Release: v3.5.1

Fix typo
2020-11-13 10:03:08 -05:00 · 2020-11-13 10:00:29 -05:00
1646 changed files with 66648 additions and 104389 deletions
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -3,7 +3,6 @@ orbs:
    gcp-gke: circleci/gcp-gke@1.0.4
    go: circleci/go@1.3.0

-
 # TPU REFERENCES
 references:
    checkout_ml_testing: &checkout_ml_testing
@@ -78,8 +77,7 @@ jobs:
                      - v0.4-torch_and_tf-{{ checksum "setup.py" }}
                      - v0.4-{{ checksum "setup.py" }}
            - run: pip install --upgrade pip
-            - run: pip install .[sklearn,tf-cpu,torch,testing,sentencepiece]
-            - run: pip install tapas torch-scatter -f https://pytorch-geometric.com/whl/torch-1.7.0+cpu.html
+            - run: pip install .[sklearn,tf-cpu,torch,testing]
            - save_cache:
                key: v0.4-{{ checksum "setup.py" }}
                paths:
@@ -105,8 +103,7 @@ jobs:
                      - v0.4-torch-{{ checksum "setup.py" }}
                      - v0.4-{{ checksum "setup.py" }}
            - run: pip install --upgrade pip
-            - run: pip install .[sklearn,torch,testing,sentencepiece]
-            - run: pip install tapas torch-scatter -f https://pytorch-geometric.com/whl/torch-1.7.0+cpu.html
+            - run: pip install .[sklearn,torch,testing]
            - save_cache:
                  key: v0.4-torch-{{ checksum "setup.py" }}
                  paths:
@@ -132,7 +129,7 @@ jobs:
                      - v0.4-tf-{{ checksum "setup.py" }}
                      - v0.4-{{ checksum "setup.py" }}
            - run: pip install --upgrade pip
-            - run: pip install .[sklearn,tf-cpu,testing,sentencepiece]
+            - run: pip install .[sklearn,tf-cpu,testing]
            - save_cache:
                  key: v0.4-tf-{{ checksum "setup.py" }}
                  paths:
@@ -158,7 +155,7 @@ jobs:
                    - 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]
+            - run: sudo pip install .[flax,sklearn,torch,testing]
            - save_cache:
                  key: v0.4-flax-{{ checksum "setup.py" }}
                  paths:
@@ -184,8 +181,7 @@ jobs:
                      - v0.4-torch-{{ checksum "setup.py" }}
                      - v0.4-{{ checksum "setup.py" }}
            - run: pip install --upgrade pip
-            - run: pip install .[sklearn,torch,testing,sentencepiece]
-            - run: pip install tapas torch-scatter -f https://pytorch-geometric.com/whl/torch-1.7.0+cpu.html
+            - run: pip install .[sklearn,torch,testing]
            - save_cache:
                  key: v0.4-torch-{{ checksum "setup.py" }}
                  paths:
@@ -211,7 +207,7 @@ jobs:
                      - v0.4-tf-{{ checksum "setup.py" }}
                      - v0.4-{{ checksum "setup.py" }}
            - run: pip install --upgrade pip
-            - run: pip install .[sklearn,tf-cpu,testing,sentencepiece]
+            - run: pip install .[sklearn,tf-cpu,testing]
            - save_cache:
                  key: v0.4-tf-{{ checksum "setup.py" }}
                  paths:
@@ -225,7 +221,7 @@ jobs:
    run_tests_custom_tokenizers:
        working_directory: ~/transformers
        docker:
-            - image: circleci/python:3.7
+            - image: circleci/python:3.6
        environment:
            RUN_CUSTOM_TOKENIZERS: yes
        steps:
@@ -235,7 +231,7 @@ jobs:
                      - v0.4-custom_tokenizers-{{ checksum "setup.py" }}
                      - v0.4-{{ 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" }}
@@ -262,8 +258,8 @@ jobs:
                      - v0.4-torch_examples-{{ checksum "setup.py" }}
                      - v0.4-{{ checksum "setup.py" }}
            - run: pip install --upgrade pip
-            - run: pip install .[sklearn,torch,sentencepiece,testing]
-            - run: pip install -r examples/_tests_requirements.txt
+            - run: pip install .[sklearn,torch,testing]
+            - run: pip install -r examples/requirements.txt
            - save_cache:
                  key: v0.4-torch_examples-{{ checksum "setup.py" }}
                  paths:
@@ -274,22 +270,6 @@ jobs:
            - store_artifacts:
                  path: ~/transformers/reports

-    run_tests_git_lfs:
-        working_directory: ~/transformers
-        docker:
-            - image: circleci/python:3.7
-        resource_class: xlarge
-        parallelism: 1
-        steps:
-            - checkout
-            - run: sudo apt-get install git-lfs
-            - run: |
-                git config --global user.email "ci@dummy.com"
-                git config --global user.name "ci"
-            - run: pip install --upgrade pip
-            - run: pip install .[testing]
-            - run: RUN_GIT_LFS_TESTS=1 python -m pytest -sv ./tests/test_hf_api.py -k "HfLargefilesTest"
-
    build_doc:
        working_directory: ~/transformers
        docker:
@@ -301,7 +281,7 @@ jobs:
                      - v0.4-build_doc-{{ checksum "setup.py" }}
                      - v0.4-{{ checksum "setup.py" }}
            - run: pip install --upgrade pip
-            - run: pip install ."[all, docs]"
+            - run: pip install .[tf,torch,sentencepiece,docs]
            - save_cache:
                  key: v0.4-build_doc-{{ checksum "setup.py" }}
                  paths:
@@ -323,7 +303,7 @@ jobs:
                  keys:
                      - v0.4-deploy_doc-{{ checksum "setup.py" }}
                      - v0.4-{{ checksum "setup.py" }}
-            - run: pip install ."[all,docs]"
+            - run: pip install .[tf,torch,sentencepiece,docs]
            - save_cache:
                  key: v0.4-deploy_doc-{{ checksum "setup.py" }}
                  paths:
@@ -344,7 +324,7 @@ jobs:
                      - v0.4-{{ checksum "setup.py" }}
            - run: pip install --upgrade pip
            - run: pip install isort
-            - run: pip install .[all,quality]
+            - run: pip install .[tf,torch,flax,quality]
            - save_cache:
                  key: v0.4-code_quality-{{ checksum "setup.py" }}
                  paths:
@@ -354,7 +334,6 @@ jobs:
            - run: flake8 examples tests src utils
            - run: python utils/style_doc.py src/transformers docs/source --max_len 119 --check_only
            - run: python utils/check_copies.py
-            - run: python utils/check_table.py
            - run: python utils/check_dummies.py
            - run: python utils/check_repo.py

@@ -418,7 +397,6 @@ workflows:
            - run_tests_flax
            - run_tests_pipelines_torch
            - run_tests_pipelines_tf
-            - run_tests_git_lfs
            - build_doc
            - deploy_doc: *workflow_filters
    tpu_testing_jobs:
--- a/.circleci/deploy.sh
+++ b/.circleci/deploy.sh
@@ -51,7 +51,4 @@ 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 "c781171" v4.0.0
-deploy_doc "bfa4ccf" # v4.1.1 Latest stable release
+deploy_doc "eb0e0ce" # v3.4.0 Latest stable release
--- a/.github/ISSUE_TEMPLATE/bug-report.md
+++ b/.github/ISSUE_TEMPLATE/bug-report.md
@@ -11,7 +11,7 @@ assignees: ''
 ## Environment info
 <!-- You can run the command `transformers-cli env` and copy-and-paste its output below.
     Don't forget to fill out the missing fields in that output! -->
-
+     
 - `transformers` version:
 - Platform:
 - Python version:
@@ -24,13 +24,13 @@ assignees: ''
 <!-- Your issue 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, GPT2, XLM: @LysandreJik
+ 
+ albert, bert, GPT2, XLM: @LysandreJik 
 tokenizers: @mfuntowicz
 Trainer: @sgugger
 Speed and Memory Benchmarks: @patrickvonplaten
 Model Cards: @julien-c
- TextGeneration: @TevenLeScao
+ TextGeneration: @TevenLeScao 
 examples/distillation: @VictorSanh
 nlp datasets: [different repo](https://github.com/huggingface/nlp)
 rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)
@@ -47,7 +47,6 @@ assignees: ''
 FSMT: @stas00
 examples/seq2seq: @patil-suraj
 examples/bert-loses-patience: @JetRunner
- ray/raytune: @richardliaw @amogkam
 tensorflow: @jplu
 examples/token-classification: @stefan-it
 documentation: @sgugger
--- a/.github/ISSUE_TEMPLATE/question-help.md
+++ b/.github/ISSUE_TEMPLATE/question-help.md
@@ -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**:
--- a/.github/PULL_REQUEST_TEMPLATE.md
+++ b/.github/PULL_REQUEST_TEMPLATE.md
@@ -20,7 +20,7 @@ Fixes # (issue)
 - [ ] 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.
+      to the 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).
@@ -58,5 +58,5 @@ members/contributors which may be interested in your PR.
 tensorflow: @jplu
 examples/token-classification: @stefan-it
 documentation: @sgugger
- FSMT: @stas00
+ FSTM: @stas00
 -->
--- a/.github/conda/build.sh
+++ b/.github/conda/build.sh
@@ -1 +0,0 @@
-$PYTHON setup.py install     # Python command to install the script.
--- a/.github/conda/meta.yaml
+++ b/.github/conda/meta.yaml
@@ -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."
--- a/.github/stale.yml
+++ b/.github/stale.yml
@@ -6,7 +6,6 @@ daysUntilClose: 7
 exemptLabels:
  - pinned
  - security
-  - Feature request
 # Label to use when marking an issue as stale
 staleLabel: wontfix
 # Comment to post when marking an issue as stale. Set to `false` to disable
--- a/.github/workflows/github-torch-hub.yml
+++ b/.github/workflows/github-torch-hub.yml
@@ -1,6 +1,6 @@
 name: Torch hub integration

-on:
+on: 
  push:
    branches:
      - "*"
@@ -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
@@ -32,10 +29,8 @@ jobs:
    - name: Install dependencies
      run: |
        pip install --upgrade pip
-        # install torch-hub specific dependencies
-        pip install -e git+https://github.com/huggingface/transformers.git#egg=transformers[torchhub]
-        # no longer needed
-        pip uninstall -y transformers
+        pip install torch
+        pip install numpy filelock protobuf requests tqdm regex sentencepiece sacremoses tokenizers packaging

    - name: Torch hub list
      run: |
--- a/.github/workflows/model-templates.yml
+++ b/.github/workflows/model-templates.yml
@@ -1,67 +0,0 @@
-name: Model templates runner
-
-on:
-  push:
-    paths:
-      - "src/**"
-      - "tests/**"
-      - ".github/**"
-      - "templates/**"
-
-jobs:
-  run_tests_templates:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v1
-
-      - name: Install Python
-        uses: actions/setup-python@v1
-        with:
-          python-version: 3.6
-
-      - name: Loading cache.
-        uses: actions/cache@v2
-        id: cache
-        with:
-          path: ~/.cache/pip
-          key: v1.2-tests_templates
-          restore-keys: |
-            v1.2-tests_templates-${{ hashFiles('setup.py') }}
-            v1.2-tests_templates
-
-      - name: Install dependencies
-        run: |
-          pip install --upgrade pip
-          pip install .[dev]
-      - name: Create model files
-        run: |
-          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/encoder-bert-tokenizer.json --path=templates/adding_a_new_model
-          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/pt-encoder-bert-tokenizer.json --path=templates/adding_a_new_model
-          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/standalone.json --path=templates/adding_a_new_model
-          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/tf-encoder-bert-tokenizer.json --path=templates/adding_a_new_model
-          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/tf-seq-2-seq-bart-tokenizer.json --path=templates/adding_a_new_model
-          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/pt-seq-2-seq-bart-tokenizer.json --path=templates/adding_a_new_model
-          make style
-          python utils/check_table.py --fix_and_overwrite
-          python utils/check_dummies.py --fix_and_overwrite
-
-      - name: Run all non-slow tests
-        run: |
-          python -m pytest -n 2 --dist=loadfile -s --make-reports=tests_templates tests/*template*
-
-      - name: Run style changes
-        run: |
-          git fetch origin master:master
-          make fixup
-
-      - name: Failure short reports
-        if: ${{ always() }}
-        run: cat reports/tests_templates_failures_short.txt
-
-      - name: Test suite reports artifacts
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v2
-        with:
-          name: run_all_tests_templates_test_reports
-          path: reports
--- a/.github/workflows/release-conda.yml
+++ b/.github/workflows/release-conda.yml
@@ -1,44 +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 list
-          conda-build .github/conda
-
-      - name: Upload to Anaconda
-        run: anaconda upload `conda-build .github/conda --output` --force
--- a/.github/workflows/self-push.yml
+++ b/.github/workflows/self-push.yml
@@ -4,19 +4,17 @@ on:
  push:
    branches:
      - master
-      - ci_*
    paths:
      - "src/**"
      - "tests/**"
      - ".github/**"
-      - "templates/**"
  # pull_request:
  repository_dispatch:


 jobs:
  run_tests_torch_gpu:
-    runs-on: [self-hosted, gpu, single-gpu]
+    runs-on: [self-hosted, single-gpu]
    steps:
      - uses: actions/checkout@v2
      - name: Python version
@@ -48,9 +46,8 @@ jobs:
        run: |
          source .env/bin/activate
          pip install --upgrade pip
-          pip install .[torch,sklearn,testing,onnxruntime,sentencepiece]
+          pip install .[torch,sklearn,testing,onnxruntime]
          pip install git+https://github.com/huggingface/datasets
-          pip install pandas torch-scatter -f https://pytorch-geometric.com/whl/torch-1.7.0+cu102.html

      - name: Are GPUs recognized by our DL frameworks
        run: |
@@ -58,14 +55,6 @@ jobs:
          python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
          python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"

-#      - name: Create model files
-#        run: |
-#          source .env/bin/activate
-#          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/encoder-bert-tokenizer.json --path=templates/adding_a_new_model
-#          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/pt-encoder-bert-tokenizer.json --path=templates/adding_a_new_model
-#          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/standalone.json --path=templates/adding_a_new_model
-#          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/tf-encoder-bert-tokenizer.json --path=templates/adding_a_new_model
-
      - name: Run all non-slow tests on GPU
        env:
          OMP_NUM_THREADS: 1
@@ -87,7 +76,7 @@ jobs:
                  

  run_tests_tf_gpu:
-    runs-on: [self-hosted, gpu, single-gpu]
+    runs-on: [self-hosted, single-gpu]
    steps:
      - uses: actions/checkout@v2
      - name: Python version
@@ -118,7 +107,7 @@ jobs:
        run: |
          source .env/bin/activate
          pip install --upgrade pip
-          pip install .[tf,sklearn,testing,onnxruntime,sentencepiece]
+          pip install .[tf,sklearn,testing,onnxruntime]
          pip install git+https://github.com/huggingface/datasets

      - name: Are GPUs recognized by our DL frameworks
@@ -127,14 +116,6 @@ jobs:
          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
@@ -154,8 +135,8 @@ jobs:
          name: run_all_tests_tf_gpu_test_reports
          path: reports

-  run_tests_torch_multi_gpu:
-    runs-on: [self-hosted, gpu, multi-gpu]
+  run_tests_torch_multiple_gpu:
+    runs-on: [self-hosted, multi-gpu]
    steps:
      - uses: actions/checkout@v2
      - name: Python version
@@ -173,7 +154,7 @@ jobs:
        id: cache
        with:
          path: .env
-          key: v1.1-tests_torch_multi_gpu-${{ hashFiles('setup.py') }}
+          key: v1.1-tests_torch_multiple_gpu-${{ hashFiles('setup.py') }}

      - name: Create new python env (on self-hosted runners we have to handle isolation ourselves)
        run: |
@@ -186,9 +167,8 @@ jobs:
        run: |
          source .env/bin/activate
          pip install --upgrade pip
-          pip install .[torch,sklearn,testing,onnxruntime,sentencepiece]
+          pip install .[torch,sklearn,testing,onnxruntime]
          pip install git+https://github.com/huggingface/datasets
-          pip install pandas torch-scatter -f https://pytorch-geometric.com/whl/torch-1.7.0+cu102.html

      - name: Are GPUs recognized by our DL frameworks
        run: |
@@ -201,11 +181,11 @@ jobs:
          OMP_NUM_THREADS: 1
        run: |
          source .env/bin/activate
-          python -m pytest -n 2 --dist=loadfile -s --make-reports=tests_torch_multi_gpu tests
+          python -m pytest -n 2 --dist=loadfile -s --make-reports=tests_torch_multiple_gpu tests

      - name: Failure short reports
        if: ${{ always() }}
-        run: cat reports/tests_torch_multi_gpu_failures_short.txt          
+        run: cat reports/tests_torch_multiple_gpu_failures_short.txt          

      - name: Test suite reports artifacts
        if: ${{ always() }}
@@ -214,8 +194,8 @@ jobs:
          name: run_all_tests_torch_multi_gpu_test_reports
          path: reports

-  run_tests_tf_multi_gpu:
-    runs-on: [self-hosted, gpu, multi-gpu]
+  run_tests_tf_multiple_gpu:
+    runs-on: [self-hosted, multi-gpu]
    steps:
      - uses: actions/checkout@v2
      - name: Python version
@@ -233,7 +213,7 @@ jobs:
        id: cache
        with:
          path: .env
-          key: v1.1-tests_tf_multi_gpu-${{ hashFiles('setup.py') }}
+          key: v1.1-tests_tf_multiple_gpu-${{ hashFiles('setup.py') }}

      - name: Create new python env (on self-hosted runners we have to handle isolation ourselves)
        run: |
@@ -246,7 +226,7 @@ jobs:
        run: |
          source .env/bin/activate
          pip install --upgrade pip
-          pip install .[tf,sklearn,testing,onnxruntime,sentencepiece]
+          pip install .[tf,sklearn,testing,onnxruntime]
          pip install git+https://github.com/huggingface/datasets

      - name: Are GPUs recognized by our DL frameworks
@@ -260,11 +240,11 @@ jobs:
          OMP_NUM_THREADS: 1
        run: |
          source .env/bin/activate
-          python -m pytest -n 2 --dist=loadfile -s --make-reports=tests_tf_multi_gpu tests
+          python -m pytest -n 2 --dist=loadfile -s --make-reports=tests_tf_multiple_gpu tests

      - name: Failure short reports
        if: ${{ always() }}
-        run: cat reports/tests_tf_multi_gpu_failures_short.txt
+        run: cat reports/tests_tf_multiple_gpu_failures_short.txt

      - name: Test suite reports artifacts
        if: ${{ always() }}
--- a/.github/workflows/self-scheduled.yml
+++ b/.github/workflows/self-scheduled.yml
@@ -6,13 +6,16 @@
 name: Self-hosted runner (scheduled)

 on:
+  push:
+    branches:
+      - ci_*
  repository_dispatch:
  schedule:
    - cron: "0 0 * * *"

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

@@ -46,7 +49,7 @@ jobs:
        run: |
          source .env/bin/activate
          pip install --upgrade pip
-          pip install .[torch,sklearn,testing,onnxruntime,sentencepiece]
+          pip install .[torch,sklearn,testing,onnxruntime]
          pip install git+https://github.com/huggingface/datasets
          pip list

@@ -75,7 +78,7 @@ jobs:
          RUN_SLOW: yes
        run: |
          source .env/bin/activate
-          pip install -r examples/_tests_requirements.txt
+          pip install -r examples/requirements.txt
          python -m pytest -n 1 --dist=loadfile -s --make-reports=examples_torch_gpu examples

      - name: Failure short reports
@@ -106,7 +109,7 @@ jobs:


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

@@ -140,7 +143,7 @@ jobs:
        run: |
          source .env/bin/activate
          pip install --upgrade pip
-          pip install .[tf,sklearn,testing,onnxruntime,sentencepiece]
+          pip install .[tf,sklearn,testing,onnxruntime]
          pip install git+https://github.com/huggingface/datasets
          pip list

@@ -184,8 +187,8 @@ jobs:
          name: run_all_tests_tf_gpu_test_reports
          path: reports
          
-  run_all_tests_torch_multi_gpu:
-    runs-on: [self-hosted, gpu, multi-gpu]
+  run_all_tests_torch_multiple_gpu:
+    runs-on: [self-hosted, multi-gpu]
    steps:
      - uses: actions/checkout@v2

@@ -219,7 +222,7 @@ jobs:
        run: |
          source .env/bin/activate
          pip install --upgrade pip
-          pip install .[torch,sklearn,testing,onnxruntime,sentencepiece]
+          pip install .[torch,sklearn,testing,onnxruntime]
          pip install git+https://github.com/huggingface/datasets
          pip list

@@ -235,11 +238,11 @@ jobs:
          RUN_SLOW: yes
        run: |
          source .env/bin/activate
-          python -m pytest -n 1 --dist=loadfile -s --make-reports=tests_torch_multi_gpu tests
+          python -m pytest -n 1 --dist=loadfile -s --make-reports=tests_torch_multiple_gpu tests

      - name: Failure short reports
        if: ${{ always() }}
-        run: cat reports/tests_torch_multi_gpu_failures_short.txt
+        run: cat reports/tests_torch_multiple_gpu_failures_short.txt

      - name: Run examples tests on multi-GPU
        env:
@@ -247,11 +250,11 @@ jobs:
          RUN_SLOW: yes
        run: |
          source .env/bin/activate
-          python -m pytest -n 1 --dist=loadfile -s --make-reports=tests_torch_examples_multi_gpu examples
+          python -m pytest -n 1 --dist=loadfile -s --make-reports=examples_torch_multiple_gpu examples

      - name: Failure short reports
        if: ${{ always() }}
-        run: cat reports/tests_torch_examples_multi_gpu_failures_short.txt
+        run: cat reports/examples_torch_multiple_gpu_failures_short.txt

      - name: Run all pipeline tests on multi-GPU
        if: ${{ always() }}
@@ -262,11 +265,11 @@ jobs:
          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
+          python -m pytest -n 1 --dist=loadfile -s -m is_pipeline_test --make-reports=tests_torch_pipeline_multiple_gpu tests

      - name: Failure short reports
        if: ${{ always() }}
-        run: cat reports/tests_torch_pipeline_multi_gpu_failures_short.txt
+        run: cat reports/tests_torch_pipeline_multiple_gpu_failures_short.txt

      - name: Test suite reports artifacts
        if: ${{ always() }}
@@ -275,8 +278,8 @@ jobs:
          name: run_all_tests_torch_multi_gpu_test_reports
          path: reports

-  run_all_tests_tf_multi_gpu:
-    runs-on: [self-hosted, gpu, multi-gpu]
+  run_all_tests_tf_multiple_gpu:
+    runs-on: [self-hosted, multi-gpu]
    steps:
      - uses: actions/checkout@v2

@@ -310,7 +313,7 @@ jobs:
        run: |
          source .env/bin/activate
          pip install --upgrade pip
-          pip install .[tf,sklearn,testing,onnxruntime,sentencepiece]
+          pip install .[tf,sklearn,testing,onnxruntime]
          pip install git+https://github.com/huggingface/datasets
          pip list

@@ -326,11 +329,11 @@ jobs:
          RUN_SLOW: yes
        run: |
          source .env/bin/activate
-          python -m pytest -n 1 --dist=loadfile -s --make-reports=tests_tf_multi_gpu tests
+          python -m pytest -n 1 --dist=loadfile -s --make-reports=tests_tf_multiple_gpu tests

      - name: Failure short reports
        if: ${{ always() }}
-        run: cat reports/tests_tf_multi_gpu_failures_short.txt
+        run: cat reports/tests_tf_multiple_gpu_failures_short.txt

      - name: Run all pipeline tests on multi-GPU
        if: ${{ always() }}
@@ -341,11 +344,11 @@ jobs:
          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
+          python -m pytest -n 1 --dist=loadfile -s -m is_pipeline_test --make-reports=tests_tf_pipelines_multiple_gpu tests
          
      - name: Failure short reports
        if: ${{ always() }}
-        run: cat reports/tests_tf_pipeline_multi_gpu_failures_short.txt
+        run: cat reports/tests_tf_multiple_gpu_pipelines_failures_short.txt

      - name: Test suite reports artifacts
        if: ${{ always() }}
--- a/.gitignore
+++ b/.gitignore
@@ -133,6 +133,7 @@ dmypy.json
 tensorflow_code

 # Models
+models
 proc_data

 # examples
@@ -159,6 +160,3 @@ tags

 # pre-commit
 .pre-commit*
-
-# .lock
-*.lock
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -1,19 +1,3 @@
-<!---
-Copyright 2020 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-->
-
 # How to contribute to transformers?

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

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

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

@@ -324,32 +308,3 @@ 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
-
-On windows, you need to configure git to transform Windows `CRLF` line endings to Linux `LF` line endings:
-
-`git config core.autocrlf input`
-
-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`
-4. Add `C:\msys64\usr\bin` to your PATH environment variable.
-
-You can now use `make` from any terminal (Powershell, cmd.exe, etc) 🎉
-
-### Syncing forked master with upstream (HuggingFace) master
-
-To avoid pinging the upstream repository which adds reference notes to each upstream PR and sends unnessary notifications to the developers involved in these PRs, 
-when syncing the master branch of a forked repository, please, follow these steps:
-1. When possible, avoid syncing with the upstream using a branch and PR on the forked repository. Instead merge directly into the forked master.
-2. If a PR is absolutely necessary, use the following steps after checking out your branch:
-```
-$ git checkout -b your-branch-for-syncing
-$ git pull --squash --no-commit upstream master
-$ git commit -m '<your message without GitHub references>'
-$ git push --set-upstream origin your-branch-for-syncing
-```
--- a/ISSUES.md
+++ b/ISSUES.md
@@ -1,275 +0,0 @@
-<!---
-Copyright 2020 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-->
-
-# How To Request Support
-
-This is an Open Source Project so please be mindful that like in any other project of this kind there is no obligation to answer all requests for help.
-
-However, we want to encourage you to ask for help whenever you think it's needed! We are happy about every  question we get because it allows us to better understand your needs, possible misunderstandings, and most importantly a way for you to help us make this library better. That being said, this document's main purpose is to provide guidelines at how you can formulate your requests to increase your chances to be understood and to get support.
-
-There are two main venues to receive support: [the forums](https://discuss.huggingface.co/) and [the GitHub issues](https://github.com/huggingface/transformers/issues).
-
-## The Forums
-
-[The user forums](https://discuss.huggingface.co/) are supported by the wide community of the library users and backed up by developers when needed.
-
-If you have a difficulty with deploying this library or some questions, or you'd like to discuss a new feature, please first consider discussing those things at the forums. Only when you feel your subject matter has been crystalized and you still need support from the library developers do proceed to file an [issue](https://github.com/huggingface/transformers/issues).
-
-In particular all "Please explain" questions or objectively very user-specific feature requests belong to the forums. Here are some example of such questions:
-
-* "I would like to use a BertModel within a RL-Agent for a customer support service. How can I use a BertForMaskedLM in my ChatBotModel?"
-
-* "Could you please explain why T5 has no positional embedding matrix under T5Model?"
-
-* "How should I set my generation parameters for translation?"
-
-* "How to train T5 on De->En translation?"
-
-
-## The GitHub Issues
-
-Everything which hints at a bug should be opened as an [issue](https://github.com/huggingface/transformers/issues).
-
-You are not required to read the following guidelines before opening an issue. However, if you notice that your issue doesn't get any replies, chances are that the developers have one or several difficulties with its quality. In this case, reading the following points and adjusting your issue accordingly could help.
-
-1. Before posting an issue, first search for already posted issues, since chances are someone has already asked a similar question before you.
-
-    If you use Google your search query should be:
-
-    ```
-    "huggingface" "transformers" your query
-    ```
-
-    The first two quoted words tell Google to limit the search to the context of the Huggingface Transformers. The remainder is your query - most commonly this would be the error message the software fails with. We will go deeper into details shortly.
-
-    The results of such a query will typically match GitHub issues, Hugging Face forums, StackExchange, and blogs.
-
-    If you find relevant hints, you may choose to continue the discussion there if you have follow up questions.
-
-    If what you found is similar but doesn't quite answer your problem, please, post a new issue and do include links to similar issues or forum discussions you may have found.
-
-    Let's look at some examples:
-
-    The error message, often referred to as an assertion, tells us what went wrong. Here is an example of an assertion:
-
-   ```python
-   Traceback (most recent call last):
-     File "<string>", line 1, in <module>
-     File "/transformers/src/transformers/__init__.py", line 34, in <module>
-       from . import dependency_versions_check
-     File "/transformers/src/transformers/dependency_versions_check.py", line 34, in <module>
-       from .file_utils import is_tokenizers_available
-     File "/transformers/src/transformers/file_utils.py", line 40, in <module>
-       from tqdm.auto import tqdm
-    ModuleNotFoundError: No module named 'tqdm.auto'
-    ```
-
-   and it typically includes a traceback, so that we can see the full stack of calls the program made before it fails. This gives us the context to know why the program failed.
-
-   Going back to the above example. If you received this error search, look at the very last line of the error which is:
-
-   ```python
-    ModuleNotFoundError: No module named 'tqdm.auto'
-    ```
-
-    And now we can use it to do the searching on your favorite search engine:
-
-    1. first for `"huggingface" "transformers" "ModuleNotFoundError: No module named 'tqdm.auto'"`
-    2. if you don't find relevant results, then search for just `"ModuleNotFoundError: No module named 'tqdm.auto'"`
-    3. and finally if nothing still comes up, then remove the outside quotes: `ModuleNotFoundError: No module named 'tqdm.auto'`
-
-   If the error includes any messages that include bits unique to your filesystem, always remove those in the search query since other users will not have the same filesystem as yours. For example:
-
-   ```bash
-   python -c 'open("/tmp/wrong_path.txt", "r")'
-   Traceback (most recent call last):
-     File "<string>", line 1, in <module>
-   FileNotFoundError: [Errno 2] No such file or directory: '/tmp/wrong_path.txt'
-   ```
-   Here you'd search for just: `"FileNotFoundError: [Errno 2] No such file or directory"`
-
-   If the local information that you removed were inside the error message and you removed them you may need to remove double quotes since your query is no longer exact. So if the error message was something like:
-
-   ```bash
-      ValueError: '/tmp/wrong_path.txt' cannot be found
-   ```
-
-   then you'd search for `"ValueError" "cannot be found"`
-
-   As you search you will notice that when you don't use quotes often the search engines will return a variety of unrelated hits, which may or may not be what you want.
-
-   Experiment with different ways and find which approach gives the most satisfactory results.
-
-2. Keep the issue short, providing the information that you think will aid the developers to understand your situation. Put yourself in the shoes of the person who has never seen your code or knows anything about your custom setup. This mental exercise will help to develop an intuition to what/what not to share"
-
-3. If there is a software failure, always provide the full traceback, for example:
-
-   ```python
-   $ python -c 'import transformers'
-   Traceback (most recent call last):
-     File "<string>", line 1, in <module>
-     File "/transformers/src/transformers/__init__.py", line 34, in <module>
-       from . import dependency_versions_check
-     File "/transformers/src/transformers/dependency_versions_check.py", line 34, in <module>
-       from .file_utils import is_tokenizers_available
-     File "/transformers/src/transformers/file_utils.py", line 40, in <module>
-       from tqdm.auto import tqdm
-   ModuleNotFoundError: No module named 'tqdm.auto'
-   ```
-
-   As compared to providing just the last line of the error message, e.g.:
-   ```python
-   ModuleNotFoundError: No module named 'tqdm.auto'
-   ```
-   which is not sufficient.
-
-   If your application is running on more than one GPU (e.g. under `DistributedDataParallel`) and typically getting every log and traceback printed multiple times, please make sure that you paste only one copy of it. At times the traceback from parallel processes may get interleaved - so either disentangle these or change the loggers to log only for `local_rank==0` so that only one process logs things.
-
-4. When quoting a traceback, command line instructions and any type of code always enclose it in triple backticks inside the editor window, that is:
-
-   ````
-   ```
-   git clone https://github.com/huggingface/transformers
-   cd transformers
-   pip install .
-   ```
-   ````
-
-   If it's a command line with a long argument list, please consider breaking it down using backslashes and new lines. Here is an example of a good command line quote:
-
-   ```bash
-    cd examples/seq2seq
-    python -m torch.distributed.launch --nproc_per_node=2 ./finetune_trainer.py \
-    --model_name_or_path sshleifer/distill-mbart-en-ro-12-4 --data_dir wmt_en_ro \
-    --output_dir output_dir --overwrite_output_dir \
-    --do_train --n_train 500 --num_train_epochs 1 \
-    --per_device_train_batch_size 1  --freeze_embeds \
-    --src_lang en_XX --tgt_lang ro_RO --task translation \
-    --fp16 --sharded_ddp
-   ```
-
-   If you don't break it up, one has to scroll horizontally which often makes it quite difficult to quickly see what's happening.
-
-   The backslashes allow us to copy the command directly into the console to run it, without needing to edit it.
-
-5. Include only the important information that you think will help the developer to quickly identify the problem.
-
-   For example applications often create huge amounts of logs. Ask yourself whether providing all or parts of the log is useful.
-
-   Pasting a 100-1000 lines of log into the issue is an immediate turn off, since it will take a lot of time to figure out where the pertinent parts of the log are.
-
-   Attaching a full log can be helpful if it's done as an attachment, if it's enclosed in the following html code in the comment editor window:
-
-   ```
-   <details>
-   <summary>Full log</summary>
-   <pre>
-
-   many
-   lines
-   go
-   here
-
-   </pre>
-   </details>
-   ```
-
-   which would result in the following entry, which can be opened if desired, but otherwise takes little space.
-
-   <details>
-   <summary>Full log</summary>
-   <pre>
-   many
-   lines
-   go
-   here
-   </pre>
-   </details>
-
-    You could also provide a link to a pastebin service, but this is less beneficial since those links tend to expire quickly and future readers of your issue might not be able to access that log file anymore and may lack some context.
-
-6. If this is an issue in your code, do try to reduce that code to a minimal example that still demonstrates the problem. Please ask at the forums if you have a hard time figuring how to do that. Please realize that we don't have the luxury of having time to try and understand all of your custom code.
-
-   If you really tried to make a short reproducible code but couldn't figure it out, it might be that having a traceback will give the developer enough information to know what's going on. But if it is not enough and we can't reproduce the problem, we can't really solve it.
-
-   Do not dispair if you can't figure it out from the begining, just share what you can and perhaps someone else will be able to help you at the forums.
-
-7. If you forked off some of this project's code or example applications, please, do not ask us to go into your code repository and figure out what you may have done. The code is already very complex and unless there is an easy way to do a diff and it's a small diff, it won't be possible to find someone with time on their hands to make a lengthy investigation. Albeit, you might find someone at the forums who will be generous to do this for you.
-
-8. Before reporting an issue, first, always try to update your environment to the latest official version of this library. We have no resources to go and debug older revisions, which could easily have bugs that have been fixed in the latest released version.
-
-   We understand that this is not always possible, especially when APIs change, in which case file an issue against the highest library version your environment can support.
-
-   Of course, if you upgrade the library, always retest that the problem is still there.
-
-9. Please do not ask us to reproduce an issue with your custom data, since we don't have it. So, either you should use some existing dataset supported by HF datasets or you need to supply a code that generates a small sample on the fly, or some another quick and simple way to get it.
-
-   Please do not send us any non-public domain data that may require a license or a permission to be used.
-
-10. Do not tag multiple developers on the issue unless you know this is expected, either because you asked them and they gave you an explicit permission to tag them or the issue template instructs you to do so.
-
-   The "who to tag for what domain" part of the issue template is there to help users direct their questions to the right developers who are designated maintainers of project's specific domains. They can then decide at their own discretion to tag other developers if they feel it'd help move the issue forward.
-
-   We currently don't have a triage service and we trust your capacity to identify the right domain and thus the persons to tag in your issue. If you are not sure, please use the forums to ask for guidance.
-
-   When in doubt, err on the side of not tagging a given person. If you tag multiple people out of context or permission don't be surprised if you get no response at all. Please remember that every time you tag someone, they get a notification and you're taking their time without their permission. Please be sensitive to that.
-
-   If you got helped by one of the developers in the past please don't tag them in future issues, unless they are listed in the issue template for the domain you are asking about or that developer gave you an explicit permission to tag them in future issues.
-
-   If you see a certain developer doing multiple and/or recent commits into a specific area of the project that you feel is relevant to your issue, it is not a good reason to tag them. Various developers may be fixing things that prevent them from moving forward, but often their work is focused on a totally different domain. And while they may or may not know how to help you with the problem at hand, it would benefit the whole community much more if they focus on the domain of their unique expertise.
-
-11. Use the Edit button. Take your time, and re-read and improve the wording and formatting to make your posts and comments as easy to understand as possible.
-
-    Avoid posting multiple comments in a row, as each comment generates a notification for the developers tagged in that issue. If you happened to post multiple comments in a row, and nobody followed up yet - consider merging those into one or a few comments while editing the combined content to be coherent.
-
-    If you choose to edit your older comments after others posted follow up comments you need to be aware that your modifications might not be noticed, so if it's not a typo fixing, try to write a new comment flagging that something has been changed in the previous comments.
-
-    For example, the very first comment is the most important one. If while the thread unfolds you realize that things aren't as they seemed to you originally you may want to edit the first post to reflect the up-to-date understanding of the issue at hand so that it helps those who read your issue in the future quickly understand what's going on and not need to sift through dozens of comments. It also helps to indicate that the post was edited. So, those reading the thread later can understand why there might be certain discontinuity in the information flow.
-
-    Use bullets and items if you have lists of items and the outcome improves overall readability.
-
-    Use backticks to refer to class and function names, e.g. `BartModel` and `generate` as these stand out and improve the speed of a reader's comprehension.
-
-    Try not use italics and bold text too much as these often make the text more difficult to read.
-
-
-12. If you are cross-referencing a specific comment in a given thread or another issue, always link to that specific comment, rather than using the issue link. If you do the latter it could be quite impossible to find which specific comment you're referring to.
-
-    To get the link to the specific comment do not copy the url from the location bar of your browser, but instead, click the `...` icon in the upper right corner of the comment and then select "Copy Link".
-
-    For example the first link is a link to an issue, and the second to a specific comment in the same issue:
-
-    1. https://github.com/huggingface/transformers/issues/9257
-    2. https://github.com/huggingface/transformers/issues/9257#issuecomment-749945162
-
-
-13. If you are replying to a last comment, it's totally fine to make your reply with just your comment in it. The readers can follow the information flow here.
-
-    But if you're replying to a comment that happened some comments back it's always a good practice to quote just the relevant lines you're replying it. The `>` is used for quoting, or you can always use the menu to do so. For example your editor box will look like:
-
-    ```
-    > How big is your gpu cluster?
-
-    Our cluster is made of 256 gpus.
-    ```
-
-    If you are addressing multiple comments, quote the relevant parts of each before your answer. Some people use the same comment to do multiple replies, others separate them into separate comments. Either way works. The latter approach helps for linking to a specific comment.
-
-In general the best way to figure out what works the best is learn from issues posted by other people - see which issues get great responses and which get little to no response - observe what the posters who received great responses did differently from those who did not.
-
-Thank you for reading this somewhat lengthy document. We would like to conclude that these are not absolute rules, but a friendly advice that will help maximize the chances for us to understand what you are trying to communicate, reproduce the problem then resolve it to your satisfaction and the benefit of the whole community.
-
-If after reading this document there are remaining questions on how and why or there is a need for further elucidation, please, don't hesitate to ask your question in [this thread](https://discuss.huggingface.co/t/how-to-request-support/3128).
--- a/1
+++ b/1
@@ -1,4 +1,3 @@
-Copyright 2018- The Hugging Face team. All rights reserved.

                                 Apache License
                           Version 2.0, January 2004
--- a/15
+++ b/15
@@ -1,4 +1,4 @@
-.PHONY: deps_table_update modified_only_fixup extra_quality_checks quality style fixup fix-copies test test-examples docs
+.PHONY: modified_only_fixup extra_quality_checks quality style fixup fix-copies test test-examples docs


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

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

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

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

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

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

 # Run tests for the library
@@ -67,4 +60,4 @@ test-examples:
 # Check that docs can build

 docs:
-	cd docs && make html SPHINXOPTS="-W -j 4"
+	cd docs && make html SPHINXOPTS="-W"
--- a/README.md
+++ b/README.md
@@ -1,19 +1,3 @@
-<!---
-Copyright 2020 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-->
-
 <p align="center">
    <br>
    <img src="https://raw.githubusercontent.com/huggingface/transformers/master/docs/source/imgs/transformers_logo_name.png" width="400"/>
@@ -47,9 +31,12 @@ limitations under the License.

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

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

-You can test most of our models directly on their pages from the [model hub](https://huggingface.co/models). We also offer [private model hosting, versioning, & an inference API](https://huggingface.co/pricing) to use those models.
+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:
 - [Masked word completion with BERT](https://huggingface.co/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
@@ -150,16 +137,14 @@ The model itself is a regular [Pytorch `nn.Module`](https://pytorch.org/docs/sta

 ## Installation

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

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

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

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

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

@@ -169,33 +154,15 @@ pip install transformers

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

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

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

 1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.
 1. **[BART](https://huggingface.co/transformers/model_doc/bart.html)** (from Facebook) released with the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/pdf/1910.13461.pdf) by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer.
-1. **[BARThez](https://huggingface.co/transformers/model_doc/barthez.html)** (from École polytechnique) released with the paper [BARThez: a Skilled Pretrained French Sequence-to-Sequence Model](https://arxiv.org/abs/2010.12321) by Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis.
 1. **[BERT](https://huggingface.co/transformers/model_doc/bert.html)** (from Google) released with the paper [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.
 1. **[BERT For Sequence Generation](https://huggingface.co/transformers/model_doc/bertgeneration.html)** (from Google) released with the paper [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
 1. **[Blenderbot](https://huggingface.co/transformers/model_doc/blenderbot.html)** (from Facebook) released with the paper [Recipes for building an open-domain chatbot](https://arxiv.org/abs/2004.13637) by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
-1. **[BlenderbotSmall](https://huggingface.co/transformers/model_doc/blenderbot_small.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.
@@ -210,13 +177,10 @@ Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
 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. **[LED](https://huggingface.co/transformers/model_doc/led.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. **[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. **[MPNet](https://huggingface.co/transformers/model_doc/mpnet.html)** (from Microsoft Research) released with the paper [MPNet: Masked and Permuted Pre-training for Language Understanding](https://arxiv.org/abs/2004.09297) by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu.
-1. **[MT5](https://huggingface.co/transformers/model_doc/mt5.html)** (from Google AI) released with the paper [mT5: A massively multilingual pre-trained text-to-text transformer](https://arxiv.org/abs/2010.11934) by Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel.
 1. **[Pegasus](https://huggingface.co/transformers/model_doc/pegasus.html)** (from Google) released with the paper [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/abs/1912.08777)> by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu.
 1. **[ProphetNet](https://huggingface.co/transformers/model_doc/prophetnet.html)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
 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.
@@ -224,16 +188,14 @@ Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
 ultilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers/tree/master/examples/distillation) and a German version of DistilBERT.
 1. **[SqueezeBert](https://huggingface.co/transformers/model_doc/squeezebert.html)** released with the paper [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer.
 1. **[T5](https://huggingface.co/transformers/model_doc/t5.html)** (from Google AI) released with the paper [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
-1. **[TAPAS](https://huggingface.co/transformers/model_doc/tapas.html)** (from Google AI) released with the paper [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://arxiv.org/abs/2004.02349) by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos.
 1. **[Transformer-XL](https://huggingface.co/transformers/model_doc/transformerxl.html)** (from Google/CMU) released with the paper [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
 1. **[XLM](https://huggingface.co/transformers/model_doc/xlm.html)** (from Facebook) released together with the paper [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample and Alexis Conneau.
 1. **[XLM-ProphetNet](https://huggingface.co/transformers/model_doc/xlmprophetnet.html)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
 1. **[XLM-RoBERTa](https://huggingface.co/transformers/model_doc/xlmroberta.html)** (from Facebook AI), released together with the paper [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Alexis Conneau*, Kartikay Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov.
 1. **[XLNet](https://huggingface.co/transformers/model_doc/xlnet.html)** (from Google/CMU) released with the paper [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
+1. **[Other community models](https://huggingface.co/models)**, contributed by the [community](https://huggingface.co/users).
 1. Want to contribute a new model? We have added a **detailed guide and templates** to guide you in the process of adding a new model. You can find them in the [`templates`](./templates) folder of the repository. Be sure to check the [contributing guidelines](./CONTRIBUTING.md) and contact the maintainers or open an issue to collect feedbacks before starting your PR.

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


@@ -251,17 +213,13 @@ These implementations have been tested on several datasets (see the example scri

 ## Citation

-We now have a [paper](https://www.aclweb.org/anthology/2020.emnlp-demos.6/) you can cite for the 🤗 Transformers library:
+We now have a [paper](https://arxiv.org/abs/1910.03771) you can cite for the 🤗 Transformers library:
 ```bibtex
-@inproceedings{wolf-etal-2020-transformers,
-    title = "Transformers: State-of-the-Art Natural Language Processing",
-    author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush",
-    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
-    month = oct,
-    year = "2020",
-    address = "Online",
-    publisher = "Association for Computational Linguistics",
-    url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6",
-    pages = "38--45"
+@article{Wolf2019HuggingFacesTS,
+  title={HuggingFace's Transformers: State-of-the-art Natural Language Processing},
+  author={Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush},
+  journal={ArXiv},
+  year={2019},
+  volume={abs/1910.03771}
 }
 ```
--- a/docs/README.md
+++ b/docs/README.md
@@ -1,19 +1,3 @@
-<!---
-Copyright 2020 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-->
-
 # Generating the documentation

 To generate the documentation, you first have to build it. Several packages are necessary to build the doc,
--- a/docs/source/_static/css/huggingface.css
+++ b/docs/source/_static/css/huggingface.css
@@ -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;
--- a/docs/source/_static/js/custom.js
+++ b/docs/source/_static/js/custom.js
@@ -1,16 +1,13 @@
 // These two things need to be updated at each release for the version selector.
 // Last stable version
-const stableVersion = "v4.1.1"
-// Dictionary doc folder to label. The last stable version should have an empty key.
+const stableVersion = "v3.4.0"
+// Dictionary doc folder to label
 const versionMapping = {
    "master": "master",
-    "": "v4.1.1 (stable)",
-    "v4.0.1": "v4.0.0/v4.0.1",
-    "v3.5.1": "v3.5.0/v3.5.1",
-    "v3.4.0": "v3.4.0",
+    "": "v3.4.0",
    "v3.3.1": "v3.3.0/v3.3.1",
    "v3.2.0": "v3.2.0",
-    "v3.1.0": "v3.1.0",
+    "v3.1.0": "v3.1.0 (stable)",
    "v3.0.2": "v3.0.0/v3.0.1/v3.0.2",
    "v2.11.0": "v2.11.0",
    "v2.10.0": "v2.10.0",
--- a/docs/source/benchmarks.rst
+++ b/docs/source/benchmarks.rst
@@ -1,22 +1,10 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 Benchmarks
 =======================================================================================================================

 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 :prefix_link:`here
-<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
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -99,7 +87,6 @@ An instantiated benchmark object can then simply be run by calling ``benchmark.r
    --------------------------------------------------------------------------------

    ====================        ENVIRONMENT INFORMATION         ====================
-
    - transformers_version: 2.11.0
    - framework: PyTorch
    - use_torchscript: False
@@ -146,7 +133,6 @@ An instantiated benchmark object can then simply be run by calling ``benchmark.r
    --------------------------------------------------------------------------------

    ====================        ENVIRONMENT INFORMATION         ====================
-
    - transformers_version: 2.11.0
    - framework: Tensorflow
    - use_xla: False
@@ -230,7 +216,6 @@ configurations must be inserted with the benchmark args as follows.
    --------------------------------------------------------------------------------

    ====================        ENVIRONMENT INFORMATION         ====================
-
    - transformers_version: 2.11.0
    - framework: PyTorch
    - use_torchscript: False
@@ -300,7 +285,6 @@ configurations must be inserted with the benchmark args as follows.
    --------------------------------------------------------------------------------

    ====================        ENVIRONMENT INFORMATION         ====================
-
    - transformers_version: 2.11.0
    - framework: Tensorflow
    - use_xla: False
@@ -357,5 +341,5 @@ The approach is detailed in the `following blogpost
 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
-:prefix_link:`here <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>`__.
--- a/docs/source/bertology.rst
+++ b/docs/source/bertology.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 BERTology
 -----------------------------------------------------------------------------------------------------------------------

@@ -33,6 +21,6 @@ help people access the inner representations, mainly adapted from the great work
 * 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: :prefix_link:`bertology.py
-<examples/research_projects/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.
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -20,17 +20,14 @@ sys.path.insert(0, os.path.abspath('../../src'))
 # -- Project information -----------------------------------------------------

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

 # The short X.Y version
 version = u''
 # The full version, including alpha/beta/rc tags
-release = u'4.2.0'
-# Prefix link to point to master, comment this during version release and uncomment below line
-extlinks = {'prefix_link': ('https://github.com/huggingface/transformers/blob/master/%s', '')}
-# Prefix link to always point to corresponding version, uncomment this during version release
-# extlinks = {'prefix_link': ('https://github.com/huggingface/transformers/blob/v'+ release + '/%s', '')}
+release = u'3.5.1'
+

 # -- General configuration ---------------------------------------------------

@@ -43,7 +40,6 @@ extlinks = {'prefix_link': ('https://github.com/huggingface/transformers/blob/ma
 # ones.
 extensions = [
    'sphinx.ext.autodoc',
-    'sphinx.ext.extlinks',
    'sphinx.ext.coverage',
    'sphinx.ext.napoleon',
    'recommonmark',
--- a/docs/source/converting_tensorflow_models.rst
+++ b/docs/source/converting_tensorflow_models.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 Converting Tensorflow Checkpoints
 =======================================================================================================================

@@ -27,8 +15,9 @@ 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
-:prefix_link:`convert_bert_original_tf_checkpoint_to_pytorch.py
-<src/transformers/convert_bert_original_tf_checkpoint_to_pytorch.py>` script.
+`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
@@ -65,8 +54,9 @@ ALBERT
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

 Convert TensorFlow model checkpoints of ALBERT to PyTorch using the
-:prefix_link:`convert_albert_original_tf_checkpoint_to_pytorch.py
-<src/transformers/convert_bert_original_tf_checkpoint_to_pytorch.py>` script.
+`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
--- a/docs/source/custom_datasets.rst
+++ b/docs/source/custom_datasets.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 Fine-tuning with custom datasets
 =======================================================================================================================

@@ -558,15 +546,12 @@ we can use the built in :func:`~transformers.BatchEncoding.char_to_token` method
        end_positions = []
        for i in range(len(answers)):
            start_positions.append(encodings.char_to_token(i, answers[i]['answer_start']))
-            end_positions.append(encodings.char_to_token(i, answers[i]['answer_end']))
-
-            # if start position is None, the answer passage has been truncated
+            end_positions.append(encodings.char_to_token(i, answers[i]['answer_end'] - 1))
+            # if None, the answer passage has been truncated
            if start_positions[-1] is None:
                start_positions[-1] = tokenizer.model_max_length
-
-            # if end position is None, the 'char_to_token' function points to the space before the correct token - > add + 1
            if end_positions[-1] is None:
-                end_positions[-1] = encodings.char_to_token(i, answers[i]['answer_end'] + 1)
+                end_positions[-1] = tokenizer.model_max_length
        encodings.update({'start_positions': start_positions, 'end_positions': end_positions})

    add_token_positions(train_encodings, train_answers)
--- a/docs/source/glossary.rst
+++ b/docs/source/glossary.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 Glossary
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

@@ -226,7 +214,7 @@ Contrary to RNNs that have the position of each token embedded within them, tran
 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`` are passed to the model, the IDs are automatically created as
+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
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -22,18 +22,6 @@ State-of-the-art NLP for everyone:
 - Hands-on practitioners
 - AI/ML/NLP teachers and educators

-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 Lower compute costs, smaller carbon footprint:

 - Researchers can share trained models instead of always retraining
@@ -47,16 +35,6 @@ 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.
-
-`All the model checkpoints <https://huggingface.co/models>`__ are seamlessly integrated from the huggingface.co `model
-hub <https://huggingface.co>`__ where they are uploaded directly by `users <https://huggingface.co/users>`__ and
-`organizations <https://huggingface.co/organizations>`__.
-
-Current number of checkpoints: |checkpoints|
-
-.. |checkpoints| image:: https://img.shields.io/endpoint?url=https://huggingface.co/api/shields/models&color=brightgreen
-
 Contents
 -----------------------------------------------------------------------------------------------------------------------

@@ -66,7 +44,7 @@ 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:

@@ -74,8 +52,8 @@ The documentation is organized in five parts:
    - **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:
+The library currently contains PyTorch and Tensorflow implementations, pre-trained 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!
@@ -88,215 +66,102 @@ and conversion utilities for the following models:
   Pre-training for Natural Language Generation, Translation, and Comprehension
   <https://arxiv.org/pdf/1910.13461.pdf>`__ by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman
   Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer.
-3. :doc:`BARThez <model_doc/barthez>` (from École polytechnique) released with the paper `BARThez: a Skilled Pretrained
-   French Sequence-to-Sequence Model <https://arxiv.org/abs/2010.12321>`__ by Moussa Kamal Eddine, Antoine J.-P.
-   Tixier, Michalis Vazirgiannis.
-4. :doc:`BERT <model_doc/bert>` (from Google) released with the paper `BERT: Pre-training of Deep Bidirectional
+3. :doc:`BERT <model_doc/bert>` (from Google) released with the paper `BERT: Pre-training of Deep Bidirectional
   Transformers for Language Understanding <https://arxiv.org/abs/1810.04805>`__ by Jacob Devlin, Ming-Wei Chang,
   Kenton Lee and Kristina Toutanova.
-5. :doc:`BERT For Sequence Generation <model_doc/bertgeneration>` (from Google) released with the paper `Leveraging
+4. :doc:`BERT For Sequence Generation <model_doc/bertgeneration>` (from Google) released with the paper `Leveraging
   Pre-trained Checkpoints for Sequence Generation Tasks <https://arxiv.org/abs/1907.12461>`__ by Sascha Rothe, Shashi
   Narayan, Aliaksei Severyn.
-6. :doc:`Blenderbot <model_doc/blenderbot>` (from Facebook) released with the paper `Recipes for building an
+5. :doc:`Blenderbot <model_doc/blenderbot>` (from Facebook) released with the paper `Recipes for building an
   open-domain chatbot <https://arxiv.org/abs/2004.13637>`__ by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary
   Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
-7. :doc:`BlenderbotSmall <model_doc/blenderbot_small>` (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.
-8. :doc:`CamemBERT <model_doc/camembert>` (from Inria/Facebook/Sorbonne) released with the paper `CamemBERT: a Tasty
+6. :doc:`CamemBERT <model_doc/camembert>` (from Inria/Facebook/Sorbonne) released with the paper `CamemBERT: a Tasty
   French Language Model <https://arxiv.org/abs/1911.03894>`__ by Louis Martin*, Benjamin Muller*, Pedro Javier Ortiz
   Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
-9. :doc:`CTRL <model_doc/ctrl>` (from Salesforce) released with the paper `CTRL: A Conditional Transformer Language
+7. :doc:`CTRL <model_doc/ctrl>` (from Salesforce) released with the paper `CTRL: A Conditional Transformer Language
   Model for Controllable Generation <https://arxiv.org/abs/1909.05858>`__ by Nitish Shirish Keskar*, Bryan McCann*,
   Lav R. Varshney, Caiming Xiong and Richard Socher.
-10. :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.
-11. :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.
-12. :doc:`DistilBERT <model_doc/distilbert>` (from HuggingFace), released together with the paper `DistilBERT, a
+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.
-13. :doc:`DPR <model_doc/dpr>` (from Facebook) released with the paper `Dense Passage Retrieval for Open-Domain
+11. :doc:`DPR <model_doc/dpr>` (from Facebook) released with the paper `Dense Passage Retrieval for Open-Domain
    Question Answering <https://arxiv.org/abs/2004.04906>`__ by Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick
    Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
-14. :doc:`ELECTRA <model_doc/electra>` (from Google Research/Stanford University) released with the paper `ELECTRA:
+12. :doc:`ELECTRA <model_doc/electra>` (from Google Research/Stanford University) released with the paper `ELECTRA:
    Pre-training text encoders as discriminators rather than generators <https://arxiv.org/abs/2003.10555>`__ by Kevin
    Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning.
-15. :doc:`FlauBERT <model_doc/flaubert>` (from CNRS) released with the paper `FlauBERT: Unsupervised Language Model
+13. :doc:`FlauBERT <model_doc/flaubert>` (from CNRS) released with the paper `FlauBERT: Unsupervised Language Model
    Pre-training for French <https://arxiv.org/abs/1912.05372>`__ by Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne,
    Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, Didier Schwab.
-16. :doc:`Funnel Transformer <model_doc/funnel>` (from CMU/Google Brain) released with the paper `Funnel-Transformer:
+14. :doc:`Funnel Transformer <model_doc/funnel>` (from CMU/Google Brain) released with the paper `Funnel-Transformer:
    Filtering out Sequential Redundancy for Efficient Language Processing <https://arxiv.org/abs/2006.03236>`__ by
    Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le.
-17. :doc:`GPT <model_doc/gpt>` (from OpenAI) released with the paper `Improving Language Understanding by Generative
+15. :doc:`GPT <model_doc/gpt>` (from OpenAI) released with the paper `Improving Language Understanding by Generative
    Pre-Training <https://blog.openai.com/language-unsupervised/>`__ by Alec Radford, Karthik Narasimhan, Tim Salimans
    and Ilya Sutskever.
-18. :doc:`GPT-2 <model_doc/gpt2>` (from OpenAI) released with the paper `Language Models are Unsupervised Multitask
+16. :doc:`GPT-2 <model_doc/gpt2>` (from OpenAI) released with the paper `Language Models are Unsupervised Multitask
    Learners <https://blog.openai.com/better-language-models/>`__ by Alec Radford*, Jeffrey Wu*, Rewon Child, David
    Luan, Dario Amodei** and Ilya Sutskever**.
-19. :doc:`LayoutLM <model_doc/layoutlm>` (from Microsoft Research Asia) released with the paper `LayoutLM: Pre-training
+17. :doc:`LayoutLM <model_doc/layoutlm>` (from Microsoft Research Asia) released with the paper `LayoutLM: Pre-training
    of Text and Layout for Document Image Understanding <https://arxiv.org/abs/1912.13318>`__ by Yiheng Xu, Minghao Li,
    Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou.
-20. :doc:`LED <model_doc/led>` (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.
-21. :doc:`Longformer <model_doc/longformer>` (from AllenAI) released with the paper `Longformer: The Long-Document
+18. :doc:`Longformer <model_doc/longformer>` (from AllenAI) released with the paper `Longformer: The Long-Document
    Transformer <https://arxiv.org/abs/2004.05150>`__ by Iz Beltagy, Matthew E. Peters, Arman Cohan.
-22. :doc:`LXMERT <model_doc/lxmert>` (from UNC Chapel Hill) released with the paper `LXMERT: Learning Cross-Modality
+19. :doc:`LXMERT <model_doc/lxmert>` (from UNC Chapel Hill) released with the paper `LXMERT: Learning Cross-Modality
    Encoder Representations from Transformers for Open-Domain Question Answering <https://arxiv.org/abs/1908.07490>`__
    by Hao Tan and Mohit Bansal.
-23. :doc:`MarianMT <model_doc/marian>` Machine translation models trained using `OPUS <http://opus.nlpl.eu/>`__ data by
+20. :doc:`MarianMT <model_doc/marian>` Machine translation models trained using `OPUS <http://opus.nlpl.eu/>`__ data by
    Jörg Tiedemann. The `Marian Framework <https://marian-nmt.github.io/>`__ is being developed by the Microsoft
    Translator Team.
-24. :doc:`MBart <model_doc/mbart>` (from Facebook) released with the paper `Multilingual Denoising Pre-training for
+21. :doc:`MBart <model_doc/mbart>` (from Facebook) released with the paper `Multilingual Denoising Pre-training for
    Neural Machine Translation <https://arxiv.org/abs/2001.08210>`__ by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li,
    Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
-25. :doc:`MPNet <model_doc/mpnet>` (from Microsoft Research) released with the paper `MPNet: Masked and Permuted
-    Pre-training for Language Understanding <https://arxiv.org/abs/2004.09297>`__ by Kaitao Song, Xu Tan, Tao Qin,
-    Jianfeng Lu, Tie-Yan Liu.
-26. :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.
-27. :doc:`Pegasus <model_doc/pegasus>` (from Google) released with the paper `PEGASUS: Pre-training with Extracted
+22. :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.
-28. :doc:`ProphetNet <model_doc/prophetnet>` (from Microsoft Research) released with the paper `ProphetNet: Predicting
+23. :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.
-29. :doc:`Reformer <model_doc/reformer>` (from Google Research) released with the paper `Reformer: The Efficient
+24. :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.
-30. :doc:`RoBERTa <model_doc/roberta>` (from Facebook), released together with the paper a `Robustly Optimized BERT
+25. :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.
-31. :doc:`SqueezeBert <model_doc/squeezebert>` released with the paper `SqueezeBERT: What can computer vision teach NLP
+26. :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.
-32. :doc:`T5 <model_doc/t5>` (from Google AI) released with the paper `Exploring the Limits of Transfer Learning with a
+27. :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.
-33. :doc:`TAPAS <model_doc/tapas>` (from Google AI) released with the paper `TAPAS: Weakly Supervised Table Parsing via
-    Pre-training <https://arxiv.org/abs/2004.02349>`__ by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller,
-    Francesco Piccinno and Julian Martin Eisenschlos.
-34. :doc:`Transformer-XL <model_doc/transformerxl>` (from Google/CMU) released with the paper `Transformer-XL:
+28. :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.
-35. :doc:`XLM <model_doc/xlm>` (from Facebook) released together with the paper `Cross-lingual Language Model
+29. :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.
-36. :doc:`XLM-ProphetNet <model_doc/xlmprophetnet>` (from Microsoft Research) released with the paper `ProphetNet:
+30. :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.
-37. :doc:`XLM-RoBERTa <model_doc/xlmroberta>` (from Facebook AI), released together with the paper `Unsupervised
+31. :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.
-38. :doc:`XLNet <model_doc/xlnet>` (from Google/CMU) released with the paper `XLNet: Generalized Autoregressive
+32. :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.
-
-
-.. _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          |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|       BlenderbotSmall       |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|            CTRL             |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|          CamemBERT          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|             DPR             |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|           DeBERTa           |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|         DistilBERT          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|           ELECTRA           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|       Encoder decoder       |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-| FairSeq Machine-Translation |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|          FlauBERT           |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|     Funnel Transformer      |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|             LED             |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|           LXMERT            |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|          LayoutLM           |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|         Longformer          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|            MPNet            |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|           Marian            |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|         MobileBERT          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|         OpenAI GPT          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|        OpenAI GPT-2         |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|           Pegasus           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|         ProphetNet          |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|             RAG             |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|          Reformer           |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|          RetriBERT          |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|           RoBERTa           |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|         SqueezeBERT         |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|             T5              |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|            TAPAS            |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|       Transformer-XL        |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|             XLM             |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|         XLM-RoBERTa         |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|        XLMProphetNet        |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|            XLNet            |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|            mBART            |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|             mT5             |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+33. `Other community models <https://huggingface.co/models>`__, contributed by the `community
+    <https://huggingface.co/users>`__.

 .. toctree::
    :maxdepth: 2
@@ -363,12 +228,9 @@ TensorFlow and/or Flax.
    model_doc/albert
    model_doc/auto
    model_doc/bart
-    model_doc/barthez
    model_doc/bert
-    model_doc/bertweet
    model_doc/bertgeneration
    model_doc/blenderbot
-    model_doc/blenderbot_small
    model_doc/camembert
    model_doc/ctrl
    model_doc/deberta
@@ -380,20 +242,15 @@ TensorFlow and/or Flax.
    model_doc/flaubert
    model_doc/fsmt
    model_doc/funnel
-    model_doc/herbert
    model_doc/layoutlm
-    model_doc/led
    model_doc/longformer
    model_doc/lxmert
    model_doc/marian
    model_doc/mbart
    model_doc/mobilebert
-    model_doc/mpnet
-    model_doc/mt5
    model_doc/gpt
    model_doc/gpt2
    model_doc/pegasus
-    model_doc/phobert
    model_doc/prophetnet
    model_doc/rag
    model_doc/reformer
@@ -401,7 +258,6 @@ TensorFlow and/or Flax.
    model_doc/roberta
    model_doc/squeezebert
    model_doc/t5
-    model_doc/tapas
    model_doc/transformerxl
    model_doc/xlm
    model_doc/xlmprophetnet
--- a/docs/source/installation.md
+++ b/docs/source/installation.md
@@ -1,19 +1,3 @@
-<!---
-Copyright 2020 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-->
-
 # Installation

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

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

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

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

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

 ```bash
@@ -89,32 +66,19 @@ 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
 (``PYTORCH_TRANSFORMERS_CACHE`` or ``PYTORCH_PRETRAINED_BERT_CACHE``), those will be used if there is no shell
@@ -133,6 +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!
--- a/docs/source/internal/generation_utils.rst
+++ b/docs/source/internal/generation_utils.rst
@@ -1,114 +1,12 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 Utilities for Generation
 -----------------------------------------------------------------------------------------------------------------------

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

-Generate Outputs
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The output of :meth:`~transformers.PreTrainedModel.generate` is an instance of a subclass of
-:class:`~transformers.file_utils.ModelOutput`. This output is a data structure containing all the information returned
-by :meth:`~transformers.PreTrainedModel.generate`, but that can also be used as tuple or dictionary.
-
-Here's an example:
-
-.. code-block::
-
-    from transformers import GPT2Tokenizer, GPT2LMHeadModel
-
-    tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
-    model = GPT2LMHeadModel.from_pretrained('gpt2')
-
-    inputs = tokenizer("Hello, my dog is cute and ", return_tensors="pt")
-    generation_output = model.generate(**inputs, return_dict_in_generate=True, output_scores=True)
-
-The ``generation_output`` object is a :class:`~transformers.generation_utils.GreedySearchDecoderOnlyOutput`, as we can
-see in the documentation of that class below, it means it has the following attributes:
-
- ``sequences``: the generated sequences of tokens
- ``scores`` (optional): the prediction scores of the language modelling head, for each generation step
- ``hidden_states`` (optional): the hidden states of the model, for each generation step
- ``attentions`` (optional): the attention weights of the model, for each generation step
-
-Here we have the ``scores`` since we passed along ``output_scores=True``, but we don't have ``hidden_states`` and
-``attentions`` because we didn't pass ``output_hidden_states=True`` or ``output_attentions=True``.
-
-You can access each attribute as you would usually do, and if that attribute has not been returned by the model, you
-will get ``None``. Here for instance ``generation_output.scores`` are all the generated prediction scores of the
-language modeling head, and ``generation_output.attentions`` is ``None``.
-
-When using our ``generation_output`` object as a tuple, it only keeps the attributes that don't have ``None`` values.
-Here, for instance, it has two elements, ``loss`` then ``logits``, so
-
-.. code-block::
-
-    generation_output[:2]
-
-will return the tuple ``(generation_output.sequences, generation_output.scores)`` for instance.
-
-When using our ``generation_output`` object as a dictionary, it only keeps the attributes that don't have ``None``
-values. Here, for instance, it has two keys that are ``sequences`` and ``scores``.
-
-We document here all output types.
-
-
-GreedySearchOutput
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. autoclass:: transformers.generation_utils.GreedySearchDecoderOnlyOutput
-    :members:
-
-.. autoclass:: transformers.generation_utils.GreedySearchEncoderDecoderOutput
-    :members:
-
-
-SampleOutput
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. autoclass:: transformers.generation_utils.SampleDecoderOnlyOutput
-    :members:
-
-.. autoclass:: transformers.generation_utils.SampleEncoderDecoderOutput
-    :members:
-
-
-BeamSearchOutput
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. autoclass:: transformers.generation_utils.BeamSearchDecoderOnlyOutput
-    :members:
-
-.. autoclass:: transformers.generation_utils.BeamSearchEncoderDecoderOutput
-    :members:
-
-
-BeamSampleOutput
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. autoclass:: transformers.generation_utils.BeamSampleDecoderOnlyOutput
-    :members:
-
-.. autoclass:: transformers.generation_utils.BeamSampleEncoderDecoderOutput
-    :members:
-
-
 LogitsProcessor
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -121,9 +19,6 @@ generation.
 .. autoclass:: transformers.LogitsProcessorList
    :members: __call__

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

@@ -145,12 +40,6 @@ generation.
 .. autoclass:: transformers.NoBadWordsLogitsProcessor
    :members: __call__

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

@@ -159,10 +48,3 @@ BeamSearch

 .. autoclass:: transformers.BeamSearchScorer
    :members: process, finalize
-
-Utilities
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autofunction:: transformers.top_k_top_p_filtering
-
-.. autofunction:: transformers.tf_top_k_top_p_filtering
--- a/docs/source/internal/modeling_utils.rst
+++ b/docs/source/internal/modeling_utils.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 Custom Layers and Utilities
 -----------------------------------------------------------------------------------------------------------------------

@@ -91,6 +79,8 @@ TensorFlow loss functions
 TensorFlow Helper Functions
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

+.. autofunction:: transformers.modeling_tf_utils.cast_bool_to_primitive
+
 .. autofunction:: transformers.modeling_tf_utils.get_initializer

 .. autofunction:: transformers.modeling_tf_utils.keras_serializable
--- a/docs/source/internal/pipelines_utils.rst
+++ b/docs/source/internal/pipelines_utils.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 Utilities for pipelines
 -----------------------------------------------------------------------------------------------------------------------

--- a/docs/source/internal/tokenization_utils.rst
+++ b/docs/source/internal/tokenization_utils.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 Utilities for Tokenizers
 -----------------------------------------------------------------------------------------------------------------------

--- a/docs/source/internal/trainer_utils.rst
+++ b/docs/source/internal/trainer_utils.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 Utilities for Trainer
 -----------------------------------------------------------------------------------------------------------------------

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

 .. autoclass:: transformers.EvalPrediction

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

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

 .. autoclass:: transformers.trainer_callback.CallbackHandler

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

 .. autoclass:: transformers.trainer_pt_utils.DistributedTensorGatherer
    :members:
-
-
-Distributed Evaluation
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.HfArgumentParser
--- a/docs/source/main_classes/callback.rst
+++ b/docs/source/main_classes/callback.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 Callbacks
 -----------------------------------------------------------------------------------------------------------------------

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

 .. autoclass:: transformers.ProgressCallback

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

 .. autoclass:: transformers.integrations.WandbCallback
--- a/docs/source/main_classes/configuration.rst
+++ b/docs/source/main_classes/configuration.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 Configuration
 -----------------------------------------------------------------------------------------------------------------------

--- a/docs/source/main_classes/logging.rst
+++ b/docs/source/main_classes/logging.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 Logging
 -----------------------------------------------------------------------------------------------------------------------

--- a/docs/source/main_classes/model.rst
+++ b/docs/source/main_classes/model.rst
@@ -1,22 +1,9 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 Models
 -----------------------------------------------------------------------------------------------------------------------

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

 :class:`~transformers.PreTrainedModel` and :class:`~transformers.TFPreTrainedModel` also implement a few methods which
 are common among all the models to:
@@ -58,13 +45,6 @@ TFModelUtilsMixin
    :members:


-FlaxPreTrainedModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxPreTrainedModel
-    :members:
-
-
 Generation
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

--- a/docs/source/main_classes/optimizer_schedules.rst
+++ b/docs/source/main_classes/optimizer_schedules.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 Optimization
 -----------------------------------------------------------------------------------------------------------------------

@@ -43,10 +31,6 @@ Schedules
 Learning Rate Schedules (Pytorch)
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

-.. autoclass:: transformers.SchedulerType
-
-.. autofunction:: transformers.get_scheduler
-
 .. autofunction:: transformers.get_constant_schedule


@@ -78,10 +62,6 @@ Learning Rate Schedules (Pytorch)
    :target: /imgs/warmup_linear_schedule.png
    :alt:

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

--- a/docs/source/main_classes/output.rst
+++ b/docs/source/main_classes/output.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 Model outputs
 -----------------------------------------------------------------------------------------------------------------------

@@ -126,6 +114,13 @@ CausalLMOutputWithCrossAttentions
    :members:


+CausalLMOutputWithPastAndCrossAttentions
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.modeling_outputs.CausalLMOutputWithPastAndCrossAttentions
+    :members:
+
+
 CausalLMOutputWithPast
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

--- a/docs/source/main_classes/pipelines.rst
+++ b/docs/source/main_classes/pipelines.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 Pipelines
 -----------------------------------------------------------------------------------------------------------------------

@@ -34,7 +22,6 @@ There are two categories of pipeline abstractions to be aware about:
    - :class:`~transformers.TranslationPipeline`
    - :class:`~transformers.ZeroShotClassificationPipeline`
    - :class:`~transformers.Text2TextGenerationPipeline`
-    - :class:`~transformers.TableQuestionAnsweringPipeline`

 The pipeline abstraction
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -74,9 +61,8 @@ FillMaskPipeline
 NerPipeline
 =======================================================================================================================

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

 QuestionAnsweringPipeline
 =======================================================================================================================
@@ -92,13 +78,6 @@ SummarizationPipeline
    :special-members: __call__
    :members:

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

@@ -127,13 +106,6 @@ TokenClassificationPipeline
    :special-members: __call__
    :members:

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

--- a/docs/source/main_classes/processors.rst
+++ b/docs/source/main_classes/processors.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 Processors
 -----------------------------------------------------------------------------------------------------------------------

@@ -168,5 +156,5 @@ Using `tensorflow_datasets` is as easy as using a data file:
    )


-Another example using these processors is given in the :prefix_link:`run_squad.py
-<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.
--- a/docs/source/main_classes/tokenizer.rst
+++ b/docs/source/main_classes/tokenizer.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 Tokenizer
 -----------------------------------------------------------------------------------------------------------------------

--- a/docs/source/main_classes/trainer.rst
+++ b/docs/source/main_classes/trainer.rst
@@ -1,15 +1,3 @@
-..
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 Trainer
 -----------------------------------------------------------------------------------------------------------------------

@@ -47,7 +35,7 @@ Here is an example of how to customize :class:`~transformers.Trainer` using a cu
    class MyTrainer(Trainer):
        def compute_loss(self, model, inputs):
            labels = inputs.pop("labels")
-            outputs = model(**inputs)
+            outputs = models(**inputs)
            logits = outputs[0]
            return my_custom_loss(logits, labels)

@@ -63,13 +51,6 @@ Trainer
    :members:


-Seq2SeqTrainer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.Seq2SeqTrainer
-    :members: evaluate, predict
-
-
 TFTrainer
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -84,450 +65,8 @@ TrainingArguments
    :members:


-Seq2SeqTrainingArguments
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.Seq2SeqTrainingArguments
-    :members:
-
-
 TFTrainingArguments
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFTrainingArguments
    :members:
-
-
-Trainer Integrations
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-The :class:`~transformers.Trainer` has been extended to support libraries that may dramatically improve your training
-time and fit much bigger models.
-
-Currently it supports third party solutions, `DeepSpeed <https://github.com/microsoft/DeepSpeed>`__ and `FairScale
-<https://github.com/facebookresearch/fairscale/>`__, which implement parts of the paper `ZeRO: Memory Optimizations
-Toward Training Trillion Parameter Models, by Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase, Yuxiong He
-<https://arxiv.org/abs/1910.02054>`__.
-
-This provided support is new and experimental as of this writing.
-
-You will need at least 2 GPUs to benefit from these features.
-
-FairScale
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-By integrating `FairScale <https://github.com/facebookresearch/fairscale/>`__ the :class:`~transformers.Trainer`
-provides support for the following features from `the ZeRO paper <https://arxiv.org/abs/1910.02054>`__:
-
-1. Optimizer State Sharding
-2. Gradient Sharding
-
-To deploy this feature:
-
-1. Install the library via pypi:
-
-   .. code-block:: bash
-
-       pip install fairscale
-
-   or find more details on `the FairScale's github page
-   <https://github.com/facebookresearch/fairscale/#installation>`__.
-
-2. Add ``--sharded_ddp`` to the command line arguments, and make sure you have added the distributed launcher ``-m
-   torch.distributed.launch --nproc_per_node=NUMBER_OF_GPUS_YOU_HAVE`` if you haven't been using it already.
-
-For example here is how you could use it for ``finetune_trainer.py`` with 2 GPUs:
-
-.. code-block:: bash
-
-    cd examples/seq2seq
-    python -m torch.distributed.launch --nproc_per_node=2 ./finetune_trainer.py \
-    --model_name_or_path sshleifer/distill-mbart-en-ro-12-4 --data_dir wmt_en_ro \
-    --output_dir output_dir --overwrite_output_dir \
-    --do_train --n_train 500 --num_train_epochs 1 \
-    --per_device_train_batch_size 1  --freeze_embeds \
-    --src_lang en_XX --tgt_lang ro_RO --task translation \
-    --fp16 --sharded_ddp
-
-Notes:
-
- This feature requires distributed training (so multiple GPUs).
- It is not implemented for TPUs.
- It works with ``--fp16`` too, to make things even faster.
- One of the main benefits of enabling ``--sharded_ddp`` is that it uses a lot less GPU memory, so you should be able
-  to use significantly larger batch sizes using the same hardware (e.g. 3x and even bigger) which should lead to
-  significantly shorter training time.
-
-
-DeepSpeed
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-
-`DeepSpeed <https://github.com/microsoft/DeepSpeed>`__ implements everything described in the `ZeRO paper
-<https://arxiv.org/abs/1910.02054>`__, except ZeRO's stage 3. "Parameter Partitioning (Pos+g+p)". Currently it provides
-full support for:
-
-1. Optimizer State Partitioning (ZeRO stage 1)
-2. Add Gradient Partitioning (ZeRO stage 2)
-
-To deploy this feature:
-
-1. Install the library via pypi:
-
-   .. code-block:: bash
-
-       pip install deepspeed
-
-   or find more details on `the DeepSpeed's github page <https://github.com/microsoft/deepspeed#installation>`__.
-
-2. Adjust the :class:`~transformers.Trainer` command line arguments as following:
-
-   1. replace ``python -m torch.distributed.launch`` with ``deepspeed``.
-   2. add a new argument ``--deepspeed ds_config.json``, where ``ds_config.json`` is the DeepSpeed configuration file
-      as documented `here <https://www.deepspeed.ai/docs/config-json/>`__. The file naming is up to you.
-
-   Therefore, if your original command line looked as following:
-
-   .. code-block:: bash
-
-       python -m torch.distributed.launch --nproc_per_node=2 your_program.py <normal cl args>
-
-   Now it should be:
-
-   .. code-block:: bash
-
-       deepspeed --num_gpus=2 your_program.py <normal cl args> --deepspeed ds_config.json
-
-   Unlike, ``torch.distributed.launch`` where you have to specify how many GPUs to use with ``--nproc_per_node``, with
-   the ``deepspeed`` launcher you don't have to use the corresponding ``--num_gpus`` if you want all of your GPUs used.
-   The full details on how to configure various nodes and GPUs can be found `here
-   <https://www.deepspeed.ai/getting-started/#resource-configuration-multi-node>`__.
-
-   Here is an example of running ``finetune_trainer.py`` under DeepSpeed deploying all available GPUs:
-
-   .. code-block:: bash
-
-       cd examples/seq2seq
-       deepspeed ./finetune_trainer.py --deepspeed ds_config.json \
-       --model_name_or_path sshleifer/distill-mbart-en-ro-12-4 --data_dir wmt_en_ro \
-       --output_dir output_dir --overwrite_output_dir \
-       --do_train --n_train 500 --num_train_epochs 1 \
-       --per_device_train_batch_size 1  --freeze_embeds \
-       --src_lang en_XX --tgt_lang ro_RO --task translation
-
-   Note that in the DeepSpeed documentation you are likely to see ``--deepspeed --deepspeed_config ds_config.json`` -
-   i.e. two DeepSpeed-related arguments, but for the sake of simplicity, and since there are already so many arguments
-   to deal with, we combined the two into a single argument.
-
-Before you can deploy DeepSpeed, let's discuss its configuration.
-
-**Configuration:**
-
-For the complete guide to the DeepSpeed configuration options that can be used in its configuration file please refer
-to the `following documentation <https://www.deepspeed.ai/docs/config-json/>`__.
-
-While you always have to supply the DeepSpeed configuration file, you can configure the DeepSpeed integration in
-several ways:
-
-1. Supply most of the configuration inside the file, and just use a few required command line arguments. This is the
-   recommended way as it puts most of the configuration params in one place.
-2. Supply just the ZeRO configuration params inside the file, and configure the rest using the normal
-   :class:`~transformers.Trainer` command line arguments.
-3. Any variation of the first two ways.
-
-To get an idea of what DeepSpeed configuration file looks like, here is one that activates ZeRO stage 2 features,
-enables FP16, uses AdamW optimizer and WarmupLR scheduler:
-
-.. code-block:: json
-
-    {
-        "fp16": {
-            "enabled": true,
-            "loss_scale": 0,
-            "loss_scale_window": 1000,
-            "hysteresis": 2,
-            "min_loss_scale": 1
-        },
-
-       "zero_optimization": {
-           "stage": 2,
-           "allgather_partitions": true,
-           "allgather_bucket_size": 5e8,
-           "overlap_comm": true,
-           "reduce_scatter": true,
-           "reduce_bucket_size": 5e8,
-           "contiguous_gradients": true,
-           "cpu_offload": true
-       },
-
-       "optimizer": {
-         "type": "AdamW",
-         "params": {
-           "lr": 3e-5,
-           "betas": [ 0.8, 0.999 ],
-           "eps": 1e-8,
-           "weight_decay": 3e-7
-         }
-       },
-       "zero_allow_untested_optimizer": true,
-
-       "scheduler": {
-         "type": "WarmupLR",
-         "params": {
-           "warmup_min_lr": 0,
-           "warmup_max_lr": 3e-5,
-           "warmup_num_steps": 500
-         }
-       }
-    }
-
-If you already have a command line that you have been using with :class:`transformers.Trainer` args, you can continue
-using those and the :class:`~transformers.Trainer` will automatically convert them into the corresponding DeepSpeed
-configuration at run time. For example, you could use the following configuration file:
-
-.. code-block:: json
-
-    {
-       "zero_optimization": {
-           "stage": 2,
-           "allgather_partitions": true,
-           "allgather_bucket_size": 5e8,
-           "overlap_comm": true,
-           "reduce_scatter": true,
-           "reduce_bucket_size": 5e8,
-           "contiguous_gradients": true,
-           "cpu_offload": true
-       }
-    }
-
-and the following command line arguments:
-
-.. code-block:: bash
-
-    --learning_rate 3e-5 --warmup_steps 500 --adam_beta1 0.8 --adam_beta2 0.999 --adam_epsilon 1e-8 \
-    --weight_decay 3e-7 --lr_scheduler_type constant_with_warmup --fp16 --fp16_backend amp
-
-to achieve the same configuration as provided by the longer json file in the first example.
-
-When you execute the program, DeepSpeed will log the configuration it received from the :class:`~transformers.Trainer`
-to the console, so you can see exactly what the final configuration was passed to it.
-
-**Shared Configuration:**
-
-Some configuration information is required by both the :class:`~transformers.Trainer` and DeepSpeed to function
-correctly, therefore, to prevent conflicting definitions, which could lead to hard to detect errors, we chose to
-configure those via the :class:`~transformers.Trainer` command line arguments.
-
-Therefore, the following DeepSpeed configuration params shouldn't be used with the :class:`~transformers.Trainer`:
-
-* ``train_batch_size``
-* ``train_micro_batch_size_per_gpu``
-* ``gradient_accumulation_steps``
-
-as these will be automatically derived from the run time environment and the following 2 command line arguments:
-
-.. code-block:: bash
-
-    --per_device_train_batch_size 8 --gradient_accumulation_steps 2
-
-which are always required to be supplied.
-
-Of course, you will need to adjust the values in this example to your situation.
-
-
-
-**ZeRO:**
-
-The ``zero_optimization`` section of the configuration file is the most important part (`docs
-<https://www.deepspeed.ai/docs/config-json/#zero-optimizations-for-fp16-training>`__), since that is where you define
-which ZeRO stages you want to enable and how to configure them.
-
-.. code-block:: json
-
-    {
-       "zero_optimization": {
-           "stage": 2,
-           "allgather_partitions": true,
-           "allgather_bucket_size": 5e8,
-           "overlap_comm": true,
-           "reduce_scatter": true,
-           "reduce_bucket_size": 5e8,
-           "contiguous_gradients": true,
-           "cpu_offload": true
-       }
-    }
-
-Notes:
-
- enabling ``cpu_offload`` should reduce GPU RAM usage (it requires ``"stage": 2``)
- ``"overlap_comm": true`` trades off increased GPU RAM usage to lower all-reduce latency. ``overlap_comm`` uses 4.5x
-  the ``allgather_bucket_size`` and ``reduce_bucket_size`` values. So if they are set to 5e8, this requires a 9GB
-  footprint (``5e8 x 2Bytes x 2 x 4.5``). Therefore, if you have a GPU with 8GB or less RAM, to avoid getting
-  OOM-errors you will need to reduce those parameters to about ``2e8``, which would require 3.6GB.
-
-This section has to be configured exclusively via DeepSpeed configuration - the :class:`~transformers.Trainer` provides
-no equivalent command line arguments.
-
-
-
-**Optimizer:**
-
-
-DeepSpeed's main optimizers are Adam, OneBitAdam, and Lamb. These have been thoroughly tested with ZeRO and are thus
-recommended to be used. It, however, can import other optimizers from ``torch``. The full documentation is `here
-<https://www.deepspeed.ai/docs/config-json/#optimizer-parameters>`__.
-
-If you don't configure the ``optimizer`` entry in the configuration file, the :class:`~transformers.Trainer` will
-automatically set it to ``AdamW`` and will use the supplied values or the defaults for the following command line
-arguments: ``--learning_rate``, ``--adam_beta1``, ``--adam_beta2``, ``--adam_epsilon`` and ``--weight_decay``.
-
-Here is an example of the pre-configured ``optimizer`` entry for AdamW:
-
-.. code-block:: json
-
-    {
-       "zero_allow_untested_optimizer": true,
-       "optimizer": {
-           "type": "AdamW",
-           "params": {
-             "lr": 0.001,
-             "betas": [0.8, 0.999],
-             "eps": 1e-8,
-             "weight_decay": 3e-7
-           }
-         }
-    }
-
-Since AdamW isn't on the list of tested with DeepSpeed/ZeRO optimizers, we have to add
-``zero_allow_untested_optimizer`` flag.
-
-If you want to use one of the officially supported optimizers, configure them explicitly in the configuration file, and
-make sure to adjust the values. e.g. if use Adam you will want ``weight_decay`` around ``0.01``.
-
-
-**Scheduler:**
-
-DeepSpeed supports LRRangeTest, OneCycle, WarmupLR and WarmupDecayLR LR schedulers. The full documentation is `here
-<https://www.deepspeed.ai/docs/config-json/#scheduler-parameters>`__.
-
-If you don't configure the ``scheduler`` entry in the configuration file, the :class:`~transformers.Trainer` will use
-the value of ``--lr_scheduler_type`` to configure it. Currently the :class:`~transformers.Trainer` supports only 2 LR
-schedulers that are also supported by DeepSpeed:
-
-* ``WarmupLR`` via ``--lr_scheduler_type constant_with_warmup``
-* ``WarmupDecayLR`` via ``--lr_scheduler_type linear``. This is also the default value for ``--lr_scheduler_type``,
-  therefore, if you don't configure the scheduler this is scheduler that will get configured by default.
-
-In either case, the values of ``--learning_rate`` and ``--warmup_steps`` will be used for the configuration.
-
-In other words, if you don't use the configuration file to set the ``scheduler`` entry, provide either:
-
-.. code-block:: bash
-
-    --lr_scheduler_type constant_with_warmup --learning_rate 3e-5 --warmup_steps 500
-
-or
-
-.. code-block:: bash
-
-    --lr_scheduler_type linear --learning_rate 3e-5 --warmup_steps 500
-
-with the desired values. If you don't pass these arguments, reasonable default values will be used instead.
-
-In the case of WarmupDecayLR ``total_num_steps`` gets set either via the ``--max_steps`` command line argument, or if
-it is not provided, derived automatically at run time based on the environment and the size of the dataset and other
-command line arguments.
-
-Here is an example of the pre-configured ``scheduler`` entry for WarmupLR (``constant_with_warmup`` in the
-:class:`~transformers.Trainer` API):
-
-.. code-block:: json
-
-    {
-       "scheduler": {
-             "type": "WarmupLR",
-             "params": {
-                 "warmup_min_lr": 0,
-                 "warmup_max_lr": 0.001,
-                 "warmup_num_steps": 1000
-             }
-         }
-    }
-
-**Automatic Mixed Precision:**
-
-You can work with FP16 in one of the following ways:
-
-1. Pytorch native amp, as documented `here <https://www.deepspeed.ai/docs/config-json/#fp16-training-options>`__.
-2. NVIDIA's apex, as documented `here
-   <https://www.deepspeed.ai/docs/config-json/#automatic-mixed-precision-amp-training-options>`__.
-
-If you want to use an equivalent of the pytorch native amp, you can either configure the ``fp16`` entry in the
-configuration file, or use the following command line arguments: ``--fp16 --fp16_backend amp``.
-
-Here is an example of the ``fp16`` configuration:
-
-.. code-block:: json
-
-    {
-        "fp16": {
-            "enabled": true,
-            "loss_scale": 0,
-            "loss_scale_window": 1000,
-            "hysteresis": 2,
-            "min_loss_scale": 1
-        },
-    }
-
-If you want to use NVIDIA's apex instead, you can can either configure the ``amp`` entry in the configuration file, or
-use the following command line arguments: ``--fp16 --fp16_backend apex --fp16_opt_level 01``.
-
-Here is an example of the ``amp`` configuration:
-
-.. code-block:: json
-
-    {
-        "amp": {
-            "enabled": true,
-            "opt_level": "O1"
-        }
-    }
-
-
-
-**Gradient Clipping:**
-
-If you don't configure the ``gradient_clipping`` entry in the configuration file, the :class:`~transformers.Trainer`
-will use the value of the ``--max_grad_norm`` command line argument to set it.
-
-Here is an example of the ``gradient_clipping`` configuration:
-
-.. code-block:: json
-
-    {
-        "gradient_clipping": 1.0,
-    }
-
-
-
-**Notes:**
-
-* DeepSpeed works with the PyTorch :class:`~transformers.Trainer` but not TF :class:`~transformers.TFTrainer`.
-* While DeepSpeed has a pip installable PyPI package, it is highly recommended that it gets installed from `source
-  <https://github.com/microsoft/deepspeed#installation>`__ to best match your hardware and also if you need to enable
-  certain features, like 1-bit Adam, which aren't available in the pypi distribution.
-* You don't have to use the :class:`~transformers.Trainer` to use DeepSpeed with HuggingFace ``transformers`` - you can
-  use any model with your own trainer, and you will have to adapt the latter according to `the DeepSpeed integration
-  instructions <https://www.deepspeed.ai/getting-started/#writing-deepspeed-models>`__.
-
-**Main DeepSpeed Resources:**
-
- `github <https://github.com/microsoft/deepspeed>`__
- `Usage docs <https://www.deepspeed.ai/getting-started/>`__
- `API docs <https://deepspeed.readthedocs.io/en/latest/index.html>`__
-
-Finally, please, remember that, HuggingFace :class:`~transformers.Trainer` only integrates DeepSpeed, therefore if you
-have any problems or questions with regards to DeepSpeed usage, please, file an issue with `DeepSpeed github
-<https://github.com/microsoft/DeepSpeed/issues>`__.
--- a/docs/source/migration.md
+++ b/docs/source/migration.md
@@ -1,186 +1,5 @@
-<!---
-Copyright 2020 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-->
-
 # Migrating from previous packages

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

 Here is a quick summary of what you should take care of when migrating from `pytorch-transformers` to 🤗 Transformers.
--- a/docs/source/model_doc/albert.rst
+++ b/docs/source/model_doc/albert.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 ALBERT
 -----------------------------------------------------------------------------------------------------------------------

@@ -60,20 +48,13 @@ AlbertTokenizer
        create_token_type_ids_from_sequences, save_vocabulary


-AlbertTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AlbertTokenizerFast
-    :members:
-
-
 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:


--- a/docs/source/model_doc/auto.rst
+++ b/docs/source/model_doc/auto.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 Auto Classes
 -----------------------------------------------------------------------------------------------------------------------

@@ -114,13 +102,6 @@ AutoModelForQuestionAnswering
    :members:


-AutoModelForTableQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AutoModelForTableQuestionAnswering
-    :members:
-
-
 TFAutoModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -182,10 +163,3 @@ TFAutoModelForQuestionAnswering

 .. autoclass:: transformers.TFAutoModelForQuestionAnswering
    :members:
-
-
-FlaxAutoModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxAutoModel
-    :members:
--- a/docs/source/model_doc/bart.rst
+++ b/docs/source/model_doc/bart.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 BART
 -----------------------------------------------------------------------------------------------------------------------

@@ -42,12 +30,10 @@ Examples
 _______________________________________________________________________________________________________________________

 - Examples and scripts for fine-tuning BART and other models for sequence to sequence tasks can be found in
-  :prefix_link:`examples/seq2seq/ <examples/seq2seq/README.md>`.
+  `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>`__.


 Implementation Notes
@@ -55,31 +41,16 @@ 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 the ``decoder_input_ids`` if they are not passed.
-  This is different than some other modeling APIs. A typical use case of this feature is mask filling.
- 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.
+- The forward pass of :class:`~transformers.BartModel` will create decoder inputs (using the helper function
+  :func:`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 :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.
-
-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']
-
+- For training/forward passes that don't involve beam search, pass :obj:`use_cache=False`.


 BartConfig
@@ -96,12 +67,6 @@ BartTokenizer
    :members:


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

 BartModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -109,6 +74,8 @@ BartModel
 .. autoclass:: transformers.BartModel
    :members: forward

+.. autofunction:: transformers.modeling_bart._prepare_bart_decoder_inputs
+

 BartForConditionalGeneration
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
--- a/docs/source/model_doc/barthez.rst
+++ b/docs/source/model_doc/barthez.rst
@@ -1,59 +0,0 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
-BARThez
-----------------------------------------------------------------------------------------------------------------------
-
-Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The BARThez model was proposed in `BARThez: a Skilled Pretrained French Sequence-to-Sequence Model`
-<https://arxiv.org/abs/2010.12321>`__ by Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis on 23 Oct,
-2020.
-
-The abstract of the paper:
-
-
-*Inductive transfer learning, enabled by self-supervised learning, have taken the entire Natural Language Processing
-(NLP) field by storm, with models such as BERT and BART setting new state of the art on countless natural language
-understanding tasks. While there are some notable exceptions, most of the available models and research have been
-conducted for the English language. In this work, we introduce BARThez, the first BART model for the French language
-(to the best of our knowledge). BARThez was pretrained on a very large monolingual French corpus from past research
-that we adapted to suit BART's perturbation schemes. Unlike already existing BERT-based French language models such as
-CamemBERT and FlauBERT, BARThez is particularly well-suited for generative tasks, since not only its encoder but also
-its decoder is pretrained. In addition to discriminative tasks from the FLUE benchmark, we evaluate BARThez on a novel
-summarization dataset, OrangeSum, that we release with this paper. We also continue the pretraining of an already
-pretrained multilingual BART on BARThez's corpus, and we show that the resulting model, which we call mBARTHez,
-provides a significant boost over vanilla BARThez, and is on par with or outperforms CamemBERT and FlauBERT.*
-
-The Authors' code can be found `here <https://github.com/moussaKam/BARThez>`__.
-
-
-Examples
-_______________________________________________________________________________________________________________________
-
- BARThez can be fine-tuned on sequence-to-sequence tasks in a similar way as BART, check:
-  :prefix_link:`examples/seq2seq/ <examples/seq2seq/README.md>`.
-
-
-BarthezTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BarthezTokenizer
-    :members:
-
-
-BarthezTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BarthezTokenizerFast
-    :members:
--- a/docs/source/model_doc/bert.rst
+++ b/docs/source/model_doc/bert.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 BERT
 -----------------------------------------------------------------------------------------------------------------------

@@ -69,10 +57,10 @@ BertTokenizerFast
 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:


@@ -200,17 +188,3 @@ TFBertForQuestionAnswering

 .. autoclass:: transformers.TFBertForQuestionAnswering
    :members: call
-
-
-FlaxBertModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxBertModel
-    :members: __call__
-
-
-FlaxBertForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxBertForMaskedLM
-    :members: __call__
--- a/docs/source/model_doc/bertgeneration.rst
+++ b/docs/source/model_doc/bertgeneration.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 BertGeneration
 -----------------------------------------------------------------------------------------------------------------------

@@ -22,7 +10,7 @@ Tasks <https://arxiv.org/abs/1907.12461>`__ by Sascha Rothe, Shashi Narayan, Ali

 The abstract from the paper is the following:

-*Unsupervised pretraining of large neural models has recently revolutionized Natural Language Processing. By
+*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
@@ -52,7 +40,7 @@ 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()


--- a/docs/source/model_doc/bertweet.rst
+++ b/docs/source/model_doc/bertweet.rst
@@ -1,64 +0,0 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
-Bertweet
-----------------------------------------------------------------------------------------------------------------------
-
-Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The BERTweet model was proposed in `BERTweet: A pre-trained language model for English Tweets
-<https://www.aclweb.org/anthology/2020.emnlp-demos.2.pdf>`__ by Dat Quoc Nguyen, Thanh Vu, Anh Tuan Nguyen.
-
-The abstract from the paper is the following:
-
-*We present BERTweet, the first public large-scale pre-trained language model for English Tweets. Our BERTweet, having
-the same architecture as BERT-base (Devlin et al., 2019), is trained using the RoBERTa pre-training procedure (Liu et
-al., 2019). Experiments show that BERTweet outperforms strong baselines RoBERTa-base and XLM-R-base (Conneau et al.,
-2020), producing better performance results than the previous state-of-the-art models on three Tweet NLP tasks:
-Part-of-speech tagging, Named-entity recognition and text classification.*
-
-Example of use:
-
-.. code-block::
-
-  import torch
-  from transformers import AutoModel, AutoTokenizer 
-
-  bertweet = AutoModel.from_pretrained("vinai/bertweet-base")
-
-  # For transformers v4.x+: 
-  tokenizer = AutoTokenizer.from_pretrained("vinai/bertweet-base", use_fast=False)
-
-  # For transformers v3.x: 
-  # tokenizer = AutoTokenizer.from_pretrained("vinai/bertweet-base")
-
-  # INPUT TWEET IS ALREADY NORMALIZED!
-  line = "SC has first two presumptive cases of coronavirus , DHEC confirms HTTPURL via @USER :cry:"
-
-  input_ids = torch.tensor([tokenizer.encode(line)])
-
-  with torch.no_grad():
-      features = bertweet(input_ids)  # Models outputs are now tuples
-
-  ## With TensorFlow 2.0+:
-  # from transformers import TFAutoModel
-  # bertweet = TFAutoModel.from_pretrained("vinai/bertweet-base")
-
-
-The original code can be found `here <https://github.com/VinAIResearch/BERTweet>`__.
-
-BertweetTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BertweetTokenizer
-    :members: 
--- a/docs/source/model_doc/blenderbot.rst
+++ b/docs/source/model_doc/blenderbot.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 Blenderbot
 -----------------------------------------------------------------------------------------------------------------------

@@ -43,10 +31,13 @@ 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>`__.
- This is the `default` Blenderbot model class. However, some smaller checkpoints, such as
-  ``facebook/blenderbot_small_90M``, have a different architecture and consequently should be used with
-  `BlenderbotSmall <https://huggingface.co/transformers/master/model_doc/blenderbot_small.html>`__.


 Usage
@@ -56,15 +47,26 @@ Here is an example of model usage:

 .. code-block::

-        >>> from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration
-        >>> mname = 'facebook/blenderbot-400M-distill'
+        >>> from transformers import BlenderbotSmallTokenizer, BlenderbotForConditionalGeneration
+        >>> mname = 'facebook/blenderbot-90M'
        >>> model = BlenderbotForConditionalGeneration.from_pretrained(mname)
-        >>> tokenizer = BlenderbotTokenizer.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.batch_decode(reply_ids))
-        ["<s> That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?</s>"]
+        >>> 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
@@ -79,14 +81,11 @@ BlenderbotTokenizer
 .. autoclass:: transformers.BlenderbotTokenizer
    :members: build_inputs_with_special_tokens

-
-BlenderbotModel
+BlenderbotSmallTokenizer
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

-See :obj:`transformers.BartModel` for arguments to `forward` and `generate`
-
-.. autoclass:: transformers.BlenderbotModel
-    :members: forward
+.. autoclass:: transformers.BlenderbotSmallTokenizer
+    :members:


 BlenderbotForConditionalGeneration
@@ -95,18 +94,13 @@ BlenderbotForConditionalGeneration
 See :obj:`transformers.BartForConditionalGeneration` for arguments to `forward` and `generate`

 .. autoclass:: transformers.BlenderbotForConditionalGeneration
-    :members: forward
-
-
-TFBlenderbotModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFBlenderbotModel
-    :members: call
+    :members:


 TFBlenderbotForConditionalGeneration
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

+See :obj:`transformers.TFBartForConditionalGeneration` for arguments to `forward` and `generate`
+
 .. autoclass:: transformers.TFBlenderbotForConditionalGeneration
-    :members: call
+    :members:
--- a/docs/source/model_doc/blenderbot_small.rst
+++ b/docs/source/model_doc/blenderbot_small.rst
@@ -1,84 +0,0 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
-Blenderbot Small
-----------------------------------------------------------------------------------------------------------------------
-
-Note that :class:`~transformers.BlenderbotSmallModel` and
-:class:`~transformers.BlenderbotSmallForConditionalGeneration` are only used in combination with the checkpoint
-`facebook/blenderbot-90M <https://huggingface.co/facebook/blenderbot-90M>`__. Larger Blenderbot checkpoints should
-instead be used with :class:`~transformers.BlenderbotModel` and
-:class:`~transformers.BlenderbotForConditionalGeneration`
-
-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>`__ .
-
-BlenderbotSmallConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BlenderbotSmallConfig
-    :members:
-
-
-BlenderbotSmallTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BlenderbotSmallTokenizer
-    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
-        create_token_type_ids_from_sequences, save_vocabulary
-
-
-BlenderbotSmallModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BlenderbotSmallModel
-    :members: forward
-
-
-BlenderbotSmallForConditionalGeneration
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BlenderbotSmallForConditionalGeneration
-    :members: forward
-
-
-TFBlenderbotSmallModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFBlenderbotSmallModel
-    :members: call
-
-
-TFBlenderbotSmallForConditionalGeneration
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFBlenderbotSmallForConditionalGeneration
-    :members: call
--- a/docs/source/model_doc/camembert.rst
+++ b/docs/source/model_doc/camembert.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 CamemBERT
 -----------------------------------------------------------------------------------------------------------------------

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


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

--- a/docs/source/model_doc/ctrl.rst
+++ b/docs/source/model_doc/ctrl.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 CTRL
 -----------------------------------------------------------------------------------------------------------------------

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


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

@@ -97,8 +78,3 @@ TFCTRLLMHeadModel
 .. autoclass:: transformers.TFCTRLLMHeadModel
    :members: call

-TFCTRLForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFCTRLForSequenceClassification
-    :members: call
--- a/docs/source/model_doc/deberta.rst
+++ b/docs/source/model_doc/deberta.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 DeBERTa
 -----------------------------------------------------------------------------------------------------------------------

@@ -32,8 +20,8 @@ disentangled attention mechanism, where each word is represented using two vecto
 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%
+of model pre-training 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.*

--- a/docs/source/model_doc/dialogpt.rst
+++ b/docs/source/model_doc/dialogpt.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 DialoGPT
 -----------------------------------------------------------------------------------------------------------------------

--- a/docs/source/model_doc/distilbert.rst
+++ b/docs/source/model_doc/distilbert.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 DistilBERT
 -----------------------------------------------------------------------------------------------------------------------

@@ -30,9 +18,9 @@ operating these large models in on-the-edge and/or under constrained computation
 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
+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 pretraining, we introduce a triple loss combining language modeling,
+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.*
--- a/docs/source/model_doc/dpr.rst
+++ b/docs/source/model_doc/dpr.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 DPR
 -----------------------------------------------------------------------------------------------------------------------

@@ -17,7 +5,7 @@ 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
+intorduced 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.

 The abstract from the paper is the following:
@@ -83,13 +71,13 @@ DPRReaderTokenizerFast
 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:


@@ -111,22 +99,3 @@ DPRReader

 .. autoclass:: transformers.DPRReader
    :members: forward
-
-TFDPRContextEncoder
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFDPRContextEncoder
-    :members: call
-
-TFDPRQuestionEncoder
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFDPRQuestionEncoder
-    :members: call
-
-
-TFDPRReader
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFDPRReader
-    :members: call
--- a/docs/source/model_doc/electra.rst
+++ b/docs/source/model_doc/electra.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 ELECTRA
 -----------------------------------------------------------------------------------------------------------------------

@@ -24,14 +12,14 @@ 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
+*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 pretraining task called replaced token detection. Instead of masking the input, our approach
+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 pretraining task is more efficient than MLM because the task is defined over all input tokens
+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
@@ -81,10 +69,10 @@ ElectraTokenizerFast
 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:


--- a/docs/source/model_doc/encoderdecoder.rst
+++ b/docs/source/model_doc/encoderdecoder.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 Encoder Decoder Models
 -----------------------------------------------------------------------------------------------------------------------

--- a/docs/source/model_doc/flaubert.rst
+++ b/docs/source/model_doc/flaubert.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 FlauBERT
 -----------------------------------------------------------------------------------------------------------------------

@@ -31,7 +19,7 @@ representations (Dai and Le, 2015; Peters et al., 2018; Howard and Ruder, 2018;
 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
+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.*

--- a/docs/source/model_doc/fsmt.rst
+++ b/docs/source/model_doc/fsmt.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 FSMT
 -----------------------------------------------------------------------------------------------------------------------

--- a/docs/source/model_doc/funnel.rst
+++ b/docs/source/model_doc/funnel.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 Funnel Transformer
 -----------------------------------------------------------------------------------------------------------------------

@@ -77,10 +65,10 @@ FunnelTokenizerFast
 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:


--- a/docs/source/model_doc/gpt.rst
+++ b/docs/source/model_doc/gpt.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 OpenAI GPT
 -----------------------------------------------------------------------------------------------------------------------

@@ -26,7 +14,7 @@ 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
+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
@@ -84,10 +72,10 @@ OpenAIGPTTokenizerFast
 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:


@@ -138,9 +126,3 @@ TFOpenAIGPTDoubleHeadsModel

 .. autoclass:: transformers.TFOpenAIGPTDoubleHeadsModel
    :members: call
-
-TFOpenAIGPTForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFOpenAIGPTForSequenceClassification
-    :members: call
--- a/docs/source/model_doc/gpt2.rst
+++ b/docs/source/model_doc/gpt2.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 OpenAI GPT2
 -----------------------------------------------------------------------------------------------------------------------

@@ -72,10 +60,10 @@ GPT2TokenizerFast
 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:


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

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


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

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


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

 .. autoclass:: transformers.TFGPT2DoubleHeadsModel
    :members: call
-
-TFGPT2ForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFGPT2ForSequenceClassification
-    :members: call
-
-TFSequenceClassifierOutputWithPast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_tf_outputs.TFSequenceClassifierOutputWithPast
-    :members:
--- a/docs/source/model_doc/herbert.rst
+++ b/docs/source/model_doc/herbert.rst
@@ -1,71 +0,0 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
-herBERT
-----------------------------------------------------------------------------------------------------------------------
-
-Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The herBERT model was proposed in `KLEJ: Comprehensive Benchmark for Polish Language Understanding
-<https://www.aclweb.org/anthology/2020.acl-main.111.pdf>`__ by Piotr Rybak, Robert Mroczkowski, Janusz Tracz, and
-Ireneusz Gawlik. It is a BERT-based Language Model trained on Polish Corpora using only MLM objective with dynamic
-masking of whole words.
-
-The abstract from the paper is the following:
-
-*In recent years, a series of Transformer-based models unlocked major improvements in general natural language
-understanding (NLU) tasks. Such a fast pace of research would not be possible without general NLU benchmarks, which
-allow for a fair comparison of the proposed methods. However, such benchmarks are available only for a handful of
-languages. To alleviate this issue, we introduce a comprehensive multi-task benchmark for the Polish language
-understanding, accompanied by an online leaderboard. It consists of a diverse set of tasks, adopted from existing
-datasets for named entity recognition, question-answering, textual entailment, and others. We also introduce a new
-sentiment analysis task for the e-commerce domain, named Allegro Reviews (AR). To ensure a common evaluation scheme and
-promote models that generalize to different NLU tasks, the benchmark includes datasets from varying domains and
-applications. Additionally, we release HerBERT, a Transformer-based model trained specifically for the Polish language,
-which has the best average performance and obtains the best results for three out of nine tasks. Finally, we provide an
-extensive evaluation, including several standard baselines and recently proposed, multilingual Transformer-based
-models.*
-
-Examples of use:
-
-.. code-block::
-
-  from transformers import HerbertTokenizer, RobertaModel
-
-  tokenizer = HerbertTokenizer.from_pretrained("allegro/herbert-klej-cased-tokenizer-v1")
-  model = RobertaModel.from_pretrained("allegro/herbert-klej-cased-v1")
-
-  encoded_input = tokenizer.encode("Kto ma lepszą sztukę, ma lepszy rząd – to jasne.", return_tensors='pt')
-  outputs = model(encoded_input)
-
-  # HerBERT can also be loaded using AutoTokenizer and AutoModel:
-  import torch
-  from transformers import AutoModel, AutoTokenizer
-
-  tokenizer = AutoTokenizer.from_pretrained("allegro/herbert-klej-cased-tokenizer-v1")
-  model = AutoModel.from_pretrained("allegro/herbert-klej-cased-v1")
-
-
-The original code can be found `here <https://github.com/allegro/HerBERT>`__.
-
-HerbertTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.HerbertTokenizer
-    :members: 
-
-HerbertTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.HerbertTokenizerFast
-    :members: 
--- a/docs/source/model_doc/layoutlm.rst
+++ b/docs/source/model_doc/layoutlm.rst
@@ -1,84 +1,32 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 LayoutLM
 -----------------------------------------------------------------------------------------------------------------------

-.. _Overview:
-
 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. It obtains state-of-the-art results
-on several downstream tasks:
-
- form understanding: the `FUNSD <https://guillaumejaume.github.io/FUNSD/>`__ dataset (a collection of 199 annotated
-  forms comprising more than 30,000 words).
- receipt understanding: the `SROIE <https://rrc.cvc.uab.es/?ch=13>`__ dataset (a collection of 626 receipts for
-  training and 347 receipts for testing).
- document image classification: the `RVL-CDIP <https://www.cs.cmu.edu/~aharley/rvl-cdip/>`__ dataset (a collection of
-  400,000 images belonging to one of 16 classes).
+Ming Zhou. It's a simple but effective pre-training 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,
+widespread use of pre-training 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 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).*
+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 pre-training. 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:

- In addition to `input_ids`, :meth:`~transformer.LayoutLMModel.forward` also expects the input :obj:`bbox`, which are
-  the bounding boxes (i.e. 2D-positions) of the input tokens. These can be obtained using an external OCR engine such
-  as Google's `Tesseract <https://github.com/tesseract-ocr/tesseract>`__ (there's a `Python wrapper
-  <https://pypi.org/project/pytesseract/>`__ available). Each bounding box should be in (x0, y0, x1, y1) format, where
-  (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1, y1) represents the
-  position of the lower right corner. Note that one first needs to normalize the bounding boxes to be on a 0-1000
-  scale. To normalize, you can use the following function:
-
-.. code-block::
-
-   def normalize_bbox(bbox, width, height):
-        return [
-            int(1000 * (bbox[0] / width)),
-            int(1000 * (bbox[1] / height)),
-            int(1000 * (bbox[2] / width)),
-            int(1000 * (bbox[3] / height)),
-        ]
-
-Here, :obj:`width` and :obj:`height` correspond to the width and height of the original document in which the token
-occurs. Those can be obtained using the Python Image Library (PIL) library for example, as follows:
-
-.. code-block::
-
-   from PIL import Image
-
-   image = Image.open("name_of_your_document - can be a png file, pdf, etc.")
-
-   width, height = image.size
-
- For a demo which shows how to fine-tune :class:`LayoutLMForTokenClassification` on the `FUNSD dataset
-  <https://guillaumejaume.github.io/FUNSD/>`__ (a collection of annotated forms), see `this notebook
-  <https://github.com/NielsRogge/Transformers-Tutorials/blob/master/LayoutLM/Fine_tuning_LayoutLMForTokenClassification_on_FUNSD.ipynb>`__.
-  It includes an inference part, which shows how to use Google's Tesseract on a new document.
+- 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>`_.

@@ -97,13 +45,6 @@ LayoutLMTokenizer
    :members:


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

@@ -118,13 +59,6 @@ LayoutLMForMaskedLM
    :members:


-LayoutLMForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.LayoutLMForSequenceClassification
-    :members:
-
-
 LayoutLMForTokenClassification
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

--- a/docs/source/model_doc/led.rst
+++ b/docs/source/model_doc/led.rst
@@ -1,149 +0,0 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
-LED
-----------------------------------------------------------------------------------------------------------------------
-
-Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The LED model was proposed in `Longformer: The Long-Document Transformer <https://arxiv.org/abs/2004.05150>`__ by Iz
-Beltagy, Matthew E. Peters, Arman Cohan.
-
-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. We finally introduce the Longformer-Encoder-Decoder (LED), a Longformer variant for supporting
-long document generative sequence-to-sequence tasks, and demonstrate its effectiveness on the arXiv summarization
-dataset.*
-
-Tips:
-
- :class:`~transformers.LEDForConditionalGeneration` is an extension of
-  :class:`~transformers.BartForConditionalGeneration` exchanging the traditional *self-attention* layer with
-  *Longformer*'s *chunked self-attention* layer. :class:`~transformers.LEDTokenizer` is an alias of
-  :class:`~transformers.BartTokenizer`.
- LED works very well on long-range *sequence-to-sequence* tasks where the ``input_ids`` largely exceed a length of
-  1024 tokens.
- LED pads the ``input_ids`` to be a multiple of ``config.attention_window`` if required. Therefore a small speed-up is
-  gained, when :class:`~transformers.LEDTokenizer` is used with the ``pad_to_multiple_of`` argument.
- LED makes use of *global attention* by means of the ``global_attention_mask`` (see
-  :class:`~transformers.LongformerModel`). For summarization, it is advised to put *global attention* only on the first
-  ``<s>`` token. For question answering, it is advised to put *global attention* on all tokens of the question.
- To fine-tune LED on all 16384, it is necessary to enable *gradient checkpointing* by setting
-  ``config.gradient_checkpointing = True``.
- A notebook showing how to evaluate LED, can be accessed `here
-  <https://colab.research.google.com/drive/12INTTR6n64TzS4RrXZxMSXfrOd9Xzamo?usp=sharing>`__.
- A notebook showing how to fine-tune LED, can be accessed `here
-  <https://colab.research.google.com/drive/12LjJazBl7Gam0XBPy_y0CTOJZeZ34c2v?usp=sharing>`__.
-
-
-LEDConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.LEDConfig
-    :members:
-
-
-LEDTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.LEDTokenizer
-    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
-        create_token_type_ids_from_sequences, save_vocabulary
-
-
-LEDTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.LEDTokenizerFast
-    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
-        create_token_type_ids_from_sequences, save_vocabulary
-
-
-LED specific outputs
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.models.led.modeling_led.LEDEncoderBaseModelOutput
-    :members: 
-
-.. autoclass:: transformers.models.led.modeling_led.LEDSeq2SeqModelOutput
-    :members: 
-
-.. autoclass:: transformers.models.led.modeling_led.LEDSeq2SeqLMOutput
-    :members: 
-
-.. autoclass:: transformers.models.led.modeling_led.LEDSeq2SeqSequenceClassifierOutput
-    :members: 
-
-.. autoclass:: transformers.models.led.modeling_led.LEDSeq2SeqQuestionAnsweringModelOutput
-    :members: 
-
-.. autoclass:: transformers.models.led.modeling_tf_led.TFLEDEncoderBaseModelOutput
-    :members: 
-
-.. autoclass:: transformers.models.led.modeling_tf_led.TFLEDSeq2SeqModelOutput
-    :members: 
-
-.. autoclass:: transformers.models.led.modeling_tf_led.TFLEDSeq2SeqLMOutput
-    :members: 
-
-
-
-
-LEDModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.LEDModel
-    :members: forward
-
-
-LEDForConditionalGeneration
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.LEDForConditionalGeneration
-    :members: forward
-
-
-LEDForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.LEDForSequenceClassification
-    :members: forward
-
-
-LEDForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.LEDForQuestionAnswering
-    :members: forward
-
-
-TFLEDModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFLEDModel
-    :members: call
-
-
-TFLEDForConditionalGeneration
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFLEDForConditionalGeneration
-    :members: call
--- a/docs/source/model_doc/longformer.rst
+++ b/docs/source/model_doc/longformer.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 Longformer
 -----------------------------------------------------------------------------------------------------------------------

@@ -34,12 +22,6 @@ contrast to most prior work, we also pretrain Longformer and finetune it on a va
 pretrained Longformer consistently outperforms RoBERTa on long document tasks and sets new state-of-the-art results on
 WikiHop and TriviaQA.*

-Tips:
-
- Since the Longformer is based on RoBERTa, it 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>`).
-
 The Authors' code can be found `here <https://github.com/allenai/longformer>`__.

 Longformer Self Attention
@@ -111,47 +93,29 @@ LongformerTokenizerFast
 Longformer specific outputs
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

-.. autoclass:: transformers.models.longformer.modeling_longformer.LongformerBaseModelOutput
+.. autoclass:: transformers.modeling_longformer.LongformerBaseModelOutput
    :members: 

-.. autoclass:: transformers.models.longformer.modeling_longformer.LongformerBaseModelOutputWithPooling
+.. autoclass:: transformers.modeling_longformer.LongformerBaseModelOutputWithPooling
    :members: 

-.. autoclass:: transformers.models.longformer.modeling_longformer.LongformerMaskedLMOutput
+.. autoclass:: transformers.modeling_longformer.LongformerMultipleChoiceModelOutput
    :members: 

-.. autoclass:: transformers.models.longformer.modeling_longformer.LongformerQuestionAnsweringModelOutput
+.. autoclass:: transformers.modeling_longformer.LongformerQuestionAnsweringModelOutput
    :members: 

-.. autoclass:: transformers.models.longformer.modeling_longformer.LongformerSequenceClassifierOutput
+.. autoclass:: transformers.modeling_tf_longformer.TFLongformerBaseModelOutput
    :members: 

-.. autoclass:: transformers.models.longformer.modeling_longformer.LongformerMultipleChoiceModelOutput
+.. autoclass:: transformers.modeling_tf_longformer.TFLongformerBaseModelOutputWithPooling
    :members: 

-.. autoclass:: transformers.models.longformer.modeling_longformer.LongformerTokenClassifierOutput
+.. autoclass:: transformers.modeling_tf_longformer.TFLongformerQuestionAnsweringModelOutput
    :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
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 LongformerModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -215,24 +179,3 @@ TFLongformerForQuestionAnswering
 .. autoclass:: transformers.TFLongformerForQuestionAnswering
    :members: call

-
-TFLongformerForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFLongformerForSequenceClassification
-    :members: call
-
-
-TFLongformerForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFLongformerForTokenClassification
-    :members: call
-
-
-TFLongformerForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFLongformerForMultipleChoice
-    :members: call
-
--- a/docs/source/model_doc/lxmert.rst
+++ b/docs/source/model_doc/lxmert.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 LXMERT
 -----------------------------------------------------------------------------------------------------------------------

@@ -31,7 +19,7 @@ Encoder Representations from Transformers) framework to learn these vision-and-l
 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),
+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
@@ -79,19 +67,19 @@ LxmertTokenizerFast
 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:


--- a/docs/source/model_doc/marian.rst
+++ b/docs/source/model_doc/marian.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 MarianMT
 -----------------------------------------------------------------------------------------------------------------------

@@ -17,7 +5,7 @@ MarianMT
 <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.
+Translations should be similar, but not identical to, output in the test set linked to in each model card.

 Implementation Notes
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -33,6 +21,7 @@ Implementation Notes
 - 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/>`),
@@ -46,107 +35,24 @@ Naming
  <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 GPU
-  <https://github.com/huggingface/transformers/blob/master/examples/research_projects/seq2seq-distillation/train_distil_marian_enro_teacher.sh>`__
- `Fine-tune on GPU with pytorch-lightning
-  <https://github.com/huggingface/transformers/blob/master/examples/research_projects/seq2seq-distillation/train_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: :obj:`Helsinki-NLP/opus-mt-{src}-{tgt}`:

-New multi-lingual models from the `Tatoeba-Challenge repo <https://github.com/Helsinki-NLP/Tatoeba-Challenge>`__
-require 3 character language codes:
+    - If :obj:`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 :obj:`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 :obj:`src_text`.
+    - You can see a tokenizer's supported language codes in ``tokenizer.supported_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',
@@ -157,12 +63,52 @@ 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 :obj:`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 :obj:`>>es_ES<<` (Spain) and :obj:`>>es_AR<<` (Argentina), that
+do not seem to change translations. I have not found these to provide different results than just using :obj:`>>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 :obj:`>>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 one target 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()]


 MarianConfig
@@ -179,29 +125,13 @@ MarianTokenizer
    :members: prepare_seq2seq_batch


-MarianModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MarianModel
-    :members: forward
-
-
 MarianMTModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.MarianMTModel
-    :members: forward
-
-
-TFMarianModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFMarianModel
-    :members: call


 TFMarianMTModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFMarianMTModel
-    :members: call
--- a/docs/source/model_doc/mbart.rst
+++ b/docs/source/model_doc/mbart.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 MBart
 -----------------------------------------------------------------------------------------------------------------------

@@ -25,19 +13,12 @@ The MBart model was presented in `Multilingual Denoising Pre-training for Neural
 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
+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
-  :prefix_link:`examples/seq2seq/ <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
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -56,8 +37,12 @@ the sequences for sequence-to-sequence fine-tuning.

    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

@@ -70,7 +55,7 @@ the sequences for sequence-to-sequence fine-tuning.
    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"
@@ -90,20 +75,6 @@ MBartTokenizer
    :members: build_inputs_with_special_tokens, prepare_seq2seq_batch


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

@@ -111,28 +82,8 @@ MBartForConditionalGeneration
    :members:


-MBartForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MBartForQuestionAnswering
-    :members:
-
-
-MBartForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MBartForSequenceClassification
-
-
-TFMBartModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFMBartModel
-    :members: call
-
-
 TFMBartForConditionalGeneration
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFMBartForConditionalGeneration
-    :members: call
+    :members:
--- a/docs/source/model_doc/mobilebert.rst
+++ b/docs/source/model_doc/mobilebert.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 MobileBERT
 -----------------------------------------------------------------------------------------------------------------------

@@ -70,10 +58,10 @@ MobileBertTokenizerFast
 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:


--- a/docs/source/model_doc/mpnet.rst
+++ b/docs/source/model_doc/mpnet.rst
@@ -1,149 +0,0 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
-MPNet
-----------------------------------------------------------------------------------------------------------------------
-
-Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The MPNet model was proposed in `MPNet: Masked and Permuted Pre-training for Language Understanding
-<https://arxiv.org/abs/2004.09297>`__ by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu.
-
-MPNet adopts a novel pre-training method, named masked and permuted language modeling, to inherit the advantages of
-masked language modeling and permuted language modeling for natural language understanding.
-
-The abstract from the paper is the following:
-
-*BERT adopts masked language modeling (MLM) for pre-training and is one of the most successful pre-training models.
-Since BERT neglects dependency among predicted tokens, XLNet introduces permuted language modeling (PLM) for
-pre-training to address this problem. However, XLNet does not leverage the full position information of a sentence and
-thus suffers from position discrepancy between pre-training and fine-tuning. In this paper, we propose MPNet, a novel
-pre-training method that inherits the advantages of BERT and XLNet and avoids their limitations. MPNet leverages the
-dependency among predicted tokens through permuted language modeling (vs. MLM in BERT), and takes auxiliary position
-information as input to make the model see a full sentence and thus reducing the position discrepancy (vs. PLM in
-XLNet). We pre-train MPNet on a large-scale dataset (over 160GB text corpora) and fine-tune on a variety of
-down-streaming tasks (GLUE, SQuAD, etc). Experimental results show that MPNet outperforms MLM and PLM by a large
-margin, and achieves better results on these tasks compared with previous state-of-the-art pre-trained methods (e.g.,
-BERT, XLNet, RoBERTa) under the same model setting.*
-
-Tips:
-
- MPNet doesn't have :obj:`token_type_ids`, you don't need to indicate which token belongs to which segment. just
-  separate your segments with the separation token :obj:`tokenizer.sep_token` (or :obj:`[sep]`).
-
-The original code can be found `here <https://github.com/microsoft/MPNet>`__.
-
-MPNetConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MPNetConfig
-    :members:
-
-
-MPNetTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MPNetTokenizer
-    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
-        create_token_type_ids_from_sequences, save_vocabulary
-
-
-MPNetTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MPNetTokenizerFast
-    :members:
-
-
-MPNetModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MPNetModel
-    :members: forward
-
-
-MPNetForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MPNetForMaskedLM
-    :members: forward
-
-
-MPNetForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MPNetForSequenceClassification
-    :members: forward
-
-
-MPNetForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MPNetForMultipleChoice
-    :members: forward
-
-
-MPNetForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MPNetForTokenClassification
-    :members: forward
-
-
-MPNetForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MPNetForQuestionAnswering
-    :members: forward
-
-
-TFMPNetModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFMPNetModel
-    :members: call
-
-
-TFMPNetForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFMPNetForMaskedLM
-    :members: call
-
-
-TFMPNetForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFMPNetForSequenceClassification
-    :members: call
-
-
-TFMPNetForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFMPNetForMultipleChoice
-    :members: call
-
-
-TFMPNetForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFMPNetForTokenClassification
-    :members: call
-
-
-TFMPNetForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFMPNetForQuestionAnswering
-    :members: call
--- a/docs/source/model_doc/mt5.rst
+++ b/docs/source/model_doc/mt5.rst
@@ -1,95 +0,0 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
-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:
-
-
-MT5Tokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MT5Tokenizer
-
-See :class:`~transformers.T5Tokenizer` for all details.
-
-
-MT5TokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MT5TokenizerFast
-
-See :class:`~transformers.T5TokenizerFast` for all details.
-
-
-MT5Model
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MT5Model
-    :members:
-
-
-MT5ForConditionalGeneration
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MT5ForConditionalGeneration
-    :members:
-
-
-MT5EncoderModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MT5EncoderModel
-    :members:
-
-
-TFMT5Model
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFMT5Model
-    :members:
-
-
-TFMT5ForConditionalGeneration
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFMT5ForConditionalGeneration
-    :members:
-
-
-TFMT5EncoderModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFMT5EncoderModel
-    :members:
--- a/docs/source/model_doc/pegasus.rst
+++ b/docs/source/model_doc/pegasus.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 Pegasus
 -----------------------------------------------------------------------------------------------------------------------

@@ -43,18 +31,10 @@ All the `checkpoints <https://huggingface.co/models?search=pegasus>`__ are fine-
 - 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>`__.
+- 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.

-Examples
-_______________________________________________________________________________________________________________________
-
- :prefix_link:`Script <examples/seq2seq/finetune_pegasus_xsum.sh>` to fine-tune pegasus on the XSUM dataset. Data
-  download instructions at :prefix_link:`examples/seq2seq/ <examples/seq2seq/README.md>`.
- FP16 is not supported (help/ideas on this appreciated!).
- The adafactor optimizer is recommended for pegasus fine-tuning.
+The gap is likely because of different alpha/length_penalty implementations in beam search.


 Implementation Notes
@@ -65,6 +45,7 @@ Implementation Notes
 - Some key configuration differences:

    - static, sinusoidal position embeddings
+    - no :obj:`layernorm_embedding` (:obj`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
@@ -88,7 +69,7 @@ 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."
@@ -110,36 +91,13 @@ warning: ``add_tokens`` does not work at the moment.
    :members: __call__, prepare_seq2seq_batch


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

 .. autoclass:: transformers.PegasusForConditionalGeneration
-    :members: forward
-
-
-TFPegasusModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFPegasusModel
-    :members: call


 TFPegasusForConditionalGeneration
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFPegasusForConditionalGeneration
-    :members: call
--- a/docs/source/model_doc/phobert.rst
+++ b/docs/source/model_doc/phobert.rst
@@ -1,59 +0,0 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
-PhoBERT
-----------------------------------------------------------------------------------------------------------------------
-
-Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The PhoBERT model was proposed in `PhoBERT: Pre-trained language models for Vietnamese
-<https://www.aclweb.org/anthology/2020.findings-emnlp.92.pdf>`__ by Dat Quoc Nguyen, Anh Tuan Nguyen.
-
-The abstract from the paper is the following:
-
-*We present PhoBERT with two versions, PhoBERT-base and PhoBERT-large, the first public large-scale monolingual
-language models pre-trained for Vietnamese. Experimental results show that PhoBERT consistently outperforms the recent
-best pre-trained multilingual model XLM-R (Conneau et al., 2020) and improves the state-of-the-art in multiple
-Vietnamese-specific NLP tasks including Part-of-speech tagging, Dependency parsing, Named-entity recognition and
-Natural language inference.*
-
-Example of use:
-
-.. code-block::
-
-  import torch
-  from transformers import AutoModel, AutoTokenizer
-
-  phobert = AutoModel.from_pretrained("vinai/phobert-base")
-  tokenizer = AutoTokenizer.from_pretrained("vinai/phobert-base")
-
-  # INPUT TEXT MUST BE ALREADY WORD-SEGMENTED!
-  line = "Tôi là sinh_viên trường đại_học Công_nghệ ."
-
-  input_ids = torch.tensor([tokenizer.encode(line)])
-
-  with torch.no_grad():
-      features = phobert(input_ids)  # Models outputs are now tuples
-
-  ## With TensorFlow 2.0+:
-  # from transformers import TFAutoModel
-  # phobert = TFAutoModel.from_pretrained("vinai/phobert-base")
-
-
-The original code can be found `here <https://github.com/VinAIResearch/PhoBERT>`__.
-
-PhobertTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.PhobertTokenizer
-    :members: 
--- a/docs/source/model_doc/prophetnet.rst
+++ b/docs/source/model_doc/prophetnet.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 ProphetNet
 -----------------------------------------------------------------------------------------------------------------------

@@ -29,7 +17,7 @@ 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
+*In this paper, we present a new sequence-to-sequence pre-training 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
@@ -37,7 +25,7 @@ step. The future n-gram prediction explicitly encourages the model to plan for t
 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.*
+state-of-the-art results on all these datasets compared to the models using the same scale pre-training corpus.*

 The Authors' code can be found `here <https://github.com/microsoft/ProphetNet>`__.

@@ -59,16 +47,16 @@ ProphetNetTokenizer
 ProphetNet specific outputs
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

-.. autoclass:: transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqLMOutput
+.. autoclass:: transformers.modeling_prophetnet.ProphetNetSeq2SeqLMOutput
    :members:

-.. autoclass:: transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqModelOutput
+.. autoclass:: transformers.modeling_prophetnet.ProphetNetSeq2SeqModelOutput
    :members:

-.. autoclass:: transformers.models.prophetnet.modeling_prophetnet.ProphetNetDecoderModelOutput
+.. autoclass:: transformers.modeling_prophetnet.ProphetNetDecoderModelOutput
    :members:

-.. autoclass:: transformers.models.prophetnet.modeling_prophetnet.ProphetNetDecoderLMOutput
+.. autoclass:: transformers.modeling_prophetnet.ProphetNetDecoderLMOutput
    :members:

 ProphetNetModel
--- a/docs/source/model_doc/rag.rst
+++ b/docs/source/model_doc/rag.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 RAG
 -----------------------------------------------------------------------------------------------------------------------

@@ -62,10 +50,10 @@ RagTokenizer
 Rag specific outputs
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

-.. autoclass:: transformers.models.rag.modeling_rag.RetrievAugLMMarginOutput
+.. autoclass:: transformers.modeling_rag.RetrievAugLMMarginOutput
    :members:

-.. autoclass:: transformers.models.rag.modeling_rag.RetrievAugLMOutput
+.. autoclass:: transformers.modeling_rag.RetrievAugLMOutput
    :members:

 RagRetriever
--- a/docs/source/model_doc/reformer.rst
+++ b/docs/source/model_doc/reformer.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 Reformer
 -----------------------------------------------------------------------------------------------------------------------

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


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

--- a/docs/source/model_doc/retribert.rst
+++ b/docs/source/model_doc/retribert.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 RetriBERT
 -----------------------------------------------------------------------------------------------------------------------

--- a/docs/source/model_doc/roberta.rst
+++ b/docs/source/model_doc/roberta.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 RoBERTa
 -----------------------------------------------------------------------------------------------------------------------

@@ -158,10 +146,3 @@ TFRobertaForQuestionAnswering

 .. autoclass:: transformers.TFRobertaForQuestionAnswering
    :members: call
-
-
-FlaxRobertaModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxRobertaModel
-    :members: __call__
--- a/docs/source/model_doc/squeezebert.rst
+++ b/docs/source/model_doc/squeezebert.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 SqueezeBERT
 -----------------------------------------------------------------------------------------------------------------------

--- a/docs/source/model_doc/t5.rst
+++ b/docs/source/model_doc/t5.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 T5
 -----------------------------------------------------------------------------------------------------------------------

@@ -29,7 +17,7 @@ The abstract from the paper is the following:
 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
+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
@@ -44,9 +32,9 @@ Tips:

  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.
+  :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>`__.

@@ -55,7 +43,7 @@ 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
+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.
@@ -76,7 +64,7 @@ token. T5 can be trained / fine-tuned both in a supervised and unsupervised fash
  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
  # the forward function automatically creates the correct decoder_input_ids
-  loss = model(input_ids=input_ids, labels=labels).loss
+  loss = model(input_ids=input_ids, labels=labels, return_dict=True).loss

 - Supervised training

@@ -89,7 +77,7 @@ token. T5 can be trained / fine-tuned both in a supervised and unsupervised fash
  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
  # the forward function automatically creates the correct decoder_input_ids
-  loss = model(input_ids=input_ids, labels=labels).loss
+  loss = model(input_ids=input_ids, labels=labels, return_dict=True).loss


 T5Config
@@ -107,31 +95,19 @@ T5Tokenizer
        create_token_type_ids_from_sequences, prepare_seq2seq_batch, save_vocabulary


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

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


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

 .. autoclass:: transformers.T5ForConditionalGeneration
-    :members: forward, parallelize, deparallelize
+    :members: forward

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

 TFT5Model
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -145,9 +121,3 @@ TFT5ForConditionalGeneration

 .. autoclass:: transformers.TFT5ForConditionalGeneration
    :members: call
-
-TFT5EncoderModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFT5EncoderModel
-    :members: call
--- a/docs/source/model_doc/tapas.rst
+++ b/docs/source/model_doc/tapas.rst
@@ -1,434 +0,0 @@
-TAPAS
-----------------------------------------------------------------------------------------------------------------------
-
-.. note::
-
-    This is a recently introduced model so the API hasn't been tested extensively. There may be some bugs or slight
-    breaking changes to fix them in the future.
-
-
-
-Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The TAPAS model was proposed in `TAPAS: Weakly Supervised Table Parsing via Pre-training
-<https://www.aclweb.org/anthology/2020.acl-main.398>`__ by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller,
-Francesco Piccinno and Julian Martin Eisenschlos. It's a BERT-based model specifically designed (and pre-trained) for
-answering questions about tabular data. Compared to BERT, TAPAS uses relative position embeddings and has 7 token types
-that encode tabular structure. TAPAS is pre-trained on the masked language modeling (MLM) objective on a large dataset
-comprising millions of tables from English Wikipedia and corresponding texts. For question answering, TAPAS has 2 heads
-on top: a cell selection head and an aggregation head, for (optionally) performing aggregations (such as counting or
-summing) among selected cells. TAPAS has been fine-tuned on several datasets: `SQA
-<https://www.microsoft.com/en-us/download/details.aspx?id=54253>`__ (Sequential Question Answering by Microsoft), `WTQ
-<https://github.com/ppasupat/WikiTableQuestions>`__ (Wiki Table Questions by Stanford University) and `WikiSQL
-<https://github.com/salesforce/WikiSQL>`__ (by Salesforce). It achieves state-of-the-art on both SQA and WTQ, while
-having comparable performance to SOTA on WikiSQL, with a much simpler architecture.
-
-The abstract from the paper is the following:
-
-*Answering natural language questions over tables is usually seen as a semantic parsing task. To alleviate the
-collection cost of full logical forms, one popular approach focuses on weak supervision consisting of denotations
-instead of logical forms. However, training semantic parsers from weak supervision poses difficulties, and in addition,
-the generated logical forms are only used as an intermediate step prior to retrieving the denotation. In this paper, we
-present TAPAS, an approach to question answering over tables without generating logical forms. TAPAS trains from weak
-supervision, and predicts the denotation by selecting table cells and optionally applying a corresponding aggregation
-operator to such selection. TAPAS extends BERT's architecture to encode tables as input, initializes from an effective
-joint pre-training of text segments and tables crawled from Wikipedia, and is trained end-to-end. We experiment with
-three different semantic parsing datasets, and find that TAPAS outperforms or rivals semantic parsing models by
-improving state-of-the-art accuracy on SQA from 55.1 to 67.2 and performing on par with the state-of-the-art on WIKISQL
-and WIKITQ, but with a simpler model architecture. We additionally find that transfer learning, which is trivial in our
-setting, from WIKISQL to WIKITQ, yields 48.7 accuracy, 4.2 points above the state-of-the-art.*
-
-In addition, the authors have further pre-trained TAPAS to recognize **table entailment**, by creating a balanced
-dataset of millions of automatically created training examples which are learned in an intermediate step prior to
-fine-tuning. The authors of TAPAS call this further pre-training intermediate pre-training (since TAPAS is first
-pre-trained on MLM, and then on another dataset). They found that intermediate pre-training further improves
-performance on SQA, achieving a new state-of-the-art as well as state-of-the-art on `TabFact
-<https://github.com/wenhuchen/Table-Fact-Checking>`__, a large-scale dataset with 16k Wikipedia tables for table
-entailment (a binary classification task). For more details, see their follow-up paper: `Understanding tables with
-intermediate pre-training <https://www.aclweb.org/anthology/2020.findings-emnlp.27/>`__ by Julian Martin Eisenschlos,
-Syrine Krichene and Thomas Müller.
-
-The original code can be found `here <https://github.com/google-research/tapas>`__.
-
-Tips:
-
- TAPAS is a model that uses relative position embeddings by default (restarting the position embeddings at every cell
-  of the table). Note that this is something that was added after the publication of the original TAPAS paper.
-  According to the authors, this usually results in a slightly better performance, and allows you to encode longer
-  sequences without running out of embeddings. This is reflected in the ``reset_position_index_per_cell`` parameter of
-  :class:`~transformers.TapasConfig`, which is set to ``True`` by default. The default versions of the models available
-  in the `model hub <https://huggingface.co/models?search=tapas>`_ all use relative position embeddings. You can still
-  use the ones with absolute position embeddings by passing in an additional argument ``revision="no_reset"`` when
-  calling the ``.from_pretrained()`` method. Note that it's usually advised to pad the inputs on the right rather than
-  the left.
- TAPAS is based on BERT, so ``TAPAS-base`` for example corresponds to a ``BERT-base`` architecture. Of course,
-  TAPAS-large will result in the best performance (the results reported in the paper are from TAPAS-large). Results of
-  the various sized models are shown on the `original Github repository <https://github.com/google-research/tapas>`_.
- TAPAS has checkpoints fine-tuned on SQA, which are capable of answering questions related to a table in a
-  conversational set-up. This means that you can ask follow-up questions such as "what is his age?" related to the
-  previous question. Note that the forward pass of TAPAS is a bit different in case of a conversational set-up: in that
-  case, you have to feed every table-question pair one by one to the model, such that the `prev_labels` token type ids
-  can be overwritten by the predicted `labels` of the model to the previous question. See "Usage" section for more
-  info.
- TAPAS is similar to BERT and therefore relies on the masked language modeling (MLM) objective. It is therefore
-  efficient at predicting masked tokens and at NLU in general, but is not optimal for text generation. Models trained
-  with a causal language modeling (CLM) objective are better in that regard.
-
-
-Usage: fine-tuning
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Here we explain how you can fine-tune :class:`~transformers.TapasForQuestionAnswering` on your own dataset.
-
-**STEP 1: Choose one of the 3 ways in which you can use TAPAS - or experiment**
-
-Basically, there are 3 different ways in which one can fine-tune :class:`~transformers.TapasForQuestionAnswering`,
-corresponding to the different datasets on which Tapas was fine-tuned:
-
-1. SQA: if you're interested in asking follow-up questions related to a table, in a conversational set-up. For example
-   if you first ask "what's the name of the first actor?" then you can ask a follow-up question such as "how old is
-   he?". Here, questions do not involve any aggregation (all questions are cell selection questions).
-2. WTQ: if you're not interested in asking questions in a conversational set-up, but rather just asking questions
-   related to a table, which might involve aggregation, such as counting a number of rows, summing up cell values or
-   averaging cell values. You can then for example ask "what's the total number of goals Cristiano Ronaldo made in his
-   career?". This case is also called **weak supervision**, since the model itself must learn the appropriate
-   aggregation operator (SUM/COUNT/AVERAGE/NONE) given only the answer to the question as supervision.
-3. WikiSQL-supervised: this dataset is based on WikiSQL with the model being given the ground truth aggregation
-   operator during training. This is also called **strong supervision**. Here, learning the appropriate aggregation
-   operator is much easier.
-
-To summarize:
-
-+------------------------------------+----------------------+-------------------------------------------------------------------------------------------------------------------+
-| **Task**                           | **Example dataset**  | **Description**                                                                                                   |
-+------------------------------------+----------------------+-------------------------------------------------------------------------------------------------------------------+
-| Conversational                     | SQA                  | Conversational, only cell selection questions                                                                     |
-+------------------------------------+----------------------+-------------------------------------------------------------------------------------------------------------------+
-| Weak supervision for aggregation   | WTQ                  | Questions might involve aggregation, and the model must learn this given only the answer as supervision           |
-+------------------------------------+----------------------+-------------------------------------------------------------------------------------------------------------------+
-| Strong supervision for aggregation | WikiSQL-supervised   | Questions might involve aggregation, and the model must learn this given the gold aggregation operator            |
-+------------------------------------+----------------------+-------------------------------------------------------------------------------------------------------------------+
-
-Initializing a model with a pre-trained base and randomly initialized classification heads from the model hub can be
-done as follows (be sure to have installed the `torch-scatter dependency <https://github.com/rusty1s/pytorch_scatter>`_
-for your environment):
-
-.. code-block::
-
-        >>> from transformers import TapasConfig, TapasForQuestionAnswering
-
-        >>> # for example, the base sized model with default SQA configuration
-        >>> model = TapasForQuestionAnswering.from_pretrained('google/tapas-base')
-
-        >>> # or, the base sized model with WTQ configuration
-        >>> config = TapasConfig.from_pretrained('google/tapas-base-finetuned-wtq')
-        >>> model = TapasForQuestionAnswering.from_pretrained('google/tapas-base', config=config)
-
-        >>> # or, the base sized model with WikiSQL configuration
-        >>> config = TapasConfig('google-base-finetuned-wikisql-supervised')
-        >>> model = TapasForQuestionAnswering.from_pretrained('google/tapas-base', config=config)
-
-
-Of course, you don't necessarily have to follow one of these three ways in which TAPAS was fine-tuned. You can also
-experiment by defining any hyperparameters you want when initializing :class:`~transformers.TapasConfig`, and then
-create a :class:`~transformers.TapasForQuestionAnswering` based on that configuration. For example, if you have a
-dataset that has both conversational questions and questions that might involve aggregation, then you can do it this
-way. Here's an example:
-
-.. code-block::
-
-        >>> from transformers import TapasConfig, TapasForQuestionAnswering
-
-        >>> # you can initialize the classification heads any way you want (see docs of TapasConfig)
-        >>> config = TapasConfig(num_aggregation_labels=3, average_logits_per_cell=True, select_one_column=False)
-        >>> # initializing the pre-trained base sized model with our custom classification heads
-        >>> model = TapasForQuestionAnswering.from_pretrained('google/tapas-base', config=config)
-
-What you can also do is start from an already fine-tuned checkpoint. A note here is that the already fine-tuned
-checkpoint on WTQ has some issues due to the L2-loss which is somewhat brittle. See `here
-<https://github.com/google-research/tapas/issues/91#issuecomment-735719340>`__ for more info.
-
-For a list of all pre-trained and fine-tuned TAPAS checkpoints available in the HuggingFace model hub, see `here
-<https://huggingface.co/models?search=tapas>`__.
-
-**STEP 2: Prepare your data in the SQA format**
-
-Second, no matter what you picked above, you should prepare your dataset in the `SQA format
-<https://www.microsoft.com/en-us/download/details.aspx?id=54253>`__. This format is a TSV/CSV file with the following
-columns:
-
- ``id``: optional, id of the table-question pair, for bookkeeping purposes.
- ``annotator``: optional, id of the person who annotated the table-question pair, for bookkeeping purposes.
- ``position``: integer indicating if the question is the first, second, third,... related to the table. Only required
-  in case of conversational setup (SQA). You don't need this column in case you're going for WTQ/WikiSQL-supervised.
- ``question``: string
- ``table_file``: string, name of a csv file containing the tabular data
- ``answer_coordinates``: list of one or more tuples (each tuple being a cell coordinate, i.e. row, column pair that is
-  part of the answer)
- ``answer_text``: list of one or more strings (each string being a cell value that is part of the answer)
- ``aggregation_label``: index of the aggregation operator. Only required in case of strong supervision for aggregation
-  (the WikiSQL-supervised case)
- ``float_answer``: the float answer to the question, if there is one (np.nan if there isn't). Only required in case of
-  weak supervision for aggregation (such as WTQ and WikiSQL)
-
-The tables themselves should be present in a folder, each table being a separate csv file. Note that the authors of the
-TAPAS algorithm used conversion scripts with some automated logic to convert the other datasets (WTQ, WikiSQL) into the
-SQA format. The author explains this `here
-<https://github.com/google-research/tapas/issues/50#issuecomment-705465960>`__. Interestingly, these conversion scripts
-are not perfect (the ``answer_coordinates`` and ``float_answer`` fields are populated based on the ``answer_text``),
-meaning that WTQ and WikiSQL results could actually be improved.
-
-**STEP 3: Convert your data into PyTorch tensors using TapasTokenizer**
-
-Third, given that you've prepared your data in this TSV/CSV format (and corresponding CSV files containing the tabular
-data), you can then use :class:`~transformers.TapasTokenizer` to convert table-question pairs into :obj:`input_ids`,
-:obj:`attention_mask`, :obj:`token_type_ids` and so on. Again, based on which of the three cases you picked above,
-:class:`~transformers.TapasForQuestionAnswering` requires different inputs to be fine-tuned:
-
-+------------------------------------+----------------------------------------------------------------------------------------------+
-| **Task**                           | **Required inputs**                                                                          |
-+------------------------------------+----------------------------------------------------------------------------------------------+
-| Conversational                     | ``input_ids``, ``attention_mask``, ``token_type_ids``, ``labels``                            |
-+------------------------------------+----------------------------------------------------------------------------------------------+
-| Weak supervision for aggregation   | ``input_ids``, ``attention_mask``, ``token_type_ids``, ``labels``, ``numeric_values``,       |
-|                                    | ``numeric_values_scale``, ``float_answer``                                                   |
-+------------------------------------+----------------------------------------------------------------------------------------------+
-| Strong supervision for aggregation | ``input ids``, ``attention mask``, ``token type ids``, ``labels``, ``aggregation_labels``    |
-+------------------------------------+----------------------------------------------------------------------------------------------+
-
-:class:`~transformers.TapasTokenizer` creates the ``labels``, ``numeric_values`` and ``numeric_values_scale`` based on
-the ``answer_coordinates`` and ``answer_text`` columns of the TSV file. The ``float_answer`` and ``aggregation_labels``
-are already in the TSV file of step 2. Here's an example:
-
-.. code-block::
-
-        >>> from transformers import TapasTokenizer
-        >>> import pandas as pd
-
-        >>> model_name = 'google/tapas-base'
-        >>> tokenizer = TapasTokenizer.from_pretrained(model_name)
-
-        >>> data = {'Actors': ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"], 'Number of movies': ["87", "53", "69"]}
-        >>> queries = ["What is the name of the first actor?", "How many movies has George Clooney played in?", "What is the total number of movies?"]
-        >>> answer_coordinates = [[(0, 0)], [(2, 1)], [(0, 1), (1, 1), (2, 1)]]
-        >>> answer_text = [["Brad Pitt"], ["69"], ["209"]]
-        >>> table = pd.DataFrame.from_dict(data)
-        >>> inputs = tokenizer(table=table, queries=queries, answer_coordinates=answer_coordinates, answer_text=answer_text, padding='max_length', return_tensors='pt')
-        >>> inputs
-        {'input_ids': tensor([[ ... ]]), 'attention_mask': tensor([[...]]), 'token_type_ids': tensor([[[...]]]),
-        'numeric_values': tensor([[ ... ]]), 'numeric_values_scale: tensor([[ ... ]]), labels: tensor([[ ... ]])}
-
-Note that :class:`~transformers.TapasTokenizer` expects the data of the table to be **text-only**. You can use
-``.astype(str)`` on a dataframe to turn it into text-only data. Of course, this only shows how to encode a single
-training example. It is advised to create a PyTorch dataset and a corresponding dataloader:
-
-.. code-block::
-
-        >>> import torch
-        >>> import pandas as pd
-
-        >>> tsv_path = "your_path_to_the_tsv_file"
-        >>> table_csv_path = "your_path_to_a_directory_containing_all_csv_files"
-
-        >>> class TableDataset(torch.utils.data.Dataset):
-        ...     def __init__(self, data, tokenizer):
-        ...         self.data = data
-        ...         self.tokenizer = tokenizer
-        ...
-        ...     def __getitem__(self, idx):
-        ...         item = data.iloc[idx]
-        ...         table = pd.read_csv(table_csv_path + item.table_file).astype(str) # be sure to make your table data text only
-        ...         encoding = self.tokenizer(table=table, 
-        ...                                   queries=item.question, 
-        ...                                   answer_coordinates=item.answer_coordinates, 
-        ...                                   answer_text=item.answer_text,
-        ...                                   truncation=True,
-        ...                                   padding="max_length",
-        ...                                   return_tensors="pt"
-        ...         )
-        ...         # remove the batch dimension which the tokenizer adds by default
-        ...         encoding = {key: val.squeeze(0) for key, val in encoding.items()}
-        ...         # add the float_answer which is also required (weak supervision for aggregation case)
-        ...         encoding["float_answer"] = torch.tensor(item.float_answer) 
-        ...         return encoding
-        ...
-        ...     def __len__(self):
-        ...        return len(self.data)
-
-        >>> data = pd.read_csv(tsv_path, sep='\t')
-        >>> train_dataset = TableDataset(data, tokenizer)
-        >>> train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=32)
-
-Note that here, we encode each table-question pair independently. This is fine as long as your dataset is **not
-conversational**. In case your dataset involves conversational questions (such as in SQA), then you should first group
-together the ``queries``, ``answer_coordinates`` and ``answer_text`` per table (in the order of their ``position``
-index) and batch encode each table with its questions. This will make sure that the ``prev_labels`` token types (see
-docs of :class:`~transformers.TapasTokenizer`) are set correctly. See `this notebook
-<https://github.com/NielsRogge/Transformers-Tutorials/blob/master/TAPAS/Fine_tuning_TapasForQuestionAnswering_on_SQA.ipynb>`__
-for more info.
-
-**STEP 4: Train (fine-tune) TapasForQuestionAnswering**
-
-You can then fine-tune :class:`~transformers.TapasForQuestionAnswering` using native PyTorch as follows (shown here for
-the weak supervision for aggregation case):
-
-.. code-block::
-
-        >>> from transformers import TapasConfig, TapasForQuestionAnswering, AdamW
-
-        >>> # this is the default WTQ configuration
-        >>> config = TapasConfig(
-        ...            num_aggregation_labels = 4,
-        ...            use_answer_as_supervision = True,
-        ...            answer_loss_cutoff = 0.664694,
-        ...            cell_selection_preference = 0.207951,
-        ...            huber_loss_delta = 0.121194,
-        ...            init_cell_selection_weights_to_zero = True,
-        ...            select_one_column = True,
-        ...            allow_empty_column_selection = False,
-        ...            temperature = 0.0352513,
-        ... )
-        >>> model = TapasForQuestionAnswering.from_pretrained("google/tapas-base", config=config)
-
-        >>> optimizer = AdamW(model.parameters(), lr=5e-5)
-
-        >>> for epoch in range(2):  # loop over the dataset multiple times
-        ...    for idx, batch in enumerate(train_dataloader):
-        ...         # get the inputs; 
-        ...         input_ids = batch["input_ids"]
-        ...         attention_mask = batch["attention_mask"]
-        ...         token_type_ids = batch["token_type_ids"]
-        ...         labels = batch["labels"]
-        ...         numeric_values = batch["numeric_values"]
-        ...         numeric_values_scale = batch["numeric_values_scale"]
-        ...         float_answer = batch["float_answer"]
-
-        ...         # zero the parameter gradients
-        ...         optimizer.zero_grad()
-
-        ...         # forward + backward + optimize
-        ...         outputs = model(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, 
-        ...                        labels=labels, numeric_values=numeric_values, numeric_values_scale=numeric_values_scale, 
-        ...                        float_answer=float_answer)
-        ...         loss = outputs.loss
-        ...         loss.backward()
-        ...         optimizer.step()
-
-Usage: inference
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Here we explain how you can use :class:`~transformers.TapasForQuestionAnswering` for inference (i.e. making predictions
-on new data). For inference, only ``input_ids``, ``attention_mask`` and ``token_type_ids`` (which you can obtain using
-:class:`~transformers.TapasTokenizer`) have to be provided to the model to obtain the logits. Next, you can use the
-handy ``convert_logits_to_predictions`` method of :class:`~transformers.TapasTokenizer` to convert these into predicted
-coordinates and optional aggregation indices.
-
-However, note that inference is **different** depending on whether or not the setup is conversational. In a
-non-conversational set-up, inference can be done in parallel on all table-question pairs of a batch. Here's an example
-of that:
-
-.. code-block::
-
-        >>> from transformers import TapasTokenizer, TapasForQuestionAnswering
-        >>> import pandas as pd 
-
-        >>> model_name = 'google/tapas-base-finetuned-wtq'
-        >>> model = TapasForQuestionAnswering.from_pretrained(model_name)
-        >>> tokenizer = TapasTokenizer.from_pretrained(model_name)
-
-        >>> data = {'Actors': ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"], 'Number of movies': ["87", "53", "69"]}
-        >>> queries = ["What is the name of the first actor?", "How many movies has George Clooney played in?", "What is the total number of movies?"]
-        >>> table = pd.DataFrame.from_dict(data)
-        >>> inputs = tokenizer(table=table, queries=queries, padding='max_length', return_tensors="pt") 
-        >>> outputs = model(**inputs)
-        >>> predicted_answer_coordinates, predicted_aggregation_indices = tokenizer.convert_logits_to_predictions(
-        ...         inputs, 
-        ...         outputs.logits.detach(), 
-        ...         outputs.logits_aggregation.detach()
-        ... )
-
-        >>> # let's print out the results:
-        >>> id2aggregation = {0: "NONE", 1: "SUM", 2: "AVERAGE", 3:"COUNT"}
-        >>> aggregation_predictions_string = [id2aggregation[x] for x in predicted_aggregation_indices]
-
-        >>> answers = []
-        >>> for coordinates in predicted_answer_coordinates:
-        ...   if len(coordinates) == 1:
-        ...     # only a single cell:
-        ...     answers.append(table.iat[coordinates[0]])
-        ...   else:
-        ...     # multiple cells
-        ...     cell_values = []
-        ...     for coordinate in coordinates:
-        ...        cell_values.append(table.iat[coordinate])
-        ...     answers.append(", ".join(cell_values))
-
-        >>> display(table)
-        >>> print("")
-        >>> for query, answer, predicted_agg in zip(queries, answers, aggregation_predictions_string):
-        ...   print(query)
-        ...   if predicted_agg == "NONE":
-        ...     print("Predicted answer: " + answer)
-        ...   else:
-        ...     print("Predicted answer: " + predicted_agg + " > " + answer)    
-        What is the name of the first actor?
-        Predicted answer: Brad Pitt
-        How many movies has George Clooney played in?
-        Predicted answer: COUNT > 69
-        What is the total number of movies?
-        Predicted answer: SUM > 87, 53, 69
-
-In case of a conversational set-up, then each table-question pair must be provided **sequentially** to the model, such
-that the ``prev_labels`` token types can be overwritten by the predicted ``labels`` of the previous table-question
-pair. Again, more info can be found in `this notebook
-<https://github.com/NielsRogge/Transformers-Tutorials/blob/master/TAPAS/Fine_tuning_TapasForQuestionAnswering_on_SQA.ipynb>`__.
-
-
-Tapas specific outputs
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.models.tapas.modeling_tapas.TableQuestionAnsweringOutput
-    :members:
-
-
-TapasConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TapasConfig
-    :members:
-
-
-TapasTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TapasTokenizer
-    :members: __call__, convert_logits_to_predictions, save_vocabulary
-
-
-TapasModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TapasModel
-    :members: forward
-
-
-TapasForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TapasForMaskedLM
-    :members: forward
-
-
-TapasForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TapasForSequenceClassification
-    :members: forward
-
-
-TapasForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TapasForQuestionAnswering
-    :members: forward
--- a/docs/source/model_doc/transformerxl.rst
+++ b/docs/source/model_doc/transformerxl.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 Transformer XL
 -----------------------------------------------------------------------------------------------------------------------

@@ -61,16 +49,16 @@ TransfoXLTokenizer
 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:


@@ -88,13 +76,6 @@ TransfoXLLMHeadModel
    :members: forward


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

@@ -107,18 +88,3 @@ TFTransfoXLLMHeadModel

 .. autoclass:: transformers.TFTransfoXLLMHeadModel
    :members: call
-
-
-TFTransfoXLForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFTransfoXLForSequenceClassification
-    :members: call
-
-
-Internal Layers
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AdaptiveEmbedding
-
-.. autoclass:: transformers.TFAdaptiveEmbedding
--- a/docs/source/model_doc/xlm.rst
+++ b/docs/source/model_doc/xlm.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 XLM
 -----------------------------------------------------------------------------------------------------------------------

@@ -62,7 +50,7 @@ XLMTokenizer
 XLM specific outputs
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

-.. autoclass:: transformers.models.xlm.modeling_xlm.XLMForQuestionAnsweringOutput
+.. autoclass:: transformers.modeling_xlm.XLMForQuestionAnsweringOutput
    :members:


--- a/docs/source/model_doc/xlmprophetnet.rst
+++ b/docs/source/model_doc/xlmprophetnet.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 XLM-ProphetNet
 -----------------------------------------------------------------------------------------------------------------------

@@ -31,7 +19,7 @@ just the next token. Its architecture is identical to ProhpetNet, but the model

 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
+*In this paper, we present a new sequence-to-sequence pre-training 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
@@ -39,7 +27,7 @@ step. The future n-gram prediction explicitly encourages the model to plan for t
 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.*
+state-of-the-art results on all these datasets compared to the models using the same scale pre-training corpus.*

 The Authors' code can be found `here <https://github.com/microsoft/ProphetNet>`__.

--- a/docs/source/model_doc/xlmroberta.rst
+++ b/docs/source/model_doc/xlmroberta.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 XLM-RoBERTa
 -----------------------------------------------------------------------------------------------------------------------

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


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

--- a/docs/source/model_doc/xlnet.rst
+++ b/docs/source/model_doc/xlnet.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 XLNet
 -----------------------------------------------------------------------------------------------------------------------

@@ -62,53 +50,46 @@ XLNetTokenizer
        create_token_type_ids_from_sequences, save_vocabulary


-XLNetTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.XLNetTokenizerFast
-    :members:
-
-
 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:


--- a/docs/source/model_sharing.rst
+++ b/docs/source/model_sharing.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 Model sharing and uploading
 =======================================================================================================================

@@ -49,7 +37,7 @@ For instance:

 .. code-block::

-    >>> model = AutoModel.from_pretrained(
+    >>> tokenizer = AutoTokenizer.from_pretrained(
    >>>   "julien-c/EsperBERTo-small",
    >>>   revision="v2.0.1" # tag name, or branch name, or commit hash
    >>> )
@@ -58,61 +46,37 @@ 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.
+users to clone it and you (and your organization members) to push to it. First, you should ensure you are logged in the
+``transformers-cli``:

-You can create a model repo **directly from `the /new page on the website <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 🤗
+Go in 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
+.. code-block::

    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
+.. code-block::

    transformers-cli repo create your-model-name

-If you want to create a repo under a specific organization, you should add a `--organization` flag:
+This creates a repo on the model hub, which can be cloned. You can then add/remove from that repo as you would with any
+other git repo.

-.. code-block:: bash
-
-    transformers-cli repo create your-model-name --organization your-org-name
-
-This creates a repo on the model hub, which can be cloned.
-
-.. code-block:: bash
-
-    # Make sure you have git-lfs installed
-    # (https://git-lfs.github.com/)
-    git lfs install
+.. code-block::

    git clone https://huggingface.co/username/your-model-name

-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
+    # Then 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 that you can go on with the workflow you're used to and the tools
-you already know.
+We are intentionally not wrapping git too much, so as to stay intuitive and easy-to-use.

-The only learning curve you might have compared to regular git is the one for git-lfs. The documentation at
-`git-lfs.github.com <https://git-lfs.github.com/>`__ is decent, but we'll work on a tutorial with some tips and tricks
-in the coming weeks!
-
-Additionally, if you want to change multiple repos at once, the `change_config.py script
-<https://github.com/huggingface/efficient_scripts/blob/main/change_config.py>`__ can probably save you some time.

 Make your model work on all frameworks
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -123,7 +87,7 @@ Make your model work on all frameworks
 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 might all be automatic). You will need to install both PyTorch and
+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.
@@ -195,25 +159,24 @@ 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
+.. code-block::

    git add --all
    git status

-Finally, the files should be committed:
+Finally, the files should be comitted:

-.. code-block:: bash
+.. code-block::

    git commit -m "First version of the your-model-name model and tokenizer."

 And pushed to the remote:

-.. code-block:: bash
+.. code-block::

    git push

@@ -223,20 +186,23 @@ This will upload the folder containing the weights, tokenizer and configuration
 Add a model card
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

-To make sure everyone knows what your model can do, what its limitations, potential bias or ethical considerations are,
-please add a README.md model card to your model repo. You can just create it, or there's also a convenient button
-titled "Add a README.md" on your model page. A model card template can be found `here
-<https://github.com/huggingface/model_card>`__ (meta-suggestions are welcome). model card template (meta-suggestions
-are welcome).
-
-.. note::
-
-    Model cards used to live in the 🤗 Transformers repo under `model_cards/`, but for consistency and scalability we
-    migrated every model card from the repo to its corresponding huggingface.co model repo.
+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
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -259,49 +225,3 @@ You may specify a revision by using the ``revision`` flag in the ``from_pretrain
    >>>   "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 either create a repo directly from `huggingface.co <https://huggingface.co/>`__ , or use the
-:obj:`transformers-cli` to create it:
-
-
-.. 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 lfs install
-
-    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 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
--- a/docs/source/model_summary.rst
+++ b/docs/source/model_summary.rst
@@ -1,22 +1,10 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 Summary of the models
 =======================================================================================================================

 This is a summary of the models available in 🤗 Transformers. It assumes you’re familiar with the original `transformer
 model <https://arxiv.org/abs/1706.03762>`_. For a gentle introduction check the `annotated transformer
 <http://nlp.seas.harvard.edu/2018/04/03/attention.html>`_. Here we focus on the high-level differences between the
-models. You can check them more in detail in their respective documentation. Also check out the :doc:`pretrained model
+models. You can check them more in detail in their respective documentation. Also checkout the :doc:`pretrained model
 page </pretrained_models>` to see the checkpoints available for each type of model and all `the community models
 <https://huggingface.co/models>`_.

@@ -30,7 +18,7 @@ Each one of the models in the library falls into one of the following categories

 Autoregressive models are pretrained on the classic language modeling task: guess the next token having read all the
 previous ones. They correspond to the decoder of the original transformer model, and a mask is used on top of the full
-sentence so that the attention heads can only see what was before in the text, and not what’s after. Although those
+sentence so that the attention heads can only see what was before in the next, and not what’s after. Although those
 models can be fine-tuned and achieve great results on many tasks, the most natural application is text generation. A
 typical example of such models is GPT.

@@ -512,8 +500,8 @@ BART
 <https://arxiv.org/abs/1910.13461>`_, Mike Lewis et al.

 Sequence-to-sequence model with an encoder and a decoder. Encoder is fed a corrupted version of the tokens, decoder is
-fed the original tokens (but has a mask to hide the future words like a regular transformers decoder). A composition of
-the following transformations are applied on the pretraining tasks for the encoder:
+fed the original tokens (but has a mask to hide the future words like a regular transformers decoder). For the encoder
+, on the pretraining tasks, a composition of the following transformations are applied:

  * mask random tokens (like in BERT)
  * delete random tokens
@@ -539,10 +527,10 @@ Pegasus
 <https://arxiv.org/pdf/1912.08777.pdf>`_, Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019.

 Sequence-to-sequence model with the same encoder-decoder model architecture as BART. Pegasus is pre-trained jointly on
-two self-supervised objective functions: Masked Language Modeling (MLM) and a novel summarization specific pretraining
+two self-supervised objective functions: Masked Language Modeling (MLM) and a novel summarization specific pre-training
 objective, called Gap Sentence Generation (GSG).

-  * MLM: encoder input tokens are randomly replaced by a mask tokens and have to be predicted by the encoder (like in
+  * MLM: encoder input tokens are randomely replaced by a mask tokens and have to be predicted by the encoder (like in
    BERT)
  * GSG: whole encoder input sentences are replaced by a second mask token and fed to the decoder, but which has a
    causal mask to hide the future words like a regular auto-regressive transformer decoder.
@@ -572,7 +560,6 @@ A framework for translation models, using the same models as BART

 The library provides a version of this model for conditional generation.

-
 T5
 -----------------------------------------------------------------------------------------------------------------------

@@ -605,28 +592,6 @@ For instance, if we have the sentence “My dog is very cute .”, and we decide

 The library provides a version of this model for conditional generation.

-
-MT5
-----------------------------------------------------------------------------------------------------------------------
-
-.. raw:: html
-
-   <a href="https://huggingface.co/models?filter=mt5">
-       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-mt5-blueviolet">
-   </a>
-   <a href="model_doc/mt5.html">
-       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-mt5-blueviolet">
-   </a>
-
-`mT5: A massively multilingual pre-trained text-to-text transformer <https://arxiv.org/abs/2010.11934>`_, Linting Xue
-et al.
-
-The model architecture is same as T5. mT5's pretraining objective includes T5's self-supervised training, but not T5's
-supervised training. mT5 is trained on 101 languages.
-
-The library provides a version of this model for conditional generation.
-
-
 MBart
 -----------------------------------------------------------------------------------------------------------------------

@@ -642,8 +607,8 @@ MBart
 `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.

-The model architecture and pretraining objective is same as BART, but MBart is trained on 25 languages and is intended
-for supervised and unsupervised machine translation. MBart is one of the first methods for pretraining a complete
+The model architecture and pre-training objective is same as BART, but MBart is trained on 25 languages and is intended
+for supervised and unsupervised machine translation. MBart is one of the first methods for pre-training a complete
 sequence-to-sequence model by denoising full texts in multiple languages,

 The library provides a version of this model for conditional generation.
@@ -670,7 +635,7 @@ ProphetNet
 `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.

-ProphetNet introduces a novel *sequence-to-sequence* pretraining objective, called *future n-gram prediction*. In
+ProphetNet introduces a novel *sequence-to-sequence* pre-training objective, called *future n-gram prediction*. In
 future n-gram prediction, the model predicts the next n tokens simultaneously based on previous context tokens at each
 time step instead instead of just the single next token. The future n-gram prediction explicitly encourages the model
 to plan for the future tokens and prevent overfitting on strong local correlations. The model architecture is based on
@@ -695,8 +660,8 @@ XLM-ProphetNet
 `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.

-XLM-ProphetNet's model architecture and pretraining objective is same as ProphetNet, but XLM-ProphetNet was pre-trained
-on the cross-lingual dataset `XGLUE <https://arxiv.org/abs/2004.01401>`__.
+XLM-ProphetNet's model architecture and pre-training objective is same as ProphetNet, but XLM-ProphetNet was
+pre-trained on the cross-lingual dataset `XGLUE <https://arxiv.org/abs/2004.01401>`__.

 The library provides a pre-trained version of this model for multi-lingual conditional generation and fine-tuned
 versions for headline generation and question generation, respectively.
--- a/docs/source/multilingual.rst
+++ b/docs/source/multilingual.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 Multi-lingual models
 =======================================================================================================================

@@ -90,8 +78,9 @@ You can then feed it all as input to your model:
    >>> outputs = model(input_ids, langs=langs)


-The example :prefix_link:`run_generation.py <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
 -----------------------------------------------------------------------------------------------------------------------
@@ -120,7 +109,7 @@ 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
+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:
--- a/docs/source/perplexity.rst
+++ b/docs/source/perplexity.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 Perplexity of fixed-length models
 =======================================================================================================================

@@ -74,7 +62,7 @@ sliding the context window so that the model has more context when making each p
 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
+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
--- a/docs/source/philosophy.rst
+++ b/docs/source/philosophy.rst
@@ -1,15 +1,3 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 Philosophy
 =======================================================================================================================

@@ -78,7 +66,7 @@ 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
+  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,
 - :obj:`save_pretrained()` lets you save a model/configuration/tokenizer locally so that it can be reloaded using
  :obj:`from_pretrained()`.
--- a/docs/source/preprocessing.rst
+++ b/docs/source/preprocessing.rst
@@ -1,26 +1,15 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 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:`quick tour </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.
+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::

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

 As you can see, the tokenizer automatically added some special tokens that the model expects. Not all models need
-special tokens; for instance, if we had used `gpt2-medium` instead of `bert-base-cased` to create our tokenizer, we
+special tokens; for instance, if we had used` gtp2-medium` instead of `bert-base-cased` to create our tokenizer, we
 would have seen the same sentence as the original one here. You can disable this behavior (which is only advised if you
 have added those special tokens yourself) by passing ``add_special_tokens=False``.

@@ -131,7 +120,7 @@ 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 from throwing those kinds of warnings.
+can safely ignore it. You can also pass ``verbose=False`` to stop the tokenizer to throw those kinds of warnings.

 .. _sentence-pairs:

@@ -216,6 +205,7 @@ 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`.

--- a/docs/source/pretrained_models.rst
+++ b/docs/source/pretrained_models.rst
@@ -1,24 +1,13 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
 Pretrained models
 =======================================================================================================================

-Here is a partial list of some of the available pretrained models together with a short presentation of each model.
+Here is the full list of the currently provided pretrained models together with a short presentation of each model.

-For the full list, 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.                                                                                                |
@@ -344,12 +333,6 @@ For the full list, refer to `https://huggingface.co/models <https://huggingface.
 |                    | ``facebook/bart-large-cnn``                                | | 24-layer, 1024-hidden, 16-heads, 406M parameters       (same as large)                                                              |
 |                    |                                                            | | bart-large base architecture finetuned on cnn summarization task                                                                    |
 +--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| BARThez            | ``moussaKam/barthez``                                      | | 12-layer,  768-hidden, 12-heads, 216M parameters                                                                                    |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/moussaKam/BARThez>`__)                                                                              |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``moussaKam/mbarthez``                                     | | 24-layer, 1024-hidden, 16-heads, 561M parameters                                                                                    |
-+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 | DialoGPT           | ``DialoGPT-small``                                         | | 12-layer, 768-hidden, 12-heads, 124M parameters                                                                                     |
 |                    |                                                            | | Trained on English text: 147M conversation-like exchanges extracted from Reddit.                                                    |
 |                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Sylvain Gugger	d5b3e56de5	Release: v3.5.1	2020-11-13 10:03:08 -05:00
Sylvain Gugger	1fb361768d	Fix typo	2020-11-13 10:00:29 -05:00
				`@@ -1 +0,0 @@`
				`$PYTHON setup.py install # Python command to install the script.`