Release: v4.16.2

* Fix Swin model outputs

* Rename pooler

.circleci/config.yml

@@ -99,7 +99,7 @@ jobs:
                   path: ~/transformers/tests_output.txt
             - store_artifacts:
                   path: ~/transformers/reports
-    
+
     run_tests_torch_and_tf_all:
         working_directory: ~/transformers
         docker:
@@ -169,7 +169,7 @@ jobs:
                   path: ~/transformers/tests_output.txt
             - store_artifacts:
                   path: ~/transformers/reports
-    
+
     run_tests_torch_and_flax_all:
         working_directory: ~/transformers
         docker:
@@ -237,7 +237,7 @@ jobs:
                   path: ~/transformers/tests_output.txt
             - store_artifacts:
                   path: ~/transformers/reports
-    
+
     run_tests_torch_all:
         working_directory: ~/transformers
         docker:
@@ -304,7 +304,7 @@ jobs:
                   path: ~/transformers/tests_output.txt
             - store_artifacts:
                   path: ~/transformers/reports
-    
+
     run_tests_tf_all:
         working_directory: ~/transformers
         docker:
@@ -370,7 +370,7 @@ jobs:
                   path: ~/transformers/tests_output.txt
             - store_artifacts:
                   path: ~/transformers/reports
-    
+
     run_tests_flax_all:
         working_directory: ~/transformers
         docker:
@@ -437,7 +437,7 @@ jobs:
                   path: ~/transformers/tests_output.txt
             - store_artifacts:
                   path: ~/transformers/reports
-    
+
     run_tests_pipelines_torch_all:
         working_directory: ~/transformers
         docker:
@@ -549,7 +549,7 @@ jobs:
                       - v0.4-custom_tokenizers-{{ checksum "setup.py" }}
                       - v0.4-{{ checksum "setup.py" }}
             - run: pip install --upgrade pip
-            - run: pip install .[ja,testing,sentencepiece,jieba]
+            - run: pip install .[ja,testing,sentencepiece,jieba,spacy,ftfy]
             - run: python -m unidic download
             - save_cache:
                   key: v0.4-custom_tokenizers-{{ checksum "setup.py" }}
@@ -557,7 +557,7 @@ jobs:
                       - '~/.cache/pip'
             - run: |
                   if [ -f test_list.txt ]; then
-                    python -m pytest -s --make-reports=tests_custom_tokenizers ./tests/test_tokenization_bert_japanese.py | tee tests_output.txt
+                    python -m pytest -s --make-reports=tests_custom_tokenizers ./tests/test_tokenization_bert_japanese.py ./tests/test_tokenization_openai.py | tee tests_output.txt
                   fi
             - store_artifacts:
                   path: ~/transformers/tests_output.txt
@@ -662,7 +662,7 @@ jobs:
                   path: ~/transformers/flax_examples_output.txt
             - store_artifacts:
                   path: ~/transformers/reports
-    
+
     run_examples_flax_all:
         working_directory: ~/transformers
         docker:
@@ -729,7 +729,7 @@ jobs:
                   path: ~/transformers/tests_output.txt
             - store_artifacts:
                   path: ~/transformers/reports
-    
+
     run_tests_hub_all:
         working_directory: ~/transformers
         docker:
@@ -795,7 +795,7 @@ jobs:
                   path: ~/transformers/tests_output.txt
             - store_artifacts:
                   path: ~/transformers/reports
-    
+
     run_tests_onnxruntime_all:
         working_directory: ~/transformers
         docker:
@@ -848,7 +848,7 @@ jobs:
             - run: isort --check-only examples tests src utils
             - run: python utils/custom_init_isort.py --check_only
             - run: flake8 examples tests src utils
-#            - run: python utils/style_doc.py src/transformers docs/source --max_len 119 --check_only
+            - run: python utils/style_doc.py src/transformers docs/source --max_len 119 --check_only
 
     check_repository_consistency:
         working_directory: ~/transformers

.github/ISSUE_TEMPLATE/bug-report.md

@@ -49,7 +49,7 @@ Library:
 - Deepspeed: @stas00
 - Ray/raytune: @richardliaw, @amogkam
 - Text generation: @patrickvonplaten @narsil
-- Tokenizers: @LysandreJik
+- Tokenizers: @SaulLu
 - Trainer: @sgugger
 - Pipelines: @Narsil
 - Speech: @patrickvonplaten, @anton-l

.github/workflows/add-model-like.yml

@@ -0,0 +1,59 @@
+name: Add model like runner
+
+on:
+  push:
+    branches:
+      - master
+  pull_request:
+    paths:
+      - "src/**"
+      - "tests/**"
+      - ".github/**"
+    types: [opened, synchronize, reopened]
+
+jobs:
+  run_tests_templates:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+
+      - name: Loading cache.
+        uses: actions/cache@v2
+        id: cache
+        with:
+          path: ~/.cache/pip
+          key: v1-tests_model_like
+          restore-keys: |
+            v1-tests_model_like-${{ hashFiles('setup.py') }}
+            v1-tests_model_like
+
+      - name: Install dependencies
+        run: |
+          pip install --upgrade pip!=21.3
+          sudo apt -y update && sudo apt install -y libsndfile1-dev
+          pip install .[dev]
+
+      - name: Create model files
+        run: |
+          transformers-cli add-new-model-like --config_file tests/fixtures/add_distilbert_like_config.json --path_to_repo .
+          make style
+          make fix-copies
+
+      - name: Run all PyTorch modeling test
+        run: |
+          python -m pytest -n 2 --dist=loadfile -s --make-reports=tests_new_models tests/test_modeling_bert_new.py
+
+      - name: Run style changes
+        run: |
+          make style && make quality && make repo-consistency
+
+      - name: Failure short reports
+        if: ${{ always() }}
+        run: cat reports/tests_new_models_failures_short.txt
+
+      - name: Test suite reports artifacts
+        if: ${{ always() }}
+        uses: actions/upload-artifact@v2
+        with:
+          name: run_all_tests_new_models_test_reports
+          path: reports

.github/workflows/build_dev_documentation.yml

@@ -0,0 +1,120 @@
+name: Build dev documentation
+
+on:
+  pull_request:
+
+
+jobs:
+  build_and_package:
+    runs-on: [self-hosted, doc-builder]
+    container:
+      image: huggingface/doc-builder-transformers
+      options: "-v /home/github_actions:/mnt"
+    env:
+      PR_NUMBER: ${{ github.event.number }}
+      EVENT_CONTEXT: ${{ toJSON(github.event) }}
+
+    steps:
+      - uses: actions/checkout@v2
+        with:
+          repository: 'huggingface/doc-builder'
+          path: doc-builder
+
+      - uses: actions/checkout@v2
+        with:
+          repository: 'huggingface/transformers'
+          path: transformers
+
+      - uses: actions/checkout@v2
+        with:
+          repository: 'huggingface/notebooks'
+          path: notebooks
+
+      - name: Set env
+        run: echo "WRITE=$(cat /mnt/WRITE)" >> $GITHUB_ENV
+
+      - name: Comment PR
+        uses: thollander/actions-comment-pull-request@v1
+        if: github.event.action == 'opened'
+
+        with:
+          message: 'The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_${{ env.PR_NUMBER }}). All of your documentation changes will be reflected on that endpoint.'
+          GITHUB_TOKEN: ${{ env.WRITE }}
+
+      - name: Find Comment
+        if: github.event.action == 'reopened'
+        uses: peter-evans/find-comment@v1
+        id: fc
+        with:
+          issue-number: ${{ env.PR_NUMBER }}
+          comment-author: HuggingFaceDocBuilder
+
+      - name: Update comment
+        if: github.event.action == 'reopened'
+        uses: peter-evans/create-or-update-comment@v1
+        with:
+          comment-id: ${{ steps.fc.outputs.comment-id }}
+          token: ${{ env.WRITE }}
+          edit-mode: replace
+          body: |
+            The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_${{ env.PR_NUMBER }}). All of your documentation changes will be reflected on that endpoint.
+
+      - name: Loading cache.
+        uses: actions/cache@v2
+        id: cache
+        with:
+          path: ~/.cache/pip
+          key: v1-test_build_doc
+          restore-keys: |
+            v1-test_build_doc-${{ hashFiles('setup.py') }}
+            v1-test_build_doc
+
+      - name: Setup environment
+        run: |
+          apt-get -y update && apt-get install -y libsndfile1-dev
+
+          pip uninstall -y doc-builder
+          pip install git+https://github.com/huggingface/doc-builder -U
+          cd transformers
+          pip install .[dev]
+          cd ..
+
+          export TORCH_VERSION=$(python -c "from torch import version; print(version.__version__.split('+')[0])")
+          pip install torch-scatter -f https://data.pyg.org/whl/torch-${TORCH_VERSION}+cpu.html
+
+          pip install torchvision
+          python -m pip install 'git+https://github.com/facebookresearch/detectron2.git'
+
+          apt install -y tesseract-ocr
+          pip install pytesseract
+          pip install pytorch-quantization --extra-index-url https://pypi.ngc.nvidia.com
+          pip install https://github.com/kpu/kenlm/archive/master.zip
+
+      - name: Setup git
+        run: |
+          git config --global user.name "Hugging Face Doc Builder"
+          git config --global user.email docs@huggingface.co
+          
+          cd doc-builder
+          git pull origin main
+          cd ..
+
+          cd notebooks
+          git pull origin master
+          cd ..
+          WRITE=`cat /mnt/WRITE`
+          
+          rm -rf doc-build-dev
+          git clone https://HuggingFaceDocBuilder:$WRITE@github.com/huggingface/doc-build-dev
+
+      - name: Make documentation
+        run: |
+          doc-builder build transformers transformers/docs/source --build_dir doc-build-dev --notebook_dir notebooks/transformers_doc --clean --version pr_$PR_NUMBER
+
+      - name: Push to repositories
+        run: |
+          cd doc-build-dev
+          ls
+          git add .
+          git commit -m "Updated with commit ${{ github.sha }} See: https://github.com/huggingface/transformers/commit/${{ github.sha }}"
+          git push origin main

.github/workflows/build_documentation.yml

@@ -17,8 +17,8 @@ jobs:
     steps:
       - uses: actions/checkout@v2
         with:
-          repository: 'huggingface/doc-builder'
-          path: doc-builder
+          repository: 'huggingface/doc-build'
+          path: doc-build
           token: ${{ secrets.HUGGINGFACE_PUSH }}
       
       - uses: actions/checkout@v2
@@ -64,10 +64,10 @@ jobs:
 
       - name: Setup git
         run: |
-          git config --global user.name "Hugging Face"
-          git config --global user.email transformers@huggingface.co
+          git config --global user.name "Hugging Face Doc Builder"
+          git config --global user.email docs@huggingface.co
           
-          cd doc-builder
+          cd doc-build
           git pull origin main
           cd ..
 
@@ -77,13 +77,13 @@ jobs:
 
       - name: Make documentation
         run: |
-          doc-builder build transformers transformers/docs/source --build_dir doc-builder/build --notebook_dir notebooks/transformers_doc --clean
+          doc-builder build transformers transformers/docs/source --build_dir doc-build --notebook_dir notebooks/transformers_doc --clean
 
       - name: Push to repositories
         run: |
-          cd doc-builder
+          cd doc-build
           if [[ `git status --porcelain` ]]; then
-            git add build
+            git add .
             git commit -m "Updated with commit ${{ github.sha }} \n\nSee: https://github.com/huggingface/transformers/commit/${{ github.sha }}"
             git push origin main
           else

.github/workflows/delete_dev_documentation.yml

@@ -0,0 +1,59 @@
+name: Delete dev documentation
+
+on:
+  pull_request:
+    types: [ closed ]
+
+
+jobs:
+  build_and_package:
+    runs-on: [self-hosted, doc-builder]
+    container:
+      image: huggingface/doc-builder-transformers
+      options: "-v /home/github_actions:/mnt"
+    env:
+      PR_NUMBER: ${{ github.event.number }}
+
+    steps:
+      - uses: actions/checkout@v2
+      - name: Set env
+        run: echo "WRITE=$(cat /mnt/WRITE)" >> $GITHUB_ENV
+
+      - uses: actions/checkout@v2
+        with:
+          repository: 'huggingface/doc-build-dev'
+          path: doc-build-dev
+          token: ${{ env.WRITE }}
+
+      - name: Setup git
+        run: |
+          git config --global user.name "Hugging Face Doc Builder"
+          git config --global user.email docs@huggingface.co
+
+      - name: Push to repositories
+        run: |
+          cd doc-build-dev
+          ls
+          rm -rf transformers/pr_$PR_NUMBER
+          ls
+          git add .
+          git commit -m "Closed PR ${GITHUB_REF##*/}"
+          git push origin main
+
+      - name: Find Comment
+        if: ${{ always() }}
+        uses: peter-evans/find-comment@v1
+        id: fc
+        with:
+          issue-number: ${{ env.PR_NUMBER }}
+          comment-author: HuggingFaceDocBuilder
+
+      - name: Update comment
+        if: ${{ always() }}
+        uses: peter-evans/create-or-update-comment@v1
+        with:
+          comment-id: ${{ steps.fc.outputs.comment-id }}
+          token: ${{ env.WRITE }}
+          edit-mode: replace
+          body: |
+            _The documentation is not available anymore as the PR was closed or merged._

.github/workflows/doctests.yml

@@ -19,7 +19,7 @@ env:
 
 jobs:
   run_doctests:
-    runs-on: [self-hosted, docker-gpu, single-gpu]
+    runs-on: [self-hosted, docker-gpu-test, single-gpu]
     container:
       image: pytorch/pytorch:1.9.0-cuda11.1-cudnn8-runtime
       options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
@@ -35,8 +35,16 @@ jobs:
         run: |
           apt -y update && apt install -y libsndfile1-dev
           pip install --upgrade pip
-          pip install .[dev]
+          pip install .[testing,torch-speech]
+
+      - name: Prepare files for doctests
+        run: |
+          python utils/prepare_for_doc_test.py src docs
 
       - name: Run doctests
         run: |
-          pytest --doctest-modules $(cat utils/documentation_tests.txt) -sv --doctest-continue-on-failure
+          pytest --doctest-modules $(cat utils/documentation_tests.txt) -sv --doctest-continue-on-failure --doctest-glob="*.mdx"
+
+      - name: Clean files after doctests
+        run: |
+          python utils/prepare_for_doc_test.py src docs --remove_new_line

.github/workflows/model-templates.yml

@@ -61,7 +61,7 @@ jobs:
       - name: Run style changes
         run: |
           git fetch origin master:master
-          make style && make quality
+          make style && make quality && make repo-consistency
 
       - name: Failure short reports
         if: ${{ always() }}

.github/workflows/self-nightly-scheduled.yml

@@ -33,9 +33,10 @@ jobs:
 
             - name: Install dependencies
               run: |
-                  apt -y update && apt install -y libsndfile1-dev git
+                  apt -y update && apt install -y libsndfile1-dev git espeak-ng
                   pip install --upgrade pip
                   pip install .[integrations,sklearn,testing,onnxruntime,sentencepiece,torch-speech,vision,timm]
+                  pip install https://github.com/kpu/kenlm/archive/master.zip
                   pip install --pre torch torchvision torchaudio -f https://download.pytorch.org/whl/nightly/cu113/torch_nightly.html -U
 
             - name: Are GPUs recognized by our DL frameworks
@@ -100,9 +101,10 @@ jobs:
 
             - name: Install dependencies
               run: |
-                  apt -y update && apt install -y libsndfile1-dev git
+                  apt -y update && apt install -y libsndfile1-dev git espeak-ng
                   pip install --upgrade pip
                   pip install .[integrations,sklearn,testing,onnxruntime,sentencepiece,torch-speech,vision,timm]
+                  pip install https://github.com/kpu/kenlm/archive/master.zip
                   pip install --pre torch torchvision torchaudio -f https://download.pytorch.org/whl/nightly/cu113/torch_nightly.html -U
 
             - name: Are GPUs recognized by our DL frameworks
@@ -152,10 +154,11 @@ jobs:
 
             - name: Install dependencies
               run: |
-                  apt -y update && apt install -y libaio-dev
+                  apt -y update && apt install -y libaio-dev libsndfile1-dev git espeak-ng
                   pip install --upgrade pip
                   pip install --pre torch torchvision torchaudio -f https://download.pytorch.org/whl/nightly/cu113/torch_nightly.html -U
                   pip install .[testing,deepspeed]
+                  pip install https://github.com/kpu/kenlm/archive/master.zip
                   pip install git+https://github.com/microsoft/DeepSpeed
 
             - name: Are GPUs recognized by our DL frameworks
@@ -193,11 +196,12 @@ jobs:
 
             - name: Install dependencies
               run: |
-                  apt -y update && apt install -y libaio-dev
+                  apt -y update && apt install -y libaio-dev libsndfile1-dev git espeak-ng
                   pip install --upgrade pip
                   pip install --pre torch torchvision torchaudio -f https://download.pytorch.org/whl/nightly/cu113/torch_nightly.html -U
                   rm -rf ~/.cache/torch_extensions/ # shared between conflicting builds
                   pip install .[testing,fairscale]
+                  pip install https://github.com/kpu/kenlm/archive/master.zip
                   pip install git+https://github.com/microsoft/DeepSpeed # testing bleeding edge
 
             - name: Are GPUs recognized by our DL frameworks

.github/workflows/self-scheduled.yml

@@ -37,6 +37,7 @@ jobs:
           pip install --upgrade pip
           pip install .[integrations,sklearn,testing,onnxruntime,sentencepiece,torch-speech,vision,timm]
           pip install https://github.com/kpu/kenlm/archive/master.zip
+          python -m pip install 'git+https://github.com/facebookresearch/detectron2.git'
 
       - name: Are GPUs recognized by our DL frameworks
         run: |
@@ -50,6 +51,10 @@ jobs:
         if: ${{ always() }}
         run: cat reports/tests_torch_gpu_failures_short.txt
 
+      - name: Test durations
+        if: ${{ always() }}
+        run: cat reports/tests_torch_gpu_durations.txt
+
       - name: Run examples tests on GPU
         if: ${{ always() }}
         env:
@@ -66,6 +71,10 @@ jobs:
         if: ${{ always() }}
         run: cat reports/examples_torch_gpu_failures_short.txt
 
+      - name: Test durations
+        if: ${{ always() }}
+        run: cat reports/examples_torch_gpu_durations.txt
+
       - name: Run all pipeline tests on GPU
         if: ${{ always() }}
         env:
@@ -77,6 +86,10 @@ jobs:
         if: ${{ always() }}
         run: cat reports/tests_torch_pipeline_gpu_failures_short.txt
 
+      - name: Test durations
+        if: ${{ always() }}
+        run: cat reports/tests_torch_pipeline_gpu_durations.txt
+
       - name: Test suite reports artifacts
         if: ${{ always() }}
         uses: actions/upload-artifact@v2
@@ -118,6 +131,10 @@ jobs:
         if: ${{ always() }}
         run: cat reports/tests_flax_gpu_failures_short.txt
 
+      - name: Test durations
+        if: ${{ always() }}
+        run: cat reports/tests_flax_gpu_durations.txt
+
       - name: Test suite reports artifacts
         if: ${{ always() }}
         uses: actions/upload-artifact@v2
@@ -162,6 +179,10 @@ jobs:
         if: ${{ always() }}
         run: cat reports/tests_tf_gpu_failures_short.txt
 
+      - name: Test durations
+        if: ${{ always() }}
+        run: cat reports/tests_tf_gpu_durations.txt
+
       - name: Run all pipeline tests on GPU
         if: ${{ always() }}
         env:
@@ -175,6 +196,10 @@ jobs:
         if: ${{ always() }}
         run: cat reports/tests_tf_pipeline_gpu_failures_short.txt
 
+      - name: Test durations
+        if: ${{ always() }}
+        run: cat reports/tests_tf_pipeline_gpu_durations.txt
+
       - name: Test suite reports artifacts
         if: ${{ always() }}
         uses: actions/upload-artifact@v2
@@ -214,6 +239,10 @@ jobs:
         if: ${{ always() }}
         run: cat reports/tests_torch_xla_tpu_failures_short.txt
 
+      - name: Tests durations
+        if: ${{ always() }}
+        run: cat reports/tests_torch_xla_tpu_durations.txt
+
       - name: Test suite reports artifacts
         if: ${{ always() }}
         uses: actions/upload-artifact@v2
@@ -241,6 +270,7 @@ jobs:
           pip install --upgrade pip
           pip install .[integrations,sklearn,testing,onnxruntime,sentencepiece,torch-speech,vision,timm]
           pip install https://github.com/kpu/kenlm/archive/master.zip
+          python -m pip install 'git+https://github.com/facebookresearch/detectron2.git'
 
       - name: Are GPUs recognized by our DL frameworks
         run: |
@@ -256,6 +286,10 @@ jobs:
         if: ${{ always() }}
         run: cat reports/tests_torch_multi_gpu_failures_short.txt
 
+      - name: Test durations
+        if: ${{ always() }}
+        run: cat reports/tests_torch_multi_gpu_durations.txt
+
       - name: Run all pipeline tests on GPU
         if: ${{ always() }}
         env:
@@ -267,6 +301,10 @@ jobs:
         if: ${{ always() }}
         run: cat reports/tests_torch_pipeline_multi_gpu_failures_short.txt
 
+      - name: Test durations
+        if: ${{ always() }}
+        run: cat reports/tests_torch_pipeline_multi_gpu_durations.txt
+
       - name: Test suite reports artifacts
         if: ${{ always() }}
         uses: actions/upload-artifact@v2
@@ -290,7 +328,7 @@ jobs:
 
       - name: Install dependencies
         run: |
-          apt -y update && apt install -y libsndfile1-dev git
+          apt -y update && apt install -y libsndfile1-dev git espeak-ng
           pip install --upgrade pip
           pip install .[sklearn,testing,onnx,sentencepiece,tf-speech,vision]
           pip install https://github.com/kpu/kenlm/archive/master.zip
@@ -311,6 +349,10 @@ jobs:
         if: ${{ always() }}
         run: cat reports/tests_tf_multi_gpu_failures_short.txt
 
+      - name: Test durations
+        if: ${{ always() }}
+        run: cat reports/tests_tf_multi_gpu_durations.txt
+
       - name: Run all pipeline tests on GPU
         if: ${{ always() }}
         env:
@@ -324,6 +366,10 @@ jobs:
         if: ${{ always() }}
         run: cat reports/tests_tf_pipeline_multi_gpu_failures_short.txt
 
+      - name: Test durations
+        if: ${{ always() }}
+        run: cat reports/tests_tf_pipeline_multi_gpu_durations.txt
+
       - name: Test suite reports artifacts
         if: ${{ always() }}
         uses: actions/upload-artifact@v2
@@ -401,6 +447,10 @@ jobs:
         if: ${{ always() }}
         run: cat reports/tests_torch_cuda_extensions_gpu_failures_short.txt
 
+      - name: Test durations
+        if: ${{ always() }}
+        run: cat reports/tests_torch_cuda_extensions_gpu_durations.txt
+
       - name: Test suite reports artifacts
         if: ${{ always() }}
         uses: actions/upload-artifact@v2
@@ -441,6 +491,10 @@ jobs:
         if: ${{ always() }}
         run: cat reports/tests_torch_cuda_extensions_multi_gpu_failures_short.txt
 
+      - name: Test durations
+        if: ${{ always() }}
+        run: cat reports/tests_torch_cuda_extensions_multi_gpu_durations.txt
+
       - name: Test suite reports artifacts
         if: ${{ always() }}
         uses: actions/upload-artifact@v2

CONTRIBUTING.md

@@ -388,7 +388,7 @@ 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,
+To avoid pinging the upstream repository which adds reference notes to each upstream PR and sends unnecessary 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:

Makefile

@@ -48,13 +48,13 @@ quality:
 	isort --check-only $(check_dirs)
 	python utils/custom_init_isort.py --check_only
 	flake8 $(check_dirs)
-#	python utils/style_doc.py src/transformers docs/source --max_len 119 --check_only
+	python utils/style_doc.py src/transformers docs/source --max_len 119 --check_only
 
 # Format source code automatically and check is there are any problems left that need manual fixing
 
 extra_style_checks:
 	python utils/custom_init_isort.py
-#	python utils/style_doc.py src/transformers docs/source --max_len 119
+	python utils/style_doc.py src/transformers docs/source --max_len 119
 
 # this target runs checks on all files and potentially modifies some of them
 

README.md

@@ -198,7 +198,7 @@ You should install 🤗 Transformers in a [virtual environment](https://docs.pyt
 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 Flax, PyTorch or TensorFlow.
-Please refer to [TensorFlow installation page](https://www.tensorflow.org/install/), [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/), [PyTorch installation page](https://pytorch.org/get-started/locally/#start-locally) and/or [Flax](https://github.com/google/flax#quick-install) and [Jax](https://github.com/google/jax#installation) installation pages regarding the specific install command for your platform.
 
 When one of those backends has been installed, 🤗 Transformers can be installed using pip as follows:
 
@@ -235,11 +235,11 @@ Current number of checkpoints: ![](https://img.shields.io/endpoint?url=https://h
 1. **[BEiT](https://huggingface.co/docs/transformers/model_doc/beit)** (from Microsoft) released with the paper [BEiT: BERT Pre-Training of Image Transformers](https://arxiv.org/abs/2106.08254) by Hangbo Bao, Li Dong, Furu Wei.
 1. **[BERT](https://huggingface.co/docs/transformers/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.
 1. **[BERTweet](https://huggingface.co/docs/transformers/model_doc/bertweet)** (from VinAI Research) released with the paper [BERTweet: A pre-trained language model for English Tweets](https://aclanthology.org/2020.emnlp-demos.2/) by Dat Quoc Nguyen, Thanh Vu and Anh Tuan Nguyen.
-1. **[BERT For Sequence Generation](https://huggingface.co/docs/transformers/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.
-1. **[BigBird-RoBERTa](https://huggingface.co/docs/transformers/model_doc/bigbird)** (from Google Research) released with the paper [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.
+1. **[BERT For Sequence Generation](https://huggingface.co/docs/transformers/model_doc/bert-generation)** (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. **[BigBird-RoBERTa](https://huggingface.co/docs/transformers/model_doc/big_bird)** (from Google Research) released with the paper [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.
 1. **[BigBird-Pegasus](https://huggingface.co/docs/transformers/model_doc/bigbird_pegasus)** (from Google Research) released with the paper [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.
 1. **[Blenderbot](https://huggingface.co/docs/transformers/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.
-1. **[BlenderbotSmall](https://huggingface.co/docs/transformers/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.
+1. **[BlenderbotSmall](https://huggingface.co/docs/transformers/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.
 1. **[BORT](https://huggingface.co/docs/transformers/model_doc/bort)** (from Alexa) released with the paper [Optimal Subarchitecture Extraction For BERT](https://arxiv.org/abs/2010.10499) by Adrian de Wynter and Daniel J. Perry.
 1. **[ByT5](https://huggingface.co/docs/transformers/model_doc/byt5)** (from Google Research) released with the paper [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626) by Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel.
 1. **[CamemBERT](https://huggingface.co/docs/transformers/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.
@@ -249,7 +249,7 @@ Current number of checkpoints: ![](https://img.shields.io/endpoint?url=https://h
 1. **[CPM](https://huggingface.co/docs/transformers/model_doc/cpm)** (from Tsinghua University) released with the paper [CPM: A Large-scale Generative Chinese Pre-trained Language Model](https://arxiv.org/abs/2012.00413) by Zhengyan Zhang, Xu Han, Hao Zhou, Pei Ke, Yuxian Gu, Deming Ye, Yujia Qin, Yusheng Su, Haozhe Ji, Jian Guan, Fanchao Qi, Xiaozhi Wang, Yanan Zheng, Guoyang Zeng, Huanqi Cao, Shengqi Chen, Daixuan Li, Zhenbo Sun, Zhiyuan Liu, Minlie Huang, Wentao Han, Jie Tang, Juanzi Li, Xiaoyan Zhu, Maosong Sun.
 1. **[CTRL](https://huggingface.co/docs/transformers/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.
 1. **[DeBERTa](https://huggingface.co/docs/transformers/model_doc/deberta)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
-1. **[DeBERTa-v2](https://huggingface.co/docs/transformers/model_doc/deberta_v2)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
+1. **[DeBERTa-v2](https://huggingface.co/docs/transformers/model_doc/deberta-v2)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
 1. **[DeiT](https://huggingface.co/docs/transformers/model_doc/deit)** (from Facebook) released with the paper [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) by Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Hervé Jégou.
 1. **[DETR](https://huggingface.co/docs/transformers/model_doc/detr)** (from Facebook) released with the paper [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) by Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko.
 1. **[DialoGPT](https://huggingface.co/docs/transformers/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.
@@ -257,12 +257,12 @@ Current number of checkpoints: ![](https://img.shields.io/endpoint?url=https://h
 1. **[DPR](https://huggingface.co/docs/transformers/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.
-1. **[EncoderDecoder](https://huggingface.co/docs/transformers/model_doc/encoderdecoder)** (from Google Research) 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. **[EncoderDecoder](https://huggingface.co/docs/transformers/model_doc/encoder-decoder)** (from Google Research) 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. **[ELECTRA](https://huggingface.co/docs/transformers/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.
 1. **[FlauBERT](https://huggingface.co/docs/transformers/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.
 1. **[FNet](https://huggingface.co/docs/transformers/model_doc/fnet)** (from Google Research) released with the paper [FNet: Mixing Tokens with Fourier Transforms](https://arxiv.org/abs/2105.03824) by James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, Santiago Ontanon.
 1. **[Funnel Transformer](https://huggingface.co/docs/transformers/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.
-1. **[GPT](https://huggingface.co/docs/transformers/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.
+1. **[GPT](https://huggingface.co/docs/transformers/model_doc/openai-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.
 1. **[GPT-2](https://huggingface.co/docs/transformers/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**.
 1. **[GPT-J](https://huggingface.co/docs/transformers/model_doc/gptj)** (from EleutherAI) released in the repository [kingoflolz/mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax/) by Ben Wang and Aran Komatsuzaki.
 1. **[GPT Neo](https://huggingface.co/docs/transformers/model_doc/gpt_neo)** (from EleutherAI) released in the repository [EleutherAI/gpt-neo](https://github.com/EleutherAI/gpt-neo) by Sid Black, Stella Biderman, Leo Gao, Phil Wang and Connor Leahy.
@@ -281,15 +281,17 @@ Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
 1. **[MarianMT](https://huggingface.co/docs/transformers/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.
 1. **[MBart](https://huggingface.co/docs/transformers/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.
 1. **[MBart-50](https://huggingface.co/docs/transformers/model_doc/mbart)** (from Facebook) released with the paper [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://arxiv.org/abs/2008.00401) by Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, Angela Fan.
-1. **[Megatron-BERT](https://huggingface.co/docs/transformers/model_doc/megatron_bert)** (from NVIDIA) released with the paper [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/abs/1909.08053) by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
+1. **[Megatron-BERT](https://huggingface.co/docs/transformers/model_doc/megatron-bert)** (from NVIDIA) released with the paper [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/abs/1909.08053) by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
 1. **[Megatron-GPT2](https://huggingface.co/docs/transformers/model_doc/megatron_gpt2)** (from NVIDIA) released with the paper [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/abs/1909.08053) by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
 1. **[MPNet](https://huggingface.co/docs/transformers/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.
 1. **[MT5](https://huggingface.co/docs/transformers/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.
+1. **[Nyströmformer](https://huggingface.co/docs/transformers/master/model_doc/nystromformer)** (from the University of Wisconsin - Madison) released with the paper [Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention](https://arxiv.org/abs/2102.03902) by Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, Vikas Singh.
 1. **[Pegasus](https://huggingface.co/docs/transformers/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.
 1. **[Perceiver IO](https://huggingface.co/docs/transformers/model_doc/perceiver)** (from Deepmind) released with the paper [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://arxiv.org/abs/2107.14795) by Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira.
 1. **[PhoBERT](https://huggingface.co/docs/transformers/model_doc/phobert)** (from VinAI Research) released with the paper [PhoBERT: Pre-trained language models for Vietnamese](https://www.aclweb.org/anthology/2020.findings-emnlp.92/) by Dat Quoc Nguyen and Anh Tuan Nguyen.
 1. **[ProphetNet](https://huggingface.co/docs/transformers/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.
 1. **[QDQBert](https://huggingface.co/docs/transformers/model_doc/qdqbert)** (from NVIDIA) released with the paper [Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation](https://arxiv.org/abs/2004.09602) by Hao Wu, Patrick Judd, Xiaojie Zhang, Mikhail Isaev and Paulius Micikevicius.
+1. **[REALM](https://huggingface.co/transformers/model_doc/realm.html)** (from Google Research) released with the paper [REALM: Retrieval-Augmented Language Model Pre-Training](https://arxiv.org/abs/2002.08909) by Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat and Ming-Wei Chang.
 1. **[Reformer](https://huggingface.co/docs/transformers/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.
 1. **[RemBERT](https://huggingface.co/docs/transformers/model_doc/rembert)** (from Google Research) released with the paper [Rethinking embedding coupling in pre-trained language models](https://arxiv.org/pdf/2010.12821.pdf) by Hyung Won Chung, Thibault Févry, Henry Tsai, M. Johnson, Sebastian Ruder.
 1. **[RoBERTa](https://huggingface.co/docs/transformers/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.
@@ -301,25 +303,29 @@ Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
 1. **[SpeechToTextTransformer2](https://huggingface.co/docs/transformers/model_doc/speech_to_text_2)** (from Facebook), released together with the paper [Large-Scale Self- and Semi-Supervised Learning for Speech Translation](https://arxiv.org/abs/2104.06678) by Changhan Wang, Anne Wu, Juan Pino, Alexei Baevski, Michael Auli, Alexis Conneau.
 1. **[Splinter](https://huggingface.co/docs/transformers/model_doc/splinter)** (from Tel Aviv University), released together with the paper [Few-Shot Question Answering by Pretraining Span Selection](https://arxiv.org/abs/2101.00438) by Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, Omer Levy.
 1. **[SqueezeBert](https://huggingface.co/docs/transformers/model_doc/squeezebert)** (from Berkeley) 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. **[Swin Transformer](https://huggingface.co/docs/transformers/master/model_doc/swin)** (from Microsoft) released with the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) by Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo.
 1. **[T5](https://huggingface.co/docs/transformers/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.
 1. **[T5v1.1](https://huggingface.co/docs/transformers/model_doc/t5v1.1)** (from Google AI) released in the repository [google-research/text-to-text-transfer-transformer](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511) 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/docs/transformers/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.
-1. **[Transformer-XL](https://huggingface.co/docs/transformers/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.
+1. **[Transformer-XL](https://huggingface.co/docs/transformers/model_doc/transfo-xl)** (from Google/CMU) released with the paper [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
 1. **[TrOCR](https://huggingface.co/docs/transformers/model_doc/trocr)** (from Microsoft), released together with the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, Furu Wei.
 1. **[UniSpeech](https://huggingface.co/docs/transformers/model_doc/unispeech)** (from Microsoft Research) released with the paper [UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data](https://arxiv.org/abs/2101.07597) by Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael Zeng, Xuedong Huang.
-1. **[UniSpeechSat](https://huggingface.co/docs/transformers/model_doc/unispeech_sat)** (from Microsoft Research) released with the paper [UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER
+1. **[UniSpeechSat](https://huggingface.co/docs/transformers/model_doc/unispeech-sat)** (from Microsoft Research) released with the paper [UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER
 AWARE PRE-TRAINING](https://arxiv.org/abs/2110.05752) by Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu.
+1. **[ViLT)](https://huggingface.co/docs/transformers/master/model_doc/vilt)** (from NAVER AI Lab/Kakao Enterprise/Kakao Brain) released with the paper [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) by Wonjae Kim, Bokyung Son, Ildoo Kim.
 1. **[Vision Transformer (ViT)](https://huggingface.co/docs/transformers/model_doc/vit)** (from Google AI) released with the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby.
+1. **[ViTMAE)](https://huggingface.co/docs/transformers/master/model_doc/vit_mae)** (from Meta AI) released with the paper [Masked Autoencoders Are Scalable Vision Learners](https://arxiv.org/abs/2111.06377) by Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick.
 1. **[VisualBERT](https://huggingface.co/docs/transformers/model_doc/visual_bert)** (from UCLA NLP) released with the paper [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://arxiv.org/pdf/1908.03557) by Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang.
 1. **[WavLM](https://huggingface.co/docs/transformers/master/model_doc/wavlm)** (from Microsoft Research) released with the paper [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://arxiv.org/abs/2110.13900) by Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Furu Wei.
 1. **[Wav2Vec2](https://huggingface.co/docs/transformers/model_doc/wav2vec2)** (from Facebook AI) released with the paper [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.
 1. **[Wav2Vec2Phoneme](https://huggingface.co/docs/master/transformers/model_doc/wav2vec2_phoneme)** (from Facebook AI) released with the paper [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition](https://arxiv.org/abs/2109.11680) by Qiantong Xu, Alexei Baevski, Michael Auli.
 1. **[XLM](https://huggingface.co/docs/transformers/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.
-1. **[XLM-ProphetNet](https://huggingface.co/docs/transformers/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.
-1. **[XLM-RoBERTa](https://huggingface.co/docs/transformers/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.
+1. **[XLM-ProphetNet](https://huggingface.co/docs/transformers/model_doc/xlm-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.
+1. **[XLM-RoBERTa](https://huggingface.co/docs/transformers/model_doc/xlm-roberta)** (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/docs/transformers/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.
 1. **[XLSR-Wav2Vec2](https://huggingface.co/docs/transformers/model_doc/xlsr_wav2vec2)** (from Facebook AI) released with the paper [Unsupervised Cross-Lingual Representation Learning For Speech Recognition](https://arxiv.org/abs/2006.13979) by Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli.
 1. **[XLS-R](https://huggingface.co/docs/master/transformers/model_doc/xls_r)** (from Facebook AI) released with the paper [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://arxiv.org/abs/2111.09296) by Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, Michael Auli.
+1. **[YOSO](https://huggingface.co/docs/transformers/master/model_doc/yoso)** (from the University of Wisconsin - Madison) released with the paper [You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling](https://arxiv.org/abs/2111.09714) by Zhanpeng Zeng, Yunyang Xiong, Sathya N. Ravi, Shailesh Acharya, Glenn Fung, Vikas Singh.
 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 Flax, PyTorch or TensorFlow, or has an associated tokenizer backed by the 🤗 Tokenizers library, refer to [this table](https://huggingface.co/docs/transformers/index#supported-frameworks).

README_ko.md

@@ -215,12 +215,12 @@ Flax, PyTorch, TensorFlow 설치 페이지에서 이들을 conda로 설치하는
 1. **[BARTpho](https://huggingface.co/docs/transformers/model_doc/bartpho)** (from VinAI Research) released with the paper [BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese](https://arxiv.org/abs/2109.09701) by Nguyen Luong Tran, Duong Minh Le and Dat Quoc Nguyen.
 1. **[BEiT](https://huggingface.co/docs/transformers/model_doc/beit)** (from Microsoft) released with the paper [BEiT: BERT Pre-Training of Image Transformers](https://arxiv.org/abs/2106.08254) by Hangbo Bao, Li Dong, Furu Wei.
 1. **[BERT](https://huggingface.co/docs/transformers/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.
-1. **[BERT For Sequence Generation](https://huggingface.co/docs/transformers/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.
+1. **[BERT For Sequence Generation](https://huggingface.co/docs/transformers/model_doc/bert-generation)** (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. **[BERTweet](https://huggingface.co/docs/transformers/model_doc/bertweet)** (from VinAI Research) released with the paper [BERTweet: A pre-trained language model for English Tweets](https://aclanthology.org/2020.emnlp-demos.2/) by Dat Quoc Nguyen, Thanh Vu and Anh Tuan Nguyen.
 1. **[BigBird-Pegasus](https://huggingface.co/docs/transformers/model_doc/bigbird_pegasus)** (from Google Research) released with the paper [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.
-1. **[BigBird-RoBERTa](https://huggingface.co/docs/transformers/model_doc/bigbird)** (from Google Research) released with the paper [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.
+1. **[BigBird-RoBERTa](https://huggingface.co/docs/transformers/model_doc/big_bird)** (from Google Research) released with the paper [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.
 1. **[Blenderbot](https://huggingface.co/docs/transformers/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.
-1. **[BlenderbotSmall](https://huggingface.co/docs/transformers/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.
+1. **[BlenderbotSmall](https://huggingface.co/docs/transformers/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.
 1. **[BORT](https://huggingface.co/docs/transformers/model_doc/bort)** (from Alexa) released with the paper [Optimal Subarchitecture Extraction For BERT](https://arxiv.org/abs/2010.10499) by Adrian de Wynter and Daniel J. Perry.
 1. **[ByT5](https://huggingface.co/docs/transformers/model_doc/byt5)** (from Google Research) released with the paper [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626) by Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel.
 1. **[CamemBERT](https://huggingface.co/docs/transformers/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.
@@ -230,18 +230,18 @@ Flax, PyTorch, TensorFlow 설치 페이지에서 이들을 conda로 설치하는
 1. **[CPM](https://huggingface.co/docs/transformers/model_doc/cpm)** (from Tsinghua University) released with the paper [CPM: A Large-scale Generative Chinese Pre-trained Language Model](https://arxiv.org/abs/2012.00413) by Zhengyan Zhang, Xu Han, Hao Zhou, Pei Ke, Yuxian Gu, Deming Ye, Yujia Qin, Yusheng Su, Haozhe Ji, Jian Guan, Fanchao Qi, Xiaozhi Wang, Yanan Zheng, Guoyang Zeng, Huanqi Cao, Shengqi Chen, Daixuan Li, Zhenbo Sun, Zhiyuan Liu, Minlie Huang, Wentao Han, Jie Tang, Juanzi Li, Xiaoyan Zhu, Maosong Sun.
 1. **[CTRL](https://huggingface.co/docs/transformers/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.
 1. **[DeBERTa](https://huggingface.co/docs/transformers/model_doc/deberta)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
-1. **[DeBERTa-v2](https://huggingface.co/docs/transformers/model_doc/deberta_v2)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
+1. **[DeBERTa-v2](https://huggingface.co/docs/transformers/model_doc/deberta-v2)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
 1. **[DeiT](https://huggingface.co/docs/transformers/model_doc/deit)** (from Facebook) released with the paper [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) by Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Hervé Jégou.
 1. **[DETR](https://huggingface.co/docs/transformers/model_doc/detr)** (from Facebook) released with the paper [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) by Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko.
 1. **[DialoGPT](https://huggingface.co/docs/transformers/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.
 1. **[DistilBERT](https://huggingface.co/docs/transformers/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.
 1. **[DPR](https://huggingface.co/docs/transformers/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.
 1. **[ELECTRA](https://huggingface.co/docs/transformers/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.
-1. **[EncoderDecoder](https://huggingface.co/docs/transformers/model_doc/encoderdecoder)** (from Google Research) 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. **[EncoderDecoder](https://huggingface.co/docs/transformers/model_doc/encoder-decoder)** (from Google Research) 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. **[FlauBERT](https://huggingface.co/docs/transformers/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.
 1. **[FNet](https://huggingface.co/docs/transformers/model_doc/fnet)** (from Google Research) released with the paper [FNet: Mixing Tokens with Fourier Transforms](https://arxiv.org/abs/2105.03824) by James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, Santiago Ontanon.
 1. **[Funnel Transformer](https://huggingface.co/docs/transformers/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.
-1. **[GPT](https://huggingface.co/docs/transformers/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.
+1. **[GPT](https://huggingface.co/docs/transformers/model_doc/openai-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.
 1. **[GPT Neo](https://huggingface.co/docs/transformers/model_doc/gpt_neo)** (from EleutherAI) released in the repository [EleutherAI/gpt-neo](https://github.com/EleutherAI/gpt-neo) by Sid Black, Stella Biderman, Leo Gao, Phil Wang and Connor Leahy.
 1. **[GPT-2](https://huggingface.co/docs/transformers/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**.
 1. **[GPT-J](https://huggingface.co/docs/transformers/model_doc/gptj)** (from EleutherAI) released in the repository [kingoflolz/mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax/) by Ben Wang and Aran Komatsuzaki.
@@ -259,16 +259,18 @@ Flax, PyTorch, TensorFlow 설치 페이지에서 이들을 conda로 설치하는
 1. **[MarianMT](https://huggingface.co/docs/transformers/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.
 1. **[MBart](https://huggingface.co/docs/transformers/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.
 1. **[MBart-50](https://huggingface.co/docs/transformers/model_doc/mbart)** (from Facebook) released with the paper [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://arxiv.org/abs/2008.00401) by Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, Angela Fan.
-1. **[Megatron-BERT](https://huggingface.co/docs/transformers/model_doc/megatron_bert)** (from NVIDIA) released with the paper [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/abs/1909.08053) by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
+1. **[Megatron-BERT](https://huggingface.co/docs/transformers/model_doc/megatron-bert)** (from NVIDIA) released with the paper [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/abs/1909.08053) by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
 1. **[Megatron-GPT2](https://huggingface.co/docs/transformers/model_doc/megatron_gpt2)** (from NVIDIA) released with the paper [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/abs/1909.08053) by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
 1. **[mLUKE](https://huggingface.co/docs/transformers/model_doc/mluke)** (from Studio Ousia) released with the paper [mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models](https://arxiv.org/abs/2110.08151) by Ryokan Ri, Ikuya Yamada, and Yoshimasa Tsuruoka.
 1. **[MPNet](https://huggingface.co/docs/transformers/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.
 1. **[MT5](https://huggingface.co/docs/transformers/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.
+1. **[Nyströmformer](https://huggingface.co/docs/transformers/master/model_doc/nystromformer)** (from the University of Wisconsin - Madison) released with the paper [Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention](https://arxiv.org/abs/2102.03902) by Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, Vikas Singh.
 1. **[Pegasus](https://huggingface.co/docs/transformers/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.
 1. **[Perceiver IO](https://huggingface.co/docs/transformers/model_doc/perceiver)** (from Deepmind) released with the paper [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://arxiv.org/abs/2107.14795) by Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira.
 1. **[PhoBERT](https://huggingface.co/docs/transformers/model_doc/phobert)** (from VinAI Research) released with the paper [PhoBERT: Pre-trained language models for Vietnamese](https://www.aclweb.org/anthology/2020.findings-emnlp.92/) by Dat Quoc Nguyen and Anh Tuan Nguyen.
 1. **[ProphetNet](https://huggingface.co/docs/transformers/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.
 1. **[QDQBert](https://huggingface.co/docs/transformers/model_doc/qdqbert)** (from NVIDIA) released with the paper [Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation](https://arxiv.org/abs/2004.09602) by Hao Wu, Patrick Judd, Xiaojie Zhang, Mikhail Isaev and Paulius Micikevicius.
+1. **[REALM](https://huggingface.co/transformers/model_doc/realm.html)** (from Google Research) released with the paper [REALM: Retrieval-Augmented Language Model Pre-Training](https://arxiv.org/abs/2002.08909) by Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat and Ming-Wei Chang.
 1. **[Reformer](https://huggingface.co/docs/transformers/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.
 1. **[RemBERT](https://huggingface.co/docs/transformers/model_doc/rembert)** (from Google Research) released with the paper [Rethinking embedding coupling in pre-trained language models](https://arxiv.org/pdf/2010.12821.pdf) by Hyung Won Chung, Thibault Févry, Henry Tsai, M. Johnson, Sebastian Ruder.
 1. **[RoBERTa](https://huggingface.co/docs/transformers/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.
@@ -280,24 +282,28 @@ Flax, PyTorch, TensorFlow 설치 페이지에서 이들을 conda로 설치하는
 1. **[SpeechToTextTransformer2](https://huggingface.co/docs/transformers/model_doc/speech_to_text_2)** (from Facebook), released together with the paper [Large-Scale Self- and Semi-Supervised Learning for Speech Translation](https://arxiv.org/abs/2104.06678) by Changhan Wang, Anne Wu, Juan Pino, Alexei Baevski, Michael Auli, Alexis Conneau.
 1. **[Splinter](https://huggingface.co/docs/transformers/model_doc/splinter)** (from Tel Aviv University), released together with the paper [Few-Shot Question Answering by Pretraining Span Selection](https://arxiv.org/abs/2101.00438) by Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, Omer Levy.
 1. **[SqueezeBert](https://huggingface.co/docs/transformers/model_doc/squeezebert)** (from Berkeley) 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. **[Swin Transformer](https://huggingface.co/docs/transformers/master/model_doc/swin)** (from Microsoft) released with the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) by Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo.
 1. **[T5](https://huggingface.co/docs/transformers/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.
 1. **[T5v1.1](https://huggingface.co/docs/transformers/model_doc/t5v1.1)** (from Google AI) released in the repository [google-research/text-to-text-transfer-transformer](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511) 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/docs/transformers/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.
-1. **[Transformer-XL](https://huggingface.co/docs/transformers/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.
+1. **[Transformer-XL](https://huggingface.co/docs/transformers/model_doc/transfo-xl)** (from Google/CMU) released with the paper [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
 1. **[TrOCR](https://huggingface.co/docs/transformers/model_doc/trocr)** (from Microsoft), released together with the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, Furu Wei.
 1. **[UniSpeech](https://huggingface.co/docs/transformers/model_doc/unispeech)** (from Microsoft Research) released with the paper [UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data](https://arxiv.org/abs/2101.07597) by Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael Zeng, Xuedong Huang.
-1. **[UniSpeechSat](https://huggingface.co/docs/transformers/model_doc/unispeech_sat)** (from Microsoft Research) released with the paper [UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER AWARE PRE-TRAINING](https://arxiv.org/abs/2110.05752) by Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu.
+1. **[UniSpeechSat](https://huggingface.co/docs/transformers/model_doc/unispeech-sat)** (from Microsoft Research) released with the paper [UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER AWARE PRE-TRAINING](https://arxiv.org/abs/2110.05752) by Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu.
+1. **[ViLT)](https://huggingface.co/docs/transformers/master/model_doc/vilt)** (from NAVER AI Lab/Kakao Enterprise/Kakao Brain) released with the paper [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) by Wonjae Kim, Bokyung Son, Ildoo Kim.
 1. **[Vision Transformer (ViT)](https://huggingface.co/docs/transformers/model_doc/vit)** (from Google AI) released with the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby.
 1. **[VisualBERT](https://huggingface.co/docs/transformers/model_doc/visual_bert)** (from UCLA NLP) released with the paper [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://arxiv.org/pdf/1908.03557) by Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang.
+1. **[ViTMAE)](https://huggingface.co/docs/transformers/master/model_doc/vit_mae)** (from Meta AI) released with the paper [Masked Autoencoders Are Scalable Vision Learners](https://arxiv.org/abs/2111.06377) by Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick.
 1. **[Wav2Vec2](https://huggingface.co/docs/transformers/model_doc/wav2vec2)** (from Facebook AI) released with the paper [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.
 1. **[Wav2Vec2Phoneme](https://huggingface.co/docs/master/transformers/model_doc/wav2vec2_phoneme)** (from Facebook AI) released with the paper [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition](https://arxiv.org/abs/2109.11680) by Qiantong Xu, Alexei Baevski, Michael Auli.
 1. **[WavLM](https://huggingface.co/docs/transformers/master/model_doc/wavlm)** (from Microsoft Research) released with the paper [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://arxiv.org/abs/2110.13900) by Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Furu Wei.
 1. **[XLM](https://huggingface.co/docs/transformers/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.
-1. **[XLM-ProphetNet](https://huggingface.co/docs/transformers/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.
-1. **[XLM-RoBERTa](https://huggingface.co/docs/transformers/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.
+1. **[XLM-ProphetNet](https://huggingface.co/docs/transformers/model_doc/xlm-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.
+1. **[XLM-RoBERTa](https://huggingface.co/docs/transformers/model_doc/xlm-roberta)** (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/docs/transformers/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.
 1. **[XLS-R](https://huggingface.co/docs/master/transformers/model_doc/xls_r)** (from Facebook AI) released with the paper [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://arxiv.org/abs/2111.09296) by Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, Michael Auli.
 1. **[XLSR-Wav2Vec2](https://huggingface.co/docs/transformers/model_doc/xlsr_wav2vec2)** (from Facebook AI) released with the paper [Unsupervised Cross-Lingual Representation Learning For Speech Recognition](https://arxiv.org/abs/2006.13979) by Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli.
+1. **[YOSO](https://huggingface.co/docs/transformers/master/model_doc/yoso)** (from the University of Wisconsin - Madison) released with the paper [You Only Sample (Almost) by Zhanpeng Zeng, Yunyang Xiong, Sathya N. Ravi, Shailesh Acharya, Glenn Fung, Vikas Singh.
 1. 새로운 모델을 올리고 싶나요? 우리가 **상세한 가이드와 템플릿** 으로 새로운 모델을 올리도록 도와드릴게요. 가이드와 템플릿은 이 저장소의 [`templates`](./templates) 폴더에서 확인하실 수 있습니다. [컨트리뷰션 가이드라인](./CONTRIBUTING.md)을 꼭 확인해주시고, PR을 올리기 전에 메인테이너에게 연락하거나 이슈를 오픈해 피드백을 받으시길 바랍니다. 
 
 각 모델이 Flax, PyTorch, TensorFlow으로 구현되었는지 또는 🤗 Tokenizers 라이브러리가 지원하는 토크나이저를 사용하는지 확인하려면, [이 표](https://huggingface.co/docs/transformers/index#supported-frameworks)를 확인하세요.

README_zh-hans.md

@@ -239,12 +239,12 @@ conda install -c huggingface transformers
 1. **[BARTpho](https://huggingface.co/docs/transformers/model_doc/bartpho)** (来自 VinAI Research) 伴随论文 [BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese](https://arxiv.org/abs/2109.09701) 由 Nguyen Luong Tran, Duong Minh Le and Dat Quoc Nguyen 发布。
 1. **[BEiT](https://huggingface.co/docs/transformers/model_doc/beit)** (来自 Microsoft) 伴随论文 [BEiT: BERT Pre-Training of Image Transformers](https://arxiv.org/abs/2106.08254) 由 Hangbo Bao, Li Dong, Furu Wei 发布。
 1. **[BERT](https://huggingface.co/docs/transformers/model_doc/bert)** (来自 Google) 伴随论文 [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) 由 Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova 发布。
-1. **[BERT For Sequence Generation](https://huggingface.co/docs/transformers/model_doc/bertgeneration)** (来自 Google) 伴随论文 [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) 由 Sascha Rothe, Shashi Narayan, Aliaksei Severyn 发布。
+1. **[BERT For Sequence Generation](https://huggingface.co/docs/transformers/model_doc/bert-generation)** (来自 Google) 伴随论文 [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) 由 Sascha Rothe, Shashi Narayan, Aliaksei Severyn 发布。
 1. **[BERTweet](https://huggingface.co/docs/transformers/model_doc/bertweet)** (来自 VinAI Research) 伴随论文 [BERTweet: A pre-trained language model for English Tweets](https://aclanthology.org/2020.emnlp-demos.2/) 由 Dat Quoc Nguyen, Thanh Vu and Anh Tuan Nguyen 发布。
 1. **[BigBird-Pegasus](https://huggingface.co/docs/transformers/model_doc/bigbird_pegasus)** (来自 Google Research) 伴随论文 [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) 由 Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed 发布。
-1. **[BigBird-RoBERTa](https://huggingface.co/docs/transformers/model_doc/bigbird)** (来自 Google Research) 伴随论文 [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) 由 Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed 发布。
+1. **[BigBird-RoBERTa](https://huggingface.co/docs/transformers/model_doc/big_bird)** (来自 Google Research) 伴随论文 [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) 由 Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed 发布。
 1. **[Blenderbot](https://huggingface.co/docs/transformers/model_doc/blenderbot)** (来自 Facebook) 伴随论文 [Recipes for building an open-domain chatbot](https://arxiv.org/abs/2004.13637) 由 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 发布。
-1. **[BlenderbotSmall](https://huggingface.co/docs/transformers/model_doc/blenderbot_small)** (来自 Facebook) 伴随论文 [Recipes for building an open-domain chatbot](https://arxiv.org/abs/2004.13637) 由 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 发布。
+1. **[BlenderbotSmall](https://huggingface.co/docs/transformers/model_doc/blenderbot-small)** (来自 Facebook) 伴随论文 [Recipes for building an open-domain chatbot](https://arxiv.org/abs/2004.13637) 由 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 发布。
 1. **[BORT](https://huggingface.co/docs/transformers/model_doc/bort)** (来自 Alexa) 伴随论文 [Optimal Subarchitecture Extraction For BERT](https://arxiv.org/abs/2010.10499) 由 Adrian de Wynter and Daniel J. Perry 发布。
 1. **[ByT5](https://huggingface.co/docs/transformers/model_doc/byt5)** (来自 Google Research) 伴随论文 [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626) 由 Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel 发布。
 1. **[CamemBERT](https://huggingface.co/docs/transformers/model_doc/camembert)** (来自 Inria/Facebook/Sorbonne) 伴随论文 [CamemBERT: a Tasty French Language Model](https://arxiv.org/abs/1911.03894) 由 Louis Martin*, Benjamin Muller*, Pedro Javier Ortiz Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot 发布。
@@ -254,18 +254,18 @@ conda install -c huggingface transformers
 1. **[CPM](https://huggingface.co/docs/transformers/model_doc/cpm)** (来自 Tsinghua University) 伴随论文 [CPM: A Large-scale Generative Chinese Pre-trained Language Model](https://arxiv.org/abs/2012.00413) 由 Zhengyan Zhang, Xu Han, Hao Zhou, Pei Ke, Yuxian Gu, Deming Ye, Yujia Qin, Yusheng Su, Haozhe Ji, Jian Guan, Fanchao Qi, Xiaozhi Wang, Yanan Zheng, Guoyang Zeng, Huanqi Cao, Shengqi Chen, Daixuan Li, Zhenbo Sun, Zhiyuan Liu, Minlie Huang, Wentao Han, Jie Tang, Juanzi Li, Xiaoyan Zhu, Maosong Sun 发布。
 1. **[CTRL](https://huggingface.co/docs/transformers/model_doc/ctrl)** (来自 Salesforce) 伴随论文 [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://arxiv.org/abs/1909.05858) 由 Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and Richard Socher 发布。
 1. **[DeBERTa](https://huggingface.co/docs/transformers/model_doc/deberta)** (来自 Microsoft) 伴随论文 [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) 由 Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen 发布。
-1. **[DeBERTa-v2](https://huggingface.co/docs/transformers/model_doc/deberta_v2)** (来自 Microsoft) 伴随论文 [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) 由 Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen 发布。
+1. **[DeBERTa-v2](https://huggingface.co/docs/transformers/model_doc/deberta-v2)** (来自 Microsoft) 伴随论文 [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) 由 Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen 发布。
 1. **[DeiT](https://huggingface.co/docs/transformers/model_doc/deit)** (来自 Facebook) 伴随论文 [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) 由 Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Hervé Jégou 发布。
 1. **[DETR](https://huggingface.co/docs/transformers/model_doc/detr)** (来自 Facebook) 伴随论文 [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) 由 Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko 发布。
 1. **[DialoGPT](https://huggingface.co/docs/transformers/model_doc/dialogpt)** (来自 Microsoft Research) 伴随论文 [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://arxiv.org/abs/1911.00536) 由 Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan 发布。
 1. **[DistilBERT](https://huggingface.co/docs/transformers/model_doc/distilbert)** (来自 HuggingFace), 伴随论文 [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 同样的方法也应用于压缩 GPT-2 到 [DistilGPT2](https://github.com/huggingface/transformers/tree/master/examples/distillation), RoBERTa 到 [DistilRoBERTa](https://github.com/huggingface/transformers/tree/master/examples/distillation), Multilingual BERT 到 [DistilmBERT](https://github.com/huggingface/transformers/tree/master/examples/distillation) 和德语版 DistilBERT。
 1. **[DPR](https://huggingface.co/docs/transformers/model_doc/dpr)** (来自 Facebook) 伴随论文 [Dense Passage Retrieval for Open-Domain Question Answering](https://arxiv.org/abs/2004.04906) 由 Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih 发布。
 1. **[ELECTRA](https://huggingface.co/docs/transformers/model_doc/electra)** (来自 Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning 发布。
-1. **[EncoderDecoder](https://huggingface.co/docs/transformers/model_doc/encoderdecoder)** (来自 Google Research) 伴随论文 [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) 由 Sascha Rothe, Shashi Narayan, Aliaksei Severyn 发布。
+1. **[EncoderDecoder](https://huggingface.co/docs/transformers/model_doc/encoder-decoder)** (来自 Google Research) 伴随论文 [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) 由 Sascha Rothe, Shashi Narayan, Aliaksei Severyn 发布。
 1. **[FlauBERT](https://huggingface.co/docs/transformers/model_doc/flaubert)** (来自 CNRS) 伴随论文 [FlauBERT: Unsupervised Language Model Pre-training for French](https://arxiv.org/abs/1912.05372) 由 Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, Didier Schwab 发布。
 1. **[FNet](https://huggingface.co/docs/transformers/model_doc/fnet)** (来自 Google Research) 伴随论文 [FNet: Mixing Tokens with Fourier Transforms](https://arxiv.org/abs/2105.03824) 由 James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, Santiago Ontanon 发布。
 1. **[Funnel Transformer](https://huggingface.co/docs/transformers/model_doc/funnel)** (来自 CMU/Google Brain) 伴随论文 [Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing](https://arxiv.org/abs/2006.03236) 由 Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le 发布。
-1. **[GPT](https://huggingface.co/docs/transformers/model_doc/gpt)** (来自 OpenAI) 伴随论文 [Improving Language Understanding by Generative Pre-Training](https://blog.openai.com/language-unsupervised/) 由 Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever 发布。
+1. **[GPT](https://huggingface.co/docs/transformers/model_doc/openai-gpt)** (来自 OpenAI) 伴随论文 [Improving Language Understanding by Generative Pre-Training](https://blog.openai.com/language-unsupervised/) 由 Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever 发布。
 1. **[GPT Neo](https://huggingface.co/docs/transformers/model_doc/gpt_neo)** (来自 EleutherAI) 随仓库 [EleutherAI/gpt-neo](https://github.com/EleutherAI/gpt-neo) 发布。作者为 Sid Black, Stella Biderman, Leo Gao, Phil Wang and Connor Leahy 发布。
 1. **[GPT-2](https://huggingface.co/docs/transformers/model_doc/gpt2)** (来自 OpenAI) 伴随论文 [Language Models are Unsupervised Multitask Learners](https://blog.openai.com/better-language-models/) 由 Alec Radford*, Jeffrey Wu*, Rewon Child, David Luan, Dario Amodei** and Ilya Sutskever** 发布。
 1. **[GPT-J](https://huggingface.co/docs/transformers/model_doc/gptj)** (来自 EleutherAI) 伴随论文 [kingoflolz/mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax/) 由 Ben Wang and Aran Komatsuzaki 发布。
@@ -283,16 +283,18 @@ conda install -c huggingface transformers
 1. **[MarianMT](https://huggingface.co/docs/transformers/model_doc/marian)** 用 [OPUS](http://opus.nlpl.eu/) 数据训练的机器翻译模型由 Jörg Tiedemann 发布。[Marian Framework](https://marian-nmt.github.io/) 由微软翻译团队开发。
 1. **[MBart](https://huggingface.co/docs/transformers/model_doc/mbart)** (来自 Facebook) 伴随论文 [Multilingual Denoising Pre-training for Neural Machine Translation](https://arxiv.org/abs/2001.08210) 由 Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer 发布。
 1. **[MBart-50](https://huggingface.co/docs/transformers/model_doc/mbart)** (来自 Facebook) 伴随论文 [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://arxiv.org/abs/2008.00401) 由 Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, Angela Fan 发布。
-1. **[Megatron-BERT](https://huggingface.co/docs/transformers/model_doc/megatron_bert)** (来自 NVIDIA) 伴随论文 [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/abs/1909.08053) 由 Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro 发布。
+1. **[Megatron-BERT](https://huggingface.co/docs/transformers/model_doc/megatron-bert)** (来自 NVIDIA) 伴随论文 [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/abs/1909.08053) 由 Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro 发布。
 1. **[Megatron-GPT2](https://huggingface.co/docs/transformers/model_doc/megatron_gpt2)** (来自 NVIDIA) 伴随论文 [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/abs/1909.08053) 由 Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro 发布。
 1. **[mLUKE](https://huggingface.co/docs/transformers/model_doc/mluke)** (来自 Studio Ousia) 伴随论文 [mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models](https://arxiv.org/abs/2110.08151) 由 Ryokan Ri, Ikuya Yamada, and Yoshimasa Tsuruoka 发布。
 1. **[MPNet](https://huggingface.co/docs/transformers/model_doc/mpnet)** (来自 Microsoft Research) 伴随论文 [MPNet: Masked and Permuted Pre-training for Language Understanding](https://arxiv.org/abs/2004.09297) 由 Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu 发布。
 1. **[MT5](https://huggingface.co/docs/transformers/model_doc/mt5)** (来自 Google AI) 伴随论文 [mT5: A massively multilingual pre-trained text-to-text transformer](https://arxiv.org/abs/2010.11934) 由 Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel 发布。
+1. **[Nyströmformer](https://huggingface.co/docs/transformers/master/model_doc/nystromformer)** (来自 the University of Wisconsin - Madison) 伴随论文 [Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention](https://arxiv.org/abs/2102.03902) 由 Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, Vikas Singh 发布。
 1. **[Pegasus](https://huggingface.co/docs/transformers/model_doc/pegasus)** (来自 Google) 伴随论文 [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/abs/1912.08777) 由 Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu 发布。
 1. **[Perceiver IO](https://huggingface.co/docs/transformers/model_doc/perceiver)** (来自 Deepmind) 伴随论文 [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://arxiv.org/abs/2107.14795) 由 Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira 发布。
 1. **[PhoBERT](https://huggingface.co/docs/transformers/model_doc/phobert)** (来自 VinAI Research) 伴随论文 [PhoBERT: Pre-trained language models for Vietnamese](https://www.aclweb.org/anthology/2020.findings-emnlp.92/) 由 Dat Quoc Nguyen and Anh Tuan Nguyen 发布。
 1. **[ProphetNet](https://huggingface.co/docs/transformers/model_doc/prophetnet)** (来自 Microsoft Research) 伴随论文 [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) 由 Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou 发布。
 1. **[QDQBert](https://huggingface.co/docs/transformers/model_doc/qdqbert)** (来自 NVIDIA) 伴随论文 [Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation](https://arxiv.org/abs/2004.09602) 由 Hao Wu, Patrick Judd, Xiaojie Zhang, Mikhail Isaev and Paulius Micikevicius 发布。
+1. **[REALM](https://huggingface.co/transformers/model_doc/realm.html)** (来自 Google Research) 伴随论文 [REALM: Retrieval-Augmented Language Model Pre-Training](https://arxiv.org/abs/2002.08909) 由 Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat and Ming-Wei Chang 发布。
 1. **[Reformer](https://huggingface.co/docs/transformers/model_doc/reformer)** (来自 Google Research) 伴随论文 [Reformer: The Efficient Transformer](https://arxiv.org/abs/2001.04451) 由 Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya 发布。
 1. **[RemBERT](https://huggingface.co/docs/transformers/model_doc/rembert)** (来自 Google Research) 伴随论文 [Rethinking embedding coupling in pre-trained language models](https://arxiv.org/pdf/2010.12821.pdf) 由 Hyung Won Chung, Thibault Févry, Henry Tsai, M. Johnson, Sebastian Ruder 发布。
 1. **[RoBERTa](https://huggingface.co/docs/transformers/model_doc/roberta)** (来自 Facebook), 伴随论文 [Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692) 由 Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov 发布。
@@ -304,24 +306,28 @@ conda install -c huggingface transformers
 1. **[SpeechToTextTransformer2](https://huggingface.co/docs/transformers/model_doc/speech_to_text_2)** (来自 Facebook) 伴随论文 [Large-Scale Self- and Semi-Supervised Learning for Speech Translation](https://arxiv.org/abs/2104.06678) 由 Changhan Wang, Anne Wu, Juan Pino, Alexei Baevski, Michael Auli, Alexis Conneau 发布。
 1. **[Splinter](https://huggingface.co/docs/transformers/model_doc/splinter)** (来自 Tel Aviv University) 伴随论文 [Few-Shot Question Answering by Pretraining Span Selection](https://arxiv.org/abs/2101.00438) 由 Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, Omer Levy 发布。
 1. **[SqueezeBert](https://huggingface.co/docs/transformers/model_doc/squeezebert)** (来自 Berkeley) 伴随论文 [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) 由 Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer 发布。
+1. **[Swin Transformer](https://huggingface.co/docs/transformers/master/model_doc/swin)** (来自 Microsoft) 伴随论文 [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) 由 Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo 发布。
 1. **[T5](https://huggingface.co/docs/transformers/model_doc/t5)** (来自 Google AI) 伴随论文 [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) 由 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 发布。
 1. **[T5v1.1](https://huggingface.co/docs/transformers/model_doc/t5v1.1)** (来自 Google AI) 伴随论文 [google-research/text-to-text-transfer-transformer](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511) 由 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 发布。
 1. **[TAPAS](https://huggingface.co/docs/transformers/model_doc/tapas)** (来自 Google AI) 伴随论文 [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://arxiv.org/abs/2004.02349) 由 Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos 发布。
-1. **[Transformer-XL](https://huggingface.co/docs/transformers/model_doc/transformerxl)** (来自 Google/CMU) 伴随论文 [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) 由 Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov 发布。
+1. **[Transformer-XL](https://huggingface.co/docs/transformers/model_doc/transfo-xl)** (来自 Google/CMU) 伴随论文 [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) 由 Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov 发布。
 1. **[TrOCR](https://huggingface.co/docs/transformers/model_doc/trocr)** (来自 Microsoft) 伴随论文 [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) 由 Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, Furu Wei 发布。
 1. **[UniSpeech](https://huggingface.co/docs/transformers/model_doc/unispeech)** (来自 Microsoft Research) 伴随论文 [UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data](https://arxiv.org/abs/2101.07597) 由 Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael Zeng, Xuedong Huang 发布。
-1. **[UniSpeechSat](https://huggingface.co/docs/transformers/model_doc/unispeech_sat)** (来自 Microsoft Research) 伴随论文 [UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER AWARE PRE-TRAINING](https://arxiv.org/abs/2110.05752) 由 Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu 发布。
+1. **[UniSpeechSat](https://huggingface.co/docs/transformers/model_doc/unispeech-sat)** (来自 Microsoft Research) 伴随论文 [UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER AWARE PRE-TRAINING](https://arxiv.org/abs/2110.05752) 由 Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu 发布。
+1. **[ViLT)](https://huggingface.co/docs/transformers/master/model_doc/vilt)** (来自 NAVER AI Lab/Kakao Enterprise/Kakao Brain) 伴随论文 [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) 由 Wonjae Kim, Bokyung Son, Ildoo Kim 发布。
 1. **[Vision Transformer (ViT)](https://huggingface.co/docs/transformers/model_doc/vit)** (来自 Google AI) 伴随论文 [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) 由 Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby 发布。
 1. **[VisualBERT](https://huggingface.co/docs/transformers/model_doc/visual_bert)** (来自 UCLA NLP) 伴随论文 [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://arxiv.org/pdf/1908.03557) 由 Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang 发布。
+1. **[ViTMAE)](https://huggingface.co/docs/transformers/master/model_doc/vit_mae)** (来自 Meta AI) 伴随论文 [Masked Autoencoders Are Scalable Vision Learners](https://arxiv.org/abs/2111.06377) 由 Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick 发布。
 1. **[Wav2Vec2](https://huggingface.co/docs/transformers/model_doc/wav2vec2)** (来自 Facebook AI) 伴随论文 [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://arxiv.org/abs/2006.11477) 由 Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli 发布。
 1. **[Wav2Vec2Phoneme](https://huggingface.co/docs/master/transformers/model_doc/wav2vec2_phoneme)** (来自 Facebook AI) 伴随论文 [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition](https://arxiv.org/abs/2109.11680) 由 Qiantong Xu, Alexei Baevski, Michael Auli 发布。
 1. **[WavLM](https://huggingface.co/docs/transformers/master/model_doc/wavlm)** (from Microsoft Research) released with the paper [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://arxiv.org/abs/2110.13900) by Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Furu Wei.
 1. **[XLM](https://huggingface.co/docs/transformers/model_doc/xlm)** (来自 Facebook) 伴随论文 [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) 由 Guillaume Lample and Alexis Conneau 发布。
-1. **[XLM-ProphetNet](https://huggingface.co/docs/transformers/model_doc/xlmprophetnet)** (来自 Microsoft Research) 伴随论文 [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) 由 Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou 发布。
-1. **[XLM-RoBERTa](https://huggingface.co/docs/transformers/model_doc/xlmroberta)** (来自 Facebook AI), 伴随论文 [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) 由 Alexis Conneau*, Kartikay Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov 发布。
+1. **[XLM-ProphetNet](https://huggingface.co/docs/transformers/model_doc/xlm-prophetnet)** (来自 Microsoft Research) 伴随论文 [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) 由 Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou 发布。
+1. **[XLM-RoBERTa](https://huggingface.co/docs/transformers/model_doc/xlm-roberta)** (来自 Facebook AI), 伴随论文 [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) 由 Alexis Conneau*, Kartikay Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov 发布。
 1. **[XLNet](https://huggingface.co/docs/transformers/model_doc/xlnet)** (来自 Google/CMU) 伴随论文 [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) 由 Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le 发布。
 1. **[XLS-R](https://huggingface.co/docs/master/transformers/model_doc/xls_r)** (来自 Facebook AI) 伴随论文 [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://arxiv.org/abs/2111.09296) 由 Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, Michael Auli 发布。
 1. **[XLSR-Wav2Vec2](https://huggingface.co/docs/transformers/model_doc/xlsr_wav2vec2)** (来自 Facebook AI) 伴随论文 [Unsupervised Cross-Lingual Representation Learning For Speech Recognition](https://arxiv.org/abs/2006.13979) 由 Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli 发布。
+1. **[YOSO](https://huggingface.co/docs/transformers/master/model_doc/yoso)** (来自 the University of Wisconsin - Madison) 伴随论文 [You Only Sample (Almost) 由 Zhanpeng Zeng, Yunyang Xiong, Sathya N. Ravi, Shailesh Acharya, Glenn Fung, Vikas Singh 发布。
 1. 想要贡献新的模型？我们这里有一份**详细指引和模板**来引导你添加新的模型。你可以在 [`templates`](./templates) 目录中找到他们。记得查看 [贡献指南](./CONTRIBUTING.md) 并在开始写 PR 前联系维护人员或开一个新的 issue 来获得反馈。
 
 要检查某个模型是否已有 Flax、PyTorch 或 TensorFlow 的实现，或其是否在 🤗 Tokenizers 库中有对应词符化器（tokenizer），敬请参阅[此表](https://huggingface.co/docs/transformers/index#supported-frameworks)。

README_zh-hant.md

@@ -251,12 +251,12 @@ conda install -c huggingface transformers
 1. **[BARTpho](https://huggingface.co/docs/transformers/model_doc/bartpho)** (from VinAI Research) released with the paper [BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese](https://arxiv.org/abs/2109.09701) by Nguyen Luong Tran, Duong Minh Le and Dat Quoc Nguyen.
 1. **[BEiT](https://huggingface.co/docs/transformers/model_doc/beit)** (from Microsoft) released with the paper [BEiT: BERT Pre-Training of Image Transformers](https://arxiv.org/abs/2106.08254) by Hangbo Bao, Li Dong, Furu Wei.
 1. **[BERT](https://huggingface.co/docs/transformers/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.
-1. **[BERT For Sequence Generation](https://huggingface.co/docs/transformers/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.
+1. **[BERT For Sequence Generation](https://huggingface.co/docs/transformers/model_doc/bert-generation)** (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. **[BERTweet](https://huggingface.co/docs/transformers/model_doc/bertweet)** (from VinAI Research) released with the paper [BERTweet: A pre-trained language model for English Tweets](https://aclanthology.org/2020.emnlp-demos.2/) by Dat Quoc Nguyen, Thanh Vu and Anh Tuan Nguyen.
 1. **[BigBird-Pegasus](https://huggingface.co/docs/transformers/model_doc/bigbird_pegasus)** (from Google Research) released with the paper [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.
-1. **[BigBird-RoBERTa](https://huggingface.co/docs/transformers/model_doc/bigbird)** (from Google Research) released with the paper [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.
+1. **[BigBird-RoBERTa](https://huggingface.co/docs/transformers/model_doc/big_bird)** (from Google Research) released with the paper [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.
 1. **[Blenderbot](https://huggingface.co/docs/transformers/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.
-1. **[BlenderbotSmall](https://huggingface.co/docs/transformers/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.
+1. **[BlenderbotSmall](https://huggingface.co/docs/transformers/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.
 1. **[BORT](https://huggingface.co/docs/transformers/model_doc/bort)** (from Alexa) released with the paper [Optimal Subarchitecture Extraction For BERT](https://arxiv.org/abs/2010.10499) by Adrian de Wynter and Daniel J. Perry.
 1. **[ByT5](https://huggingface.co/docs/transformers/model_doc/byt5)** (from Google Research) released with the paper [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626) by Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel.
 1. **[CamemBERT](https://huggingface.co/docs/transformers/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.
@@ -266,18 +266,18 @@ conda install -c huggingface transformers
 1. **[CPM](https://huggingface.co/docs/transformers/model_doc/cpm)** (from Tsinghua University) released with the paper [CPM: A Large-scale Generative Chinese Pre-trained Language Model](https://arxiv.org/abs/2012.00413) by Zhengyan Zhang, Xu Han, Hao Zhou, Pei Ke, Yuxian Gu, Deming Ye, Yujia Qin, Yusheng Su, Haozhe Ji, Jian Guan, Fanchao Qi, Xiaozhi Wang, Yanan Zheng, Guoyang Zeng, Huanqi Cao, Shengqi Chen, Daixuan Li, Zhenbo Sun, Zhiyuan Liu, Minlie Huang, Wentao Han, Jie Tang, Juanzi Li, Xiaoyan Zhu, Maosong Sun.
 1. **[CTRL](https://huggingface.co/docs/transformers/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.
 1. **[DeBERTa](https://huggingface.co/docs/transformers/model_doc/deberta)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
-1. **[DeBERTa-v2](https://huggingface.co/docs/transformers/model_doc/deberta_v2)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
+1. **[DeBERTa-v2](https://huggingface.co/docs/transformers/model_doc/deberta-v2)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
 1. **[DeiT](https://huggingface.co/docs/transformers/model_doc/deit)** (from Facebook) released with the paper [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) by Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Hervé Jégou.
 1. **[DETR](https://huggingface.co/docs/transformers/model_doc/detr)** (from Facebook) released with the paper [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) by Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko.
 1. **[DialoGPT](https://huggingface.co/docs/transformers/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.
 1. **[DistilBERT](https://huggingface.co/docs/transformers/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.
 1. **[DPR](https://huggingface.co/docs/transformers/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.
 1. **[ELECTRA](https://huggingface.co/docs/transformers/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.
-1. **[EncoderDecoder](https://huggingface.co/docs/transformers/model_doc/encoderdecoder)** (from Google Research) 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. **[EncoderDecoder](https://huggingface.co/docs/transformers/model_doc/encoder-decoder)** (from Google Research) 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. **[FlauBERT](https://huggingface.co/docs/transformers/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.
 1. **[FNet](https://huggingface.co/docs/transformers/model_doc/fnet)** (from Google Research) released with the paper [FNet: Mixing Tokens with Fourier Transforms](https://arxiv.org/abs/2105.03824) by James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, Santiago Ontanon.
 1. **[Funnel Transformer](https://huggingface.co/docs/transformers/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.
-1. **[GPT](https://huggingface.co/docs/transformers/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.
+1. **[GPT](https://huggingface.co/docs/transformers/model_doc/openai-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.
 1. **[GPT Neo](https://huggingface.co/docs/transformers/model_doc/gpt_neo)** (from EleutherAI) released in the repository [EleutherAI/gpt-neo](https://github.com/EleutherAI/gpt-neo) by Sid Black, Stella Biderman, Leo Gao, Phil Wang and Connor Leahy.
 1. **[GPT-2](https://huggingface.co/docs/transformers/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**.
 1. **[GPT-J](https://huggingface.co/docs/transformers/model_doc/gptj)** (from EleutherAI) released with the paper [kingoflolz/mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax/) by Ben Wang and Aran Komatsuzaki.
@@ -295,16 +295,18 @@ conda install -c huggingface transformers
 1. **[MarianMT](https://huggingface.co/docs/transformers/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.
 1. **[MBart](https://huggingface.co/docs/transformers/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.
 1. **[MBart-50](https://huggingface.co/docs/transformers/model_doc/mbart)** (from Facebook) released with the paper [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://arxiv.org/abs/2008.00401) by Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, Angela Fan.
-1. **[Megatron-BERT](https://huggingface.co/docs/transformers/model_doc/megatron_bert)** (from NVIDIA) released with the paper [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/abs/1909.08053) by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
+1. **[Megatron-BERT](https://huggingface.co/docs/transformers/model_doc/megatron-bert)** (from NVIDIA) released with the paper [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/abs/1909.08053) by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
 1. **[Megatron-GPT2](https://huggingface.co/docs/transformers/model_doc/megatron_gpt2)** (from NVIDIA) released with the paper [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/abs/1909.08053) by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
 1. **[mLUKE](https://huggingface.co/docs/transformers/model_doc/mluke)** (from Studio Ousia) released with the paper [mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models](https://arxiv.org/abs/2110.08151) by Ryokan Ri, Ikuya Yamada, and Yoshimasa Tsuruoka.
 1. **[MPNet](https://huggingface.co/docs/transformers/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.
 1. **[MT5](https://huggingface.co/docs/transformers/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.
+1. **[Nyströmformer](https://huggingface.co/docs/transformers/master/model_doc/nystromformer)** (from the University of Wisconsin - Madison) released with the paper [Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention](https://arxiv.org/abs/2102.03902) by Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, Vikas Singh.
 1. **[Pegasus](https://huggingface.co/docs/transformers/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.
 1. **[Perceiver IO](https://huggingface.co/docs/transformers/model_doc/perceiver)** (from Deepmind) released with the paper [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://arxiv.org/abs/2107.14795) by Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira.
 1. **[PhoBERT](https://huggingface.co/docs/transformers/model_doc/phobert)** (from VinAI Research) released with the paper [PhoBERT: Pre-trained language models for Vietnamese](https://www.aclweb.org/anthology/2020.findings-emnlp.92/) by Dat Quoc Nguyen and Anh Tuan Nguyen.
 1. **[ProphetNet](https://huggingface.co/docs/transformers/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.
 1. **[QDQBert](https://huggingface.co/docs/transformers/model_doc/qdqbert)** (from NVIDIA) released with the paper [Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation](https://arxiv.org/abs/2004.09602) by Hao Wu, Patrick Judd, Xiaojie Zhang, Mikhail Isaev and Paulius Micikevicius.
+1. **[REALM](https://huggingface.co/transformers/model_doc/realm.html)** (from Google Research) released with the paper [REALM: Retrieval-Augmented Language Model Pre-Training](https://arxiv.org/abs/2002.08909) by Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat and Ming-Wei Chang.
 1. **[Reformer](https://huggingface.co/docs/transformers/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.
 1. **[RemBERT](https://huggingface.co/docs/transformers/model_doc/rembert)** (from Google Research) released with the paper [Rethinking embedding coupling in pre-trained language models](https://arxiv.org/pdf/2010.12821.pdf) by Hyung Won Chung, Thibault Févry, Henry Tsai, M. Johnson, Sebastian Ruder.
 1. **[RoBERTa](https://huggingface.co/docs/transformers/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.
@@ -316,24 +318,28 @@ conda install -c huggingface transformers
 1. **[SpeechToTextTransformer2](https://huggingface.co/docs/transformers/model_doc/speech_to_text_2)** (from Facebook) released with the paper [Large-Scale Self- and Semi-Supervised Learning for Speech Translation](https://arxiv.org/abs/2104.06678) by Changhan Wang, Anne Wu, Juan Pino, Alexei Baevski, Michael Auli, Alexis Conneau.
 1. **[Splinter](https://huggingface.co/docs/transformers/model_doc/splinter)** (from Tel Aviv University) released with the paper [Few-Shot Question Answering by Pretraining Span Selection](https://arxiv.org/abs/2101.00438) by Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, Omer Levy.
 1. **[SqueezeBert](https://huggingface.co/docs/transformers/model_doc/squeezebert)** (from Berkeley) 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. **[Swin Transformer](https://huggingface.co/docs/transformers/master/model_doc/swin)** (from Microsoft) released with the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) by Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo.
 1. **[T5](https://huggingface.co/docs/transformers/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.
 1. **[T5v1.1](https://huggingface.co/docs/transformers/model_doc/t5v1.1)** (from Google AI) released with the paper [google-research/text-to-text-transfer-transformer](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511) 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/docs/transformers/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.
-1. **[Transformer-XL](https://huggingface.co/docs/transformers/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.
+1. **[Transformer-XL](https://huggingface.co/docs/transformers/model_doc/transfo-xl)** (from Google/CMU) released with the paper [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
 1. **[TrOCR](https://huggingface.co/docs/transformers/model_doc/trocr)** (from Microsoft) released with the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, Furu Wei.
 1. **[UniSpeech](https://huggingface.co/docs/transformers/model_doc/unispeech)** (from Microsoft Research) released with the paper [UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data](https://arxiv.org/abs/2101.07597) by Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael Zeng, Xuedong Huang.
-1. **[UniSpeechSat](https://huggingface.co/docs/transformers/model_doc/unispeech_sat)** (from Microsoft Research) released with the paper [UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER AWARE PRE-TRAINING](https://arxiv.org/abs/2110.05752) by Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu.
+1. **[UniSpeechSat](https://huggingface.co/docs/transformers/model_doc/unispeech-sat)** (from Microsoft Research) released with the paper [UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER AWARE PRE-TRAINING](https://arxiv.org/abs/2110.05752) by Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu.
+1. **[ViLT)](https://huggingface.co/docs/transformers/master/model_doc/vilt)** (from NAVER AI Lab/Kakao Enterprise/Kakao Brain) released with the paper [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) by Wonjae Kim, Bokyung Son, Ildoo Kim.
 1. **[Vision Transformer (ViT)](https://huggingface.co/docs/transformers/model_doc/vit)** (from Google AI) released with the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby. 
 1. **[VisualBERT](https://huggingface.co/docs/transformers/model_doc/visual_bert)** (from UCLA NLP) released with the paper [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://arxiv.org/pdf/1908.03557) by Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang.
+1. **[ViTMAE)](https://huggingface.co/docs/transformers/master/model_doc/vit_mae)** (from Meta AI) released with the paper [Masked Autoencoders Are Scalable Vision Learners](https://arxiv.org/abs/2111.06377) by Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick.
 1. **[Wav2Vec2](https://huggingface.co/docs/transformers/model_doc/wav2vec2)** (from Facebook AI) released with the paper [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.
 1. **[Wav2Vec2Phoneme](https://huggingface.co/docs/master/transformers/model_doc/wav2vec2_phoneme)** (from Facebook AI) released with the paper [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition](https://arxiv.org/abs/2109.11680) by Qiantong Xu, Alexei Baevski, Michael Auli.
 1. **[WavLM](https://huggingface.co/docs/transformers/master/model_doc/wavlm)** (from Microsoft Research) released with the paper [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://arxiv.org/abs/2110.13900) by Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Furu Wei.
 1. **[XLM](https://huggingface.co/docs/transformers/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.
-1. **[XLM-ProphetNet](https://huggingface.co/docs/transformers/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.
-1. **[XLM-RoBERTa](https://huggingface.co/docs/transformers/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.
+1. **[XLM-ProphetNet](https://huggingface.co/docs/transformers/model_doc/xlm-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.
+1. **[XLM-RoBERTa](https://huggingface.co/docs/transformers/model_doc/xlm-roberta)** (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/docs/transformers/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.
 1. **[XLS-R](https://huggingface.co/docs/master/transformers/model_doc/xls_r)** (from Facebook AI) released with the paper [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://arxiv.org/abs/2111.09296) by Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, Michael Auli.
 1. **[XLSR-Wav2Vec2](https://huggingface.co/docs/transformers/model_doc/xlsr_wav2vec2)** (from Facebook AI) released with the paper [Unsupervised Cross-Lingual Representation Learning For Speech Recognition](https://arxiv.org/abs/2006.13979) by Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli.
+1. **[YOSO](https://huggingface.co/docs/transformers/master/model_doc/yoso)** (from the University of Wisconsin - Madison) released with the paper [You Only Sample (Almost) by Zhanpeng Zeng, Yunyang Xiong, Sathya N. Ravi, Shailesh Acharya, Glenn Fung, Vikas Singh.
 1. 想要貢獻新的模型？我們這裡有一份**詳細指引和模板**來引導你加入新的模型。你可以在 [`templates`](./templates) 目錄中找到它們。記得查看[貢獻指引](./CONTRIBUTING.md)並在開始寫 PR 前聯繫維護人員或開一個新的 issue 來獲得 feedbacks。
 
 要檢查某個模型是否已有 Flax、PyTorch 或 TensorFlow 的實作，或其是否在🤗 Tokenizers 函式庫中有對應的 tokenizer，敬請參閱[此表](https://huggingface.co/docs/transformers/index#supported-frameworks)。

docs/README.md

@@ -40,7 +40,7 @@ check how they look like before committing for instance). You don't have to comm
 ## Building the documentation
 
 Once you have setup the `doc-builder` and additional packages, you can generate the documentation by typing th
-folowwing command:
+following command:
 
 ```bash
 doc-builder build transformers docs/source/ --build_dir ~/tmp/test-build
@@ -53,15 +53,14 @@ Markdown editor.
 ---
 **NOTE**
 
-It's not possible to see locally how the final documentation will look like for now. We are working on solutions to
-enable this, but any pre-visualiser of Markdown file should already give you a good idea of the result!
+It's not possible to see locally how the final documentation will look like for now. Once you have opened a PR, you
+will see a bot add a comment to a link where the documentation with your changes lives.
 
 ---
 
 ## Adding a new element to the navigation bar
 
-Accepted files are reStructuredText (.rst) and Markdown (.md or .mdx). We are progressively moving away from rst so you should
-create any new documentation file in the .mdx format. 
+Accepted files are Markdown (.md or .mdx).
 
 Create a file with its extension and put it in the source directory. You can then link it to the toc-tree by putting
 the filename without the extension in the [`_toctree.yml`](https://github.com/huggingface/transformers/blob/master/docs/source/_toctree.yml) file.
@@ -92,17 +91,11 @@ Use the relative style to link to the new file so that the versioned docs contin
 For an example of a rich moved sections set please see the very end of [the Trainer doc](https://github.com/huggingface/transformers/blob/master/docs/source/main_classes/trainer.mdx).
 
 
-## Preview the documentation in a pull request
-
-Coming soon!
-
 ## Writing Documentation - Specification
 
 The `huggingface/transformers` documentation follows the
 [Google documentation](https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html) style for docstrings,
-although we can write them directly in Markdown. Parts of it are written in ReStructuredText
-([Sphinx simple documentation](https://www.sphinx-doc.org/en/master/usage/restructuredtext/index.html) but we are
-updating those.
+although we can write them directly in Markdown.
 
 ### Adding a new tutorial
 
@@ -134,6 +127,8 @@ When adding a new model:
     - PyTorch head models
     - TensorFlow base model
     - TensorFlow head models
+    - Flax base model
+    - Flax head models
 
 These classes should be added using our Markdown syntax. Usually as follows:
 
@@ -179,7 +174,7 @@ function to be in the main package.
 If you want to create a link to some internal class or function, you need to
 provide its path. For instance: \[\`file_utils.ModelOutput\`\]. This will be converted into a link with
 `file_utils.ModelOutput` in the description. To get rid of the path and only keep the name of the object you are
-linking to, add a ~: \[\`~file_utils.ModelOutput\`\] will generate a link with `ModelOutput` in the description.
+linking to in the description, add a ~: \[\`~file_utils.ModelOutput\`\] will generate a link with `ModelOutput` in the description.
 
 The same wroks for methods so you can either use \[\`XXXClass.method\`\] or \[~\`XXXClass.method\`\].
 
@@ -279,3 +274,12 @@ them by URL. We recommend putting them in the following dataset: [huggingface/do
 If an external contribution, feel free to add the images to your PR and ask a Hugging Face member to migrate your images
 to this dataset.
 
+## Styling the docstring
+
+We have an automatic script running with the `make style` comment that will make sure that:
+- the docstrings fully take advantage of the line width
+- all code examples are formatted using black, like the code of the Transformers library
+
+This script may have some weird failures if you made a syntax mistake or if you uncover a bug. Therefore, it's
+recommended to commit your changes before running `make style`, so you can revert the changes done by that script
+easily.

docs/source/_toctree.yml

@@ -19,6 +19,8 @@
     title: Preprocessing data
   - local: training
     title: Fine-tuning a pretrained model
+  - local: accelerate
+    title: Distributed training with 🤗 Accelerate
   - local: model_sharing
     title: Model sharing and uploading
   - local: tokenizer_summary
@@ -60,7 +62,7 @@
   - local: debugging
     title: Debugging
   - local: serialization
-    title: Exporting transformers models
+    title: Exporting 🤗 Transformers models
   - local: pr_checks
     title: Checks on a Pull Request
   title: Advanced guides
@@ -86,6 +88,8 @@
       title: Logging
     - local: main_classes/model
       title: Models
+    - local: main_classes/onnx
+      title: ONNX
     - local: main_classes/optimizer_schedules
       title: Optimization
     - local: main_classes/output
@@ -120,17 +124,17 @@
       title: BERT
     - local: model_doc/bertweet
       title: Bertweet
-    - local: model_doc/bertgeneration
+    - local: model_doc/bert-generation
       title: BertGeneration
-    - local: model_doc/bert_japanese
+    - local: model_doc/bert-japanese
       title: BertJapanese
-    - local: model_doc/bigbird
+    - local: model_doc/big_bird
       title: BigBird
     - local: model_doc/bigbird_pegasus
       title: BigBirdPegasus
     - local: model_doc/blenderbot
       title: Blenderbot
-    - local: model_doc/blenderbot_small
+    - local: model_doc/blenderbot-small
       title: Blenderbot Small
     - local: model_doc/bort
       title: BORT
@@ -150,7 +154,7 @@
       title: CTRL
     - local: model_doc/deberta
       title: DeBERTa
-    - local: model_doc/deberta_v2
+    - local: model_doc/deberta-v2
       title: DeBERTa-v2
     - local: model_doc/deit
       title: DeiT
@@ -164,7 +168,7 @@
       title: DPR
     - local: model_doc/electra
       title: ELECTRA
-    - local: model_doc/encoderdecoder
+    - local: model_doc/encoder-decoder
       title: Encoder Decoder Models
     - local: model_doc/flaubert
       title: FlauBERT
@@ -175,7 +179,7 @@
     - local: model_doc/funnel
       title: Funnel Transformer
     - local: model_doc/herbert
-      title: herBERT
+      title: HerBERT
     - local: model_doc/ibert
       title: I-BERT
     - local: model_doc/imagegpt
@@ -200,7 +204,7 @@
       title: M2M100
     - local: model_doc/mbart
       title: MBart and MBart-50
-    - local: model_doc/megatron_bert
+    - local: model_doc/megatron-bert
       title: MegatronBERT
     - local: model_doc/megatron_gpt2
       title: MegatronGPT2
@@ -214,7 +218,9 @@
       title: MPNet
     - local: model_doc/mt5
       title: MT5
-    - local: model_doc/gpt
+    - local: model_doc/nystromformer
+      title: Nyströmformer
+    - local: model_doc/openai-gpt
       title: OpenAI GPT
     - local: model_doc/gpt2
       title: OpenAI GPT2
@@ -236,6 +242,8 @@
       title: QDQBert
     - local: model_doc/rag
       title: RAG
+    - local: model_doc/realm
+      title: REALM
     - local: model_doc/reformer
       title: Reformer
     - local: model_doc/rembert
@@ -250,9 +258,9 @@
       title: SegFormer
     - local: model_doc/sew
       title: SEW
-    - local: model_doc/sew_d
+    - local: model_doc/sew-d
       title: SEW-D
-    - local: model_doc/speechencoderdecoder
+    - local: model_doc/speech-encoder-decoder
       title: Speech Encoder Decoder Models
     - local: model_doc/speech_to_text
       title: Speech2Text
@@ -262,26 +270,32 @@
       title: Splinter
     - local: model_doc/squeezebert
       title: SqueezeBERT
+    - local: model_doc/swin
+      title: Swin Transformer
     - local: model_doc/t5
       title: T5
     - local: model_doc/t5v1.1
       title: T5v1.1
     - local: model_doc/tapas
       title: TAPAS
-    - local: model_doc/transformerxl
+    - local: model_doc/transfo-xl
       title: Transformer XL
     - local: model_doc/trocr
       title: TrOCR
     - local: model_doc/unispeech
       title: UniSpeech
-    - local: model_doc/unispeech_sat
+    - local: model_doc/unispeech-sat
       title: UniSpeech-SAT
-    - local: model_doc/visionencoderdecoder
+    - local: model_doc/vilt
+      title: ViLT
+    - local: model_doc/vision-encoder-decoder
       title: Vision Encoder Decoder Models
-    - local: model_doc/vision_text_dual_encoder
+    - local: model_doc/vision-text-dual-encoder
       title: Vision Text Dual Encoder
     - local: model_doc/vit
       title: Vision Transformer (ViT)
+    - local: model_doc/vit_mae
+      title: ViTMAE
     - local: model_doc/visual_bert
       title: VisualBERT
     - local: model_doc/wav2vec2
@@ -292,9 +306,9 @@
       title: WavLM
     - local: model_doc/xlm
       title: XLM
-    - local: model_doc/xlmprophetnet
+    - local: model_doc/xlm-prophetnet
       title: XLM-ProphetNet
-    - local: model_doc/xlmroberta
+    - local: model_doc/xlm-roberta
       title: XLM-RoBERTa
     - local: model_doc/xlnet
       title: XLNet
@@ -302,6 +316,8 @@
       title: XLSR-Wav2Vec2
     - local: model_doc/xls_r
       title: XLS-R
+    - local: model_doc/yoso
+      title: YOSO
     title: Models
   - sections:
     - local: internal/modeling_utils

docs/source/accelerate.mdx

@@ -0,0 +1,92 @@
+<!--Copyright 2022 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.
+-->
+
+# Distributed training with 🤗 Accelerate
+
+As models get bigger, parallelism has emerged as a strategy for training larger models on limited hardware and accelerating training speed by several orders of magnitude. At Hugging Face, we created the [🤗 Accelerate](https://huggingface.co/docs/accelerate/index.html) library to help users easily train a 🤗 Transformers model on any type of distributed setup, whether it is multiple GPU's on one machine or multiple GPU's across several machines. In this tutorial, learn how to customize your native PyTorch training loop to enable training in a distributed environment.
+
+## Setup
+
+Get started by installing 🤗 Accelerate:
+
+```bash
+pip install accelerate
+```
+
+Then import and create an [`Accelerator`](https://huggingface.co/docs/accelerate/accelerator.html#accelerate.Accelerator) object. [`Accelerator`] will automatically detect your type of distributed setup and initialize all the necessary components for training. You don't need to explicitly place your model on a device.
+
+```py
+>>> from accelerate import Accelerator
+
+>>> accelerator = Accelerator()
+```
+
+## Prepare to accelerate
+
+The next step is to pass all the relevant training objects to [`prepare`](https://huggingface.co/docs/accelerate/accelerator.html#accelerate.Accelerator.prepare). This includes your training and evaluation DataLoaders, a model and an optimizer:
+
+```py
+>>> train_dataloader, eval_dataloader, model, optimizer = accelerator.prepare(
+...     train_dataloader, eval_dataloader, model, optimizer
+... )
+```
+
+## Backward
+
+The last addition is to replace the typical `loss.backward()` in your training loop with 🤗 Accelerate's [`backward`](https://huggingface.co/docs/accelerate/accelerator.html#accelerate.Accelerator.backward):
+
+```py
+>>> for epoch in range(num_epochs):
+...     for batch in train_dataloader:
+...         outputs = model(**batch)
+...         loss = outputs.loss
+...         accelerator.backward(loss)
+
+...         optimizer.step()
+...         lr_scheduler.step()
+...         optimizer.zero_grad()
+...         progress_bar.update(1)
+```
+
+As you can see in the following image, you only need to add four additional lines of code to your training loop to enable distributed training!
+
+![accelerate](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/accelerate.png)
+
+## Train
+
+Once you've added the relevant lines of code, launch your training in a script or a notebook like Colaboratory.
+
+### Train with a script
+
+If you are running your training from a script, run the following command to create and save a configuration file:
+
+```bash
+accelerate config
+```
+
+Then launch your training with:
+
+```bash
+accelerate launch train.py
+```
+
+### Train with a notebook
+
+🤗 Accelerate can also run in a notebook if you're planning on using Colaboratory's TPUs. Wrap all the code responsible for training in a function, and pass it to `notebook_launcher`:
+
+```py
+>>> from accelerate import notebook_launcher
+
+>>> notebook_launcher(training_function)
+```
+
+For more information about 🤗 Accelerate and it's rich features, refer to the [documentation](https://huggingface.co/docs/accelerate/index.html).

docs/source/add_new_pipeline.mdx

@@ -0,0 +1,140 @@
+<!--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
+-->
+
+# How to add a pipeline to 🤗 Transformers?
+
+First and foremost, you need to decide the raw entries the pipeline will be able to take. It can be strings, raw bytes,
+dictionaries or whatever seems to be the most likely desired input. Try to keep these inputs as pure Python as possible
+as it makes compatibility easier (even through other languages via JSON). Those will be the `inputs` of the
+pipeline (`preprocess`).
+
+Then define the `outputs`. Same policy as the `inputs`. The simpler, the better. Those will be the outputs of
+`postprocess` method.
+
+Start by inheriting the base class `Pipeline`. with the 4 methods needed to implement `preprocess`,
+`_forward`, `postprocess` and `_sanitize_parameters`.
+
+
+```python
+from transformers import Pipeline
+
+
+class MyPipeline(Pipeline):
+    def _sanitize_parameters(self, **kwargs):
+        preprocess_kwargs = {}
+        if "maybe_arg" in kwargs:
+            preprocess_kwargs["maybe_arg"] = kwargs["maybe_arg"]
+        return preprocess_kwargs, {}, {}
+
+    def preprocess(self, inputs, maybe_arg=2):
+        model_input = Tensor(inputs["input_ids"])
+        return {"model_input": model_input}
+
+    def _forward(self, model_inputs):
+        # model_inputs == {"model_input": model_input}
+        outputs = self.model(**model_inputs)
+        # Maybe {"logits": Tensor(...)}
+        return outputs
+
+    def postprocess(self, model_outputs):
+        best_class = model_outputs["logits"].softmax(-1)
+        return best_class
+```
+
+The structure of this breakdown is to support relatively seamless support for CPU/GPU, while supporting doing
+pre/postprocessing on the CPU on different threads
+
+`preprocess` will take the originally defined inputs, and turn them into something feedable to the model. It might
+contain more information and is usually a `Dict`.
+
+`_forward` is the implementation detail and is not meant to be called directly. `forward` is the preferred
+called method as it contains safeguards to make sure everything is working on the expected device. If anything is
+linked to a real model it belongs in the `_forward` method, anything else is in the preprocess/postprocess.
+
+`postprocess` methods will take the output of `_forward` and turn it into the final output that were decided
+earlier.
+
+`_sanitize_parameters` exists to allow users to pass any parameters whenever they wish, be it at initialization
+time `pipeline(...., maybe_arg=4)` or at call time `pipe = pipeline(...); output = pipe(...., maybe_arg=4)`.
+
+The returns of `_sanitize_parameters` are the 3 dicts of kwargs that will be passed directly to `preprocess`,
+`_forward` and `postprocess`. Don't fill anything if the caller didn't call with any extra parameter. That
+allows to keep the default arguments in the function definition which is always more "natural".
+
+A classic example would be a `top_k` argument in the post processing in classification tasks.
+
+```python
+>>> pipe = pipeline("my-new-task")
+>>> pipe("This is a test")
+[{"label": "1-star", "score": 0.8}, {"label": "2-star", "score": 0.1}, {"label": "3-star", "score": 0.05}
+{"label": "4-star", "score": 0.025}, {"label": "5-star", "score": 0.025}]
+
+>>> pipe("This is a test", top_k=2)
+[{"label": "1-star", "score": 0.8}, {"label": "2-star", "score": 0.1}]
+```
+
+In order to achieve that, we'll update our `postprocess` method with a default parameter to `5`. and edit
+`_sanitize_parameters` to allow this new parameter.
+
+
+```python
+def postprocess(self, model_outputs, top_k=5):
+    best_class = model_outputs["logits"].softmax(-1)
+    # Add logic to handle top_k
+    return best_class
+
+
+def _sanitize_parameters(self, **kwargs):
+    preprocess_kwargs = {}
+    if "maybe_arg" in kwargs:
+        preprocess_kwargs["maybe_arg"] = kwargs["maybe_arg"]
+
+    postprocess_kwargs = {}
+    if "top_k" in kwargs:
+        preprocess_kwargs["top_k"] = kwargs["top_k"]
+    return preprocess_kwargs, {}, postprocess_kwargs
+```
+
+Try to keep the inputs/outputs very simple and ideally JSON-serializable as it makes the pipeline usage very easy
+without requiring users to understand new kind of objects. It's also relatively common to support many different types
+of arguments for ease of use (audio files, can be filenames, URLs or pure bytes)
+
+
+
+## Adding it to the list of supported tasks
+
+Go to `src/transformers/pipelines/__init__.py` and fill in `SUPPORTED_TASKS` with your newly created pipeline.
+If possible it should provide a default model.
+
+## Adding tests
+
+Create a new file `tests/test_pipelines_MY_PIPELINE.py` with example with the other tests.
+
+The `run_pipeline_test` function will be very generic and run on small random models on every possible
+architecture as defined by `model_mapping` and `tf_model_mapping`.
+
+This is very important to test future compatibility, meaning if someone adds a new model for
+`XXXForQuestionAnswering` then the pipeline test will attempt to run on it. Because the models are random it's
+impossible to check for actual values, that's why There is a helper `ANY` that will simply attempt to match the
+output of the pipeline TYPE.
+
+You also *need* to implement 2 (ideally 4) tests.
+
+- `test_small_model_pt` : Define 1 small model for this pipeline (doesn't matter if the results don't make sense)
+  and test the pipeline outputs. The results should be the same as `test_small_model_tf`.
+- `test_small_model_tf` : Define 1 small model for this pipeline (doesn't matter if the results don't make sense)
+  and test the pipeline outputs. The results should be the same as `test_small_model_pt`.
+- `test_large_model_pt` (`optional`): Tests the pipeline on a real pipeline where the results are supposed to
+  make sense. These tests are slow and should be marked as such. Here the goal is to showcase the pipeline and to make
+  sure there is no drift in future releases
+- `test_large_model_tf` (`optional`): Tests the pipeline on a real pipeline where the results are supposed to
+  make sense. These tests are slow and should be marked as such. Here the goal is to showcase the pipeline and to make
+  sure there is no drift in future releases

docs/source/add_new_pipeline.rst

@@ -1,143 +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
-
-How to add a pipeline to 🤗 Transformers?
-=======================================================================================================================
-
-First and foremost, you need to decide the raw entries the pipeline will be able to take. It can be strings, raw bytes,
-dictionaries or whatever seems to be the most likely desired input. Try to keep these inputs as pure Python as possible
-as it makes compatibility easier (even through other languages via JSON). Those will be the :obj:`inputs` of the
-pipeline (:obj:`preprocess`).
-
-Then define the :obj:`outputs`. Same policy as the :obj:`inputs`. The simpler, the better. Those will be the outputs of
-:obj:`postprocess` method.
-
-Start by inheriting the base class :obj:`Pipeline`. with the 4 methods needed to implement :obj:`preprocess`,
-:obj:`_forward`, :obj:`postprocess` and :obj:`_sanitize_parameters`.
-
-
-.. code-block::
-
-    from transformers import Pipeline
-
-    class MyPipeline(Pipeline):
-        def _sanitize_parameters(self, **kwargs):
-            preprocess_kwargs = {}
-            if "maybe_arg" in kwargs:
-                preprocess_kwargs["maybe_arg"] = kwargs["maybe_arg"]
-            return preprocess_kwargs, {}, {}
-
-        def preprocess(self, inputs, maybe_arg=2):
-            model_input = Tensor(....)
-            return {"model_input": model_input}
-
-        def _forward(self, model_inputs):
-            # model_inputs == {"model_input": model_input}
-            outputs = self.model(**model_inputs)
-            # Maybe {"logits": Tensor(...)}
-            return outputs
-
-        def postprocess(self, model_outputs):
-            best_class = model_outputs["logits"].softmax(-1)
-            return best_class
-
-
-The structure of this breakdown is to support relatively seamless support for CPU/GPU, while supporting doing
-pre/postprocessing on the CPU on different threads
-
-:obj:`preprocess` will take the originally defined inputs, and turn them into something feedable to the model. It might
-contain more information and is usually a :obj:`Dict`.
-
-:obj:`_forward` is the implementation detail and is not meant to be called directly. :obj:`forward` is the preferred
-called method as it contains safeguards to make sure everything is working on the expected device. If anything is
-linked to a real model it belongs in the :obj:`_forward` method, anything else is in the preprocess/postprocess.
-
-:obj:`postprocess` methods will take the output of :obj:`_forward` and turn it into the final output that were decided
-earlier.
-
-:obj:`_sanitize_parameters` exists to allow users to pass any parameters whenever they wish, be it at initialization
-time ``pipeline(...., maybe_arg=4)`` or at call time ``pipe = pipeline(...); output = pipe(...., maybe_arg=4)``.
-
-The returns of :obj:`_sanitize_parameters` are the 3 dicts of kwargs that will be passed directly to :obj:`preprocess`,
-:obj:`_forward` and :obj:`postprocess`. Don't fill anything if the caller didn't call with any extra parameter. That
-allows to keep the default arguments in the function definition which is always more "natural".
-
-A classic example would be a :obj:`top_k` argument in the post processing in classification tasks.
-
-.. code-block::
-
-    >>> pipe = pipeline("my-new-task")
-    >>> pipe("This is a test")
-    [{"label": "1-star", "score": 0.8}, {"label": "2-star", "score": 0.1}, {"label": "3-star", "score": 0.05}
-    {"label": "4-star", "score": 0.025}, {"label": "5-star", "score": 0.025}]
-
-    >>> pipe("This is a test", top_k=2)
-    [{"label": "1-star", "score": 0.8}, {"label": "2-star", "score": 0.1}]
-
-In order to achieve that, we'll update our :obj:`postprocess` method with a default parameter to :obj:`5`. and edit
-:obj:`_sanitize_parameters` to allow this new parameter.
-
-
-.. code-block::
-
-
-        def postprocess(self, model_outputs, top_k=5):
-            best_class = model_outputs["logits"].softmax(-1)
-            # Add logic to handle top_k
-            return best_class
-
-        def _sanitize_parameters(self, **kwargs):
-            preprocess_kwargs = {}
-            if "maybe_arg" in kwargs:
-                preprocess_kwargs["maybe_arg"] = kwargs["maybe_arg"]
-
-            postprocess_kwargs = {}
-            if "top_k" in kwargs:
-                preprocess_kwargs["top_k"] = kwargs["top_k"]
-            return preprocess_kwargs, {}, postprocess_kwargs
-
-Try to keep the inputs/outputs very simple and ideally JSON-serializable as it makes the pipeline usage very easy
-without requiring users to understand new kind of objects. It's also relatively common to support many different types
-of arguments for ease of use (audio files, can be filenames, URLs or pure bytes)
-
-
-
-Adding it to the list of supported tasks
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Go to ``src/transformers/pipelines/__init__.py`` and fill in :obj:`SUPPORTED_TASKS` with your newly created pipeline.
-If possible it should provide a default model.
-
-Adding tests
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Create a new file ``tests/test_pipelines_MY_PIPELINE.py`` with example with the other tests.
-
-The :obj:`run_pipeline_test` function will be very generic and run on small random models on every possible
-architecture as defined by :obj:`model_mapping` and :obj:`tf_model_mapping`.
-
-This is very important to test future compatibility, meaning if someone adds a new model for
-:obj:`XXXForQuestionAnswering` then the pipeline test will attempt to run on it. Because the models are random it's
-impossible to check for actual values, that's why There is a helper :obj:`ANY` that will simply attempt to match the
-output of the pipeline TYPE.
-
-You also *need* to implement 2 (ideally 4) tests.
-
-- :obj:`test_small_model_pt` : Define 1 small model for this pipeline (doesn't matter if the results don't make sense)
-  and test the pipeline outputs. The results should be the same as :obj:`test_small_model_tf`.
-- :obj:`test_small_model_tf` : Define 1 small model for this pipeline (doesn't matter if the results don't make sense)
-  and test the pipeline outputs. The results should be the same as :obj:`test_small_model_pt`.
-- :obj:`test_large_model_pt` (:obj:`optional`): Tests the pipeline on a real pipeline where the results are supposed to
-  make sense. These tests are slow and should be marked as such. Here the goal is to showcase the pipeline and to make
-  sure there is no drift in future releases
-- :obj:`test_large_model_tf` (:obj:`optional`): Tests the pipeline on a real pipeline where the results are supposed to
-  make sense. These tests are slow and should be marked as such. Here the goal is to showcase the pipeline and to make
-  sure there is no drift in future releases

docs/source/benchmarks.mdx

@@ -14,13 +14,13 @@ specific language governing permissions and limitations under the License.
 
 [[open-in-colab]]
 
-Let's take a look at how 🤗 Transformer models can be benchmarked, best practices, and already available benchmarks.
+Let's take a look at how 🤗 Transformers models can be benchmarked, best practices, and already available benchmarks.
 
-A notebook explaining in more detail how to benchmark 🤗 Transformer models can be found [here](https://github.com/huggingface/notebooks/tree/master/examples/benchmark.ipynb).
+A notebook explaining in more detail how to benchmark 🤗 Transformers models can be found [here](https://github.com/huggingface/notebooks/tree/master/examples/benchmark.ipynb).
 
-## How to benchmark 🤗 Transformer models
+## How to benchmark 🤗 Transformers models
 
-The classes [`PyTorchBenchmark`] and [`TensorFlowBenchmark`] allow to flexibly benchmark 🤗 Transformer models. The benchmark classes allow us to measure the _peak memory usage_ and _required time_ for both _inference_ and _training_.
+The classes [`PyTorchBenchmark`] and [`TensorFlowBenchmark`] allow to flexibly benchmark 🤗 Transformers models. The benchmark classes allow us to measure the _peak memory usage_ and _required time_ for both _inference_ and _training_.
 
 <Tip>
 
@@ -37,11 +37,12 @@ The benchmark classes [`PyTorchBenchmark`] and [`TensorFlowBenchmark`] expect an
 
 >>> args = PyTorchBenchmarkArguments(models=["bert-base-uncased"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512])
 >>> benchmark = PyTorchBenchmark(args)
-
 ===PT-TF-SPLIT===
 >>> from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
 
->>> args = TensorFlowBenchmarkArguments(models=["bert-base-uncased"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512])
+>>> args = TensorFlowBenchmarkArguments(
+...     models=["bert-base-uncased"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512]
+... )
 >>> benchmark = TensorFlowBenchmark(args)
 ```
 
@@ -174,7 +175,9 @@ configurations must be inserted with the benchmark args as follows.
 ```py
 >>> from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments, BertConfig
 
->>> args = PyTorchBenchmarkArguments(models=["bert-base", "bert-384-hid", "bert-6-lay"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512])
+>>> args = PyTorchBenchmarkArguments(
+...     models=["bert-base", "bert-384-hid", "bert-6-lay"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512]
+... )
 >>> config_base = BertConfig()
 >>> config_384_hid = BertConfig(hidden_size=384)
 >>> config_6_lay = BertConfig(num_hidden_layers=6)
@@ -244,7 +247,9 @@ bert-6-lay                 8              512            1359
 ===PT-TF-SPLIT===
 >>> from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments, BertConfig
 
->>> args = TensorFlowBenchmarkArguments(models=["bert-base", "bert-384-hid", "bert-6-lay"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512])
+>>> args = TensorFlowBenchmarkArguments(
+...     models=["bert-base", "bert-384-hid", "bert-6-lay"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512]
+... )
 >>> config_base = BertConfig()
 >>> config_384_hid = BertConfig(hidden_size=384)
 >>> config_6_lay = BertConfig(num_hidden_layers=6)

docs/source/bertology.mdx

@@ -0,0 +1,36 @@
+<!--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
+
+There is a growing field of study concerned with investigating the inner working of large-scale transformers like BERT
+(that some call "BERTology"). Some good examples of this field are:
+
+
+- BERT Rediscovers the Classical NLP Pipeline by Ian Tenney, Dipanjan Das, Ellie Pavlick:
+  https://arxiv.org/abs/1905.05950
+- Are Sixteen Heads Really Better than One? by Paul Michel, Omer Levy, Graham Neubig: https://arxiv.org/abs/1905.10650
+- What Does BERT Look At? An Analysis of BERT's Attention by Kevin Clark, Urvashi Khandelwal, Omer Levy, Christopher D.
+  Manning: https://arxiv.org/abs/1906.04341
+
+In order to help this new field develop, we have included a few additional features in the BERT/GPT/GPT-2 models to
+help people access the inner representations, mainly adapted from the great work of Paul Michel
+(https://arxiv.org/abs/1905.10650):
+
+
+- accessing all the hidden-states of BERT/GPT/GPT-2,
+- accessing all the attention weights for each head of BERT/GPT/GPT-2,
+- retrieving heads output values and gradients to be able to compute head importance score and prune head as explained
+  in https://arxiv.org/abs/1905.10650.
+
+To help you understand and use these features, we have added a specific example script: [bertology.py](https://github.com/huggingface/transformers/tree/master/examples/research_projects/bertology/run_bertology.py) while extract information and prune a model pre-trained on
+GLUE.

docs/source/bertology.rst

@@ -1,38 +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.
-
-BERTology
------------------------------------------------------------------------------------------------------------------------
-
-There is a growing field of study concerned with investigating the inner working of large-scale transformers like BERT
-(that some call "BERTology"). Some good examples of this field are:
-
-
-* BERT Rediscovers the Classical NLP Pipeline by Ian Tenney, Dipanjan Das, Ellie Pavlick:
-  https://arxiv.org/abs/1905.05950
-* Are Sixteen Heads Really Better than One? by Paul Michel, Omer Levy, Graham Neubig: https://arxiv.org/abs/1905.10650
-* What Does BERT Look At? An Analysis of BERT's Attention by Kevin Clark, Urvashi Khandelwal, Omer Levy, Christopher D.
-  Manning: https://arxiv.org/abs/1906.04341
-
-In order to help this new field develop, we have included a few additional features in the BERT/GPT/GPT-2 models to
-help people access the inner representations, mainly adapted from the great work of Paul Michel
-(https://arxiv.org/abs/1905.10650):
-
-
-* accessing all the hidden-states of BERT/GPT/GPT-2,
-* accessing all the attention weights for each head of BERT/GPT/GPT-2,
-* retrieving heads output values and gradients to be able to compute head importance score and prune head as explained
-  in https://arxiv.org/abs/1905.10650.
-
-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.

docs/source/converting_tensorflow_models.mdx

@@ -0,0 +1,162 @@
+<!--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
+
+A command-line interface is provided to convert original Bert/GPT/GPT-2/Transformer-XL/XLNet/XLM checkpoints to models
+that can be loaded using the `from_pretrained` methods of the library.
+
+<Tip>
+
+Since 2.3.0 the conversion script is now part of the transformers CLI (**transformers-cli**) available in any
+transformers >= 2.3.0 installation.
+
+The documentation below reflects the **transformers-cli convert** command format.
+
+</Tip>
+
+## BERT
+
+You can convert any TensorFlow checkpoint for BERT (in particular [the pre-trained models released by Google](https://github.com/google-research/bert#pre-trained-models)) in a PyTorch save file by using the
+[convert_bert_original_tf_checkpoint_to_pytorch.py](https://github.com/huggingface/transformers/tree/master/src/transformers/models/bert/convert_bert_original_tf_checkpoint_to_pytorch.py) script.
+
+This CLI takes as input a TensorFlow checkpoint (three files starting with `bert_model.ckpt`) and the associated
+configuration file (`bert_config.json`), and creates a PyTorch model for this configuration, loads the weights from
+the TensorFlow checkpoint in the PyTorch model and saves the resulting model in a standard PyTorch save file that can
+be imported using `from_pretrained()` (see example in [quicktour](quicktour) , [run_glue.py](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification/run_glue.py) ).
+
+You only need to run this conversion script **once** to get a PyTorch model. You can then disregard the TensorFlow
+checkpoint (the three files starting with `bert_model.ckpt`) but be sure to keep the configuration file (\
+`bert_config.json`) and the vocabulary file (`vocab.txt`) as these are needed for the PyTorch model too.
+
+To run this specific conversion script you will need to have TensorFlow and PyTorch installed (`pip install tensorflow`). The rest of the repository only requires PyTorch.
+
+Here is an example of the conversion process for a pre-trained `BERT-Base Uncased` model:
+
+```bash
+export BERT_BASE_DIR=/path/to/bert/uncased_L-12_H-768_A-12
+
+transformers-cli convert --model_type bert \
+  --tf_checkpoint $BERT_BASE_DIR/bert_model.ckpt \
+  --config $BERT_BASE_DIR/bert_config.json \
+  --pytorch_dump_output $BERT_BASE_DIR/pytorch_model.bin
+```
+
+You can download Google's pre-trained models for the conversion [here](https://github.com/google-research/bert#pre-trained-models).
+
+## ALBERT
+
+Convert TensorFlow model checkpoints of ALBERT to PyTorch using the
+[convert_albert_original_tf_checkpoint_to_pytorch.py](https://github.com/huggingface/transformers/tree/master/src/transformers/models/albert/convert_albert_original_tf_checkpoint_to_pytorch.py) script.
+
+The CLI takes as input a TensorFlow checkpoint (three files starting with `model.ckpt-best`) and the accompanying
+configuration file (`albert_config.json`), then creates and saves a PyTorch model. To run this conversion you will
+need to have TensorFlow and PyTorch installed.
+
+Here is an example of the conversion process for the pre-trained `ALBERT Base` model:
+
+```bash
+export ALBERT_BASE_DIR=/path/to/albert/albert_base
+
+transformers-cli convert --model_type albert \
+  --tf_checkpoint $ALBERT_BASE_DIR/model.ckpt-best \
+  --config $ALBERT_BASE_DIR/albert_config.json \
+  --pytorch_dump_output $ALBERT_BASE_DIR/pytorch_model.bin
+```
+
+You can download Google's pre-trained models for the conversion [here](https://github.com/google-research/albert#pre-trained-models).
+
+## OpenAI GPT
+
+Here is an example of the conversion process for a pre-trained OpenAI GPT model, assuming that your NumPy checkpoint
+save as the same format than OpenAI pretrained model (see [here](https://github.com/openai/finetune-transformer-lm)\
+)
+
+```bash
+export OPENAI_GPT_CHECKPOINT_FOLDER_PATH=/path/to/openai/pretrained/numpy/weights
+
+transformers-cli convert --model_type gpt \
+  --tf_checkpoint $OPENAI_GPT_CHECKPOINT_FOLDER_PATH \
+  --pytorch_dump_output $PYTORCH_DUMP_OUTPUT \
+  [--config OPENAI_GPT_CONFIG] \
+  [--finetuning_task_name OPENAI_GPT_FINETUNED_TASK] \
+```
+
+## OpenAI GPT-2
+
+Here is an example of the conversion process for a pre-trained OpenAI GPT-2 model (see [here](https://github.com/openai/gpt-2))
+
+```bash
+export OPENAI_GPT2_CHECKPOINT_PATH=/path/to/gpt2/pretrained/weights
+
+transformers-cli convert --model_type gpt2 \
+  --tf_checkpoint $OPENAI_GPT2_CHECKPOINT_PATH \
+  --pytorch_dump_output $PYTORCH_DUMP_OUTPUT \
+  [--config OPENAI_GPT2_CONFIG] \
+  [--finetuning_task_name OPENAI_GPT2_FINETUNED_TASK]
+```
+
+## Transformer-XL
+
+Here is an example of the conversion process for a pre-trained Transformer-XL model (see [here](https://github.com/kimiyoung/transformer-xl/tree/master/tf#obtain-and-evaluate-pretrained-sota-models))
+
+```bash
+export TRANSFO_XL_CHECKPOINT_FOLDER_PATH=/path/to/transfo/xl/checkpoint
+
+transformers-cli convert --model_type transfo_xl \
+  --tf_checkpoint $TRANSFO_XL_CHECKPOINT_FOLDER_PATH \
+  --pytorch_dump_output $PYTORCH_DUMP_OUTPUT \
+  [--config TRANSFO_XL_CONFIG] \
+  [--finetuning_task_name TRANSFO_XL_FINETUNED_TASK]
+```
+
+## XLNet
+
+Here is an example of the conversion process for a pre-trained XLNet model:
+
+```bash
+export TRANSFO_XL_CHECKPOINT_PATH=/path/to/xlnet/checkpoint
+export TRANSFO_XL_CONFIG_PATH=/path/to/xlnet/config
+
+transformers-cli convert --model_type xlnet \
+  --tf_checkpoint $TRANSFO_XL_CHECKPOINT_PATH \
+  --config $TRANSFO_XL_CONFIG_PATH \
+  --pytorch_dump_output $PYTORCH_DUMP_OUTPUT \
+  [--finetuning_task_name XLNET_FINETUNED_TASK] \
+```
+
+## XLM
+
+Here is an example of the conversion process for a pre-trained XLM model:
+
+```bash
+export XLM_CHECKPOINT_PATH=/path/to/xlm/checkpoint
+
+transformers-cli convert --model_type xlm \
+  --tf_checkpoint $XLM_CHECKPOINT_PATH \
+  --pytorch_dump_output $PYTORCH_DUMP_OUTPUT
+ [--config XML_CONFIG] \
+ [--finetuning_task_name XML_FINETUNED_TASK]
+```
+
+## T5
+
+Here is an example of the conversion process for a pre-trained T5 model:
+
+```bash
+export T5=/path/to/t5/uncased_L-12_H-768_A-12
+
+transformers-cli convert --model_type t5 \
+  --tf_checkpoint $T5/t5_model.ckpt \
+  --config $T5/t5_config.json \
+  --pytorch_dump_output $T5/pytorch_model.bin
+```

docs/source/converting_tensorflow_models.rst

@@ -1,181 +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.
-
-Converting Tensorflow Checkpoints
-=======================================================================================================================
-
-A command-line interface is provided to convert original Bert/GPT/GPT-2/Transformer-XL/XLNet/XLM checkpoints to models
-that can be loaded using the ``from_pretrained`` methods of the library.
-
-.. note::
-    Since 2.3.0 the conversion script is now part of the transformers CLI (**transformers-cli**) available in any
-    transformers >= 2.3.0 installation.
-
-    The documentation below reflects the **transformers-cli convert** command format.
-
-BERT
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-You can convert any TensorFlow checkpoint for BERT (in particular `the pre-trained models released by Google
-<https://github.com/google-research/bert#pre-trained-models>`_) in a PyTorch save file by using the
-:prefix_link:`convert_bert_original_tf_checkpoint_to_pytorch.py
-<src/transformers/models/bert/convert_bert_original_tf_checkpoint_to_pytorch.py>` script.
-
-This CLI takes as input a TensorFlow checkpoint (three files starting with ``bert_model.ckpt``) and the associated
-configuration file (``bert_config.json``), and creates a PyTorch model for this configuration, loads the weights from
-the TensorFlow checkpoint in the PyTorch model and saves the resulting model in a standard PyTorch save file that can
-be imported using ``from_pretrained()`` (see example in :doc:`quicktour` , :prefix_link:`run_glue.py
-<examples/pytorch/text-classification/run_glue.py>` ).
-
-You only need to run this conversion script **once** to get a PyTorch model. You can then disregard the TensorFlow
-checkpoint (the three files starting with ``bert_model.ckpt``) but be sure to keep the configuration file (\
-``bert_config.json``) and the vocabulary file (``vocab.txt``) as these are needed for the PyTorch model too.
-
-To run this specific conversion script you will need to have TensorFlow and PyTorch installed (``pip install
-tensorflow``). The rest of the repository only requires PyTorch.
-
-Here is an example of the conversion process for a pre-trained ``BERT-Base Uncased`` model:
-
-.. code-block:: shell
-
-    export BERT_BASE_DIR=/path/to/bert/uncased_L-12_H-768_A-12
-
-    transformers-cli convert --model_type bert \
-      --tf_checkpoint $BERT_BASE_DIR/bert_model.ckpt \
-      --config $BERT_BASE_DIR/bert_config.json \
-      --pytorch_dump_output $BERT_BASE_DIR/pytorch_model.bin
-
-You can download Google's pre-trained models for the conversion `here
-<https://github.com/google-research/bert#pre-trained-models>`__.
-
-ALBERT
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Convert TensorFlow model checkpoints of ALBERT to PyTorch using the
-:prefix_link:`convert_albert_original_tf_checkpoint_to_pytorch.py
-<src/transformers/models/albert/convert_albert_original_tf_checkpoint_to_pytorch.py>` script.
-
-The CLI takes as input a TensorFlow checkpoint (three files starting with ``model.ckpt-best``) and the accompanying
-configuration file (``albert_config.json``), then creates and saves a PyTorch model. To run this conversion you will
-need to have TensorFlow and PyTorch installed.
-
-Here is an example of the conversion process for the pre-trained ``ALBERT Base`` model:
-
-.. code-block:: shell
-
-    export ALBERT_BASE_DIR=/path/to/albert/albert_base
-
-    transformers-cli convert --model_type albert \
-      --tf_checkpoint $ALBERT_BASE_DIR/model.ckpt-best \
-      --config $ALBERT_BASE_DIR/albert_config.json \
-      --pytorch_dump_output $ALBERT_BASE_DIR/pytorch_model.bin
-
-You can download Google's pre-trained models for the conversion `here
-<https://github.com/google-research/albert#pre-trained-models>`__.
-
-OpenAI GPT
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Here is an example of the conversion process for a pre-trained OpenAI GPT model, assuming that your NumPy checkpoint
-save as the same format than OpenAI pretrained model (see `here <https://github.com/openai/finetune-transformer-lm>`__\
-)
-
-.. code-block:: shell
-
-    export OPENAI_GPT_CHECKPOINT_FOLDER_PATH=/path/to/openai/pretrained/numpy/weights
-
-    transformers-cli convert --model_type gpt \
-      --tf_checkpoint $OPENAI_GPT_CHECKPOINT_FOLDER_PATH \
-      --pytorch_dump_output $PYTORCH_DUMP_OUTPUT \
-      [--config OPENAI_GPT_CONFIG] \
-      [--finetuning_task_name OPENAI_GPT_FINETUNED_TASK] \
-
-
-OpenAI GPT-2
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Here is an example of the conversion process for a pre-trained OpenAI GPT-2 model (see `here
-<https://github.com/openai/gpt-2>`__)
-
-.. code-block:: shell
-
-    export OPENAI_GPT2_CHECKPOINT_PATH=/path/to/gpt2/pretrained/weights
-
-    transformers-cli convert --model_type gpt2 \
-      --tf_checkpoint $OPENAI_GPT2_CHECKPOINT_PATH \
-      --pytorch_dump_output $PYTORCH_DUMP_OUTPUT \
-      [--config OPENAI_GPT2_CONFIG] \
-      [--finetuning_task_name OPENAI_GPT2_FINETUNED_TASK]
-
-Transformer-XL
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Here is an example of the conversion process for a pre-trained Transformer-XL model (see `here
-<https://github.com/kimiyoung/transformer-xl/tree/master/tf#obtain-and-evaluate-pretrained-sota-models>`__)
-
-.. code-block:: shell
-
-    export TRANSFO_XL_CHECKPOINT_FOLDER_PATH=/path/to/transfo/xl/checkpoint
-
-    transformers-cli convert --model_type transfo_xl \
-      --tf_checkpoint $TRANSFO_XL_CHECKPOINT_FOLDER_PATH \
-      --pytorch_dump_output $PYTORCH_DUMP_OUTPUT \
-      [--config TRANSFO_XL_CONFIG] \
-      [--finetuning_task_name TRANSFO_XL_FINETUNED_TASK]
-
-
-XLNet
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Here is an example of the conversion process for a pre-trained XLNet model:
-
-.. code-block:: shell
-
-    export TRANSFO_XL_CHECKPOINT_PATH=/path/to/xlnet/checkpoint
-    export TRANSFO_XL_CONFIG_PATH=/path/to/xlnet/config
-
-    transformers-cli convert --model_type xlnet \
-      --tf_checkpoint $TRANSFO_XL_CHECKPOINT_PATH \
-      --config $TRANSFO_XL_CONFIG_PATH \
-      --pytorch_dump_output $PYTORCH_DUMP_OUTPUT \
-      [--finetuning_task_name XLNET_FINETUNED_TASK] \
-
-
-XLM
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Here is an example of the conversion process for a pre-trained XLM model:
-
-.. code-block:: shell
-
-    export XLM_CHECKPOINT_PATH=/path/to/xlm/checkpoint
-
-    transformers-cli convert --model_type xlm \
-      --tf_checkpoint $XLM_CHECKPOINT_PATH \
-      --pytorch_dump_output $PYTORCH_DUMP_OUTPUT
-     [--config XML_CONFIG] \
-     [--finetuning_task_name XML_FINETUNED_TASK]
-
-
-T5
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Here is an example of the conversion process for a pre-trained T5 model:
-
-.. code-block:: shell
-
-    export T5=/path/to/t5/uncased_L-12_H-768_A-12
-
-    transformers-cli convert --model_type t5 \
-      --tf_checkpoint $T5/t5_model.ckpt \
-      --config $T5/t5_config.json \
-      --pytorch_dump_output $T5/pytorch_model.bin

docs/source/custom_datasets.mdx

@@ -54,6 +54,7 @@ The 🤗 Datasets library makes it simple to load a dataset:
 
 ```python
 from datasets import load_dataset
+
 imdb = load_dataset("imdb")
 ```
 
@@ -61,8 +62,9 @@ This loads a `DatasetDict` object which you can index into to view an example:
 
 ```python
 imdb["train"][0]
-{'label': 1,
- 'text': 'Bromwell High is a cartoon comedy. It ran at the same time as some other programs about school life, such as "Teachers". My 35 years in the teaching profession lead me to believe that Bromwell High\'s satire is much closer to reality than is "Teachers". The scramble to survive financially, the insightful students who can see right through their pathetic teachers\' pomp, the pettiness of the whole situation, all remind me of the schools I knew and their students. When I saw the episode in which a student repeatedly tried to burn down the school, I immediately recalled ......... at .......... High. A classic line: INSPECTOR: I\'m here to sack one of your teachers. STUDENT: Welcome to Bromwell High. I expect that many adults of my age think that Bromwell High is far fetched. What a pity that it isn\'t!'
+{
+    "label": 1,
+    "text": "Bromwell High is a cartoon comedy. It ran at the same time as some other programs about school life, such as \"Teachers\". My 35 years in the teaching profession lead me to believe that Bromwell High's satire is much closer to reality than is \"Teachers\". The scramble to survive financially, the insightful students who can see right through their pathetic teachers' pomp, the pettiness of the whole situation, all remind me of the schools I knew and their students. When I saw the episode in which a student repeatedly tried to burn down the school, I immediately recalled ......... at .......... High. A classic line: INSPECTOR: I'm here to sack one of your teachers. STUDENT: Welcome to Bromwell High. I expect that many adults of my age think that Bromwell High is far fetched. What a pity that it isn't!",
 }
 ```
 
@@ -74,6 +76,7 @@ model was trained with to ensure appropriately tokenized words. Load the DistilB
 
 ```python
 from transformers import AutoTokenizer
+
 tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
 ```
 
@@ -99,6 +102,7 @@ batch. This is known as **dynamic padding**. You can do this with the `DataColla
 
 ```python
 from transformers import DataCollatorWithPadding
+
 data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
 ```
 
@@ -108,6 +112,7 @@ Now load your model with the [`AutoModelForSequenceClassification`] class along
 
 ```python
 from transformers import AutoModelForSequenceClassification
+
 model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased", num_labels=2)
 ```
 
@@ -121,7 +126,7 @@ At this point, only three steps remain:
 from transformers import TrainingArguments, Trainer
 
 training_args = TrainingArguments(
-    output_dir='./results',
+    output_dir="./results",
     learning_rate=2e-5,
     per_device_train_batch_size=16,
     per_device_eval_batch_size=16,
@@ -150,6 +155,7 @@ Make sure you set `return_tensors="tf"` to return `tf.Tensor` outputs instead of
 
 ```python
 from transformers import DataCollatorWithPadding
+
 data_collator = DataCollatorWithPadding(tokenizer, return_tensors="tf")
 ```
 
@@ -158,14 +164,14 @@ Next, convert your datasets to the `tf.data.Dataset` format with `to_tf_dataset`
 
 ```python
 tf_train_dataset = tokenized_imdb["train"].to_tf_dataset(
-    columns=['attention_mask', 'input_ids', 'label'],
+    columns=["attention_mask", "input_ids", "label"],
     shuffle=True,
     batch_size=16,
     collate_fn=data_collator,
 )
 
 tf_validation_dataset = tokenized_imdb["train"].to_tf_dataset(
-    columns=['attention_mask', 'input_ids', 'label'],
+    columns=["attention_mask", "input_ids", "label"],
     shuffle=False,
     batch_size=16,
     collate_fn=data_collator,
@@ -182,17 +188,14 @@ batch_size = 16
 num_epochs = 5
 batches_per_epoch = len(tokenized_imdb["train"]) // batch_size
 total_train_steps = int(batches_per_epoch * num_epochs)
-optimizer, schedule = create_optimizer(
-    init_lr=2e-5, 
-    num_warmup_steps=0, 
-    num_train_steps=total_train_steps
-)
+optimizer, schedule = create_optimizer(init_lr=2e-5, num_warmup_steps=0, num_train_steps=total_train_steps)
 ```
 
 Load your model with the [`TFAutoModelForSequenceClassification`] class along with the number of expected labels:
 
 ```python
 from transformers import TFAutoModelForSequenceClassification
+
 model = TFAutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased", num_labels=2)
 ```
 
@@ -200,6 +203,7 @@ Compile the model:
 
 ```python
 import tensorflow as tf
+
 model.compile(optimizer=optimizer)
 ```
 
@@ -234,14 +238,15 @@ or [TensorFlow notebook](https://colab.research.google.com/github/huggingface/no
 Load the WNUT 17 dataset from the 🤗 Datasets library:
 
 ```python
-from datasets import load_dataset
-wnut = load_dataset("wnut_17")
+>>> from datasets import load_dataset
+
+>>> wnut = load_dataset("wnut_17")
 ```
 
 A quick look at the dataset shows the labels associated with each word in the sentence:
 
 ```python
-wnut["train"][0]
+>>> wnut["train"][0]
 {'id': '0',
  'ner_tags': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0],
  'tokens': ['@paulwalk', 'It', "'s", 'the', 'view', 'from', 'where', 'I', "'m", 'living', 'for', 'two', 'weeks', '.', 'Empire', 'State', 'Building', '=', 'ESB', '.', 'Pretty', 'bad', 'storm', 'here', 'last', 'evening', '.']
@@ -251,21 +256,22 @@ wnut["train"][0]
 View the specific NER tags by:
 
 ```python
-label_list = wnut["train"].features[f"ner_tags"].feature.names
-label_list
-['O',
- 'B-corporation',
- 'I-corporation',
- 'B-creative-work',
- 'I-creative-work',
- 'B-group',
- 'I-group',
- 'B-location',
- 'I-location',
- 'B-person',
- 'I-person',
- 'B-product',
- 'I-product'
+>>> label_list = wnut["train"].features[f"ner_tags"].feature.names
+>>> label_list
+[
+    "O",
+    "B-corporation",
+    "I-corporation",
+    "B-creative-work",
+    "I-creative-work",
+    "B-group",
+    "I-group",
+    "B-location",
+    "I-location",
+    "B-person",
+    "I-person",
+    "B-product",
+    "I-product",
 ]
 ```
 
@@ -282,6 +288,7 @@ Now you need to tokenize the text. Load the DistilBERT tokenizer with an [`AutoT
 
 ```python
 from transformers import AutoTokenizer
+
 tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
 ```
 
@@ -289,9 +296,9 @@ Since the input has already been split into words, set `is_split_into_words=True
 subwords:
 
 ```python
-tokenized_input = tokenizer(example["tokens"], is_split_into_words=True)
-tokens = tokenizer.convert_ids_to_tokens(tokenized_input["input_ids"])
-tokens
+>>> tokenized_input = tokenizer(example["tokens"], is_split_into_words=True)
+>>> tokens = tokenizer.convert_ids_to_tokens(tokenized_input["input_ids"])
+>>> tokens
 ['[CLS]', '@', 'paul', '##walk', 'it', "'", 's', 'the', 'view', 'from', 'where', 'i', "'", 'm', 'living', 'for', 'two', 'weeks', '.', 'empire', 'state', 'building', '=', 'es', '##b', '.', 'pretty', 'bad', 'storm', 'here', 'last', 'evening', '.', '[SEP]']
 ```
 
@@ -314,12 +321,14 @@ def tokenize_and_align_labels(examples):
         word_ids = tokenized_inputs.word_ids(batch_index=i)  # Map tokens to their respective word.
         previous_word_idx = None
         label_ids = []
-        for word_idx in word_ids:                            # Set the special tokens to -100.
+        for word_idx in word_ids:  # Set the special tokens to -100.
             if word_idx is None:
                 label_ids.append(-100)
-            elif word_idx != previous_word_idx:              # Only label the first token of a given word.
+            elif word_idx != previous_word_idx:  # Only label the first token of a given word.
                 label_ids.append(label[word_idx])
-
+            else:
+                label_ids.append(-100)
+            previous_word_idx = word_idx
         labels.append(label_ids)
 
     tokenized_inputs["labels"] = labels
@@ -336,6 +345,7 @@ Finally, pad your text and labels, so they are a uniform length:
 
 ```python
 from transformers import DataCollatorForTokenClassification
+
 data_collator = DataCollatorForTokenClassification(tokenizer)
 ```
 
@@ -345,6 +355,7 @@ Load your model with the [`AutoModelForTokenClassification`] class along with th
 
 ```python
 from transformers import AutoModelForTokenClassification, TrainingArguments, Trainer
+
 model = AutoModelForTokenClassification.from_pretrained("distilbert-base-uncased", num_labels=len(label_list))
 ```
 
@@ -352,7 +363,7 @@ Gather your training arguments in [`TrainingArguments`]:
 
 ```python
 training_args = TrainingArguments(
-    output_dir='./results',
+    output_dir="./results",
     evaluation_strategy="epoch",
     learning_rate=2e-5,
     per_device_train_batch_size=16,
@@ -387,6 +398,7 @@ Batch your examples together and pad your text and labels, so they are a uniform
 
 ```python
 from transformers import DataCollatorForTokenClassification
+
 data_collator = DataCollatorForTokenClassification(tokenizer, return_tensors="tf")
 ```
 
@@ -412,6 +424,7 @@ Load the model with the [`TFAutoModelForTokenClassification`] class along with t
 
 ```python
 from transformers import TFAutoModelForTokenClassification
+
 model = TFAutoModelForTokenClassification.from_pretrained("distilbert-base-uncased", num_labels=len(label_list))
 ```
 
@@ -435,6 +448,7 @@ Compile the model:
 
 ```python
 import tensorflow as tf
+
 model.compile(optimizer=optimizer)
 ```
 
@@ -469,13 +483,14 @@ Load the SQuAD dataset from the 🤗 Datasets library:
 
 ```python
 from datasets import load_dataset
+
 squad = load_dataset("squad")
 ```
 
 Take a look at an example from the dataset:
 
 ```python
-squad["train"][0]
+>>> squad["train"][0]
 {'answers': {'answer_start': [515], 'text': ['Saint Bernadette Soubirous']},
  'context': 'Architecturally, the school has a Catholic character. Atop the Main Building\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend "Venite Ad Me Omnes". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.',
  'id': '5733be284776f41900661182',
@@ -490,6 +505,7 @@ Load the DistilBERT tokenizer with an [`AutoTokenizer`]:
 
 ```python
 from transformers import AutoTokenizer
+
 tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
 ```
 
@@ -567,6 +583,7 @@ Batch the processed examples together:
 
 ```python
 from transformers import default_data_collator
+
 data_collator = default_data_collator
 ```
 
@@ -576,6 +593,7 @@ Load your model with the [`AutoModelForQuestionAnswering`] class:
 
 ```python
 from transformers import AutoModelForQuestionAnswering, TrainingArguments, Trainer
+
 model = AutoModelForQuestionAnswering.from_pretrained("distilbert-base-uncased")
 ```
 
@@ -583,7 +601,7 @@ Gather your training arguments in [`TrainingArguments`]:
 
 ```python
 training_args = TrainingArguments(
-    output_dir='./results',
+    output_dir="./results",
     evaluation_strategy="epoch",
     learning_rate=2e-5,
     per_device_train_batch_size=16,
@@ -618,6 +636,7 @@ Batch the processed examples together with a TensorFlow default data collator:
 
 ```python
 from transformers.data.data_collator import tf_default_collator
+
 data_collator = tf_default_collator
 ```
 
@@ -650,8 +669,8 @@ batch_size = 16
 num_epochs = 2
 total_train_steps = (len(tokenized_squad["train"]) // batch_size) * num_epochs
 optimizer, schedule = create_optimizer(
-    init_lr=2e-5, 
-    num_warmup_steps=0, 
+    init_lr=2e-5,
+    num_warmup_steps=0,
     num_train_steps=total_train_steps,
 )
 ```
@@ -660,6 +679,7 @@ Load your model with the [`TFAutoModelForQuestionAnswering`] class:
 
 ```python
 from transformers import TFAutoModelForQuestionAnswering
+
 model = TFAutoModelForQuestionAnswering("distilbert-base-uncased")
 ```
 
@@ -667,6 +687,7 @@ Compile the model:
 
 ```python
 import tensorflow as tf
+
 model.compile(optimizer=optimizer)
 ```
 

docs/source/debugging.mdx

@@ -49,6 +49,7 @@ If you're using your own training loop or another Trainer you can accomplish the
 
 ```python
 from .debug_utils import DebugUnderflowOverflow
+
 debug_overflow = DebugUnderflowOverflow(model)
 ```
 
@@ -200,13 +201,16 @@ def _forward(self, hidden_states):
     hidden_states = self.wo(hidden_states)
     return hidden_states
 
+
 import torch
+
+
 def forward(self, hidden_states):
     if torch.is_autocast_enabled():
-         with torch.cuda.amp.autocast(enabled=False):
-             return self._forward(hidden_states)
-     else:
-         return self._forward(hidden_states)
+        with torch.cuda.amp.autocast(enabled=False):
+            return self._forward(hidden_states)
+    else:
+        return self._forward(hidden_states)
 ```
 
 Since the automatic detector only reports on inputs and outputs of full frames, once you know where to look, you may
@@ -216,8 +220,10 @@ want to analyse the intermediary stages of any specific `forward` function as we
 ```python
 from debug_utils import detect_overflow
 
+
 class T5LayerFF(nn.Module):
     [...]
+
     def forward(self, hidden_states):
         forwarded_states = self.layer_norm(hidden_states)
         detect_overflow(forwarded_states, "after layer_norm")
@@ -237,6 +243,7 @@ its default, e.g.:
 
 ```python
 from .debug_utils import DebugUnderflowOverflow
+
 debug_overflow = DebugUnderflowOverflow(model, max_frames_to_save=100)
 ```
 
@@ -248,7 +255,7 @@ Let's say you want to watch the absolute min and max values for all the ingredie
 batch, and only do that for batches 1 and 3. Then you instantiate this class as:
 
 ```python
-debug_overflow = DebugUnderflowOverflow(model, trace_batch_nums=[1,3])
+debug_overflow = DebugUnderflowOverflow(model, trace_batch_nums=[1, 3])
 ```
 
 And now full batches 1 and 3 will be traced using the same format as the underflow/overflow detector does.
@@ -295,5 +302,5 @@ numbers started to diverge.
 You can also specify the batch number after which to stop the training, with:
 
 ```python
-debug_overflow = DebugUnderflowOverflow(model, trace_batch_nums=[1,3], abort_after_batch_num=3)
+debug_overflow = DebugUnderflowOverflow(model, trace_batch_nums=[1, 3], abort_after_batch_num=3)
 ```

docs/source/fast_tokenizers.mdx

@@ -0,0 +1,70 @@
+<!--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.
+-->
+
+# Using tokenizers from 🤗 Tokenizers
+
+The [`PreTrainedTokenizerFast`] depends on the [🤗 Tokenizers](https://huggingface.co/docs/tokenizers) library. The tokenizers obtained from the 🤗 Tokenizers library can be
+loaded very simply into 🤗 Transformers.
+
+Before getting in the specifics, let's first start by creating a dummy tokenizer in a few lines:
+
+```python
+>>> from tokenizers import Tokenizer
+>>> from tokenizers.models import BPE
+>>> from tokenizers.trainers import BpeTrainer
+>>> from tokenizers.pre_tokenizers import Whitespace
+
+>>> tokenizer = Tokenizer(BPE(unk_token="[UNK]"))
+>>> trainer = BpeTrainer(special_tokens=["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"])
+
+>>> tokenizer.pre_tokenizer = Whitespace()
+>>> files = [...]
+>>> tokenizer.train(files, trainer)
+```
+
+We now have a tokenizer trained on the files we defined. We can either continue using it in that runtime, or save it to
+a JSON file for future re-use.
+
+## Loading directly from the tokenizer object
+
+Let's see how to leverage this tokenizer object in the 🤗 Transformers library. The
+[`PreTrainedTokenizerFast`] class allows for easy instantiation, by accepting the instantiated
+*tokenizer* object as an argument:
+
+```python
+>>> from transformers import PreTrainedTokenizerFast
+
+>>> fast_tokenizer = PreTrainedTokenizerFast(tokenizer_object=tokenizer)
+```
+
+This object can now be used with all the methods shared by the 🤗 Transformers tokenizers! Head to [the tokenizer
+page](main_classes/tokenizer) for more information.
+
+## Loading from a JSON file
+
+In order to load a tokenizer from a JSON file, let's first start by saving our tokenizer:
+
+```python
+>>> tokenizer.save("tokenizer.json")
+```
+
+The path to which we saved this file can be passed to the [`PreTrainedTokenizerFast`] initialization
+method using the `tokenizer_file` parameter:
+
+```python
+>>> from transformers import PreTrainedTokenizerFast
+
+>>> fast_tokenizer = PreTrainedTokenizerFast(tokenizer_file="tokenizer.json")
+```
+
+This object can now be used with all the methods shared by the 🤗 Transformers tokenizers! Head to [the tokenizer
+page](main_classes/tokenizer) for more information.

docs/source/fast_tokenizers.rst

@@ -1,62 +0,0 @@
-Using tokenizers from 🤗 Tokenizers
-=======================================================================================================================
-
-The :class:`~transformers.PreTrainedTokenizerFast` depends on the `tokenizers
-<https://huggingface.co/docs/tokenizers>`__ library. The tokenizers obtained from the 🤗 Tokenizers library can be
-loaded very simply into 🤗 Transformers.
-
-Before getting in the specifics, let's first start by creating a dummy tokenizer in a few lines:
-
-.. code-block::
-
-    >>> from tokenizers import Tokenizer
-    >>> from tokenizers.models import BPE
-    >>> from tokenizers.trainers import BpeTrainer
-    >>> from tokenizers.pre_tokenizers import Whitespace
-
-    >>> tokenizer = Tokenizer(BPE(unk_token="[UNK]"))
-    >>> trainer = BpeTrainer(special_tokens=["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"])
-
-    >>> tokenizer.pre_tokenizer = Whitespace()
-    >>> files = [...]
-    >>> tokenizer.train(files, trainer)
-
-We now have a tokenizer trained on the files we defined. We can either continue using it in that runtime, or save it to
-a JSON file for future re-use.
-
-Loading directly from the tokenizer object
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Let's see how to leverage this tokenizer object in the 🤗 Transformers library. The
-:class:`~transformers.PreTrainedTokenizerFast` class allows for easy instantiation, by accepting the instantiated
-`tokenizer` object as an argument:
-
-.. code-block::
-
-    >>> from transformers import PreTrainedTokenizerFast
-
-    >>> fast_tokenizer = PreTrainedTokenizerFast(tokenizer_object=tokenizer)
-
-This object can now be used with all the methods shared by the 🤗 Transformers tokenizers! Head to :doc:`the tokenizer
-page <main_classes/tokenizer>` for more information.
-
-Loading from a JSON file
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-In order to load a tokenizer from a JSON file, let's first start by saving our tokenizer:
-
-.. code-block::
-
-    >>> tokenizer.save("tokenizer.json")
-
-The path to which we saved this file can be passed to the :class:`~transformers.PreTrainedTokenizerFast` initialization
-method using the :obj:`tokenizer_file` parameter:
-
-.. code-block::
-
-    >>> from transformers import PreTrainedTokenizerFast
-
-    >>> fast_tokenizer = PreTrainedTokenizerFast(tokenizer_file="tokenizer.json")
-
-This object can now be used with all the methods shared by the 🤗 Transformers tokenizers! Head to :doc:`the tokenizer
-page <main_classes/tokenizer>` for more information.

docs/source/glossary.mdx

@@ -0,0 +1,300 @@
+<!--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
+
+## General terms
+
+- autoencoding models: see MLM
+- autoregressive models: see CLM
+- CLM: causal language modeling, a pretraining task where the model reads the texts in order and has to predict the
+  next word. It's usually done by reading the whole sentence but using a mask inside the model to hide the future
+  tokens at a certain timestep.
+- deep learning: machine learning algorithms which uses neural networks with several layers.
+- MLM: masked language modeling, a pretraining task where the model sees a corrupted version of the texts, usually done
+  by masking some tokens randomly, and has to predict the original text.
+- multimodal: a task that combines texts with another kind of inputs (for instance images).
+- NLG: natural language generation, all tasks related to generating text (for instance talk with transformers,
+  translation).
+- NLP: natural language processing, a generic way to say "deal with texts".
+- NLU: natural language understanding, all tasks related to understanding what is in a text (for instance classifying
+  the whole text, individual words).
+- pretrained model: a model that has been pretrained on some data (for instance all of Wikipedia). Pretraining methods
+  involve a self-supervised objective, which can be reading the text and trying to predict the next word (see CLM) or
+  masking some words and trying to predict them (see MLM).
+- RNN: recurrent neural network, a type of model that uses a loop over a layer to process texts.
+- self-attention: each element of the input finds out which other elements of the input they should attend to.
+- seq2seq or sequence-to-sequence: models that generate a new sequence from an input, like translation models, or
+  summarization models (such as [Bart](model_doc/bart) or [T5](model_doc/t5)).
+- token: a part of a sentence, usually a word, but can also be a subword (non-common words are often split in subwords)
+  or a punctuation symbol.
+- transformer: self-attention based deep learning model architecture.
+
+## Model inputs
+
+Every model is different yet bears similarities with the others. Therefore most models use the same inputs, which are
+detailed here alongside usage examples.
+
+<a id='input-ids'></a>
+
+### Input IDs
+
+The input ids are often the only required parameters to be passed to the model as input. *They are token indices,
+numerical representations of tokens building the sequences that will be used as input by the model*.
+
+<Youtube id="VFp38yj8h3A"/>
+
+Each tokenizer works differently but the underlying mechanism remains the same. Here's an example using the BERT
+tokenizer, which is a [WordPiece](https://arxiv.org/pdf/1609.08144.pdf) tokenizer:
+
+```python
+>>> from transformers import BertTokenizer
+
+>>> tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
+
+>>> sequence = "A Titan RTX has 24GB of VRAM"
+```
+
+The tokenizer takes care of splitting the sequence into tokens available in the tokenizer vocabulary.
+
+```python
+>>> tokenized_sequence = tokenizer.tokenize(sequence)
+```
+
+The tokens are either words or subwords. Here for instance, "VRAM" wasn't in the model vocabulary, so it's been split
+in "V", "RA" and "M". To indicate those tokens are not separate words but parts of the same word, a double-hash prefix
+is added for "RA" and "M":
+
+```python
+>>> print(tokenized_sequence)
+['A', 'Titan', 'R', '##T', '##X', 'has', '24', '##GB', 'of', 'V', '##RA', '##M']
+```
+
+These tokens can then be converted into IDs which are understandable by the model. This can be done by directly feeding
+the sentence to the tokenizer, which leverages the Rust implementation of [🤗 Tokenizers](https://github.com/huggingface/tokenizers) for peak performance.
+
+```python
+>>> inputs = tokenizer(sequence)
+```
+
+The tokenizer returns a dictionary with all the arguments necessary for its corresponding model to work properly. The
+token indices are under the key "input_ids":
+
+```python
+>>> encoded_sequence = inputs["input_ids"]
+>>> print(encoded_sequence)
+[101, 138, 18696, 155, 1942, 3190, 1144, 1572, 13745, 1104, 159, 9664, 2107, 102]
+```
+
+Note that the tokenizer automatically adds "special tokens" (if the associated model relies on them) which are special
+IDs the model sometimes uses.
+
+If we decode the previous sequence of ids,
+
+```python
+>>> decoded_sequence = tokenizer.decode(encoded_sequence)
+```
+
+we will see
+
+```python
+>>> print(decoded_sequence)
+[CLS] A Titan RTX has 24GB of VRAM [SEP]
+```
+
+because this is the way a [`BertModel`] is going to expect its inputs.
+
+<a id='attention-mask'></a>
+
+### Attention mask
+
+The attention mask is an optional argument used when batching sequences together.
+
+<Youtube id="M6adb1j2jPI"/>
+
+This argument indicates to the model which tokens should be attended to, and which should not.
+
+For example, consider these two sequences:
+
+```python
+>>> from transformers import BertTokenizer
+
+>>> tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
+
+>>> sequence_a = "This is a short sequence."
+>>> sequence_b = "This is a rather long sequence. It is at least longer than the sequence A."
+
+>>> encoded_sequence_a = tokenizer(sequence_a)["input_ids"]
+>>> encoded_sequence_b = tokenizer(sequence_b)["input_ids"]
+```
+
+The encoded versions have different lengths:
+
+```python
+>>> len(encoded_sequence_a), len(encoded_sequence_b)
+(8, 19)
+```
+
+Therefore, we can't put them together in the same tensor as-is. The first sequence needs to be padded up to the length
+of the second one, or the second one needs to be truncated down to the length of the first one.
+
+In the first case, the list of IDs will be extended by the padding indices. We can pass a list to the tokenizer and ask
+it to pad like this:
+
+```python
+>>> padded_sequences = tokenizer([sequence_a, sequence_b], padding=True)
+```
+
+We can see that 0s have been added on the right of the first sentence to make it the same length as the second one:
+
+```python
+>>> padded_sequences["input_ids"]
+[[101, 1188, 1110, 170, 1603, 4954, 119, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [101, 1188, 1110, 170, 1897, 1263, 4954, 119, 1135, 1110, 1120, 1655, 2039, 1190, 1103, 4954, 138, 119, 102]]
+```
+
+This can then be converted into a tensor in PyTorch or TensorFlow. The attention mask is a binary tensor indicating the
+position of the padded indices so that the model does not attend to them. For the [`BertTokenizer`],
+`1` indicates a value that should be attended to, while `0` indicates a padded value. This attention mask is
+in the dictionary returned by the tokenizer under the key "attention_mask":
+
+```python
+>>> padded_sequences["attention_mask"]
+[[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
+```
+
+<a id='token-type-ids'></a>
+
+### Token Type IDs
+
+Some models' purpose is to do classification on pairs of sentences or question answering.
+
+<Youtube id="0u3ioSwev3s"/>
+
+These require two different sequences to be joined in a single "input_ids" entry, which usually is performed with the
+help of special tokens, such as the classifier (`[CLS]`) and separator (`[SEP]`) tokens. For example, the BERT
+model builds its two sequence input as such:
+
+```python
+>>> # [CLS] SEQUENCE_A [SEP] SEQUENCE_B [SEP]
+```
+
+We can use our tokenizer to automatically generate such a sentence by passing the two sequences to `tokenizer` as two
+arguments (and not a list, like before) like this:
+
+```python
+>>> from transformers import BertTokenizer
+
+>>> tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
+>>> sequence_a = "HuggingFace is based in NYC"
+>>> sequence_b = "Where is HuggingFace based?"
+
+>>> encoded_dict = tokenizer(sequence_a, sequence_b)
+>>> decoded = tokenizer.decode(encoded_dict["input_ids"])
+```
+
+which will return:
+
+```python
+>>> print(decoded)
+[CLS] HuggingFace is based in NYC [SEP] Where is HuggingFace based? [SEP]
+```
+
+This is enough for some models to understand where one sequence ends and where another begins. However, other models,
+such as BERT, also deploy token type IDs (also called segment IDs). They are represented as a binary mask identifying
+the two types of sequence in the model.
+
+The tokenizer returns this mask as the "token_type_ids" entry:
+
+```python
+>>> encoded_dict["token_type_ids"]
+[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]
+```
+
+The first sequence, the "context" used for the question, has all its tokens represented by a `0`, whereas the
+second sequence, corresponding to the "question", has all its tokens represented by a `1`.
+
+Some models, like [`XLNetModel`] use an additional token represented by a `2`.
+
+<a id='position-ids'></a>
+
+### Position IDs
+
+Contrary to RNNs that have the position of each token embedded within them, transformers are unaware of the position of
+each token. Therefore, the position IDs (`position_ids`) are used by the model to identify each token's position in
+the list of tokens.
+
+They are an optional parameter. If no `position_ids` are passed to the model, the IDs are automatically created as
+absolute positional embeddings.
+
+Absolute positional embeddings are selected in the range `[0, config.max_position_embeddings - 1]`. Some models use
+other types of positional embeddings, such as sinusoidal position embeddings or relative position embeddings.
+
+<a id='labels'></a>
+
+### Labels
+
+The labels are an optional argument which can be passed in order for the model to compute the loss itself. These labels
+should be the expected prediction of the model: it will use the standard loss in order to compute the loss between its
+predictions and the expected value (the label).
+
+These labels are different according to the model head, for example:
+
+- For sequence classification models (e.g., [`BertForSequenceClassification`]), the model expects a
+  tensor of dimension `(batch_size)` with each value of the batch corresponding to the expected label of the
+  entire sequence.
+- For token classification models (e.g., [`BertForTokenClassification`]), the model expects a tensor
+  of dimension `(batch_size, seq_length)` with each value corresponding to the expected label of each individual
+  token.
+- For masked language modeling (e.g., [`BertForMaskedLM`]), the model expects a tensor of dimension
+  `(batch_size, seq_length)` with each value corresponding to the expected label of each individual token: the
+  labels being the token ID for the masked token, and values to be ignored for the rest (usually -100).
+- For sequence to sequence tasks,(e.g., [`BartForConditionalGeneration`],
+  [`MBartForConditionalGeneration`]), the model expects a tensor of dimension `(batch_size, tgt_seq_length)` with each value corresponding to the target sequences associated with each input sequence. During
+  training, both *BART* and *T5* will make the appropriate *decoder_input_ids* and decoder attention masks internally.
+  They usually do not need to be supplied. This does not apply to models leveraging the Encoder-Decoder framework. See
+  the documentation of each model for more information on each specific model's labels.
+
+The base models (e.g., [`BertModel`]) do not accept labels, as these are the base transformer
+models, simply outputting features.
+
+<a id='decoder-input-ids'></a>
+
+### Decoder input IDs
+
+This input is specific to encoder-decoder models, and contains the input IDs that will be fed to the decoder. These
+inputs should be used for sequence to sequence tasks, such as translation or summarization, and are usually built in a
+way specific to each model.
+
+Most encoder-decoder models (BART, T5) create their `decoder_input_ids` on their own from the `labels`. In
+such models, passing the `labels` is the preferred way to handle training.
+
+Please check each model's docs to see how they handle these input IDs for sequence to sequence training.
+
+<a id='feed-forward-chunking'></a>
+
+### Feed Forward Chunking
+
+In each residual attention block in transformers the self-attention layer is usually followed by 2 feed forward layers.
+The intermediate embedding size of the feed forward layers is often bigger than the hidden size of the model (e.g., for
+`bert-base-uncased`).
+
+For an input of size `[batch_size, sequence_length]`, the memory required to store the intermediate feed forward
+embeddings `[batch_size, sequence_length, config.intermediate_size]` can account for a large fraction of the memory
+use. The authors of [Reformer: The Efficient Transformer](https://arxiv.org/abs/2001.04451) noticed that since the
+computation is independent of the `sequence_length` dimension, it is mathematically equivalent to compute the output
+embeddings of both feed forward layers `[batch_size, config.hidden_size]_0, ..., [batch_size, config.hidden_size]_n`
+individually and concat them afterward to `[batch_size, sequence_length, config.hidden_size]` with `n = sequence_length`, which trades increased computation time against reduced memory use, but yields a mathematically
+**equivalent** result.
+
+For models employing the function [`apply_chunking_to_forward`], the `chunk_size` defines the
+number of output embeddings that are computed in parallel and thus defines the trade-off between memory and time
+complexity. If `chunk_size` is set to 0, no feed forward chunking is done.

docs/source/glossary.rst

@@ -1,322 +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.
-
-Glossary
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-General terms
------------------------------------------------------------------------------------------------------------------------
-
-- autoencoding models: see MLM
-- autoregressive models: see CLM
-- CLM: causal language modeling, a pretraining task where the model reads the texts in order and has to predict the
-  next word. It's usually done by reading the whole sentence but using a mask inside the model to hide the future
-  tokens at a certain timestep.
-- deep learning: machine learning algorithms which uses neural networks with several layers.
-- MLM: masked language modeling, a pretraining task where the model sees a corrupted version of the texts, usually done
-  by masking some tokens randomly, and has to predict the original text.
-- multimodal: a task that combines texts with another kind of inputs (for instance images).
-- NLG: natural language generation, all tasks related to generating text (for instance talk with transformers,
-  translation).
-- NLP: natural language processing, a generic way to say "deal with texts".
-- NLU: natural language understanding, all tasks related to understanding what is in a text (for instance classifying
-  the whole text, individual words).
-- pretrained model: a model that has been pretrained on some data (for instance all of Wikipedia). Pretraining methods
-  involve a self-supervised objective, which can be reading the text and trying to predict the next word (see CLM) or
-  masking some words and trying to predict them (see MLM).
-- RNN: recurrent neural network, a type of model that uses a loop over a layer to process texts.
-- self-attention: each element of the input finds out which other elements of the input they should attend to.
-- seq2seq or sequence-to-sequence: models that generate a new sequence from an input, like translation models, or
-  summarization models (such as :doc:`Bart </model_doc/bart>` or :doc:`T5 </model_doc/t5>`).
-- token: a part of a sentence, usually a word, but can also be a subword (non-common words are often split in subwords)
-  or a punctuation symbol.
-- transformer: self-attention based deep learning model architecture.
-
-Model inputs
------------------------------------------------------------------------------------------------------------------------
-
-Every model is different yet bears similarities with the others. Therefore most models use the same inputs, which are
-detailed here alongside usage examples.
-
-.. _input-ids:
-
-Input IDs
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The input ids are often the only required parameters to be passed to the model as input. *They are token indices,
-numerical representations of tokens building the sequences that will be used as input by the model*.
-
-.. raw:: html
-
-   <iframe width="560" height="315" src="https://www.youtube.com/embed/VFp38yj8h3A" title="YouTube video player"
-   frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope;
-   picture-in-picture" allowfullscreen></iframe>
-
-Each tokenizer works differently but the underlying mechanism remains the same. Here's an example using the BERT
-tokenizer, which is a `WordPiece <https://arxiv.org/pdf/1609.08144.pdf>`__ tokenizer:
-
-.. code-block::
-
-    >>> from transformers import BertTokenizer
-    >>> tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
-
-    >>> sequence = "A Titan RTX has 24GB of VRAM"
-
-The tokenizer takes care of splitting the sequence into tokens available in the tokenizer vocabulary.
-
-.. code-block::
-
-    >>> tokenized_sequence = tokenizer.tokenize(sequence)
-
-The tokens are either words or subwords. Here for instance, "VRAM" wasn't in the model vocabulary, so it's been split
-in "V", "RA" and "M". To indicate those tokens are not separate words but parts of the same word, a double-hash prefix
-is added for "RA" and "M":
-
-.. code-block::
-
-    >>> print(tokenized_sequence)
-    ['A', 'Titan', 'R', '##T', '##X', 'has', '24', '##GB', 'of', 'V', '##RA', '##M']
-
-These tokens can then be converted into IDs which are understandable by the model. This can be done by directly feeding
-the sentence to the tokenizer, which leverages the Rust implementation of `huggingface/tokenizers
-<https://github.com/huggingface/tokenizers>`__ for peak performance.
-
-.. code-block::
-
-    >>> inputs = tokenizer(sequence)
-
-The tokenizer returns a dictionary with all the arguments necessary for its corresponding model to work properly. The
-token indices are under the key "input_ids":
-
-.. code-block::
-
-    >>> encoded_sequence = inputs["input_ids"]
-    >>> print(encoded_sequence)
-    [101, 138, 18696, 155, 1942, 3190, 1144, 1572, 13745, 1104, 159, 9664, 2107, 102]
-
-Note that the tokenizer automatically adds "special tokens" (if the associated model relies on them) which are special
-IDs the model sometimes uses.
-
-If we decode the previous sequence of ids,
-
-.. code-block::
-
-    >>> decoded_sequence = tokenizer.decode(encoded_sequence)
-
-we will see
-
-.. code-block::
-
-    >>> print(decoded_sequence)
-    [CLS] A Titan RTX has 24GB of VRAM [SEP]
-
-because this is the way a :class:`~transformers.BertModel` is going to expect its inputs.
-
-.. _attention-mask:
-
-Attention mask
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The attention mask is an optional argument used when batching sequences together.
-
-.. raw:: html
-
-   <iframe width="560" height="315" src="https://www.youtube.com/embed/M6adb1j2jPI" title="YouTube video player"
-   frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope;
-   picture-in-picture" allowfullscreen></iframe>
-
-This argument indicates to the model which tokens should be attended to, and which should not.
-
-For example, consider these two sequences:
-
-.. code-block::
-
-    >>> from transformers import BertTokenizer
-    >>> tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
-
-    >>> sequence_a = "This is a short sequence."
-    >>> sequence_b = "This is a rather long sequence. It is at least longer than the sequence A."
-
-    >>> encoded_sequence_a = tokenizer(sequence_a)["input_ids"]
-    >>> encoded_sequence_b = tokenizer(sequence_b)["input_ids"]
-
-The encoded versions have different lengths:
-
-.. code-block::
-
-    >>> len(encoded_sequence_a), len(encoded_sequence_b)
-    (8, 19)
-
-Therefore, we can't put them together in the same tensor as-is. The first sequence needs to be padded up to the length
-of the second one, or the second one needs to be truncated down to the length of the first one.
-
-In the first case, the list of IDs will be extended by the padding indices. We can pass a list to the tokenizer and ask
-it to pad like this:
-
-.. code-block::
-
-    >>> padded_sequences = tokenizer([sequence_a, sequence_b], padding=True)
-
-We can see that 0s have been added on the right of the first sentence to make it the same length as the second one:
-
-.. code-block::
-
-    >>> padded_sequences["input_ids"]
-    [[101, 1188, 1110, 170, 1603, 4954, 119, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [101, 1188, 1110, 170, 1897, 1263, 4954, 119, 1135, 1110, 1120, 1655, 2039, 1190, 1103, 4954, 138, 119, 102]]
-
-This can then be converted into a tensor in PyTorch or TensorFlow. The attention mask is a binary tensor indicating the
-position of the padded indices so that the model does not attend to them. For the :class:`~transformers.BertTokenizer`,
-:obj:`1` indicates a value that should be attended to, while :obj:`0` indicates a padded value. This attention mask is
-in the dictionary returned by the tokenizer under the key "attention_mask":
-
-.. code-block::
-
-    >>> padded_sequences["attention_mask"]
-    [[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
-
-.. _token-type-ids:
-
-Token Type IDs
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Some models' purpose is to do classification on pairs of sentences or question answering.
-
-.. raw:: html
-
-   <iframe width="560" height="315" src="https://www.youtube.com/embed/0u3ioSwev3s" title="YouTube video player"
-   frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope;
-   picture-in-picture" allowfullscreen></iframe>
-
-These require two different sequences to be joined in a single "input_ids" entry, which usually is performed with the
-help of special tokens, such as the classifier (``[CLS]``) and separator (``[SEP]``) tokens. For example, the BERT
-model builds its two sequence input as such:
-
-.. code-block::
-
-    >>> # [CLS] SEQUENCE_A [SEP] SEQUENCE_B [SEP]
-
-We can use our tokenizer to automatically generate such a sentence by passing the two sequences to ``tokenizer`` as two
-arguments (and not a list, like before) like this:
-
-.. code-block::
-
-    >>> from transformers import BertTokenizer
-    >>> tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
-    >>> sequence_a = "HuggingFace is based in NYC"
-    >>> sequence_b = "Where is HuggingFace based?"
-
-    >>> encoded_dict = tokenizer(sequence_a, sequence_b)
-    >>> decoded = tokenizer.decode(encoded_dict["input_ids"])
-
-which will return:
-
-.. code-block::
-
-    >>> print(decoded)
-    [CLS] HuggingFace is based in NYC [SEP] Where is HuggingFace based? [SEP]
-
-This is enough for some models to understand where one sequence ends and where another begins. However, other models,
-such as BERT, also deploy token type IDs (also called segment IDs). They are represented as a binary mask identifying
-the two types of sequence in the model.
-
-The tokenizer returns this mask as the "token_type_ids" entry:
-
-.. code-block::
-
-    >>> encoded_dict['token_type_ids']
-    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]
-
-The first sequence, the "context" used for the question, has all its tokens represented by a :obj:`0`, whereas the
-second sequence, corresponding to the "question", has all its tokens represented by a :obj:`1`.
-
-Some models, like :class:`~transformers.XLNetModel` use an additional token represented by a :obj:`2`.
-
-.. _position-ids:
-
-Position IDs
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Contrary to RNNs that have the position of each token embedded within them, transformers are unaware of the position of
-each token. Therefore, the position IDs (``position_ids``) are used by the model to identify each token's position in
-the list of tokens.
-
-They are an optional parameter. If no ``position_ids`` are passed to the model, the IDs are automatically created as
-absolute positional embeddings.
-
-Absolute positional embeddings are selected in the range ``[0, config.max_position_embeddings - 1]``. Some models use
-other types of positional embeddings, such as sinusoidal position embeddings or relative position embeddings.
-
-.. _labels:
-
-Labels
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The labels are an optional argument which can be passed in order for the model to compute the loss itself. These labels
-should be the expected prediction of the model: it will use the standard loss in order to compute the loss between its
-predictions and the expected value (the label).
-
-These labels are different according to the model head, for example:
-
-- For sequence classification models (e.g., :class:`~transformers.BertForSequenceClassification`), the model expects a
-  tensor of dimension :obj:`(batch_size)` with each value of the batch corresponding to the expected label of the
-  entire sequence.
-- For token classification models (e.g., :class:`~transformers.BertForTokenClassification`), the model expects a tensor
-  of dimension :obj:`(batch_size, seq_length)` with each value corresponding to the expected label of each individual
-  token.
-- For masked language modeling (e.g., :class:`~transformers.BertForMaskedLM`), the model expects a tensor of dimension
-  :obj:`(batch_size, seq_length)` with each value corresponding to the expected label of each individual token: the
-  labels being the token ID for the masked token, and values to be ignored for the rest (usually -100).
-- For sequence to sequence tasks,(e.g., :class:`~transformers.BartForConditionalGeneration`,
-  :class:`~transformers.MBartForConditionalGeneration`), the model expects a tensor of dimension :obj:`(batch_size,
-  tgt_seq_length)` with each value corresponding to the target sequences associated with each input sequence. During
-  training, both `BART` and `T5` will make the appropriate `decoder_input_ids` and decoder attention masks internally.
-  They usually do not need to be supplied. This does not apply to models leveraging the Encoder-Decoder framework. See
-  the documentation of each model for more information on each specific model's labels.
-
-The base models (e.g., :class:`~transformers.BertModel`) do not accept labels, as these are the base transformer
-models, simply outputting features.
-
-.. _decoder-input-ids:
-
-Decoder input IDs
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-This input is specific to encoder-decoder models, and contains the input IDs that will be fed to the decoder. These
-inputs should be used for sequence to sequence tasks, such as translation or summarization, and are usually built in a
-way specific to each model.
-
-Most encoder-decoder models (BART, T5) create their :obj:`decoder_input_ids` on their own from the :obj:`labels`. In
-such models, passing the :obj:`labels` is the preferred way to handle training.
-
-Please check each model's docs to see how they handle these input IDs for sequence to sequence training.
-
-.. _feed-forward-chunking:
-
-Feed Forward Chunking
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-In each residual attention block in transformers the self-attention layer is usually followed by 2 feed forward layers.
-The intermediate embedding size of the feed forward layers is often bigger than the hidden size of the model (e.g., for
-``bert-base-uncased``).
-
-For an input of size ``[batch_size, sequence_length]``, the memory required to store the intermediate feed forward
-embeddings ``[batch_size, sequence_length, config.intermediate_size]`` can account for a large fraction of the memory
-use. The authors of `Reformer: The Efficient Transformer <https://arxiv.org/abs/2001.04451>`_ noticed that since the
-computation is independent of the ``sequence_length`` dimension, it is mathematically equivalent to compute the output
-embeddings of both feed forward layers ``[batch_size, config.hidden_size]_0, ..., [batch_size, config.hidden_size]_n``
-individually and concat them afterward to ``[batch_size, sequence_length, config.hidden_size]`` with ``n =
-sequence_length``, which trades increased computation time against reduced memory use, but yields a mathematically
-**equivalent** result.
-
-For models employing the function :func:`~.transformers.apply_chunking_to_forward`, the ``chunk_size`` defines the
-number of output embeddings that are computed in parallel and thus defines the trade-off between memory and time
-complexity. If ``chunk_size`` is set to 0, no feed forward chunking is done.

docs/source/index.mdx

@@ -97,11 +97,11 @@ conversion utilities for the following models.
 1. **[BEiT](model_doc/beit)** (from Microsoft) released with the paper [BEiT: BERT Pre-Training of Image Transformers](https://arxiv.org/abs/2106.08254) by Hangbo Bao, Li Dong, Furu Wei.
 1. **[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.
 1. **[BERTweet](model_doc/bertweet)** (from VinAI Research) released with the paper [BERTweet: A pre-trained language model for English Tweets](https://aclanthology.org/2020.emnlp-demos.2/) by Dat Quoc Nguyen, Thanh Vu and Anh Tuan Nguyen.
-1. **[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.
-1. **[BigBird-RoBERTa](model_doc/bigbird)** (from Google Research) released with the paper [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.
+1. **[BERT For Sequence Generation](model_doc/bert-generation)** (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. **[BigBird-RoBERTa](model_doc/big_bird)** (from Google Research) released with the paper [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.
 1. **[BigBird-Pegasus](model_doc/bigbird_pegasus)** (from Google Research) released with the paper [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.
 1. **[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.
-1. **[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.
+1. **[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.
 1. **[BORT](model_doc/bort)** (from Alexa) released with the paper [Optimal Subarchitecture Extraction For BERT](https://arxiv.org/abs/2010.10499) by Adrian de Wynter and Daniel J. Perry.
 1. **[ByT5](model_doc/byt5)** (from Google Research) released with the paper [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626) by Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel.
 1. **[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.
@@ -111,18 +111,18 @@ conversion utilities for the following models.
 1. **[CPM](model_doc/cpm)** (from Tsinghua University) released with the paper [CPM: A Large-scale Generative Chinese Pre-trained Language Model](https://arxiv.org/abs/2012.00413) by Zhengyan Zhang, Xu Han, Hao Zhou, Pei Ke, Yuxian Gu, Deming Ye, Yujia Qin, Yusheng Su, Haozhe Ji, Jian Guan, Fanchao Qi, Xiaozhi Wang, Yanan Zheng, Guoyang Zeng, Huanqi Cao, Shengqi Chen, Daixuan Li, Zhenbo Sun, Zhiyuan Liu, Minlie Huang, Wentao Han, Jie Tang, Juanzi Li, Xiaoyan Zhu, Maosong Sun.
 1. **[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.
 1. **[DeBERTa](model_doc/deberta)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
-1. **[DeBERTa-v2](model_doc/deberta_v2)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
+1. **[DeBERTa-v2](model_doc/deberta-v2)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
 1. **[DeiT](model_doc/deit)** (from Facebook) released with the paper [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) by Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Hervé Jégou.
 1. **[DETR](model_doc/detr)** (from Facebook) released with the paper [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) by Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko.
 1. **[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.
 1. **[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/research_projects/distillation), RoBERTa into [DistilRoBERTa](https://github.com/huggingface/transformers/tree/master/examples/research_projects/distillation), Multilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers/tree/master/examples/research_projects/distillation) and a German version of DistilBERT.
 1. **[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.
-1. **[EncoderDecoder](model_doc/encoderdecoder)** (from Google Research) 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. **[EncoderDecoder](model_doc/encoder-decoder)** (from Google Research) 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. **[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.
 1. **[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.
 1. **[FNet](model_doc/fnet)** (from Google Research) released with the paper [FNet: Mixing Tokens with Fourier Transforms](https://arxiv.org/abs/2105.03824) by James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, Santiago Ontanon.
 1. **[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.
-1. **[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.
+1. **[GPT](model_doc/openai-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.
 1. **[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**.
 1. **[GPT-J](model_doc/gptj)** (from EleutherAI) released in the repository [kingoflolz/mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax/) by Ben Wang and Aran Komatsuzaki.
 1. **[GPT Neo](model_doc/gpt_neo)** (from EleutherAI) released in the repository [EleutherAI/gpt-neo](https://github.com/EleutherAI/gpt-neo) by Sid Black, Stella Biderman, Leo Gao, Phil Wang and Connor Leahy.
@@ -141,15 +141,17 @@ conversion utilities for the following models.
 1. **[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.
 1. **[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.
 1. **[MBart-50](model_doc/mbart)** (from Facebook) released with the paper [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://arxiv.org/abs/2008.00401) by Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, Angela Fan.
-1. **[Megatron-BERT](model_doc/megatron_bert)** (from NVIDIA) released with the paper [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/abs/1909.08053) by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
+1. **[Megatron-BERT](model_doc/megatron-bert)** (from NVIDIA) released with the paper [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/abs/1909.08053) by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
 1. **[Megatron-GPT2](model_doc/megatron_gpt2)** (from NVIDIA) released with the paper [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/abs/1909.08053) by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
 1. **[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.
 1. **[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.
+1. **[Nyströmformer](model_doc/nystromformer)** (from the University of Wisconsin - Madison) released with the paper [Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention](https://arxiv.org/abs/2102.03902) by Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, Vikas Singh.
 1. **[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.
 1. **[Perceiver IO](model_doc/perceiver)** (from Deepmind) released with the paper [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://arxiv.org/abs/2107.14795) by Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira.
 1. **[PhoBERT](model_doc/phobert)** (from VinAI Research) released with the paper [PhoBERT: Pre-trained language models for Vietnamese](https://www.aclweb.org/anthology/2020.findings-emnlp.92/) by Dat Quoc Nguyen and Anh Tuan Nguyen.
 1. **[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.
 1. **[QDQBert](model_doc/qdqbert)** (from NVIDIA) released with the paper [Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation](https://arxiv.org/abs/2004.09602) by Hao Wu, Patrick Judd, Xiaojie Zhang, Mikhail Isaev and Paulius Micikevicius.
+1. **[REALM](https://huggingface.co/transformers/model_doc/realm.html)** (from Google Research) released with the paper [REALM: Retrieval-Augmented Language Model Pre-Training](https://arxiv.org/abs/2002.08909) by Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat and Ming-Wei Chang.
 1. **[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.
 1. **[RemBERT](model_doc/rembert)** (from Google Research) released with the paper [Rethinking embedding coupling in pre-trained language models](https://arxiv.org/pdf/2010.12821.pdf) by Hyung Won Chung, Thibault Févry, Henry Tsai, M. Johnson, Sebastian Ruder.
 1. **[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.
@@ -161,24 +163,28 @@ conversion utilities for the following models.
 1. **[SpeechToTextTransformer2](model_doc/speech_to_text_2)** (from Facebook), released together with the paper [Large-Scale Self- and Semi-Supervised Learning for Speech Translation](https://arxiv.org/abs/2104.06678) by Changhan Wang, Anne Wu, Juan Pino, Alexei Baevski, Michael Auli, Alexis Conneau.
 1. **[Splinter](model_doc/splinter)** (from Tel Aviv University), released together with the paper [Few-Shot Question Answering by Pretraining Span Selection](https://arxiv.org/abs/2101.00438) by Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, Omer Levy.
 1. **[SqueezeBert](model_doc/squeezebert)** (from Berkeley) 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. **[Swin Transformer](model_doc/swin)** (from Microsoft) released with the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) by Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo.
 1. **[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.
 1. **[T5v1.1](model_doc/t5v1.1)** (from Google AI) released in the repository [google-research/text-to-text-transfer-transformer](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511) 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](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.
-1. **[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.
+1. **[Transformer-XL](model_doc/transfo-xl)** (from Google/CMU) released with the paper [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
 1. **[TrOCR](model_doc/trocr)** (from Microsoft), released together with the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, Furu Wei.
 1. **[UniSpeech](model_doc/unispeech)** (from Microsoft Research) released with the paper [UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data](https://arxiv.org/abs/2101.07597) by Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael Zeng, Xuedong Huang.
-1. **[UniSpeechSat](model_doc/unispeech_sat)** (from Microsoft Research) released with the paper [UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER AWARE PRE-TRAINING](https://arxiv.org/abs/2110.05752) by Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu.
+1. **[UniSpeechSat](model_doc/unispeech-sat)** (from Microsoft Research) released with the paper [UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER AWARE PRE-TRAINING](https://arxiv.org/abs/2110.05752) by Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu.
+1. **[ViLT)](model_doc/vilt)** (from NAVER AI Lab/Kakao Enterprise/Kakao Brain) released with the paper [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) by Wonjae Kim, Bokyung Son, Ildoo Kim.
 1. **[Vision Transformer (ViT)](model_doc/vit)** (from Google AI) released with the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby.
+1. **[ViTMAE)](model_doc/vit_mae)** (from Meta AI) released with the paper [Masked Autoencoders Are Scalable Vision Learners](https://arxiv.org/abs/2111.06377) by Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick.
 1. **[VisualBERT](model_doc/visual_bert)** (from UCLA NLP) released with the paper [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://arxiv.org/pdf/1908.03557) by Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang.
 1. **[WavLM](model_doc/wavlm)** (from Microsoft Research) released with the paper [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://arxiv.org/abs/2110.13900) by Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Furu Wei.
 1. **[Wav2Vec2](model_doc/wav2vec2)** (from Facebook AI) released with the paper [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.
 1. **[Wav2Vec2Phoneme](https://huggingface.co/docs/master/transformers/model_doc/wav2vec2_phoneme)** (from Facebook AI) released with the paper [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition](https://arxiv.org/abs/2109.11680) by Qiantong Xu, Alexei Baevski, Michael Auli.
 1. **[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.
-1. **[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.
-1. **[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.
+1. **[XLM-ProphetNet](model_doc/xlm-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.
+1. **[XLM-RoBERTa](model_doc/xlm-roberta)** (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](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.
 1. **[XLSR-Wav2Vec2](model_doc/xlsr_wav2vec2)** (from Facebook AI) released with the paper [Unsupervised Cross-Lingual Representation Learning For Speech Recognition](https://arxiv.org/abs/2006.13979) by Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli.
 1. **[XLS-R](https://huggingface.co/docs/master/transformers/model_doc/xls_r)** (from Facebook AI) released with the paper [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://arxiv.org/abs/2111.09296) by Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, Michael Auli.
+1. **[YOSO](model_doc/yoso)** (from the University of Wisconsin - Madison) released with the paper [You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling](https://arxiv.org/abs/2111.09714) by Zhanpeng Zeng, Yunyang Xiong, Sathya N. Ravi, Shailesh Acharya, Glenn Fung, Vikas Singh.
 
 
 ### Supported frameworks
@@ -190,7 +196,7 @@ Flax), PyTorch, and/or TensorFlow.
 <!--This table is updated automatically from the auto modules with _make fix-copies_. Do not update manually!-->
 
 |            Model            | Tokenizer slow | Tokenizer fast | PyTorch support | TensorFlow support | Flax Support |
-|-----------------------------|----------------|----------------|-----------------|--------------------|--------------|
+|:---------------------------:|:--------------:|:--------------:|:---------------:|:------------------:|:------------:|
 |           ALBERT            |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
 |            BART             |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
 |            BEiT             |       ❌       |       ❌       |       ✅        |         ❌         |      ✅      |
@@ -202,7 +208,7 @@ Flax), PyTorch, and/or TensorFlow.
 |       BlenderbotSmall       |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
 |          CamemBERT          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 |           Canine            |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
-|            CLIP             |       ✅       |       ✅       |       ✅        |         ❌         |      ✅      |
+|            CLIP             |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
 |          ConvBERT           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 |            CTRL             |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 |           DeBERTa           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
@@ -235,6 +241,7 @@ Flax), PyTorch, and/or TensorFlow.
 |         MobileBERT          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 |            MPNet            |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 |             mT5             |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+|        Nystromformer        |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |         OpenAI GPT          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 |        OpenAI GPT-2         |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
 |           Pegasus           |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
@@ -242,11 +249,12 @@ Flax), PyTorch, and/or TensorFlow.
 |         ProphetNet          |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
 |           QDQBert           |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |             RAG             |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
+|            Realm            |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
 |          Reformer           |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
 |           RemBERT           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 |          RetriBERT          |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
 |           RoBERTa           |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
-|          RoFormer           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+|          RoFormer           |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
 |          SegFormer          |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |             SEW             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |            SEW-D            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
@@ -255,21 +263,25 @@ Flax), PyTorch, and/or TensorFlow.
 |        Speech2Text2         |       ✅       |       ❌       |       ❌        |         ❌         |      ❌      |
 |          Splinter           |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
 |         SqueezeBERT         |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
+|            Swin             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |             T5              |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
 |            TAPAS            |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 |       Transformer-XL        |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 |            TrOCR            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |          UniSpeech          |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |        UniSpeechSat         |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-|   Vision Encoder decoder    |       ❌       |       ❌       |       ✅        |         ❌         |      ✅      |
+|            ViLT             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|   Vision Encoder decoder    |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |
 |    VisionTextDualEncoder    |       ❌       |       ❌       |       ✅        |         ❌         |      ✅      |
 |         VisualBert          |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |             ViT             |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |
+|           ViTMAE            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |          Wav2Vec2           |       ✅       |       ❌       |       ✅        |         ✅         |      ✅      |
 |            WavLM            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |             XLM             |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 |         XLM-RoBERTa         |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 |        XLMProphetNet        |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
 |            XLNet            |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+|            YOSO             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 
 <!-- End table-->

docs/source/internal/file_utils.mdx

@@ -0,0 +1,46 @@
+<!--Copyright 2021 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.
+-->
+
+# General Utilities
+
+This page lists all of Transformers general utility functions that are found in the file `file_utils.py`.
+
+Most of those are only useful if you are studying the general code in the library.
+
+
+## Enums and namedtuples
+
+[[autodoc]] file_utils.ExplicitEnum
+
+[[autodoc]] file_utils.PaddingStrategy
+
+[[autodoc]] file_utils.TensorType
+
+## Special Decorators
+
+[[autodoc]] file_utils.add_start_docstrings
+
+[[autodoc]] file_utils.add_start_docstrings_to_model_forward
+
+[[autodoc]] file_utils.add_end_docstrings
+
+[[autodoc]] file_utils.add_code_sample_docstrings
+
+[[autodoc]] file_utils.replace_return_docstrings
+
+## Special Properties
+
+[[autodoc]] file_utils.cached_property
+
+## Other Utilities
+
+[[autodoc]] file_utils._LazyModule

docs/source/internal/file_utils.rst

@@ -1,54 +0,0 @@
-.. 
-    Copyright 2021 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.
-
-General Utilities
------------------------------------------------------------------------------------------------------------------------
-
-This page lists all of Transformers general utility functions that are found in the file ``file_utils.py``.
-
-Most of those are only useful if you are studying the general code in the library.
-
-
-Enums and namedtuples
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.file_utils.ExplicitEnum
-
-.. autoclass:: transformers.file_utils.PaddingStrategy
-
-.. autoclass:: transformers.file_utils.TensorType
-
-
-Special Decorators
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autofunction:: transformers.file_utils.add_start_docstrings
-
-.. autofunction:: transformers.file_utils.add_start_docstrings_to_model_forward
-
-.. autofunction:: transformers.file_utils.add_end_docstrings
-
-.. autofunction:: transformers.file_utils.add_code_sample_docstrings
-
-.. autofunction:: transformers.file_utils.replace_return_docstrings
-
-
-Special Properties
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.file_utils.cached_property
-
-
-Other Utilities
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.file_utils._LazyModule

docs/source/internal/generation_utils.mdx

@@ -0,0 +1,207 @@
+<!--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 [`~generation_utils.GenerationMixin.generate`],
+[`~generation_utils.GenerationMixin.greedy_search`],
+[`~generation_utils.GenerationMixin.sample`],
+[`~generation_utils.GenerationMixin.beam_search`],
+[`~generation_utils.GenerationMixin.beam_sample`], and
+[`~generation_utils.GenerationMixin.group_beam_search`].
+
+Most of those are only useful if you are studying the code of the generate methods in the library.
+
+## Generate Outputs
+
+The output of [`~generation_utils.GenerationMixin.generate`] is an instance of a subclass of
+[`~file_utils.ModelOutput`]. This output is a data structure containing all the information returned
+by [`~generation_utils.GenerationMixin.generate`], but that can also be used as tuple or dictionary.
+
+Here's an example:
+
+```python
+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 [`~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
+
+```python
+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
+
+[[autodoc]] generation_utils.GreedySearchDecoderOnlyOutput
+
+[[autodoc]] generation_utils.GreedySearchEncoderDecoderOutput
+
+[[autodoc]] generation_flax_utils.FlaxGreedySearchOutput
+
+### SampleOutput
+
+[[autodoc]] generation_utils.SampleDecoderOnlyOutput
+
+[[autodoc]] generation_utils.SampleEncoderDecoderOutput
+
+[[autodoc]] generation_flax_utils.FlaxSampleOutput
+
+### BeamSearchOutput
+
+[[autodoc]] generation_utils.BeamSearchDecoderOnlyOutput
+
+[[autodoc]] generation_utils.BeamSearchEncoderDecoderOutput
+
+### BeamSampleOutput
+
+[[autodoc]] generation_utils.BeamSampleDecoderOnlyOutput
+
+[[autodoc]] generation_utils.BeamSampleEncoderDecoderOutput
+
+## LogitsProcessor
+
+A [`LogitsProcessor`] can be used to modify the prediction scores of a language model head for
+generation.
+
+[[autodoc]] LogitsProcessor
+    - __call__
+
+[[autodoc]] LogitsProcessorList
+    - __call__
+
+[[autodoc]] LogitsWarper
+    - __call__
+
+[[autodoc]] MinLengthLogitsProcessor
+    - __call__
+
+[[autodoc]] TemperatureLogitsWarper
+    - __call__
+
+[[autodoc]] RepetitionPenaltyLogitsProcessor
+    - __call__
+
+[[autodoc]] TopPLogitsWarper
+    - __call__
+
+[[autodoc]] TopKLogitsWarper
+    - __call__
+
+[[autodoc]] NoRepeatNGramLogitsProcessor
+    - __call__
+
+[[autodoc]] NoBadWordsLogitsProcessor
+    - __call__
+
+[[autodoc]] PrefixConstrainedLogitsProcessor
+    - __call__
+
+[[autodoc]] HammingDiversityLogitsProcessor
+    - __call__
+
+[[autodoc]] ForcedBOSTokenLogitsProcessor
+    - __call__
+
+[[autodoc]] ForcedEOSTokenLogitsProcessor
+    - __call__
+
+[[autodoc]] InfNanRemoveLogitsProcessor
+    - __call__
+
+[[autodoc]] FlaxLogitsProcessor
+    - __call__
+
+[[autodoc]] FlaxLogitsProcessorList
+    - __call__
+
+[[autodoc]] FlaxLogitsWarper
+    - __call__
+
+[[autodoc]] FlaxTemperatureLogitsWarper
+    - __call__
+
+[[autodoc]] FlaxTopPLogitsWarper
+    - __call__
+
+[[autodoc]] FlaxTopKLogitsWarper
+    - __call__
+
+[[autodoc]] FlaxForcedBOSTokenLogitsProcessor
+    - __call__
+
+[[autodoc]] FlaxForcedEOSTokenLogitsProcessor
+    - __call__
+
+[[autodoc]] FlaxMinLengthLogitsProcessor
+    - __call__
+
+## StoppingCriteria
+
+A [`StoppingCriteria`] can be used to change when to stop generation (other than EOS token).
+
+[[autodoc]] StoppingCriteria
+    - __call__
+
+[[autodoc]] StoppingCriteriaList
+    - __call__
+
+[[autodoc]] MaxLengthCriteria
+    - __call__
+
+[[autodoc]] MaxTimeCriteria
+    - __call__
+
+## BeamSearch
+
+[[autodoc]] BeamScorer
+    - process
+    - finalize
+
+[[autodoc]] BeamSearchScorer
+    - process
+    - finalize
+
+## Utilities
+
+[[autodoc]] top_k_top_p_filtering
+
+[[autodoc]] tf_top_k_top_p_filtering

docs/source/internal/generation_utils.rst

@@ -1,230 +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.
-
-Utilities for Generation
------------------------------------------------------------------------------------------------------------------------
-
-This page lists all the utility functions used by :meth:`~transformers.generation_utils.GenerationMixin.generate`,
-:meth:`~transformers.generation_utils.GenerationMixin.greedy_search`,
-:meth:`~transformers.generation_utils.GenerationMixin.sample`,
-:meth:`~transformers.generation_utils.GenerationMixin.beam_search`,
-:meth:`~transformers.generation_utils.GenerationMixin.beam_sample`, and
-:meth:`~transformers.generation_utils.GenerationMixin.group_beam_search`.
-
-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.generation_utils.GenerationMixin.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.generation_utils.GenerationMixin.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:
-
-.. autoclass:: transformers.generation_flax_utils.FlaxGreedySearchOutput
-    :members:
-
-
-SampleOutput
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. autoclass:: transformers.generation_utils.SampleDecoderOnlyOutput
-    :members:
-
-.. autoclass:: transformers.generation_utils.SampleEncoderDecoderOutput
-    :members:
-
-.. autoclass:: transformers.generation_flax_utils.FlaxSampleOutput
-    :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
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-A :class:`~transformers.LogitsProcessor` can be used to modify the prediction scores of a language model head for
-generation.
-
-.. autoclass:: transformers.LogitsProcessor
-    :members: __call__
-
-.. autoclass:: transformers.LogitsProcessorList
-    :members: __call__
-
-.. autoclass:: transformers.LogitsWarper
-    :members: __call__
-
-.. autoclass:: transformers.MinLengthLogitsProcessor
-    :members: __call__
-
-.. autoclass:: transformers.TemperatureLogitsWarper
-    :members: __call__
-
-.. autoclass:: transformers.RepetitionPenaltyLogitsProcessor
-    :members: __call__
-
-.. autoclass:: transformers.TopPLogitsWarper
-    :members: __call__
-
-.. autoclass:: transformers.TopKLogitsWarper
-    :members: __call__
-
-.. autoclass:: transformers.NoRepeatNGramLogitsProcessor
-    :members: __call__
-
-.. autoclass:: transformers.NoBadWordsLogitsProcessor
-    :members: __call__
-
-.. autoclass:: transformers.PrefixConstrainedLogitsProcessor
-    :members: __call__
-
-.. autoclass:: transformers.HammingDiversityLogitsProcessor
-    :members: __call__
-
-.. autoclass:: transformers.ForcedBOSTokenLogitsProcessor
-    :members: __call__
-
-.. autoclass:: transformers.ForcedEOSTokenLogitsProcessor
-    :members: __call__
-
-.. autoclass:: transformers.InfNanRemoveLogitsProcessor
-    :members: __call__
-
-.. autoclass:: transformers.FlaxLogitsProcessor
-    :members: __call__
-
-.. autoclass:: transformers.FlaxLogitsProcessorList
-    :members: __call__
-
-.. autoclass:: transformers.FlaxLogitsWarper
-    :members: __call__
-
-.. autoclass:: transformers.FlaxTemperatureLogitsWarper
-    :members: __call__
-
-.. autoclass:: transformers.FlaxTopPLogitsWarper
-    :members: __call__
-
-.. autoclass:: transformers.FlaxTopKLogitsWarper
-    :members: __call__
-
-.. autoclass:: transformers.FlaxForcedBOSTokenLogitsProcessor
-    :members: __call__
-
-.. autoclass:: transformers.FlaxForcedEOSTokenLogitsProcessor
-    :members: __call__
-
-.. autoclass:: transformers.FlaxMinLengthLogitsProcessor
-    :members: __call__
-
-
-StoppingCriteria
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-A :class:`~transformers.StoppingCriteria` can be used to change when to stop generation (other than EOS token).
-
-.. autoclass:: transformers.StoppingCriteria
-    :members: __call__
-
-.. autoclass:: transformers.StoppingCriteriaList
-    :members: __call__
-
-.. autoclass:: transformers.MaxLengthCriteria
-    :members: __call__
-
-.. autoclass:: transformers.MaxTimeCriteria
-    :members: __call__
-
-BeamSearch
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BeamScorer
-    :members: process, finalize
-
-.. autoclass:: transformers.BeamSearchScorer
-    :members: process, finalize
-
-Utilities
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autofunction:: transformers.top_k_top_p_filtering
-
-.. autofunction:: transformers.tf_top_k_top_p_filtering

docs/source/internal/modeling_utils.mdx

@@ -0,0 +1,82 @@
+<!--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
+
+This page lists all the custom layers used by the library, as well as the utility functions it provides for modeling.
+
+Most of those are only useful if you are studying the code of the models in the library.
+
+
+## Pytorch custom modules
+
+[[autodoc]] modeling_utils.Conv1D
+
+[[autodoc]] modeling_utils.PoolerStartLogits
+    - forward
+
+[[autodoc]] modeling_utils.PoolerEndLogits
+    - forward
+
+[[autodoc]] modeling_utils.PoolerAnswerClass
+    - forward
+
+[[autodoc]] modeling_utils.SquadHeadOutput
+
+[[autodoc]] modeling_utils.SQuADHead
+    - forward
+
+[[autodoc]] modeling_utils.SequenceSummary
+    - forward
+
+## PyTorch Helper Functions
+
+[[autodoc]] apply_chunking_to_forward
+
+[[autodoc]] modeling_utils.find_pruneable_heads_and_indices
+
+[[autodoc]] modeling_utils.prune_layer
+
+[[autodoc]] modeling_utils.prune_conv1d_layer
+
+[[autodoc]] modeling_utils.prune_linear_layer
+
+## TensorFlow custom layers
+
+[[autodoc]] modeling_tf_utils.TFConv1D
+
+[[autodoc]] modeling_tf_utils.TFSharedEmbeddings
+    - call
+
+[[autodoc]] modeling_tf_utils.TFSequenceSummary
+
+## TensorFlow loss functions
+
+[[autodoc]] modeling_tf_utils.TFCausalLanguageModelingLoss
+
+[[autodoc]] modeling_tf_utils.TFMaskedLanguageModelingLoss
+
+[[autodoc]] modeling_tf_utils.TFMultipleChoiceLoss
+
+[[autodoc]] modeling_tf_utils.TFQuestionAnsweringLoss
+
+[[autodoc]] modeling_tf_utils.TFSequenceClassificationLoss
+
+[[autodoc]] modeling_tf_utils.TFTokenClassificationLoss
+
+## TensorFlow Helper Functions
+
+[[autodoc]] modeling_tf_utils.get_initializer
+
+[[autodoc]] modeling_tf_utils.keras_serializable
+
+[[autodoc]] modeling_tf_utils.shape_list

docs/source/internal/modeling_utils.rst

@@ -1,97 +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.
-
-Custom Layers and Utilities
------------------------------------------------------------------------------------------------------------------------
-
-This page lists all the custom layers used by the library, as well as the utility functions it provides for modeling.
-
-Most of those are only useful if you are studying the code of the models in the library.
-
-
-Pytorch custom modules
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_utils.Conv1D
-
-.. autoclass:: transformers.modeling_utils.PoolerStartLogits
-    :members: forward
-
-.. autoclass:: transformers.modeling_utils.PoolerEndLogits
-    :members: forward
-
-.. autoclass:: transformers.modeling_utils.PoolerAnswerClass
-    :members: forward
-
-.. autoclass:: transformers.modeling_utils.SquadHeadOutput
-
-.. autoclass:: transformers.modeling_utils.SQuADHead
-    :members: forward
-
-.. autoclass:: transformers.modeling_utils.SequenceSummary
-    :members: forward
-
-
-PyTorch Helper Functions
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autofunction:: transformers.apply_chunking_to_forward
-
-.. autofunction:: transformers.modeling_utils.find_pruneable_heads_and_indices
-
-.. autofunction:: transformers.modeling_utils.prune_layer
-
-.. autofunction:: transformers.modeling_utils.prune_conv1d_layer
-
-.. autofunction:: transformers.modeling_utils.prune_linear_layer
-
-TensorFlow custom layers
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_tf_utils.TFConv1D
-
-.. autoclass:: transformers.modeling_tf_utils.TFSharedEmbeddings
-    :members: call
-
-.. autoclass:: transformers.modeling_tf_utils.TFSequenceSummary
-
-
-TensorFlow loss functions
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_tf_utils.TFCausalLanguageModelingLoss
-    :members:
-
-.. autoclass:: transformers.modeling_tf_utils.TFMaskedLanguageModelingLoss
-    :members:
-
-.. autoclass:: transformers.modeling_tf_utils.TFMultipleChoiceLoss
-    :members:
-
-.. autoclass:: transformers.modeling_tf_utils.TFQuestionAnsweringLoss
-    :members:
-
-.. autoclass:: transformers.modeling_tf_utils.TFSequenceClassificationLoss
-    :members:
-
-.. autoclass:: transformers.modeling_tf_utils.TFTokenClassificationLoss
-    :members:
-
-
-TensorFlow Helper Functions
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autofunction:: transformers.modeling_tf_utils.get_initializer
-
-.. autofunction:: transformers.modeling_tf_utils.keras_serializable
-
-.. autofunction:: transformers.modeling_tf_utils.shape_list

docs/source/internal/pipelines_utils.mdx

@@ -0,0 +1,40 @@
+<!--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
+
+This page lists all the utility functions the library provides for pipelines.
+
+Most of those are only useful if you are studying the code of the models in the library.
+
+
+## Argument handling
+
+[[autodoc]] pipelines.ArgumentHandler
+
+[[autodoc]] pipelines.ZeroShotClassificationArgumentHandler
+
+[[autodoc]] pipelines.QuestionAnsweringArgumentHandler
+
+## Data format
+
+[[autodoc]] pipelines.PipelineDataFormat
+
+[[autodoc]] pipelines.CsvPipelineDataFormat
+
+[[autodoc]] pipelines.JsonPipelineDataFormat
+
+[[autodoc]] pipelines.PipedPipelineDataFormat
+
+## Utilities
+
+[[autodoc]] pipelines.PipelineException

docs/source/internal/pipelines_utils.rst

@@ -1,50 +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.
-
-Utilities for pipelines
------------------------------------------------------------------------------------------------------------------------
-
-This page lists all the utility functions the library provides for pipelines.
-
-Most of those are only useful if you are studying the code of the models in the library.
-
-
-Argument handling
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.pipelines.ArgumentHandler
-
-.. autoclass:: transformers.pipelines.ZeroShotClassificationArgumentHandler
-
-.. autoclass:: transformers.pipelines.QuestionAnsweringArgumentHandler
-
-
-Data format
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.pipelines.PipelineDataFormat
-    :members:
-
-.. autoclass:: transformers.pipelines.CsvPipelineDataFormat
-    :members:
-
-.. autoclass:: transformers.pipelines.JsonPipelineDataFormat
-    :members:
-
-.. autoclass:: transformers.pipelines.PipedPipelineDataFormat
-    :members:
-
-
-Utilities
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.pipelines.PipelineException

docs/source/internal/tokenization_utils.mdx

@@ -0,0 +1,38 @@
+<!--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
+
+This page lists all the utility functions used by the tokenizers, mainly the class
+[`~tokenization_utils_base.PreTrainedTokenizerBase`] that implements the common methods between
+[`PreTrainedTokenizer`] and [`PreTrainedTokenizerFast`] and the mixin
+[`~tokenization_utils_base.SpecialTokensMixin`].
+
+Most of those are only useful if you are studying the code of the tokenizers in the library.
+
+## PreTrainedTokenizerBase
+
+[[autodoc]] tokenization_utils_base.PreTrainedTokenizerBase
+    - __call__
+    - all
+
+## SpecialTokensMixin
+
+[[autodoc]] tokenization_utils_base.SpecialTokensMixin
+
+## Enums and namedtuples
+
+[[autodoc]] tokenization_utils_base.TruncationStrategy
+
+[[autodoc]] tokenization_utils_base.CharSpan
+
+[[autodoc]] tokenization_utils_base.TokenSpan

docs/source/internal/tokenization_utils.rst

@@ -1,45 +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.
-
-Utilities for Tokenizers
------------------------------------------------------------------------------------------------------------------------
-
-This page lists all the utility functions used by the tokenizers, mainly the class
-:class:`~transformers.tokenization_utils_base.PreTrainedTokenizerBase` that implements the common methods between
-:class:`~transformers.PreTrainedTokenizer` and :class:`~transformers.PreTrainedTokenizerFast` and the mixin
-:class:`~transformers.tokenization_utils_base.SpecialTokensMixin`.
-
-Most of those are only useful if you are studying the code of the tokenizers in the library.
-
-PreTrainedTokenizerBase
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.tokenization_utils_base.PreTrainedTokenizerBase
-    :special-members: __call__
-    :members:
-
-
-SpecialTokensMixin
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.tokenization_utils_base.SpecialTokensMixin
-    :members:
-
-
-Enums and namedtuples
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.tokenization_utils_base.TruncationStrategy
-
-.. autoclass:: transformers.tokenization_utils_base.CharSpan
-
-.. autoclass:: transformers.tokenization_utils_base.TokenSpan

docs/source/internal/trainer_utils.mdx

@@ -0,0 +1,43 @@
+<!--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
+
+This page lists all the utility functions used by [`Trainer`].
+
+Most of those are only useful if you are studying the code of the Trainer in the library.
+
+## Utilities
+
+[[autodoc]] EvalPrediction
+
+[[autodoc]] IntervalStrategy
+
+[[autodoc]] set_seed
+
+[[autodoc]] torch_distributed_zero_first
+
+## Callbacks internals
+
+[[autodoc]] trainer_callback.CallbackHandler
+
+## Distributed Evaluation
+
+[[autodoc]] trainer_pt_utils.DistributedTensorGatherer
+
+## Distributed Evaluation
+
+[[autodoc]] HfArgumentParser
+
+## Debug Utilities
+
+[[autodoc]] debug_utils.DebugUnderflowOverflow

docs/source/internal/trainer_utils.rst

@@ -1,54 +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.
-
-Utilities for Trainer
------------------------------------------------------------------------------------------------------------------------
-
-This page lists all the utility functions used by :class:`~transformers.Trainer`.
-
-Most of those are only useful if you are studying the code of the Trainer in the library.
-
-Utilities
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.EvalPrediction
-
-.. autoclass:: transformers.IntervalStrategy
-
-.. autofunction:: transformers.set_seed
-
-.. autofunction:: transformers.torch_distributed_zero_first
-
-
-Callbacks internals
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.trainer_callback.CallbackHandler
-
-
-Distributed Evaluation
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.trainer_pt_utils.DistributedTensorGatherer
-    :members:
-
-
-Distributed Evaluation
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.HfArgumentParser
-
-
-Debug Utilities
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.debug_utils.DebugUnderflowOverflow

docs/source/main_classes/callback.mdx

@@ -0,0 +1,107 @@
+<!--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
+
+Callbacks are objects that can customize the behavior of the training loop in the PyTorch
+[`Trainer`] (this feature is not yet implemented in TensorFlow) that can inspect the training loop
+state (for progress reporting, logging on TensorBoard or other ML platforms...) and take decisions (like early
+stopping).
+
+Callbacks are "read only" pieces of code, apart from the [`TrainerControl`] object they return, they
+cannot change anything in the training loop. For customizations that require changes in the training loop, you should
+subclass [`Trainer`] and override the methods you need (see [trainer](trainer) for examples).
+
+By default a [`Trainer`] will use the following callbacks:
+
+- [`DefaultFlowCallback`] which handles the default behavior for logging, saving and evaluation.
+- [`PrinterCallback`] or [`ProgressCallback`] to display progress and print the
+  logs (the first one is used if you deactivate tqdm through the [`TrainingArguments`], otherwise
+  it's the second one).
+- [`~integrations.TensorBoardCallback`] if tensorboard is accessible (either through PyTorch >= 1.4
+  or tensorboardX).
+- [`~integrations.WandbCallback`] if [wandb](https://www.wandb.com/) is installed.
+- [`~integrations.CometCallback`] if [comet_ml](https://www.comet.ml/site/) is installed.
+- [`~integrations.MLflowCallback`] if [mlflow](https://www.mlflow.org/) is installed.
+- [`~integrations.AzureMLCallback`] if [azureml-sdk](https://pypi.org/project/azureml-sdk/) is
+  installed.
+
+The main class that implements callbacks is [`TrainerCallback`]. It gets the
+[`TrainingArguments`] used to instantiate the [`Trainer`], can access that
+Trainer's internal state via [`TrainerState`], and can take some actions on the training loop via
+[`TrainerControl`].
+
+
+## Available Callbacks
+
+Here is the list of the available [`TrainerCallback`] in the library:
+
+[[autodoc]] integrations.CometCallback
+    - setup
+
+[[autodoc]] DefaultFlowCallback
+
+[[autodoc]] PrinterCallback
+
+[[autodoc]] ProgressCallback
+
+[[autodoc]] EarlyStoppingCallback
+
+[[autodoc]] integrations.TensorBoardCallback
+
+[[autodoc]] integrations.WandbCallback
+    - setup
+
+[[autodoc]] integrations.MLflowCallback
+    - setup
+
+[[autodoc]] integrations.AzureMLCallback
+
+## TrainerCallback
+
+[[autodoc]] TrainerCallback
+
+Here is an example of how to register a custom callback with the PyTorch [`Trainer`]:
+
+```python
+class MyCallback(TrainerCallback):
+    "A callback that prints a message at the beginning of training"
+
+    def on_train_begin(self, args, state, control, **kwargs):
+        print("Starting training")
+
+
+trainer = Trainer(
+    model,
+    args,
+    train_dataset=train_dataset,
+    eval_dataset=eval_dataset,
+    callbacks=[MyCallback],  # We can either pass the callback class this way or an instance of it (MyCallback())
+)
+```
+
+Another way to register a callback is to call `trainer.add_callback()` as follows:
+
+```python
+trainer = Trainer(...)
+trainer.add_callback(MyCallback)
+# Alternatively, we can pass an instance of the callback class
+trainer.add_callback(MyCallback())
+```
+
+## TrainerState
+
+[[autodoc]] TrainerState
+
+## TrainerControl
+
+[[autodoc]] TrainerControl

docs/source/main_classes/callback.rst

@@ -1,115 +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.
-
-Callbacks
------------------------------------------------------------------------------------------------------------------------
-
-Callbacks are objects that can customize the behavior of the training loop in the PyTorch
-:class:`~transformers.Trainer` (this feature is not yet implemented in TensorFlow) that can inspect the training loop
-state (for progress reporting, logging on TensorBoard or other ML platforms...) and take decisions (like early
-stopping).
-
-Callbacks are "read only" pieces of code, apart from the :class:`~transformers.TrainerControl` object they return, they
-cannot change anything in the training loop. For customizations that require changes in the training loop, you should
-subclass :class:`~transformers.Trainer` and override the methods you need (see :doc:`trainer` for examples).
-
-By default a :class:`~transformers.Trainer` will use the following callbacks:
-
-- :class:`~transformers.DefaultFlowCallback` which handles the default behavior for logging, saving and evaluation.
-- :class:`~transformers.PrinterCallback` or :class:`~transformers.ProgressCallback` to display progress and print the
-  logs (the first one is used if you deactivate tqdm through the :class:`~transformers.TrainingArguments`, otherwise
-  it's the second one).
-- :class:`~transformers.integrations.TensorBoardCallback` if tensorboard is accessible (either through PyTorch >= 1.4
-  or tensorboardX).
-- :class:`~transformers.integrations.WandbCallback` if `wandb <https://www.wandb.com/>`__ is installed.
-- :class:`~transformers.integrations.CometCallback` if `comet_ml <https://www.comet.ml/site/>`__ is installed.
-- :class:`~transformers.integrations.MLflowCallback` if `mlflow <https://www.mlflow.org/>`__ is installed.
-- :class:`~transformers.integrations.AzureMLCallback` if `azureml-sdk <https://pypi.org/project/azureml-sdk/>`__ is
-  installed.
-
-The main class that implements callbacks is :class:`~transformers.TrainerCallback`. It gets the
-:class:`~transformers.TrainingArguments` used to instantiate the :class:`~transformers.Trainer`, can access that
-Trainer's internal state via :class:`~transformers.TrainerState`, and can take some actions on the training loop via
-:class:`~transformers.TrainerControl`.
-
-
-Available Callbacks
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Here is the list of the available :class:`~transformers.TrainerCallback` in the library:
-
-.. autoclass:: transformers.integrations.CometCallback
-    :members: setup
-
-.. autoclass:: transformers.DefaultFlowCallback
-
-.. autoclass:: transformers.PrinterCallback
-
-.. autoclass:: transformers.ProgressCallback
-
-.. autoclass:: transformers.EarlyStoppingCallback
-
-.. autoclass:: transformers.integrations.TensorBoardCallback
-
-.. autoclass:: transformers.integrations.WandbCallback
-    :members: setup
-
-.. autoclass:: transformers.integrations.MLflowCallback
-    :members: setup
-
-.. autoclass:: transformers.integrations.AzureMLCallback
-
-TrainerCallback
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TrainerCallback
-    :members:
-
-Here is an example of how to register a custom callback with the PyTorch :class:`~transformers.Trainer`:
-
-.. code-block:: python
-
-    class MyCallback(TrainerCallback):
-        "A callback that prints a message at the beginning of training"
-
-        def on_train_begin(self, args, state, control, **kwargs):
-            print("Starting training")
-
-    trainer = Trainer(
-        model,
-        args,
-        train_dataset=train_dataset,
-        eval_dataset=eval_dataset,
-        callbacks=[MyCallback]  # We can either pass the callback class this way or an instance of it (MyCallback())
-    )
-
-Another way to register a callback is to call ``trainer.add_callback()`` as follows:
-
-.. code-block:: python
-
-    trainer = Trainer(...)
-    trainer.add_callback(MyCallback)
-    # Alternatively, we can pass an instance of the callback class
-    trainer.add_callback(MyCallback())
-
-TrainerState
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TrainerState
-    :members:
-
-
-TrainerControl
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TrainerControl
-    :members:

docs/source/main_classes/configuration.mdx

@@ -0,0 +1,28 @@
+<!--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
+
+The base class [`PretrainedConfig`] implements the common methods for loading/saving a configuration
+either from a local file or directory, or from a pretrained model configuration provided by the library (downloaded
+from HuggingFace's AWS S3 repository).
+
+Each derived config class implements model specific attributes. Common attributes present in all config classes are:
+`hidden_size`, `num_attention_heads`, and `num_hidden_layers`. Text models further implement:
+`vocab_size`.
+
+
+## PretrainedConfig
+
+[[autodoc]] PretrainedConfig
+    - push_to_hub
+    - all

docs/source/main_classes/configuration.rst

@@ -1,31 +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.
-
-Configuration
------------------------------------------------------------------------------------------------------------------------
-
-The base class :class:`~transformers.PretrainedConfig` implements the common methods for loading/saving a configuration
-either from a local file or directory, or from a pretrained model configuration provided by the library (downloaded
-from HuggingFace's AWS S3 repository).
-
-Each derived config class implements model specific attributes. Common attributes present in all config classes are:
-:obj:`hidden_size`, :obj:`num_attention_heads`, and :obj:`num_hidden_layers`. Text models further implement:
-:obj:`vocab_size`.
-
-
-
-PretrainedConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.PretrainedConfig
-    :special-members: push_to_hub
-    :members:

docs/source/main_classes/data_collator.mdx

@@ -0,0 +1,64 @@
+<!--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.
+-->
+
+# Data Collator
+
+Data collators are objects that will form a batch by using a list of dataset elements as input. These elements are of
+the same type as the elements of `train_dataset` or `eval_dataset`.
+
+To be able to build batches, data collators may apply some processing (like padding). Some of them (like
+[`DataCollatorForLanguageModeling`]) also apply some random data augmentation (like random masking)
+on the formed batch.
+
+Examples of use can be found in the [example scripts](../examples) or [example notebooks](../notebooks).
+
+
+## Default data collator
+
+[[autodoc]] data.data_collator.default_data_collator
+
+## DefaultDataCollator
+
+[[autodoc]] data.data_collator.DefaultDataCollator
+
+## DataCollatorWithPadding
+
+[[autodoc]] data.data_collator.DataCollatorWithPadding
+
+## DataCollatorForTokenClassification
+
+[[autodoc]] data.data_collator.DataCollatorForTokenClassification
+
+## DataCollatorForSeq2Seq
+
+[[autodoc]] data.data_collator.DataCollatorForSeq2Seq
+
+## DataCollatorForLanguageModeling
+
+[[autodoc]] data.data_collator.DataCollatorForLanguageModeling
+    - numpy_mask_tokens
+    - tf_mask_tokens
+    - torch_mask_tokens
+
+## DataCollatorForWholeWordMask
+
+[[autodoc]] data.data_collator.DataCollatorForWholeWordMask
+    - numpy_mask_tokens
+    - tf_mask_tokens
+    - torch_mask_tokens
+
+## DataCollatorForPermutationLanguageModeling
+
+[[autodoc]] data.data_collator.DataCollatorForPermutationLanguageModeling
+    - numpy_mask_tokens
+    - tf_mask_tokens
+    - torch_mask_tokens

docs/source/main_classes/data_collator.rst

@@ -1,78 +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.
-
-Data Collator
------------------------------------------------------------------------------------------------------------------------
-
-Data collators are objects that will form a batch by using a list of dataset elements as input. These elements are of
-the same type as the elements of :obj:`train_dataset` or :obj:`eval_dataset`.
-
-To be able to build batches, data collators may apply some processing (like padding). Some of them (like
-:class:`~transformers.DataCollatorForLanguageModeling`) also apply some random data augmentation (like random masking)
-on the formed batch.
-
-Examples of use can be found in the :doc:`example scripts <../examples>` or :doc:`example notebooks <../notebooks>`.
-
-
-Default data collator
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autofunction:: transformers.data.data_collator.default_data_collator
-
-
-DefaultDataCollator
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.data.data_collator.DefaultDataCollator
-    :members:
-
-
-DataCollatorWithPadding
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.data.data_collator.DataCollatorWithPadding
-    :members:
-
-
-DataCollatorForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.data.data_collator.DataCollatorForTokenClassification
-    :members:
-
-
-DataCollatorForSeq2Seq
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.data.data_collator.DataCollatorForSeq2Seq
-    :members:
-
-
-DataCollatorForLanguageModeling
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.data.data_collator.DataCollatorForLanguageModeling
-    :members: numpy_mask_tokens, tf_mask_tokens, torch_mask_tokens
-
-
-DataCollatorForWholeWordMask
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.data.data_collator.DataCollatorForWholeWordMask
-    :members: numpy_mask_tokens, tf_mask_tokens, torch_mask_tokens
-
-
-DataCollatorForPermutationLanguageModeling
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.data.data_collator.DataCollatorForPermutationLanguageModeling
-    :members: numpy_mask_tokens, tf_mask_tokens, torch_mask_tokens

docs/source/main_classes/deepspeed.mdx

@@ -31,7 +31,7 @@ won't be possible on a single GPU.
 
 🤗 Transformers integrates [DeepSpeed](https://github.com/microsoft/DeepSpeed) via 2 options:
 
-1. Integration of the core DeepSpeed features via [`Trainer`]. This is everything done for you type
+1. Integration of the core DeepSpeed features via [`Trainer`]. This is an everything-done-for-you type
    of integration - just supply your custom config file or use our template and you have nothing else to do. Most of
    this document is focused on this feature.
 2. If you don't use [`Trainer`] and want to use your own Trainer where you integrated DeepSpeed
@@ -97,7 +97,7 @@ TORCH_CUDA_ARCH_LIST="8.6" DS_BUILD_CPU_ADAM=1 DS_BUILD_UTILS=1 pip install . \
 --disable-pip-version-check 2>&1 | tee build.log
 ```
 
-If you intend to use NVMe offload you will need to also include `DS_BUILD_AIO=1` in the instructions above (and also
+If you intend to use NVMe offload you will also need to include `DS_BUILD_AIO=1` in the instructions above (and also
 install *libaio-dev* system-wide).
 
 Edit `TORCH_CUDA_ARCH_LIST` to insert the code for the architectures of the GPU cards you intend to use. Assuming all
@@ -134,7 +134,7 @@ You can check the archs pytorch was built with using:
 python -c "import torch; print(torch.cuda.get_arch_list())"
 ```
 
-Here is how to find out the arch for one of the installed GPU. For example, for GPU 0:
+Here is how to find out the arch for one of the installed GPUs. For example, for GPU 0:
 
 ```bash
 CUDA_VISIBLE_DEVICES=0 python -c "import torch; \
@@ -169,7 +169,7 @@ following:
 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:
+Therefore, if your original command line looked as follows:
 
 ```bash
 python -m torch.distributed.launch --nproc_per_node=2 your_program.py <normal cl args>
@@ -214,7 +214,7 @@ For some practical usage examples, please, see this [post](https://github.com/hu
 
 ### Deployment with one GPU
 
-To deploy DeepSpeed with one GPU adjust the [`Trainer`] command line arguments as following:
+To deploy DeepSpeed with one GPU adjust the [`Trainer`] command line arguments as follows:
 
 ```bash
 deepspeed --num_gpus=1 examples/pytorch/translation/run_translation.py \
@@ -295,11 +295,12 @@ If you're using only 1 GPU, here is how you'd have to adjust your training code
 # DeepSpeed requires a distributed environment even when only one process is used.
 # This emulates a launcher in the notebook
 import os
-os.environ['MASTER_ADDR'] = 'localhost'
-os.environ['MASTER_PORT'] = '9994' # modify if RuntimeError: Address already in use
-os.environ['RANK'] = "0"
-os.environ['LOCAL_RANK'] = "0"
-os.environ['WORLD_SIZE'] = "1"
+
+os.environ["MASTER_ADDR"] = "localhost"
+os.environ["MASTER_PORT"] = "9994"  # modify if RuntimeError: Address already in use
+os.environ["RANK"] = "0"
+os.environ["LOCAL_RANK"] = "0"
+os.environ["WORLD_SIZE"] = "1"
 
 # Now proceed as normal, plus pass the deepspeed config file
 training_args = TrainingArguments(..., deepspeed="ds_config_zero3.json")
@@ -316,7 +317,7 @@ at the beginning of this section.
 If you want to create the config file on the fly in the notebook in the current directory, you could have a dedicated
 cell with:
 
-```python
+```python no-style
 %%bash
 cat <<'EOT' > ds_config_zero3.json
 {
@@ -382,14 +383,14 @@ EOT
 If the training script is in a normal file and not in the notebook cells, you can launch `deepspeed` normally via
 shell from a cell. For example, to use `run_translation.py` you would launch it with:
 
-```python
+```python no-style
 !git clone https://github.com/huggingface/transformers
 !cd transformers; deepspeed examples/pytorch/translation/run_translation.py ...
 ```
 
 or with `%%bash` magic, where you can write a multi-line code for the shell program to run:
 
-```python
+```python no-style
 %%bash
 
 git clone https://github.com/huggingface/transformers
@@ -512,7 +513,7 @@ TrainingArguments(..., deepspeed="/path/to/ds_config.json")
 or:
 
 ```python
-ds_config_dict=dict(scheduler=scheduler_params, optimizer=optimizer_params)
+ds_config_dict = dict(scheduler=scheduler_params, optimizer=optimizer_params)
 TrainingArguments(..., deepspeed=ds_config_dict)
 ```
 
@@ -559,7 +560,7 @@ Do note that some values, such as `scheduler.params.total_num_steps` are calcula
 ### ZeRO
 
 [Zero Redundancy Optimizer (ZeRO)](https://www.deepspeed.ai/tutorials/zero/) is the workhorse of DeepSpeed. It
-support 3 different levels (stages) of optimization. The first one is not quite interesting for scalability purposes,
+supports 3 different levels (stages) of optimization. The first one is not quite interesting for scalability purposes,
 therefore this document focuses on stages 2 and 3. Stage 3 is further improved by the latest addition of ZeRO-Infinity.
 You will find more indepth information in the DeepSpeed documentation.
 
@@ -580,7 +581,7 @@ going to use.
 
 #### ZeRO-2 Config
 
-The following is an example configuration for ZeRO stage 2:
+The following is an example of configuration for ZeRO stage 2:
 
 ```json
 {
@@ -608,8 +609,8 @@ The following is an example configuration for ZeRO stage 2:
   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. You will want to do
   the same on larger capacity GPU as well, if you're starting to hit OOM.
-- when reducing these buffers you're trading communication speed to avail more GPU RAM. The smaller the buffer size,
-  the slower the communication, and the more GPU RAM will be available to other tasks. So if a bigger batch size is
+- when reducing these buffers you're trading communication speed to avail more GPU RAM. The smaller the buffer size is,
+  the slower the communication gets, and the more GPU RAM will be available to other tasks. So if a bigger batch size is
   important, getting a slightly slower training time could be a good trade.
 
 
@@ -618,7 +619,7 @@ The following is an example configuration for ZeRO stage 2:
 
 #### ZeRO-3 Config
 
-The following is an example configuration for ZeRO stage 3:
+The following is an example of configuration for ZeRO stage 3:
 
 ```json
 {
@@ -661,7 +662,7 @@ and its typically accessed much faster than normal CPU memory.
 
 If hitting OOM reduce `stage3_max_live_parameters` and `stage3_max_reuse_distance`. They should have minimal impact
 on performance unless you are doing activation checkpointing. `1e9` would consume ~2GB. The memory is shared by
-`stage3_max_live_parameters` and `stage3_max_reuse_distance`, so its not additive, its just 2GB total.
+`stage3_max_live_parameters` and `stage3_max_reuse_distance`, so it's not additive, it's just 2GB total.
 
 `stage3_max_live_parameters` is the upper limit on how many full parameters you want to keep on the GPU at any given
 time. "reuse distance" is a metric we are using to figure out when will a parameter be used again in the future, and we
@@ -1430,6 +1431,7 @@ If you have saved at least one checkpoint, and you want to use the latest one, y
 ```python
 from transformers.trainer_utils import get_last_checkpoint
 from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint
+
 checkpoint_dir = get_last_checkpoint(trainer.args.output_dir)
 fp32_model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)
 ```
@@ -1439,6 +1441,7 @@ checkpoint), then you can finish the training by first saving the final model ex
 
 ```python
 from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint
+
 checkpoint_dir = os.path.join(trainer.args.output_dir, "checkpoint-final")
 trainer.deepspeed.save_checkpoint(checkpoint_dir)
 fp32_model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)
@@ -1461,7 +1464,8 @@ these yourself as is shown in the following example:
 
 ```python
 from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
-state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # already on cpu
+
+state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir)  # already on cpu
 model = model.cpu()
 model.load_state_dict(state_dict)
 ```
@@ -1529,9 +1533,10 @@ context manager (which is also a function decorator), like so:
 ```python
 from transformers import T5ForConditionalGeneration, T5Config
 import deepspeed
+
 with deepspeed.zero.Init():
-   config = T5Config.from_pretrained("t5-small")
-   model = T5ForConditionalGeneration(config)
+    config = T5Config.from_pretrained("t5-small")
+    model = T5ForConditionalGeneration(config)
 ```
 
 As you can see this gives you a randomly initialized model.
@@ -1544,6 +1549,7 @@ section. Thus you must create the [`TrainingArguments`] object **before** callin
 
 ```python
 from transformers import AutoModel, Trainer, TrainingArguments
+
 training_args = TrainingArguments(..., deepspeed=ds_config)
 model = AutoModel.from_pretrained("t5-small")
 trainer = Trainer(model=model, args=training_args, ...)
@@ -1574,7 +1580,7 @@ limitations.
 Also under ZeRO-3, if you write your own code and run into a model parameter weight that looks like:
 
 ```python
-tensor([1.], device='cuda:0', dtype=torch.float16, requires_grad=True)
+tensor([1.0], device="cuda:0", dtype=torch.float16, requires_grad=True)
 ```
 
 stress on `tensor([1.])`, or if you get an error where it says the parameter is of size `1`, instead of some much
@@ -1701,13 +1707,13 @@ Work is being done to enable estimating how much memory is needed for a specific
 
 ## Non-Trainer Deepspeed Integration
 
-The [`~integrations.HfDeepSpeedConfig`] is used to integrate Deepspeed into the 🤗 Transformers core
+The [`~deepspeed.HfDeepSpeedConfig`] is used to integrate Deepspeed into the 🤗 Transformers core
 functionality, when [`Trainer`] is not used.
 
 When using [`Trainer`] everything is automatically taken care of.
 
 When not using [`Trainer`], to efficiently deploy DeepSpeed stage 3, you must instantiate the
-[`~integrations.HfDeepSpeedConfig`] object before instantiating the model.
+[`~deepspeed.HfDeepSpeedConfig`] object before instantiating the model.
 
 For example for a pretrained model:
 
@@ -1715,9 +1721,9 @@ For example for a pretrained model:
 from transformers.deepspeed import HfDeepSpeedConfig
 from transformers import AutoModel, deepspeed
 
-ds_config = { ... } # deepspeed config object or path to the file
+ds_config = {...}  # deepspeed config object or path to the file
 # must run before instantiating the model
-dschf = HfDeepSpeedConfig(ds_config) # keep this object alive
+dschf = HfDeepSpeedConfig(ds_config)  # keep this object alive
 model = AutoModel.from_pretrained("gpt2")
 engine = deepspeed.initialize(model=model, config_params=ds_config, ...)
 ```
@@ -1728,9 +1734,9 @@ or for non-pretrained model:
 from transformers.deepspeed import HfDeepSpeedConfig
 from transformers import AutoModel, AutoConfig, deepspeed
 
-ds_config = { ... } # deepspeed config object or path to the file
+ds_config = {...}  # deepspeed config object or path to the file
 # must run before instantiating the model
-dschf = HfDeepSpeedConfig(ds_config) # keep this object alive
+dschf = HfDeepSpeedConfig(ds_config)  # keep this object alive
 config = AutoConfig.from_pretrained("gpt2")
 model = AutoModel.from_config(config)
 engine = deepspeed.initialize(model=model, config_params=ds_config, ...)

docs/source/main_classes/feature_extractor.mdx

@@ -0,0 +1,38 @@
+<!--Copyright 2021 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.
+-->
+
+# Feature Extractor
+
+A feature extractor is in charge of preparing input features for a multi-modal model. This includes feature extraction
+from sequences, *e.g.*, pre-processing audio files to Log-Mel Spectrogram features, feature extraction from images
+*e.g.* cropping image image files, but also padding, normalization, and conversion to Numpy, PyTorch, and TensorFlow
+tensors.
+
+
+## FeatureExtractionMixin
+
+[[autodoc]] feature_extraction_utils.FeatureExtractionMixin
+    - from_pretrained
+    - save_pretrained
+
+## SequenceFeatureExtractor
+
+[[autodoc]] SequenceFeatureExtractor
+    - pad
+
+## BatchFeature
+
+[[autodoc]] BatchFeature
+
+## ImageFeatureExtractionMixin
+
+[[autodoc]] image_utils.ImageFeatureExtractionMixin

docs/source/main_classes/feature_extractor.rst

@@ -1,48 +0,0 @@
-.. 
-    Copyright 2021 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.
-
-
-Feature Extractor
------------------------------------------------------------------------------------------------------------------------
-
-A feature extractor is in charge of preparing input features for a multi-modal model. This includes feature extraction
-from sequences, *e.g.*, pre-processing audio files to Log-Mel Spectrogram features, feature extraction from images
-*e.g.* cropping image image files, but also padding, normalization, and conversion to Numpy, PyTorch, and TensorFlow
-tensors.
-
-
-FeatureExtractionMixin
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.feature_extraction_utils.FeatureExtractionMixin
-    :members: from_pretrained, save_pretrained
-
-
-SequenceFeatureExtractor
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.SequenceFeatureExtractor
-    :members: pad
-
-
-BatchFeature
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BatchFeature
-    :members:
-
-
-ImageFeatureExtractionMixin
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.image_utils.ImageFeatureExtractionMixin
-    :members:

docs/source/main_classes/keras_callbacks.mdx

@@ -0,0 +1,24 @@
+<!--Copyright 2021 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.
+-->
+
+# Keras callbacks
+
+When training a Transformers model with Keras, there are some library-specific callbacks available to automate common
+tasks:
+
+## KerasMetricCallback
+
+[[autodoc]] KerasMetricCallback
+
+## PushToHubCallback
+
+[[autodoc]] PushToHubCallback

docs/source/main_classes/keras_callbacks.rst

@@ -1,22 +0,0 @@
-..
-    Copyright 2021 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.
-
-Keras callbacks
-=======================================================================================================================
-
-When training a Transformers model with Keras, there are some library-specific callbacks available to automate common
-tasks:
-
-PushToHubCallback
------------------------------------------------------------------------------------------------------------------------
-
-.. autoclass:: transformers.keras_callbacks.PushToHubCallback

docs/source/main_classes/logging.mdx

@@ -0,0 +1,87 @@
+<!--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
+
+🤗 Transformers has a centralized logging system, so that you can setup the verbosity of the library easily.
+
+Currently the default verbosity of the library is `WARNING`.
+
+To change the level of verbosity, just use one of the direct setters. For instance, here is how to change the verbosity
+to the INFO level.
+
+```python
+import transformers
+
+transformers.logging.set_verbosity_info()
+```
+
+You can also use the environment variable `TRANSFORMERS_VERBOSITY` to override the default verbosity. You can set it
+to one of the following: `debug`, `info`, `warning`, `error`, `critical`. For example:
+
+```bash
+TRANSFORMERS_VERBOSITY=error ./myprogram.py
+```
+
+Additionally, some `warnings` can be disabled by setting the environment variable
+`TRANSFORMERS_NO_ADVISORY_WARNINGS` to a true value, like *1*. This will disable any warning that is logged using
+[`logger.warning_advice`]. For example:
+
+
+```bash
+TRANSFORMERS_NO_ADVISORY_WARNINGS=1 ./myprogram.py
+```
+
+All the methods of this logging module are documented below, the main ones are
+[`logging.get_verbosity`] to get the current level of verbosity in the logger and
+[`logging.set_verbosity`] to set the verbosity to the level of your choice. In order (from the least
+verbose to the most verbose), those levels (with their corresponding int values in parenthesis) are:
+
+- `transformers.logging.CRITICAL` or `transformers.logging.FATAL` (int value, 50): only report the most
+  critical errors.
+- `transformers.logging.ERROR` (int value, 40): only report errors.
+- `transformers.logging.WARNING` or `transformers.logging.WARN` (int value, 30): only reports error and
+  warnings. This the default level used by the library.
+- `transformers.logging.INFO` (int value, 20): reports error, warnings and basic information.
+- `transformers.logging.DEBUG` (int value, 10): report all information.
+
+By default, `tqdm` progress bars will be displayed during model download. [`logging.disable_progress_bar`] and [`logging.enable_progress_bar`] can be used to suppress or unsuppress this behavior. 
+
+## Base setters
+
+[[autodoc]] logging.set_verbosity_error
+
+[[autodoc]] logging.set_verbosity_warning
+
+[[autodoc]] logging.set_verbosity_info
+
+[[autodoc]] logging.set_verbosity_debug
+
+## Other functions
+
+[[autodoc]] logging.get_verbosity
+
+[[autodoc]] logging.set_verbosity
+
+[[autodoc]] logging.get_logger
+
+[[autodoc]] logging.enable_default_handler
+
+[[autodoc]] logging.disable_default_handler
+
+[[autodoc]] logging.enable_explicit_format
+
+[[autodoc]] logging.reset_format
+
+[[autodoc]] logging.enable_progress_bar
+
+[[autodoc]] logging.disable_progress_bar

docs/source/main_classes/logging.rst

@@ -1,83 +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.
-
-Logging
------------------------------------------------------------------------------------------------------------------------
-
-🤗 Transformers has a centralized logging system, so that you can setup the verbosity of the library easily.
-
-Currently the default verbosity of the library is ``WARNING``.
-
-To change the level of verbosity, just use one of the direct setters. For instance, here is how to change the verbosity
-to the INFO level.
-
-.. code-block:: python
-
-    import transformers
-    transformers.logging.set_verbosity_info()
-
-You can also use the environment variable ``TRANSFORMERS_VERBOSITY`` to override the default verbosity. You can set it
-to one of the following: ``debug``, ``info``, ``warning``, ``error``, ``critical``. For example:
-
-.. code-block:: bash
-
-    TRANSFORMERS_VERBOSITY=error ./myprogram.py
-
-Additionally, some ``warnings`` can be disabled by setting the environment variable
-``TRANSFORMERS_NO_ADVISORY_WARNINGS`` to a true value, like `1`. This will disable any warning that is logged using
-:meth:`logger.warning_advice`. For example:
-
-
-.. code-block:: bash
-
-    TRANSFORMERS_NO_ADVISORY_WARNINGS=1 ./myprogram.py
-
-All the methods of this logging module are documented below, the main ones are
-:func:`transformers.logging.get_verbosity` to get the current level of verbosity in the logger and
-:func:`transformers.logging.set_verbosity` to set the verbosity to the level of your choice. In order (from the least
-verbose to the most verbose), those levels (with their corresponding int values in parenthesis) are:
-
-- :obj:`transformers.logging.CRITICAL` or :obj:`transformers.logging.FATAL` (int value, 50): only report the most
-  critical errors.
-- :obj:`transformers.logging.ERROR` (int value, 40): only report errors.
-- :obj:`transformers.logging.WARNING` or :obj:`transformers.logging.WARN` (int value, 30): only reports error and
-  warnings. This the default level used by the library.
-- :obj:`transformers.logging.INFO` (int value, 20): reports error, warnings and basic information.
-- :obj:`transformers.logging.DEBUG` (int value, 10): report all information.
-
-Base setters
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autofunction:: transformers.logging.set_verbosity_error
-
-.. autofunction:: transformers.logging.set_verbosity_warning
-
-.. autofunction:: transformers.logging.set_verbosity_info
-
-.. autofunction:: transformers.logging.set_verbosity_debug
-
-Other functions
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autofunction:: transformers.logging.get_verbosity
-
-.. autofunction:: transformers.logging.set_verbosity
-
-.. autofunction:: transformers.logging.get_logger
-
-.. autofunction:: transformers.logging.enable_default_handler
-
-.. autofunction:: transformers.logging.disable_default_handler
-
-.. autofunction:: transformers.logging.enable_explicit_format
-
-.. autofunction:: transformers.logging.reset_format

docs/source/main_classes/model.mdx

@@ -0,0 +1,99 @@
+<!--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 [`PreTrainedModel`], [`TFPreTrainedModel`], and
+[`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).
+
+[`PreTrainedModel`] and [`TFPreTrainedModel`] also implement a few methods which
+are common among all the models to:
+
+- resize the input token embeddings when new tokens are added to the vocabulary
+- prune the attention heads of the model.
+
+The other methods that are common to each model are defined in [`~modeling_utils.ModuleUtilsMixin`]
+(for the PyTorch models) and [`~modeling_tf_utils.TFModuleUtilsMixin`] (for the TensorFlow models) or
+for text generation, [`~generation_utils.GenerationMixin`] (for the PyTorch models),
+[`~generation_tf_utils.TFGenerationMixin`] (for the TensorFlow models) and
+[`~generation_flax_utils.FlaxGenerationMixin`] (for the Flax/JAX models).
+
+
+## PreTrainedModel
+
+[[autodoc]] PreTrainedModel
+    - push_to_hub
+    - all
+
+<a id='from_pretrained-torch-dtype'></a>
+
+### Model Instantiation dtype
+
+Under Pytorch a model normally gets instantiated with `torch.float32` format. This can be an issue if one tries to
+load a model whose weights are in fp16, since it'd require twice as much memory. To overcome this limitation, you can
+either explicitly pass the desired `dtype` using `torch_dtype` argument:
+
+```python
+model = T5ForConditionalGeneration.from_pretrained("t5", torch_dtype=torch.float16)
+```
+
+or, if you want the model to always load in the most optimal memory pattern, you can use the special value `"auto"`,
+and then `dtype` will be automatically derived from the model's weights:
+
+```python
+model = T5ForConditionalGeneration.from_pretrained("t5", torch_dtype="auto")
+```
+
+Models instantiated from scratch can also be told which `dtype` to use with:
+
+```python
+config = T5Config.from_pretrained("t5")
+model = AutoModel.from_config(config)
+```
+
+Due to Pytorch design, this functionality is only available for floating dtypes.
+
+
+
+## ModuleUtilsMixin
+
+[[autodoc]] modeling_utils.ModuleUtilsMixin
+
+## TFPreTrainedModel
+
+[[autodoc]] TFPreTrainedModel
+    - push_to_hub
+    - all
+
+## TFModelUtilsMixin
+
+[[autodoc]] modeling_tf_utils.TFModelUtilsMixin
+
+## FlaxPreTrainedModel
+
+[[autodoc]] FlaxPreTrainedModel
+    - push_to_hub
+    - all
+
+## Generation
+
+[[autodoc]] generation_utils.GenerationMixin
+
+[[autodoc]] generation_tf_utils.TFGenerationMixin
+
+[[autodoc]] generation_flax_utils.FlaxGenerationMixin
+
+## Pushing to the Hub
+
+[[autodoc]] file_utils.PushToHubMixin

docs/source/main_classes/model.rst

@@ -1,120 +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.
-
-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).
-
-:class:`~transformers.PreTrainedModel` and :class:`~transformers.TFPreTrainedModel` also implement a few methods which
-are common among all the models to:
-
-- resize the input token embeddings when new tokens are added to the vocabulary
-- prune the attention heads of the model.
-
-The other methods that are common to each model are defined in :class:`~transformers.modeling_utils.ModuleUtilsMixin`
-(for the PyTorch models) and :class:`~transformers.modeling_tf_utils.TFModuleUtilsMixin` (for the TensorFlow models) or
-for text generation, :class:`~transformers.generation_utils.GenerationMixin` (for the PyTorch models),
-:class:`~transformers.generation_tf_utils.TFGenerationMixin` (for the TensorFlow models) and
-:class:`~transformers.generation_flax_utils.FlaxGenerationMixin` (for the Flax/JAX models).
-
-
-PreTrainedModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.PreTrainedModel
-    :special-members: push_to_hub
-    :members:
-
-
-.. _from_pretrained-torch-dtype:
-
-Model Instantiation dtype
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Under Pytorch a model normally gets instantiated with ``torch.float32`` format. This can be an issue if one tries to
-load a model whose weights are in fp16, since it'd require twice as much memory. To overcome this limitation, you can
-either explicitly pass the desired ``dtype`` using ``torch_dtype`` argument:
-
-.. code-block:: python
-
-    model = T5ForConditionalGeneration.from_pretrained("t5", torch_dtype=torch.float16)
-
-or, if you want the model to always load in the most optimal memory pattern, you can use the special value ``"auto"``,
-and then ``dtype`` will be automatically derived from the model's weights:
-
-.. code-block:: python
-
-    model = T5ForConditionalGeneration.from_pretrained("t5", torch_dtype="auto")
-
-Models instantiated from scratch can also be told which ``dtype`` to use with:
-
-.. code-block:: python
-
-    config = T5Config.from_pretrained("t5")
-    model = AutoModel.from_config(config)
-
-Due to Pytorch design, this functionality is only available for floating dtypes.
-
-
-
-ModuleUtilsMixin
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_utils.ModuleUtilsMixin
-    :members:
-
-
-TFPreTrainedModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFPreTrainedModel
-    :special-members: push_to_hub
-    :members:
-
-
-TFModelUtilsMixin
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_tf_utils.TFModelUtilsMixin
-    :members:
-
-
-FlaxPreTrainedModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxPreTrainedModel
-    :special-members: push_to_hub
-    :members:
-
-
-Generation
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.generation_utils.GenerationMixin
-    :members:
-
-.. autoclass:: transformers.generation_tf_utils.TFGenerationMixin
-    :members:
-
-.. autoclass:: transformers.generation_flax_utils.FlaxGenerationMixin
-    :members:
-
-
-Pushing to the Hub
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.file_utils.PushToHubMixin
-    :members:

docs/source/main_classes/onnx.mdx

@@ -0,0 +1,50 @@
+<!--Copyright 2020 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# Exporting 🤗 Transformers models to ONNX
+
+🤗 Transformers provides a `transformers.onnx` package that enables you to
+convert model checkpoints to an ONNX graph by leveraging configuration objects.
+
+See the [guide](../serialization) on exporting 🤗 Transformers models for more
+details.
+
+## ONNX Configurations
+
+We provide three abstract classes that you should inherit from, depending on the
+type of model architecture you wish to export:
+
+* Encoder-based models inherit from [`~onnx.config.OnnxConfig`]
+* Decoder-based models inherit from [`~onnx.config.OnnxConfigWithPast`]
+* Encoder-decoder models inherit from [`~onnx.config.OnnxSeq2SeqConfigWithPast`]
+
+### OnnxConfig
+
+[[autodoc]] onnx.config.OnnxConfig
+
+### OnnxConfigWithPast
+
+[[autodoc]] onnx.config.OnnxConfigWithPast
+
+### OnnxSeq2SeqConfigWithPast
+
+[[autodoc]] onnx.config.OnnxSeq2SeqConfigWithPast
+
+## ONNX Features
+
+Each ONNX configuration is associated with a set of _features_ that enable you
+to export models for different types of topologies or tasks.
+
+### FeaturesManager
+
+[[autodoc]] onnx.features.FeaturesManager
+

docs/source/main_classes/optimizer_schedules.mdx

@@ -0,0 +1,71 @@
+<!--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
+
+The `.optimization` module provides:
+
+- an optimizer with weight decay fixed that can be used to fine-tuned models, and
+- several schedules in the form of schedule objects that inherit from `_LRSchedule`:
+- a gradient accumulation class to accumulate the gradients of multiple batches
+
+## AdamW (PyTorch)
+
+[[autodoc]] AdamW
+
+## AdaFactor (PyTorch)
+
+[[autodoc]] Adafactor
+
+## AdamWeightDecay (TensorFlow)
+
+[[autodoc]] AdamWeightDecay
+
+[[autodoc]] create_optimizer
+
+## Schedules
+
+### Learning Rate Schedules (Pytorch)
+
+[[autodoc]] SchedulerType
+
+[[autodoc]] get_scheduler
+
+[[autodoc]] get_constant_schedule
+
+[[autodoc]] get_constant_schedule_with_warmup
+
+<img alt="" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/warmup_constant_schedule.png"/>
+
+[[autodoc]] get_cosine_schedule_with_warmup
+
+<img alt="" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/warmup_cosine_schedule.png"/>
+
+[[autodoc]] get_cosine_with_hard_restarts_schedule_with_warmup
+
+<img alt="" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/warmup_cosine_hard_restarts_schedule.png"/>
+
+[[autodoc]] get_linear_schedule_with_warmup
+
+<img alt="" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/warmup_linear_schedule.png"/>
+
+[[autodoc]] get_polynomial_decay_schedule_with_warmup
+
+### Warmup (TensorFlow)
+
+[[autodoc]] WarmUp
+
+## Gradient Strategies
+
+### GradientAccumulator (TensorFlow)
+
+[[autodoc]] GradientAccumulator

docs/source/main_classes/optimizer_schedules.rst

@@ -1,97 +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.
-
-Optimization
------------------------------------------------------------------------------------------------------------------------
-
-The ``.optimization`` module provides:
-
-- an optimizer with weight decay fixed that can be used to fine-tuned models, and
-- several schedules in the form of schedule objects that inherit from ``_LRSchedule``:
-- a gradient accumulation class to accumulate the gradients of multiple batches
-
-AdamW (PyTorch)
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AdamW
-    :members:
-
-AdaFactor (PyTorch)
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.Adafactor
-
-AdamWeightDecay (TensorFlow)
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AdamWeightDecay
-
-.. autofunction:: transformers.create_optimizer
-
-Schedules
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Learning Rate Schedules (Pytorch)
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. autoclass:: transformers.SchedulerType
-
-.. autofunction:: transformers.get_scheduler
-
-.. autofunction:: transformers.get_constant_schedule
-
-
-.. autofunction:: transformers.get_constant_schedule_with_warmup
-
-.. image:: https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/warmup_constant_schedule.png
-    :target: https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/warmup_constant_schedule.png
-    :alt:
-
-
-.. autofunction:: transformers.get_cosine_schedule_with_warmup
-
-.. image:: https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/warmup_cosine_schedule.png
-    :target: https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/warmup_cosine_schedule.png
-    :alt:
-
-
-.. autofunction:: transformers.get_cosine_with_hard_restarts_schedule_with_warmup
-
-.. image:: https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/warmup_cosine_hard_restarts_schedule.png
-    :target: https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/warmup_cosine_hard_restarts_schedule.png
-    :alt:
-
-
-
-.. autofunction:: transformers.get_linear_schedule_with_warmup
-
-.. image:: https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/warmup_linear_schedule.png
-    :target: https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/warmup_linear_schedule.png
-    :alt:
-
-
-.. autofunction:: transformers.get_polynomial_decay_schedule_with_warmup
-
-
-Warmup (TensorFlow)
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. autoclass:: transformers.WarmUp
-    :members:
-
-Gradient Strategies
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-GradientAccumulator (TensorFlow)
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. autoclass:: transformers.GradientAccumulator

docs/source/main_classes/output.mdx

@@ -0,0 +1,269 @@
+<!--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
+
+All models have outputs that are instances of subclasses of [`~file_utils.ModelOutput`]. Those are
+data structures containing all the information returned by the model, but that can also be used as tuples or
+dictionaries.
+
+Let's see of this looks on an example:
+
+```python
+from transformers import BertTokenizer, BertForSequenceClassification
+import torch
+
+tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
+model = BertForSequenceClassification.from_pretrained("bert-base-uncased")
+
+inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
+labels = torch.tensor([1]).unsqueeze(0)  # Batch size 1
+outputs = model(**inputs, labels=labels)
+```
+
+The `outputs` object is a [`~modeling_outputs.SequenceClassifierOutput`], as we can see in the
+documentation of that class below, it means it has an optional `loss`, a `logits` an optional `hidden_states` and
+an optional `attentions` attribute. Here we have the `loss` since we passed along `labels`, 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 `outputs.loss` is the loss computed by the model, and `outputs.attentions` is
+`None`.
+
+When considering our `outputs` object as tuple, it only considers the attributes that don't have `None` values.
+Here for instance, it has two elements, `loss` then `logits`, so
+
+```python
+outputs[:2]
+```
+
+will return the tuple `(outputs.loss, outputs.logits)` for instance.
+
+When considering our `outputs` object as dictionary, it only considers the attributes that don't have `None`
+values. Here for instance, it has two keys that are `loss` and `logits`.
+
+We document here the generic model outputs that are used by more than one model type. Specific output types are
+documented on their corresponding model page.
+
+## ModelOutput
+
+[[autodoc]] file_utils.ModelOutput
+    - to_tuple
+
+## BaseModelOutput
+
+[[autodoc]] modeling_outputs.BaseModelOutput
+
+## BaseModelOutputWithPooling
+
+[[autodoc]] modeling_outputs.BaseModelOutputWithPooling
+
+## BaseModelOutputWithCrossAttentions
+
+[[autodoc]] modeling_outputs.BaseModelOutputWithCrossAttentions
+
+## BaseModelOutputWithPoolingAndCrossAttentions
+
+[[autodoc]] modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions
+
+## BaseModelOutputWithPast
+
+[[autodoc]] modeling_outputs.BaseModelOutputWithPast
+
+## BaseModelOutputWithPastAndCrossAttentions
+
+[[autodoc]] modeling_outputs.BaseModelOutputWithPastAndCrossAttentions
+
+## Seq2SeqModelOutput
+
+[[autodoc]] modeling_outputs.Seq2SeqModelOutput
+
+## CausalLMOutput
+
+[[autodoc]] modeling_outputs.CausalLMOutput
+
+## CausalLMOutputWithCrossAttentions
+
+[[autodoc]] modeling_outputs.CausalLMOutputWithCrossAttentions
+
+## CausalLMOutputWithPast
+
+[[autodoc]] modeling_outputs.CausalLMOutputWithPast
+
+## MaskedLMOutput
+
+[[autodoc]] modeling_outputs.MaskedLMOutput
+
+## Seq2SeqLMOutput
+
+[[autodoc]] modeling_outputs.Seq2SeqLMOutput
+
+## NextSentencePredictorOutput
+
+[[autodoc]] modeling_outputs.NextSentencePredictorOutput
+
+## SequenceClassifierOutput
+
+[[autodoc]] modeling_outputs.SequenceClassifierOutput
+
+## Seq2SeqSequenceClassifierOutput
+
+[[autodoc]] modeling_outputs.Seq2SeqSequenceClassifierOutput
+
+## MultipleChoiceModelOutput
+
+[[autodoc]] modeling_outputs.MultipleChoiceModelOutput
+
+## TokenClassifierOutput
+
+[[autodoc]] modeling_outputs.TokenClassifierOutput
+
+## QuestionAnsweringModelOutput
+
+[[autodoc]] modeling_outputs.QuestionAnsweringModelOutput
+
+## Seq2SeqQuestionAnsweringModelOutput
+
+[[autodoc]] modeling_outputs.Seq2SeqQuestionAnsweringModelOutput
+
+## TFBaseModelOutput
+
+[[autodoc]] modeling_tf_outputs.TFBaseModelOutput
+
+## TFBaseModelOutputWithPooling
+
+[[autodoc]] modeling_tf_outputs.TFBaseModelOutputWithPooling
+
+## TFBaseModelOutputWithPoolingAndCrossAttentions
+
+[[autodoc]] modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions
+
+## TFBaseModelOutputWithPast
+
+[[autodoc]] modeling_tf_outputs.TFBaseModelOutputWithPast
+
+## TFBaseModelOutputWithPastAndCrossAttentions
+
+[[autodoc]] modeling_tf_outputs.TFBaseModelOutputWithPastAndCrossAttentions
+
+## TFSeq2SeqModelOutput
+
+[[autodoc]] modeling_tf_outputs.TFSeq2SeqModelOutput
+
+## TFCausalLMOutput
+
+[[autodoc]] modeling_tf_outputs.TFCausalLMOutput
+
+## TFCausalLMOutputWithCrossAttentions
+
+[[autodoc]] modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions
+
+## TFCausalLMOutputWithPast
+
+[[autodoc]] modeling_tf_outputs.TFCausalLMOutputWithPast
+
+## TFMaskedLMOutput
+
+[[autodoc]] modeling_tf_outputs.TFMaskedLMOutput
+
+## TFSeq2SeqLMOutput
+
+[[autodoc]] modeling_tf_outputs.TFSeq2SeqLMOutput
+
+## TFNextSentencePredictorOutput
+
+[[autodoc]] modeling_tf_outputs.TFNextSentencePredictorOutput
+
+## TFSequenceClassifierOutput
+
+[[autodoc]] modeling_tf_outputs.TFSequenceClassifierOutput
+
+## TFSeq2SeqSequenceClassifierOutput
+
+[[autodoc]] modeling_tf_outputs.TFSeq2SeqSequenceClassifierOutput
+
+## TFMultipleChoiceModelOutput
+
+[[autodoc]] modeling_tf_outputs.TFMultipleChoiceModelOutput
+
+## TFTokenClassifierOutput
+
+[[autodoc]] modeling_tf_outputs.TFTokenClassifierOutput
+
+## TFQuestionAnsweringModelOutput
+
+[[autodoc]] modeling_tf_outputs.TFQuestionAnsweringModelOutput
+
+## TFSeq2SeqQuestionAnsweringModelOutput
+
+[[autodoc]] modeling_tf_outputs.TFSeq2SeqQuestionAnsweringModelOutput
+
+## FlaxBaseModelOutput
+
+[[autodoc]] modeling_flax_outputs.FlaxBaseModelOutput
+
+## FlaxBaseModelOutputWithPast
+
+[[autodoc]] modeling_flax_outputs.FlaxBaseModelOutputWithPast
+
+## FlaxBaseModelOutputWithPooling
+
+[[autodoc]] modeling_flax_outputs.FlaxBaseModelOutputWithPooling
+
+## FlaxBaseModelOutputWithPastAndCrossAttentions
+
+[[autodoc]] modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions
+
+## FlaxSeq2SeqModelOutput
+
+[[autodoc]] modeling_flax_outputs.FlaxSeq2SeqModelOutput
+
+## FlaxCausalLMOutputWithCrossAttentions
+
+[[autodoc]] modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions
+
+## FlaxMaskedLMOutput
+
+[[autodoc]] modeling_flax_outputs.FlaxMaskedLMOutput
+
+## FlaxSeq2SeqLMOutput
+
+[[autodoc]] modeling_flax_outputs.FlaxSeq2SeqLMOutput
+
+## FlaxNextSentencePredictorOutput
+
+[[autodoc]] modeling_flax_outputs.FlaxNextSentencePredictorOutput
+
+## FlaxSequenceClassifierOutput
+
+[[autodoc]] modeling_flax_outputs.FlaxSequenceClassifierOutput
+
+## FlaxSeq2SeqSequenceClassifierOutput
+
+[[autodoc]] modeling_flax_outputs.FlaxSeq2SeqSequenceClassifierOutput
+
+## FlaxMultipleChoiceModelOutput
+
+[[autodoc]] modeling_flax_outputs.FlaxMultipleChoiceModelOutput
+
+## FlaxTokenClassifierOutput
+
+[[autodoc]] modeling_flax_outputs.FlaxTokenClassifierOutput
+
+## FlaxQuestionAnsweringModelOutput
+
+[[autodoc]] modeling_flax_outputs.FlaxQuestionAnsweringModelOutput
+
+## FlaxSeq2SeqQuestionAnsweringModelOutput
+
+[[autodoc]] modeling_flax_outputs.FlaxSeq2SeqQuestionAnsweringModelOutput

docs/source/main_classes/output.rst

@@ -1,412 +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.
-
-Model outputs
------------------------------------------------------------------------------------------------------------------------
-
-All models have outputs that are instances of subclasses of :class:`~transformers.file_utils.ModelOutput`. Those are
-data structures containing all the information returned by the model, but that can also be used as tuples or
-dictionaries.
-
-Let's see of this looks on an example:
-
-.. code-block::
-
-    from transformers import BertTokenizer, BertForSequenceClassification
-    import torch
-
-    tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-    model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
-
-    inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
-    labels = torch.tensor([1]).unsqueeze(0)  # Batch size 1
-    outputs = model(**inputs, labels=labels)
-
-The ``outputs`` object is a :class:`~transformers.modeling_outputs.SequenceClassifierOutput`, as we can see in the
-documentation of that class below, it means it has an optional ``loss``, a ``logits`` an optional ``hidden_states`` and
-an optional ``attentions`` attribute. Here we have the ``loss`` since we passed along ``labels``, 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 ``outputs.loss`` is the loss computed by the model, and ``outputs.attentions`` is
-``None``.
-
-When considering our ``outputs`` object as tuple, it only considers the attributes that don't have ``None`` values.
-Here for instance, it has two elements, ``loss`` then ``logits``, so
-
-.. code-block::
-
-    outputs[:2]
-
-will return the tuple ``(outputs.loss, outputs.logits)`` for instance.
-
-When considering our ``outputs`` object as dictionary, it only considers the attributes that don't have ``None``
-values. Here for instance, it has two keys that are ``loss`` and ``logits``.
-
-We document here the generic model outputs that are used by more than one model type. Specific output types are
-documented on their corresponding model page.
-
-ModelOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.file_utils.ModelOutput
-    :members: to_tuple
-
-
-BaseModelOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_outputs.BaseModelOutput
-    :members:
-
-
-BaseModelOutputWithPooling
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_outputs.BaseModelOutputWithPooling
-    :members:
-
-
-BaseModelOutputWithCrossAttentions
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_outputs.BaseModelOutputWithCrossAttentions
-    :members:
-
-
-BaseModelOutputWithPoolingAndCrossAttentions
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions
-    :members:
-
-
-BaseModelOutputWithPast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_outputs.BaseModelOutputWithPast
-    :members:
-
-
-BaseModelOutputWithPastAndCrossAttentions
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions
-    :members:
-
-
-Seq2SeqModelOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_outputs.Seq2SeqModelOutput
-    :members:
-
-
-CausalLMOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_outputs.CausalLMOutput
-    :members:
-
-
-CausalLMOutputWithCrossAttentions
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_outputs.CausalLMOutputWithCrossAttentions
-    :members:
-
-
-CausalLMOutputWithPast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_outputs.CausalLMOutputWithPast
-    :members:
-
-
-MaskedLMOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_outputs.MaskedLMOutput
-    :members:
-
-
-Seq2SeqLMOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_outputs.Seq2SeqLMOutput
-    :members:
-
-
-NextSentencePredictorOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_outputs.NextSentencePredictorOutput
-    :members:
-
-
-SequenceClassifierOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_outputs.SequenceClassifierOutput
-    :members:
-
-
-Seq2SeqSequenceClassifierOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput
-    :members:
-
-
-MultipleChoiceModelOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_outputs.MultipleChoiceModelOutput
-    :members:
-
-
-TokenClassifierOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_outputs.TokenClassifierOutput
-    :members:
-
-
-QuestionAnsweringModelOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_outputs.QuestionAnsweringModelOutput
-    :members:
-
-
-Seq2SeqQuestionAnsweringModelOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput
-    :members:
-
-
-TFBaseModelOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_tf_outputs.TFBaseModelOutput
-    :members:
-
-
-TFBaseModelOutputWithPooling
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling
-    :members:
-
-
-TFBaseModelOutputWithPoolingAndCrossAttentions
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions
-    :members:
-
-
-TFBaseModelOutputWithPast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_tf_outputs.TFBaseModelOutputWithPast
-    :members:
-
-
-TFBaseModelOutputWithPastAndCrossAttentions
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_tf_outputs.TFBaseModelOutputWithPastAndCrossAttentions
-    :members:
-
-
-TFSeq2SeqModelOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_tf_outputs.TFSeq2SeqModelOutput
-    :members:
-
-
-TFCausalLMOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_tf_outputs.TFCausalLMOutput
-    :members:
-
-
-TFCausalLMOutputWithCrossAttentions
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions
-    :members:
-
-
-TFCausalLMOutputWithPast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_tf_outputs.TFCausalLMOutputWithPast
-    :members:
-
-
-TFMaskedLMOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_tf_outputs.TFMaskedLMOutput
-    :members:
-
-
-TFSeq2SeqLMOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_tf_outputs.TFSeq2SeqLMOutput
-    :members:
-
-
-TFNextSentencePredictorOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_tf_outputs.TFNextSentencePredictorOutput
-    :members:
-
-
-TFSequenceClassifierOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_tf_outputs.TFSequenceClassifierOutput
-    :members:
-
-
-TFSeq2SeqSequenceClassifierOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_tf_outputs.TFSeq2SeqSequenceClassifierOutput
-    :members:
-
-
-TFMultipleChoiceModelOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput
-    :members:
-
-
-TFTokenClassifierOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_tf_outputs.TFTokenClassifierOutput
-    :members:
-
-
-TFQuestionAnsweringModelOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput
-    :members:
-
-
-TFSeq2SeqQuestionAnsweringModelOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_tf_outputs.TFSeq2SeqQuestionAnsweringModelOutput
-    :members:
-
-
-FlaxBaseModelOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_flax_outputs.FlaxBaseModelOutput
-
-
-FlaxBaseModelOutputWithPast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPast
-
-
-FlaxBaseModelOutputWithPooling
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling
-
-
-FlaxBaseModelOutputWithPastAndCrossAttentions
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions
-
-
-FlaxSeq2SeqModelOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput
-
-
-FlaxCausalLMOutputWithCrossAttentions
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions
-
-
-FlaxMaskedLMOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_flax_outputs.FlaxMaskedLMOutput
-
-
-FlaxSeq2SeqLMOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput
-
-
-FlaxNextSentencePredictorOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_flax_outputs.FlaxNextSentencePredictorOutput
-
-
-FlaxSequenceClassifierOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput
-
-
-FlaxSeq2SeqSequenceClassifierOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_flax_outputs.FlaxSeq2SeqSequenceClassifierOutput
-
-
-FlaxMultipleChoiceModelOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput
-
-
-FlaxTokenClassifierOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_flax_outputs.FlaxTokenClassifierOutput
-
-
-FlaxQuestionAnsweringModelOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput
-
-
-FlaxSeq2SeqQuestionAnsweringModelOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_flax_outputs.FlaxSeq2SeqQuestionAnsweringModelOutput

docs/source/main_classes/pipelines.mdx

@@ -0,0 +1,433 @@
+<!--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
+
+The pipelines are a great and easy way to use models for inference. These pipelines are objects that abstract most of
+the complex code from the library, offering a simple API dedicated to several tasks, including Named Entity
+Recognition, Masked Language Modeling, Sentiment Analysis, Feature Extraction and Question Answering. See the
+[task summary](../task_summary) for examples of use.
+
+There are two categories of pipeline abstractions to be aware about:
+
+- The [`pipeline`] which is the most powerful object encapsulating all other pipelines.
+- The other task-specific pipelines:
+
+  - [`AudioClassificationPipeline`]
+  - [`AutomaticSpeechRecognitionPipeline`]
+  - [`ConversationalPipeline`]
+  - [`FeatureExtractionPipeline`]
+  - [`FillMaskPipeline`]
+  - [`ImageClassificationPipeline`]
+  - [`ImageSegmentationPipeline`]
+  - [`ObjectDetectionPipeline`]
+  - [`QuestionAnsweringPipeline`]
+  - [`SummarizationPipeline`]
+  - [`TableQuestionAnsweringPipeline`]
+  - [`TextClassificationPipeline`]
+  - [`TextGenerationPipeline`]
+  - [`Text2TextGenerationPipeline`]
+  - [`TokenClassificationPipeline`]
+  - [`TranslationPipeline`]
+  - [`ZeroShotClassificationPipeline`]
+
+## The pipeline abstraction
+
+The *pipeline* abstraction is a wrapper around all the other available pipelines. It is instantiated as any other
+pipeline but can provide additional quality of life.
+
+Simple call on one item:
+
+```python
+>>> pipe = pipeline("text-classification")
+>>> pipe("This restaurant is awesome")
+[{'label': 'POSITIVE', 'score': 0.9998743534088135}]
+```
+
+If you want to use a specific model from the [hub](https://huggingface.co) you can ignore the task if the model on
+the hub already defines it:
+
+```python
+>>> pipe = pipeline(model="roberta-large-mnli")
+>>> pipe("This restaurant is awesome")
+[{'label': 'POSITIVE', 'score': 0.9998743534088135}]
+```
+
+To call a pipeline on many items, you can either call with a *list*.
+
+```python
+>>> pipe = pipeline("text-classification")
+>>> pipe(["This restaurant is awesome", "This restaurant is aweful"])
+[{'label': 'POSITIVE', 'score': 0.9998743534088135},
+ {'label': 'NEGATIVE', 'score': 0.9996669292449951}]
+```
+
+To iterate of full datasets it is recommended to use a `dataset` directly. This means you don't need to allocate
+the whole dataset at once, nor do you need to do batching yourself. This should work just as fast as custom loops on
+GPU. If it doesn't don't hesitate to create an issue.
+
+```python
+import datasets
+from transformers import pipeline
+from transformers.pipelines.base import KeyDataset
+from tqdm.auto import tqdm
+
+pipe = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-960h", device=0)
+dataset = datasets.load_dataset("superb", name="asr", split="test")
+
+# KeyDataset (only *pt*) will simply return the item in the dict returned by the dataset item
+# as we're not interested in the *target* part of the dataset.
+for out in tqdm(pipe(KeyDataset(dataset, "file"))):
+    print(out)
+    # {"text": "NUMBER TEN FRESH NELLY IS WAITING ON YOU GOOD NIGHT HUSBAND"}
+    # {"text": ....}
+    # ....
+```
+
+For ease of use, a generator is also possible:
+
+
+```python
+from transformers import pipeline
+
+pipe = pipeline("text-classification")
+
+
+def data():
+    while True:
+        # This could come from a dataset, a database, a queue or HTTP request
+        # in a server
+        # Caveat: because this is iterative, you cannot use `num_workers > 1` variable
+        # to use multiple threads to preprocess data. You can still have 1 thread that
+        # does the preprocessing while the main runs the big inference
+        yield "This is a test"
+
+
+for out in pipe(data()):
+    print(out)
+    # {"text": "NUMBER TEN FRESH NELLY IS WAITING ON YOU GOOD NIGHT HUSBAND"}
+    # {"text": ....}
+    # ....
+```
+
+[[autodoc]] pipeline
+
+## Pipeline batching
+
+All pipelines can use batching. This will work
+whenever the pipeline uses its streaming ability (so when passing lists or `Dataset` or `generator`).
+
+```python
+from transformers import pipeline
+from transformers.pipelines.base import KeyDataset
+import datasets
+
+dataset = datasets.load_dataset("imdb", name="plain_text", split="unsupervised")
+pipe = pipeline("text-classification", device=0)
+for out in pipe(KeyDataset(dataset, "text"), batch_size=8, truncation="only_first"):
+    print(out)
+    # [{'label': 'POSITIVE', 'score': 0.9998743534088135}]
+    # Exactly the same output as before, but the content are passed
+    # as batches to the model
+```
+
+<Tip warning={true}>
+
+However, this is not automatically a win for performance. It can be either a 10x speedup or 5x slowdown depending
+on hardware, data and the actual model being used.
+
+Example where it's mostly a speedup:
+
+</Tip>
+
+```python
+from transformers import pipeline
+from torch.utils.data import Dataset
+from tqdm.auto import tqdm
+
+pipe = pipeline("text-classification", device=0)
+
+
+class MyDataset(Dataset):
+    def __len__(self):
+        return 5000
+
+    def __getitem__(self, i):
+        return "This is a test"
+
+
+dataset = MyDataset()
+
+for batch_size in [1, 8, 64, 256]:
+    print("-" * 30)
+    print(f"Streaming batch_size={batch_size}")
+    for out in tqdm(pipe(dataset, batch_size=batch_size), total=len(dataset)):
+        pass
+```
+
+```
+# On GTX 970
+------------------------------
+Streaming no batching
+100%|██████████████████████████████████████████████████████████████████████| 5000/5000 [00:26<00:00, 187.52it/s]
+------------------------------
+Streaming batch_size=8
+100%|█████████████████████████████████████████████████████████████████████| 5000/5000 [00:04<00:00, 1205.95it/s]
+------------------------------
+Streaming batch_size=64
+100%|█████████████████████████████████████████████████████████████████████| 5000/5000 [00:02<00:00, 2478.24it/s]
+------------------------------
+Streaming batch_size=256
+100%|█████████████████████████████████████████████████████████████████████| 5000/5000 [00:01<00:00, 2554.43it/s]
+(diminishing returns, saturated the GPU)
+```
+
+Example where it's most a slowdown:
+
+```python
+class MyDataset(Dataset):
+    def __len__(self):
+        return 5000
+
+    def __getitem__(self, i):
+        if i % 64 == 0:
+            n = 100
+        else:
+            n = 1
+        return "This is a test" * n
+```
+
+This is a occasional very long sentence compared to the other. In that case, the **whole** batch will need to be 400
+tokens long, so the whole batch will be [64, 400] instead of [64, 4], leading to the high slowdown. Even worse, on
+bigger batches, the program simply crashes.
+
+
+```
+------------------------------
+Streaming no batching
+100%|█████████████████████████████████████████████████████████████████████| 1000/1000 [00:05<00:00, 183.69it/s]
+------------------------------
+Streaming batch_size=8
+100%|█████████████████████████████████████████████████████████████████████| 1000/1000 [00:03<00:00, 265.74it/s]
+------------------------------
+Streaming batch_size=64
+100%|██████████████████████████████████████████████████████████████████████| 1000/1000 [00:26<00:00, 37.80it/s]
+------------------------------
+Streaming batch_size=256
+  0%|                                                                                 | 0/1000 [00:00<?, ?it/s]
+Traceback (most recent call last):
+  File "/home/nicolas/src/transformers/test.py", line 42, in <module>
+    for out in tqdm(pipe(dataset, batch_size=256), total=len(dataset)):
+....
+    q = q / math.sqrt(dim_per_head)  # (bs, n_heads, q_length, dim_per_head)
+RuntimeError: CUDA out of memory. Tried to allocate 376.00 MiB (GPU 0; 3.95 GiB total capacity; 1.72 GiB already allocated; 354.88 MiB free; 2.46 GiB reserved in total by PyTorch)
+```
+
+There are no good (general) solutions for this problem, and your mileage may vary depending on your use cases. Rule of
+thumb:
+
+For users, a rule of thumb is:
+
+- **Measure performance on your load, with your hardware. Measure, measure, and keep measuring. Real numbers are the
+  only way to go.**
+- If you are latency constrained (live product doing inference), don't batch
+- If you are using CPU, don't batch.
+- If you are using throughput (you want to run your model on a bunch of static data), on GPU, then:
+
+  - If you have no clue about the size of the sequence_length ("natural" data), by default don't batch, measure and
+    try tentatively to add it, add OOM checks to recover when it will fail (and it will at some point if you don't
+    control the sequence_length.)
+  - If your sequence_length is super regular, then batching is more likely to be VERY interesting, measure and push
+    it until you get OOMs.
+  - The larger the GPU the more likely batching is going to be more interesting
+- As soon as you enable batching, make sure you can handle OOMs nicely.
+
+## Pipeline chunk batching
+
+`zero-shot-classification` and `question-answering` are slightly specific in the sense, that a single input might yield
+multiple forward pass of a model. Under normal circumstances, this would yield issues with `batch_size` argument.
+
+In order to circumvent this issue, both of these pipelines are a bit specific, they are `ChunkPipeline` instead of
+regular `Pipeline`. In short:
+
+
+```python
+preprocessed = pipe.preprocess(inputs)
+model_outputs = pipe.forward(preprocessed)
+outputs = pipe.postprocess(model_outputs)
+```
+
+Now becomes:
+
+
+```python
+all_model_outputs = []
+for preprocessed in pipe.preprocess(inputs):
+    model_outputs = pipe.forward(preprocessed)
+    all_model_outputs.append(model_outputs)
+outputs = pipe.postprocess(all_model_outputs)
+```
+
+This should be very transparent to your code because the pipelines are used in
+the same way.
+
+This is a simplified view, since the pipeline can handle automatically the batch to ! Meaning you don't have to care
+about how many forward passes you inputs are actually going to trigger, you can optimize the `batch_size`
+independently of the inputs. The caveats from the previous section still apply.
+
+## Pipeline custom code
+
+If you want to override a specific pipeline.
+
+Don't hesitate to create an issue for your task at hand, the goal of the pipeline is to be easy to use and support most
+cases, so `transformers` could maybe support your use case.
+
+
+If you want to try simply you can:
+
+- Subclass your pipeline of choice
+
+```python
+class MyPipeline(TextClassificationPipeline):
+    def postprocess():
+        # Your code goes here
+        scores = scores * 100
+        # And here
+
+
+my_pipeline = MyPipeline(model=model, tokenizer=tokenizer, ...)
+# or if you use *pipeline* function, then:
+my_pipeline = pipeline(model="xxxx", pipeline_class=MyPipeline)
+```
+
+That should enable you to do all the custom code you want.
+
+
+## Implementing a pipeline
+
+[Implementing a new pipeline](../add_new_pipeline)
+
+## The task specific pipelines
+
+
+### AudioClassificationPipeline
+
+[[autodoc]] AudioClassificationPipeline
+    - __call__
+    - all
+
+### AutomaticSpeechRecognitionPipeline
+
+[[autodoc]] AutomaticSpeechRecognitionPipeline
+    - __call__
+    - all
+
+### ConversationalPipeline
+
+[[autodoc]] Conversation
+
+[[autodoc]] ConversationalPipeline
+    - __call__
+    - all
+
+### FeatureExtractionPipeline
+
+[[autodoc]] FeatureExtractionPipeline
+    - __call__
+    - all
+
+### FillMaskPipeline
+
+[[autodoc]] FillMaskPipeline
+    - __call__
+    - all
+
+### ImageClassificationPipeline
+
+[[autodoc]] ImageClassificationPipeline
+    - __call__
+    - all
+
+### ImageSegmentationPipeline
+
+[[autodoc]] ImageSegmentationPipeline
+    - __call__
+    - all
+
+### NerPipeline
+
+[[autodoc]] NerPipeline
+
+See [`TokenClassificationPipeline`] for all details.
+
+### ObjectDetectionPipeline
+
+[[autodoc]] ObjectDetectionPipeline
+    - __call__
+    - all
+
+### QuestionAnsweringPipeline
+
+[[autodoc]] QuestionAnsweringPipeline
+    - __call__
+    - all
+
+### SummarizationPipeline
+
+[[autodoc]] SummarizationPipeline
+    - __call__
+    - all
+
+### TableQuestionAnsweringPipeline
+
+[[autodoc]] TableQuestionAnsweringPipeline
+    - __call__
+
+### TextClassificationPipeline
+
+[[autodoc]] TextClassificationPipeline
+    - __call__
+    - all
+
+### TextGenerationPipeline
+
+[[autodoc]] TextGenerationPipeline
+    - __call__
+    - all
+
+### Text2TextGenerationPipeline
+
+[[autodoc]] Text2TextGenerationPipeline
+    - __call__
+    - all
+
+### TokenClassificationPipeline
+
+[[autodoc]] TokenClassificationPipeline
+    - __call__
+    - all
+
+### TranslationPipeline
+
+[[autodoc]] TranslationPipeline
+    - __call__
+    - all
+
+### ZeroShotClassificationPipeline
+
+[[autodoc]] ZeroShotClassificationPipeline
+    - __call__
+    - all
+
+## Parent class: `Pipeline`
+
+[[autodoc]] Pipeline

docs/source/main_classes/pipelines.rst

@@ -1,407 +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.
-
-Pipelines
------------------------------------------------------------------------------------------------------------------------
-
-The pipelines are a great and easy way to use models for inference. These pipelines are objects that abstract most of
-the complex code from the library, offering a simple API dedicated to several tasks, including Named Entity
-Recognition, Masked Language Modeling, Sentiment Analysis, Feature Extraction and Question Answering. See the
-:doc:`task summary <../task_summary>` for examples of use.
-
-There are two categories of pipeline abstractions to be aware about:
-
-- The :func:`~transformers.pipeline` which is the most powerful object encapsulating all other pipelines.
-- The other task-specific pipelines:
-
-    - :class:`~transformers.AudioClassificationPipeline`
-    - :class:`~transformers.AutomaticSpeechRecognitionPipeline`
-    - :class:`~transformers.ConversationalPipeline`
-    - :class:`~transformers.FeatureExtractionPipeline`
-    - :class:`~transformers.FillMaskPipeline`
-    - :class:`~transformers.ImageClassificationPipeline`
-    - :class:`~transformers.ImageSegmentationPipeline`
-    - :class:`~transformers.ObjectDetectionPipeline`
-    - :class:`~transformers.QuestionAnsweringPipeline`
-    - :class:`~transformers.SummarizationPipeline`
-    - :class:`~transformers.TableQuestionAnsweringPipeline`
-    - :class:`~transformers.TextClassificationPipeline`
-    - :class:`~transformers.TextGenerationPipeline`
-    - :class:`~transformers.Text2TextGenerationPipeline`
-    - :class:`~transformers.TokenClassificationPipeline`
-    - :class:`~transformers.TranslationPipeline`
-    - :class:`~transformers.ZeroShotClassificationPipeline`
-
-The pipeline abstraction
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The `pipeline` abstraction is a wrapper around all the other available pipelines. It is instantiated as any other
-pipeline but can provide additional quality of life.
-
-Simple call on one item:
-
-.. code-block::
-
-    >>> pipe = pipeline("text-classification")
-    >>> pipe("This restaurant is awesome")
-    [{'label': 'POSITIVE', 'score': 0.9998743534088135}]
-
-If you want to use a specific model from the `hub <https://huggingface.co>`__ you can ignore the task if the model on
-the hub already defines it:
-
-.. code-block::
-
-    >>> pipe = pipeline(model="roberta-large-mnli")
-    >>> pipe("This restaurant is awesome")
-    [{'label': 'POSITIVE', 'score': 0.9998743534088135}]
-
-To call a pipeline on many items, you can either call with a `list`.
-
-.. code-block::
-
-    >>> pipe = pipeline("text-classification")
-    >>> pipe(["This restaurant is awesome", "This restaurant is aweful"])
-    [{'label': 'POSITIVE', 'score': 0.9998743534088135},
-     {'label': 'NEGATIVE', 'score': 0.9996669292449951}]
-
-
-To iterate of full datasets it is recommended to use a :obj:`dataset` directly. This means you don't need to allocate
-the whole dataset at once, nor do you need to do batching yourself. This should work just as fast as custom loops on
-GPU. If it doesn't don't hesitate to create an issue.
-
-.. code-block::
-
-    import datasets
-    from transformers import pipeline
-    from transformers.pipelines.base import KeyDataset
-    import tqdm
-
-    pipe = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-960h", device=0)
-    dataset = datasets.load_dataset("superb", name="asr", split="test")
-
-    # KeyDataset (only `pt`) will simply return the item in the dict returned by the dataset item
-    # as we're not interested in the `target` part of the dataset.
-    for out in tqdm.tqdm(pipe(KeyDataset(dataset, "file"))):
-        print(out)
-        # {"text": "NUMBER TEN FRESH NELLY IS WAITING ON YOU GOOD NIGHT HUSBAND"}
-        # {"text": ....}
-        # ....
-
-
-.. autofunction:: transformers.pipeline
-
-Pipeline batching
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-All pipelines (except `zero-shot-classification` and `question-answering` currently) can use batching. This will work
-whenever the pipeline uses its streaming ability (so when passing lists or :obj:`Dataset`).
-
-.. code-block::
-
-    from transformers import pipeline                                                   
-    from transformers.pipelines.base import KeyDataset
-    import datasets
-    import tqdm                                                                         
-
-    dataset = datasets.load_dataset("imdb", name="plain_text", split="unsupervised")
-    pipe = pipeline("text-classification", device=0)
-    for out in pipe(KeyDataset(dataset, "text"), batch_size=8, truncation="only_first"):
-        print(out)
-        # [{'label': 'POSITIVE', 'score': 0.9998743534088135}]
-        # Exactly the same output as before, but the content are passed
-        # as batches to the model
-
-
-.. warning::
-
-    However, this is not automatically a win for performance. It can be either a 10x speedup or 5x slowdown depending
-    on hardware, data and the actual model being used.
-
-    Example where it's most a speedup:
-
-
-.. code-block::
-
-    from transformers import pipeline                                                   
-    from torch.utils.data import Dataset                                                
-    import tqdm                                                                         
-
-
-    pipe = pipeline("text-classification", device=0)                                    
-
-
-    class MyDataset(Dataset):                                                           
-        def __len__(self):                                                              
-            return 5000                                                                 
-
-        def __getitem__(self, i):                                                       
-            return "This is a test"                                                     
-
-
-    dataset = MyDataset()   
-
-    for batch_size in [1, 8, 64, 256]:
-        print("-" * 30)                                                                     
-        print(f"Streaming batch_size={batch_size}")    
-        for out in tqdm.tqdm(pipe(dataset, batch_size=batch_size), total=len(dataset)):              
-            pass
-
-
-.. code-block::
-
-    # On GTX 970
-    ------------------------------
-    Streaming no batching
-    100%|██████████████████████████████████████████████████████████████████████| 5000/5000 [00:26<00:00, 187.52it/s]
-    ------------------------------
-    Streaming batch_size=8
-    100%|█████████████████████████████████████████████████████████████████████| 5000/5000 [00:04<00:00, 1205.95it/s]
-    ------------------------------
-    Streaming batch_size=64
-    100%|█████████████████████████████████████████████████████████████████████| 5000/5000 [00:02<00:00, 2478.24it/s]
-    ------------------------------
-    Streaming batch_size=256
-    100%|█████████████████████████████████████████████████████████████████████| 5000/5000 [00:01<00:00, 2554.43it/s]
-    (diminishing returns, saturated the GPU)
-
-
-Example where it's most a slowdown:
-
-.. code-block::
-
-    class MyDataset(Dataset):                                                           
-        def __len__(self):                                                              
-            return 5000                                                                 
-
-        def __getitem__(self, i):                                                       
-            if i % 64 == 0:                                                          
-                n = 100                                                              
-            else:                                                                    
-                n = 1                                                                
-            return "This is a test" * n
-
-This is a occasional very long sentence compared to the other. In that case, the **whole** batch will need to be 400
-tokens long, so the whole batch will be [64, 400] instead of [64, 4], leading to the high slowdown. Even worse, on
-bigger batches, the program simply crashes.
-
-
-.. code-block::
-
-    ------------------------------
-    Streaming no batching
-    100%|█████████████████████████████████████████████████████████████████████| 1000/1000 [00:05<00:00, 183.69it/s]
-    ------------------------------
-    Streaming batch_size=8
-    100%|█████████████████████████████████████████████████████████████████████| 1000/1000 [00:03<00:00, 265.74it/s]
-    ------------------------------
-    Streaming batch_size=64
-    100%|██████████████████████████████████████████████████████████████████████| 1000/1000 [00:26<00:00, 37.80it/s]
-    ------------------------------
-    Streaming batch_size=256
-      0%|                                                                                 | 0/1000 [00:00<?, ?it/s]
-    Traceback (most recent call last):
-      File "/home/nicolas/src/transformers/test.py", line 42, in <module>
-        for out in tqdm.tqdm(pipe(dataset, batch_size=256), total=len(dataset)):
-    ....
-        q = q / math.sqrt(dim_per_head)  # (bs, n_heads, q_length, dim_per_head)
-    RuntimeError: CUDA out of memory. Tried to allocate 376.00 MiB (GPU 0; 3.95 GiB total capacity; 1.72 GiB already allocated; 354.88 MiB free; 2.46 GiB reserved in total by PyTorch)
-
-
-There are no good (general) solutions for this problem, and your mileage may vary depending on your use cases. Rule of
-thumb:
-
-For users, a rule of thumb is:
-
-- **Measure performance on your load, with your hardware. Measure, measure, and keep measuring. Real numbers are the
-  only way to go.**
-- If you are latency constrained (live product doing inference), don't batch
-- If you are using CPU, don't batch.
-- If you are using throughput (you want to run your model on a bunch of static data), on GPU, then:
-
-      - If you have no clue about the size of the sequence_length ("natural" data), by default don't batch, measure and
-        try tentatively to add it, add OOM checks to recover when it will fail (and it will at some point if you don't
-        control the sequence_length.)
-      - If your sequence_length is super regular, then batching is more likely to be VERY interesting, measure and push
-        it until you get OOMs.
-      - The larger the GPU the more likely batching is going to be more interesting
-- As soon as you enable batching, make sure you can handle OOMs nicely.
-
-Pipeline custom code
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-If you want to override a specific pipeline.
-
-Don't hesitate to create an issue for your task at hand, the goal of the pipeline is to be easy to use and support most
-cases, so :obj:`transformers` could maybe support your use case.
-
-
-If you want to try simply you can:
-
-- Subclass your pipeline of choice
-
-.. code-block::
-
-    class MyPipeline(TextClassificationPipeline):
-        def postprocess(...):
-            ...
-            scores = scores * 100
-            ...
-
-    my_pipeline = MyPipeline(model=model, tokenizer=tokenizer, ...)
-    # or if you use `pipeline` function, then:
-    my_pipeline = pipeline(model="xxxx", pipeline_class=MyPipeline)
-
-That should enable you to do all the custom code you want.
-
-
-Implementing a pipeline
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-:doc:`Implementing a new pipeline <../add_new_pipeline>`
-
-The task specific pipelines
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-AudioClassificationPipeline
-=======================================================================================================================
-
-.. autoclass:: transformers.AudioClassificationPipeline
-    :special-members: __call__
-    :members:
-
-AutomaticSpeechRecognitionPipeline
-=======================================================================================================================
-
-.. autoclass:: transformers.AutomaticSpeechRecognitionPipeline
-    :special-members: __call__
-    :members:
-
-ConversationalPipeline
-=======================================================================================================================
-
-.. autoclass:: transformers.Conversation
-
-.. autoclass:: transformers.ConversationalPipeline
-    :special-members: __call__
-    :members:
-
-FeatureExtractionPipeline
-=======================================================================================================================
-
-.. autoclass:: transformers.FeatureExtractionPipeline
-    :special-members: __call__
-    :members:
-
-FillMaskPipeline
-=======================================================================================================================
-
-.. autoclass:: transformers.FillMaskPipeline
-    :special-members: __call__
-    :members:
-
-ImageClassificationPipeline
-=======================================================================================================================
-
-.. autoclass:: transformers.ImageClassificationPipeline
-    :special-members: __call__
-    :members:
-
-ImageSegmentationPipeline
-=======================================================================================================================
-
-.. autoclass:: transformers.ImageSegmentationPipeline
-    :special-members: __call__
-    :members:
-
-NerPipeline
-=======================================================================================================================
-
-.. autoclass:: transformers.NerPipeline
-
-See :class:`~transformers.TokenClassificationPipeline` for all details.
-
-ObjectDetectionPipeline
-=======================================================================================================================
-
-.. autoclass:: transformers.ObjectDetectionPipeline
-    :special-members: __call__
-    :members:
-
-QuestionAnsweringPipeline
-=======================================================================================================================
-
-.. autoclass:: transformers.QuestionAnsweringPipeline
-    :special-members: __call__
-    :members:
-
-SummarizationPipeline
-=======================================================================================================================
-
-.. autoclass:: transformers.SummarizationPipeline
-    :special-members: __call__
-    :members:
-
-TableQuestionAnsweringPipeline
-=======================================================================================================================
-
-.. autoclass:: transformers.TableQuestionAnsweringPipeline
-    :special-members: __call__
-
-
-TextClassificationPipeline
-=======================================================================================================================
-
-.. autoclass:: transformers.TextClassificationPipeline
-    :special-members: __call__
-    :members:
-
-TextGenerationPipeline
-=======================================================================================================================
-
-.. autoclass:: transformers.TextGenerationPipeline
-    :special-members: __call__
-    :members:
-
-Text2TextGenerationPipeline
-=======================================================================================================================
-
-.. autoclass:: transformers.Text2TextGenerationPipeline
-    :special-members: __call__
-    :members:
-
-TokenClassificationPipeline
-=======================================================================================================================
-
-.. autoclass:: transformers.TokenClassificationPipeline
-    :special-members: __call__
-    :members:
-
-TranslationPipeline
-=======================================================================================================================
-
-.. autoclass:: transformers.TranslationPipeline
-    :special-members: __call__
-    :members:
-
-ZeroShotClassificationPipeline
-=======================================================================================================================
-
-.. autoclass:: transformers.ZeroShotClassificationPipeline
-    :special-members: __call__
-    :members:
-
-Parent class: :obj:`Pipeline`
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.Pipeline
-    :members:

docs/source/main_classes/processors.mdx

@@ -0,0 +1,152 @@
+<!--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
+
+This library includes processors for several traditional tasks. These processors can be used to process a dataset into
+examples that can be fed to a model.
+
+## Processors
+
+All processors follow the same architecture which is that of the
+[`~data.processors.utils.DataProcessor`]. The processor returns a list of
+[`~data.processors.utils.InputExample`]. These
+[`~data.processors.utils.InputExample`] can be converted to
+[`~data.processors.utils.InputFeatures`] in order to be fed to the model.
+
+[[autodoc]] data.processors.utils.DataProcessor
+
+[[autodoc]] data.processors.utils.InputExample
+
+[[autodoc]] data.processors.utils.InputFeatures
+
+## GLUE
+
+[General Language Understanding Evaluation (GLUE)](https://gluebenchmark.com/) is a benchmark that evaluates the
+performance of models across a diverse set of existing NLU tasks. It was released together with the paper [GLUE: A
+multi-task benchmark and analysis platform for natural language understanding](https://openreview.net/pdf?id=rJ4km2R5t7)
+
+This library hosts a total of 10 processors for the following tasks: MRPC, MNLI, MNLI (mismatched), CoLA, SST2, STSB,
+QQP, QNLI, RTE and WNLI.
+
+Those processors are:
+
+- [`~data.processors.utils.MrpcProcessor`]
+- [`~data.processors.utils.MnliProcessor`]
+- [`~data.processors.utils.MnliMismatchedProcessor`]
+- [`~data.processors.utils.Sst2Processor`]
+- [`~data.processors.utils.StsbProcessor`]
+- [`~data.processors.utils.QqpProcessor`]
+- [`~data.processors.utils.QnliProcessor`]
+- [`~data.processors.utils.RteProcessor`]
+- [`~data.processors.utils.WnliProcessor`]
+
+Additionally, the following method can be used to load values from a data file and convert them to a list of
+[`~data.processors.utils.InputExample`].
+
+automethod,transformers.data.processors.glue.glue_convert_examples_to_features
+
+
+### Example usage
+
+An example using these processors is given in the [run_glue.py](https://github.com/huggingface/transformers/tree/master/examples/legacy/text-classification/run_glue.py) script.
+
+
+## XNLI
+
+[The Cross-Lingual NLI Corpus (XNLI)](https://www.nyu.edu/projects/bowman/xnli/) is a benchmark that evaluates the
+quality of cross-lingual text representations. XNLI is crowd-sourced dataset based on [*MultiNLI*](http://www.nyu.edu/projects/bowman/multinli/): pairs of text are labeled with textual entailment annotations for 15
+different languages (including both high-resource language such as English and low-resource languages such as Swahili).
+
+It was released together with the paper [XNLI: Evaluating Cross-lingual Sentence Representations](https://arxiv.org/abs/1809.05053)
+
+This library hosts the processor to load the XNLI data:
+
+- [`~data.processors.utils.XnliProcessor`]
+
+Please note that since the gold labels are available on the test set, evaluation is performed on the test set.
+
+An example using these processors is given in the [run_xnli.py](https://github.com/huggingface/transformers/tree/master/examples/legacy/text-classification/run_xnli.py) script.
+
+
+## SQuAD
+
+[The Stanford Question Answering Dataset (SQuAD)](https://rajpurkar.github.io/SQuAD-explorer//) is a benchmark that
+evaluates the performance of models on question answering. Two versions are available, v1.1 and v2.0. The first version
+(v1.1) was released together with the paper [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://arxiv.org/abs/1606.05250). The second version (v2.0) was released alongside the paper [Know What You Don't
+Know: Unanswerable Questions for SQuAD](https://arxiv.org/abs/1806.03822).
+
+This library hosts a processor for each of the two versions:
+
+### Processors
+
+Those processors are:
+
+- [`~data.processors.utils.SquadV1Processor`]
+- [`~data.processors.utils.SquadV2Processor`]
+
+They both inherit from the abstract class [`~data.processors.utils.SquadProcessor`]
+
+[[autodoc]] data.processors.squad.SquadProcessor
+    - all
+
+Additionally, the following method can be used to convert SQuAD examples into
+[`~data.processors.utils.SquadFeatures`] that can be used as model inputs.
+
+automethod,transformers.data.processors.squad.squad_convert_examples_to_features
+
+
+These processors as well as the aforementionned method can be used with files containing the data as well as with the
+*tensorflow_datasets* package. Examples are given below.
+
+
+### Example usage
+
+Here is an example using the processors as well as the conversion method using data files:
+
+```python
+# Loading a V2 processor
+processor = SquadV2Processor()
+examples = processor.get_dev_examples(squad_v2_data_dir)
+
+# Loading a V1 processor
+processor = SquadV1Processor()
+examples = processor.get_dev_examples(squad_v1_data_dir)
+
+features = squad_convert_examples_to_features(
+    examples=examples,
+    tokenizer=tokenizer,
+    max_seq_length=max_seq_length,
+    doc_stride=args.doc_stride,
+    max_query_length=max_query_length,
+    is_training=not evaluate,
+)
+```
+
+Using *tensorflow_datasets* is as easy as using a data file:
+
+```python
+# tensorflow_datasets only handle Squad V1.
+tfds_examples = tfds.load("squad")
+examples = SquadV1Processor().get_examples_from_dataset(tfds_examples, evaluate=evaluate)
+
+features = squad_convert_examples_to_features(
+    examples=examples,
+    tokenizer=tokenizer,
+    max_seq_length=max_seq_length,
+    doc_stride=args.doc_stride,
+    max_query_length=max_query_length,
+    is_training=not evaluate,
+)
+```
+
+Another example using these processors is given in the [run_squad.py](https://github.com/huggingface/transformers/tree/master/examples/legacy/question-answering/run_squad.py) script.

docs/source/main_classes/processors.rst

@@ -1,172 +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.
-
-Processors
------------------------------------------------------------------------------------------------------------------------
-
-This library includes processors for several traditional tasks. These processors can be used to process a dataset into
-examples that can be fed to a model.
-
-Processors
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-All processors follow the same architecture which is that of the
-:class:`~transformers.data.processors.utils.DataProcessor`. The processor returns a list of
-:class:`~transformers.data.processors.utils.InputExample`. These
-:class:`~transformers.data.processors.utils.InputExample` can be converted to
-:class:`~transformers.data.processors.utils.InputFeatures` in order to be fed to the model.
-
-.. autoclass:: transformers.data.processors.utils.DataProcessor
-    :members:
-
-
-.. autoclass:: transformers.data.processors.utils.InputExample
-    :members:
-
-
-.. autoclass:: transformers.data.processors.utils.InputFeatures
-    :members:
-
-
-GLUE
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-`General Language Understanding Evaluation (GLUE) <https://gluebenchmark.com/>`__ is a benchmark that evaluates the
-performance of models across a diverse set of existing NLU tasks. It was released together with the paper `GLUE: A
-multi-task benchmark and analysis platform for natural language understanding
-<https://openreview.net/pdf?id=rJ4km2R5t7>`__
-
-This library hosts a total of 10 processors for the following tasks: MRPC, MNLI, MNLI (mismatched), CoLA, SST2, STSB,
-QQP, QNLI, RTE and WNLI.
-
-Those processors are:
-
-    - :class:`~transformers.data.processors.utils.MrpcProcessor`
-    - :class:`~transformers.data.processors.utils.MnliProcessor`
-    - :class:`~transformers.data.processors.utils.MnliMismatchedProcessor`
-    - :class:`~transformers.data.processors.utils.Sst2Processor`
-    - :class:`~transformers.data.processors.utils.StsbProcessor`
-    - :class:`~transformers.data.processors.utils.QqpProcessor`
-    - :class:`~transformers.data.processors.utils.QnliProcessor`
-    - :class:`~transformers.data.processors.utils.RteProcessor`
-    - :class:`~transformers.data.processors.utils.WnliProcessor`
-
-Additionally, the following method can be used to load values from a data file and convert them to a list of
-:class:`~transformers.data.processors.utils.InputExample`.
-
-.. automethod:: transformers.data.processors.glue.glue_convert_examples_to_features
-
-Example usage
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-An example using these processors is given in the :prefix_link:`run_glue.py
-<examples/legacy/text-classification/run_glue.py>` script.
-
-
-XNLI
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-`The Cross-Lingual NLI Corpus (XNLI) <https://www.nyu.edu/projects/bowman/xnli/>`__ is a benchmark that evaluates the
-quality of cross-lingual text representations. XNLI is crowd-sourced dataset based on `MultiNLI
-<http://www.nyu.edu/projects/bowman/multinli/>`: pairs of text are labeled with textual entailment annotations for 15
-different languages (including both high-resource language such as English and low-resource languages such as Swahili).
-
-It was released together with the paper `XNLI: Evaluating Cross-lingual Sentence Representations
-<https://arxiv.org/abs/1809.05053>`__
-
-This library hosts the processor to load the XNLI data:
-
-    - :class:`~transformers.data.processors.utils.XnliProcessor`
-
-Please note that since the gold labels are available on the test set, evaluation is performed on the test set.
-
-An example using these processors is given in the :prefix_link:`run_xnli.py
-<examples/legacy/text-classification/run_xnli.py>` script.
-
-
-SQuAD
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-`The Stanford Question Answering Dataset (SQuAD) <https://rajpurkar.github.io/SQuAD-explorer//>`__ is a benchmark that
-evaluates the performance of models on question answering. Two versions are available, v1.1 and v2.0. The first version
-(v1.1) was released together with the paper `SQuAD: 100,000+ Questions for Machine Comprehension of Text
-<https://arxiv.org/abs/1606.05250>`__. The second version (v2.0) was released alongside the paper `Know What You Don't
-Know: Unanswerable Questions for SQuAD <https://arxiv.org/abs/1806.03822>`__.
-
-This library hosts a processor for each of the two versions:
-
-Processors
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Those processors are:
-
-    - :class:`~transformers.data.processors.utils.SquadV1Processor`
-    - :class:`~transformers.data.processors.utils.SquadV2Processor`
-
-They both inherit from the abstract class :class:`~transformers.data.processors.utils.SquadProcessor`
-
-.. autoclass:: transformers.data.processors.squad.SquadProcessor
-    :members:
-
-Additionally, the following method can be used to convert SQuAD examples into
-:class:`~transformers.data.processors.utils.SquadFeatures` that can be used as model inputs.
-
-.. automethod:: transformers.data.processors.squad.squad_convert_examples_to_features
-
-These processors as well as the aforementionned method can be used with files containing the data as well as with the
-`tensorflow_datasets` package. Examples are given below.
-
-
-Example usage
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Here is an example using the processors as well as the conversion method using data files:
-
-.. code-block::
-
-    # Loading a V2 processor
-    processor = SquadV2Processor()
-    examples = processor.get_dev_examples(squad_v2_data_dir)
-
-    # Loading a V1 processor
-    processor = SquadV1Processor()
-    examples = processor.get_dev_examples(squad_v1_data_dir)
-
-    features = squad_convert_examples_to_features( 
-        examples=examples,
-        tokenizer=tokenizer,
-        max_seq_length=max_seq_length,
-        doc_stride=args.doc_stride,
-        max_query_length=max_query_length,
-        is_training=not evaluate,
-    )
-
-Using `tensorflow_datasets` is as easy as using a data file:
-
-.. code-block::
-
-    # tensorflow_datasets only handle Squad V1.
-    tfds_examples = tfds.load("squad")
-    examples = SquadV1Processor().get_examples_from_dataset(tfds_examples, evaluate=evaluate)
-
-    features = squad_convert_examples_to_features( 
-        examples=examples,
-        tokenizer=tokenizer,
-        max_seq_length=max_seq_length,
-        doc_stride=args.doc_stride,
-        max_query_length=max_query_length,
-        is_training=not evaluate,
-    )
-
-
-Another example using these processors is given in the :prefix_link:`run_squad.py
-<examples/legacy/question-answering/run_squad.py>` script.

docs/source/main_classes/tokenizer.mdx

@@ -0,0 +1,77 @@
+<!--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 tokenizer is in charge of preparing the inputs for a model. The library contains tokenizers for all the models. Most
+of the tokenizers are available in two flavors: a full python implementation and a "Fast" implementation based on the
+Rust library [🤗 Tokenizers](https://github.com/huggingface/tokenizers). The "Fast" implementations allows:
+
+1. a significant speed-up in particular when doing batched tokenization and
+2. additional methods to map between the original string (character and words) and the token space (e.g. getting the
+   index of the token comprising a given character or the span of characters corresponding to a given token). Currently
+   no "Fast" implementation is available for the SentencePiece-based tokenizers (for T5, ALBERT, CamemBERT, XLM-RoBERTa
+   and XLNet models).
+
+The base classes [`PreTrainedTokenizer`] and [`PreTrainedTokenizerFast`]
+implement the common methods for encoding string inputs in model inputs (see below) and instantiating/saving python and
+"Fast" tokenizers either from a local file or directory or from a pretrained tokenizer provided by the library
+(downloaded from HuggingFace's AWS S3 repository). They both rely on
+[`~tokenization_utils_base.PreTrainedTokenizerBase`] that contains the common methods, and
+[`~tokenization_utils_base.SpecialTokensMixin`].
+
+[`PreTrainedTokenizer`] and [`PreTrainedTokenizerFast`] thus implement the main
+methods for using all the tokenizers:
+
+- Tokenizing (splitting strings in sub-word token strings), converting tokens strings to ids and back, and
+  encoding/decoding (i.e., tokenizing and converting to integers).
+- Adding new tokens to the vocabulary in a way that is independent of the underlying structure (BPE, SentencePiece...).
+- Managing special tokens (like mask, beginning-of-sentence, etc.): adding them, assigning them to attributes in the
+  tokenizer for easy access and making sure they are not split during tokenization.
+
+[`BatchEncoding`] holds the output of the
+[`~tokenization_utils_base.PreTrainedTokenizerBase`]'s encoding methods (`__call__`,
+`encode_plus` and `batch_encode_plus`) and is derived from a Python dictionary. When the tokenizer is a pure python
+tokenizer, this class behaves just like a standard python dictionary and holds the various model inputs computed by
+these methods (`input_ids`, `attention_mask`...). When the tokenizer is a "Fast" tokenizer (i.e., backed by
+HuggingFace [tokenizers library](https://github.com/huggingface/tokenizers)), this class provides in addition
+several advanced alignment methods which can be used to map between the original string (character and words) and the
+token space (e.g., getting the index of the token comprising a given character or the span of characters corresponding
+to a given token).
+
+
+## PreTrainedTokenizer
+
+[[autodoc]] PreTrainedTokenizer
+    - __call__
+    - batch_decode
+    - decode
+    - encode
+    - push_to_hub
+    - all
+
+## PreTrainedTokenizerFast
+
+The [`PreTrainedTokenizerFast`] depend on the [tokenizers](https://huggingface.co/docs/tokenizers) library. The tokenizers obtained from the 🤗 tokenizers library can be
+loaded very simply into 🤗 transformers. Take a look at the [Using tokenizers from 🤗 tokenizers](../fast_tokenizers) page to understand how this is done.
+
+[[autodoc]] PreTrainedTokenizerFast
+    - __call__
+    - batch_decode
+    - decode
+    - encode
+    - push_to_hub
+    - all
+
+## BatchEncoding
+
+[[autodoc]] BatchEncoding

docs/source/main_classes/tokenizer.rst

@@ -1,78 +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.
-
-Tokenizer
------------------------------------------------------------------------------------------------------------------------
-
-A tokenizer is in charge of preparing the inputs for a model. The library contains tokenizers for all the models. Most
-of the tokenizers are available in two flavors: a full python implementation and a "Fast" implementation based on the
-Rust library `tokenizers <https://github.com/huggingface/tokenizers>`__. The "Fast" implementations allows:
-
-1. a significant speed-up in particular when doing batched tokenization and
-2. additional methods to map between the original string (character and words) and the token space (e.g. getting the
-   index of the token comprising a given character or the span of characters corresponding to a given token). Currently
-   no "Fast" implementation is available for the SentencePiece-based tokenizers (for T5, ALBERT, CamemBERT, XLM-RoBERTa
-   and XLNet models).
-
-The base classes :class:`~transformers.PreTrainedTokenizer` and :class:`~transformers.PreTrainedTokenizerFast`
-implement the common methods for encoding string inputs in model inputs (see below) and instantiating/saving python and
-"Fast" tokenizers either from a local file or directory or from a pretrained tokenizer provided by the library
-(downloaded from HuggingFace's AWS S3 repository). They both rely on
-:class:`~transformers.tokenization_utils_base.PreTrainedTokenizerBase` that contains the common methods, and
-:class:`~transformers.tokenization_utils_base.SpecialTokensMixin`.
-
-:class:`~transformers.PreTrainedTokenizer` and :class:`~transformers.PreTrainedTokenizerFast` thus implement the main
-methods for using all the tokenizers:
-
-- Tokenizing (splitting strings in sub-word token strings), converting tokens strings to ids and back, and
-  encoding/decoding (i.e., tokenizing and converting to integers).
-- Adding new tokens to the vocabulary in a way that is independent of the underlying structure (BPE, SentencePiece...).
-- Managing special tokens (like mask, beginning-of-sentence, etc.): adding them, assigning them to attributes in the
-  tokenizer for easy access and making sure they are not split during tokenization.
-
-:class:`~transformers.BatchEncoding` holds the output of the
-:class:`~transformers.tokenization_utils_base.PreTrainedTokenizerBase`'s encoding methods (``__call__``,
-``encode_plus`` and ``batch_encode_plus``) and is derived from a Python dictionary. When the tokenizer is a pure python
-tokenizer, this class behaves just like a standard python dictionary and holds the various model inputs computed by
-these methods (``input_ids``, ``attention_mask``...). When the tokenizer is a "Fast" tokenizer (i.e., backed by
-HuggingFace `tokenizers library <https://github.com/huggingface/tokenizers>`__), this class provides in addition
-several advanced alignment methods which can be used to map between the original string (character and words) and the
-token space (e.g., getting the index of the token comprising a given character or the span of characters corresponding
-to a given token).
-
-
-PreTrainedTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.PreTrainedTokenizer
-    :special-members: __call__, batch_decode, decode, encode, push_to_hub
-    :members: 
-
-
-PreTrainedTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The :class:`~transformers.PreTrainedTokenizerFast` depend on the `tokenizers
-<https://huggingface.co/docs/tokenizers>`__ library. The tokenizers obtained from the 🤗 tokenizers library can be
-loaded very simply into 🤗 transformers. Take a look at the :doc:`Using tokenizers from 🤗 tokenizers
-<../fast_tokenizers>` page to understand how this is done.
-
-.. autoclass:: transformers.PreTrainedTokenizerFast
-    :special-members: __call__, batch_decode, decode, encode, push_to_hub
-    :members:
-
-
-BatchEncoding
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BatchEncoding
-    :members:

docs/source/main_classes/trainer.mdx

@@ -47,20 +47,22 @@ when you use it on other models. When using it on your own model, make sure:
 
 </Tip>
 
-Here is an example of how to customize [`Trainer`] using a custom loss function for multi-label classification:
+Here is an example of how to customize [`Trainer`] to use a weighted loss (useful when you have an unbalanced training set):
 
 ```python
 from torch import nn
 from transformers import Trainer
 
-class MultilabelTrainer(Trainer):
+
+class CustomTrainer(Trainer):
     def compute_loss(self, model, inputs, return_outputs=False):
         labels = inputs.get("labels")
+        # forward pass
         outputs = model(**inputs)
-        logits = outputs.get('logits')
-        loss_fct = nn.BCEWithLogitsLoss()
-        loss = loss_fct(logits.view(-1, self.model.config.num_labels),
-                        labels.float().view(-1, self.model.config.num_labels))
+        logits = outputs.get("logits")
+        # compute custom loss (suppose one has 3 labels with different weights)
+        loss_fct = nn.CrossEntropyLoss(weight=torch.tensor([1.0, 2.0, 3.0]))
+        loss = loss_fct(logits.view(-1, self.model.config.num_labels), labels.view(-1))
         return (loss, outputs) if return_outputs else loss
 ```
 

docs/source/migration.mdx

@@ -209,7 +209,7 @@ Here is a `pytorch-pretrained-bert` to 🤗 Transformers conversion example for
 
 ```python
 # Let's load our model
-model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
+model = BertForSequenceClassification.from_pretrained("bert-base-uncased")
 
 # If you used to have this line in pytorch-pretrained-bert:
 loss = model(input_ids, labels=labels)
@@ -222,7 +222,7 @@ loss = outputs[0]
 loss, logits = outputs[:2]
 
 # And even the attention weights if you configure the model to output them (and other outputs too, see the docstrings and documentation)
-model = BertForSequenceClassification.from_pretrained('bert-base-uncased', output_attentions=True)
+model = BertForSequenceClassification.from_pretrained("bert-base-uncased", output_attentions=True)
 outputs = model(input_ids, labels=labels)
 loss, logits, attentions = outputs
 ```
@@ -241,23 +241,23 @@ Here is an example:
 
 ```python
 ### Let's load a model and tokenizer
-model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
-tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+model = BertForSequenceClassification.from_pretrained("bert-base-uncased")
+tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
 
 ### Do some stuff to our model and tokenizer
 # Ex: add new tokens to the vocabulary and embeddings of our model
-tokenizer.add_tokens(['[SPECIAL_TOKEN_1]', '[SPECIAL_TOKEN_2]'])
+tokenizer.add_tokens(["[SPECIAL_TOKEN_1]", "[SPECIAL_TOKEN_2]"])
 model.resize_token_embeddings(len(tokenizer))
 # Train our model
 train(model)
 
 ### Now let's save our model and tokenizer to a directory
-model.save_pretrained('./my_saved_model_directory/')
-tokenizer.save_pretrained('./my_saved_model_directory/')
+model.save_pretrained("./my_saved_model_directory/")
+tokenizer.save_pretrained("./my_saved_model_directory/")
 
 ### Reload the model and the tokenizer
-model = BertForSequenceClassification.from_pretrained('./my_saved_model_directory/')
-tokenizer = BertTokenizer.from_pretrained('./my_saved_model_directory/')
+model = BertForSequenceClassification.from_pretrained("./my_saved_model_directory/")
+tokenizer = BertTokenizer.from_pretrained("./my_saved_model_directory/")
 ```
 
 ### Optimizers: BertAdam & OpenAIAdam are now AdamW, schedules are standard PyTorch schedules
@@ -283,7 +283,13 @@ num_warmup_steps = 100
 warmup_proportion = float(num_warmup_steps) / float(num_training_steps)  # 0.1
 
 ### Previously BertAdam optimizer was instantiated like this:
-optimizer = BertAdam(model.parameters(), lr=lr, schedule='warmup_linear', warmup=warmup_proportion, num_training_steps=num_training_steps)
+optimizer = BertAdam(
+    model.parameters(),
+    lr=lr,
+    schedule="warmup_linear",
+    warmup=warmup_proportion,
+    num_training_steps=num_training_steps,
+)
 ### and used like this:
 for batch in train_data:
     loss = model(batch)
@@ -291,13 +297,19 @@ for batch in train_data:
     optimizer.step()
 
 ### In 🤗 Transformers, optimizer and schedules are split and instantiated like this:
-optimizer = AdamW(model.parameters(), lr=lr, correct_bias=False)  # To reproduce BertAdam specific behavior set correct_bias=False
-scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps)  # PyTorch scheduler
+optimizer = AdamW(
+    model.parameters(), lr=lr, correct_bias=False
+)  # To reproduce BertAdam specific behavior set correct_bias=False
+scheduler = get_linear_schedule_with_warmup(
+    optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps
+)  # PyTorch scheduler
 ### and used like this:
 for batch in train_data:
     loss = model(batch)
     loss.backward()
-    torch.nn.utils.clip_grad_norm_(model.parameters(), max_grad_norm)  # Gradient clipping is not in AdamW anymore (so you can use amp without issue)
+    torch.nn.utils.clip_grad_norm_(
+        model.parameters(), max_grad_norm
+    )  # Gradient clipping is not in AdamW anymore (so you can use amp without issue)
     optimizer.step()
     scheduler.step()
 ```

docs/source/model_doc/auto.mdx

@@ -0,0 +1,247 @@
+<!--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
+
+In many cases, the architecture you want to use can be guessed from the name or the path of the pretrained model you
+are supplying to the `from_pretrained()` method. AutoClasses are here to do this job for you so that you
+automatically retrieve the relevant model given the name/path to the pretrained weights/config/vocabulary.
+
+Instantiating one of [`AutoConfig`], [`AutoModel`], and
+[`AutoTokenizer`] will directly create a class of the relevant architecture. For instance
+
+
+```python
+model = AutoModel.from_pretrained("bert-base-cased")
+```
+
+will create a model that is an instance of [`BertModel`].
+
+There is one class of `AutoModel` for each task, and for each backend (PyTorch, TensorFlow, or Flax).
+
+## Extending the Auto Classes
+
+Each of the auto classes has a method to be extended with your custom classes. For instance, if you have defined a
+custom class of model `NewModel`, make sure you have a `NewModelConfig` then you can add those to the auto
+classes like this:
+
+```python
+from transformers import AutoConfig, AutoModel
+
+AutoConfig.register("new-model", NewModelConfig)
+AutoModel.register(NewModelConfig, NewModel)
+```
+
+You will then be able to use the auto classes like you would usually do!
+
+<Tip warning={true}>
+
+If your `NewModelConfig` is a subclass of [`~transformer.PretrainedConfig`], make sure its
+`model_type` attribute is set to the same key you use when registering the config (here `"new-model"`).
+
+Likewise, if your `NewModel` is a subclass of [`PreTrainedModel`], make sure its
+`config_class` attribute is set to the same class you use when registering the model (here
+`NewModelConfig`).
+
+</Tip>
+
+## AutoConfig
+
+[[autodoc]] AutoConfig
+
+## AutoTokenizer
+
+[[autodoc]] AutoTokenizer
+
+## AutoFeatureExtractor
+
+[[autodoc]] AutoFeatureExtractor
+
+## AutoProcessor
+
+[[autodoc]] AutoProcessor
+
+## AutoModel
+
+[[autodoc]] AutoModel
+
+## AutoModelForPreTraining
+
+[[autodoc]] AutoModelForPreTraining
+
+## AutoModelForCausalLM
+
+[[autodoc]] AutoModelForCausalLM
+
+## AutoModelForMaskedLM
+
+[[autodoc]] AutoModelForMaskedLM
+
+## AutoModelForSeq2SeqLM
+
+[[autodoc]] AutoModelForSeq2SeqLM
+
+## AutoModelForSequenceClassification
+
+[[autodoc]] AutoModelForSequenceClassification
+
+## AutoModelForMultipleChoice
+
+[[autodoc]] AutoModelForMultipleChoice
+
+## AutoModelForNextSentencePrediction
+
+[[autodoc]] AutoModelForNextSentencePrediction
+
+## AutoModelForTokenClassification
+
+[[autodoc]] AutoModelForTokenClassification
+
+## AutoModelForQuestionAnswering
+
+[[autodoc]] AutoModelForQuestionAnswering
+
+## AutoModelForTableQuestionAnswering
+
+[[autodoc]] AutoModelForTableQuestionAnswering
+
+## AutoModelForImageClassification
+
+[[autodoc]] AutoModelForImageClassification
+
+## AutoModelForVision2Seq
+
+[[autodoc]] AutoModelForVision2Seq
+
+## AutoModelForAudioClassification
+
+[[autodoc]] AutoModelForAudioClassification
+
+## AutoModelForAudioFrameClassification
+
+[[autodoc]] AutoModelForAudioFrameClassification
+
+## AutoModelForCTC
+
+[[autodoc]] AutoModelForCTC
+
+## AutoModelForSpeechSeq2Seq
+
+[[autodoc]] AutoModelForSpeechSeq2Seq
+
+## AutoModelForAudioXVector
+
+[[autodoc]] AutoModelForAudioXVector
+
+## AutoModelForObjectDetection
+
+[[autodoc]] AutoModelForObjectDetection
+
+## AutoModelForImageSegmentation
+
+[[autodoc]] AutoModelForImageSegmentation
+
+## TFAutoModel
+
+[[autodoc]] TFAutoModel
+
+## TFAutoModelForPreTraining
+
+[[autodoc]] TFAutoModelForPreTraining
+
+## TFAutoModelForCausalLM
+
+[[autodoc]] TFAutoModelForCausalLM
+
+## TFAutoModelForImageClassification
+
+[[autodoc]] TFAutoModelForImageClassification
+
+## TFAutoModelForMaskedLM
+
+[[autodoc]] TFAutoModelForMaskedLM
+
+## TFAutoModelForSeq2SeqLM
+
+[[autodoc]] TFAutoModelForSeq2SeqLM
+
+## TFAutoModelForSequenceClassification
+
+[[autodoc]] TFAutoModelForSequenceClassification
+
+## TFAutoModelForMultipleChoice
+
+[[autodoc]] TFAutoModelForMultipleChoice
+
+## TFAutoModelForTableQuestionAnswering
+
+[[autodoc]] TFAutoModelForTableQuestionAnswering
+
+## TFAutoModelForTokenClassification
+
+[[autodoc]] TFAutoModelForTokenClassification
+
+## TFAutoModelForQuestionAnswering
+
+[[autodoc]] TFAutoModelForQuestionAnswering
+
+## TFAutoModelForVision2Seq
+
+[[autodoc]] TFAutoModelForVision2Seq
+
+## FlaxAutoModel
+
+[[autodoc]] FlaxAutoModel
+
+## FlaxAutoModelForCausalLM
+
+[[autodoc]] FlaxAutoModelForCausalLM
+
+## FlaxAutoModelForPreTraining
+
+[[autodoc]] FlaxAutoModelForPreTraining
+
+## FlaxAutoModelForMaskedLM
+
+[[autodoc]] FlaxAutoModelForMaskedLM
+
+## FlaxAutoModelForSeq2SeqLM
+
+[[autodoc]] FlaxAutoModelForSeq2SeqLM
+
+## FlaxAutoModelForSequenceClassification
+
+[[autodoc]] FlaxAutoModelForSequenceClassification
+
+## FlaxAutoModelForQuestionAnswering
+
+[[autodoc]] FlaxAutoModelForQuestionAnswering
+
+## FlaxAutoModelForTokenClassification
+
+[[autodoc]] FlaxAutoModelForTokenClassification
+
+## FlaxAutoModelForMultipleChoice
+
+[[autodoc]] FlaxAutoModelForMultipleChoice
+
+## FlaxAutoModelForNextSentencePrediction
+
+[[autodoc]] FlaxAutoModelForNextSentencePrediction
+
+## FlaxAutoModelForImageClassification
+
+[[autodoc]] FlaxAutoModelForImageClassification
+
+## FlaxAutoModelForVision2Seq
+
+[[autodoc]] FlaxAutoModelForVision2Seq

docs/source/model_doc/auto.rst

@@ -1,384 +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.
-
-Auto Classes
------------------------------------------------------------------------------------------------------------------------
-
-In many cases, the architecture you want to use can be guessed from the name or the path of the pretrained model you
-are supplying to the :obj:`from_pretrained()` method. AutoClasses are here to do this job for you so that you
-automatically retrieve the relevant model given the name/path to the pretrained weights/config/vocabulary.
-
-Instantiating one of :class:`~transformers.AutoConfig`, :class:`~transformers.AutoModel`, and
-:class:`~transformers.AutoTokenizer` will directly create a class of the relevant architecture. For instance
-
-
-.. code-block:: python
-
-    model = AutoModel.from_pretrained('bert-base-cased')
-
-will create a model that is an instance of :class:`~transformers.BertModel`.
-
-There is one class of :obj:`AutoModel` for each task, and for each backend (PyTorch, TensorFlow, or Flax).
-
-Extending the Auto Classes
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Each of the auto classes has a method to be extended with your custom classes. For instance, if you have defined a
-custom class of model :obj:`NewModel`, make sure you have a :obj:`NewModelConfig` then you can add those to the auto
-classes like this:
-
-.. code-block::
-
-    from transformers import AutoConfig, AutoModel
-
-    AutoConfig.register("new-model", NewModelConfig)
-    AutoModel.register(NewModelConfig, NewModel)
-
-You will then be able to use the auto classes like you would usually do!
-
-.. warning::
-
-    If your :obj:`NewModelConfig` is a subclass of :class:`~transformer.PretrainedConfig`, make sure its
-    :obj:`model_type` attribute is set to the same key you use when registering the config (here :obj:`"new-model"`).
-
-    Likewise, if your :obj:`NewModel` is a subclass of :class:`~transformers.PreTrainedModel`, make sure its
-    :obj:`config_class` attribute is set to the same class you use when registering the model (here
-    :obj:`NewModelConfig`).
-
-
-AutoConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AutoConfig
-    :members:
-
-
-AutoTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AutoTokenizer
-    :members:
-
-
-AutoFeatureExtractor
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AutoFeatureExtractor
-    :members:
-
-
-AutoProcessor
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AutoProcessor
-    :members:
-
-
-AutoModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AutoModel
-    :members:
-
-
-AutoModelForPreTraining
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AutoModelForPreTraining
-    :members:
-
-
-AutoModelForCausalLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AutoModelForCausalLM
-    :members:
-
-
-AutoModelForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AutoModelForMaskedLM
-    :members:
-
-
-AutoModelForSeq2SeqLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AutoModelForSeq2SeqLM
-    :members:
-
-
-AutoModelForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AutoModelForSequenceClassification
-    :members:
-
-
-AutoModelForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AutoModelForMultipleChoice
-    :members:
-
-
-AutoModelForNextSentencePrediction
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AutoModelForNextSentencePrediction
-    :members:
-
-
-AutoModelForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AutoModelForTokenClassification
-    :members:
-
-
-AutoModelForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AutoModelForQuestionAnswering
-    :members:
-
-
-AutoModelForTableQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AutoModelForTableQuestionAnswering
-    :members:
-
-
-AutoModelForImageClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AutoModelForImageClassification
-    :members:
-
-
-AutoModelForVision2Seq
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AutoModelForVision2Seq
-    :members:
-
-
-AutoModelForAudioClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AutoModelForAudioClassification
-    :members:
-
-
-AutoModelForAudioFrameClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AutoModelForAudioFrameClassification
-    :members:
-
-
-AutoModelForCTC
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AutoModelForCTC
-    :members:
-
-
-AutoModelForSpeechSeq2Seq
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AutoModelForSpeechSeq2Seq
-    :members:
-
-
-AutoModelForAudioXVector
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AutoModelForAudioXVector
-    :members:
-
-
-AutoModelForObjectDetection
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AutoModelForObjectDetection
-    :members:
-
-
-AutoModelForImageSegmentation
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AutoModelForImageSegmentation
-    :members:
-
-
-TFAutoModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFAutoModel
-    :members:
-
-
-TFAutoModelForPreTraining
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFAutoModelForPreTraining
-    :members:
-
-
-TFAutoModelForCausalLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFAutoModelForCausalLM
-    :members:
-
-
-TFAutoModelForImageClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFAutoModelForImageClassification
-    :members:
-
-
-TFAutoModelForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFAutoModelForMaskedLM
-    :members:
-
-
-TFAutoModelForSeq2SeqLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFAutoModelForSeq2SeqLM
-    :members:
-
-
-TFAutoModelForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFAutoModelForSequenceClassification
-    :members:
-
-
-TFAutoModelForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFAutoModelForMultipleChoice
-    :members:
-
-
-TFAutoModelForTableQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFAutoModelForTableQuestionAnswering
-    :members:
-
-
-TFAutoModelForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFAutoModelForTokenClassification
-    :members:
-
-
-TFAutoModelForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFAutoModelForQuestionAnswering
-    :members:
-
-
-FlaxAutoModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxAutoModel
-    :members:
-
-
-FlaxAutoModelForCausalLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxAutoModelForCausalLM
-    :members:
-
-
-FlaxAutoModelForPreTraining
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxAutoModelForPreTraining
-    :members:
-
-
-FlaxAutoModelForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxAutoModelForMaskedLM
-    :members:
-
-
-FlaxAutoModelForSeq2SeqLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxAutoModelForSeq2SeqLM
-    :members:
-
-
-FlaxAutoModelForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxAutoModelForSequenceClassification
-    :members:
-
-
-FlaxAutoModelForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxAutoModelForQuestionAnswering
-    :members:
-
-
-FlaxAutoModelForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxAutoModelForTokenClassification
-    :members:
-
-
-FlaxAutoModelForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxAutoModelForMultipleChoice
-    :members:
-
-
-FlaxAutoModelForNextSentencePrediction
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxAutoModelForNextSentencePrediction
-    :members:
-
-
-FlaxAutoModelForImageClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxAutoModelForImageClassification
-    :members:
-
-
-FlaxAutoModelForVision2Seq
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxAutoModelForVision2Seq
-    :members:

docs/source/model_doc/bart.mdx

@@ -64,12 +64,15 @@ The `facebook/bart-base` and `facebook/bart-large` checkpoints can be used to fi
 
 ```python
 from transformers import BartForConditionalGeneration, BartTokenizer
+
 model = BartForConditionalGeneration.from_pretrained("facebook/bart-large", forced_bos_token_id=0)
 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']
+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"
+]
 ```
 
 ## BartConfig

docs/source/model_doc/bartpho.mdx

@@ -44,6 +44,7 @@ Example of use:
 
 >>> # With TensorFlow 2.0+:
 >>> from transformers import TFAutoModel
+
 >>> bartpho = TFAutoModel.from_pretrained("vinai/bartpho-syllable")
 >>> input_ids = tokenizer(line, return_tensors="tf")
 >>> features = bartpho(**input_ids)
@@ -58,9 +59,10 @@ Tips:
 
 ```python
 >>> from transformers import MBartForConditionalGeneration
+
 >>> bartpho = MBartForConditionalGeneration.from_pretrained("vinai/bartpho-syllable")
->>> TXT = 'Chúng tôi là <mask> nghiên cứu viên.'
->>> input_ids = tokenizer([TXT], return_tensors='pt')['input_ids']
+>>> TXT = "Chúng tôi là <mask> nghiên cứu viên."
+>>> input_ids = tokenizer([TXT], return_tensors="pt")["input_ids"]
 >>> logits = bartpho(input_ids).logits
 >>> masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero().item()
 >>> probs = logits[0, masked_index].softmax(dim=0)

docs/source/model_doc/bert-generation.mdx

@@ -39,14 +39,18 @@ Usage:
 >>> # use BERT's cls token as BOS token and sep token as EOS token
 >>> encoder = BertGenerationEncoder.from_pretrained("bert-large-uncased", bos_token_id=101, eos_token_id=102)
 >>> # add cross attention layers and use BERT's cls token as BOS token and sep token as EOS token
->>> decoder = BertGenerationDecoder.from_pretrained("bert-large-uncased", add_cross_attention=True, is_decoder=True, bos_token_id=101, eos_token_id=102)
+>>> decoder = BertGenerationDecoder.from_pretrained(
+...     "bert-large-uncased", add_cross_attention=True, is_decoder=True, bos_token_id=101, eos_token_id=102
+... )
 >>> bert2bert = EncoderDecoderModel(encoder=encoder, decoder=decoder)
 
 >>> # create tokenizer...
 >>> tokenizer = BertTokenizer.from_pretrained("bert-large-uncased")
 
->>> input_ids = tokenizer('This is a long article to summarize', add_special_tokens=False, return_tensors="pt").input_ids
->>> labels = tokenizer('This is a short summary', return_tensors="pt").input_ids
+>>> input_ids = tokenizer(
+...     "This is a long article to summarize", add_special_tokens=False, return_tensors="pt"
+>>> ).input_ids
+>>> 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
@@ -61,7 +65,9 @@ Usage:
 >>> sentence_fuser = EncoderDecoderModel.from_pretrained("google/roberta2roberta_L-24_discofuse")
 >>> tokenizer = AutoTokenizer.from_pretrained("google/roberta2roberta_L-24_discofuse")
 
->>> input_ids = tokenizer('This is the first sentence. This is the second sentence.', add_special_tokens=False, return_tensors="pt").input_ids
+>>> input_ids = tokenizer(
+...     "This is the first sentence. This is the second sentence.", add_special_tokens=False, return_tensors="pt"
+>>> ).input_ids
 
 >>> outputs = sentence_fuser.generate(input_ids)
 

docs/source/model_doc/bert-japanese.mdx

@@ -30,7 +30,7 @@ Example of using a model with MeCab and WordPiece tokenization:
 
 ```python
 >>> import torch
->>> from transformers import AutoModel, AutoTokenizer 
+>>> from transformers import AutoModel, AutoTokenizer
 
 >>> bertjapanese = AutoModel.from_pretrained("cl-tohoku/bert-base-japanese")
 >>> tokenizer = AutoTokenizer.from_pretrained("cl-tohoku/bert-base-japanese")
@@ -40,7 +40,7 @@ Example of using a model with MeCab and WordPiece tokenization:
 
 >>> inputs = tokenizer(line, return_tensors="pt")
 
->>> print(tokenizer.decode(inputs['input_ids'][0]))
+>>> print(tokenizer.decode(inputs["input_ids"][0]))
 [CLS] 吾輩 は 猫 で ある 。 [SEP]
 
 >>> outputs = bertjapanese(**inputs)
@@ -57,7 +57,7 @@ Example of using a model with Character tokenization:
 
 >>> inputs = tokenizer(line, return_tensors="pt")
 
->>> print(tokenizer.decode(inputs['input_ids'][0]))
+>>> print(tokenizer.decode(inputs["input_ids"][0]))
 [CLS] 吾 輩 は 猫 で あ る 。 [SEP]
 
 >>> outputs = bertjapanese(**inputs)

docs/source/model_doc/bertweet.mdx

@@ -28,14 +28,14 @@ Example of use:
 
 ```python
 >>> import torch
->>> from transformers import AutoModel, AutoTokenizer 
+>>> from transformers import AutoModel, AutoTokenizer
 
 >>> bertweet = AutoModel.from_pretrained("vinai/bertweet-base")
 
->>> # For transformers v4.x+: 
+>>> # For transformers v4.x+:
 >>> tokenizer = AutoTokenizer.from_pretrained("vinai/bertweet-base", use_fast=False)
 
->>> # For transformers v3.x: 
+>>> # For transformers v3.x:
 >>> # tokenizer = AutoTokenizer.from_pretrained("vinai/bertweet-base")
 
 >>> # INPUT TWEET IS ALREADY NORMALIZED!

docs/source/model_doc/blenderbot.mdx

@@ -41,7 +41,7 @@ This model was contributed by [sshleifer](https://huggingface.co/sshleifer). The
 - 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](blenderbot_small).
+  [BlenderbotSmall](blenderbot-small).
 
 
 ## Usage
@@ -50,11 +50,12 @@ Here is an example of model usage:
 
 ```python
 >>> from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration
->>> mname = 'facebook/blenderbot-400M-distill'
+
+>>> mname = "facebook/blenderbot-400M-distill"
 >>> model = BlenderbotForConditionalGeneration.from_pretrained(mname)
 >>> tokenizer = BlenderbotTokenizer.from_pretrained(mname)
 >>> UTTERANCE = "My friends are cool but they eat too many carbs."
->>> inputs = tokenizer([UTTERANCE], return_tensors='pt')
+>>> 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>"]

docs/source/model_doc/byt5.mdx

@@ -51,12 +51,14 @@ ByT5 works on raw UTF-8 bytes, so it can be used without a tokenizer:
 from transformers import T5ForConditionalGeneration
 import torch
 
-model = T5ForConditionalGeneration.from_pretrained('google/byt5-small')
+model = T5ForConditionalGeneration.from_pretrained("google/byt5-small")
 
 input_ids = torch.tensor([list("Life is like a box of chocolates.".encode("utf-8"))]) + 3  # add 3 for special tokens
-labels = torch.tensor([list("La vie est comme une boîte de chocolat.".encode("utf-8"))]) + 3  # add 3 for special tokens
+labels = (
+    torch.tensor([list("La vie est comme une boîte de chocolat.".encode("utf-8"))]) + 3
+)  # add 3 for special tokens
 
-loss = model(input_ids, labels=labels).loss # forward pass
+loss = model(input_ids, labels=labels).loss  # forward pass
 ```
 
 For batched inference and training it is however recommended to make use of the tokenizer:
@@ -64,13 +66,17 @@ For batched inference and training it is however recommended to make use of the
 ```python
 from transformers import T5ForConditionalGeneration, AutoTokenizer
 
-model = T5ForConditionalGeneration.from_pretrained('google/byt5-small')
-tokenizer = AutoTokenizer.from_pretrained('google/byt5-small')
+model = T5ForConditionalGeneration.from_pretrained("google/byt5-small")
+tokenizer = AutoTokenizer.from_pretrained("google/byt5-small")
 
-model_inputs = tokenizer(["Life is like a box of chocolates.", "Today is Monday."], padding="longest", return_tensors="pt")
-labels = tokenizer(["La vie est comme une boîte de chocolat.", "Aujourd'hui c'est lundi."], padding="longest", return_tensors="pt").input_ids
+model_inputs = tokenizer(
+    ["Life is like a box of chocolates.", "Today is Monday."], padding="longest", return_tensors="pt"
+)
+labels = tokenizer(
+    ["La vie est comme une boîte de chocolat.", "Aujourd'hui c'est lundi."], padding="longest", return_tensors="pt"
+).input_ids
 
-loss = model(**model_inputs, labels=labels).loss # forward pass
+loss = model(**model_inputs, labels=labels).loss  # forward pass
 ```
 
 ## ByT5Tokenizer

docs/source/model_doc/canine.mdx

@@ -64,13 +64,13 @@ CANINE works on raw characters, so it can be used without a tokenizer:
 >>> from transformers import CanineModel
 >>> import torch
 
->>> model = CanineModel.from_pretrained('google/canine-c') # model pre-trained with autoregressive character loss
+>>> model = CanineModel.from_pretrained("google/canine-c")  # model pre-trained with autoregressive character loss
 
 >>> text = "hello world"
 >>> # use Python's built-in ord() function to turn each character into its unicode code point id
 >>> input_ids = torch.tensor([[ord(char) for char in text]])
 
->>> outputs = model(input_ids) # forward pass
+>>> outputs = model(input_ids)  # forward pass
 >>> pooled_output = outputs.pooler_output
 >>> sequence_output = outputs.last_hidden_state
 ```
@@ -81,13 +81,13 @@ sequences to the same length):
 ```python
 >>> from transformers import CanineTokenizer, CanineModel
 
->>> model = CanineModel.from_pretrained('google/canine-c')
->>> tokenizer = CanineTokenizer.from_pretrained('google/canine-c')
+>>> model = CanineModel.from_pretrained("google/canine-c")
+>>> tokenizer = CanineTokenizer.from_pretrained("google/canine-c")
 
 >>> inputs = ["Life is like a box of chocolates.", "You never know what you gonna get."]
 >>> encoding = tokenizer(inputs, padding="longest", truncation=True, return_tensors="pt")
 
->>> outputs = model(**encoding) # forward pass
+>>> outputs = model(**encoding)  # forward pass
 >>> pooled_output = outputs.pooler_output
 >>> sequence_output = outputs.last_hidden_state
 ```

docs/source/model_doc/clip.mdx

@@ -69,8 +69,8 @@ encode the text and prepare the images. The following example shows how to get t
 >>> inputs = processor(text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True)
 
 >>> outputs = model(**inputs)
->>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score
->>> probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities
+>>> logits_per_image = outputs.logits_per_image  # this is the image-text similarity score
+>>> probs = logits_per_image.softmax(dim=1)  # we can take the softmax to get the label probabilities
 ```
 
 This model was contributed by [valhalla](https://huggingface.co/valhalla). The original code can be found [here](https://github.com/openai/CLIP).
@@ -125,6 +125,23 @@ This model was contributed by [valhalla](https://huggingface.co/valhalla). The o
 [[autodoc]] CLIPVisionModel
     - forward
 
+## TFCLIPModel
+
+[[autodoc]] TFCLIPModel
+    - call
+    - get_text_features
+    - get_image_features
+
+## TFCLIPTextModel
+
+[[autodoc]] TFCLIPTextModel
+    - call
+
+## TFCLIPVisionModel
+
+[[autodoc]] TFCLIPVisionModel
+    - call
+
 ## FlaxCLIPModel
 
 [[autodoc]] FlaxCLIPModel

docs/source/model_doc/electra.mdx

@@ -83,6 +83,11 @@ This model was contributed by [lysandre](https://huggingface.co/lysandre). The o
 [[autodoc]] ElectraForPreTraining
     - forward
 
+## ElectraForCausalLM
+
+[[autodoc]] ElectraForCausalLM
+    - forward
+
 ## ElectraForMaskedLM
 
 [[autodoc]] ElectraForMaskedLM

docs/source/model_doc/gpt_neo.mdx

@@ -29,16 +29,24 @@ The `generate()` method can be used to generate text using GPT Neo model.
 
 ```python
 >>> from transformers import GPTNeoForCausalLM, GPT2Tokenizer
+
 >>> model = GPTNeoForCausalLM.from_pretrained("EleutherAI/gpt-neo-1.3B")
 >>> tokenizer = GPT2Tokenizer.from_pretrained("EleutherAI/gpt-neo-1.3B")
 
->>> prompt = "In a shocking finding, scientists discovered a herd of unicorns living in a remote, " \
-...          "previously unexplored valley, in the Andes Mountains. Even more surprising to the " \
-...          "researchers was the fact that the unicorns spoke perfect English."
+>>> prompt = (
+...     "In a shocking finding, scientists discovered a herd of unicorns living in a remote, "
+...     "previously unexplored valley, in the Andes Mountains. Even more surprising to the "
+...     "researchers was the fact that the unicorns spoke perfect English."
+... )
 
 >>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids
 
->>> gen_tokens = model.generate(input_ids, do_sample=True, temperature=0.9, max_length=100,)
+>>> gen_tokens = model.generate(
+...     input_ids,
+...     do_sample=True,
+...     temperature=0.9,
+...     max_length=100,
+... )
 >>> gen_text = tokenizer.batch_decode(gen_tokens)[0]
 ```
 

docs/source/model_doc/gptj.mdx

@@ -33,7 +33,9 @@ Tips:
 >>> from transformers import GPTJForCausalLM
 >>> import torch
 
->>> model = GPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B", revision="float16", torch_dtype=torch.float16, low_cpu_mem_usage=True)
+>>> model = GPTJForCausalLM.from_pretrained(
+...     "EleutherAI/gpt-j-6B", revision="float16", torch_dtype=torch.float16, low_cpu_mem_usage=True
+... )
 ```
 
 - The model should fit on 16GB GPU for inference. For training/fine-tuning it would take much more GPU RAM. Adam
@@ -56,16 +58,24 @@ model.
 
 ```python
 >>> from transformers import AutoModelForCausalLM, AutoTokenizer
+
 >>> model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-j-6B")
 >>> tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B")
 
->>> prompt = "In a shocking finding, scientists discovered a herd of unicorns living in a remote, " \
-...          "previously unexplored valley, in the Andes Mountains. Even more surprising to the " \
-...          "researchers was the fact that the unicorns spoke perfect English."
+>>> prompt = (
+...     "In a shocking finding, scientists discovered a herd of unicorns living in a remote, "
+...     "previously unexplored valley, in the Andes Mountains. Even more surprising to the "
+...     "researchers was the fact that the unicorns spoke perfect English."
+... )
 
 >>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids
 
->>> gen_tokens = model.generate(input_ids, do_sample=True, temperature=0.9, max_length=100,)
+>>> gen_tokens = model.generate(
+...     input_ids,
+...     do_sample=True,
+...     temperature=0.9,
+...     max_length=100,
+... )
 >>> gen_text = tokenizer.batch_decode(gen_tokens)[0]
 ```
 
@@ -78,13 +88,20 @@ model.
 >>> model = GPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B", torch_dtype=torch.float16)
 >>> tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B")
 
->>> prompt = "In a shocking finding, scientists discovered a herd of unicorns living in a remote, " \
-...          "previously unexplored valley, in the Andes Mountains. Even more surprising to the " \
-...          "researchers was the fact that the unicorns spoke perfect English."
+>>> prompt = (
+...     "In a shocking finding, scientists discovered a herd of unicorns living in a remote, "
+...     "previously unexplored valley, in the Andes Mountains. Even more surprising to the "
+...     "researchers was the fact that the unicorns spoke perfect English."
+... )
 
 >>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids
 
->>> gen_tokens = model.generate(input_ids, do_sample=True, temperature=0.9, max_length=100,)
+>>> gen_tokens = model.generate(
+...     input_ids,
+...     do_sample=True,
+...     temperature=0.9,
+...     max_length=100,
+... )
 >>> gen_text = tokenizer.batch_decode(gen_tokens)[0]
 ```
 

docs/source/model_doc/herbert.mdx

@@ -41,7 +41,7 @@ Examples of use:
 >>> 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')
+>>> 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:

docs/source/model_doc/imagegpt.mdx

@@ -40,7 +40,7 @@ Tips:
 
 - Demo notebooks for ImageGPT can be found
   [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/ImageGPT).
-- ImageGPT is almost exactly the same as [GPT-2](./model_doc/gpt2), with the exception that a different activation
+- ImageGPT is almost exactly the same as [GPT-2](gpt2), with the exception that a different activation
   function is used (namely "quick gelu"), and the layer normalization layers don't mean center the inputs. ImageGPT
   also doesn't have tied input- and output embeddings.
 - As the time- and memory requirements of the attention mechanism of Transformers scales quadratically in the sequence

docs/source/model_doc/layoutlm.mdx

@@ -53,12 +53,12 @@ Tips:
 
 ```python
 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)),
-     ]
+    return [
+        int(1000 * (bbox[0] / width)),
+        int(1000 * (bbox[1] / height)),
+        int(1000 * (bbox[2] / width)),
+        int(1000 * (bbox[3] / height)),
+    ]
 ```
 
 Here, `width` and `height` correspond to the width and height of the original document in which the token

docs/source/model_doc/layoutlmv2.mdx

@@ -70,12 +70,12 @@ Tips:
 
 ```python
 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)),
-     ]
+    return [
+        int(1000 * (bbox[0] / width)),
+        int(1000 * (bbox[1] / height)),
+        int(1000 * (bbox[2] / width)),
+        int(1000 * (bbox[3] / height)),
+    ]
 ```
 
 Here, `width` and `height` correspond to the width and height of the original document in which the token
@@ -123,7 +123,7 @@ modality.
 ```python
 from transformers import LayoutLMv2FeatureExtractor, LayoutLMv2TokenizerFast, LayoutLMv2Processor
 
-feature_extractor = LayoutLMv2FeatureExtractor() # apply_ocr is set to True by default
+feature_extractor = LayoutLMv2FeatureExtractor()  # apply_ocr is set to True by default
 tokenizer = LayoutLMv2TokenizerFast.from_pretrained("microsoft/layoutlmv2-base-uncased")
 processor = LayoutLMv2Processor(feature_extractor, tokenizer)
 ```
@@ -158,7 +158,9 @@ from PIL import Image
 processor = LayoutLMv2Processor.from_pretrained("microsoft/layoutlmv2-base-uncased")
 
 image = Image.open("name_of_your_document - can be a png file, pdf, etc.").convert("RGB")
-encoding = processor(image, return_tensors="pt") # you can also add all tokenizer parameters here such as padding, truncation
+encoding = processor(
+    image, return_tensors="pt"
+)  # you can also add all tokenizer parameters here such as padding, truncation
 print(encoding.keys())
 # dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'bbox', 'image'])
 ```
@@ -177,7 +179,7 @@ processor = LayoutLMv2Processor.from_pretrained("microsoft/layoutlmv2-base-uncas
 
 image = Image.open("name_of_your_document - can be a png file, pdf, etc.").convert("RGB")
 words = ["hello", "world"]
-boxes = [[1, 2, 3, 4], [5, 6, 7, 8]] # make sure to normalize your bounding boxes
+boxes = [[1, 2, 3, 4], [5, 6, 7, 8]]  # make sure to normalize your bounding boxes
 encoding = processor(image, words, boxes=boxes, return_tensors="pt")
 print(encoding.keys())
 # dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'bbox', 'image'])
@@ -199,7 +201,7 @@ processor = LayoutLMv2Processor.from_pretrained("microsoft/layoutlmv2-base-uncas
 
 image = Image.open("name_of_your_document - can be a png file, pdf, etc.").convert("RGB")
 words = ["hello", "world"]
-boxes = [[1, 2, 3, 4], [5, 6, 7, 8]] # make sure to normalize your bounding boxes
+boxes = [[1, 2, 3, 4], [5, 6, 7, 8]]  # make sure to normalize your bounding boxes
 word_labels = [1, 2]
 encoding = processor(image, words, boxes=boxes, word_labels=word_labels, return_tensors="pt")
 print(encoding.keys())
@@ -219,7 +221,7 @@ processor = LayoutLMv2Processor.from_pretrained("microsoft/layoutlmv2-base-uncas
 
 image = Image.open("name_of_your_document - can be a png file, pdf, etc.").convert("RGB")
 question = "What's his name?"
-encoding = processor(image, question, return_tensors="pt") 
+encoding = processor(image, question, return_tensors="pt")
 print(encoding.keys())
 # dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'bbox', 'image'])
 ```
@@ -238,8 +240,8 @@ processor = LayoutLMv2Processor.from_pretrained("microsoft/layoutlmv2-base-uncas
 image = Image.open("name_of_your_document - can be a png file, pdf, etc.").convert("RGB")
 question = "What's his name?"
 words = ["hello", "world"]
-boxes = [[1, 2, 3, 4], [5, 6, 7, 8]] # make sure to normalize your bounding boxes
-encoding = processor(image, question, words, boxes=boxes, return_tensors="pt")  
+boxes = [[1, 2, 3, 4], [5, 6, 7, 8]]  # make sure to normalize your bounding boxes
+encoding = processor(image, question, words, boxes=boxes, return_tensors="pt")
 print(encoding.keys())
 # dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'bbox', 'image'])
 ```

docs/source/model_doc/layoutxlm.mdx

@@ -34,7 +34,7 @@ One can directly plug in the weights of LayoutXLM into a LayoutLMv2 model, like
 ```python
 from transformers import LayoutLMv2Model
 
-model = LayoutLMv2Model.from_pretrained('microsoft/layoutxlm-base')
+model = LayoutLMv2Model.from_pretrained("microsoft/layoutxlm-base")
 ```
 
 Note that LayoutXLM has its own tokenizer, based on
@@ -44,7 +44,7 @@ follows:
 ```python
 from transformers import LayoutXLMTokenizer
 
-tokenizer = LayoutXLMTokenizer.from_pretrained('microsoft/layoutxlm-base')
+tokenizer = LayoutXLMTokenizer.from_pretrained("microsoft/layoutxlm-base")
 ```
 
 Similar to LayoutLMv2, you can use [`LayoutXLMProcessor`] (which internally applies

docs/source/model_doc/longformer.mdx

@@ -75,8 +75,8 @@ For more information, please refer to the official [paper](https://arxiv.org/pdf
 trained and should be used as follows:
 
 ```python
-input_ids = tokenizer.encode('This is a sentence from [MASK] training data', return_tensors='pt')
-mlm_labels = tokenizer.encode('This is a sentence from the training data', return_tensors='pt')
+input_ids = tokenizer.encode("This is a sentence from [MASK] training data", return_tensors="pt")
+mlm_labels = tokenizer.encode("This is a sentence from the training data", return_tensors="pt")
 
 loss = model(input_ids, labels=input_ids, masked_lm_labels=mlm_labels)[0]
 ```