-XLMProphetNetDecoder
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.XLMProphetNetDecoder
-
-
-XLMProphetNetForConditionalGeneration
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.XLMProphetNetForConditionalGeneration
-
-
-XLMProphetNetForCausalLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.XLMProphetNetForCausalLM
@@ -1,49 +1,48 @@
 XLM-RoBERTa
-----------------------------------------------------------------------------------------------------------------------
+------------------------------------------

 Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

-The XLM-RoBERTa model was proposed in `Unsupervised Cross-lingual Representation Learning at Scale
-<https://arxiv.org/abs/1911.02116>`__ by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume
-Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov. It is based on Facebook's
-RoBERTa model released in 2019. It is a large multi-lingual language model, trained on 2.5TB of filtered CommonCrawl
-data.
+The XLM-RoBERTa model was proposed in `Unsupervised Cross-lingual Representation Learning at Scale <https://arxiv.org/abs/1911.02116>`__
+by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán,
+Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov. It is based on Facebook's RoBERTa model released in 2019.
+It is a large multi-lingual language model, trained on 2.5TB of filtered CommonCrawl data.

 The abstract from the paper is the following:

-*This paper shows that pretraining multilingual language models at scale leads to significant performance gains for a
-wide range of cross-lingual transfer tasks. We train a Transformer-based masked language model on one hundred
+*This paper shows that pretraining multilingual language models at scale leads to significant performance gains for
+a wide range of cross-lingual transfer tasks. We train a Transformer-based masked language model on one hundred
 languages, using more than two terabytes of filtered CommonCrawl data. Our model, dubbed XLM-R, significantly
-outperforms multilingual BERT (mBERT) on a variety of cross-lingual benchmarks, including +13.8% average accuracy on
-XNLI, +12.3% average F1 score on MLQA, and +2.1% average F1 score on NER. XLM-R performs particularly well on
-low-resource languages, improving 11.8% in XNLI accuracy for Swahili and 9.2% for Urdu over the previous XLM model. We
-also present a detailed empirical evaluation of the key factors that are required to achieve these gains, including the
-trade-offs between (1) positive transfer and capacity dilution and (2) the performance of high and low resource
-languages at scale. Finally, we show, for the first time, the possibility of multilingual modeling without sacrificing
-per-language performance; XLM-Ris very competitive with strong monolingual models on the GLUE and XNLI benchmarks. We
-will make XLM-R code, data, and models publicly available.*
+outperforms multilingual BERT (mBERT) on a variety of cross-lingual benchmarks, including +13.8% average accuracy
+on XNLI, +12.3% average F1 score on MLQA, and +2.1% average F1 score on NER. XLM-R performs particularly well on
+low-resource languages, improving 11.8% in XNLI accuracy for Swahili and 9.2% for Urdu over the previous XLM model.
+We also present a detailed empirical evaluation of the key factors that are required to achieve these gains,
+including the trade-offs between (1) positive transfer and capacity dilution and (2) the performance of high and
+low resource languages at scale. Finally, we show, for the first time, the possibility of multilingual modeling
+without sacrificing per-language performance; XLM-Ris very competitive with strong monolingual models on the GLUE
+and XNLI benchmarks. We will make XLM-R code, data, and models publicly available.*

 Tips:

- XLM-RoBERTa is a multilingual model trained on 100 different languages. Unlike some XLM multilingual models, it does
-  not require :obj:`lang` tensors to understand which language is used, and should be able to determine the correct
+- XLM-R is a multilingual model trained on 100 different languages. Unlike some XLM multilingual models, it does
+  not require `lang` tensors to understand which language is used, and should be able to determine the correct
  language from the input ids.
- This implementation is the same as RoBERTa. Refer to the :doc:`documentation of RoBERTa <roberta>` for usage examples
-  as well as the information relative to the inputs and outputs.
+- This implementation is the same as RoBERTa. Refer to the `documentation of RoBERTa <./roberta.html>`__ for usage
+  examples as well as the information relative to the inputs and outputs.

-The original code can be found `here <https://github.com/pytorch/fairseq/tree/master/examples/xlmr>`__.
+The original code can be found `here <https://github.com/pytorch/fairseq/tree/master/examples/xlmr>`_.


 XLMRobertaConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.XLMRobertaConfig
    :members:


 XLMRobertaTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.XLMRobertaTokenizer
    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
@@ -51,91 +50,91 @@ XLMRobertaTokenizer


 XLMRobertaModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.XLMRobertaModel
-    :members: forward
+    :members:


 XLMRobertaForCausalLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.XLMRobertaForCausalLM
-    :members: forward
+    :members:


 XLMRobertaForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.XLMRobertaForMaskedLM
-    :members: forward
+    :members:


 XLMRobertaForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.XLMRobertaForSequenceClassification
-    :members: forward
+    :members:


 XLMRobertaForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.XLMRobertaForMultipleChoice
-    :members: forward
+    :members:


 XLMRobertaForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.XLMRobertaForTokenClassification
-    :members: forward
+    :members:


 XLMRobertaForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.XLMRobertaForQuestionAnswering
-    :members: forward
+    :members:


 TFXLMRobertaModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFXLMRobertaModel
-    :members: call
+    :members:


 TFXLMRobertaForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFXLMRobertaForMaskedLM
-    :members: call
+    :members:


 TFXLMRobertaForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFXLMRobertaForSequenceClassification
-    :members: call
+    :members:


 TFXLMRobertaForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFXLMRobertaForMultipleChoice
-    :members: call
+    :members:


 TFXLMRobertaForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFXLMRobertaForTokenClassification
-    :members: call
+    :members:


 TFXLMRobertaForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFXLMRobertaForQuestionAnswering
-    :members: call
+    :members:
@@ -1,14 +1,14 @@
 XLNet
-----------------------------------------------------------------------------------------------------------------------
+----------------------------------------------------

 Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~

-The XLNet model was proposed in `XLNet: Generalized Autoregressive Pretraining for Language Understanding
-<https://arxiv.org/abs/1906.08237>`_ by Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov,
-Quoc V. Le. XLnet is an extension of the Transformer-XL model pre-trained using an autoregressive method to learn
-bidirectional contexts by maximizing the expected likelihood over all permutations of the input sequence factorization
-order.
+The XLNet model was proposed in `XLNet: Generalized Autoregressive Pretraining for Language Understanding <https://arxiv.org/abs/1906.08237>`_
+by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
+XLnet is an extension of the Transformer-XL model pre-trained using an autoregressive method
+to learn bidirectional contexts by maximizing the expected likelihood over all permutations
+of the input sequence factorization order.

 The abstract from the paper is the following:

@@ -16,34 +16,34 @@ The abstract from the paper is the following:
 better performance than pretraining approaches based on autoregressive language modeling. However, relying on
 corrupting the input with masks, BERT neglects dependency between the masked positions and suffers from a
 pretrain-finetune discrepancy. In light of these pros and cons, we propose XLNet, a generalized autoregressive
-pretraining method that (1) enables learning bidirectional contexts by maximizing the expected likelihood over all
-permutations of the factorization order and (2) overcomes the limitations of BERT thanks to its autoregressive
-formulation. Furthermore, XLNet integrates ideas from Transformer-XL, the state-of-the-art autoregressive model, into
-pretraining. Empirically, under comparable experiment settings, XLNet outperforms BERT on 20 tasks, often by a large
-margin, including question answering, natural language inference, sentiment analysis, and document ranking.*
+pretraining method that (1) enables learning bidirectional contexts by maximizing the expected likelihood over
+all permutations of the factorization order and (2) overcomes the limitations of BERT thanks to its autoregressive
+formulation. Furthermore, XLNet integrates ideas from Transformer-XL, the state-of-the-art autoregressive model,
+into pretraining. Empirically, under comparable experiment settings, XLNet outperforms BERT on 20 tasks, often by
+a large margin, including question answering, natural language inference, sentiment analysis, and document ranking.*

 Tips:

- The specific attention pattern can be controlled at training and test time using the :obj:`perm_mask` input.
- Due to the difficulty of training a fully auto-regressive model over various factorization order, XLNet is pretrained
-  using only a sub-set of the output tokens as target which are selected with the :obj:`target_mapping` input.
- To use XLNet for sequential decoding (i.e. not in fully bi-directional setting), use the :obj:`perm_mask` and
-  :obj:`target_mapping` inputs to control the attention span and outputs (see examples in
-  `examples/text-generation/run_generation.py`)
+- The specific attention pattern can be controlled at training and test time using the `perm_mask` input.
+- Due to the difficulty of training a fully auto-regressive model over various factorization order,
+  XLNet is pretrained using only a sub-set of the output tokens as target which are selected
+  with the `target_mapping` input.
+- To use XLNet for sequential decoding (i.e. not in fully bi-directional setting), use the `perm_mask` and
+  `target_mapping` inputs to control the attention span and outputs (see examples in `examples/text-generation/run_generation.py`)
 - XLNet is one of the few models that has no sequence length limit.

-The original code can be found `here <https://github.com/zihangdai/xlnet/>`__.
+The original code can be found `here <https://github.com/zihangdai/xlnet/>`_.


 XLNetConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.XLNetConfig
    :members:


 XLNetTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.XLNetTokenizer
    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
@@ -51,134 +51,134 @@ XLNetTokenizer


 XLNet specific outputs
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~

-.. autoclass:: transformers.models.xlnet.modeling_xlnet.XLNetModelOutput
+.. autoclass:: transformers.modeling_xlnet.XLNetModelOutput
    :members:

-.. autoclass:: transformers.models.xlnet.modeling_xlnet.XLNetLMHeadModelOutput
+.. autoclass:: transformers.modeling_xlnet.XLNetLMHeadModelOutput
    :members:

-.. autoclass:: transformers.models.xlnet.modeling_xlnet.XLNetForSequenceClassificationOutput
+.. autoclass:: transformers.modeling_xlnet.XLNetForSequenceClassificationOutput
    :members:

-.. autoclass:: transformers.models.xlnet.modeling_xlnet.XLNetForMultipleChoiceOutput
+.. autoclass:: transformers.modeling_xlnet.XLNetForMultipleChoiceOutput
    :members:

-.. autoclass:: transformers.models.xlnet.modeling_xlnet.XLNetForTokenClassificationOutput
+.. autoclass:: transformers.modeling_xlnet.XLNetForTokenClassificationOutput
    :members:

-.. autoclass:: transformers.models.xlnet.modeling_xlnet.XLNetForQuestionAnsweringSimpleOutput
+.. autoclass:: transformers.modeling_xlnet.XLNetForQuestionAnsweringSimpleOutput
    :members:

-.. autoclass:: transformers.models.xlnet.modeling_xlnet.XLNetForQuestionAnsweringOutput
+.. autoclass:: transformers.modeling_xlnet.XLNetForQuestionAnsweringOutput
    :members:

-.. autoclass:: transformers.models.xlnet.modeling_tf_xlnet.TFXLNetModelOutput
+.. autoclass:: transformers.modeling_tf_xlnet.TFXLNetModelOutput
    :members:

-.. autoclass:: transformers.models.xlnet.modeling_tf_xlnet.TFXLNetLMHeadModelOutput
+.. autoclass:: transformers.modeling_tf_xlnet.TFXLNetLMHeadModelOutput
    :members:

-.. autoclass:: transformers.models.xlnet.modeling_tf_xlnet.TFXLNetForSequenceClassificationOutput
+.. autoclass:: transformers.modeling_tf_xlnet.TFXLNetForSequenceClassificationOutput
    :members:

-.. autoclass:: transformers.models.xlnet.modeling_tf_xlnet.TFXLNetForMultipleChoiceOutput
+.. autoclass:: transformers.modeling_tf_xlnet.TFXLNetForMultipleChoiceOutput
    :members:

-.. autoclass:: transformers.models.xlnet.modeling_tf_xlnet.TFXLNetForTokenClassificationOutput
+.. autoclass:: transformers.modeling_tf_xlnet.TFXLNetForTokenClassificationOutput
    :members:

-.. autoclass:: transformers.models.xlnet.modeling_tf_xlnet.TFXLNetForQuestionAnsweringSimpleOutput
+.. autoclass:: transformers.modeling_tf_xlnet.TFXLNetForQuestionAnsweringSimpleOutput
    :members:


 XLNetModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.XLNetModel
-    :members: forward
+    :members:


 XLNetLMHeadModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.XLNetLMHeadModel
-    :members: forward
+    :members:


 XLNetForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.XLNetForSequenceClassification
-    :members: forward
+    :members:


 XLNetForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.XLNetForMultipleChoice
-    :members: forward
+    :members:


 XLNetForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.XLNetForTokenClassification
-    :members: forward
+    :members:


 XLNetForQuestionAnsweringSimple
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.XLNetForQuestionAnsweringSimple
-    :members: forward
+    :members:


 XLNetForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.XLNetForQuestionAnswering
-    :members: forward
+    :members:


 TFXLNetModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFXLNetModel
-    :members: call
+    :members:


 TFXLNetLMHeadModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFXLNetLMHeadModel
-    :members: call
+    :members:


 TFXLNetForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFXLNetForSequenceClassification
-    :members: call
+    :members:


 TFLNetForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFXLNetForMultipleChoice
-    :members: call
+    :members:


 TFXLNetForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFXLNetForTokenClassification
-    :members: call
+    :members:


 TFXLNetForQuestionAnsweringSimple
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFXLNetForQuestionAnsweringSimple
-    :members: call
+    :members:
@@ -1,283 +1,224 @@
-Model sharing and uploading
-=======================================================================================================================
-
-In this page, we will show you how to share a model you have trained or fine-tuned on new data with the community on
-the `model hub <https://huggingface.co/models>`__.
-
-.. note::
-
-    You will need to create an account on `huggingface.co <https://huggingface.co/join>`__ for this.
-
-    Optionally, you can join an existing organization or create a new one.
-
-Prepare your model for uploading
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-We have seen in the :doc:`training tutorial <training>`: how to fine-tune a model on a given task. You have probably
-done something similar on your task, either using the model directly in your own training loop or using the
-:class:`~.transformers.Trainer`/:class:`~.transformers.TFTrainer` class. Let's see how you can share the result on the
-`model hub <https://huggingface.co/models>`__.
-
-Model versioning
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Since version v3.5.0, the model hub has built-in model versioning based on git and git-lfs. It is based on the paradigm
-that one model *is* one repo.
-
-This allows:
-
- built-in versioning
- access control
- scalability
-
-This is built around *revisions*, which is a way to pin a specific version of a model, using a commit hash, tag or
-branch.
-
-For instance:
-
-.. code-block::
-
-    >>> model = AutoModel.from_pretrained(
-    >>>   "julien-c/EsperBERTo-small",
-    >>>   revision="v2.0.1" # tag name, or branch name, or commit hash
-    >>> )
-
-Basic steps
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-In order to upload a model, you'll need to first create a git repo. This repo will live on the model hub, allowing
-users to clone it and you (and your organization members) to push to it.
-
-You can create a model repo directly from the website, `here <https://huggingface.co/new>`.
-
-Alternatively, you can use the ``transformers-cli``. The next steps describe that process:
-
-Go to a terminal and run the following command. It should be in the virtual environment where you installed 🤗
-Transformers, since that command :obj:`transformers-cli` comes from the library.
-
-.. code-block:: bash
-
-    transformers-cli login
-
-
-Once you are logged in with your model hub credentials, you can start building your repositories. To create a repo:
-
-.. code-block:: bash
-
-    transformers-cli repo create your-model-name
-
-This creates a repo on the model hub, which can be cloned.
-
-.. code-block:: bash
-
-    git clone https://huggingface.co/username/your-model-name
-
-    # Make sure you have git-lfs installed
-    # (https://git-lfs.github.com/)
-    git lfs install
-
-When you have your local clone of your repo and lfs installed, you can then add/remove from that clone as you would
-with any other git repo.
-
-.. code-block:: bash
-
-    # Commit as usual
-    cd your-model-name
-    echo "hello" >> README.md
-    git add . && git commit -m "Update from $USER"
-
-We are intentionally not wrapping git too much, so as to stay intuitive and easy-to-use.
-
-
-Make your model work on all frameworks
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. 
-    TODO Sylvain: make this automatic during the upload
-
-You probably have your favorite framework, but so will other users! That's why it's best to upload your model with both
-PyTorch `and` TensorFlow checkpoints to make it easier to use (if you skip this step, users will still be able to load
-your model in another framework, but it will be slower, as it will have to be converted on the fly). Don't worry, it's
-super easy to do (and in a future version, it will all be automatic). You will need to install both PyTorch and
-TensorFlow for this step, but you don't need to worry about the GPU, so it should be very easy. Check the `TensorFlow
-installation page <https://www.tensorflow.org/install/pip#tensorflow-2.0-rc-is-available>`__ and/or the `PyTorch
-installation page <https://pytorch.org/get-started/locally/#start-locally>`__ to see how.
-
-First check that your model class exists in the other framework, that is try to import the same model by either adding
-or removing TF. For instance, if you trained a :class:`~transformers.DistilBertForSequenceClassification`, try to type
-
-.. code-block::
-
-    >>> from transformers import TFDistilBertForSequenceClassification
-
-and if you trained a :class:`~transformers.TFDistilBertForSequenceClassification`, try to type
-
-.. code-block::
-
-    >>> from transformers import DistilBertForSequenceClassification
-
-This will give back an error if your model does not exist in the other framework (something that should be pretty rare
-since we're aiming for full parity between the two frameworks). In this case, skip this and go to the next step.
-
-Now, if you trained your model in PyTorch and have to create a TensorFlow version, adapt the following code to your
-model class:
-
-.. code-block::
-
-    >>> tf_model = TFDistilBertForSequenceClassification.from_pretrained("path/to/awesome-name-you-picked", from_pt=True)
-    >>> tf_model.save_pretrained("path/to/awesome-name-you-picked")
-
-and if you trained your model in TensorFlow and have to create a PyTorch version, adapt the following code to your
-model class:
-
-.. code-block::
-
-    >>> pt_model = DistilBertForSequenceClassification.from_pretrained("path/to/awesome-name-you-picked", from_tf=True)
-    >>> pt_model.save_pretrained("path/to/awesome-name-you-picked")
-
-That's all there is to it!
-
-Check the directory before pushing to the model hub.
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Make sure there are no garbage files in the directory you'll upload. It should only have:
-
- a `config.json` file, which saves the :doc:`configuration <main_classes/configuration>` of your model ;
- a `pytorch_model.bin` file, which is the PyTorch checkpoint (unless you can't have it for some reason) ;
- a `tf_model.h5` file, which is the TensorFlow checkpoint (unless you can't have it for some reason) ;
- a `special_tokens_map.json`, which is part of your :doc:`tokenizer <main_classes/tokenizer>` save;
- a `tokenizer_config.json`, which is part of your :doc:`tokenizer <main_classes/tokenizer>` save;
- files named `vocab.json`, `vocab.txt`, `merges.txt`, or similar, which contain the vocabulary of your tokenizer, part
-  of your :doc:`tokenizer <main_classes/tokenizer>` save;
- maybe a `added_tokens.json`, which is part of your :doc:`tokenizer <main_classes/tokenizer>` save.
-
-Other files can safely be deleted.
-
-
-Uploading your files
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Once the repo is cloned, you can add the model, configuration and tokenizer files. For instance, saving the model and
-tokenizer files:
-
-.. code-block::
-
-    >>> model.save_pretrained("path/to/repo/clone/your-model-name")
-    >>> tokenizer.save_pretrained("path/to/repo/clone/your-model-name")
-
-Or, if you're using the Trainer API
-
-.. code-block::
-
-    >>> trainer.save_model("path/to/awesome-name-you-picked")
-    >>> tokenizer.save_pretrained("path/to/repo/clone/your-model-name")
-
-You can then add these files to the staging environment and verify that they have been correctly staged with the ``git
-status`` command:
-
-.. code-block:: bash
-
-    git add --all
-    git status
-
-Finally, the files should be comitted:
-
-.. code-block:: bash
-
-    git commit -m "First version of the your-model-name model and tokenizer."
-
-And pushed to the remote:
-
-.. code-block:: bash
-
-    git push
-
-This will upload the folder containing the weights, tokenizer and configuration we have just prepared.
-
-
-Add a model card
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-To make sure everyone knows what your model can do, what its limitations and potential bias or ethetical
-considerations, please add a README.md model card to the 🤗 Transformers repo under `model_cards/`. It should then be
-placed in a subfolder with your username or organization, then another subfolder named like your model
-(`awesome-name-you-picked`). Or just click on the "Create a model card on GitHub" button on the model page, it will get
-you directly to the right location. If you need one, `here <https://github.com/huggingface/model_card>`__ is a model
-card template (meta-suggestions are welcome).
-
-If your model is fine-tuned from another model coming from the model hub (all 🤗 Transformers pretrained models do),
-don't forget to link to its model card so that people can fully trace how your model was built.
-
-If you have never made a pull request to the 🤗 Transformers repo, look at the :doc:`contributing guide <contributing>`
-to see the steps to follow.
-
-.. note::
-
-    You can also send your model card in the folder you uploaded with the CLI by placing it in a `README.md` file
-    inside `path/to/awesome-name-you-picked/`.
-
-Using your model
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Your model now has a page on huggingface.co/models 🔥
-
-Anyone can load it from code:
-
-.. code-block::
-
-    >>> tokenizer = AutoTokenizer.from_pretrained("namespace/awesome-name-you-picked")
-    >>> model = AutoModel.from_pretrained("namespace/awesome-name-you-picked")
-
-
-You may specify a revision by using the ``revision`` flag in the ``from_pretrained`` method:
-
-.. code-block::
-
-    >>> tokenizer = AutoTokenizer.from_pretrained(
-    >>>   "julien-c/EsperBERTo-small",
-    >>>   revision="v2.0.1" # tag name, or branch name, or commit hash
-    >>> )
-
-Workflow in a Colab notebook
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-If you're in a Colab notebook (or similar) with no direct access to a terminal, here is the workflow you can use to
-upload your model. You can execute each one of them in a cell by adding a ! at the beginning.
-
-First you need to install `git-lfs` in the environment used by the notebook:
-
-.. code-block:: bash
-
-    sudo apt-get install git-lfs
-
-Then you can use the :obj:`transformers-cli` to create your new repo:
-
-
-.. code-block:: bash
-
-    transformers-cli login
-    transformers-cli repo create your-model-name
-
-Once it's created, you can clone it and configure it (replace username by your username on huggingface.co):
-
-.. code-block:: bash
-
-    git clone https://username:password@huggingface.co/username/your-model-name
-    # Alternatively if you have a token,
-    # you can use it instead of your password
-    git clone https://username:token@huggingface.co/username/your-model-name
-
-    cd your-model-name
-    git lfs install
-    git config --global user.email "email@example.com"
-    # Tip: using the same email than for your huggingface.co account will link your commits to your profile
-    git config --global user.name "Your name"
-
-Once you've saved your model inside, and your clone is setup with the right remote URL, you can add it and push it with
-usual git commands.
-
-.. code-block:: bash
-
-    git add .
-    git commit -m "Initial commit"
-    git push
+Model sharing and uploading
+===========================
+
+In this page, we will show you how to share a model you have trained or fine-tuned on new data with the community on
+the `model hub <https://huggingface.co/models>`__.
+
+.. note::
+
+    You will need to create an account on `huggingface.co <https://huggingface.co/join>`__ for this.
+
+    Optionally, you can join an existing organization or create a new one.
+
+Prepare your model for uploading
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+We have seen in the :doc:`training tutorial <training>`: how to fine-tune a model on a given task. You have probably
+done something similar on your task, either using the model directly in your own training loop or using the
+:class:`~.transformers.Trainer`/:class:`~.transformers.TFTrainer` class. Let's see how you can share the result on
+the `model hub <https://huggingface.co/models>`__.
+
+Basic steps
+^^^^^^^^^^^
+
+.. 
+    When #5258 is merged, we can remove the need to create the directory.
+
+First, pick a directory with the name you want your model to have on the model hub (its full name will then be
+`username/awesome-name-you-picked` or `organization/awesome-name-you-picked`) and create it with either
+
+::
+
+    mkdir path/to/awesome-name-you-picked
+
+or in python
+
+::
+
+    import os
+    os.makedirs("path/to/awesome-name-you-picked")
+
+then you can save your model and tokenizer with:
+
+::
+
+    model.save_pretrained("path/to/awesome-name-you-picked")
+    tokenizer.save_pretrained("path/to/awesome-name-you-picked")
+
+Or, if you're using the Trainer API
+
+::
+
+    trainer.save_model("path/to/awesome-name-you-picked")
+    tokenizer.save_pretrained("path/to/awesome-name-you-picked")
+
+Make your model work on all frameworks
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. 
+    TODO Sylvain: make this automatic during the upload
+
+You probably have your favorite framework, but so will other users! That's why it's best to upload your model with both
+PyTorch `and` TensorFlow checkpoints to make it easier to use (if you skip this step, users will still be able to load
+your model in another framework, but it will be slower, as it will have to be converted on the fly). Don't worry, it's super easy to do (and in a future version,
+it will all be automatic). You will need to install both PyTorch and TensorFlow for this step, but you don't need to
+worry about the GPU, so it should be very easy. Check the
+`TensorFlow installation page <https://www.tensorflow.org/install/pip#tensorflow-2.0-rc-is-available>`__ 
+and/or the `PyTorch installation page <https://pytorch.org/get-started/locally/#start-locally>`__ to see how.
+
+First check that your model class exists in the other framework, that is try to import the same model by either adding
+or removing TF. For instance, if you trained a :class:`~transformers.DistilBertForSequenceClassification`, try to
+type
+
+::
+
+    from transformers import TFDistilBertForSequenceClassification
+
+and if you trained a :class:`~transformers.TFDistilBertForSequenceClassification`, try to
+type
+
+::
+
+    from transformers import DistilBertForSequenceClassification
+
+This will give back an error if your model does not exist in the other framework (something that should be pretty rare
+since we're aiming for full parity between the two frameworks). In this case, skip this and go to the next step.
+
+Now, if you trained your model in PyTorch and have to create a TensorFlow version, adapt the following code to your
+model class:
+
+::
+
+    tf_model = TFDistilBertForSequenceClassification.from_pretrained("path/to/awesome-name-you-picked", from_pt=True)
+    tf_model.save_pretrained("path/to/awesome-name-you-picked")
+
+and if you trained your model in TensorFlow and have to create a PyTorch version, adapt the following code to your
+model class:
+
+::
+
+    pt_model = DistilBertForSequenceClassification.from_pretrained("path/to/awesome-name-you-picked", from_tf=True)
+    pt_model.save_pretrained("path/to/awesome-name-you-picked")
+
+That's all there is to it!
+
+Check the directory before uploading
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Make sure there are no garbage files in the directory you'll upload. It should only have:
+
+- a `config.json` file, which saves the :doc:`configuration <main_classes/configuration>` of your model ;
+- a `pytorch_model.bin` file, which is the PyTorch checkpoint (unless you can't have it for some reason) ;
+- a `tf_model.h5` file, which is the TensorFlow checkpoint (unless you can't have it for some reason) ;
+- a `special_tokens_map.json`, which is part of your :doc:`tokenizer <main_classes/tokenizer>` save;
+- a `tokenizer_config.json`, which is part of your :doc:`tokenizer <main_classes/tokenizer>` save;
+- a `vocab.txt`, which is the vocabulary of your tokenizer, part of your :doc:`tokenizer <main_classes/tokenizer>`
+  save;
+- maybe a `added_tokens.json`, which is part of your :doc:`tokenizer <main_classes/tokenizer>` save.
+
+Other files can safely be deleted.
+
+Upload your model with the CLI
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Now go in a terminal and run the following command. It should be in the virtual enviromnent where you installed 🤗
+Transformers, since that command :obj:`transformers-cli` comes from the library.
+
+::
+
+    transformers-cli login
+
+Then log in using the same credentials as on huggingface.co. To upload your model, just type
+
+::
+
+    transformers-cli upload path/to/awesome-name-you-picked/
+
+This will upload the folder containing the weights, tokenizer and configuration we prepared in the previous section.
+
+By default you will be prompted to confirm that you want these files to be uploaded. If you are uploading multiple models and need to script that process, you can add `-y` to bypass the prompt. For example:
+
+::
+
+    transformers-cli upload -y path/to/awesome-name-you-picked/
+
+
+If you want to upload a single file (a new version of your model, or the other framework checkpoint you want to add),
+just type:
+
+::
+
+    transformers-cli upload path/to/awesome-name-you-picked/that-file 
+
+or
+
+::
+
+   transformers-cli upload path/to/awesome-name-you-picked/that-file --filename awesome-name-you-picked/new_name
+
+if you want to change its filename.
+
+This uploads the model to your personal account. If you want your model to be namespaced by your organization name
+rather than your username, add the following flag to any command:
+
+::
+
+    --organization organization_name
+
+so for instance:
+
+::
+
+    transformers-cli upload path/to/awesome-name-you-picked/ --organization organization_name
+
+Your model will then be accessible through its identifier, which is, as we saw above,
+`username/awesome-name-you-picked` or `organization/awesome-name-you-picked`.
+
+Add a model card
+^^^^^^^^^^^^^^^^
+
+To make sure everyone knows what your model can do, what its limitations and potential bias or ethetical
+considerations, please add a README.md model card to the 🤗 Transformers repo under `model_cards/`. It should then be
+placed in a subfolder with your username or organization, then another subfolder named like your model
+(`awesome-name-you-picked`). Or just click on the "Create a model card on GitHub" button on the model page, it will
+get you directly to the right location. If you need one, `here <https://github.com/huggingface/model_card>`__ is a
+model card template (meta-suggestions are welcome).
+
+If your model is fine-tuned from another model coming from the model hub (all 🤗 Transformers pretrained models do),
+don't forget to link to its model card so that people can fully trace how your model was built.
+
+If you have never made a pull request to the 🤗 Transformers repo, look at the
+:doc:`contributing guide <contributing>` to see the steps to follow.
+
+.. Note::
+
+    You can also send your model card in the folder you uploaded with the CLI by placing it in a `README.md` file
+    inside `path/to/awesome-name-you-picked/`.
+
+Using your model
+^^^^^^^^^^^^^^^^
+
+Your model now has a page on huggingface.co/models 🔥
+
+Anyone can load it from code:
+
+::
+
+    tokenizer = AutoTokenizer.from_pretrained("namespace/awesome-name-you-picked")
+    model = AutoModel.from_pretrained("namespace/awesome-name-you-picked")
+
+Additional commands
+^^^^^^^^^^^^^^^^^^^
+
+You can list all the files you uploaded on the hub like this:
+
+::
+
+    transformers-cli s3 ls
+
+You can also delete unneeded files with
+
+::
+
+    transformers-cli s3 rm awesome-name-you-picked/filename
+
@@ -1,20 +1,20 @@
 Multi-lingual models
-=======================================================================================================================
+================================================

-Most of the models available in this library are mono-lingual models (English, Chinese and German). A few multi-lingual
-models are available and have a different mechanisms than mono-lingual models. This page details the usage of these
-models.
+Most of the models available in this library are mono-lingual models (English, Chinese and German). A few
+multi-lingual models are available and have a different mechanisms than mono-lingual models.
+This page details the usage of these models.

 The two models that currently support multiple languages are BERT and XLM.

 XLM
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

 XLM has a total of 10 different checkpoints, only one of which is mono-lingual. The 9 remaining model checkpoints can
 be split in two categories: the checkpoints that make use of language embeddings, and those that don't

 XLM & Language Embeddings
-----------------------------------------------------------------------------------------------------------------------
+------------------------------------------------

 This section concerns the following checkpoints:

@@ -28,8 +28,8 @@ This section concerns the following checkpoints:

 These checkpoints require language embeddings that will specify the language used at inference time. These language
 embeddings are represented as a tensor that is of the same shape as the input ids passed to the model. The values in
-these tensors depend on the language used and are identifiable using the ``lang2id`` and ``id2lang`` attributes from
-the tokenizer.
+these tensors depend on the language used and are identifiable using the ``lang2id`` and ``id2lang`` attributes
+from the tokenizer.

 Here is an example using the ``xlm-clm-enfr-1024`` checkpoint (Causal language modeling, English-French):

@@ -78,39 +78,38 @@ You can then feed it all as input to your model:
    >>> outputs = model(input_ids, langs=langs)


-The example `run_generation.py
-<https://github.com/huggingface/transformers/blob/master/examples/text-generation/run_generation.py>`__ can generate
-text using the CLM checkpoints from XLM, using the language embeddings.
+The example `run_generation.py <https://github.com/huggingface/transformers/blob/master/examples/text-generation/run_generation.py>`__
+can generate text using the CLM checkpoints from XLM, using the language embeddings.

 XLM without Language Embeddings
-----------------------------------------------------------------------------------------------------------------------
+------------------------------------------------

 This section concerns the following checkpoints:

 - ``xlm-mlm-17-1280`` (Masked language modeling, 17 languages)
 - ``xlm-mlm-100-1280`` (Masked language modeling, 100 languages)

-These checkpoints do not require language embeddings at inference time. These models are used to have generic sentence
-representations, differently from previously-mentioned XLM checkpoints.
+These checkpoints do not require language embeddings at inference time. These models are used to have generic
+sentence representations, differently from previously-mentioned XLM checkpoints.


 BERT
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

 BERT has two checkpoints that can be used for multi-lingual tasks:

 - ``bert-base-multilingual-uncased`` (Masked language modeling + Next sentence prediction, 102 languages)
 - ``bert-base-multilingual-cased`` (Masked language modeling + Next sentence prediction, 104 languages)

-These checkpoints do not require language embeddings at inference time. They should identify the language used in the
-context and infer accordingly.
+These checkpoints do not require language embeddings at inference time. They should identify the language
+used in the context and infer accordingly.

 XLM-RoBERTa
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

-XLM-RoBERTa was trained on 2.5TB of newly created clean CommonCrawl data in 100 languages. It provides strong gains
-over previously released multi-lingual models like mBERT or XLM on downstream tasks like classification, sequence
-labeling and question answering.
+XLM-RoBERTa was trained on 2.5TB of newly created clean CommonCrawl data in 100 languages. It provides strong
+gains over previously released multi-lingual models like mBERT or XLM on downstream taks like classification,
+sequence labeling and question answering.

 Two XLM-RoBERTa checkpoints can be used for multi-lingual tasks:

@@ -1,72 +1,89 @@
 Perplexity of fixed-length models
-=======================================================================================================================
+=================================

-Perplexity (PPL) is one of the most common metrics for evaluating language models. Before diving in, we should note
-that the metric applies specifically to classical language models (sometimes called autoregressive or causal language
-models) and is not well defined for masked language models like BERT (see :doc:`summary of the models
-<model_summary>`).
+Perplexity (PPL) is one of the most common metrics for evaluating language
+models. Before diving in, we should note that the metric applies specifically
+to classical language models (sometimes called autoregressive or causal
+language models) and is not well defined for masked language models like BERT
+(see :doc:`summary of the models <model_summary>`).

-Perplexity is defined as the exponentiated average log-likelihood of a sequence. If we have a tokenized sequence
-:math:`X = (x_0, x_1, \dots, x_t)`, then the perplexity of :math:`X` is,
+Perplexity is defined as the exponentiated average log-likelihood of a
+sequence. If we have a tokenized sequence :math:`X = (x_0, x_1, \dots, x_t)`,
+then the perplexity of :math:`X` is,

 .. math::

    \text{PPL}(X)
    = \exp \left\{ {-\frac{1}{t}\sum_i^t \log p_\theta (x_i|x_{<i}) } \right\}

-where :math:`\log p_\theta (x_i|x_{<i})` is the log-likelihood of the ith token conditioned on the preceding tokens
-:math:`x_{<i}` according to our model. Intuitively, it can be thought of as an evaluation of the model's ability to
-predict uniformly among the set of specified tokens in a corpus. Importantly, this means that the tokenization
-procedure has a direct impact on a model's perplexity which should always be taken into consideration when comparing
-different models.
+where :math:`\log p_\theta (x_i|x_{<i})` is the log-likelihood of the ith
+token conditioned on the preceding tokens :math:`x_{<i}` according to our
+model. Intuitively, it can be thought of as an evaluation of the model's
+ability to predict uniformly among the set of specified tokens in a corpus.
+Importantly, this means that the tokenization procedure has a direct impact
+on a model's perplexity which should always be taken into consideration when
+comparing different models.

-This is also equivalent to the exponentiation of the cross-entropy between the data and model predictions. For more
-intuition about perplexity and its relationship to Bits Per Character (BPC) and data compression, check out this
-`fantastic blog post on The Gradient <https://thegradient.pub/understanding-evaluation-metrics-for-language-models/>`_.
+This is also equivalent to the exponentiation of the cross-entropy between
+the data and model predictions. For more intuition about perplexity and its
+relationship to Bits Per Character (BPC) and data compression, check out this
+`fantastic blog post on The Gradient
+<https://thegradient.pub/understanding-evaluation-metrics-for-language-models/>`_.

 Calculating PPL with fixed-length models
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

-If we weren't limited by a model's context size, we would evaluate the model's perplexity by autoregressively
-factorizing a sequence and conditioning on the entire preceding subsequence at each step, as shown below.
+If we weren't limited by a model's context size, we would evaluate the
+model's perplexity by autoregressively factorizing a sequence and
+conditioning on the entire preceding subsequence at each step, as shown
+below.

 .. image:: imgs/ppl_full.gif
    :width: 600
    :alt: Full decomposition of a sequence with unlimited context length

-When working with approximate models, however, we typically have a constraint on the number of tokens the model can
-process. The largest version of :doc:`GPT-2 <model_doc/gpt2>`, for example, has a fixed length of 1024 tokens, so we
-cannot calculate :math:`p_\theta(x_t|x_{<t})` directly when :math:`t` is greater than 1024.
+When working with approximate models, however, we typically have a constraint
+on the number of tokens the model can process. The largest version
+of :doc:`GPT-2 <model_doc/gpt2>`, for example, has a fixed length of 1024
+tokens, so we cannot calculate :math:`p_\theta(x_t|x_{<t})` directly when
+:math:`t` is greater than 1024.

-Instead, the sequence is typically broken into subsequences equal to the model's maximum input size. If a model's max
-input size is :math:`k`, we then approximate the likelihood of a token :math:`x_t` by conditioning only on the
-:math:`k-1` tokens that precede it rather than the entire context. When evaluating the model's perplexity of a
-sequence, a tempting but suboptimal approach is to break the sequence into disjoint chunks and add up the decomposed
-log-likelihoods of each segment independently.
+Instead, the sequence is typically broken into subsequences equal to the
+model's maximum input size. If a model's max input size is :math:`k`, we
+then approximate the likelihood of a token :math:`x_t` by conditioning only
+on the :math:`k-1` tokens that precede it rather than the entire context.
+When evaluating the model's perplexity of a sequence, a tempting but
+suboptimal approach is to break the sequence into disjoint chunks and
+add up the decomposed log-likelihoods of each segment independently.

 .. image:: imgs/ppl_chunked.gif
    :width: 600
    :alt: Suboptimal PPL not taking advantage of full available context

-This is quick to compute since the perplexity of each segment can be computed in one forward pass, but serves as a poor
-approximation of the fully-factorized perplexity and will typically yield a higher (worse) PPL because the model will
-have less context at most of the prediction steps.
+This is quick to compute since the perplexity of each segment can be computed
+in one forward pass, but serves as a poor approximation of the
+fully-factorized perplexity and will typically yield a higher (worse) PPL
+because the model will have less context at most of the prediction steps.

-Instead, the PPL of fixed-length models should be evaluated with a sliding-window strategy. This involves repeatedly
-sliding the context window so that the model has more context when making each prediction.
+Instead, the PPL of fixed-length models should be evaluated with a
+sliding-window strategy. This involves repeatedly sliding the
+context window so that the model has more context when making each
+prediction.

 .. image:: imgs/ppl_sliding.gif
    :width: 600
    :alt: Sliding window PPL taking advantage of all available context

-This is a closer approximation to the true decomposition of the sequence probability and will typically yield a more
-favorable score. The downside is that it requires a separate forward pass for each token in the corpus. A good
-practical compromise is to employ a strided sliding window, moving the context by larger strides rather than sliding by
-1 token a time. This allows computation to proceed much faster while still giving the model a large context to make
-predictions at each step.
+This is a closer approximation to the true decomposition of the
+sequence probability and will typically yield a more favorable score.
+The downside is that it requires a separate forward pass for each token in
+the corpus. A good practical compromise is to employ a strided sliding
+window, moving the context by larger strides rather than sliding by 1 token a
+time. This allows computation to procede much faster while still giving the
+model a large context to make predictions at each step.

 Example: Calculating perplexity with GPT-2 in 🤗 Transformers
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

 Let's demonstrate this process with GPT-2.

@@ -78,9 +95,10 @@ Let's demonstrate this process with GPT-2.
    model = GPT2LMHeadModel.from_pretrained(model_id).to(device)
    tokenizer = GPT2TokenizerFast.from_pretrained(model_id)

-We'll load in the WikiText-2 dataset and evaluate the perplexity using a few different sliding-window strategies. Since
-this dataset is small and we're just doing one forward pass over the set, we can just load and encode the entire
-dataset in memory.
+We'll load in the WikiText-2 dataset and evaluate the perplexity using a few
+different sliding-window strategies. Since this dataset is small and we're
+just doing one forward pass over the set, we can just load and encode the
+entire dataset in memory.

 .. code-block:: python

@@ -88,13 +106,16 @@ dataset in memory.
    test = load_dataset('wikitext', 'wikitext-2-raw-v1', split='test')
    encodings = tokenizer('\n\n'.join(test['text']), return_tensors='pt')

-With 🤗 Transformers, we can simply pass the ``input_ids`` as the ``labels`` to our model, and the average
-log-likelihood for each token is returned as the loss. With our sliding window approach, however, there is overlap in
-the tokens we pass to the model at each iteration. We don't want the log-likelihood for the tokens we're just treating
-as context to be included in our loss, so we can set these targets to ``-100`` so that they are ignored. The following
-is an example of how we could do this with a stride of ``512``. This means that the model will have at least 512 tokens
-for context when calculating the conditional likelihood of any one token (provided there are 512 preceding tokens
-available to condition on).
+With 🤗 Transformers, we can simply pass the ``input_ids`` as the ``labels``
+to our model, and the average log-likelihood for each token is returned as
+the loss. With our sliding window approach, however, there is overlap in the
+tokens we pass to the model at each iteration. We don't want the
+log-likelihood for the tokens we're just treating as context to be included
+in our loss, so we can set these targets to ``-100`` so that they are
+ignored. The following is an example of how we could do this with a stride of
+``512``. This means that the model will have at least 512 tokens for context
+when calculating the conditional likelihood of any one token (provided there
+are 512 preceding tokens available to condition on).

 .. code-block:: python

@@ -104,25 +125,27 @@ available to condition on).
    lls = []
    for i in tqdm(range(0, encodings.input_ids.size(1), stride)):
        begin_loc = max(i + stride - max_length, 0)
-        end_loc = min(i + stride, encodings.input_ids.size(1))
-        trg_len = end_loc - i    # may be different from stride on last loop
+        end_loc = i + stride
        input_ids = encodings.input_ids[:,begin_loc:end_loc].to(device)
        target_ids = input_ids.clone()
-        target_ids[:,:-trg_len] = -100
+        target_ids[:,:-stride] = -100

        with torch.no_grad():
            outputs = model(input_ids, labels=target_ids)
-            log_likelihood = outputs[0] * trg_len
+            log_likelihood = outputs[0] * stride

        lls.append(log_likelihood)
+    
+    ppl = torch.exp(torch.stack(lls).sum() / i)

-    ppl = torch.exp(torch.stack(lls).sum() / end_loc)
+Running this with the stride length equal to the max input length is
+equivalent to the suboptimal, non-sliding-window strategy we discussed above.
+The smaller the stride, the more context the model will have in making each
+prediction, and the better the reported perplexity will typically be.

-Running this with the stride length equal to the max input length is equivalent to the suboptimal, non-sliding-window
-strategy we discussed above. The smaller the stride, the more context the model will have in making each prediction,
-and the better the reported perplexity will typically be.
-
-When we run the above with ``stride = 1024``, i.e. no overlap, the resulting PPL is ``19.64``, which is about the same
-as the ``19.93`` reported in the GPT-2 paper. By using ``stride = 512`` and thereby employing our striding window
-strategy, this jumps down to ``16.53``. This is not only a more favorable score, but is calculated in a way that is
-closer to the true autoregressive decomposition of a sequence likelihood.
+When we run the above with ``stride = 1024``, i.e. no overlap, the resulting
+PPL is ``19.64``, which is about the same as the ``19.93`` reported in the
+GPT-2 paper. By using ``stride = 512`` and thereby employing our striding
+window strategy, this jumps down to ``16.53``. This is not only a more
+favorable score, but is calculated in a way that is closer to the true
+autoregressive decomposition of a sequence likelihood.
@@ -1,5 +1,5 @@
 Philosophy
-=======================================================================================================================
+==========

 🤗 Transformers is an opinionated library built for:

@@ -12,15 +12,15 @@ The library was designed with two strong goals in mind:
 - Be as easy and fast to use as possible:

    - We strongly limited the number of user-facing abstractions to learn, in fact, there are almost no abstractions,
-      just three standard classes required to use each model: :doc:`configuration <main_classes/configuration>`,
+      just three standard classes required to use each model: :doc:`configuration <main_classes/configuration>`, 
      :doc:`models <main_classes/model>` and :doc:`tokenizer <main_classes/tokenizer>`.
    - All of these classes can be initialized in a simple and unified way from pretrained instances by using a common
      :obj:`from_pretrained()` instantiation method which will take care of downloading (if needed), caching and
-      loading the related class instance and associated data (configurations' hyper-parameters, tokenizers' vocabulary,
-      and models' weights) from a pretrained checkpoint provided on `Hugging Face Hub
-      <https://huggingface.co/models>`__ or your own saved checkpoint.
+      loading the related class instance and associated data (configurations' hyper-parameters, tokenizers' vocabulary, 
+      and models' weights) from a pretrained checkpoint provided on 
+      `Hugging Face Hub <https://huggingface.co/models>`__ or your own saved checkpoint.
    - On top of those three base classes, the library provides two APIs: :func:`~transformers.pipeline` for quickly
-      using a model (plus its associated tokenizer and configuration) on a given task and
+      using a model (plus its associated tokenizer and configuration) on a given task and 
      :func:`~transformers.Trainer`/:func:`~transformers.TFTrainer` to quickly train or fine-tune a given model.
    - As a consequence, this library is NOT a modular toolbox of building blocks for neural nets. If you want to
      extend/build-upon the library, just use regular Python/PyTorch/TensorFlow/Keras modules and inherit from the base
@@ -48,14 +48,14 @@ A few other goals:
 - Switch easily between PyTorch and TensorFlow 2.0, allowing training using one framework and inference using another.

 Main concepts
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~

 The library is built around three types of classes for each model:

- **Model classes** such as :class:`~transformers.BertModel`, which are 30+ PyTorch models (`torch.nn.Module
-  <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__) or Keras models (`tf.keras.Model
-  <https://www.tensorflow.org/api_docs/python/tf/keras/Model>`__) that work with the pretrained weights provided in the
-  library.
+- **Model classes**  such as :class:`~transformers.BertModel`, which are 30+ PyTorch models 
+  (`torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__) or Keras models 
+  (`tf.keras.Model <https://www.tensorflow.org/api_docs/python/tf/keras/Model>`__) that work with the pretrained
+  weights provided in the library.
 - **Configuration classes** such as :class:`~transformers.BertConfig`, which store all the parameters required to build
  a model. You don't always need to instantiate these yourself. In particular, if you are using a pretrained model
  without any modification, creating the model will automatically take care of instantiating the configuration (which
@@ -66,8 +66,8 @@ The library is built around three types of classes for each model:
 All these classes can be instantiated from pretrained instances and saved locally using two methods:

 - :obj:`from_pretrained()` lets you instantiate a model/configuration/tokenizer from a pretrained version either
-  provided by the library itself (the supported models are provided in the list :doc:`here <pretrained_models>` or
-  stored locally (or on a server) by the user,
+  provided by the library itself (the suported models are provided in the list :doc:`here <pretrained_models>`
+  or stored locally (or on a server) by the user,
 - :obj:`save_pretrained()` lets you save a model/configuration/tokenizer locally so that it can be reloaded using
  :obj:`from_pretrained()`.

@@ -1,343 +1,343 @@
-Preprocessing data
-=======================================================================================================================
-
-In this tutorial, we'll explore how to preprocess your data using 🤗 Transformers. The main tool for this is what we
-
-call a :doc:`tokenizer <main_classes/tokenizer>`. You can build one using the tokenizer class associated to the model
-you would like to use, or directly with the :class:`~transformers.AutoTokenizer` class.
-
-As we saw in the :doc:`quicktour </quicktour>`, the tokenizer will first split a given text in words (or part of words,
-punctuation symbols, etc.) usually called `tokens`. Then it will convert those `tokens` into numbers, to be able to
-build a tensor out of them and feed them to the model. It will also add any additional inputs the model might expect to
-work properly.
-
-.. note::
-
-    If you plan on using a pretrained model, it's important to use the associated pretrained tokenizer: it will split
-    the text you give it in tokens the same way for the pretraining corpus, and it will use the same correspondence
-    token to index (that we usually call a `vocab`) as during pretraining.
-
-To automatically download the vocab used during pretraining or fine-tuning a given model, you can use the
-:func:`~transformers.AutoTokenizer.from_pretrained` method:
-
-.. code-block::
-
-    from transformers import AutoTokenizer
-    tokenizer = AutoTokenizer.from_pretrained('bert-base-cased')
-
-Base use
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-A :class:`~transformers.PreTrainedTokenizer` has many methods, but the only one you need to remember for preprocessing
-is its ``__call__``: you just need to feed your sentence to your tokenizer object.
-
-.. code-block::
-
-    >>> encoded_input = tokenizer("Hello, I'm a single sentence!")
-    >>> print(encoded_input)
-    {'input_ids': [101, 138, 18696, 155, 1942, 3190, 1144, 1572, 13745, 1104, 159, 9664, 2107, 102], 
-     'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 
-     'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
-
-This returns a dictionary string to list of ints. The `input_ids <glossary.html#input-ids>`__ are the indices
-corresponding to each token in our sentence. We will see below what the `attention_mask
-<glossary.html#attention-mask>`__ is used for and in :ref:`the next section <sentence-pairs>` the goal of
-`token_type_ids <glossary.html#token-type-ids>`__.
-
-The tokenizer can decode a list of token ids in a proper sentence:
-
-.. code-block::
-
-    >>> tokenizer.decode(encoded_input["input_ids"])
-    "[CLS] Hello, I'm a single sentence! [SEP]"
-
-As you can see, the tokenizer automatically added some special tokens that the model expects. Not all models need
-special tokens; for instance, if we had used` gtp2-medium` instead of `bert-base-cased` to create our tokenizer, we
-would have seen the same sentence as the original one here. You can disable this behavior (which is only advised if you
-have added those special tokens yourself) by passing ``add_special_tokens=False``.
-
-If you have several sentences you want to process, you can do this efficiently by sending them as a list to the
-tokenizer:
-
-.. code-block::
-
-    >>> batch_sentences = ["Hello I'm a single sentence",
-    ...                    "And another sentence",
-    ...                    "And the very very last one"]
-    >>> encoded_inputs = tokenizer(batch_sentences)
-    >>> print(encoded_inputs)
-    {'input_ids': [[101, 8667, 146, 112, 182, 170, 1423, 5650, 102],
-                   [101, 1262, 1330, 5650, 102],
-                   [101, 1262, 1103, 1304, 1304, 1314, 1141, 102]],
-     'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0],
-                        [0, 0, 0, 0, 0],
-                        [0, 0, 0, 0, 0, 0, 0, 0]],
-     'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1],
-                        [1, 1, 1, 1, 1],
-                        [1, 1, 1, 1, 1, 1, 1, 1]]}
-
-We get back a dictionary once again, this time with values being lists of lists of ints.
-
-If the purpose of sending several sentences at a time to the tokenizer is to build a batch to feed the model, you will
-probably want:
-
- To pad each sentence to the maximum length there is in your batch.
- To truncate each sentence to the maximum length the model can accept (if applicable).
- To return tensors.
-
-You can do all of this by using the following options when feeding your list of sentences to the tokenizer:
-
-.. code-block::
-
-    >>> ## PYTORCH CODE
-    >>> batch = tokenizer(batch_sentences, padding=True, truncation=True, return_tensors="pt")
-    >>> print(batch)
-    {'input_ids': tensor([[ 101, 8667,  146,  112,  182,  170, 1423, 5650,  102],
-                          [ 101, 1262, 1330, 5650,  102,    0,    0,    0,    0],
-                          [ 101, 1262, 1103, 1304, 1304, 1314, 1141,  102,    0]]),
-     'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0],
-                               [0, 0, 0, 0, 0, 0, 0, 0, 0],
-                               [0, 0, 0, 0, 0, 0, 0, 0, 0]]), 
-     'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1],
-                               [1, 1, 1, 1, 1, 0, 0, 0, 0],
-                               [1, 1, 1, 1, 1, 1, 1, 1, 0]])}
-    >>> ## TENSORFLOW CODE
-    >>> batch = tokenizer(batch_sentences, padding=True, truncation=True, return_tensors="tf")
-    >>> print(batch)
-    {'input_ids': tf.Tensor([[ 101, 8667,  146,  112,  182,  170, 1423, 5650,  102],
-                          [ 101, 1262, 1330, 5650,  102,    0,    0,    0,    0],
-                          [ 101, 1262, 1103, 1304, 1304, 1314, 1141,  102,    0]]),
-     'token_type_ids': tf.Tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0],
-                               [0, 0, 0, 0, 0, 0, 0, 0, 0],
-                               [0, 0, 0, 0, 0, 0, 0, 0, 0]]), 
-     'attention_mask': tf.Tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1],
-                               [1, 1, 1, 1, 1, 0, 0, 0, 0],
-                               [1, 1, 1, 1, 1, 1, 1, 1, 0]])}
-
-It returns a dictionary with string keys and tensor values. We can now see what the `attention_mask
-<glossary.html#attention-mask>`__ is all about: it points out which tokens the model should pay attention to and which
-ones it should not (because they represent padding in this case).
-
-
-Note that if your model does not have a maximum length associated to it, the command above will throw a warning. You
-can safely ignore it. You can also pass ``verbose=False`` to stop the tokenizer to throw those kinds of warnings.
-
-.. _sentence-pairs:
-
-Preprocessing pairs of sentences
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Sometimes you need to feed a pair of sentences to your model. For instance, if you want to classify if two sentences in
-a pair are similar, or for question-answering models, which take a context and a question. For BERT models, the input
-is then represented like this: :obj:`[CLS] Sequence A [SEP] Sequence B [SEP]`
-
-You can encode a pair of sentences in the format expected by your model by supplying the two sentences as two arguments
-(not a list since a list of two sentences will be interpreted as a batch of two single sentences, as we saw before).
-This will once again return a dict string to list of ints:
-
-.. code-block::
-
-    >>> encoded_input = tokenizer("How old are you?", "I'm 6 years old")
-    >>> print(encoded_input)
-    {'input_ids': [101, 1731, 1385, 1132, 1128, 136, 102, 146, 112, 182, 127, 1201, 1385, 102], 
-     'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1], 
-     'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
-
-This shows us what the `token_type_ids <glossary.html#token-type-ids>`__ are for: they indicate to the model which part
-of the inputs correspond to the first sentence and which part corresponds to the second sentence. Note that
-`token_type_ids` are not required or handled by all models. By default, a tokenizer will only return the inputs that
-its associated model expects. You can force the return (or the non-return) of any of those special arguments by using
-``return_input_ids`` or ``return_token_type_ids``.
-
-If we decode the token ids we obtained, we will see that the special tokens have been properly added.
-
-.. code-block::
-
-    >>> tokenizer.decode(encoded_input["input_ids"])
-    "[CLS] How old are you? [SEP] I'm 6 years old [SEP]"
-
-If you have a list of pairs of sequences you want to process, you should feed them as two lists to your tokenizer: the
-list of first sentences and the list of second sentences:
-
-.. code-block::
-
-    >>> batch_sentences = ["Hello I'm a single sentence",
-    ...                    "And another sentence",
-    ...                    "And the very very last one"]
-    >>> batch_of_second_sentences = ["I'm a sentence that goes with the first sentence",
-    ...                              "And I should be encoded with the second sentence",
-    ...                              "And I go with the very last one"]
-    >>> encoded_inputs = tokenizer(batch_sentences, batch_of_second_sentences)
-    >>> print(encoded_inputs)
-    {'input_ids': [[101, 8667, 146, 112, 182, 170, 1423, 5650, 102, 146, 112, 182, 170, 5650, 1115, 2947, 1114, 1103, 1148, 5650, 102], 
-                   [101, 1262, 1330, 5650, 102, 1262, 146, 1431, 1129, 12544, 1114, 1103, 1248, 5650, 102], 
-                   [101, 1262, 1103, 1304, 1304, 1314, 1141, 102, 1262, 146, 1301, 1114, 1103, 1304, 1314, 1141, 102]], 
-    'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 
-                       [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 
-                       [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 
-    'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 
-                       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 
-                       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
-
-As we can see, it returns a dictionary where each value is a list of lists of ints.
-
-To double-check what is fed to the model, we can decode each list in `input_ids` one by one:
-
-.. code-block::
-
-    >>> for ids in encoded_inputs["input_ids"]:
-    >>>     print(tokenizer.decode(ids))
-    [CLS] Hello I'm a single sentence [SEP] I'm a sentence that goes with the first sentence [SEP]
-    [CLS] And another sentence [SEP] And I should be encoded with the second sentence [SEP]
-    [CLS] And the very very last one [SEP] And I go with the very last one [SEP]
-
-Once again, you can automatically pad your inputs to the maximum sentence length in the batch, truncate to the maximum
-length the model can accept and return tensors directly with the following:
-
-.. code-block::
-
-    ## PYTORCH CODE
-    batch = tokenizer(batch_sentences, batch_of_second_sentences, padding=True, truncation=True, return_tensors="pt")
-    ## TENSORFLOW CODE
-    batch = tokenizer(batch_sentences, batch_of_second_sentences, padding=True, truncation=True, return_tensors="tf")
-
-Everything you always wanted to know about padding and truncation
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-We have seen the commands that will work for most cases (pad your batch to the length of the maximum sentence and
-
-truncate to the maximum length the mode can accept). However, the API supports more strategies if you need them. The
-three arguments you need to know for this are :obj:`padding`, :obj:`truncation` and :obj:`max_length`.
-
- :obj:`padding` controls the padding. It can be a boolean or a string which should be:
-
-    - :obj:`True` or :obj:`'longest'` to pad to the longest sequence in the batch (doing no padding if you only provide
-      a single sequence).
-    - :obj:`'max_length'` to pad to a length specified by the :obj:`max_length` argument or the maximum length accepted
-      by the model if no :obj:`max_length` is provided (``max_length=None``). If you only provide a single sequence,
-      padding will still be applied to it.
-    - :obj:`False` or :obj:`'do_not_pad'` to not pad the sequences. As we have seen before, this is the default
-      behavior.
-
- :obj:`truncation` controls the truncation. It can be a boolean or a string which should be:
-
-    - :obj:`True` or :obj:`'only_first'` truncate to a maximum length specified by the :obj:`max_length` argument or
-      the maximum length accepted by the model if no :obj:`max_length` is provided (``max_length=None``). This will
-      only truncate the first sentence of a pair if a pair of sequence (or a batch of pairs of sequences) is provided.
-    - :obj:`'only_second'` truncate to a maximum length specified by the :obj:`max_length` argument or the maximum
-      length accepted by the model if no :obj:`max_length` is provided (``max_length=None``). This will only truncate
-      the second sentence of a pair if a pair of sequence (or a batch of pairs of sequences) is provided.
-    - :obj:`'longest_first'` truncate to a maximum length specified by the :obj:`max_length` argument or the maximum
-      length accepted by the model if no :obj:`max_length` is provided (``max_length=None``). This will truncate token
-      by token, removing a token from the longest sequence in the pair until the proper length is reached.
-    - :obj:`False` or :obj:`'do_not_truncate'` to not truncate the sequences. As we have seen before, this is the
-      default behavior.
-
- :obj:`max_length` to control the length of the padding/truncation. It can be an integer or :obj:`None`, in which case
-  it will default to the maximum length the model can accept. If the model has no specific maximum input length,
-  truncation/padding to :obj:`max_length` is deactivated.
-
-Here is a table summarizing the recommend way to setup padding and truncation. If you use pair of inputs sequence in
-any of the following examples, you can replace :obj:`truncation=True` by a :obj:`STRATEGY` selected in
-:obj:`['only_first', 'only_second', 'longest_first']`, i.e. :obj:`truncation='only_second'` or :obj:`truncation=
-'longest_first'` to control how both sequence in the pair are truncated as detailed before.
-
-+--------------------------------------+-----------------------------------+---------------------------------------------------------------------------------------------+
-| Truncation                           | Padding                           | Instruction                                                                                 |
-+======================================+===================================+=============================================================================================+
-| no truncation                        | no padding                        | :obj:`tokenizer(batch_sentences)`                                                           |
-|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
-|                                      | padding to max sequence in batch  | :obj:`tokenizer(batch_sentences, padding=True)` or                                          |
-|                                      |                                   | :obj:`tokenizer(batch_sentences, padding='longest')`                                        |
-|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
-|                                      | padding to max model input length | :obj:`tokenizer(batch_sentences, padding='max_length')`                                     |
-|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
-|                                      | padding to specific length        | :obj:`tokenizer(batch_sentences, padding='max_length', max_length=42)`                      |
-+--------------------------------------+-----------------------------------+---------------------------------------------------------------------------------------------+
-| truncation to max model input length | no padding                        | :obj:`tokenizer(batch_sentences, truncation=True)` or                                       |
-|                                      |                                   | :obj:`tokenizer(batch_sentences, truncation=STRATEGY)`                                      |
-|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
-|                                      | padding to max sequence in batch  | :obj:`tokenizer(batch_sentences, padding=True, truncation=True)` or                         |
-|                                      |                                   | :obj:`tokenizer(batch_sentences, padding=True, truncation=STRATEGY)`                        |
-|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
-|                                      | padding to max model input length | :obj:`tokenizer(batch_sentences, padding='max_length', truncation=True)` or                 |
-|                                      |                                   | :obj:`tokenizer(batch_sentences, padding='max_length', truncation=STRATEGY)`                |
-|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
-|                                      | padding to specific length        | Not possible                                                                                |
-+--------------------------------------+-----------------------------------+---------------------------------------------------------------------------------------------+
-| truncation to specific length        | no padding                        | :obj:`tokenizer(batch_sentences, truncation=True, max_length=42)` or                        |
-|                                      |                                   | :obj:`tokenizer(batch_sentences, truncation=STRATEGY, max_length=42)`                       |
-|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
-|                                      | padding to max sequence in batch  | :obj:`tokenizer(batch_sentences, padding=True, truncation=True, max_length=42)` or          |
-|                                      |                                   | :obj:`tokenizer(batch_sentences, padding=True, truncation=STRATEGY, max_length=42)`         |
-|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
-|                                      | padding to max model input length | Not possible                                                                                |
-|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
-|                                      | padding to specific length        | :obj:`tokenizer(batch_sentences, padding='max_length', truncation=True, max_length=42)` or  |
-|                                      |                                   | :obj:`tokenizer(batch_sentences, padding='max_length', truncation=STRATEGY, max_length=42)` |
-+--------------------------------------+-----------------------------------+---------------------------------------------------------------------------------------------+
-
-Pre-tokenized inputs
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The tokenizer also accept pre-tokenized inputs. This is particularly useful when you want to compute labels and extract
-predictions in `named entity recognition (NER) <https://en.wikipedia.org/wiki/Named-entity_recognition>`__ or
-`part-of-speech tagging (POS tagging) <https://en.wikipedia.org/wiki/Part-of-speech_tagging>`__.
-
-.. warning::
-
-    Pre-tokenized does not mean your inputs are already tokenized (you wouldn't need to pass them through the tokenizer
-    if that was the case) but just split into words (which is often the first step in subword tokenization algorithms
-    like BPE).
-
-If you want to use pre-tokenized inputs, just set :obj:`is_split_into_words=True` when passing your inputs to the
-tokenizer. For instance, we have:
-
-.. code-block::
-
-    >>> encoded_input = tokenizer(["Hello", "I'm", "a", "single", "sentence"], is_split_into_words=True)
-    >>> print(encoded_input)
-    {'input_ids': [101, 8667, 146, 112, 182, 170, 1423, 5650, 102],
-     'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0], 
-     'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1]}
-
-Note that the tokenizer still adds the ids of special tokens (if applicable) unless you pass
-``add_special_tokens=False``.
-
-This works exactly as before for batch of sentences or batch of pairs of sentences. You can encode a batch of sentences
-like this:
-
-.. code-block::
-
-    batch_sentences = [["Hello", "I'm", "a", "single", "sentence"],
-                       ["And", "another", "sentence"],
-                       ["And", "the", "very", "very", "last", "one"]]
-    encoded_inputs = tokenizer(batch_sentences, is_split_into_words=True)
-
-or a batch of pair sentences like this:
-
-.. code-block::
-
-    batch_of_second_sentences = [["I'm", "a", "sentence", "that", "goes", "with", "the", "first", "sentence"],
-                                 ["And", "I", "should", "be", "encoded", "with", "the", "second", "sentence"],
-                                 ["And", "I", "go", "with", "the", "very", "last", "one"]]
-    encoded_inputs = tokenizer(batch_sentences, batch_of_second_sentences, is_split_into_words=True)
-
-And you can add padding, truncation as well as directly return tensors like before:
-
-.. code-block::
-
-    ## PYTORCH CODE
-    batch = tokenizer(batch_sentences,
-                      batch_of_second_sentences,
-                      is_split_into_words=True,
-                      padding=True,
-                      truncation=True,
-                      return_tensors="pt")
-    ## TENSORFLOW CODE
-    batch = tokenizer(batch_sentences,
-                      batch_of_second_sentences,
-                      is_split_into_words=True,
-                      padding=True,
-                      truncation=True,
-                      return_tensors="tf")
+Preprocessing data
+==================
+
+In this tutorial, we'll explore how to preprocess your data using 🤗 Transformers. The main tool for this is what we
+
+call a :doc:`tokenizer <main_classes/tokenizer>`. You can build one using the tokenizer class associated to the model
+you would like to use, or directly with the :class:`~transformers.AutoTokenizer` class.
+
+As we saw in the :doc:`quicktour </quicktour>`, the tokenizer will first split a given text in words (or part of words,
+punctuation symbols, etc.) usually called `tokens`. Then it will convert those `tokens` into numbers, to be able to
+build a tensor out of them and feed them to the model. It will also add any additional inputs the model might expect to
+work properly.
+
+.. note::
+
+    If you plan on using a pretrained model, it's important to use the associated pretrained tokenizer: it will split
+    the text you give it in tokens the same way for the pretraining corpus, and it will use the same correspondence
+    token to index (that we usually call a `vocab`) as during pretraining.
+
+To automatically download the vocab used during pretraining or fine-tuning a given model, you can use the 
+:func:`~transformers.AutoTokenizer.from_pretrained` method:
+
+.. code-block::
+
+    from transformers import AutoTokenizer
+    tokenizer = AutoTokenizer.from_pretrained('bert-base-cased')
+
+Base use
+~~~~~~~~
+
+A :class:`~transformers.PreTrainedTokenizer` has many methods, but the only one you need to remember for preprocessing
+is its ``__call__``: you just need to feed your sentence to your tokenizer object.
+
+.. code-block::
+
+    >>> encoded_input = tokenizer("Hello, I'm a single sentence!")
+    >>> print(encoded_input)
+    {'input_ids': [101, 138, 18696, 155, 1942, 3190, 1144, 1572, 13745, 1104, 159, 9664, 2107, 102], 
+     'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 
+     'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
+
+This returns a dictionary string to list of ints.
+The `input_ids <glossary.html#input-ids>`__ are the indices corresponding to each token in our sentence. We will see
+below what the `attention_mask <glossary.html#attention-mask>`__ is used for and in
+:ref:`the next section <sentence-pairs>` the goal of `token_type_ids <glossary.html#token-type-ids>`__.
+
+The tokenizer can decode a list of token ids in a proper sentence:
+
+.. code-block::
+
+    >>> tokenizer.decode(encoded_input["input_ids"])
+    "[CLS] Hello, I'm a single sentence! [SEP]"
+
+As you can see, the tokenizer automatically added some special tokens that the model expect. Not all model need special
+tokens; for instance, if we had used` gtp2-medium` instead of `bert-base-cased` to create our tokenizer, we would have
+seen the same sentence as the original one here. You can disable this behavior (which is only advised if you have added
+those special tokens yourself) by passing ``add_special_tokens=False``.
+
+If you have several sentences you want to process, you can do this efficiently by sending them as a list to the
+tokenizer:
+
+.. code-block::
+
+    >>> batch_sentences = ["Hello I'm a single sentence",
+    ...                    "And another sentence",
+    ...                    "And the very very last one"]
+    >>> encoded_inputs = tokenizer(batch_sentences)
+    >>> print(encoded_inputs)
+    {'input_ids': [[101, 8667, 146, 112, 182, 170, 1423, 5650, 102],
+                   [101, 1262, 1330, 5650, 102],
+                   [101, 1262, 1103, 1304, 1304, 1314, 1141, 102]],
+     'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0],
+                        [0, 0, 0, 0, 0],
+                        [0, 0, 0, 0, 0, 0, 0, 0]],
+     'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1],
+                        [1, 1, 1, 1, 1],
+                        [1, 1, 1, 1, 1, 1, 1, 1]]}
+
+We get back a dictionary once again, this time with values being list of list of ints.
+
+If the purpose of sending several sentences at a time to the tokenizer is to build a batch to feed the model, you will
+probably want:
+
+- To pad each sentence to the maximum length there is in your batch.
+- To truncate each sentence to the maximum length the model can accept (if applicable).
+- To return tensors.
+
+You can do all of this by using the following options when feeding your list of sentences to the tokenizer:
+
+.. code-block::
+
+    >>> ## PYTORCH CODE
+    >>> batch = tokenizer(batch_sentences, padding=True, truncation=True, return_tensors="pt")
+    >>> print(batch)
+    {'input_ids': tensor([[ 101, 8667,  146,  112,  182,  170, 1423, 5650,  102],
+                          [ 101, 1262, 1330, 5650,  102,    0,    0,    0,    0],
+                          [ 101, 1262, 1103, 1304, 1304, 1314, 1141,  102,    0]]),
+     'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0],
+                               [0, 0, 0, 0, 0, 0, 0, 0, 0],
+                               [0, 0, 0, 0, 0, 0, 0, 0, 0]]), 
+     'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1],
+                               [1, 1, 1, 1, 1, 0, 0, 0, 0],
+                               [1, 1, 1, 1, 1, 1, 1, 1, 0]])}
+    >>> ## TENSORFLOW CODE
+    >>> batch = tokenizer(batch_sentences, padding=True, truncation=True, return_tensors="tf")
+    >>> print(batch)
+    {'input_ids': tf.Tensor([[ 101, 8667,  146,  112,  182,  170, 1423, 5650,  102],
+                          [ 101, 1262, 1330, 5650,  102,    0,    0,    0,    0],
+                          [ 101, 1262, 1103, 1304, 1304, 1314, 1141,  102,    0]]),
+     'token_type_ids': tf.Tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0],
+                               [0, 0, 0, 0, 0, 0, 0, 0, 0],
+                               [0, 0, 0, 0, 0, 0, 0, 0, 0]]), 
+     'attention_mask': tf.Tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1],
+                               [1, 1, 1, 1, 1, 0, 0, 0, 0],
+                               [1, 1, 1, 1, 1, 1, 1, 1, 0]])}
+
+It returns a dictionary string to tensor. We can now see what the `attention_mask <glossary.html#attention-mask>`__ is
+all about: it points out which tokens the model should pay attention to and which ones it should not (because they
+represent padding in this case).
+
+
+Note that if your model does not have a maximum length associated to it, the command above will throw a warning. You
+can safely ignore it. You can also pass ``verbose=False`` to stop the tokenizer to throw those kinds of warnings.
+
+.. _sentence-pairs:
+
+Preprocessing pairs of sentences
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Sometimes you need to feed pair of sentences to your model. For instance, if you want to classify if two sentences in a
+pair are similar, or for question-answering models, which take a context and a question. For BERT models, the input is
+then represented like this: :obj:`[CLS] Sequence A [SEP] Sequence B [SEP]`
+
+You can encode a pair of sentences in the format expected by your model by supplying the two sentences as two arguments
+(not a list since a list of two sentences will be interpreted as a batch of two single sentences, as we saw before).
+This will once again return a dict string to list of ints:
+
+.. code-block::
+
+    >>> encoded_input = tokenizer("How old are you?", "I'm 6 years old")
+    >>> print(encoded_input)
+    {'input_ids': [101, 1731, 1385, 1132, 1128, 136, 102, 146, 112, 182, 127, 1201, 1385, 102], 
+     'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1], 
+     'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
+
+This shows us what the `token_type_ids <glossary.html#token-type-ids>`__ are for: they indicate to the model which part
+of the inputs correspond to the first sentence and which part corresponds to the second sentence. Note that
+`token_type_ids` are not required or handled by all models. By default, a tokenizer will only return the inputs that
+its associated model expects. You can force the return (or the non-return) of any of those special arguments by
+using ``return_input_ids`` or ``return_token_type_ids``.
+
+If we decode the token ids we obtained, we will see that the special tokens have been properly added.
+
+.. code-block::
+
+    >>> tokenizer.decode(encoded_input["input_ids"])
+    "[CLS] How old are you? [SEP] I'm 6 years old [SEP]"
+
+If you have a list of pairs of sequences you want to process, you should feed them as two lists to your tokenizer: the
+list of first sentences and the list of second sentences:
+
+.. code-block::
+
+    >>> batch_sentences = ["Hello I'm a single sentence",
+    ...                    "And another sentence",
+    ...                    "And the very very last one"]
+    >>> batch_of_second_sentences = ["I'm a sentence that goes with the first sentence",
+    ...                              "And I should be encoded with the second sentence",
+    ...                              "And I go with the very last one"]
+    >>> encoded_inputs = tokenizer(batch_sentences, batch_of_second_sentences)
+    >>> print(encoded_inputs)
+    {'input_ids': [[101, 8667, 146, 112, 182, 170, 1423, 5650, 102, 146, 112, 182, 170, 5650, 1115, 2947, 1114, 1103, 1148, 5650, 102], 
+                   [101, 1262, 1330, 5650, 102, 1262, 146, 1431, 1129, 12544, 1114, 1103, 1248, 5650, 102], 
+                   [101, 1262, 1103, 1304, 1304, 1314, 1141, 102, 1262, 146, 1301, 1114, 1103, 1304, 1314, 1141, 102]], 
+    'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 
+                       [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 
+                       [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 
+    'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 
+                       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 
+                       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
+
+As we can see, it returns a dictionary with the values being list of lists of ints.
+
+To double-check what is fed to the model, we can decode each list in `input_ids` one by one:
+
+.. code-block::
+
+    >>> for ids in encoded_inputs["input_ids"]:
+    >>>     print(tokenizer.decode(ids))
+    [CLS] Hello I'm a single sentence [SEP] I'm a sentence that goes with the first sentence [SEP]
+    [CLS] And another sentence [SEP] And I should be encoded with the second sentence [SEP]
+    [CLS] And the very very last one [SEP] And I go with the very last one [SEP]
+
+Once again, you can automatically pad your inputs to the maximum sentence length in the batch, truncate to the maximum
+length the model can accept and return tensors directly with the following:
+
+.. code-block::
+
+    ## PYTORCH CODE
+    batch = tokenizer(batch_sentences, batch_of_second_sentences, padding=True, truncation=True, return_tensors="pt")
+    ## TENSORFLOW CODE
+    batch = tokenizer(batch_sentences, batch_of_second_sentences, padding=True, truncation=True, return_tensors="tf")
+
+Everything you always wanted to know about padding and truncation
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+We have seen the commands that will work for most cases (pad your batch to the length of the maximum sentence and
+
+truncate to the maximum length the mode can accept). However, the API supports more strategies if you need them. The
+three arguments you need to know for this are :obj:`padding`, :obj:`truncation` and :obj:`max_length`.
+
+- :obj:`padding` controls the padding. It can be a boolean or a string which should be:
+
+    - :obj:`True` or :obj:`'longest'` to pad to the longest sequence in the batch (doing no padding if you only provide
+      a single sequence).
+    - :obj:`'max_length'` to pad to a length specified by the :obj:`max_length` argument or the maximum length accepted
+      by the model if no :obj:`max_length` is provided (``max_length=None``). If you only provide a single sequence,
+      padding will still be applied to it. 
+    - :obj:`False` or :obj:`'do_not_pad'` to not pad the sequences. As we have seen before, this is the default
+      behavior.
+
+- :obj:`truncation` controls the truncation. It can be a boolean or a string which should be:
+
+    - :obj:`True` or :obj:`'only_first'` truncate to a maximum length specified by the :obj:`max_length` argument or
+      the maximum length accepted by the model if no :obj:`max_length` is provided (``max_length=None``). This will
+      only truncate the first sentence of a pair if a pair of sequence (or a batch of pairs of sequences) is provided.
+    - :obj:`'only_second'` truncate to a maximum length specified by the :obj:`max_length` argument or the maximum
+      length accepted by the model if no :obj:`max_length` is provided (``max_length=None``). This will only truncate
+      the second sentence of a pair if a pair of sequence (or a batch of pairs of sequences) is provided.
+    - :obj:`'longest_first'` truncate to a maximum length specified by the :obj:`max_length` argument or the maximum
+      length accepted by the model if no :obj:`max_length` is provided (``max_length=None``). This will truncate token
+      by token, removing a token from the longest sequence in the pair until the proper length is reached.
+    - :obj:`False` or :obj:`'do_not_truncate'` to not truncate the sequences. As we have seen before, this is the
+      default behavior.
+
+- :obj:`max_length` to control the length of the padding/truncation. It can be an integer or :obj:`None`, in which case
+  it will default to the maximum length the model can accept. If the model has no specific maximum input length,
+  truncation/padding to :obj:`max_length` is deactivated.
+
+Here is a table summarizing the recommend way to setup padding and truncation. If you use pair of inputs sequence in
+any of the following examples, you can replace :obj:`truncation=True` by a :obj:`STRATEGY` selected in 
+:obj:`['only_first', 'only_second', 'longest_first']`, i.e. :obj:`truncation='only_second'` or
+:obj:`truncation= 'longest_first'` to control how both sequence in the pair are truncated as detailed before.
+
+--------------------------------------+-----------------------------------+---------------------------------------------------------------------------------------------+
+| Truncation                           | Padding                           | Instruction                                                                                 |
+======================================+===================================+=============================================================================================+
+| no truncation                        | no padding                        | :obj:`tokenizer(batch_sentences)`                                                           |
+|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
+|                                      | padding to max sequence in batch  | :obj:`tokenizer(batch_sentences, padding=True)` or                                          |
+|                                      |                                   | :obj:`tokenizer(batch_sentences, padding='longest')`                                        |
+|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
+|                                      | padding to max model input length | :obj:`tokenizer(batch_sentences, padding='max_length')`                                     |
+|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
+|                                      | padding to specific length        | :obj:`tokenizer(batch_sentences, padding='max_length', max_length=42)`                      |
+--------------------------------------+-----------------------------------+---------------------------------------------------------------------------------------------+
+| truncation to max model input length | no padding                        | :obj:`tokenizer(batch_sentences, truncation=True)` or                                       |
+|                                      |                                   | :obj:`tokenizer(batch_sentences, truncation=STRATEGY)`                                      |
+|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
+|                                      | padding to max sequence in batch  | :obj:`tokenizer(batch_sentences, padding=True, truncation=True)` or                         |
+|                                      |                                   | :obj:`tokenizer(batch_sentences, padding=True, truncation=STRATEGY)`                        |
+|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
+|                                      | padding to max model input length | :obj:`tokenizer(batch_sentences, padding='max_length', truncation=True)` or                 |
+|                                      |                                   | :obj:`tokenizer(batch_sentences, padding='max_length', truncation=STRATEGY)`                |
+|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
+|                                      | padding to specific length        | Not possible                                                                                |
+--------------------------------------+-----------------------------------+---------------------------------------------------------------------------------------------+
+| truncation to specific length        | no padding                        | :obj:`tokenizer(batch_sentences, truncation=True, max_length=42)` or                        |
+|                                      |                                   | :obj:`tokenizer(batch_sentences, truncation=STRATEGY, max_length=42)`                       |
+|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
+|                                      | padding to max sequence in batch  | :obj:`tokenizer(batch_sentences, padding=True, truncation=True, max_length=42)` or          |
+|                                      |                                   | :obj:`tokenizer(batch_sentences, padding=True, truncation=STRATEGY, max_length=42)`         |
+|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
+|                                      | padding to max model input length | Not possible                                                                                |
+|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
+|                                      | padding to specific length        | :obj:`tokenizer(batch_sentences, padding='max_length', truncation=True, max_length=42)` or  |
+|                                      |                                   | :obj:`tokenizer(batch_sentences, padding='max_length', truncation=STRATEGY, max_length=42)` |
+--------------------------------------+-----------------------------------+---------------------------------------------------------------------------------------------+
+
+Pre-tokenized inputs
+~~~~~~~~~~~~~~~~~~~~
+
+The tokenizer also accept pre-tokenized inputs. This is particularly useful when you want to compute labels and extract
+predictions in `named entity recognition (NER) <https://en.wikipedia.org/wiki/Named-entity_recognition>`__ or
+`part-of-speech tagging (POS tagging) <https://en.wikipedia.org/wiki/Part-of-speech_tagging>`__.
+
+.. warning::
+
+    Pre-tokenized does not mean your inputs are already tokenized (you wouldn't need to pass them though the tokenizer
+    if that was the case) but just split into words (which is often the first step in subword tokenization algorithms
+    like BPE).
+
+If you want to use pre-tokenized inputs, just set :obj:`is_pretokenized=True` when passing your inputs to the
+tokenizer. For instance, we have:
+
+.. code-block::
+
+    >>> encoded_input = tokenizer(["Hello", "I'm", "a", "single", "sentence"], is_pretokenized=True)
+    >>> print(encoded_input)
+    {'input_ids': [101, 8667, 146, 112, 182, 170, 1423, 5650, 102],
+     'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0], 
+     'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1]}
+
+Note that the tokenizer still adds the ids of special tokens (if applicable) unless you pass
+``add_special_tokens=False``.
+
+This works exactly as before for batch of sentences or batch of pairs of sentences. You can encode a batch of sentences
+like this:
+
+.. code-block::
+
+    batch_sentences = [["Hello", "I'm", "a", "single", "sentence"],
+                       ["And", "another", "sentence"],
+                       ["And", "the", "very", "very", "last", "one"]]
+    encoded_inputs = tokenizer(batch_sentences, is_pretokenized=True)
+
+or a batch of pair sentences like this:
+
+.. code-block::
+
+    batch_of_second_sentences = [["I'm", "a", "sentence", "that", "goes", "with", "the", "first", "sentence"],
+                                 ["And", "I", "should", "be", "encoded", "with", "the", "second", "sentence"],
+                                 ["And", "I", "go", "with", "the", "very", "last", "one"]]
+    encoded_inputs = tokenizer(batch_sentences, batch_of_second_sentences, is_pretokenized=True)
+
+And you can add padding, truncation as well as directly return tensors like before:
+
+.. code-block::
+
+    ## PYTORCH CODE
+    batch = tokenizer(batch_sentences,
+                      batch_of_second_sentences,
+                      is_pretokenized=True,
+                      padding=True,
+                      truncation=True,
+                      return_tensors="pt")
+    ## TENSORFLOW CODE
+    batch = tokenizer(batch_sentences,
+                      batch_of_second_sentences,
+                      is_pretokenized=True,
+                      padding=True,
+                      truncation=True,
+                      return_tensors="tf")
@@ -1,37 +1,36 @@
 Pretrained models
-=======================================================================================================================
+================================================

 Here is the full list of the currently provided pretrained models together with a short presentation of each model.

-For a list that includes all community-uploaded models, refer to `https://huggingface.co/models
-<https://huggingface.co/models>`__.
+For a list that includes community-uploaded models, refer to `https://huggingface.co/models <https://huggingface.co/models>`__.

 +--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| Architecture       | Model id                                                   | Details of the model                                                                                                                  |
+| Architecture       | Shortcut name                                              | Details of the model                                                                                                                  |
 +====================+============================================================+=======================================================================================================================================+
 | BERT               | ``bert-base-uncased``                                      | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
 |                    |                                                            | | Trained on lower-cased English text.                                                                                                |
 |                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``bert-large-uncased``                                     | | 24-layer, 1024-hidden, 16-heads, 336M parameters.                                                                                   |
+|                    | ``bert-large-uncased``                                     | | 24-layer, 1024-hidden, 16-heads, 340M parameters.                                                                                   |
 |                    |                                                            | | Trained on lower-cased English text.                                                                                                |
 |                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``bert-base-cased``                                        | | 12-layer, 768-hidden, 12-heads, 109M parameters.                                                                                    |
+|                    | ``bert-base-cased``                                        | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
 |                    |                                                            | | Trained on cased English text.                                                                                                      |
 |                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``bert-large-cased``                                       | | 24-layer, 1024-hidden, 16-heads, 335M parameters.                                                                                   |
+|                    | ``bert-large-cased``                                       | | 24-layer, 1024-hidden, 16-heads, 340M parameters.                                                                                   |
 |                    |                                                            | | Trained on cased English text.                                                                                                      |
 |                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``bert-base-multilingual-uncased``                         | | (Original, not recommended) 12-layer, 768-hidden, 12-heads, 168M parameters.                                                        |
+|                    | ``bert-base-multilingual-uncased``                         | | (Original, not recommended) 12-layer, 768-hidden, 12-heads, 110M parameters.                                                        |
 |                    |                                                            | | Trained on lower-cased text in the top 102 languages with the largest Wikipedias                                                    |
 |                    |                                                            |                                                                                                                                       |
 |                    |                                                            | (see `details <https://github.com/google-research/bert/blob/master/multilingual.md>`__).                                              |
 |                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``bert-base-multilingual-cased``                           | | (New, **recommended**) 12-layer, 768-hidden, 12-heads, 179M parameters.                                                             |
+|                    | ``bert-base-multilingual-cased``                           | | (New, **recommended**) 12-layer, 768-hidden, 12-heads, 110M parameters.                                                             |
 |                    |                                                            | | Trained on cased text in the top 104 languages with the largest Wikipedias                                                          |
 |                    |                                                            |                                                                                                                                       |
 |                    |                                                            | (see `details <https://github.com/google-research/bert/blob/master/multilingual.md>`__).                                              |
 |                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``bert-base-chinese``                                      | | 12-layer, 768-hidden, 12-heads, 103M parameters.                                                                                    |
+|                    | ``bert-base-chinese``                                      | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
 |                    |                                                            | | Trained on cased Chinese Simplified and Traditional text.                                                                           |
 |                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                    | ``bert-base-german-cased``                                 | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
@@ -39,22 +38,22 @@ For a list that includes all community-uploaded models, refer to `https://huggin
 |                    |                                                            |                                                                                                                                       |
 |                    |                                                            | (see `details on deepset.ai website <https://deepset.ai/german-bert>`__).                                                             |
 |                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``bert-large-uncased-whole-word-masking``                  | | 24-layer, 1024-hidden, 16-heads, 336M parameters.                                                                                   |
+|                    | ``bert-large-uncased-whole-word-masking``                  | | 24-layer, 1024-hidden, 16-heads, 340M parameters.                                                                                   |
 |                    |                                                            | | Trained on lower-cased English text using Whole-Word-Masking                                                                        |
 |                    |                                                            |                                                                                                                                       |
 |                    |                                                            | (see `details <https://github.com/google-research/bert/#bert>`__).                                                                    |
 |                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``bert-large-cased-whole-word-masking``                    | | 24-layer, 1024-hidden, 16-heads, 335M parameters.                                                                                   |
+|                    | ``bert-large-cased-whole-word-masking``                    | | 24-layer, 1024-hidden, 16-heads, 340M parameters.                                                                                   |
 |                    |                                                            | | Trained on cased English text using Whole-Word-Masking                                                                              |
 |                    |                                                            |                                                                                                                                       |
 |                    |                                                            | (see `details <https://github.com/google-research/bert/#bert>`__).                                                                    |
 |                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``bert-large-uncased-whole-word-masking-finetuned-squad``  | | 24-layer, 1024-hidden, 16-heads, 336M parameters.                                                                                   |
+|                    | ``bert-large-uncased-whole-word-masking-finetuned-squad``  | | 24-layer, 1024-hidden, 16-heads, 340M parameters.                                                                                   |
 |                    |                                                            | | The ``bert-large-uncased-whole-word-masking`` model fine-tuned on SQuAD                                                             |
 |                    |                                                            |                                                                                                                                       |
 |                    |                                                            | (see details of fine-tuning in the `example section <https://github.com/huggingface/transformers/tree/master/examples>`__).           |
 |                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``bert-large-cased-whole-word-masking-finetuned-squad``    | | 24-layer, 1024-hidden, 16-heads, 335M parameters                                                                                    |
+|                    | ``bert-large-cased-whole-word-masking-finetuned-squad``    | | 24-layer, 1024-hidden, 16-heads, 340M parameters                                                                                    |
 |                    |                                                            | | The ``bert-large-cased-whole-word-masking`` model fine-tuned on SQuAD                                                               |
 |                    |                                                            |                                                                                                                                       |
 |                    |                                                            | (see `details of fine-tuning in the example section <https://huggingface.co/transformers/examples.html>`__)                           |
@@ -74,31 +73,31 @@ For a list that includes all community-uploaded models, refer to `https://huggin
 |                    |                                                            |                                                                                                                                       |
 |                    |                                                            | (see `details on dbmdz repository <https://github.com/dbmdz/german-bert>`__).                                                         |
 |                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``cl-tohoku/bert-base-japanese``                           | | 12-layer, 768-hidden, 12-heads, 111M parameters.                                                                                    |
+|                    | ``cl-tohoku/bert-base-japanese``                           | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
 |                    |                                                            | | Trained on Japanese text. Text is tokenized with MeCab and WordPiece and this requires some extra dependencies,                     |
 |                    |                                                            | | `fugashi <https://github.com/polm/fugashi>`__ which is a wrapper around `MeCab <https://taku910.github.io/mecab/>`__.               |
 |                    |                                                            | | Use ``pip install transformers["ja"]`` (or ``pip install -e .["ja"]`` if you install from source) to install them.                  |
 |                    |                                                            |                                                                                                                                       |
 |                    |                                                            | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__).                                               |
 |                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``cl-tohoku/bert-base-japanese-whole-word-masking``        | | 12-layer, 768-hidden, 12-heads, 111M parameters.                                                                                    |
+|                    | ``cl-tohoku/bert-base-japanese-whole-word-masking``        | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
 |                    |                                                            | | Trained on Japanese text. Text is tokenized with MeCab and WordPiece and this requires some extra dependencies,                     |
 |                    |                                                            | | `fugashi <https://github.com/polm/fugashi>`__ which is a wrapper around `MeCab <https://taku910.github.io/mecab/>`__.               |
 |                    |                                                            | | Use ``pip install transformers["ja"]`` (or ``pip install -e .["ja"]`` if you install from source) to install them.                  |
 |                    |                                                            |                                                                                                                                       |
 |                    |                                                            | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__).                                               |
 |                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``cl-tohoku/bert-base-japanese-char``                      | | 12-layer, 768-hidden, 12-heads, 90M parameters.                                                                                     |
+|                    | ``cl-tohoku/bert-base-japanese-char``                      | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
 |                    |                                                            | | Trained on Japanese text. Text is tokenized into characters.                                                                        |
 |                    |                                                            |                                                                                                                                       |
 |                    |                                                            | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__).                                               |
 |                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``cl-tohoku/bert-base-japanese-char-whole-word-masking``   | | 12-layer, 768-hidden, 12-heads, 90M parameters.                                                                                     |
+|                    | ``cl-tohoku/bert-base-japanese-char-whole-word-masking``   | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
 |                    |                                                            | | Trained on Japanese text using Whole-Word-Masking. Text is tokenized into characters.                                               |
 |                    |                                                            |                                                                                                                                       |
 |                    |                                                            | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__).                                               |
 |                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``TurkuNLP/bert-base-finnish-cased-v1``                    | | 12-layer, 768-hidden, 12-heads, 125M parameters.                                                                                    |
+|                    | ``TurkuNLP/bert-base-finnish-cased-v1``                    | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
 |                    |                                                            | | Trained on cased Finnish text.                                                                                                      |
 |                    |                                                            |                                                                                                                                       |
 |                    |                                                            | (see `details on turkunlp.org <http://turkunlp.org/FinBERT/>`__).                                                                     |
@@ -295,10 +294,10 @@ For a list that includes all community-uploaded models, refer to `https://huggin
 |                    | ``t5-11B``                                                 | | ~11B parameters with 24-layers, 1024-hidden-state, 65536 feed-forward hidden-state, 128-heads,                                      |
 |                    |                                                            | | Trained on English text: the Colossal Clean Crawled Corpus (C4)                                                                     |
 +--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| XLM-RoBERTa        | ``xlm-roberta-base``                                       | | ~270M parameters with 12-layers, 768-hidden-state, 3072 feed-forward hidden-state, 8-heads,                                         |
+| XLM-RoBERTa        | ``xlm-roberta-base``                                       | | ~125M parameters with 12-layers, 768-hidden-state, 3072 feed-forward hidden-state, 8-heads,                                         |
 |                    |                                                            | | Trained on on 2.5 TB of newly created clean CommonCrawl data in 100 languages                                                       |
 |                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``xlm-roberta-large``                                      | | ~550M parameters with 24-layers, 1024-hidden-state, 4096 feed-forward hidden-state, 16-heads,                                       |
+|                    | ``xlm-roberta-large``                                      | | ~355M parameters with 24-layers, 1027-hidden-state, 4096 feed-forward hidden-state, 16-heads,                                       |
 |                    |                                                            | | Trained on 2.5 TB of newly created clean CommonCrawl data in 100 languages                                                          |
 +--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 | FlauBERT           | ``flaubert/flaubert_small_cased``                          | | 6-layer, 512-hidden, 8-heads, 54M parameters                                                                                        |
@@ -330,7 +329,7 @@ For a list that includes all community-uploaded models, refer to `https://huggin
 |                    | ``facebook/bart-large-mnli``                               | | Adds a 2 layer classification head with 1 million parameters                                                                        |
 |                    |                                                            | | bart-large base architecture with a classification head, finetuned on MNLI                                                          |
 |                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``facebook/bart-large-cnn``                                | | 24-layer, 1024-hidden, 16-heads, 406M parameters       (same as large)                                                              |
+|                    | ``facebook/bart-large-cnn``                                | | 12-layer, 1024-hidden, 16-heads, 406M parameters       (same as base)                                                               |
 |                    |                                                            | | bart-large base architecture finetuned on cnn summarization task                                                                    |
 +--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 | DialoGPT           | ``DialoGPT-small``                                         | | 12-layer, 768-hidden, 12-heads, 124M parameters                                                                                     |
@@ -409,31 +408,3 @@ For a list that includes all community-uploaded models, refer to `https://huggin
 |                    |                                                            |                                                                                                                                       |
 |                    |                                                            | (see `details <https://github.com/laiguokun/Funnel-Transformer>`__)                                                                   |
 +--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| LayoutLM           | ``microsoft/layoutlm-base-uncased``                        | | 12 layers, 768-hidden, 12-heads, 113M parameters                                                                                    |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/microsoft/unilm/tree/master/layoutlm>`__)                                                           |
-+                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``microsoft/layoutlm-large-uncased``                       | | 24 layers, 1024-hidden, 16-heads, 343M parameters                                                                                   |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/microsoft/unilm/tree/master/layoutlm>`__)                                                           |
-+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| DeBERTa            | ``microsoft/deberta-base``                                 | | 12-layer, 768-hidden, 12-heads, ~125M parameters                                                                                    |
-|                    |                                                            | | DeBERTa using the BERT-base architecture                                                                                            |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/microsoft/DeBERTa>`__)                                                                              |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``microsoft/deberta-large``                                | | 24-layer, 1024-hidden, 16-heads, ~390M parameters                                                                                   |
-|                    |                                                            | | DeBERTa using the BERT-large architecture                                                                                           |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/microsoft/DeBERTa>`__)                                                                              |
-+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| SqueezeBERT        | ``squeezebert/squeezebert-uncased``                        | | 12-layer, 768-hidden, 12-heads, 51M parameters, 4.3x faster than bert-base-uncased on a smartphone.                                 |
-|                    |                                                            | | SqueezeBERT architecture pretrained from scratch on masked language model (MLM) and sentence order prediction (SOP) tasks.          |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``squeezebert/squeezebert-mnli``                           | | 12-layer, 768-hidden, 12-heads, 51M parameters, 4.3x faster than bert-base-uncased on a smartphone.                                 |
-|                    |                                                            | | This is the squeezebert-uncased model finetuned on MNLI sentence pair classification task with distillation from electra-base.      |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``squeezebert/squeezebert-mnli-headless``                  | | 12-layer, 768-hidden, 12-heads, 51M parameters, 4.3x faster than bert-base-uncased on a smartphone.                                 |
-|                    |                                                            | | This is the squeezebert-uncased model finetuned on MNLI sentence pair classification task with distillation from electra-base.      |
-|                    |                                                            | | The final classification layer is removed, so when you finetune, the final layer will be reinitialized.                             |
-+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
@@ -1,8 +1,8 @@
 Quick tour
-=======================================================================================================================
+==========

-Let's have a quick look at the 🤗 Transformers library features. The library downloads pretrained models for Natural
-Language Understanding (NLU) tasks, such as analyzing the sentiment of a text, and Natural Language Generation (NLG),
+Let's have a quick look at the 🤗 Transformers library features. The library downloads pretrained models for
+Natural Language Understanding (NLU) tasks, such as analyzing the sentiment of a text, and Natural Language Generation (NLG),
 such as completing a prompt with new text or translating in another language.

 First we will see how to easily leverage the pipeline API to quickly use those pretrained models at inference. Then, we
@@ -14,7 +14,7 @@ will dig a little bit more and see how the library gives you access to those mod
    not, the code is expected to work for both backends without any change needed.

 Getting started on a task with a pipeline
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 The easiest way to use a pretrained model on a given task is to use :func:`~transformers.pipeline`. 🤗 Transformers
 provides the following tasks out of the box:
@@ -29,8 +29,8 @@ provides the following tasks out of the box:
 - Translation: translate a text in another language.
 - Feature extraction: return a tensor representation of the text.

-Let's see how this work for sentiment analysis (the other tasks are all covered in the :doc:`task summary
-</task_summary>`):
+Let's see how this work for sentiment analysis (the other tasks are all covered in the
+:doc:`task summary </task_summary>`):

 .. code-block::

@@ -123,7 +123,7 @@ to share your fine-tuned model on the hub with the community, using :doc:`this t
 .. _pretrained-model:

 Under the hood: pretrained models
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 Let's now see what happens beneath the hood when using those pipelines. As we saw, the model and tokenizer are created
 using the :obj:`from_pretrained` method:
@@ -142,7 +142,7 @@ using the :obj:`from_pretrained` method:
    >>> tokenizer = AutoTokenizer.from_pretrained(model_name)

 Using the tokenizer
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^

 We mentioned the tokenizer is responsible for the preprocessing of your texts. First, it will split a given text in
 words (or part of words, punctuation symbols, etc.) usually called `tokens`. There are multiple rules that can govern
@@ -160,10 +160,9 @@ To apply these steps on a given text, we can just feed it to our tokenizer:

    >>> inputs = tokenizer("We are very happy to show you the 🤗 Transformers library.")

-This returns a dictionary string to list of ints. It contains the `ids of the tokens <glossary.html#input-ids>`__, as
-mentioned before, but also additional arguments that will be useful to the model. Here for instance, we also have an
-`attention mask <glossary.html#attention-mask>`__ that the model will use to have a better understanding of the
-sequence:
+This returns a dictionary string to list of ints. It contains the `ids of the tokens <glossary.html#input-ids>`__,
+as mentioned before, but also additional arguments that will be useful to the model. Here for instance, we also have an
+`attention mask <glossary.html#attention-mask>`__ that the model will use to have a better understanding of the sequence:


 .. code-block::
@@ -192,8 +191,8 @@ and get tensors back. You can specify all of that to the tokenizer:
    ...     return_tensors="tf"
    ... )

-The padding is automatically applied on the side expected by the model (in this case, on the right), with the padding
-token the model was pretrained with. The attention mask is also adapted to take the padding into account:
+The padding is automatically applied on the side expected by the model (in this case, on the right), with the
+padding token the model was pretrained with. The attention mask is also adapted to take the padding into account:

 .. code-block::

@@ -211,11 +210,11 @@ token the model was pretrained with. The attention mask is also adapted to take
 You can learn more about tokenizers :doc:`here <preprocessing>`.

 Using the model
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^

 Once your input has been preprocessed by the tokenizer, you can send it directly to the model. As we mentioned, it will
-contain all the relevant information the model needs. If you're using a TensorFlow model, you can pass the dictionary
-keys directly to tensors, for a PyTorch model, you need to unpack the dictionary by adding :obj:`**`.
+contain all the relevant information the model needs. If you're using a TensorFlow model, you can pass the
+dictionary keys directly to tensors, for a PyTorch model, you need to unpack the dictionary by adding :obj:`**`.

 .. code-block::

@@ -224,8 +223,8 @@ keys directly to tensors, for a PyTorch model, you need to unpack the dictionary
    >>> ## TENSORFLOW CODE
    >>> tf_outputs = tf_model(tf_batch)

-In 🤗 Transformers, all outputs are tuples (with only one element potentially). Here, we get a tuple with just the final
-activations of the model.
+In 🤗 Transformers, all outputs are tuples (with only one element potentially). Here, we get a tuple with just the
+final activations of the model.

 .. code-block::

@@ -240,10 +239,11 @@ 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.
+    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.

 Let's apply the SoftMax activation to get predictions.

@@ -281,11 +281,11 @@ If you have labels, you can provide them to the model, it will return a tuple wi
    >>> import tensorflow as tf
    >>> tf_outputs = tf_model(tf_batch, labels = tf.constant([1, 0]))

-Models are standard `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__ or `tf.keras.Model
-<https://www.tensorflow.org/api_docs/python/tf/keras/Model>`__ so you can use them in your usual training loop. 🤗
-Transformers also provides a :class:`~transformers.Trainer` (or :class:`~transformers.TFTrainer` if you are using
-TensorFlow) class to help with your training (taking care of things such as distributed training, mixed precision,
-etc.). See the :doc:`training tutorial <training>` for more details.
+Models are standard `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__ or
+`tf.keras.Model <https://www.tensorflow.org/api_docs/python/tf/keras/Model>`__ so you can use them in your usual
+training loop. 🤗 Transformers also provides a :class:`~transformers.Trainer` (or :class:`~transformers.TFTrainer` if
+you are using TensorFlow) class to help with your training (taking care of things such as distributed training, mixed
+precision, etc.). See the :doc:`training tutorial <training>` for more details.

 .. note::

@@ -330,19 +330,19 @@ Lastly, you can also ask the model to return all hidden states and all attention
    >>> all_hidden_states, all_attentions = tf_outputs[-2:]

 Accessing the code
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^

 The :obj:`AutoModel` and :obj:`AutoTokenizer` classes are just shortcuts that will automatically work with any
 pretrained model. Behind the scenes, the library has one model class per combination of architecture plus class, so the
 code is easy to access and tweak if you need to.

-In our previous example, the model was called "distilbert-base-uncased-finetuned-sst-2-english", which means it's using
-the :doc:`DistilBERT </model_doc/distilbert>` architecture. As
-:class:`~transformers.AutoModelForSequenceClassification` (or
-:class:`~transformers.TFAutoModelForSequenceClassification` if you are using TensorFlow) was used, the model
-automatically created is then a :class:`~transformers.DistilBertForSequenceClassification`. You can look at its
-documentation for all details relevant to that specific model, or browse the source code. This is how you would
-directly instantiate model and tokenizer without the auto magic:
+In our previous example, the model was called "distilbert-base-uncased-finetuned-sst-2-english", which means it's
+using the :doc:`DistilBERT </model_doc/distilbert>` architecture. As
+:class:`~transformers.AutoModelForSequenceClassification` (or :class:`~transformers.TFAutoModelForSequenceClassification`
+if you are using TensorFlow) was used, the model automatically created is then a
+:class:`~transformers.DistilBertForSequenceClassification`. You can look at its documentation for all details relevant
+to that specific model, or browse the source code. This is how you would directly instantiate model and tokenizer
+without the auto magic:

 .. code-block::

@@ -358,7 +358,7 @@ directly instantiate model and tokenizer without the auto magic:
    >>> tokenizer = DistilBertTokenizer.from_pretrained(model_name)

 Customizing the model
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^

 If you want to change how the model itself is built, you can define your custom configuration class. Each architecture
 comes with its own relevant configuration (in the case of DistilBERT, :class:`~transformers.DistilBertConfig`) which
@@ -1,22 +1,20 @@
-***********************************************************************************************************************
+**********************************************
 Exporting transformers models
-***********************************************************************************************************************
+**********************************************

 ONNX / ONNXRuntime
-=======================================================================================================================
+==============================================

-Projects `ONNX (Open Neural Network eXchange) <http://onnx.ai>`_ and `ONNXRuntime (ORT)
-<https://microsoft.github.io/onnxruntime/>`_ are part of an effort from leading industries in the AI field to provide a
-unified and community-driven format to store and, by extension, efficiently execute neural network leveraging a variety
+Projects `ONNX (Open Neural Network eXchange) <http://onnx.ai>`_ and `ONNXRuntime (ORT) <https://microsoft.github.io/onnxruntime/>`_ are part of an effort from leading industries in the AI field
+to provide a unified and community-driven format to store and, by extension, efficiently execute neural network leveraging a variety
 of hardware and dedicated optimizations.

 Starting from transformers v2.10.0 we partnered with ONNX Runtime to provide an easy export of transformers models to
-the ONNX format. You can have a look at the effort by looking at our joint blog post `Accelerate your NLP pipelines
-using Hugging Face Transformers and ONNX Runtime
-<https://medium.com/microsoftazure/accelerate-your-nlp-pipelines-using-hugging-face-transformers-and-onnx-runtime-2443578f4333>`_.
+the ONNX format. You can have a look at the effort by looking at our joint blog post `Accelerate your NLP pipelines using
+Hugging Face Transformers and ONNX Runtime <https://medium.com/microsoftazure/accelerate-your-nlp-pipelines-using-hugging-face-transformers-and-onnx-runtime-2443578f4333>`_.

-Exporting a model is done through the script `convert_graph_to_onnx.py` at the root of the transformers sources. The
-following command shows how easy it is to export a BERT model from the library, simply run:
+Exporting a model is done through the script `convert_graph_to_onnx.py` at the root of the transformers sources.
+The following command shows how easy it is to export a BERT model from the library, simply run:

 .. code-block:: bash

@@ -29,66 +27,62 @@ The conversion tool works for both PyTorch and Tensorflow models and ensures:
 * The generated model can be correctly loaded through onnxruntime.

 .. note::
-    Currently, inputs and outputs are always exported with dynamic sequence axes preventing some optimizations on the
-    ONNX Runtime. If you would like to see such support for fixed-length inputs/outputs, please open up an issue on
-    transformers.
+    Currently, inputs and outputs are always exported with dynamic sequence axes preventing some optimizations
+    on the ONNX Runtime. If you would like to see such support for fixed-length inputs/outputs, please
+    open up an issue on transformers.


 Also, the conversion tool supports different options which let you tune the behavior of the generated model:

-* **Change the target opset version of the generated model.** (More recent opset generally supports more operators and
-  enables faster inference)
+* **Change the target opset version of the generated model.**  (More recent opset generally supports more operators and enables faster inference)

-* **Export pipeline-specific prediction heads.** (Allow to export model along with its task-specific prediction
-  head(s))
+* **Export pipeline-specific prediction heads.**  (Allow to export model along with its task-specific prediction head(s))

-* **Use the external data format (PyTorch only).** (Lets you export model which size is above 2Gb (`More info
-  <https://github.com/pytorch/pytorch/pull/33062>`_))
+* **Use the external data format (PyTorch only).**  (Lets you export model which size is above 2Gb (`More info <https://github.com/pytorch/pytorch/pull/33062>`_))


 Optimizations
-----------------------------------------------------------------------------------------------------------------------
+------------------------------------------------

-ONNXRuntime includes some transformers-specific transformations to leverage optimized operations in the graph. Below
-are some of the operators which can be enabled to speed up inference through ONNXRuntime (*see note below*):
+ONNXRuntime includes some transformers-specific transformations to leverage optimized operations in the graph.
+Below are some of the operators which can be enabled to speed up inference through ONNXRuntime (*see note below*):

 * Constant folding
 * Attention Layer fusing
 * Skip connection LayerNormalization fusing
 * FastGeLU approximation

-Some of the optimizations performed by ONNX runtime can be hardware specific and thus lead to different performances if
-used on another machine with a different hardware configuration than the one used for exporting the model. For this
-reason, when using ``convert_graph_to_onnx.py`` optimizations are not enabled, ensuring the model can be easily
-exported to various hardware. Optimizations can then be enabled when loading the model through ONNX runtime for
-inference.
+Some of the optimizations performed by ONNX runtime can be hardware specific and thus lead to different performances
+if used on another machine with a different hardware configuration than the one used for exporting the model.
+For this reason, when using ``convert_graph_to_onnx.py`` optimizations are not enabled,
+ensuring the model can be easily exported to various hardware.
+Optimizations can then be enabled when loading the model through ONNX runtime for inference.


 .. note::
-    When quantization is enabled (see below), ``convert_graph_to_onnx.py`` script will enable optimizations on the
-    model because quantization would modify the underlying graph making it impossible for ONNX runtime to do the
-    optimizations afterwards.
+    When quantization is enabled (see below), ``convert_graph_to_onnx.py`` script will enable optimizations on the model
+    because quantization would modify the underlying graph making it impossible for ONNX runtime to do the 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
-----------------------------------------------------------------------------------------------------------------------
+------------------------------------------------

 ONNX exporter supports generating a quantized version of the model to allow efficient inference.

-Quantization works by converting the memory representation of the parameters in the neural network to a compact integer
-format. By default, weights of a neural network are stored as single-precision float (`float32`) which can express a
-wide-range of floating-point numbers with decent precision. These properties are especially interesting at training
-where you want fine-grained representation.
+Quantization works by converting the memory representation of the parameters in the neural network
+to a compact integer format. By default, weights of a neural network are stored as single-precision float (`float32`)
+which can express a wide-range of floating-point numbers with decent precision.
+These properties are especially interesting at training where you want fine-grained representation.

-On the other hand, after the training phase, it has been shown one can greatly reduce the range and the precision of
-`float32` numbers without changing the performances of the neural network.
+On the other hand, after the training phase, it has been shown one can greatly reduce the range and the precision of `float32` numbers
+without changing the performances of the neural network.

-More technically, `float32` parameters are converted to a type requiring fewer bits to represent each number, thus
-reducing the overall size of the model. Here, we are enabling `float32` mapping to `int8` values (a non-floating,
-single byte, number representation) according to the following formula:
+More technically, `float32` parameters are converted to a type requiring fewer bits to represent each number, thus reducing
+the overall size of the model. Here, we are enabling `float32` mapping to `int8` values (a non-floating, single byte, number representation)
+according to the following formula:

 .. math::
    y_{float32} = scale * x_{int8} - zero\_point
@@ -102,9 +96,9 @@ Leveraging tiny-integers has numerous advantages when it comes to inference:
 * Integer operations execute a magnitude faster on modern hardware
 * Integer operations require less power to do the computations

-In order to convert a transformers model to ONNX IR with quantized weights you just need to specify ``--quantize`` when
-using ``convert_graph_to_onnx.py``. Also, you can have a look at the ``quantize()`` utility-method in this same script
-file.
+In order to convert a transformers model to ONNX IR with quantized weights you just need to specify ``--quantize``
+when using ``convert_graph_to_onnx.py``. Also, you can have a look at the ``quantize()`` utility-method in this
+same script file.

 Example of quantized BERT model export:

@@ -117,27 +111,26 @@ Example of quantized BERT model export:

 .. note::
    When exporting quantized model you will end up with two different ONNX files. The one specified at the end of the
-    above command will contain the original ONNX model storing `float32` weights. The second one, with ``-quantized``
-    suffix, will hold the quantized parameters.
+    above command will contain the original ONNX model storing `float32` weights.
+    The second one, with ``-quantized`` suffix, will hold the quantized parameters.


 TorchScript
-=======================================================================================================================
+=======================================

 .. note::
-    This is the very beginning of our experiments with TorchScript and we are still exploring its capabilities with
-    variable-input-size models. It is a focus of interest to us and we will deepen our analysis in upcoming releases,
-    with more code examples, a more flexible implementation, and benchmarks comparing python-based codes with compiled
-    TorchScript.
+    This is the very beginning of our experiments with TorchScript and we are still exploring its capabilities
+    with variable-input-size models. It is a focus of interest to us and we will deepen our analysis in upcoming
+    releases, with more code examples, a more flexible implementation, and benchmarks comparing python-based codes
+    with compiled TorchScript.


-According to Pytorch's documentation: "TorchScript is a way to create serializable and optimizable models from PyTorch
-code". Pytorch's two modules `JIT and TRACE <https://pytorch.org/docs/stable/jit.html>`_ allow the developer to export
+According to Pytorch's documentation: "TorchScript is a way to create serializable and optimizable models from PyTorch code".
+Pytorch's two modules `JIT and TRACE <https://pytorch.org/docs/stable/jit.html>`_ allow the developer to export
 their model to be re-used in other programs, such as efficiency-oriented C++ programs.

-We have provided an interface that allows the export of 🤗 Transformers models to TorchScript so that they can be reused
-in a different environment than a Pytorch-based python program. Here we explain how to export and use our models using
-TorchScript.
+We have provided an interface that allows the export of 🤗 Transformers models to TorchScript so that they can
+be reused in a different environment than a Pytorch-based python program. Here we explain how to export and use our models using TorchScript.

 Exporting a model requires two things:

@@ -148,28 +141,27 @@ These necessities imply several things developers should be careful about. These


 Implications
-----------------------------------------------------------------------------------------------------------------------
+------------------------------------------------

 TorchScript flag and tied weights
-----------------------------------------------------------------------------------------------------------------------
-
+------------------------------------------------
 This flag is necessary because most of the language models in this repository have tied weights between their
-``Embedding`` layer and their ``Decoding`` layer. TorchScript does not allow the export of models that have tied
-weights, therefore it is necessary to untie and clone the weights beforehand.
+``Embedding`` layer and their ``Decoding`` layer. TorchScript does not allow the export of models that have tied weights, therefore
+it is necessary to untie and clone the weights beforehand.

-This implies that models instantiated with the ``torchscript`` flag have their ``Embedding`` layer and ``Decoding``
-layer separate, which means that they should not be trained down the line. Training would de-synchronize the two
-layers, leading to unexpected results.
+This implies that models instantiated with the ``torchscript`` flag have their ``Embedding`` layer and ``Decoding`` layer
+separate, which means that they should not be trained down the line. Training would de-synchronize the two layers,
+leading to unexpected results.

 This is not the case for models that do not have a Language Model head, as those do not have tied weights. These models
 can be safely exported without the ``torchscript`` flag.

 Dummy inputs and standard lengths
-----------------------------------------------------------------------------------------------------------------------
+------------------------------------------------

 The dummy inputs are used to do a model forward pass. While the inputs' values are propagating through the layers,
-Pytorch keeps track of the different operations executed on each tensor. These recorded operations are then used to
-create the "trace" of the model.
+Pytorch keeps track of the different operations executed on each tensor. These recorded operations are then used
+to create the "trace" of the model.

 The trace is created relatively to the inputs' dimensions. It is therefore constrained by the dimensions of the dummy
 input, and will not work for any other sequence length or batch size. When trying with a different size, an error such
@@ -186,15 +178,15 @@ It is recommended to be careful of the total number of operations done on each i
 when exporting varying sequence-length models.

 Using TorchScript in Python
-----------------------------------------------------------------------------------------------------------------------
+-------------------------------------------------

 Below is an example, showing how to save, load models as well as how to use the trace for inference.

 Saving a model
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

-This snippet shows how to use TorchScript to export a ``BertModel``. Here the ``BertModel`` is instantiated according
-to a ``BertConfig`` class and then saved to disk under the filename ``traced_bert.pt``
+This snippet shows how to use TorchScript to export a ``BertModel``. Here the ``BertModel`` is instantiated
+according to a ``BertConfig`` class and then saved to disk under the filename ``traced_bert.pt``

 .. code-block:: python

@@ -237,7 +229,7 @@ to a ``BertConfig`` class and then saved to disk under the filename ``traced_ber
    torch.jit.save(traced_model, "traced_bert.pt")

 Loading a model
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

 This snippet shows how to load the ``BertModel`` that was previously saved to disk under the name ``traced_bert.pt``.
 We are re-using the previously initialised ``dummy_input``.
@@ -250,7 +242,7 @@ We are re-using the previously initialised ``dummy_input``.
    all_encoder_layers, pooled_output = loaded_model(*dummy_input)

 Using a traced model for inference
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

 Using the traced model for inference is as simple as using its ``__call__`` dunder method:

@@ -1,31 +1,31 @@
 Summary of the tasks
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

 This page shows the most frequent use-cases when using the library. The models available allow for many different
-configurations and a great versatility in use-cases. The most simple ones are presented here, showcasing usage for
-tasks such as question answering, sequence classification, named entity recognition and others.
+configurations and a great versatility in use-cases. The most simple ones are presented here, showcasing usage
+for tasks such as question answering, sequence classification, named entity recognition and others.

 These examples leverage auto-models, which are classes that will instantiate a model according to a given checkpoint,
 automatically selecting the correct model architecture. Please check the :class:`~transformers.AutoModel` documentation
-for more information. Feel free to modify the code to be more specific and adapt it to your specific use-case.
+for more information.
+Feel free to modify the code to be more specific and adapt it to your specific use-case.

 In order for a model to perform well on a task, it must be loaded from a checkpoint corresponding to that task. These
 checkpoints are usually pre-trained on a large corpus of data and fine-tuned on a specific task. This means the
 following:

 - Not all models were fine-tuned on all tasks. If you want to fine-tune a model on a specific task, you can leverage
-  one of the `run_$TASK.py` scripts in the `examples
-  <https://github.com/huggingface/transformers/tree/master/examples>`__ directory.
- Fine-tuned models were fine-tuned on a specific dataset. This dataset may or may not overlap with your use-case and
-  domain. As mentioned previously, you may leverage the `examples
-  <https://github.com/huggingface/transformers/tree/master/examples>`__ scripts to fine-tune your model, or you may
-  create your own training script.
+  one of the `run_$TASK.py` scripts in the
+  `examples <https://github.com/huggingface/transformers/tree/master/examples>`__ directory.
+- Fine-tuned models were fine-tuned on a specific dataset. This dataset may or may not overlap with your use-case
+  and domain. As mentioned previously, you may leverage the
+  `examples <https://github.com/huggingface/transformers/tree/master/examples>`__ scripts to fine-tune your model, or you
+  may create your own training script.

 In order to do an inference on a task, several mechanisms are made available by the library:

 - Pipelines: very easy-to-use abstractions, which require as little as two lines of code.
- Direct model use: Less abstractions, but more flexibility and power via a direct access to a tokenizer
-  (PyTorch/TensorFlow) and full inference capacity.
+- Direct model use: Less abstractions, but more flexibility and power via a direct access to a tokenizer (PyTorch/TensorFlow) and full inference capacity.

 Both approaches are showcased here.

@@ -38,19 +38,17 @@ Both approaches are showcased here.
    This would produce random output.

 Sequence Classification
-----------------------------------------------------------------------------------------------------------------------
+--------------------------

-Sequence classification is the task of classifying sequences according to a given number of classes. An example of
-sequence classification is the GLUE dataset, which is entirely based on that task. If you would like to fine-tune a
-model on a GLUE sequence classification task, you may leverage the `run_glue.py
-<https://github.com/huggingface/transformers/tree/master/examples/text-classification/run_glue.py>`__ and
-`run_pl_glue.py
-<https://github.com/huggingface/transformers/tree/master/examples/text-classification/run_pl_glue.py>`__ or
-`run_tf_glue.py
-<https://github.com/huggingface/transformers/tree/master/examples/text-classification/run_tf_glue.py>`__ scripts.
+Sequence classification is the task of classifying sequences according to a given number of classes. An example
+of sequence classification is the GLUE dataset, which is entirely based on that task. If you would like to fine-tune
+a model on a GLUE sequence classification task, you may leverage the
+`run_glue.py <https://github.com/huggingface/transformers/tree/master/examples/text-classification/run_glue.py>`__ and
+`run_pl_glue.py <https://github.com/huggingface/transformers/tree/master/examples/text-classification/run_pl_glue.py>`__ or
+`run_tf_glue.py <https://github.com/huggingface/transformers/tree/master/examples/text-classification/run_tf_glue.py>`__ scripts.

-Here is an example of using pipelines to do sentiment analysis: identifying if a sequence is positive or negative. It
-leverages a fine-tuned model on sst2, which is a GLUE task.
+Here is an example of using pipelines to do sentiment analysis: identifying if a sequence is positive or negative.
+It leverages a fine-tuned model on sst2, which is a GLUE task.

 This returns a label ("POSITIVE" or "NEGATIVE") alongside a score, as follows:

@@ -69,16 +67,18 @@ This returns a label ("POSITIVE" or "NEGATIVE") alongside a score, as follows:
    label: POSITIVE, with score: 0.9999


-Here is an example of doing a sequence classification using a model to determine if two sequences are paraphrases of
-each other. The process is the following:
+Here is an example of doing a sequence classification using a model to determine if two sequences are paraphrases
+of each other. The process is the following:

-1. Instantiate a tokenizer and a model from the checkpoint name. The model is identified as a BERT model and loads it
-   with the weights stored in the checkpoint.
-2. Build a sequence from the two sentences, with the correct model-specific separators token type ids and attention
-   masks (:func:`~transformers.PreTrainedTokenizer.encode` and :func:`~transformers.PreTrainedTokenizer.__call__` take
-   care of this).
-3. Pass this sequence through the model so that it is classified in one of the two available classes: 0 (not a
-   paraphrase) and 1 (is a paraphrase).
+1. Instantiate a tokenizer and a model from the checkpoint name. The model is
+   identified as a BERT model and loads it with the weights stored in the
+   checkpoint.
+2. Build a sequence from the two sentences, with the correct model-specific
+   separators token type ids and attention masks
+   (:func:`~transformers.PreTrainedTokenizer.encode` and
+   :func:`~transformers.PreTrainedTokenizer.__call__` take care of this).
+3. Pass this sequence through the model so that it is classified in one of the
+   two available classes: 0 (not a paraphrase) and 1 (is a paraphrase).
 4. Compute the softmax of the result to get probabilities over the classes.
 5. Print the results.

@@ -152,18 +152,17 @@ each other. The process is the following:
    is paraphrase: 6%

 Extractive Question Answering
-----------------------------------------------------------------------------------------------------------------------
+----------------------------------------------------

 Extractive Question Answering is the task of extracting an answer from a text given a question. An example of a
-question answering dataset is the SQuAD dataset, which is entirely based on that task. If you would like to fine-tune a
-model on a SQuAD task, you may leverage the `run_squad.py
-<https://github.com/huggingface/transformers/tree/master/examples/question-answering/run_squad.py>`__ and
-`run_tf_squad.py
-<https://github.com/huggingface/transformers/tree/master/examples/question-answering/run_tf_squad.py>`__ scripts.
+question answering dataset is the SQuAD dataset, which is entirely based on that task. If you would like to fine-tune
+a model on a SQuAD task, you may leverage the
+`run_squad.py <https://github.com/huggingface/transformers/tree/master/examples/question-answering/run_squad.py>`__ and
+`run_tf_squad.py <https://github.com/huggingface/transformers/tree/master/examples/question-answering/run_tf_squad.py>`__ scripts.


-Here is an example of using pipelines to do question answering: extracting an answer from a text given a question. It
-leverages a fine-tuned model on SQuAD.
+Here is an example of using pipelines to do question answering: extracting an answer from a text given a question.
+It leverages a fine-tuned model on SQuAD.

 .. code-block::

@@ -177,8 +176,8 @@ leverages a fine-tuned model on SQuAD.
    ... a model on a SQuAD task, you may leverage the examples/question-answering/run_squad.py script.
    ... """

-This returns an answer extracted from the text, a confidence score, alongside "start" and "end" values, which are the
-positions of the extracted answer in the text.
+This returns an answer extracted from the text, a confidence score, alongside "start" and "end" values, which
+are the positions of the extracted answer in the text.

 .. code-block::

@@ -193,13 +192,16 @@ positions of the extracted answer in the text.

 Here is an example of question answering using a model and a tokenizer. The process is the following:

-1. Instantiate a tokenizer and a model from the checkpoint name. The model is identified as a BERT model and loads it
-   with the weights stored in the checkpoint.
+1. Instantiate a tokenizer and a model from the checkpoint name. The model is
+   identified as a BERT model and loads it with the weights stored in the
+   checkpoint.
 2. Define a text and a few questions.
-3. Iterate over the questions and build a sequence from the text and the current question, with the correct
-   model-specific separators token type ids and attention masks.
-4. Pass this sequence through the model. This outputs a range of scores across the entire sequence tokens (question and
-   text), for both the start and end positions.
+3. Iterate over the questions and build a sequence from the text and the current
+   question, with the correct model-specific separators token type ids and
+   attention masks.
+4. Pass this sequence through the model. This outputs a range of scores across
+   the entire sequence tokens (question and text), for both the start and end
+   positions.
 5. Compute the softmax of the result to get probabilities over the tokens.
 6. Fetch the tokens from the identified start and stop values, convert those tokens to a string.
 7. Print the results.
@@ -231,9 +233,7 @@ Here is an example of question answering using a model and a tokenizer. The proc
    ...     input_ids = inputs["input_ids"].tolist()[0]
    ...
    ...     text_tokens = tokenizer.convert_ids_to_tokens(input_ids)
-    ...     outputs = model(**inputs)
-    ...     answer_start_scores = outputs.start_logits
-    ...     answer_end_scores = outputs.end_logits
+    ...     answer_start_scores, answer_end_scores = model(**inputs)
    ...
    ...     answer_start = torch.argmax(
    ...         answer_start_scores
@@ -275,9 +275,7 @@ Here is an example of question answering using a model and a tokenizer. The proc
    ...     input_ids = inputs["input_ids"].numpy()[0]
    ...
    ...     text_tokens = tokenizer.convert_ids_to_tokens(input_ids)
-    ...     outputs = model(inputs)
-    ...     answer_start_scores = outputs.start_logits
-    ...     answer_end_scores = outputs.end_logits
+    ...     answer_start_scores, answer_end_scores = model(inputs)
    ...
    ...     answer_start = tf.argmax(
    ...         answer_start_scores, axis=1
@@ -299,24 +297,24 @@ Here is an example of question answering using a model and a tokenizer. The proc


 Language Modeling
-----------------------------------------------------------------------------------------------------------------------
+----------------------------------------------------

-Language modeling is the task of fitting a model to a corpus, which can be domain specific. All popular
-transformer-based models are trained using a variant of language modeling, e.g. BERT with masked language modeling,
-GPT-2 with causal language modeling.
+Language modeling is the task of fitting a model to a corpus, which can be domain specific. All popular transformer-based
+models are trained using a variant of language modeling, e.g. BERT with masked language modeling, GPT-2 with
+causal language modeling.

-Language modeling can be useful outside of pretraining as well, for example to shift the model distribution to be
-domain-specific: using a language model trained over a very large corpus, and then fine-tuning it to a news dataset or
-on scientific papers e.g. `LysandreJik/arxiv-nlp <https://huggingface.co/lysandre/arxiv-nlp>`__.
+Language modeling can be useful outside of pre-training as well, for example to shift the model distribution to be
+domain-specific: using a language model trained over a very large corpus, and then fine-tuning it to a news dataset
+or on scientific papers e.g. `LysandreJik/arxiv-nlp <https://huggingface.co/lysandre/arxiv-nlp>`__.

 Masked Language Modeling
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 Masked language modeling is the task of masking tokens in a sequence with a masking token, and prompting the model to
 fill that mask with an appropriate token. This allows the model to attend to both the right context (tokens on the
-right of the mask) and the left context (tokens on the left of the mask). Such a training creates a strong basis for
-downstream tasks, requiring bi-directional context such as SQuAD (question answering, see `Lewis, Lui, Goyal et al.
-<https://arxiv.org/abs/1910.13461>`__, part 4.2).
+right of the mask) and the left context (tokens on the left of the mask). Such a training creates a strong basis
+for downstream tasks, requiring bi-directional context such as SQuAD (question answering,
+see `Lewis, Lui, Goyal et al. <https://arxiv.org/abs/1910.13461>`__, part 4.2).

 Here is an example of using pipelines to replace a mask from a sequence:

@@ -326,7 +324,8 @@ Here is an example of using pipelines to replace a mask from a sequence:

    >>> nlp = pipeline("fill-mask")

-This outputs the sequences with the mask filled, the confidence score, and the token id in the tokenizer vocabulary:
+This outputs the sequences with the mask filled, the confidence score, and the token id in the tokenizer
+vocabulary:

 .. code-block::

@@ -360,12 +359,14 @@ This outputs the sequences with the mask filled, the confidence score, and the t

 Here is an example of doing masked language modeling using a model and a tokenizer. The process is the following:

-1. Instantiate a tokenizer and a model from the checkpoint name. The model is identified as a DistilBERT model and
-   loads it with the weights stored in the checkpoint.
+1. Instantiate a tokenizer and a model from the checkpoint name. The model is
+   identified as a DistilBERT model and loads it with the weights stored in the
+   checkpoint.
 2. Define a sequence with a masked token, placing the :obj:`tokenizer.mask_token` instead of a word.
 3. Encode that sequence into a list of IDs and find the position of the masked token in that list.
-4. Retrieve the predictions at the index of the mask token: this tensor has the same size as the vocabulary, and the
-   values are the scores attributed to each token. The model gives higher score to tokens it deems probable in that
+4. Retrieve the predictions at the index of the mask token: this tensor has the
+   same size as the vocabulary, and the values are the scores attributed to each
+   token. The model gives higher score to tokens it deems probable in that
   context.
 5. Retrieve the top 5 tokens using the PyTorch :obj:`topk` or TensorFlow :obj:`top_k` methods.
 6. Replace the mask token by the tokens and print the results
@@ -420,18 +421,15 @@ This prints five sequences, with the top 5 tokens predicted by the model:


 Causal Language Modeling
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 Causal language modeling is the task of predicting the token following a sequence of tokens. In this situation, the
 model only attends to the left context (tokens on the left of the mask). Such a training is particularly interesting
 for generation tasks.

-Usually, the next token is predicted by sampling from the logits of the last hidden state the model produces from the
-input sequence.
+Usually, the next token is predicted by sampling from the logits of the last hidden state the model produces from the input sequence.

-Here is an example of using the tokenizer and model and leveraging the
-:func:`~transformers.PreTrainedModel.top_k_top_p_filtering` method to sample the next token following an input sequence
-of tokens.
+Here is an example of using the tokenizer and model and leveraging the :func:`~transformers.PreTrainedModel.top_k_top_p_filtering` method to sample the next token following an input sequence of tokens.

 .. code-block::

@@ -492,16 +490,12 @@ This outputs a (hopefully) coherent next token following the original sequence,
    >>> print(resulting_string)
    Hugging Face is based in DUMBO, New York City, and has

-In the next section, we show how this functionality is leveraged in :func:`~transformers.PreTrainedModel.generate` to
-generate multiple tokens up to a user-defined length.
+In the next section, we show how this functionality is leveraged in :func:`~transformers.PreTrainedModel.generate` to generate multiple tokens up to a user-defined length.

 Text Generation
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

-In text generation (*a.k.a* *open-ended text generation*) the goal is to create a coherent portion of text that is a
-continuation from the given context. The following example shows how *GPT-2* can be used in pipelines to generate text.
-As a default all models apply *Top-K* sampling when used in pipelines, as configured in their respective configurations
-(see `gpt-2 config <https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-config.json>`__ for example).
+In text generation (*a.k.a* *open-ended text generation*) the goal is to create a coherent portion of text that is a continuation from the given context. The following example shows how *GPT-2* can be used in pipelines to generate text. As a default all models apply *Top-K* sampling when used in pipelines, as configured in their respective configurations (see `gpt-2 config <https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-config.json>`__ for example).

 .. code-block::

@@ -513,11 +507,10 @@ As a default all models apply *Top-K* sampling when used in pipelines, as config



-Here, the model generates a random text with a total maximal length of *50* tokens from context *"As far as I am
-concerned, I will"*. The default arguments of ``PreTrainedModel.generate()`` can be directly overridden in the
-pipeline, as is shown above for the argument ``max_length``.
+Here, the model generates a random text with a total maximal length of *50* tokens from context *"As far as I am concerned, I will"*.
+The default arguments of ``PreTrainedModel.generate()`` can be directly overriden in the pipeline, as is shown above for the argument ``max_length``.

-Here is an example of text generation using ``XLNet`` and its tokenizer.
+Here is an example of text generation using ``XLNet`` and its tokenzier.

 .. code-block::

@@ -576,30 +569,25 @@ Here is an example of text generation using ``XLNet`` and its tokenizer.
    >>> print(generated)
    Today the weather is really nice and I am planning on anning on taking a nice...... of a great time!<eop>...............

-Text generation is currently possible with *GPT-2*, *OpenAi-GPT*, *CTRL*, *XLNet*, *Transfo-XL* and *Reformer* in
-PyTorch and for most models in Tensorflow as well. As can be seen in the example above *XLNet* and *Transfo-XL* often
-need to be padded to work well. GPT-2 is usually a good choice for *open-ended text generation* because it was trained
-on millions of webpages with a causal language modeling objective.
+Text generation is currently possible with *GPT-2*, *OpenAi-GPT*, *CTRL*, *XLNet*, *Transfo-XL* and *Reformer* in PyTorch and for most models in Tensorflow as well. As can be seen in the example above *XLNet* and *Transfo-XL* often need to be padded to work well.
+GPT-2 is usually a good choice for *open-ended text generation* because it was trained on millions of webpages with a causal language modeling objective.

-For more information on how to apply different decoding strategies for text generation, please also refer to our text
-generation blog post `here <https://huggingface.co/blog/how-to-generate>`__.
+For more information on how to apply different decoding strategies for text generation, please also refer to our text generation blog post `here <https://huggingface.co/blog/how-to-generate>`__.


 Named Entity Recognition
-----------------------------------------------------------------------------------------------------------------------
+----------------------------------------------------

-Named Entity Recognition (NER) is the task of classifying tokens according to a class, for example, identifying a token
-as a person, an organisation or a location. An example of a named entity recognition dataset is the CoNLL-2003 dataset,
-which is entirely based on that task. If you would like to fine-tune a model on an NER task, you may leverage the
-`run_ner.py <https://github.com/huggingface/transformers/tree/master/examples/token-classification/run_ner.py>`__
-(PyTorch), `run_pl_ner.py
-<https://github.com/huggingface/transformers/tree/master/examples/token-classification/run_pl_ner.py>`__ (leveraging
-pytorch-lightning) or the `run_tf_ner.py
-<https://github.com/huggingface/transformers/tree/master/examples/token-classification/run_tf_ner.py>`__ (TensorFlow)
-scripts.
+Named Entity Recognition (NER) is the task of classifying tokens according to a class, for example, identifying a
+token as a person, an organisation or a location.
+An example of a named entity recognition dataset is the CoNLL-2003 dataset, which is entirely based on that task.
+If you would like to fine-tune a model on an NER task, you may leverage the
+`run_ner.py <https://github.com/huggingface/transformers/tree/master/examples/token-classification/run_ner.py>`__ (PyTorch),
+`run_pl_ner.py <https://github.com/huggingface/transformers/tree/master/examples/token-classification/run_pl_ner.py>`__ (leveraging pytorch-lightning) or the
+`run_tf_ner.py <https://github.com/huggingface/transformers/tree/master/examples/token-classification/run_tf_ner.py>`__ (TensorFlow) scripts.

-Here is an example of using pipelines to do named entity recognition, specifically, trying to identify tokens as
-belonging to one of 9 classes:
+Here is an example of using pipelines to do named entity recognition, specifically, trying to identify tokens as belonging to one
+of 9 classes:

 - O, Outside of a named entity
 - B-MIS, Beginning of a miscellaneous entity right after another miscellaneous entity
@@ -611,8 +599,8 @@ belonging to one of 9 classes:
 - B-LOC, Beginning of a location right after another location
 - I-LOC, Location

-It leverages a fine-tuned model on CoNLL-2003, fine-tuned by `@stefan-it <https://github.com/stefan-it>`__ from `dbmdz
-<https://github.com/dbmdz>`__.
+It leverages a fine-tuned model on CoNLL-2003, fine-tuned by `@stefan-it <https://github.com/stefan-it>`__ from
+`dbmdz <https://github.com/dbmdz>`__.

 .. code-block::

@@ -624,8 +612,8 @@ It leverages a fine-tuned model on CoNLL-2003, fine-tuned by `@stefan-it <https:
    ...            "close to the Manhattan Bridge which is visible from the window."


-This outputs a list of all words that have been identified as one of the entities from the 9 classes defined above.
-Here are the expected results:
+This outputs a list of all words that have been identified as one of the entities from the 9 classes defined above. Here are the
+expected results:

 .. code-block::

@@ -645,21 +633,24 @@ Here are the expected results:
        {'word': 'Bridge', 'score': 0.990249514579773, 'entity': 'I-LOC'}
    ]

-Note, how the tokens of the sequence "Hugging Face" have been identified as an organisation, and "New York City",
-"DUMBO" and "Manhattan Bridge" have been identified as locations.
+Note, how the tokens of the sequence "Hugging Face" have been identified as an organisation, and "New York City", "DUMBO" and
+"Manhattan Bridge" have been identified as locations.

 Here is an example of doing named entity recognition, using a model and a tokenizer. The process is the following:

-1. Instantiate a tokenizer and a model from the checkpoint name. The model is identified as a BERT model and loads it
-   with the weights stored in the checkpoint.
+1. Instantiate a tokenizer and a model from the checkpoint name. The model is
+   identified as a BERT model and loads it with the weights stored in the
+   checkpoint.
 2. Define the label list with which the model was trained on.
 3. Define a sequence with known entities, such as "Hugging Face" as an organisation and "New York City" as a location.
-4. Split words into tokens so that they can be mapped to predictions. We use a small hack by, first, completely
-   encoding and decoding the sequence, so that we're left with a string that contains the special tokens.
+4. Split words into tokens so that they can be mapped to predictions. We use a
+   small hack by, first, completely encoding and decoding the sequence, so that
+   we're left with a string that contains the special tokens.
 5. Encode that sequence into IDs (special tokens are added automatically).
-6. Retrieve the predictions by passing the input to the model and getting the first output. This results in a
-   distribution over the 9 possible classes for each token. We take the argmax to retrieve the most likely class for
-   each token.
+6. Retrieve the predictions by passing the input to the model and getting the
+   first output. This results in a distribution over the 9 possible classes for
+   each token. We take the argmax to retrieve the most likely class for each
+   token.
 7. Zip together each token with its prediction and print it.

 .. code-block::
@@ -722,9 +713,9 @@ Here is an example of doing named entity recognition, using a model and a tokeni
    >>> predictions = tf.argmax(outputs, axis=2)


-This outputs a list of each token mapped to its corresponding prediction. Differently from the pipeline, here every
-token has a prediction as we didn't remove the "0"th class, which means that no particular entity was found on that
-token. The following array should be the output:
+This outputs a list of each token mapped to its corresponding prediction. Differently from the pipeline, here every token has
+a prediction as we didn't remove the "0"th class, which means that no particular entity was found on that token. The
+following array should be the output:

 .. code-block::

@@ -732,17 +723,15 @@ token. The following array should be the output:
    [('[CLS]', 'O'), ('Hu', 'I-ORG'), ('##gging', 'I-ORG'), ('Face', 'I-ORG'), ('Inc', 'I-ORG'), ('.', 'O'), ('is', 'O'), ('a', 'O'), ('company', 'O'), ('based', 'O'), ('in', 'O'), ('New', 'I-LOC'), ('York', 'I-LOC'), ('City', 'I-LOC'), ('.', 'O'), ('Its', 'O'), ('headquarters', 'O'), ('are', 'O'), ('in', 'O'), ('D', 'I-LOC'), ('##UM', 'I-LOC'), ('##BO', 'I-LOC'), (',', 'O'), ('therefore', 'O'), ('very', 'O'), ('##c', 'O'), ('##lose', 'O'), ('to', 'O'), ('the', 'O'), ('Manhattan', 'I-LOC'), ('Bridge', 'I-LOC'), ('.', 'O'), ('[SEP]', 'O')]

 Summarization
-----------------------------------------------------------------------------------------------------------------------
+----------------------------------------------------

 Summarization is the task of summarizing a document or an article into a shorter text.

-An example of a summarization dataset is the CNN / Daily Mail dataset, which consists of long news articles and was
-created for the task of summarization. If you would like to fine-tune a model on a summarization task, various
-approaches are described in this `document
-<https://github.com/huggingface/transformers/blob/master/examples/seq2seq/README.md>`__.
+An example of a summarization dataset is the CNN / Daily Mail dataset, which consists of long news articles and was created for the task of summarization.
+If you would like to fine-tune a model on a summarization task, various approaches are described in this
+`document <https://github.com/huggingface/transformers/blob/master/examples/seq2seq/README.md>`__.

-Here is an example of using the pipelines to do summarization. It leverages a Bart model that was fine-tuned on the CNN
-/ Daily Mail data set.
+Here is an example of using the pipelines to do summarization. It leverages a Bart model that was fine-tuned on the CNN / Daily Mail data set.

 .. code-block::

@@ -769,9 +758,9 @@ Here is an example of using the pipelines to do summarization. It leverages a Ba
    ... If convicted, Barrientos faces up to four years in prison.  Her next court appearance is scheduled for May 18.
    ... """

-Because the summarization pipeline depends on the ``PreTrainedModel.generate()`` method, we can override the default
-arguments of ``PreTrainedModel.generate()`` directly in the pipeline for ``max_length`` and ``min_length`` as shown
-below. This outputs the following summary:
+Because the summarization pipeline depends on the ``PretrainedModel.generate()`` method, we can override the default arguments
+of ``PretrainedModel.generate()`` directly in the pipeline for ``max_length`` and ``min_length`` as shown below.
+This outputs the following summary:

 .. code-block::

@@ -780,14 +769,12 @@ below. This outputs the following summary:

 Here is an example of doing summarization using a model and a tokenizer. The process is the following:

-1. Instantiate a tokenizer and a model from the checkpoint name. Summarization is usually done using an encoder-decoder
-   model, such as ``Bart`` or ``T5``.
+1. Instantiate a tokenizer and a model from the checkpoint name. Summarization is usually done using an encoder-decoder model, such as ``Bart`` or ``T5``.
 2. Define the article that should be summarized.
 3. Add the T5 specific prefix "summarize: ".
-4. Use the ``PreTrainedModel.generate()`` method to generate the summary.
+4. Use the ``PretrainedModel.generate()`` method to generate the summary.

-In this example we use Google`s T5 model. Even though it was pre-trained only on a multi-task mixed dataset (including
-CNN / Daily Mail), it yields very good results.
+In this example we use Google`s T5 model. Even though it was pre-trained only on a multi-task mixed dataset (including CNN / Daily Mail), it yields very good results.

 .. code-block::

@@ -811,17 +798,18 @@ CNN / Daily Mail), it yields very good results.
    >>> outputs = model.generate(inputs, max_length=150, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True)

 Translation
-----------------------------------------------------------------------------------------------------------------------
+----------------------------------------------------

 Translation is the task of translating a text from one language to another.

-An example of a translation dataset is the WMT English to German dataset, which has sentences in English as the input
-data and the corresponding sentences in German as the target data. If you would like to fine-tune a model on a
-translation task, various approaches are described in this `document
-<https://github.com/huggingface/transformers/blob/master/examples/seq2seq/README.md>`__.
+An example of a translation dataset is the WMT English to German dataset, which has sentences in English as the input data
+and the corresponding sentences in German as the target data.
+If you would like to fine-tune a model on a translation task, various approaches are described in this
+`document <https://github.com/huggingface/transformers/blob/master/examples/seq2seq/README.md>`__.

-Here is an example of using the pipelines to do translation. It leverages a T5 model that was only pre-trained on a
-multi-task mixture dataset (including WMT), yet, yielding impressive translation results.
+Here is an example of using the pipelines to do translation.
+It leverages a T5 model that was only pre-trained on a multi-task mixture dataset (including WMT), yet, yielding impressive
+translation results.

 .. code-block::

@@ -831,16 +819,15 @@ multi-task mixture dataset (including WMT), yet, yielding impressive translation
    >>> print(translator("Hugging Face is a technology company based in New York and Paris", max_length=40))
    [{'translation_text': 'Hugging Face ist ein Technologieunternehmen mit Sitz in New York und Paris.'}]

-Because the translation pipeline depends on the ``PreTrainedModel.generate()`` method, we can override the default
-arguments of ``PreTrainedModel.generate()`` directly in the pipeline as is shown for ``max_length`` above.
+Because the translation pipeline depends on the ``PretrainedModel.generate()`` method, we can override the default arguments
+of ``PretrainedModel.generate()`` directly in the pipeline as is shown for ``max_length`` above.

 Here is an example of doing translation using a model and a tokenizer. The process is the following:

-1. Instantiate a tokenizer and a model from the checkpoint name. Summarization is usually done using an encoder-decoder
-   model, such as ``Bart`` or ``T5``.
-2. Define the article that should be summarized.
+1. Instantiate a tokenizer and a model from the checkpoint name. Summarization is usually done using an encoder-decoder model, such as ``Bart`` or ``T5``.
+2. Define the article that should be summarizaed.
 3. Add the T5 specific prefix "translate English to German: "
-4. Use the ``PreTrainedModel.generate()`` method to perform the translation.
+4. Use the ``PretrainedModel.generate()`` method to perform the translation.

 .. code-block::

@@ -1,264 +1,243 @@
-Summary of the tokenizers
-----------------------------------------------------------------------------------------------------------------------
-
-On this page, we will have a closer look at tokenization. As we saw in :doc:`the preprocessing tutorial
-<preprocessing>`, tokenizing a text is splitting it into words or subwords, which then are converted to ids through a
-look-up table. Converting words or subwords to ids is straightforward, so in this summary, we will focus on splitting a
-text into words or subwords (i.e. tokenizing a text). More specifically, we will look at the three main types of
-tokenizers used in 🤗 Transformers: :ref:`Byte-Pair Encoding (BPE) <byte-pair-encoding>`, :ref:`WordPiece <wordpiece>`,
-and :ref:`SentencePiece <sentencepiece>`, and show exemplary which tokenizer type is used by which model.
-
-Note that on each model page, you can look at the documentation of the associated tokenizer to know which tokenizer
-type was used by the pretrained model. For instance, if we look at :class:`~transformers.BertTokenizer`, we can see
-that the model uses :ref:`WordPiece <wordpiece>`.
-
-Introduction
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Splitting a text into smaller chunks is a task that is harder than it looks, and there are multiple ways of doing so.
-For instance, let's look at the sentence ``"Don't you love 🤗 Transformers? We sure do."`` A simple way of tokenizing
-this text is to split it by spaces, which would give:
-
-.. code-block::
-
-    ["Don't", "you", "love", "🤗", "Transformers?", "We", "sure", "do."]
-
-This is a sensible first step, but if we look at the tokens ``"Transformers?"`` and ``"do."``, we notice that the
-punctuation is attached to the words ``"Transformer"`` and ``"do"``, which is suboptimal. We should take the
-punctuation into account so that a model does not have to learn a different representation of a word and every possible
-punctuation symbol that could follow it, which would explode the number of representations the model has to learn.
-Taking punctuation into account, tokenizing our exemplary text would give:
-
-.. code-block::
-
-    ["Don", "'", "t", "you", "love", "🤗", "Transformers", "?", "We", "sure", "do", "."]
-
-Better. However, it is disadvantageous, how the tokenization dealt with the word ``"Don't"``. ``"Don't"`` stands for
-``"do not"``, so it would be better tokenized as ``["Do", "n't"]``. This is where things start getting complicated, and
-part of the reason each model has its own tokenizer type. Depending on the rules we apply for tokenizing a text, a
-different tokenized output is generated for the same text. A pretrained model only performs properly if you feed it an
-input that was tokenized with the same rules that were used to tokenize its training data.
-
-`spaCy <https://spacy.io/>`__ and `Moses <http://www.statmt.org/moses/?n=Development.GetStarted>`__ are two popular
-rule-based tokenizers. Applying them on our example, *spaCy* and *Moses* would output something like:
-
-.. code-block::
-
-    ["Do", "n't", "you", "love", "🤗", "Transformers", "?", "We", "sure", "do", "."]
-
-As can be seen space and punctuation tokenization, as well as rule-based tokenization, is used here. Space and
-punctuation tokenization and rule-based tokenization are both examples of word tokenization, which is loosely defined
-as splitting sentences into words. While it's the most intuitive way to split texts into smaller chunks, this
-tokenization method can lead to problems for massive text corpora. In this case, space and punctuation tokenization
-usually generates a very big vocabulary (the set of all unique words and tokens used). *E.g.*, :doc:`Transformer XL
-<model_doc/transformerxl>` uses space and punctuation tokenization, resulting in a vocabulary size of 267,735!
-
-Such a big vocabulary size forces the model to have an enormous embedding matrix as the input and output layer, which
-causes both an increased memory and time complexity. In general, transformers models rarely have a vocabulary size
-greater than 50,000, especially if they are pretrained only on a single language.
-
-So if simple space and punctuation tokenization is unsatisfactory, why not simply tokenize on characters? While
-character tokenization is very simple and would greatly reduce memory and time complexity it makes it much harder for
-the model to learn meaningful input representations. *E.g.* learning a meaningful context-independent representation
-for the letter ``"t"`` is much harder as learning a context-independent representation for the word ``"today"``.
-Therefore, character tokenization is often accompanied by a loss of performance. So to get the best of both worlds,
-transformers models use a hybrid between word-level and character-level tokenization called **subword** tokenization.
-
-Subword tokenization
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Subword tokenization algorithms rely on the principle that frequently used words should not be split into smaller
-subwords, but rare words should be decomposed into meaningful subwords. For instance ``"annoyingly"`` might be
-considered a rare word and could be decomposed into ``"annoying"`` and ``"ly"``. Both ``"annoying"`` and ``"ly"`` as
-stand-alone subwords would appear more frequently while at the same time the meaning of ``"annoyingly"`` is kept by the
-composite meaning of ``"annoying"`` and ``"ly"``. This is especially useful in agglutinative languages such as Turkish,
-where you can form (almost) arbitrarily long complex words by stringing together subwords.
-
-Subword tokenization allows the model to have a reasonable vocabulary size while being able to learn meaningful
-context-independent representations. In addition, subword tokenization enables the model to process words it has never
-seen before, by decomposing them into known subwords. For instance, the :class:`~transformers.BertTokenizer` tokenizes
-``"I have a new GPU!"`` as follows:
-
-.. code-block::
-
-    >>> from transformers import BertTokenizer
-    >>> tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
-    >>> tokenizer.tokenize("I have a new GPU!")
-    ["i", "have", "a", "new", "gp", "##u", "!"]
-
-Because we are considering the uncased model, the sentence was lowercased first. We can see that the words ``["i",
-"have", "a", "new"]`` are present in the tokenizer's vocabulary, but the word ``"gpu"`` is not. Consequently, the
-tokenizer splits ``"gpu"`` into known subwords: ``["gp" and "##u"]``. ``"##"`` means that the rest of the token should
-be attached to the previous one, without space (for decoding or reversal of the tokenization).
-
-As another example, :class:`~transformers.XLNetTokenizer` tokenizes our previously exemplary text as follows:
-
-.. code-block::
-
-    >>> from transformers import XLNetTokenizer
-    >>> tokenizer = XLNetTokenizer.from_pretrained("xlnet-base-cased")
-    >>> tokenizer.tokenize("Don't you love 🤗 Transformers? We sure do.")
-    ["▁Don", "'", "t", "▁you", "▁love", "▁", "🤗", "▁", "Transform", "ers", "?", "▁We", "▁sure", "▁do", "."]
-
-We'll get back to the meaning of those ``"▁"`` when we look at :ref:`SentencePiece <sentencepiece>`. As one can see,
-the rare word ``"Transformers"`` has been split into the more frequent subwords ``"Transform"`` and ``"ers"``.
-
-Let's now look at how the different subword tokenization algorithms work. Note that all of those tokenization
-algorithms rely on some form of training which is usually done on the corpus the corresponding model will be trained
-on.
-
-.. _byte-pair-encoding:
-
-Byte-Pair Encoding (BPE)
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Byte-Pair Encoding (BPE) was introduced in `Neural Machine Translation of Rare Words with Subword Units (Sennrich et
-al., 2015) <https://arxiv.org/abs/1508.07909>`__. BPE relies on a pre-tokenizer that splits the training data into
-words. Pretokenization can be as simple as space tokenization, e.g. :doc:`GPT-2 <model_doc/gpt2>`, :doc:`Roberta
-<model_doc/roberta>`. More advanced pre-tokenization include rule-based tokenization, e.g. :doc:`XLM <model_doc/xlm>`,
-:doc:`FlauBERT <model_doc/flaubert>` which uses Moses for most languages, or :doc:`GPT <model_doc/gpt>` which uses
-Spacy and ftfy, to count the frequency of each word in the training corpus.
-
-After pre-tokenization, a set of unique words has been created and the frequency of each word it occurred in the
-training data has been determined. Next, BPE creates a base vocabulary consisting of all symbols that occur in the set
-of unique words and learns merge rules to form a new symbol from two symbols of the base vocabulary. It does so until
-the vocabulary has attained the desired vocabulary size. Note that the desired vocabulary size is a hyperparameter to
-define before training the tokenizer.
-
-As an example, let's assume that after pre-tokenization, the following set of words including their frequency has been
-determined:
-
-.. code-block::
-
-    ("hug", 10), ("pug", 5), ("pun", 12), ("bun", 4), ("hugs", 5)
-
-Consequently, the base vocabulary is ``["b", "g", "h", "n", "p", "s", "u"]``. Splitting all words into symbols of the
-base vocabulary, we obtain:
-
-.. code-block::
-
-    ("h" "u" "g", 10), ("p" "u" "g", 5), ("p" "u" "n", 12), ("b" "u" "n", 4), ("h" "u" "g" "s", 5)
-
-BPE then counts the frequency of each possible symbol pair and picks the symbol pair that occurs most frequently. In
-the example above ``"h"`` followed by ``"u"`` is present `10 + 5 = 15` times (10 times in the 10 occurrences of
-``"hug"``, 5 times in the 5 occurrences of "hugs"). However, the most frequent symbol pair is ``"u"`` followed by "g",
-occurring `10 + 5 + 5 = 20` times in total. Thus, the first merge rule the tokenizer learns is to group all ``"u"``
-symbols followed by a ``"g"`` symbol together. Next, "ug" is added to the vocabulary. The set of words then becomes
-
-.. code-block::
-
-    ("h" "ug", 10), ("p" "ug", 5), ("p" "u" "n", 12), ("b" "u" "n", 4), ("h" "ug" "s", 5)
-
-BPE then identifies the next most common symbol pair. It's ``"u"`` followed by ``"n"``, which occurs 16 times. ``"u"``,
-``"n"`` is merged to ``"un"`` and added to the vocabulary. The next most frequent symbol pair is ``"h"`` followed by
-``"ug"``, occurring 15 times. Again the pair is merged and ``"hug"`` can be added to the vocabulary.
-
-At this stage, the vocabulary is ``["b", "g", "h", "n", "p", "s", "u", "ug", "un", "hug"]`` and our set of unique words
-is represented as
-
-.. code-block::
-
-    ("hug", 10), ("p" "ug", 5), ("p" "un", 12), ("b" "un", 4), ("hug" "s", 5)
-
-Assuming, that the Byte-Pair Encoding training would stop at this point, the learned merge rules would then be applied
-to new words (as long as those new words do not include symbols that were not in the base vocabulary). For instance,
-the word ``"bug"`` would be tokenized to ``["b", "ug"]`` but ``"mug"`` would be tokenized as ``["<unk>", "ug"]`` since
-the symbol ``"m"`` is not in the base vocabulary. In general, single letters such as ``"m"`` are not replaced by the
-``"<unk>"`` symbol because the training data usually includes at least one occurrence of each letter, but it is likely
-to happen for very special characters like emojis.
-
-As mentioned earlier, the vocabulary size, *i.e.* the base vocabulary size + the number of merges, is a hyperparameter
-to choose. For instance :doc:`GPT <model_doc/gpt>` has a vocabulary size of 40,478 since they have 478 base characters
-and chose to stop training after 40,000 merges.
-
-Byte-level BPE
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-A base vocabulary that includes all possible base characters can be quite large if *e.g.* all unicode characters are
-considered as base characters. To have a better base vocabulary, `GPT-2
-<https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf>`__ uses bytes
-as the base vocabulary, which is a clever trick to force the base vocabulary to be of size 256 while ensuring that
-every base character is included in the vocabulary. With some additional rules to deal with punctuation, the GPT2's
-tokenizer can tokenize every text without the need for the <unk> symbol. :doc:`GPT-2 <model_doc/gpt>` has a vocabulary
-size of 50,257, which corresponds to the 256 bytes base tokens, a special end-of-text token and the symbols learned
-with 50,000 merges.
-
-.. _wordpiece:
-
-WordPiece
-=======================================================================================================================
-
-WordPiece is the subword tokenization algorithm used for :doc:`BERT <model_doc/bert>`, :doc:`DistilBERT
-<model_doc/distilbert>`, and :doc:`Electra <model_doc/electra>`. The algorithm was outlined in `Japanese and Korean
-Voice Seach (Schuster et al., 2012)
-<https://static.googleusercontent.com/media/research.google.com/ja//pubs/archive/37842.pdf>`__ and is very similar to
-BPE. WordPiece first initializes the vocabulary to include every character present in the training data and
-progressively learn a given number of merge rules. In contrast to BPE, WordPiece does not choose the most frequent
-symbol pair, but the one that maximizes the likelihood of the training data once added to the vocabulary.
-
-So what does this mean exactly? Referring to the previous example, maximizing the likelihood of the training data is
-equivalent to finding the symbol pair, whose probability divided by the probabilities of its first symbol followed by
-its second symbol is the greatest among all symbol pairs. *E.g.* ``"u"``, followed by ``"g"`` would have only been
-merged if the probability of ``"ug"`` divided by ``"u"``, ``"g"`` would have been greater than for any other symbol
-pair. Intuitively, WordPiece is slightly different to BPE in that it evaluates what it `loses` by merging two symbols
-to make ensure it's `worth it`.
-
-.. _unigram:
-
-Unigram
-=======================================================================================================================
-
-Unigram is a subword tokenization algorithm introduced in `Subword Regularization: Improving Neural Network Translation
-Models with Multiple Subword Candidates (Kudo, 2018) <https://arxiv.org/pdf/1804.10959.pdf>`__. In contrast to BPE or
-WordPiece, Unigram initializes its base vocabulary to a large number of symbols and progressively trims down each
-symbol to obtain a smaller vocabulary. The base vocabulary could for instance correspond to all pre-tokenized words and
-the most common substrings. Unigram is not used directly for any of the models in the transformers, but it's used in
-conjunction with :ref:`SentencePiece <sentencepiece>`.
-
-At each training step, the Unigram algorithm defines a loss (often defined as the log-likelihood) over the training
-data given the current vocabulary and a unigram language model. Then, for each symbol in the vocabulary, the algorithm
-computes how much the overall loss would increase if the symbol was to be removed from the vocabulary. Unigram then
-removes p (with p usually being 10% or 20%) percent of the symbols whose loss increase is the lowest, *i.e.* those
-symbols that least affect the overall loss over the training data. This process is repeated until the vocabulary has
-reached the desired size. The Unigram algorithm always keeps the base characters so that any word can be tokenized.
-
-Because Unigram is not based on merge rules (in contrast to BPE and WordPiece), the algorithm has several ways of
-tokenizing new text after training. As an example, if a trained Unigram tokenizer exhibits the vocabulary:
-
-.. code-block::
-
-    ["b", "g", "h", "n", "p", "s", "u", "ug", "un", "hug"],
-
-``"hugs"`` could be tokenized both as ``["hug", "s"]``, ``["h", "ug", "s"]`` or ``["h", "u", "g", "s"]``. So which one
-to choose? Unigram saves the probability of each token in the training corpus on top of saving the vocabulary so that
-the probability of each possible tokenization can be computed after training. The algorithm simply picks the most
-likely tokenization in practice, but also offers the possibility to sample a possible tokenization according to their
-probabilities.
-
-Those probabilities are defined by the loss the tokenizer is trained on. Assuming that the training data consists of
-the words :math:`x_{1}, \dots, x_{N}` and that the set of all possible tokenizations for a word :math:`x_{i}` is
-defined as :math:`S(x_{i})`, then the overall loss is defined as
-
-.. math::
-    \mathcal{L} = -\sum_{i=1}^{N} \log \left ( \sum_{x \in S(x_{i})} p(x) \right )
-
-.. _sentencepiece:
-
-SentencePiece
-=======================================================================================================================
-
-All tokenization algorithms described so far have the same problem: It is assumed that the input text uses spaces to
-separate words. However, not all languages use spaces to separate words. One possible solution is to use language
-specific pre-tokenizers, *e.g.* :doc:`XLM <model_doc/xlm>` uses a specific Chinese, Japanese, and Thai pre-tokenizer).
-To solve this problem more generally, `SentencePiece: A simple and language independent subword tokenizer and
-detokenizer for Neural Text Processing (Kudo et al., 2018) <https://arxiv.org/pdf/1808.06226.pdf>`__ treats the input
-as a raw input stream, thus including the space in the set of characters to use. It then uses the BPE or unigram
-algorithm to construct the appropriate vocabulary.
-
-The :class:`~transformers.XLNetTokenizer` uses SentencePiece for example, which is also why in the example earlier the
-``"▁"`` character was included in the vocabulary. Decoding with SentencePiece is very easy since all tokens can just be
-concatenated and ``"▁"`` is replaced by a space.
-
-All transformers models in the library that use SentencePiece use it in combination with unigram. Examples of models
-using SentencePiece are :doc:`ALBERT <model_doc/albert>`, :doc:`XLNet <model_doc/xlnet>`, :doc:`Marian
-<model_doc/marian>`, and :doc:`T5 <model_doc/t5>`.
+Tokenizer summary
+-----------------
+
+In this page, we will have a closer look at tokenization. As we saw in
+:doc:`the preprocessing tutorial <preprocessing>`, tokenizing a text is splitting it into words or subwords, which then
+are converted to ids. The second part is pretty straightforward, here we will focus on the first part. More
+specifically, we will look at the three main different kinds of tokenizers used in 🤗 Transformers:
+:ref:`Byte-Pair Encoding (BPE) <byte-pair-encoding>`, :ref:`WordPiece <wordpiece>` and
+:ref:`SentencePiece <sentencepiece>`, and provide examples of models using each of those.
+
+Note that on each model page, you can look at the documentation of the associated tokenizer to know which of those
+algorithms the pretrained model used. For instance, if we look at :class:`~transformers.BertTokenizer`, we can see it's
+using :ref:`WordPiece <wordpiece>`.
+
+Introduction to tokenization
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Splitting a text in smaller chunks is a task that's harder than it looks, and there are multiple ways of doing it. For
+instance, let's look at the sentence "Don't you love 🤗 Transformers? We sure do." A first simple way of tokenizing
+this text is just to split it by spaces, which would give:
+
+::
+
+    ["Don't", "you", "love", "🤗", "Transformers?", "We", "sure", "do."]
+
+This is a nice first step, but if we look at the tokens "Transformers?" or "do.", we can see we can do better. Those
+will be different than the tokens "Transformers" and "do" for our model, so we should probably take the punctuation
+into account. This would give:
+
+::
+
+    ["Don", "'", "t", "you", "love", "🤗", "Transformers", "?", "We", "sure", "do", "."]
+
+which is better already. One thing that is annoying though is how it dealt with "Don't". "Don't" stands for do not, so
+it should probably be better tokenized as ``["Do", "n't"]``. This is where things start getting more complicated, and
+part of the reason each kind of model has its own tokenizer class. Depending on the rules we apply to split our texts
+into tokens, we'll get different tokenized versions of the same text. And of course, a given pretrained model won't
+perform properly if you don't use the exact same rules as the persons who pretrained it.
+
+`spaCy <https://spacy.io/>`__ and `Moses <http://www.statmt.org/moses/?n=Development.GetStarted>`__ are two popular
+rule-based tokenizers. On the text above, they'd output something like:
+
+::
+
+    ["Do", "n't", "you", "love", "🤗", "Transformers", "?", "We", "sure", "do", "."]
+
+Space/punctuation-tokenization and rule-based tokenization are both examples of word tokenization, which is splitting a
+sentence into words. While it's the most intuitive way to separate texts in smaller chunks, it can have a problem when
+you have a huge corpus: it usually yields a very big vocabulary (the set of all unique tokens used).
+:doc:`Transformer XL <model_doc/transformerxl>` for instance uses space/punctuation-tokenization, and has a vocabulary
+size of 267,735!
+
+A huge vocabulary size means a huge embedding matrix at the start of the model, which will cause memory problems.
+TransformerXL deals with it by using a special kind of embeddings called adaptive embeddings, but in general,
+transformers models rarely have a vocabulary size greater than 50,000, especially if they are trained on a single
+language.
+
+So if tokenizing on words is unsatisfactory, we could go on the opposite direction and simply tokenize on characters.
+While it's very simple and would save a lot of memory, this doesn't allow the model to learn representations of texts
+as meaningful as when using a word tokenization, leading to a loss of performance. So to get the best of both worlds,
+all transformers models use a hybrid between word-level and character-level tokenization called subword tokenization.
+
+Subword tokenization
+^^^^^^^^^^^^^^^^^^^^
+
+Subword tokenization algorithms rely on the principle that most common words should be left as is, but rare words
+should be decomposed in meaningful subword units. For instance "annoyingly" might be considered a rare word and
+decomposed as "annoying" and "ly". This is especially useful in agglutinative languages such as Turkish, where you can
+form (almost) arbitrarily long complex words by stringing together some subwords.
+
+This allows the model to keep a reasonable vocabulary while still learning useful representations for common words or
+subwords. This also enables the model to process words it has never seen before, by decomposing them into
+subwords it knows. For instance, the base :class:`~transformers.BertTokenizer` will tokenize "I have a new GPU!" like
+this:
+
+.. code-block::
+
+    >>> from transformers import BertTokenizer
+    >>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+    >>> tokenizer.tokenize("I have a new GPU!")
+    ['i', 'have', 'a', 'new', 'gp', '##u', '!']
+
+Since we are considering the uncased model, the sentence was lowercased first. Then all the words were present in the
+vocabulary of the tokenizer, except for "gpu", so the tokenizer split it in subwords it knows: "gp" and "##u". The "##"
+means that the rest of the token should be attached to the previous one, without space (for when we need to decode
+predictions and reverse the tokenization).
+
+Another example is when we use the base :class:`~transformers.XLNetTokenizer` to tokenize our previous text:
+
+.. code-block::
+
+    >>> from transformers import XLNetTokenizer
+    >>> tokenizer = XLNetTokenizer.from_pretrained('xlnet-base-cased')
+    >>> tokenizer.tokenize("Don't you love 🤗 Transformers? We sure do.")
+    ['▁Don', "'", 't', '▁you', '▁love', '▁', '🤗', '▁', 'Transform', 'ers', '?', '▁We', '▁sure', '▁do', '.']
+
+We'll get back to the meaning of those '▁' when we look at :ref:`SentencePiece <sentencepiece>` but you can see
+Transformers has been split into "Transform" and "ers".
+
+Let's now look at how the different subword tokenization algorithms work. Note that they all rely on some form of
+training which is usually done on the corpus the corresponding model will be trained on.
+
+.. _byte-pair-encoding:
+
+Byte-Pair Encoding
+~~~~~~~~~~~~~~~~~~
+
+Byte-Pair Encoding was introduced in `this paper <https://arxiv.org/abs/1508.07909>`__. It relies on a pretokenizer
+splitting the training data into words, which can be a simple space tokenization
+(:doc:`GPT-2 <model_doc/gpt2>` and :doc:`Roberta <model_doc/roberta>` uses this for instance) or a rule-based tokenizer
+(:doc:`XLM <model_doc/xlm>` use Moses for most languages, as does :doc:`FlauBERT <model_doc/flaubert>`),
+
+:doc:`GPT <model_doc/gpt>` uses Spacy and ftfy, and counts the frequency of each word in the training corpus.
+
+It then begins from the list of all characters, and will learn merge rules to form a new token from two symbols in the
+vocabulary until it has learned a vocabulary of the desired size (this is a hyperparameter to pick).
+
+Let's say that after the pre-tokenization we have the following words (the number indicating the frequency of each
+word):
+
+::
+
+    ('hug', 10), ('pug', 5), ('pun', 12), ('bun', 4), ('hugs', 5)
+
+Then the base vocabulary is ['b', 'g', 'h', 'n', 'p', 's', 'u'] and all our words are first split by character:
+
+::
+
+    ('h' 'u' 'g', 10), ('p' 'u' 'g', 5), ('p' 'u' 'n', 12), ('b' 'u' 'n', 4), ('h' 'u' 'g' 's', 5)
+
+We then take each pair of symbols and look at the most frequent. For instance 'hu' is present `10 + 5 = 15` times (10
+times in the 10 occurrences of 'hug', 5 times in the 5 occurrences of 'hugs'). The most frequent here is 'ug', present
+`10 + 5 + 5 = 20` times in total. So the first merge rule the tokenizer learns is to group all 'u' and 'g' together
+then it adds 'ug' to the vocabulary. Our corpus then becomes
+
+::
+
+    ('h' 'ug', 10), ('p' 'ug', 5), ('p' 'u' 'n', 12), ('b' 'u' 'n', 4), ('h' 'ug' 's', 5)
+
+and we continue by looking at the next most common pair of symbols. It's 'un', present 16 times, so we merge those two
+and add 'un' to the vocabulary. Then it's 'hug' (as 'h' + 'ug'), present 15 times, so we merge those two and add 'hug'
+to the vocabulary.
+
+At this stage, the vocabulary is ``['b', 'g', 'h', 'n', 'p', 's', 'u', 'ug', 'un', 'hug']`` and our corpus is
+represented as
+
+::
+
+    ('hug', 10), ('p' 'ug', 5), ('p' 'un', 12), ('b' 'un', 4), ('hug' 's', 5)
+
+If we stop there, the tokenizer can apply the rules it learned to new words (as long as they don't contain characters that
+were not in the base vocabulary). For instance 'bug' would be tokenized as ``['b', 'ug']`` but mug would be tokenized as
+``['<unk>', 'ug']`` since the 'm' is not in the base vocabulary. This doesn't happen to letters in general (since the
+base corpus uses all of them), but to special characters like emojis.
+
+As we said before, the vocabulary size (which is the base vocabulary size + the number of merges) is a hyperparameter
+to choose. For instance :doc:`GPT <model_doc/gpt>` has a vocabulary size of 40,478 since they have 478 base characters
+and chose to stop the training of the tokenizer at 40,000 merges.
+
+Byte-level BPE
+^^^^^^^^^^^^^^
+
+To deal with the fact the base vocabulary needs to get all base characters, which can be quite big if one allows for
+all unicode characters, the
+`GPT-2 paper <https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf>`__
+introduces a clever trick, which is to use bytes as the base vocabulary (which gives a size of 256). With some
+additional rules to deal with punctuation, this manages to be able to tokenize every text without needing an unknown
+token. For instance, the :doc:`GPT-2 model <model_doc/gpt>` has a vocabulary size of 50,257, which corresponds to the
+256 bytes base tokens, a special end-of-text token and the symbols learned with 50,000 merges.
+
+.. _wordpiece:
+
+WordPiece
+=========
+
+WordPiece is the subword tokenization algorithm used for :doc:`BERT <model_doc/bert>` (as well as
+:doc:`DistilBERT <model_doc/distilbert>` and :doc:`Electra <model_doc/electra>`) and was outlined in
+`this paper <https://static.googleusercontent.com/media/research.google.com/ja//pubs/archive/37842.pdf>`__. It relies
+on the same base as BPE, which is to initialize the vocabulary to every character present in the corpus and
+progressively learn a given number of merge rules, the difference is that it doesn't choose the pair that is the most
+frequent but the one that will maximize the likelihood on the corpus once merged.
+
+What does this mean? Well, in the previous example, it means we would only merge 'u' and 'g' if the probability of
+having 'ug' divided by the probability of having 'u' then 'g' is greater than for any other pair of symbols. It's
+subtly different from what BPE does in the sense that it evaluates what it "loses" by merging two symbols and makes
+sure it's `worth it`.
+
+.. _unigram:
+
+Unigram
+=======
+
+Unigram is a subword tokenization algorithm introduced in `this paper <https://arxiv.org/pdf/1804.10959.pdf>`__.
+Instead of starting with a group of base symbols and learning merges with some rule, like BPE or WordPiece, it starts
+from a large vocabulary (for instance, all pretokenized words and the most common substrings) that it will trim down
+progressively. It's not used directly for any of the pretrained models in the library, but it's used in conjunction
+with :ref:`SentencePiece <sentencepiece>`.
+
+More specifically, at a given step, unigram computes a loss from the corpus we have and the current vocabulary, then,
+for each subword, evaluate how much the loss would augment if the subword was removed from the vocabulary. It then
+sorts the subwords by this quantity (that represents how worse the loss becomes if the token is removed) and removes
+all the worst p tokens (for instance p could be 10% or 20%). It then repeats the process until the vocabulary has
+reached the desired size, always keeping the base characters (to be able to tokenize any word written with them, like
+BPE or WordPiece).
+
+Contrary to BPE and WordPiece that work out rules in a certain order that you can then apply in the same order when
+tokenizing new text, Unigram will have several ways of tokenizing a new text. For instance, if it ends up with the
+vocabulary
+
+::
+
+    ['b', 'g', 'h', 'n', 'p', 's', 'u', 'ug', 'un', 'hug']
+
+we had before, it could tokenize "hugs" as ``['hug', 's']``, ``['h', 'ug', 's']`` or ``['h', 'u', 'g', 's']``. So which
+one choose? On top of saving the vocabulary, the trained tokenizer will save the probability of each token in the
+training corpus. You can then give a probability to each tokenization (which is the product of the probabilities of the
+tokens forming it) and pick the most likely one (or if you want to apply some data augmentation, you could sample one
+of the tokenization according to their probabilities).
+
+Those probabilities define the loss that trains the tokenizer: if our corpus consists of the
+words :math:`x_{1}, \dots, x_{N}` and if for the word :math:`x_{i}` we note :math:`S(x_{i})` the set of all possible
+tokenizations of :math:`x_{i}` (with the current vocabulary), then the loss is defined as
+
+.. math::
+    \mathcal{L} = -\sum_{i=1}^{N} \log \left ( \sum_{x \in S(x_{i})} p(x) \right )
+
+.. _sentencepiece:
+
+SentencePiece
+=============
+
+All the methods we have been looking at so far required some form of pretokenization, which has a central problem: not
+all languages use spaces to separate words. This is a problem :doc:`XLM <model_doc/xlm>` solves by using specific
+pretokenizers for each of those languages (in this case, Chinese, Japanese and Thai). To solve this problem,
+SentencePiece (introduced in `this paper <https://arxiv.org/pdf/1808.06226.pdf>`__) treats the input as a raw stream,
+includes the space in the set of characters to use, then uses BPE or unigram to construct the appropriate vocabulary.
+
+That's why in the example we saw before using :class:`~transformers.XLNetTokenizer` (which uses SentencePiece), we had
+the '▁' character, that represents space. Decoding a tokenized text is then super easy: we just have to concatenate
+all of them together and replace '▁' with space.
+
+All transformers models in the library that use SentencePiece use it with unigram. Examples of models using it are
+:doc:`ALBERT <model_doc/albert>`, :doc:`XLNet <model_doc/xlnet>` or the :doc:`Marian framework <model_doc/marian>`.
@@ -1,14 +1,18 @@
 Training and fine-tuning
-=======================================================================================================================
+========================

-Model classes in 🤗 Transformers are designed to be compatible with native PyTorch and TensorFlow 2 and can be used
-seemlessly with either. In this quickstart, we will show how to fine-tune (or train from scratch) a model using the
-standard training tools available in either framework. We will also show how to use our included
-:func:`~transformers.Trainer` class which handles much of the complexity of training for you.
+Model classes in 🤗 Transformers are designed to be compatible with native
+PyTorch and TensorFlow 2 and can be used seemlessly with either. In this
+quickstart, we will show how to fine-tune (or train from scratch) a model
+using the standard training tools available in either framework. We will also
+show how to use our included :func:`~transformers.Trainer` class which
+handles much of the complexity of training for you.

-This guide assume that you are already familiar with loading and use our models for inference; otherwise, see the
-:doc:`task summary <task_summary>`. We also assume that you are familiar with training deep neural networks in either
-PyTorch or TF2, and focus specifically on the nuances and tools for training models in 🤗 Transformers.
+This guide assume that you are already familiar with loading and use our
+models for inference; otherwise, see the :doc:`task summary <task_summary>`. We also assume
+that you are familiar with training deep neural networks in either PyTorch or
+TF2, and focus specifically on the nuances and tools for training models in
+🤗 Transformers.

 Sections:

@@ -20,39 +24,48 @@ Sections:
 .. _pytorch:

 Fine-tuning in native PyTorch
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

-Model classes in 🤗 Transformers that don't begin with ``TF`` are `PyTorch Modules
-<https://pytorch.org/docs/master/generated/torch.nn.Module.html>`_, meaning that you can use them just as you would any
-model in PyTorch for both inference and optimization.
+Model classes in 🤗 Transformers that don't begin with ``TF`` are
+`PyTorch Modules <https://pytorch.org/docs/master/generated/torch.nn.Module.html>`_,
+meaning that you can use them just as you would any model in PyTorch for
+both inference and optimization.

-Let's consider the common task of fine-tuning a masked language model like BERT on a sequence classification dataset.
-When we instantiate a model with :func:`~transformers.PreTrainedModel.from_pretrained`, the model configuration and
-pre-trained weights of the specified model are used to initialize the model. The library also includes a number of
-task-specific final layers or 'heads' whose weights are instantiated randomly when not present in the specified
+Let's consider the common task of fine-tuning a masked language model like
+BERT on a sequence classification dataset. When we instantiate a model with
+:func:`~transformers.PreTrainedModel.from_pretrained`, the model
+configuration and pre-trained weights
+of the specified model are used to initialize the model. The
+library also includes a number of task-specific final layers or 'heads' whose
+weights are instantiated randomly when not present in the specified
 pre-trained model. For example, instantiating a model with
-``BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)`` will create a BERT model instance
-with encoder weights copied from the ``bert-base-uncased`` model and a randomly initialized sequence classification
-head on top of the encoder with an output size of 2. Models are initialized in ``eval`` mode by default. We can call
-``model.train()`` to put it in train mode.
+``BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)``
+will create a BERT model instance with encoder weights copied from the
+``bert-base-uncased`` model and a randomly initialized sequence
+classification head on top of the encoder with an output size of 2. Models
+are initialized in ``eval`` mode by default. We can call ``model.train()`` to
+put it in train mode.

 .. code-block:: python

    from transformers import BertForSequenceClassification
-    model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
+    model = BertForSequenceClassification.from_pretrained('bert-base-uncased', return_dict=True)
    model.train()

-This is useful because it allows us to make use of the pre-trained BERT encoder and easily train it on whatever
-sequence classification dataset we choose. We can use any PyTorch optimizer, but our library also provides the
-:func:`~transformers.AdamW` optimizer which implements gradient bias correction as well as weight decay.
+This is useful because it allows us to make use of the pre-trained BERT
+encoder and easily train it on whatever sequence classification dataset we
+choose. We can use any PyTorch optimizer, but our library also provides the
+:func:`~transformers.AdamW` optimizer which implements gradient bias
+correction as well as weight decay.

 .. code-block:: python

    from transformers import AdamW
    optimizer = AdamW(model.parameters(), lr=1e-5)

-The optimizer allows us to apply different hyperpameters for specific parameter groups. For example, we can apply
-weight decay to all parameters other than bias and layer normalization terms:
+The optimizer allows us to apply different hyperpameters for specific
+parameter groups. For example, we can apply weight decay to all parameters
+other than bias and layer normalization terms:

 .. code-block:: python

@@ -62,9 +75,11 @@ weight decay to all parameters other than bias and layer normalization terms:
        {'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
    ]
    optimizer = AdamW(optimizer_grouped_parameters, lr=1e-5)
-
-Now we can set up a simple dummy training batch using :func:`~transformers.PreTrainedTokenizer.__call__`. This returns
-a :func:`~transformers.BatchEncoding` instance which prepares everything we might need to pass to the model.
+    
+Now we can set up a simple dummy training batch using
+:func:`~transformers.PreTrainedTokenizer.__call__`. This returns a
+:func:`~transformers.BatchEncoding` instance which
+prepares everything we might need to pass to the model.

 .. code-block:: python

@@ -75,9 +90,10 @@ a :func:`~transformers.BatchEncoding` instance which prepares everything we migh
    input_ids = encoding['input_ids']
    attention_mask = encoding['attention_mask']

-When we call a classification model with the ``labels`` argument, the first returned element is the Cross Entropy loss
-between the predictions and the passed labels. Having already set up our optimizer, we can then do a backwards pass and
-update the weights:
+When we call a classification model with the ``labels`` argument, the first
+returned element is the Cross Entropy loss between the predictions and the
+passed labels. Having already set up our optimizer, we can then do a
+backwards pass and update the weights:

 .. code-block:: python

@@ -87,22 +103,24 @@ update the weights:
    loss.backward()
    optimizer.step()

-Alternatively, you can just get the logits and calculate the loss yourself. The following is equivalent to the previous
-example:
+Alternatively, you can just get the logits and calculate the loss yourself.
+The following is equivalent to the previous example:

 .. code-block:: python

    from torch.nn import functional as F
-    labels = torch.tensor([1,0])
+    labels = torch.tensor([1,0]).unsqueeze(0)
    outputs = model(input_ids, attention_mask=attention_mask)
-    loss = F.cross_entropy(outputs.logits, labels)
+    loss = F.cross_entropy(labels, outputs.logitd)
    loss.backward()
    optimizer.step()

-Of course, you can train on GPU by calling ``to('cuda')`` on the model and inputs as usual.
+Of course, you can train on GPU by calling ``to('cuda')`` on the model and
+inputs as usual.

-We also provide a few learning rate scheduling tools. With the following, we can set up a scheduler which warms up for
-``num_warmup_steps`` and then linearly decays to 0 by the end of training.
+We also provide a few learning rate scheduling tools. With the following, we
+can set up a scheduler which warms up for ``num_warmup_steps`` and then
+linearly decays to 0 by the end of training.

 .. code-block:: python

@@ -117,16 +135,19 @@ Then all we have to do is call ``scheduler.step()`` after ``optimizer.step()``.
    optimizer.step()
    scheduler.step()

-We highly recommend using :func:`~transformers.Trainer`, discussed below, which conveniently handles the moving parts
-of training 🤗 Transformers models with features like mixed precision and easy tensorboard logging.
+We highly recommend using :func:`~transformers.Trainer`, discussed below,
+which conveniently handles the moving parts of training 🤗 Transformers models
+with features like mixed precision and easy tensorboard logging.


 Freezing the encoder
-----------------------------------------------------------------------------------------------------------------------
+--------------------

-In some cases, you might be interested in keeping the weights of the pre-trained encoder frozen and optimizing only the
-weights of the head layers. To do so, simply set the ``requires_grad`` attribute to ``False`` on the encoder
-parameters, which can be accessed with the ``base_model`` submodule on any task-specific model in the library:
+In some cases, you might be interested in keeping the weights of the
+pre-trained encoder frozen and optimizing only the weights of the head
+layers. To do so, simply set the ``requires_grad`` attribute to ``False`` on
+the encoder parameters, which can be accessed with the ``base_model``
+submodule on any task-specific model in the library:

 .. code-block:: python

@@ -137,10 +158,12 @@ parameters, which can be accessed with the ``base_model`` submodule on any task-
 .. _tensorflow:

 Fine-tuning in native TensorFlow 2
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

-Models can also be trained natively in TensorFlow 2. Just as with PyTorch, TensorFlow models can be instantiated with
-:func:`~transformers.PreTrainedModel.from_pretrained` to load the weights of the encoder from a pretrained model.
+Models can also be trained natively in TensorFlow 2. Just as with PyTorch,
+TensorFlow models can be instantiated with
+:func:`~transformers.PreTrainedModel.from_pretrained` to load the weights of
+the encoder from a pretrained model.

 .. code-block:: python

@@ -148,9 +171,11 @@ Models can also be trained natively in TensorFlow 2. Just as with PyTorch, Tenso
    model = TFBertForSequenceClassification.from_pretrained('bert-base-uncased')

 Let's use ``tensorflow_datasets`` to load in the `MRPC dataset
-<https://www.tensorflow.org/datasets/catalog/glue#gluemrpc>`_ from GLUE. We can then use our built-in
-:func:`~transformers.data.processors.glue.glue_convert_examples_to_features` to tokenize MRPC and convert it to a
-TensorFlow ``Dataset`` object. Note that tokenizers are framework-agnostic, so there is no need to prepend ``TF`` to
+<https://www.tensorflow.org/datasets/catalog/glue#gluemrpc>`_ from GLUE. We
+can then use our built-in
+:func:`~transformers.data.processors.glue.glue_convert_examples_to_features`
+to tokenize MRPC and convert it to a TensorFlow ``Dataset`` object. Note that
+tokenizers are framework-agnostic, so there is no need to prepend ``TF`` to
 the pretrained tokenizer name.

 .. code-block:: python
@@ -172,8 +197,8 @@ The model can then be compiled and trained as any Keras model:
    model.compile(optimizer=optimizer, loss=loss)
    model.fit(train_dataset, epochs=2, steps_per_epoch=115)

-With the tight interoperability between TensorFlow and PyTorch models, you can even save the model and then reload it
-as a PyTorch model (or vice-versa):
+With the tight interoperability between TensorFlow and PyTorch models, you
+can even save the model and then reload it as a PyTorch model (or vice-versa):

 .. code-block:: python

@@ -185,11 +210,14 @@ as a PyTorch model (or vice-versa):
 .. _trainer:

 Trainer
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^

-We also provide a simple but feature-complete training and evaluation interface through :func:`~transformers.Trainer`
-and :func:`~transformers.TFTrainer`. You can train, fine-tune, and evaluate any 🤗 Transformers model with a wide range
-of training options and with built-in features like logging, gradient accumulation, and mixed precision.
+We also provide a simple but feature-complete training and evaluation
+interface through :func:`~transformers.Trainer` and
+:func:`~transformers.TFTrainer`. You can train, fine-tune,
+and evaluate any 🤗 Transformers model with a wide range of training options and
+with built-in features like logging, gradient accumulation, and mixed
+precision.

 .. code-block:: python

@@ -236,16 +264,21 @@ of training options and with built-in features like logging, gradient accumulati
        eval_dataset=tfds_test_dataset       # tensorflow_datasets evaluation dataset
    )

-Now simply call ``trainer.train()`` to train and ``trainer.evaluate()`` to evaluate. You can use your own module as
-well, but the first argument returned from ``forward`` must be the loss which you wish to optimize.
+Now simply call ``trainer.train()`` to train and ``trainer.evaluate()`` to
+evaluate. You can use your own module as well, but the first
+argument returned from ``forward`` must be the loss which you wish to
+optimize.

-:func:`~transformers.Trainer` uses a built-in default function to collate batches and prepare them to be fed into the
-model. If needed, you can also use the ``data_collator`` argument to pass your own collator function which takes in the
-data in the format provided by your dataset and returns a batch ready to be fed into the model. Note that
-:func:`~transformers.TFTrainer` expects the passed datasets to be dataset objects from ``tensorflow_datasets``.
+:func:`~transformers.Trainer` uses a built-in default function to collate
+batches and prepare them to be fed into the model. If needed, you can also
+use the ``data_collator`` argument to pass your own collator function which
+takes in the data in the format provided by your dataset and returns a
+batch ready to be fed into the model. Note that
+:func:`~transformers.TFTrainer` expects the passed datasets to be dataset
+objects from ``tensorflow_datasets``.

-To calculate additional metrics in addition to the loss, you can also define your own ``compute_metrics`` function and
-pass it to the trainer.
+To calculate additional metrics in addition to the loss, you can also define
+your own ``compute_metrics`` function and pass it to the trainer.

 .. code-block:: python

@@ -263,24 +296,23 @@ pass it to the trainer.
            'recall': recall
        }

-Finally, you can view the results, including any calculated metrics, by launching tensorboard in your specified
-``logging_dir`` directory.
+Finally, you can view the results, including any calculated metrics, by
+launching tensorboard in your specified ``logging_dir`` directory.


 .. _additional-resources:

 Additional resources
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^

 - `A lightweight colab demo <https://colab.research.google.com/drive/1-JIJlao4dI-Ilww_NnTc0rxtp-ymgDgM?usp=sharing>`_
  which uses ``Trainer`` for IMDb sentiment classification.

- `🤗 Transformers Examples <https://github.com/huggingface/transformers/tree/master/examples>`_ including scripts for
-  training and fine-tuning on GLUE, SQuAD, and several other tasks.
+- `🤗 Transformers Examples <https://github.com/huggingface/transformers/tree/master/examples>`_
+  including scripts for training and fine-tuning on GLUE, SQuAD, and several other tasks.

- `How to train a language model
-  <https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/01_how_to_train.ipynb>`_, a detailed
-  colab notebook which uses ``Trainer`` to train a masked language model from scratch on Esperanto.
+- `How to train a language model <https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/01_how_to_train.ipynb>`_,
+  a detailed colab notebook which uses ``Trainer`` to train a masked language model from scratch on Esperanto.

 - `🤗 Transformers Notebooks <notebooks.html>`_ which contain dozens of example notebooks from the community for
  training and using 🤗 Transformers on a variety of tasks.
@@ -1,22 +1,41 @@
 # 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.
+Version 2.9 of 🤗 Transformers introduces a new [`Trainer`](https://github.com/huggingface/transformers/blob/master/src/transformers/trainer.py) class for PyTorch, and its equivalent [`TFTrainer`](https://github.com/huggingface/transformers/blob/master/src/transformers/trainer_tf.py) for TF 2.
 Running the examples requires PyTorch 1.3.1+ or TensorFlow 2.2+.

 Here is the list of all our examples:
 - **grouped by task** (all official examples work for multiple models)
- with information on whether they are **built on top of `Trainer`/`TFTrainer`** (if not, they still work, they might
-  just lack some features),
- whether or not they leverage the [🤗 Datasets](https://github.com/huggingface/datasets) library.
+- with information on whether they are **built on top of `Trainer`/`TFTrainer`** (if not, they still work, they might just lack some features),
+- whether they also include examples for **`pytorch-lightning`**, which is a great fully-featured, general-purpose training library for PyTorch,
 - links to **Colab notebooks** to walk through the scripts and run them easily,
 - links to **Cloud deployments** to be able to deploy large-scale trainings in the Cloud with little to no setup.

+This is still a work-in-progress – in particular documentation is still sparse – so please **contribute improvements/pull requests.**
+
+
+## The Big Table of Tasks
+
+| Task | Example datasets | Trainer support | TFTrainer support | pytorch-lightning | Colab
+|---|---|:---:|:---:|:---:|:---:|
+| [**`language-modeling`**](https://github.com/huggingface/transformers/tree/master/examples/language-modeling)       | Raw text        | ✅ | -  | -  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/01_how_to_train.ipynb)
+| [**`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://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/trainer/01_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           | ✅ | ✅ | -  | -
+| [**`text-generation`**](https://github.com/huggingface/transformers/tree/master/examples/text-generation)           | -               | n/a | n/a | n/a | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/02_how_to_generate.ipynb)
+| [**`distillation`**](https://github.com/huggingface/transformers/tree/master/examples/distillation)       | All               | -  | -  | -  | -
+| [**`summarization`**](https://github.com/huggingface/transformers/tree/master/examples/seq2seq)     | CNN/Daily Mail    | -  | -  | ✅  | -
+| [**`translation`**](https://github.com/huggingface/transformers/tree/master/examples/seq2seq)         | WMT               | -  | -  | ✅  | -
+| [**`bertology`**](https://github.com/huggingface/transformers/tree/master/examples/bertology)             | -                 | -  | -  | -  | -
+| [**`adversarial`**](https://github.com/huggingface/transformers/tree/master/examples/adversarial)         | HANS              | ✅ | -  | -  | -
+
+
+<br>

 ## Important note

 **Important**
-
-To make sure you can successfully run the latest versions of the example scripts, you have to **install the library from source** and install some example-specific requirements.
+To make sure you can successfully run the latest versions of the example scripts, you have to install the library from source and install some example-specific requirements.
 Execute the following steps in a new virtual environment:

 ```bash
@@ -26,33 +45,11 @@ pip install .
 pip install -r ./examples/requirements.txt
 ```

-Alternatively, you can run the version of the examples as they were for your current version of Transformers via (for instance with v3.4.0):
-```bash
-git checkout tags/v3.4.0
-```
-
-## The Big Table of Tasks
-
-| Task | Example datasets | Trainer support | TFTrainer support | 🤗 Datasets | Colab
-|---|---|:---:|:---:|:---:|:---:|
-| [**`language-modeling`**](https://github.com/huggingface/transformers/tree/master/examples/language-modeling)       | Raw text        | ✅ | -  | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/01_how_to_train.ipynb)
-| [**`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           | ✅ | ✅ | - | -
-| [**`text-generation`**](https://github.com/huggingface/transformers/tree/master/examples/text-generation)           | -               | n/a | n/a | - | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/02_how_to_generate.ipynb)
-| [**`distillation`**](https://github.com/huggingface/transformers/tree/master/examples/distillation)                 | All             | - | -  | - | -
-| [**`summarization`**](https://github.com/huggingface/transformers/tree/master/examples/seq2seq)                     | CNN/Daily Mail  | ✅  | - | - | -
-| [**`translation`**](https://github.com/huggingface/transformers/tree/master/examples/seq2seq)                       | WMT             | ✅  | - | - | -
-| [**`bertology`**](https://github.com/huggingface/transformers/tree/master/examples/bertology)                       | -               | - | - | - | -
-| [**`adversarial`**](https://github.com/huggingface/transformers/tree/master/examples/adversarial)                   | HANS            | ✅ | - | - | -
-
-
-<br>
-
 ## One-click Deploy to Cloud (wip)

-**Coming soon!**
+#### Azure
+
+[![Deploy to Azure](https://aka.ms/deploytoazurebutton)](https://portal.azure.com/#create/Microsoft.Template/uri/https%3A%2F%2Fraw.githubusercontent.com%2FAzure%2Fazure-quickstart-templates%2Fmaster%2F101-storage-account-create%2Fazuredeploy.json)

 ## Running on TPUs

@@ -62,14 +59,13 @@ When using PyTorch, we support TPUs thanks to `pytorch/xla`. For more context an
 very detailed [pytorch/xla README](https://github.com/pytorch/xla/blob/master/README.md).

 In this repo, we provide a very simple launcher script named [xla_spawn.py](https://github.com/huggingface/transformers/tree/master/examples/xla_spawn.py) that lets you run our example scripts on multiple TPU cores without any boilerplate.
-Just pass a `--num_cores` flag to this script, then your regular training script with its arguments (this is similar to the `torch.distributed.launch` helper for torch.distributed). 
-Note that this approach does not work for examples that use `pytorch-lightning`.
+Just pass a `--num_cores` flag to this script, then your regular training script with its arguments (this is similar to the `torch.distributed.launch` helper for torch.distributed).

 For example for `run_glue`:

 ```bash
 python examples/xla_spawn.py --num_cores 8 \
-	examples/text-classification/run_glue.py \
+	examples/text-classification/run_glue.py
 	--model_name_or_path bert-base-cased \
 	--task_name mnli \
 	--data_dir ./data/glue_data/MNLI \
@@ -23,7 +23,6 @@ from typing import Dict, List, Optional
 import numpy as np
 import torch

-import transformers
 from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
@@ -34,7 +33,6 @@ from transformers import (
    default_data_collator,
    set_seed,
 )
-from transformers.trainer_utils import is_main_process
 from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels


@@ -57,8 +55,7 @@ class ModelArguments:
        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"},
+        default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
    )


@@ -127,11 +124,6 @@ def main():
        bool(training_args.local_rank != -1),
        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()
-        transformers.utils.logging.enable_default_handler()
-        transformers.utils.logging.enable_explicit_format()
    logger.info("Training/evaluation parameters %s", training_args)

    # Set seed
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Sam Shleifer	c1e44a3147	boom boom	2020-09-21 20:12:16 -04:00
Sam Shleifer	21f2672f5b	boom boom	2020-09-21 20:08:10 -04:00
Sam Shleifer	248d2e7b91	debug: log more to json	2020-09-21 19:51:33 -04:00
Sam Shleifer	cd3f362621	type hints	2020-09-21 15:46:44 -04:00
Sam Shleifer	d449ef0cc6	Set dataset size	2020-09-21 15:24:54 -04:00
Sam Shleifer	c06ecf10cb	Merge branch 'master' into bump-pl	2020-09-21 13:26:46 -04:00
Sam Shleifer	87f69d6566	Wierd failure	2020-09-21 13:23:49 -04:00
Sam Shleifer	cb36ccb53f	Merge branch 'master' into bump-pl	2020-09-21 13:06:33 -04:00
Sam Shleifer	09846c2ff8	Merge branch 'master' into bump-pl	2020-09-10 17:44:38 -04:00
Sam Shleifer	6b68d3121f	bump pl=0.9.0	2020-09-10 13:58:23 -04:00
				`@@ -1 +0,0 @@`
				`$PYTHON setup.py install # Python command to install the script.`