Release: v4.13.0

Fix typo in toctree (#14704 )
add str hub token to repository when provided else fallback to default (#14682 )
2021-12-09 16:55:21 +01:00 · 2021-12-09 09:25:31 -05:00 · 2021-12-09 08:42:23 -05:00 · 2021-12-09 08:32:35 -05:00 · 2021-12-09 07:01:03 -05:00 · 2021-12-08 19:59:44 -05:00
539 changed files with 57033 additions and 10558 deletions
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -65,7 +65,7 @@ jobs:
    run_tests_torch_and_tf:
        working_directory: ~/transformers
        docker:
-            - image: circleci/python:3.6
+            - image: circleci/python:3.7
        environment:
            OMP_NUM_THREADS: 1
            RUN_PT_TF_CROSS_TESTS: yes
@@ -81,7 +81,9 @@ jobs:
            - run: sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev
            - run: pip install --upgrade pip
            - run: pip install .[sklearn,tf-cpu,torch,testing,sentencepiece,torch-speech,vision]
-            - run: pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.9.0+cpu.html
+            - run: pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.10.0+cpu.html
+            - run: pip install tensorflow_probability
+            - run: pip install https://github.com/kpu/kenlm/archive/master.zip
            - save_cache:
                key: v0.4-{{ checksum "setup.py" }}
                paths:
@@ -101,7 +103,7 @@ jobs:
    run_tests_torch_and_tf_all:
        working_directory: ~/transformers
        docker:
-            - image: circleci/python:3.6
+            - image: circleci/python:3.7
        environment:
            OMP_NUM_THREADS: 1
            RUN_PT_TF_CROSS_TESTS: yes
@@ -117,7 +119,9 @@ jobs:
            - run: sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev
            - run: pip install --upgrade pip
            - run: pip install .[sklearn,tf-cpu,torch,testing,sentencepiece,torch-speech,vision]
-            - run: pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.9.0+cpu.html
+            - run: pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.10.0+cpu.html
+            - run: pip install tensorflow_probability
+            - run: pip install https://github.com/kpu/kenlm/archive/master.zip
            - save_cache:
                key: v0.4-{{ checksum "setup.py" }}
                paths:
@@ -148,7 +152,8 @@ jobs:
            - run: sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev
            - run: pip install --upgrade pip
            - run: pip install .[sklearn,flax,torch,testing,sentencepiece,torch-speech,vision]
-            - run: pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.9.0+cpu.html
+            - run: pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.10.0+cpu.html
+            - run: pip install https://github.com/kpu/kenlm/archive/master.zip
            - save_cache:
                key: v0.4-{{ checksum "setup.py" }}
                paths:
@@ -184,7 +189,8 @@ jobs:
            - run: sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev
            - run: pip install --upgrade pip
            - run: pip install .[sklearn,flax,torch,testing,sentencepiece,torch-speech,vision]
-            - run: pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.9.0+cpu.html
+            - run: pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.10.0+cpu.html
+            - run: pip install https://github.com/kpu/kenlm/archive/master.zip
            - save_cache:
                key: v0.4-{{ checksum "setup.py" }}
                paths:
@@ -214,7 +220,8 @@ jobs:
            - run: sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev
            - run: pip install --upgrade pip
            - run: pip install .[sklearn,torch,testing,sentencepiece,torch-speech,vision,timm]
-            - run: pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.9.0+cpu.html
+            - run: pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.10.0+cpu.html
+            - run: pip install https://github.com/kpu/kenlm/archive/master.zip
            - save_cache:
                  key: v0.4-torch-{{ checksum "setup.py" }}
                  paths:
@@ -249,7 +256,8 @@ jobs:
            - run: sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev
            - run: pip install --upgrade pip
            - run: pip install .[sklearn,torch,testing,sentencepiece,torch-speech,vision,timm]
-            - run: pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.9.0+cpu.html
+            - run: pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.10.0+cpu.html
+            - run: pip install https://github.com/kpu/kenlm/archive/master.zip
            - save_cache:
                  key: v0.4-torch-{{ checksum "setup.py" }}
                  paths:
@@ -276,8 +284,11 @@ jobs:
                  keys:
                      - v0.4-tf-{{ checksum "setup.py" }}
                      - v0.4-{{ checksum "setup.py" }}
+            - run: sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev
            - run: pip install --upgrade pip
-            - run: pip install .[sklearn,tf-cpu,testing,sentencepiece,tf-speech]
+            - run: pip install .[sklearn,tf-cpu,testing,sentencepiece,tf-speech,vision]
+            - run: pip install tensorflow_probability
+            - run: pip install https://github.com/kpu/kenlm/archive/master.zip
            - save_cache:
                  key: v0.4-tf-{{ checksum "setup.py" }}
                  paths:
@@ -309,8 +320,11 @@ jobs:
                  keys:
                      - v0.4-tf-{{ checksum "setup.py" }}
                      - v0.4-{{ checksum "setup.py" }}
+            - run: sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev
            - run: pip install --upgrade pip
-            - run: pip install .[sklearn,tf-cpu,testing,sentencepiece,tf-speech]
+            - run: pip install .[sklearn,tf-cpu,testing,sentencepiece,tf-speech,vision]
+            - run: pip install tensorflow_probability
+            - run: pip install https://github.com/kpu/kenlm/archive/master.zip
            - save_cache:
                  key: v0.4-tf-{{ checksum "setup.py" }}
                  paths:
@@ -337,8 +351,10 @@ jobs:
                keys:
                    - v0.4-flax-{{ checksum "setup.py" }}
                    - v0.4-{{ checksum "setup.py" }}
+            - run: sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev
            - run: pip install --upgrade pip
-            - run: sudo pip install .[flax,testing,sentencepiece,flax-speech,vision]
+            - run: pip install .[flax,testing,sentencepiece,flax-speech,vision]
+            - run: pip install https://github.com/kpu/kenlm/archive/master.zip
            - save_cache:
                  key: v0.4-flax-{{ checksum "setup.py" }}
                  paths:
@@ -370,8 +386,10 @@ jobs:
                keys:
                    - v0.4-flax-{{ checksum "setup.py" }}
                    - v0.4-{{ checksum "setup.py" }}
+            - run: sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev
            - run: pip install --upgrade pip
-            - run: sudo pip install .[flax,testing,sentencepiece,vision,flax-speech]
+            - run: pip install .[flax,testing,sentencepiece,vision,flax-speech]
+            - run: pip install https://github.com/kpu/kenlm/archive/master.zip
            - save_cache:
                  key: v0.4-flax-{{ checksum "setup.py" }}
                  paths:
@@ -401,8 +419,9 @@ jobs:
                      - v0.4-{{ checksum "setup.py" }}
            - run: sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev
            - run: pip install --upgrade pip
-            - run: pip install .[sklearn,torch,testing,sentencepiece,torch-speech,vision]
-            - run: pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.9.0+cpu.html
+            - run: pip install .[sklearn,torch,testing,sentencepiece,torch-speech,vision,timm]
+            - run: pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.10.0+cpu.html
+            - run: pip install https://github.com/kpu/kenlm/archive/master.zip
            - save_cache:
                  key: v0.4-torch-{{ checksum "setup.py" }}
                  paths:
@@ -437,8 +456,9 @@ jobs:
                      - v0.4-{{ checksum "setup.py" }}
            - run: sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev
            - run: pip install --upgrade pip
-            - run: pip install .[sklearn,torch,testing,sentencepiece,torch-speech,vision]
-            - run: pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.9.0+cpu.html
+            - run: pip install .[sklearn,torch,testing,sentencepiece,torch-speech,vision,timm]
+            - run: pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.10.0+cpu.html
+            - run: pip install https://github.com/kpu/kenlm/archive/master.zip
            - save_cache:
                  key: v0.4-torch-{{ checksum "setup.py" }}
                  paths:
@@ -468,6 +488,7 @@ jobs:
                      - v0.4-{{ checksum "setup.py" }}
            - run: pip install --upgrade pip
            - run: pip install .[sklearn,tf-cpu,testing,sentencepiece]
+            - run: pip install tensorflow_probability
            - save_cache:
                  key: v0.4-tf-{{ checksum "setup.py" }}
                  paths:
@@ -502,6 +523,7 @@ jobs:
                      - v0.4-{{ checksum "setup.py" }}
            - run: pip install --upgrade pip
            - run: pip install .[sklearn,tf-cpu,testing,sentencepiece]
+            - run: pip install tensorflow_probability
            - save_cache:
                  key: v0.4-tf-{{ checksum "setup.py" }}
                  paths:
@@ -576,7 +598,7 @@ jobs:
                  path: ~/transformers/examples_output.txt
            - store_artifacts:
                  path: ~/transformers/reports
-    
+
    run_examples_torch_all:
        working_directory: ~/transformers
        docker:
@@ -607,6 +629,69 @@ jobs:
            - store_artifacts:
                  path: ~/transformers/reports

+    run_examples_flax:
+        working_directory: ~/transformers
+        docker:
+            - image: circleci/python:3.7
+        environment:
+            OMP_NUM_THREADS: 1
+            TRANSFORMERS_IS_CI: yes
+        resource_class: xlarge
+        parallelism: 1
+        steps:
+            - checkout
+            - restore_cache:
+                keys:
+                    - v0.4-flax_examples-{{ checksum "setup.py" }}
+                    - v0.4-{{ checksum "setup.py" }}
+            - run: pip install --upgrade pip
+            - run: sudo pip install .[flax,testing,sentencepiece]
+            - run: pip install -r examples/flax/_tests_requirements.txt
+            - save_cache:
+                  key: v0.4-flax_examples-{{ checksum "setup.py" }}
+                  paths:
+                      - '~/.cache/pip'
+            - run: python utils/tests_fetcher.py --filters examples tests | tee test_preparation.txt
+            - store_artifacts:
+                  path: ~/transformers/test_preparation.txt
+            - run: |
+                  if [ -f test_list.txt ]; then
+                    python -m pytest -n 8 --dist=loadfile -s --make-reports=examples_flax ./examples/flax/ | tee tests_output.txt
+                  fi
+            - store_artifacts:
+                  path: ~/transformers/flax_examples_output.txt
+            - store_artifacts:
+                  path: ~/transformers/reports
+    
+    run_examples_flax_all:
+        working_directory: ~/transformers
+        docker:
+            - image: circleci/python:3.7
+        environment:
+            OMP_NUM_THREADS: 1
+            TRANSFORMERS_IS_CI: yes
+        resource_class: xlarge
+        parallelism: 1
+        steps:
+            - checkout
+            - restore_cache:
+                keys:
+                    - v0.4-flax_examples-{{ checksum "setup.py" }}
+                    - v0.4-{{ checksum "setup.py" }}
+            - run: pip install --upgrade pip
+            - run: sudo pip install .[flax,testing,sentencepiece]
+            - run: pip install -r examples/flax/_tests_requirements.txt
+            - save_cache:
+                  key: v0.4-flax_examples-{{ checksum "setup.py" }}
+                  paths:
+                      - '~/.cache/pip'
+            - run: |
+                  TRANSFORMERS_IS_CI=1 python -m pytest -n 8 --dist=loadfile -s --make-reports=examples_flax ./examples/flax/ | tee examples_output.txt
+            - store_artifacts:
+                  path: ~/transformers/flax_examples_output.txt
+            - store_artifacts:
+                  path: ~/transformers/reports
+
    run_tests_hub:
        working_directory: ~/transformers
        docker:
@@ -739,52 +824,6 @@ jobs:
            - store_artifacts:
                  path: ~/transformers/reports

-    build_doc:
-        working_directory: ~/transformers
-        docker:
-            - image: circleci/python:3.6
-        resource_class: large
-        steps:
-            - checkout
-            - restore_cache:
-                  keys:
-                      - v0.4-build_doc-{{ checksum "setup.py" }}
-                      - v0.4-{{ checksum "setup.py" }}
-            - run: sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev
-            - run: pip install --upgrade pip
-            - run: pip install ."[docs]"
-            - run: pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.9.0+cpu.html
-            - save_cache:
-                  key: v0.4-build_doc-{{ checksum "setup.py" }}
-                  paths:
-                      - '~/.cache/pip'
-            - run: cd docs && make html SPHINXOPTS="-W -j 4"
-            - store_artifacts:
-                path: ./docs/_build
-
-    deploy_doc:
-        working_directory: ~/transformers
-        docker:
-            - image: circleci/python:3.6
-        resource_class: large
-        steps:
-            - add_ssh_keys:
-                fingerprints:
-                    - "5b:7a:95:18:07:8c:aa:76:4c:60:35:88:ad:60:56:71"
-            - checkout
-            - restore_cache:
-                  keys:
-                      - v0.4-deploy_doc-{{ checksum "setup.py" }}
-                      - v0.4-{{ checksum "setup.py" }}
-            - run: sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev
-            - run: pip install --upgrade pip
-            - run: pip install ."[docs]"
-            - save_cache:
-                  key: v0.4-deploy_doc-{{ checksum "setup.py" }}
-                  paths:
-                      - '~/.cache/pip'
-            - run: ./.circleci/deploy.sh
-
    check_code_quality:
        working_directory: ~/transformers
        docker:
@@ -800,7 +839,6 @@ jobs:
                      - v0.4-code_quality-{{ checksum "setup.py" }}
                      - v0.4-{{ checksum "setup.py" }}
            - run: pip install --upgrade pip
-            - run: pip install isort GitPython
            - run: pip install .[all,quality]
            - save_cache:
                  key: v0.4-code_quality-{{ checksum "setup.py" }}
@@ -811,6 +849,27 @@ jobs:
            - 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
+
+    check_repository_consistency:
+        working_directory: ~/transformers
+        docker:
+            - image: circleci/python:3.6
+        resource_class: large
+        environment:
+            TRANSFORMERS_IS_CI: yes
+        parallelism: 1
+        steps:
+            - checkout
+            - restore_cache:
+                  keys:
+                      - v0.4-repository_consistency-{{ checksum "setup.py" }}
+                      - v0.4-{{ checksum "setup.py" }}
+            - run: pip install --upgrade pip
+            - run: pip install .[all,quality]
+            - save_cache:
+                  key: v0.4-repository_consistency-{{ checksum "setup.py" }}
+                  paths:
+                      - '~/.cache/pip'
            - run: python utils/check_copies.py
            - run: python utils/check_table.py
            - run: python utils/check_dummies.py
@@ -819,17 +878,6 @@ jobs:
            - run: make deps_table_check_updated
            - run: python utils/tests_fetcher.py --sanity_check

-    check_repository_consistency:
-        working_directory: ~/transformers
-        docker:
-            - image: circleci/python:3.6
-        resource_class: small
-        parallelism: 1
-        steps:
-            - checkout
-            - run: pip install requests
-            - run: python ./utils/link_tester.py
-
    run_tests_layoutlmv2:
        working_directory: ~/transformers
        docker:
@@ -911,6 +959,7 @@ workflows:
            - check_code_quality
            - check_repository_consistency
            - run_examples_torch
+            - run_examples_flax
            - run_tests_custom_tokenizers
            - run_tests_torch_and_tf
            - run_tests_torch_and_flax
@@ -921,9 +970,7 @@ workflows:
            - run_tests_pipelines_tf
            - run_tests_onnxruntime
            - run_tests_hub
-            - build_doc
            - run_tests_layoutlmv2
-            - deploy_doc: *workflow_filters
    nightly:
        triggers:
            - schedule:
@@ -934,6 +981,7 @@ workflows:
                            - master
        jobs:
            - run_examples_torch_all
+            - run_examples_flax_all
            - run_tests_torch_and_tf_all
            - run_tests_torch_and_flax_all
            - run_tests_torch_all
--- a/.circleci/deploy.sh
+++ b/.circleci/deploy.sh
@@ -1,79 +0,0 @@
-cd docs
-
-function deploy_doc(){
-	echo "Creating doc at commit $1 and pushing to folder $2"
-	git checkout $1
-	pip install -U ..
-	if [ ! -z "$2" ]
-	then
-		if [ "$2" == "master" ]; then
-		    echo "Pushing master"
-			make clean && make html && scp -r -oStrictHostKeyChecking=no _build/html/* $doc:$dir/$2/
-			cp -r _build/html/_static .
-		elif ssh -oStrictHostKeyChecking=no $doc "[ -d $dir/$2 ]"; then
-			echo "Directory" $2 "already exists"
-			scp -r -oStrictHostKeyChecking=no _static/* $doc:$dir/$2/_static/
-		else
-			echo "Pushing version" $2
-			make clean && make html
-			rm -rf _build/html/_static
-			cp -r _static _build/html
-			scp -r -oStrictHostKeyChecking=no _build/html $doc:$dir/$2
-		fi
-	else
-		echo "Pushing stable"
-		make clean && make html
-		rm -rf _build/html/_static
-		cp -r _static _build/html
-		scp -r -oStrictHostKeyChecking=no _build/html/* $doc:$dir
-	fi
-}
-
-# You can find the commit for each tag on https://github.com/huggingface/transformers/tags
-deploy_doc "master" master
-deploy_doc "b33a385" v1.0.0
-deploy_doc "fe02e45" v1.1.0
-deploy_doc "89fd345" v1.2.0
-deploy_doc "fc9faa8" v2.0.0
-deploy_doc "3ddce1d" v2.1.1
-deploy_doc "3616209" v2.2.0
-deploy_doc "d0f8b9a" v2.3.0
-deploy_doc "6664ea9" v2.4.0
-deploy_doc "fb560dc" v2.5.0
-deploy_doc "b90745c" v2.5.1
-deploy_doc "fbc5bf1" v2.6.0
-deploy_doc "6f5a12a" v2.7.0
-deploy_doc "11c3257" v2.8.0
-deploy_doc "e7cfc1a" v2.9.0
-deploy_doc "7cb203f" v2.9.1
-deploy_doc "10d7239" v2.10.0
-deploy_doc "b42586e" v2.11.0
-deploy_doc "7fb8bdf" v3.0.2
-deploy_doc "4b3ee9c" v3.1.0
-deploy_doc "3ebb1b3" v3.2.0
-deploy_doc "0613f05" v3.3.1
-deploy_doc "eb0e0ce" v3.4.0
-deploy_doc "818878d" v3.5.1
-deploy_doc "c781171" v4.0.1
-deploy_doc "bfa4ccf" v4.1.1
-deploy_doc "7d9a9d0" v4.2.2
-deploy_doc "bae0c79" v4.3.3
-deploy_doc "c988db5" v4.4.0
-deploy_doc "c5d6a28" v4.4.1
-deploy_doc "6bc89ed" v4.4.2
-deploy_doc "4906a29" v4.5.0
-deploy_doc "4bae96e" v4.5.1
-deploy_doc "25dee4a" v4.6.0
-deploy_doc "7a6c9fa" v4.7.0
-deploy_doc "9252a51" v4.8.0
-deploy_doc "1366172" v4.8.1
-deploy_doc "96d1cfb" v4.8.2
-deploy_doc "72aee83" v4.9.0
-deploy_doc "bff1c71" v4.9.1
-deploy_doc "41981a2" v4.9.2
-deploy_doc "39cb6f5" v4.10.0
-deploy_doc "28e2787" v4.10.1
-deploy_doc "dc193c9" v4.11.0
-deploy_doc "54f9d62" v4.11.1
-deploy_doc "7655f11" v4.11.2
-deploy_doc "65659a2"  # v4.11.3 Latest stable release
--- a/.github/workflows/build_doc_test.yml
+++ b/.github/workflows/build_doc_test.yml
@@ -0,0 +1,49 @@
+name: Documentation test build
+
+on:
+  pull_request:
+    paths:
+      - "src/**"
+      - "docs/**"
+      - ".github/**"
+
+jobs:
+  build_and_package:
+    runs-on: ubuntu-latest
+    defaults:
+      run:
+        shell: bash -l {0}
+
+    steps:
+      - uses: actions/checkout@v2
+      
+      - name: Loading cache.
+        uses: actions/cache@v2
+        id: cache
+        with:
+          path: ~/.cache/pip
+          key: v1-test_build_doc
+          restore-keys: |
+            v1-test_build_doc-${{ hashFiles('setup.py') }}
+            v1-test_build_doc
+
+      - name: Setup environment
+        run: |
+          sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev
+
+          pip install git+https://github.com/huggingface/doc-builder
+          pip install git+https://github.com/huggingface/transformers#egg=transformers[dev]
+
+          export TORCH_VERSION=$(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'
+
+          sudo apt install tesseract-ocr
+          pip install pytesseract
+          pip install pytorch-quantization --extra-index-url https://pypi.ngc.nvidia.com
+
+      - name: Make documentation
+        run: |
+          doc-builder build transformers ./transformers/docs/source
--- a/.github/workflows/build_documentation.yml
+++ b/.github/workflows/build_documentation.yml
@@ -0,0 +1,69 @@
+name: Build documentation
+
+on:
+  push:
+    branches:
+      - master
+      - doc-builder*
+
+jobs:
+  build_and_package:
+    runs-on: ubuntu-latest
+    defaults:
+      run:
+        shell: bash -l {0}
+
+    steps:
+      - uses: actions/checkout@v2
+        with:
+          repository: 'huggingface/doc-builder'
+          token: ${{ secrets.HUGGINGFACE_PUSH }}
+
+      - name: Clone transformers
+        run: |
+          git clone https://github.com/huggingface/transformers
+      
+      - name: Loading cache.
+        uses: actions/cache@v2
+        id: cache
+        with:
+          path: ~/.cache/pip
+          key: v1-test_build_doc
+          restore-keys: |
+            v1-test_build_doc-${{ hashFiles('setup.py') }}
+            v1-test_build_doc
+
+      - name: Setup environment
+        run: |
+          sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev
+
+          pip install git+https://github.com/huggingface/doc-builder
+          pip install git+https://github.com/huggingface/transformers#egg=transformers[dev]
+
+          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'
+
+          sudo apt install 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"
+          git config --global user.email transformers@huggingface.co
+          git pull origin main
+
+      - name: Make documentation
+        run: |
+          doc-builder build transformers ./transformers/docs/source
+
+      - name: Push to repository
+        run: |
+          git add build
+          git commit -m "Updated with commit ${{ github.sha }}"
+          git push origin main
+
--- a/.github/workflows/self-nightly-scheduled.yml
+++ b/.github/workflows/self-nightly-scheduled.yml
@@ -40,10 +40,7 @@ jobs:

            - name: Are GPUs recognized by our DL frameworks
              run: |
-                  python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
-                  python -c "import torch; print('Cuda version:', torch.version.cuda)"
-                  python -c "import torch; print('CuDNN version:', torch.backends.cudnn.version())"
-                  python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"
+                utils/print_env_pt.py

            - name: Run all tests on GPU
              run: |
@@ -110,10 +107,7 @@ jobs:

            - name: Are GPUs recognized by our DL frameworks
              run: |
-                  python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
-                  python -c "import torch; print('Cuda version:', torch.version.cuda)"
-                  python -c "import torch; print('CuDNN version:', torch.backends.cudnn.version())"
-                  python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"
+                utils/print_env_pt.py

            - name: Run all tests on GPU
              env:
@@ -166,10 +160,7 @@ jobs:

            - name: Are GPUs recognized by our DL frameworks
              run: |
-                  python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
-                  python -c "import torch; print('Cuda version:', torch.version.cuda)"
-                  python -c "import torch; print('CuDNN version:', torch.backends.cudnn.version())"
-                  python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"
+                utils/print_env_pt.py

            - name: Run all tests on GPU
              run: |
@@ -205,20 +196,18 @@ jobs:
                  apt -y update && apt install -y libaio-dev
                  pip install --upgrade pip
                  pip install --pre torch torchvision torchaudio -f https://download.pytorch.org/whl/nightly/cu111/torch_nightly.html -U
-                  pip install .[testing,deepspeed,fairscale]
-                  pip install git+https://github.com/microsoft/DeepSpeed
+                  rm -rf ~/.cache/torch_extensions/ # shared between conflicting builds
+                  pip install .[testing,fairscale]
+                  pip install git+https://github.com/microsoft/DeepSpeed # testing bleeding edge

            - name: Are GPUs recognized by our DL frameworks
              run: |
-                  python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
-                  python -c "import torch; print('Cuda version:', torch.version.cuda)"
-                  python -c "import torch; print('CuDNN version:', torch.backends.cudnn.version())"
-                  python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"
+                utils/print_env_pt.py

            - name: Run all tests on GPU
              run: |
                  python -m pytest -n 1 -v --dist=loadfile --make-reports=tests_torch_cuda_extensions_multi_gpu tests/deepspeed tests/extended
- 
+
            - name: Failure short reports
              if: ${{ always() }}
              run: cat reports/tests_torch_cuda_extensions_multi_gpu_failures_short.txt
--- a/.github/workflows/self-push.yml
+++ b/.github/workflows/self-push.yml
@@ -34,6 +34,7 @@ jobs:
          apt install -y libsndfile1-dev
          pip install --upgrade pip
          pip install .[sklearn,testing,onnxruntime,sentencepiece,torch-speech,vision,timm]
+          pip install https://github.com/kpu/kenlm/archive/master.zip

      - name: Launcher docker
        uses: actions/checkout@v2
@@ -46,11 +47,8 @@ jobs:

      - name: Are GPUs recognized by our DL frameworks
        run: |
-          python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
-          python -c "import torch; print('Cuda version:', torch.version.cuda)"
-          python -c "import torch; print('CuDNN version:', torch.backends.cudnn.version())"
-          python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"
-      
+          utils/print_env_pt.py
+
      - name: Fetch the tests to run
        run: |
          python utils/tests_fetcher.py --diff_with_last_commit | tee test_preparation.txt
@@ -90,6 +88,7 @@ jobs:
          pip install --upgrade "jax[cuda111]" -f https://storage.googleapis.com/jax-releases/jax_releases.html
          pip install --upgrade pip
          pip install .[sklearn,testing,sentencepiece,flax,flax-speech,vision]
+          pip install https://github.com/kpu/kenlm/archive/master.zip

      - name: Launcher docker
        uses: actions/checkout@v2
@@ -105,7 +104,7 @@ jobs:
        run: |
          python -c "from jax.lib import xla_bridge; print('GPU available:', xla_bridge.get_backend().platform)"
          python -c "import jax; print('Number of GPUs available:', len(jax.local_devices()))"
-      
+
      - name: Fetch the tests to run
        run: |
          python utils/tests_fetcher.py --diff_with_last_commit | tee test_preparation.txt
@@ -145,6 +144,7 @@ jobs:
 #          apt -y update && apt install -y software-properties-common && apt -y update && add-apt-repository -y ppa:git-core/ppa && apt -y update && apt install -y git
 #          pip install --upgrade pip
 #          pip install .[sklearn,testing,onnxruntime,sentencepiece,tf-speech]
+#          pip install https://github.com/kpu/kenlm/archive/master.zip
 #
 #      - name: Launcher docker
 #        uses: actions/checkout@v2
@@ -203,7 +203,7 @@ jobs:
          apt install -y libsndfile1-dev
          pip install --upgrade pip
          pip install .[sklearn,testing,onnxruntime,sentencepiece,torch-speech,vision,timm]
-      
+          pip install https://github.com/kpu/kenlm/archive/master.zip
      - name: Launcher docker
        uses: actions/checkout@v2
        with:
@@ -216,10 +216,7 @@ jobs:

      - name: Are GPUs recognized by our DL frameworks
        run: |
-          python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
-          python -c "import torch; print('Cuda version:', torch.version.cuda)"
-          python -c "import torch; print('CuDNN version:', torch.backends.cudnn.version())"
-          python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"
+          utils/print_env_pt.py

      - name: Fetch the tests to run
        run: |
@@ -262,6 +259,7 @@ jobs:
 #          pip install --upgrade "jax[cuda111]" -f https://storage.googleapis.com/jax-releases/jax_releases.html
 #          pip install --upgrade pip
 #          pip install .[sklearn,testing,sentencepiece,flax,flax-speech,vision]
+#          pip install https://github.com/kpu/kenlm/archive/master.zip
 #
 #      - name: Launcher docker
 #        uses: actions/checkout@v2
@@ -277,7 +275,7 @@ jobs:
 #        run: |
 #          python -c "from jax.lib import xla_bridge; print('GPU available:', xla_bridge.get_backend().platform)"
 #          python -c "import jax; print('Number of GPUs available:', len(jax.local_devices()))"
-#      
+#
 #      - name: Fetch the tests to run
 #        run: |
 #          python utils/tests_fetcher.py --diff_with_last_commit | tee test_preparation.txt
@@ -317,6 +315,7 @@ jobs:
 #          apt -y update && apt install -y software-properties-common && apt -y update && add-apt-repository -y ppa:git-core/ppa && apt -y update && apt install -y git
 #          pip install --upgrade pip
 #          pip install .[sklearn,testing,onnxruntime,sentencepiece,tf-speech]
+#          pip install https://github.com/kpu/kenlm/archive/master.zip
 #
 #      - name: Launcher docker
 #        uses: actions/checkout@v2
@@ -385,15 +384,12 @@ jobs:

      - name: Are GPUs recognized by our DL frameworks
        run: |
-          python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
-          python -c "import torch; print('Cuda version:', torch.version.cuda)"
-          python -c "import torch; print('CuDNN version:', torch.backends.cudnn.version())"
-          python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"
-      
+          utils/print_env_pt.py
+
      - name: Fetch the tests to run
        run: |
          python utils/tests_fetcher.py --diff_with_last_commit --filters tests/deepspeed tests/extended | tee test_preparation.txt
-      
+
      - name: Report fetched tests
        uses: actions/upload-artifact@v2
        with:
@@ -437,14 +433,12 @@ jobs:
        run: |
          apt -y update && apt install -y libaio-dev
          pip install --upgrade pip
+          rm -rf ~/.cache/torch_extensions/ # shared between conflicting builds
          pip install .[testing,deepspeed,fairscale]

      - name: Are GPUs recognized by our DL frameworks
        run: |
-          python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
-          python -c "import torch; print('Cuda version:', torch.version.cuda)"
-          python -c "import torch; print('CuDNN version:', torch.backends.cudnn.version())"
-          python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"
+          utils/print_env_pt.py

      - name: Fetch the tests to run
        run: |
--- a/.github/workflows/self-scheduled.yml
+++ b/.github/workflows/self-scheduled.yml
@@ -36,13 +36,11 @@ jobs:
          apt -y update && apt install -y libsndfile1-dev git
          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

      - name: Are GPUs recognized by our DL frameworks
        run: |
-          python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
-          python -c "import torch; print('Cuda version:', torch.version.cuda)"
-          python -c "import torch; print('CuDNN version:', torch.backends.cudnn.version())"
-          python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"
+          utils/print_env_pt.py

      - name: Run all tests on GPU
        run: |
@@ -105,6 +103,7 @@ jobs:
          pip install --upgrade pip
          pip install --upgrade "jax[cuda111]" -f https://storage.googleapis.com/jax-releases/jax_releases.html
          pip install .[flax,integrations,sklearn,testing,sentencepiece,flax-speech,vision]
+          pip install https://github.com/kpu/kenlm/archive/master.zip

      - name: Are GPUs recognized by our DL frameworks
        run: |
@@ -143,7 +142,9 @@ jobs:
        run: |
          apt -y update && apt install -y libsndfile1-dev git
          pip install --upgrade pip
-          pip install .[sklearn,testing,onnx,sentencepiece,tf-speech]
+          pip install .[sklearn,testing,onnx,sentencepiece,tf-speech,vision]
+          pip install https://github.com/kpu/kenlm/archive/master.zip
+

      - name: Are GPUs recognized by our DL frameworks
        run: |
@@ -239,13 +240,11 @@ jobs:
          apt -y update && apt install -y libsndfile1-dev git
          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

      - name: Are GPUs recognized by our DL frameworks
        run: |
-          python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
-          python -c "import torch; print('Cuda version:', torch.version.cuda)"
-          python -c "import torch; print('CuDNN version:', torch.backends.cudnn.version())"
-          python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"
+          utils/print_env_pt.py

      - name: Run all tests on GPU
        env:
@@ -293,7 +292,8 @@ jobs:
        run: |
          apt -y update && apt install -y libsndfile1-dev git
          pip install --upgrade pip
-          pip install .[sklearn,testing,onnx,sentencepiece,tf-speech]
+          pip install .[sklearn,testing,onnx,sentencepiece,tf-speech,vision]
+          pip install https://github.com/kpu/kenlm/archive/master.zip

      - name: Are GPUs recognized by our DL frameworks
        run: |
@@ -391,10 +391,7 @@ jobs:

      - name: Are GPUs recognized by our DL frameworks
        run: |
-          python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
-          python -c "import torch; print('Cuda version:', torch.version.cuda)"
-          python -c "import torch; print('CuDNN version:', torch.backends.cudnn.version())"
-          python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"
+          utils/print_env_pt.py

      - name: Run all tests on GPU
        run: |
@@ -429,14 +426,12 @@ jobs:
        run: |
          apt -y update && apt install -y libaio-dev
          pip install --upgrade pip
+          rm -rf ~/.cache/torch_extensions/ # shared between conflicting builds
          pip install .[testing,deepspeed,fairscale]

      - name: Are GPUs recognized by our DL frameworks
        run: |
-          python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
-          python -c "import torch; print('Cuda version:', torch.version.cuda)"
-          python -c "import torch; print('CuDNN version:', torch.backends.cudnn.version())"
-          python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"
+          utils/print_env_pt.py

      - name: Run all tests on GPU
        run: |
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -273,9 +273,11 @@ Follow these steps to start contributing:
   - If you are adding a new tokenizer, write tests, and make sure
     `RUN_SLOW=1 python -m pytest tests/test_tokenization_{your_model_name}.py` passes.
   CircleCI does not run the slow tests, but github actions does every night!
-6. All public methods must have informative docstrings that work nicely with sphinx. See `modeling_ctrl.py` for an
+6. All public methods must have informative docstrings that work nicely with sphinx. See `modeling_bert.py` for an
   example.

+See more about the checks run on a pull request in our [PR guide](pr_checks)
+
 ### Tests

 An extensive test suite is included to test the library behavior and several examples. Library tests can be found in
--- a/10
+++ b/10
@@ -31,9 +31,9 @@ deps_table_check_updated:

 autogenerate_code: deps_table_update

-# Check that source code meets quality standards
+# Check that the repo is in a good state

-extra_quality_checks:
+repo-consistency:
 	python utils/check_copies.py
 	python utils/check_table.py
 	python utils/check_dummies.py
@@ -42,12 +42,13 @@ extra_quality_checks:
 	python utils/tests_fetcher.py --sanity_check

 # this target runs checks on all files
+
 quality:
 	black --check $(check_dirs)
 	isort --check-only $(check_dirs)
 	python utils/custom_init_isort.py --check_only
 	flake8 $(check_dirs)
-	${MAKE} extra_quality_checks
+	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

@@ -56,6 +57,7 @@ extra_style_checks:
 	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
+
 style:
 	black $(check_dirs)
 	isort $(check_dirs)
@@ -64,7 +66,7 @@ style:

 # Super fast fix and check target that only works on relevant modified files since the branch was made

-fixup: modified_only_fixup extra_style_checks autogenerate_code extra_quality_checks
+fixup: modified_only_fixup extra_style_checks autogenerate_code repo-consistency

 # Make marked copies of snippets of codes conform to the original

--- a/README.md
+++ b/README.md
@@ -26,8 +26,8 @@ limitations under the License.
    <a href="https://github.com/huggingface/transformers/blob/master/LICENSE">
        <img alt="GitHub" src="https://img.shields.io/github/license/huggingface/transformers.svg?color=blue">
    </a>
-    <a href="https://huggingface.co/transformers/index.html">
-        <img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/transformers/index.html.svg?down_color=red&down_message=offline&up_message=online">
+    <a href="https://huggingface.co/docs/transformers/index">
+        <img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/docs/transformers/index.svg?down_color=red&down_message=offline&up_message=online">
    </a>
    <a href="https://github.com/huggingface/transformers/releases">
        <img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/transformers.svg">
@@ -48,14 +48,22 @@ limitations under the License.
 </h4>

 <h3 align="center">
-    <p>State-of-the-art Natural Language Processing for Jax, PyTorch and TensorFlow</p>
+    <p>State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow</p>
 </h3>

 <h3 align="center">
    <a href="https://hf.co/course"><img src="https://raw.githubusercontent.com/huggingface/transformers/master/docs/source/imgs/course_banner.png"></a>
 </h3>

-🤗 Transformers provides thousands of pretrained models to perform tasks on texts such as classification, information extraction, question answering, summarization, translation, text generation and more in over 100 languages. Its aim is to make cutting-edge NLP easier to use for everyone.
+🤗 Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio. 
+
+These models can be applied on:
+
+* 📝 Text, for tasks like text classification, information extraction, question answering, summarization, translation, text generation, in over 100 languages. 
+* 🖼️ Images, for tasks like image classification, object detection, and segmentation. 
+* 🗣️ Audio, for tasks like speech recognition and audio classification. 
+
+Transformer models can also perform tasks on **several modalities combined**, such as table question answering, optical character recognition, information extraction from scanned documents, video classification, and visual question answering.

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

@@ -66,6 +74,8 @@ limitations under the License.
 You can test most of our models directly on their pages from the [model hub](https://huggingface.co/models). We also offer [private model hosting, versioning, & an inference API](https://huggingface.co/pricing) for public and private models.

 Here are a few examples:
+
+ In Natural Language Processing:
 - [Masked word completion with BERT](https://huggingface.co/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
 - [Name Entity Recognition with Electra](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city)
 - [Text generation with GPT-2](https://huggingface.co/gpt2?text=A+long+time+ago%2C+)
@@ -74,6 +84,15 @@ Here are a few examples:
 - [Question answering with DistilBERT](https://huggingface.co/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
 - [Translation with T5](https://huggingface.co/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)

+In Computer Vision:
+- [Image classification with ViT](https://huggingface.co/google/vit-base-patch16-224)
+- [Object Detection with DETR](https://huggingface.co/facebook/detr-resnet-50)
+- [Image Segmentation with DETR](https://huggingface.co/facebook/detr-resnet-50-panoptic)
+
+In Audio:
+- [Automatic Speech Recognition with Wav2Vec2](https://huggingface.co/facebook/wav2vec2-base-960h)
+- [Keyword Spotting with Wav2Vec2](https://huggingface.co/superb/wav2vec2-base-superb-ks)
+
 **[Write With Transformer](https://transformer.huggingface.co)**, built by the Hugging Face team, is the official demo of this repo’s text generation capabilities.

 ## If you are looking for custom support from the Hugging Face team
@@ -84,7 +103,7 @@ Here are a few examples:

 ## Quick tour

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

 ```python
 >>> from transformers import pipeline
@@ -112,7 +131,7 @@ Many NLP tasks have a pre-trained `pipeline` ready to go. For example, we can ea

 ```

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

 To download and use any of the pretrained models on your given task, all it takes is three lines of code. Here is the PyTorch version:
 ```python
@@ -137,12 +156,12 @@ And here is the equivalent code for TensorFlow:

 The tokenizer is responsible for all the preprocessing the pretrained model expects, and can be called directly on a single string (as in the above examples) or a list. It will output a dictionary that you can use in downstream code or simply directly pass to your model using the ** argument unpacking operator.

-The model itself is a regular [Pytorch `nn.Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) or a [TensorFlow `tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model) (depending on your backend) which you can use normally. [This tutorial](https://huggingface.co/transformers/training.html) explains how to integrate such a model into a classic PyTorch or TensorFlow training loop, or how to use our `Trainer` API to quickly fine-tune on a new dataset.
+The model itself is a regular [Pytorch `nn.Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) or a [TensorFlow `tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model) (depending on your backend) which you can use normally. [This tutorial](https://huggingface.co/docs/transformers/training) explains how to integrate such a model into a classic PyTorch or TensorFlow training loop, or how to use our `Trainer` API to quickly fine-tune on a new dataset.

 ## Why should I use transformers?

 1. Easy-to-use state-of-the-art models:
-    - High performance on NLU and NLG tasks.
+    - High performance on natural language understanding & generation, computer vision, and audio tasks.
    - Low barrier to entry for educators and practitioners.
    - Few user-facing abstractions with just three classes to learn.
    - A unified API for using all our pretrained models.
@@ -150,11 +169,11 @@ The model itself is a regular [Pytorch `nn.Module`](https://pytorch.org/docs/sta
 1. Lower compute costs, smaller carbon footprint:
    - Researchers can share trained models instead of always retraining.
    - Practitioners can reduce compute time and production costs.
-    - Dozens of architectures with over 2,000 pretrained models, some in more than 100 languages.
+    - Dozens of architectures with over 20,000 pretrained models, some in more than 100 languages.

 1. Choose the right framework for every part of a model's lifetime:
    - Train state-of-the-art models in 3 lines of code.
-    - Move a single model between TF2.0/PyTorch frameworks at will.
+    - Move a single model between TF2.0/PyTorch/JAX frameworks at will.
    - Seamlessly pick the right framework for training, evaluation and production.

 1. Easily customize a model or an example to your needs:
@@ -187,7 +206,7 @@ When one of those backends has been installed, 🤗 Transformers can be installe
 pip install transformers
 ```

-If you'd like to play with the examples or need the bleeding edge of the code and can't wait for a new release, you must [install the library from source](https://huggingface.co/transformers/installation.html#installing-from-source).
+If you'd like to play with the examples or need the bleeding edge of the code and can't wait for a new release, you must [install the library from source](https://huggingface.co/docs/transformers/installation#installing-from-source).

 ### With conda

@@ -207,111 +226,115 @@ Follow the installation pages of Flax, PyTorch or TensorFlow to see how to insta

 Current number of checkpoints: ![](https://img.shields.io/endpoint?url=https://huggingface.co/api/shields/models&color=brightgreen)

-🤗 Transformers currently provides the following architectures (see [here](https://huggingface.co/transformers/model_summary.html) for a high-level summary of each them):
+🤗 Transformers currently provides the following architectures (see [here](https://huggingface.co/docs/transformers/model_summary) for a high-level summary of each them):

-1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.
-1. **[BART](https://huggingface.co/transformers/model_doc/bart.html)** (from Facebook) released with the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/pdf/1910.13461.pdf) by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer.
-1. **[BARThez](https://huggingface.co/transformers/model_doc/barthez.html)** (from École polytechnique) released with the paper [BARThez: a Skilled Pretrained French Sequence-to-Sequence Model](https://arxiv.org/abs/2010.12321) by Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis.
-1. **[BARTpho](https://huggingface.co/transformers/model_doc/bartpho.html)** (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/transformers/model_doc/beit.html)** (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/transformers/model_doc/bert.html)** (from Google) released with the paper [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.
-1. **[BERTweet](https://huggingface.co/transformers/model_doc/bertweet.html)** (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/transformers/model_doc/bertgeneration.html)** (from Google) released with the paper [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
-1. **[BigBird-RoBERTa](https://huggingface.co/transformers/model_doc/bigbird.html)** (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/transformers/model_doc/bigbird_pegasus.html)** (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/transformers/model_doc/blenderbot.html)** (from Facebook) released with the paper [Recipes for building an open-domain chatbot](https://arxiv.org/abs/2004.13637) by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
-1. **[BlenderbotSmall](https://huggingface.co/transformers/model_doc/blenderbot_small.html)** (from Facebook) released with the paper [Recipes for building an open-domain chatbot](https://arxiv.org/abs/2004.13637) by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
-1. **[BORT](https://huggingface.co/transformers/model_doc/bort.html)** (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/transformers/model_doc/byt5.html)** (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/transformers/model_doc/camembert.html)** (from Inria/Facebook/Sorbonne) released with the paper [CamemBERT: a Tasty French Language Model](https://arxiv.org/abs/1911.03894) by Louis Martin*, Benjamin Muller*, Pedro Javier Ortiz Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
-1. **[CANINE](https://huggingface.co/transformers/model_doc/canine.html)** (from Google Research) released with the paper [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://arxiv.org/abs/2103.06874) by Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting.
-1. **[CLIP](https://huggingface.co/transformers/model_doc/clip.html)** (from OpenAI) released with the paper [Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/abs/2103.00020) by Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever.
-1. **[ConvBERT](https://huggingface.co/transformers/model_doc/convbert.html)** (from YituTech) released with the paper [ConvBERT: Improving BERT with Span-based Dynamic Convolution](https://arxiv.org/abs/2008.02496) by Zihang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan.
-1. **[CPM](https://huggingface.co/transformers/model_doc/cpm.html)** (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/transformers/model_doc/ctrl.html)** (from Salesforce) released with the paper [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://arxiv.org/abs/1909.05858) by Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and Richard Socher.
-1. **[DeBERTa](https://huggingface.co/transformers/model_doc/deberta.html)** (from Microsoft) 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/transformers/model_doc/deberta_v2.html)** (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/transformers/model_doc/deit.html)** (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/transformers/model_doc/detr.html)** (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/transformers/model_doc/dialogpt.html)** (from Microsoft Research) released with the paper [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://arxiv.org/abs/1911.00536) by Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan.
-1. **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace), released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into [DistilGPT2](https://github.com/huggingface/transformers/tree/master/examples/distillation), RoBERTa into [DistilRoBERTa](https://github.com/huggingface/transformers/tree/master/examples/distillation), Multilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers/tree/master/examples/distillation) and a German version of DistilBERT.
-1. **[DPR](https://huggingface.co/transformers/model_doc/dpr.html)** (from Facebook) released with the paper [Dense Passage Retrieval
+1. **[ALBERT](https://huggingface.co/docs/transformers/model_doc/albert)** (from Google Research and the Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.
+1. **[BART](https://huggingface.co/docs/transformers/model_doc/bart)** (from Facebook) released with the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/pdf/1910.13461.pdf) by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer.
+1. **[BARThez](https://huggingface.co/docs/transformers/model_doc/barthez)** (from École polytechnique) released with the paper [BARThez: a Skilled Pretrained French Sequence-to-Sequence Model](https://arxiv.org/abs/2010.12321) by Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis.
+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. **[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. **[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. **[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.
+1. **[CANINE](https://huggingface.co/docs/transformers/model_doc/canine)** (from Google Research) released with the paper [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://arxiv.org/abs/2103.06874) by Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting.
+1. **[CLIP](https://huggingface.co/docs/transformers/model_doc/clip)** (from OpenAI) released with the paper [Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/abs/2103.00020) by Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever.
+1. **[ConvBERT](https://huggingface.co/docs/transformers/model_doc/convbert)** (from YituTech) released with the paper [ConvBERT: Improving BERT with Span-based Dynamic Convolution](https://arxiv.org/abs/2008.02496) by Zihang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan.
+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. **[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. **[EncoderDecoder](https://huggingface.co/transformers/model_doc/encoderdecoder.html)** (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/transformers/model_doc/electra.html)** (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning.
-1. **[FlauBERT](https://huggingface.co/transformers/model_doc/flaubert.html)** (from CNRS) released with the paper [FlauBERT: Unsupervised Language Model Pre-training for French](https://arxiv.org/abs/1912.05372) by Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, Didier Schwab.
-1. **[FNet](https://huggingface.co/transformers/model_doc/fnet.html)** (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/transformers/model_doc/funnel.html)** (from CMU/Google Brain) released with the paper [Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing](https://arxiv.org/abs/2006.03236) by Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le.
-1. **[GPT](https://huggingface.co/transformers/model_doc/gpt.html)** (from OpenAI) released with the paper [Improving Language Understanding by Generative Pre-Training](https://blog.openai.com/language-unsupervised/) by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever.
-1. **[GPT-2](https://huggingface.co/transformers/model_doc/gpt2.html)** (from OpenAI) released with the paper [Language Models are Unsupervised Multitask Learners](https://blog.openai.com/better-language-models/) by Alec Radford*, Jeffrey Wu*, Rewon Child, David Luan, Dario Amodei** and Ilya Sutskever**.
-1. **[GPT-J](https://huggingface.co/transformers/model_doc/gptj.html)** (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/transformers/model_doc/gpt_neo.html)** (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. **[Hubert](https://huggingface.co/transformers/model_doc/hubert.html)** (from Facebook) released with the paper [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://arxiv.org/abs/2106.07447) by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed.
-1. **[I-BERT](https://huggingface.co/transformers/model_doc/ibert.html)** (from Berkeley) released with the paper [I-BERT: Integer-only BERT Quantization](https://arxiv.org/abs/2101.01321) by Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer.
-1. **[LayoutLM](https://huggingface.co/transformers/model_doc/layoutlm.html)** (from Microsoft Research Asia) released with the paper [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://arxiv.org/abs/1912.13318) by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou.
-1. **[LayoutLMv2](https://huggingface.co/transformers/model_doc/layoutlmv2.html)** (from Microsoft Research Asia) released with the paper [LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding](https://arxiv.org/abs/2012.14740) by Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou.
-1. **[LayoutXLM](https://huggingface.co/transformers/model_doc/layoutlmv2.html)** (from Microsoft Research Asia) released with the paper [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://arxiv.org/abs/2104.08836) by Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei.
-1. **[LED](https://huggingface.co/transformers/model_doc/led.html)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
-1. **[Longformer](https://huggingface.co/transformers/model_doc/longformer.html)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
-1. **[LUKE](https://huggingface.co/transformers/model_doc/luke.html)** (from Studio Ousia) released with the paper [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://arxiv.org/abs/2010.01057) by Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yuji Matsumoto.
-1. **[LXMERT](https://huggingface.co/transformers/model_doc/lxmert.html)** (from UNC Chapel Hill) released with the paper [LXMERT: Learning Cross-Modality Encoder Representations from Transformers for Open-Domain Question Answering](https://arxiv.org/abs/1908.07490) by Hao Tan and Mohit Bansal.
-1. **[M2M100](https://huggingface.co/transformers/model_doc/m2m_100.html)** (from Facebook) released with the paper [Beyond English-Centric Multilingual Machine Translation](https://arxiv.org/abs/2010.11125) by Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin.
-1. **[MarianMT](https://huggingface.co/transformers/model_doc/marian.html)** Machine translation models trained using [OPUS](http://opus.nlpl.eu/) data by Jörg Tiedemann. The [Marian Framework](https://marian-nmt.github.io/) is being developed by the Microsoft Translator Team.
-1. **[MBart](https://huggingface.co/transformers/model_doc/mbart.html)** (from Facebook) released with the paper [Multilingual Denoising Pre-training for Neural Machine Translation](https://arxiv.org/abs/2001.08210) by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
-1. **[MBart-50](https://huggingface.co/transformers/model_doc/mbart.html)** (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/transformers/model_doc/megatron_bert.html)** (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/transformers/model_doc/megatron_gpt2.html)** (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/transformers/model_doc/mpnet.html)** (from Microsoft Research) released with the paper [MPNet: Masked and Permuted Pre-training for Language Understanding](https://arxiv.org/abs/2004.09297) by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu.
-1. **[MT5](https://huggingface.co/transformers/model_doc/mt5.html)** (from Google AI) released with the paper [mT5: A massively multilingual pre-trained text-to-text transformer](https://arxiv.org/abs/2010.11934) by Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel.
-1. **[Pegasus](https://huggingface.co/transformers/model_doc/pegasus.html)** (from Google) released with the paper [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/abs/1912.08777) by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu.
-1. **[PhoBERT](https://huggingface.co/transformers/model_doc/phobert.html)** (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/transformers/model_doc/prophetnet.html)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
-1. **[Reformer](https://huggingface.co/transformers/model_doc/reformer.html)** (from Google Research) released with the paper [Reformer: The Efficient Transformer](https://arxiv.org/abs/2001.04451) by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
-1. **[RemBERT](https://huggingface.co/transformers/model_doc/rembert.html)** (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/transformers/model_doc/roberta.html)** (from Facebook), released together with the paper a [Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692) by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
-1. **[RoFormer](https://huggingface.co/transformers/model_doc/roformer.html)** (from ZhuiyiTechnology), released together with the paper a [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://arxiv.org/pdf/2104.09864v1.pdf) by Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu.
-1. **[SegFormer](https://huggingface.co/transformers/model_doc/segformer.html)** (from NVIDIA) released with the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo.
-1. **[SEW](https://huggingface.co/transformers/model_doc/sew.html)** (from ASAPP) released with the paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
-1. **[SEW-D](https://huggingface.co/transformers/model_doc/sew_d.html)** (from ASAPP) released with the paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
-1. **[SpeechToTextTransformer](https://huggingface.co/transformers/model_doc/speech_to_text.html)** (from Facebook), released together with the paper [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171) by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino.
-1. **[SpeechToTextTransformer2](https://huggingface.co/transformers/model_doc/speech_to_text_2.html)** (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/transformers/model_doc/splinter.html)** (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/transformers/model_doc/squeezebert.html)** (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. **[T5](https://huggingface.co/transformers/model_doc/t5.html)** (from Google AI) released with the paper [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
-1. **[T5v1.1](https://huggingface.co/transformers/model_doc/t5v1.1.html)** (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/transformers/model_doc/tapas.html)** (from Google AI) released with the paper [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://arxiv.org/abs/2004.02349) by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos.
-1. **[Transformer-XL](https://huggingface.co/transformers/model_doc/transformerxl.html)** (from Google/CMU) released with the paper [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
-1. **[TrOCR](https://huggingface.co/transformers/model_doc/trocr.html)** (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/transformers/model_doc/unispeech.html)** (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/transformers/model_doc/unispeech_sat.html)** (from Microsoft Research) released with the paper [UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER
+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. **[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-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.
+1. **[Hubert](https://huggingface.co/docs/transformers/model_doc/hubert)** (from Facebook) released with the paper [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://arxiv.org/abs/2106.07447) by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed.
+1. **[I-BERT](https://huggingface.co/docs/transformers/model_doc/ibert)** (from Berkeley) released with the paper [I-BERT: Integer-only BERT Quantization](https://arxiv.org/abs/2101.01321) by Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer.
+1. **[ImageGPT](https://huggingface.co/docs/transformers/master/model_doc/imagegpt)** (from OpenAI) released with the paper [Generative Pretraining from Pixels](https://openai.com/blog/image-gpt/) by Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever.
+1. **[LayoutLM](https://huggingface.co/docs/transformers/model_doc/layoutlm)** (from Microsoft Research Asia) released with the paper [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://arxiv.org/abs/1912.13318) by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou.
+1. **[LayoutLMv2](https://huggingface.co/docs/transformers/model_doc/layoutlmv2)** (from Microsoft Research Asia) released with the paper [LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding](https://arxiv.org/abs/2012.14740) by Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou.
+1. **[LayoutXLM](https://huggingface.co/docs/transformers/model_doc/layoutlmv2)** (from Microsoft Research Asia) released with the paper [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://arxiv.org/abs/2104.08836) by Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei.
+1. **[LED](https://huggingface.co/docs/transformers/model_doc/led)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
+1. **[Longformer](https://huggingface.co/docs/transformers/model_doc/longformer)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
+1. **[LUKE](https://huggingface.co/docs/transformers/model_doc/luke)** (from Studio Ousia) released with the paper [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://arxiv.org/abs/2010.01057) by Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yuji Matsumoto.
+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. **[LXMERT](https://huggingface.co/docs/transformers/model_doc/lxmert)** (from UNC Chapel Hill) released with the paper [LXMERT: Learning Cross-Modality Encoder Representations from Transformers for Open-Domain Question Answering](https://arxiv.org/abs/1908.07490) by Hao Tan and Mohit Bansal.
+1. **[M2M100](https://huggingface.co/docs/transformers/model_doc/m2m_100)** (from Facebook) released with the paper [Beyond English-Centric Multilingual Machine Translation](https://arxiv.org/abs/2010.11125) by Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin.
+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-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. **[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. **[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.
+1. **[RoFormer](https://huggingface.co/docs/transformers/model_doc/roformer)** (from ZhuiyiTechnology), released together with the paper a [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://arxiv.org/pdf/2104.09864v1.pdf) by Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu.
+1. **[SegFormer](https://huggingface.co/docs/transformers/model_doc/segformer)** (from NVIDIA) released with the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo.
+1. **[SEW](https://huggingface.co/docs/transformers/model_doc/sew)** (from ASAPP) released with the paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
+1. **[SEW-D](https://huggingface.co/docs/transformers/model_doc/sew_d)** (from ASAPP) released with the paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
+1. **[SpeechToTextTransformer](https://huggingface.co/docs/transformers/model_doc/speech_to_text)** (from Facebook), released together with the paper [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171) by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino.
+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. **[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. **[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. **[Vision Transformer (ViT)](https://huggingface.co/transformers/model_doc/vit.html)** (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/transformers/model_doc/visual_bert.html)** (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. **[Wav2Vec2](https://huggingface.co/transformers/model_doc/wav2vec2.html)** (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. **[XLM](https://huggingface.co/transformers/model_doc/xlm.html)** (from Facebook) released together with the paper [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample and Alexis Conneau.
-1. **[XLM-ProphetNet](https://huggingface.co/transformers/model_doc/xlmprophetnet.html)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
-1. **[XLM-RoBERTa](https://huggingface.co/transformers/model_doc/xlmroberta.html)** (from Facebook AI), released together with the paper [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Alexis Conneau*, Kartikay Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov.
-1. **[XLNet](https://huggingface.co/transformers/model_doc/xlnet.html)** (from Google/CMU) released with the paper [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
-1. **[XLSR-Wav2Vec2](https://huggingface.co/transformers/model_doc/xlsr_wav2vec2.html)** (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. **[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. **[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. **[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. **[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. 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/transformers/index.html#supported-frameworks).
+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).

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


 ## Learn more

 | Section | Description |
 |-|-|
-| [Documentation](https://huggingface.co/transformers/) | Full API documentation and tutorials |
-| [Task summary](https://huggingface.co/transformers/task_summary.html) | Tasks supported by 🤗 Transformers |
-| [Preprocessing tutorial](https://huggingface.co/transformers/preprocessing.html) | Using the `Tokenizer` class to prepare data for the models |
-| [Training and fine-tuning](https://huggingface.co/transformers/training.html) | Using the models provided by 🤗 Transformers in a PyTorch/TensorFlow training loop and the `Trainer` API |
+| [Documentation](https://huggingface.co/docs/transformers/) | Full API documentation and tutorials |
+| [Task summary](https://huggingface.co/docs/transformers/task_summary) | Tasks supported by 🤗 Transformers |
+| [Preprocessing tutorial](https://huggingface.co/docstransformers/preprocessing) | Using the `Tokenizer` class to prepare data for the models |
+| [Training and fine-tuning](https://huggingface.co/docs/transformers/training) | Using the models provided by 🤗 Transformers in a PyTorch/TensorFlow training loop and the `Trainer` API |
 | [Quick tour: Fine-tuning/usage scripts](https://github.com/huggingface/transformers/tree/master/examples) | Example scripts for fine-tuning models on a wide range of tasks |
-| [Model sharing and uploading](https://huggingface.co/transformers/model_sharing.html) | Upload and share your fine-tuned models with the community |
-| [Migration](https://huggingface.co/transformers/migration.html) | Migrate to 🤗 Transformers from `pytorch-transformers` or `pytorch-pretrained-bert` |
+| [Model sharing and uploading](https://huggingface.co/docs/transformers/model_sharing) | Upload and share your fine-tuned models with the community |
+| [Migration](https://huggingface.co/docs/transformers/migration) | Migrate to 🤗 Transformers from `pytorch-transformers` or `pytorch-pretrained-bert` |

 ## Citation

--- a/README_ko.md
+++ b/README_ko.md
@@ -26,8 +26,8 @@ limitations under the License.
    <a href="https://github.com/huggingface/transformers/blob/master/LICENSE">
        <img alt="GitHub" src="https://img.shields.io/github/license/huggingface/transformers.svg?color=blue">
    </a>
-    <a href="https://huggingface.co/transformers/index.html">
-        <img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/transformers/index.html.svg?down_color=red&down_message=offline&up_message=online">
+    <a href="https://huggingface.co/docs/transformers/index">
+        <img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/docs/transformers/index.svg?down_color=red&down_message=offline&up_message=online">
    </a>
    <a href="https://github.com/huggingface/transformers/releases">
        <img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/transformers.svg">
@@ -112,7 +112,7 @@ limitations under the License.

 ```

-답변뿐만 아니라, 여기에 사용된 사전학습 모델은 확신도와 토크나이즈된 문장 속 답변의 시작점, 끝점까지 반환합니다. [이 튜토리얼](https://huggingface.co/transformers/task_summary.html)에서 `pipeline` API가 지원하는 다양한 과제를 확인할 수 있습니다.
+답변뿐만 아니라, 여기에 사용된 사전학습 모델은 확신도와 토크나이즈된 문장 속 답변의 시작점, 끝점까지 반환합니다. [이 튜토리얼](https://huggingface.co/docs/transformers/task_summary)에서 `pipeline` API가 지원하는 다양한 과제를 확인할 수 있습니다.

 코드 3줄로 원하는 과제에 맞게 사전학습 모델을 다운로드 받고 사용할 수 있습니다. 다음은 PyTorch 버전입니다:
 ```python
@@ -187,7 +187,7 @@ limitations under the License.
 pip install transformers
 ```

-예시들을 체험해보고 싶거나, 최최최첨단 코드를 원하거나, 새로운 버전이 나올 때까지 기다릴 수 없다면 [라이브러리를 소스에서 바로 설치](https://huggingface.co/transformers/installation.html#installing-from-source)하셔야 합니다.
+예시들을 체험해보고 싶거나, 최최최첨단 코드를 원하거나, 새로운 버전이 나올 때까지 기다릴 수 없다면 [라이브러리를 소스에서 바로 설치](https://huggingface.co/docs/transformers/installation#installing-from-source)하셔야 합니다.

 ### conda로 설치하기

@@ -207,107 +207,111 @@ Flax, PyTorch, TensorFlow 설치 페이지에서 이들을 conda로 설치하는

 현재 사용 가능한 모델 체크포인트의 개수: ![](https://img.shields.io/endpoint?url=https://huggingface.co/api/shields/models&color=brightgreen)

-🤗 Transformers는 다음 모델들을 제공합니다 (각 모델의 요약은 [여기](https://huggingface.co/transformers/model_summary.html)서 확인하세요):
+🤗 Transformers는 다음 모델들을 제공합니다 (각 모델의 요약은 [여기](https://huggingface.co/docs/transformers/model_summary)서 확인하세요):

-1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.
-1. **[BART](https://huggingface.co/transformers/model_doc/bart.html)** (from Facebook) released with the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/pdf/1910.13461.pdf) by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer.
-1. **[BARThez](https://huggingface.co/transformers/model_doc/barthez.html)** (from École polytechnique) released with the paper [BARThez: a Skilled Pretrained French Sequence-to-Sequence Model](https://arxiv.org/abs/2010.12321) by Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis.
-1. **[BARTpho](https://huggingface.co/transformers/model_doc/bartpho.html)** (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/transformers/model_doc/beit.html)** (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/transformers/model_doc/bert.html)** (from Google) released with the paper [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.
-1. **[BERT For Sequence Generation](https://huggingface.co/transformers/model_doc/bertgeneration.html)** (from Google) released with the paper [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
-1. **[BERTweet](https://huggingface.co/transformers/model_doc/bertweet.html)** (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/transformers/model_doc/bigbird_pegasus.html)** (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/transformers/model_doc/bigbird.html)** (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/transformers/model_doc/blenderbot.html)** (from Facebook) released with the paper [Recipes for building an open-domain chatbot](https://arxiv.org/abs/2004.13637) by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
-1. **[BlenderbotSmall](https://huggingface.co/transformers/model_doc/blenderbot_small.html)** (from Facebook) released with the paper [Recipes for building an open-domain chatbot](https://arxiv.org/abs/2004.13637) by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
-1. **[BORT](https://huggingface.co/transformers/model_doc/bort.html)** (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/transformers/model_doc/byt5.html)** (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/transformers/model_doc/camembert.html)** (from Inria/Facebook/Sorbonne) released with the paper [CamemBERT: a Tasty French Language Model](https://arxiv.org/abs/1911.03894) by Louis Martin*, Benjamin Muller*, Pedro Javier Ortiz Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
-1. **[CANINE](https://huggingface.co/transformers/model_doc/canine.html)** (from Google Research) released with the paper [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://arxiv.org/abs/2103.06874) by Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting.
-1. **[CLIP](https://huggingface.co/transformers/model_doc/clip.html)** (from OpenAI) released with the paper [Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/abs/2103.00020) by Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever.
-1. **[ConvBERT](https://huggingface.co/transformers/model_doc/convbert.html)** (from YituTech) released with the paper [ConvBERT: Improving BERT with Span-based Dynamic Convolution](https://arxiv.org/abs/2008.02496) by Zihang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan.
-1. **[CPM](https://huggingface.co/transformers/model_doc/cpm.html)** (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/transformers/model_doc/ctrl.html)** (from Salesforce) released with the paper [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://arxiv.org/abs/1909.05858) by Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and Richard Socher.
-1. **[DeBERTa](https://huggingface.co/transformers/model_doc/deberta.html)** (from Microsoft) 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/transformers/model_doc/deberta_v2.html)** (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/transformers/model_doc/deit.html)** (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/transformers/model_doc/detr.html)** (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/transformers/model_doc/dialogpt.html)** (from Microsoft Research) released with the paper [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://arxiv.org/abs/1911.00536) by Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan.
-1. **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace), released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into [DistilGPT2](https://github.com/huggingface/transformers/tree/master/examples/distillation), RoBERTa into [DistilRoBERTa](https://github.com/huggingface/transformers/tree/master/examples/distillation), Multilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers/tree/master/examples/distillation) and a German version of DistilBERT.
-1. **[DPR](https://huggingface.co/transformers/model_doc/dpr.html)** (from Facebook) released with the paper [Dense Passage Retrieval for Open-Domain Question Answering](https://arxiv.org/abs/2004.04906) by Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
-1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning.
-1. **[EncoderDecoder](https://huggingface.co/transformers/model_doc/encoderdecoder.html)** (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/transformers/model_doc/flaubert.html)** (from CNRS) released with the paper [FlauBERT: Unsupervised Language Model Pre-training for French](https://arxiv.org/abs/1912.05372) by Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, Didier Schwab.
-1. **[FNet](https://huggingface.co/transformers/model_doc/fnet.html)** (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/transformers/model_doc/funnel.html)** (from CMU/Google Brain) released with the paper [Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing](https://arxiv.org/abs/2006.03236) by Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le.
-1. **[GPT](https://huggingface.co/transformers/model_doc/gpt.html)** (from OpenAI) released with the paper [Improving Language Understanding by Generative Pre-Training](https://blog.openai.com/language-unsupervised/) by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever.
-1. **[GPT Neo](https://huggingface.co/transformers/model_doc/gpt_neo.html)** (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/transformers/model_doc/gpt2.html)** (from OpenAI) released with the paper [Language Models are Unsupervised Multitask Learners](https://blog.openai.com/better-language-models/) by Alec Radford*, Jeffrey Wu*, Rewon Child, David Luan, Dario Amodei** and Ilya Sutskever**.
-1. **[GPT-J](https://huggingface.co/transformers/model_doc/gptj.html)** (from EleutherAI) released in the repository [kingoflolz/mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax/) by Ben Wang and Aran Komatsuzaki.
-1. **[Hubert](https://huggingface.co/transformers/model_doc/hubert.html)** (from Facebook) released with the paper [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://arxiv.org/abs/2106.07447) by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed.
-1. **[I-BERT](https://huggingface.co/transformers/model_doc/ibert.html)** (from Berkeley) released with the paper [I-BERT: Integer-only BERT Quantization](https://arxiv.org/abs/2101.01321) by Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer.
-1. **[LayoutLM](https://huggingface.co/transformers/model_doc/layoutlm.html)** (from Microsoft Research Asia) released with the paper [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://arxiv.org/abs/1912.13318) by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou.
-1. **[LayoutLMv2](https://huggingface.co/transformers/model_doc/layoutlmv2.html)** (from Microsoft Research Asia) released with the paper [LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding](https://arxiv.org/abs/2012.14740) by Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou.
-1. **[LayoutXLM](https://huggingface.co/transformers/model_doc/layoutlmv2.html)** (from Microsoft Research Asia) released with the paper [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://arxiv.org/abs/2104.08836) by Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei.
-1. **[LED](https://huggingface.co/transformers/model_doc/led.html)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
-1. **[Longformer](https://huggingface.co/transformers/model_doc/longformer.html)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
-1. **[LUKE](https://huggingface.co/transformers/model_doc/luke.html)** (from Studio Ousia) released with the paper [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://arxiv.org/abs/2010.01057) by Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yuji Matsumoto.
-1. **[LXMERT](https://huggingface.co/transformers/model_doc/lxmert.html)** (from UNC Chapel Hill) released with the paper [LXMERT: Learning Cross-Modality Encoder Representations from Transformers for Open-Domain Question Answering](https://arxiv.org/abs/1908.07490) by Hao Tan and Mohit Bansal.
-1. **[M2M100](https://huggingface.co/transformers/model_doc/m2m_100.html)** (from Facebook) released with the paper [Beyond English-Centric Multilingual Machine Translation](https://arxiv.org/abs/2010.11125) by Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin.
-1. **[MarianMT](https://huggingface.co/transformers/model_doc/marian.html)** Machine translation models trained using [OPUS](http://opus.nlpl.eu/) data by Jörg Tiedemann. The [Marian Framework](https://marian-nmt.github.io/) is being developed by the Microsoft Translator Team.
-1. **[MBart](https://huggingface.co/transformers/model_doc/mbart.html)** (from Facebook) released with the paper [Multilingual Denoising Pre-training for Neural Machine Translation](https://arxiv.org/abs/2001.08210) by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
-1. **[MBart-50](https://huggingface.co/transformers/model_doc/mbart.html)** (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/transformers/model_doc/megatron_bert.html)** (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/transformers/model_doc/megatron_gpt2.html)** (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/transformers/model_doc/mpnet.html)** (from Microsoft Research) released with the paper [MPNet: Masked and Permuted Pre-training for Language Understanding](https://arxiv.org/abs/2004.09297) by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu.
-1. **[MT5](https://huggingface.co/transformers/model_doc/mt5.html)** (from Google AI) released with the paper [mT5: A massively multilingual pre-trained text-to-text transformer](https://arxiv.org/abs/2010.11934) by Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel.
-1. **[Pegasus](https://huggingface.co/transformers/model_doc/pegasus.html)** (from Google) released with the paper [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/abs/1912.08777) by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu.
-1. **[PhoBERT](https://huggingface.co/transformers/model_doc/phobert.html)** (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/transformers/model_doc/prophetnet.html)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
-1. **[Reformer](https://huggingface.co/transformers/model_doc/reformer.html)** (from Google Research) released with the paper [Reformer: The Efficient Transformer](https://arxiv.org/abs/2001.04451) by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
-1. **[RemBERT](https://huggingface.co/transformers/model_doc/rembert.html)** (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/transformers/model_doc/roberta.html)** (from Facebook), released together with the paper a [Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692) by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
-1. **[RoFormer](https://huggingface.co/transformers/model_doc/roformer.html)** (from ZhuiyiTechnology), released together with the paper a [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://arxiv.org/pdf/2104.09864v1.pdf) by Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu.
-1. **[SegFormer](https://huggingface.co/transformers/model_doc/segformer.html)** (from NVIDIA) released with the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo.
-1. **[SEW](https://huggingface.co/transformers/model_doc/sew.html)** (from ASAPP) released with the paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
-1. **[SEW-D](https://huggingface.co/transformers/model_doc/sew_d.html)** (from ASAPP) released with the paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
-1. **[SpeechToTextTransformer](https://huggingface.co/transformers/model_doc/speech_to_text.html)** (from Facebook), released together with the paper [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171) by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino.
-1. **[SpeechToTextTransformer2](https://huggingface.co/transformers/model_doc/speech_to_text_2.html)** (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/transformers/model_doc/splinter.html)** (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/transformers/model_doc/squeezebert.html)** (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. **[T5](https://huggingface.co/transformers/model_doc/t5.html)** (from Google AI) released with the paper [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
-1. **[T5v1.1](https://huggingface.co/transformers/model_doc/t5v1.1.html)** (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/transformers/model_doc/tapas.html)** (from Google AI) released with the paper [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://arxiv.org/abs/2004.02349) by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos.
-1. **[Transformer-XL](https://huggingface.co/transformers/model_doc/transformerxl.html)** (from Google/CMU) released with the paper [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
-1. **[TrOCR](https://huggingface.co/transformers/model_doc/trocr.html)** (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/transformers/model_doc/unispeech.html)** (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/transformers/model_doc/unispeech_sat.html)** (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. **[Vision Transformer (ViT)](https://huggingface.co/transformers/model_doc/vit.html)** (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/transformers/model_doc/visual_bert.html)** (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. **[Wav2Vec2](https://huggingface.co/transformers/model_doc/wav2vec2.html)** (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. **[XLM](https://huggingface.co/transformers/model_doc/xlm.html)** (from Facebook) released together with the paper [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample and Alexis Conneau.
-1. **[XLM-ProphetNet](https://huggingface.co/transformers/model_doc/xlmprophetnet.html)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
-1. **[XLM-RoBERTa](https://huggingface.co/transformers/model_doc/xlmroberta.html)** (from Facebook AI), released together with the paper [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Alexis Conneau*, Kartikay Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov.
-1. **[XLNet](https://huggingface.co/transformers/model_doc/xlnet.html)** (from Google/CMU) released with the paper [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
-1. **[XLSR-Wav2Vec2](https://huggingface.co/transformers/model_doc/xlsr_wav2vec2.html)** (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. **[ALBERT](https://huggingface.co/docs/transformers/model_doc/albert)** (from Google Research and the Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.
+1. **[BART](https://huggingface.co/docs/transformers/model_doc/bart)** (from Facebook) released with the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/pdf/1910.13461.pdf) by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer.
+1. **[BARThez](https://huggingface.co/docs/transformers/model_doc/barthez)** (from École polytechnique) released with the paper [BARThez: a Skilled Pretrained French Sequence-to-Sequence Model](https://arxiv.org/abs/2010.12321) by Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis.
+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. **[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. **[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. **[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.
+1. **[CANINE](https://huggingface.co/docs/transformers/model_doc/canine)** (from Google Research) released with the paper [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://arxiv.org/abs/2103.06874) by Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting.
+1. **[CLIP](https://huggingface.co/docs/transformers/model_doc/clip)** (from OpenAI) released with the paper [Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/abs/2103.00020) by Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever.
+1. **[ConvBERT](https://huggingface.co/docs/transformers/model_doc/convbert)** (from YituTech) released with the paper [ConvBERT: Improving BERT with Span-based Dynamic Convolution](https://arxiv.org/abs/2008.02496) by Zihang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan.
+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. **[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. **[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 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.
+1. **[Hubert](https://huggingface.co/docs/transformers/model_doc/hubert)** (from Facebook) released with the paper [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://arxiv.org/abs/2106.07447) by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed.
+1. **[I-BERT](https://huggingface.co/docs/transformers/model_doc/ibert)** (from Berkeley) released with the paper [I-BERT: Integer-only BERT Quantization](https://arxiv.org/abs/2101.01321) by Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer.
+1. **[ImageGPT](https://huggingface.co/docs/transformers/master/model_doc/imagegpt)** (from OpenAI) released with the paper [Generative Pretraining from Pixels](https://openai.com/blog/image-gpt/) by Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever.
+1. **[LayoutLM](https://huggingface.co/docs/transformers/model_doc/layoutlm)** (from Microsoft Research Asia) released with the paper [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://arxiv.org/abs/1912.13318) by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou.
+1. **[LayoutLMv2](https://huggingface.co/docs/transformers/model_doc/layoutlmv2)** (from Microsoft Research Asia) released with the paper [LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding](https://arxiv.org/abs/2012.14740) by Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou.
+1. **[LayoutXLM](https://huggingface.co/docs/transformers/model_doc/layoutlmv2)** (from Microsoft Research Asia) released with the paper [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://arxiv.org/abs/2104.08836) by Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei.
+1. **[LED](https://huggingface.co/docs/transformers/model_doc/led)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
+1. **[Longformer](https://huggingface.co/docs/transformers/model_doc/longformer)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
+1. **[LUKE](https://huggingface.co/docs/transformers/model_doc/luke)** (from Studio Ousia) released with the paper [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://arxiv.org/abs/2010.01057) by Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yuji Matsumoto.
+1. **[LXMERT](https://huggingface.co/docs/transformers/model_doc/lxmert)** (from UNC Chapel Hill) released with the paper [LXMERT: Learning Cross-Modality Encoder Representations from Transformers for Open-Domain Question Answering](https://arxiv.org/abs/1908.07490) by Hao Tan and Mohit Bansal.
+1. **[M2M100](https://huggingface.co/docs/transformers/model_doc/m2m_100)** (from Facebook) released with the paper [Beyond English-Centric Multilingual Machine Translation](https://arxiv.org/abs/2010.11125) by Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin.
+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-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. **[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. **[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.
+1. **[RoFormer](https://huggingface.co/docs/transformers/model_doc/roformer)** (from ZhuiyiTechnology), released together with the paper a [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://arxiv.org/pdf/2104.09864v1.pdf) by Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu.
+1. **[SegFormer](https://huggingface.co/docs/transformers/model_doc/segformer)** (from NVIDIA) released with the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo.
+1. **[SEW](https://huggingface.co/docs/transformers/model_doc/sew)** (from ASAPP) released with the paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
+1. **[SEW-D](https://huggingface.co/docs/transformers/model_doc/sew_d)** (from ASAPP) released with the paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
+1. **[SpeechToTextTransformer](https://huggingface.co/docs/transformers/model_doc/speech_to_text)** (from Facebook), released together with the paper [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171) by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino.
+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. **[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. **[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. **[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. **[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. **[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. **[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. 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/transformers/index.html#supported-frameworks)를 확인하세요.
+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.

-이 구현은 여러 데이터로 검증되었고 (예시 스크립트를 참고하세요) 오리지널 구현의 성능과 같아야 합니다. [도큐먼트](https://huggingface.co/transformers/examples.html)의 Examples 섹션에서 성능에 대한 자세한 설명을 확인할 수 있습니다.
+이 구현은 여러 데이터로 검증되었고 (예시 스크립트를 참고하세요) 오리지널 구현의 성능과 같아야 합니다. [도큐먼트](https://huggingface.co/docs/transformers/examples)의 Examples 섹션에서 성능에 대한 자세한 설명을 확인할 수 있습니다.

 ## 더 알아보기

 | 섹션 | 설명 |
 |-|-|
 | [도큐먼트](https://huggingface.co/transformers/) | 전체 API 도큐먼트와 튜토리얼 |
-| [과제 요약](https://huggingface.co/transformers/task_summary.html) | 🤗 Transformers가 지원하는 과제들 |
-| [전처리 튜토리얼](https://huggingface.co/transformers/preprocessing.html) | `Tokenizer` 클래스를 이용해 모델을 위한 데이터 준비하기 |
-| [학습과 fine-tuning](https://huggingface.co/transformers/training.html) | 🤗 Transformers가 제공하는 모델 PyTorch/TensorFlow 학습 과정과 `Trainer` API에서 사용하기 |
+| [과제 요약](https://huggingface.co/docs/transformers/task_summary) | 🤗 Transformers가 지원하는 과제들 |
+| [전처리 튜토리얼](https://huggingface.co/docs/transformers/preprocessing) | `Tokenizer` 클래스를 이용해 모델을 위한 데이터 준비하기 |
+| [학습과 fine-tuning](https://huggingface.co/docs/transformers/training) | 🤗 Transformers가 제공하는 모델 PyTorch/TensorFlow 학습 과정과 `Trainer` API에서 사용하기 |
 | [퀵 투어: Fine-tuning/사용 스크립트](https://github.com/huggingface/transformers/tree/master/examples) | 다양한 과제에서 모델 fine-tuning하는 예시 스크립트 |
-| [모델 공유 및 업로드](https://huggingface.co/transformers/model_sharing.html) | 커뮤니티에 fine-tune된 모델을 업로드 및 공유하기 |
-| [마이그레이션](https://huggingface.co/transformers/migration.html) | `pytorch-transformers`나 `pytorch-pretrained-bert`에서 🤗 Transformers로 이동하기|
+| [모델 공유 및 업로드](https://huggingface.co/docs/transformers/model_sharing) | 커뮤니티에 fine-tune된 모델을 업로드 및 공유하기 |
+| [마이그레이션](https://huggingface.co/docs/transformers/migration) | `pytorch-transformers`나 `pytorch-pretrained-bert`에서 🤗 Transformers로 이동하기|

 ## 인용

--- a/README_zh-hans.md
+++ b/README_zh-hans.md
@@ -51,8 +51,8 @@ checkpoint: 检查点
    <a href="https://github.com/huggingface/transformers/blob/master/LICENSE">
        <img alt="GitHub" src="https://img.shields.io/github/license/huggingface/transformers.svg?color=blue">
    </a>
-    <a href="https://huggingface.co/transformers/index.html">
-        <img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/transformers/index.html.svg?down_color=red&down_message=offline&up_message=online">
+    <a href="https://huggingface.co/docs/transformers/index">
+        <img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/docs/transformers/index.svg?down_color=red&down_message=offline&up_message=online">
    </a>
    <a href="https://github.com/huggingface/transformers/releases">
        <img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/transformers.svg">
@@ -137,7 +137,7 @@ checkpoint: 检查点

 ```

-除了给出答案，预训练模型还给出了对应的置信度分数、答案在词符化 (tokenized) 后的文本中开始和结束的位置。你可以从[这个教程](https://huggingface.co/transformers/task_summary.html)了解更多流水线API支持的任务。
+除了给出答案，预训练模型还给出了对应的置信度分数、答案在词符化 (tokenized) 后的文本中开始和结束的位置。你可以从[这个教程](https://huggingface.co/docs/transformers/task_summary)了解更多流水线API支持的任务。

 要在你的任务上下载和使用任意预训练模型也很简单，只需三行代码。这里是 PyTorch 版的示例：
 ```python
@@ -211,7 +211,7 @@ checkpoint: 检查点
 pip install transformers
 ```

-如果你想要试试用例或者想在正式发布前使用最新的开发中代码，你得[从源代码安装](https://huggingface.co/transformers/installation.html#installing-from-source)。
+如果你想要试试用例或者想在正式发布前使用最新的开发中代码，你得[从源代码安装](https://huggingface.co/docs/transformers/installation#installing-from-source)。

 ### 使用 conda

@@ -231,97 +231,99 @@ conda install -c huggingface transformers

 目前的检查点数量： ![](https://img.shields.io/endpoint?url=https://huggingface.co/api/shields/models&color=brightgreen)

-🤗 Transformers 目前支持如下的架构（模型概述请阅[这里](https://huggingface.co/transformers/model_summary.html)）：
+🤗 Transformers 目前支持如下的架构（模型概述请阅[这里](https://huggingface.co/docs/transformers/model_summary)）：

-1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。
-1. **[BART](https://huggingface.co/transformers/model_doc/bart.html)** (来自 Facebook) 伴随论文 [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/pdf/1910.13461.pdf) 由 Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer 发布。
-1. **[BARThez](https://huggingface.co/transformers/model_doc/barthez.html)** (来自 École polytechnique) 伴随论文 [BARThez: a Skilled Pretrained French Sequence-to-Sequence Model](https://arxiv.org/abs/2010.12321) 由 Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis 发布。
-1. **[BARTpho](https://huggingface.co/transformers/model_doc/bartpho.html)** (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/transformers/model_doc/beit.html)** (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/transformers/model_doc/bert.html)** (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/transformers/model_doc/bertgeneration.html)** (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/transformers/model_doc/bertweet.html)** (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/transformers/model_doc/bigbird_pegasus.html)** (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/transformers/model_doc/bigbird.html)** (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/transformers/model_doc/blenderbot.html)** (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/transformers/model_doc/blenderbot_small.html)** (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/transformers/model_doc/bort.html)** (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/transformers/model_doc/byt5.html)** (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/transformers/model_doc/camembert.html)** (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。
-1. **[CANINE](https://huggingface.co/transformers/model_doc/canine.html)** (来自 Google Research) 伴随论文 [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://arxiv.org/abs/2103.06874) 由 Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting 发布。
-1. **[CLIP](https://huggingface.co/transformers/model_doc/clip.html)** (来自 OpenAI) 伴随论文 [Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/abs/2103.00020) 由 Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever 发布。
-1. **[ConvBERT](https://huggingface.co/transformers/model_doc/convbert.html)** (来自 YituTech) 伴随论文 [ConvBERT: Improving BERT with Span-based Dynamic Convolution](https://arxiv.org/abs/2008.02496) 由 Zihang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan 发布。
-1. **[CPM](https://huggingface.co/transformers/model_doc/cpm.html)** (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/transformers/model_doc/ctrl.html)** (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/transformers/model_doc/deberta.html)** (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/transformers/model_doc/deberta_v2.html)** (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/transformers/model_doc/deit.html)** (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/transformers/model_doc/detr.html)** (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/transformers/model_doc/dialogpt.html)** (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/transformers/model_doc/distilbert.html)** (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/transformers/model_doc/dpr.html)** (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/transformers/model_doc/electra.html)** (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/transformers/model_doc/encoderdecoder.html)** (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/transformers/model_doc/flaubert.html)** (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/transformers/model_doc/fnet.html)** (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/transformers/model_doc/funnel.html)** (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/transformers/model_doc/gpt.html)** (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/transformers/model_doc/gpt_neo.html)** (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/transformers/model_doc/gpt2.html)** (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/transformers/model_doc/gptj.html)** (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。
-1. **[Hubert](https://huggingface.co/transformers/model_doc/hubert.html)** (来自 Facebook) 伴随论文 [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://arxiv.org/abs/2106.07447) 由 Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed 发布。
-1. **[I-BERT](https://huggingface.co/transformers/model_doc/ibert.html)** (来自 Berkeley) 伴随论文 [I-BERT: Integer-only BERT Quantization](https://arxiv.org/abs/2101.01321) 由 Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer 发布。
-1. **[LayoutLM](https://huggingface.co/transformers/model_doc/layoutlm.html)** (来自 Microsoft Research Asia) 伴随论文 [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://arxiv.org/abs/1912.13318) 由 Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou 发布。
-1. **[LayoutLMv2](https://huggingface.co/transformers/model_doc/layoutlmv2.html)** (来自 Microsoft Research Asia) 伴随论文 [LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding](https://arxiv.org/abs/2012.14740) 由 Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou 发布。
-1. **[LayoutXLM](https://huggingface.co/transformers/model_doc/layoutlmv2.html)** (来自 Microsoft Research Asia) 伴随论文 [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://arxiv.org/abs/2104.08836) 由 Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei 发布。
-1. **[LED](https://huggingface.co/transformers/model_doc/led.html)** (来自 AllenAI) 伴随论文 [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) 由 Iz Beltagy, Matthew E. Peters, Arman Cohan 发布。
-1. **[Longformer](https://huggingface.co/transformers/model_doc/longformer.html)** (来自 AllenAI) 伴随论文 [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) 由 Iz Beltagy, Matthew E. Peters, Arman Cohan 发布。
-1. **[LUKE](https://huggingface.co/transformers/model_doc/luke.html)** (来自 Studio Ousia) 伴随论文 [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://arxiv.org/abs/2010.01057) 由 Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yuji Matsumoto 发布。
-1. **[LXMERT](https://huggingface.co/transformers/model_doc/lxmert.html)** (来自 UNC Chapel Hill) 伴随论文 [LXMERT: Learning Cross-Modality Encoder Representations from Transformers for Open-Domain Question Answering](https://arxiv.org/abs/1908.07490) 由 Hao Tan and Mohit Bansal 发布。
-1. **[M2M100](https://huggingface.co/transformers/model_doc/m2m_100.html)** (来自 Facebook) 伴随论文 [Beyond English-Centric Multilingual Machine Translation](https://arxiv.org/abs/2010.11125) 由 Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin 发布。
-1. **[MarianMT](https://huggingface.co/transformers/model_doc/marian.html)** 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/transformers/model_doc/mbart.html)** (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/transformers/model_doc/mbart.html)** (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/transformers/model_doc/megatron_bert.html)** (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/transformers/model_doc/megatron_gpt2.html)** (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. **[MPNet](https://huggingface.co/transformers/model_doc/mpnet.html)** (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/transformers/model_doc/mt5.html)** (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. **[Pegasus](https://huggingface.co/transformers/model_doc/pegasus.html)** (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. **[PhoBERT](https://huggingface.co/transformers/model_doc/phobert.html)** (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/transformers/model_doc/prophetnet.html)** (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. **[Reformer](https://huggingface.co/transformers/model_doc/reformer.html)** (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/transformers/model_doc/rembert.html)** (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/transformers/model_doc/roberta.html)** (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。
-1. **[RoFormer](https://huggingface.co/transformers/model_doc/roformer.html)** (来自 ZhuiyiTechnology), 伴随论文 [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://arxiv.org/pdf/2104.09864v1.pdf) 由 Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu 发布。
-1. **[SegFormer](https://huggingface.co/transformers/model_doc/segformer.html)** (来自 NVIDIA) 伴随论文 [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) 由 Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo 发布。
-1. **[SEW](https://huggingface.co/transformers/model_doc/sew.html)** (来自 ASAPP) 伴随论文 [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) 由 Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi 发布。
-1. **[SEW-D](https://huggingface.co/transformers/model_doc/sew_d.html)** (来自 ASAPP) 伴随论文 [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) 由 Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi 发布。
-1. **[SpeechEncoderDecoder](https://huggingface.co/transformers/model_doc/speechencoderdecoder.html)** 
-1. **[SpeechToTextTransformer](https://huggingface.co/transformers/model_doc/speech_to_text.html)** (来自 Facebook), 伴随论文 [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171) 由 Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino 发布。
-1. **[SpeechToTextTransformer2](https://huggingface.co/transformers/model_doc/speech_to_text_2.html)** (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/transformers/model_doc/splinter.html)** (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/transformers/model_doc/squeezebert.html)** (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. **[T5](https://huggingface.co/transformers/model_doc/t5.html)** (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/transformers/model_doc/t5v1.1.html)** (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/transformers/model_doc/tapas.html)** (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/transformers/model_doc/transformerxl.html)** (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/transformers/model_doc/trocr.html)** (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/transformers/model_doc/unispeech.html)** (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/transformers/model_doc/unispeech_sat.html)** (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. **[Vision Transformer (ViT)](https://huggingface.co/transformers/model_doc/vit.html)** (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. **[VisionEncoderDecoder](https://huggingface.co/transformers/model_doc/visionencoderdecoder.html)** 
-1. **[VisualBERT](https://huggingface.co/transformers/model_doc/visual_bert.html)** (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. **[Wav2Vec2](https://huggingface.co/transformers/model_doc/wav2vec2.html)** (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. **[XLM](https://huggingface.co/transformers/model_doc/xlm.html)** (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/transformers/model_doc/xlmprophetnet.html)** (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/transformers/model_doc/xlmroberta.html)** (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/transformers/model_doc/xlnet.html)** (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. **[XLSR-Wav2Vec2](https://huggingface.co/transformers/model_doc/xlsr_wav2vec2.html)** (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. **[ALBERT](https://huggingface.co/docs/transformers/model_doc/albert)** (来自 Google Research and the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。
+1. **[BART](https://huggingface.co/docs/transformers/model_doc/bart)** (来自 Facebook) 伴随论文 [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/pdf/1910.13461.pdf) 由 Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer 发布。
+1. **[BARThez](https://huggingface.co/docs/transformers/model_doc/barthez)** (来自 École polytechnique) 伴随论文 [BARThez: a Skilled Pretrained French Sequence-to-Sequence Model](https://arxiv.org/abs/2010.12321) 由 Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis 发布。
+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. **[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. **[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. **[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。
+1. **[CANINE](https://huggingface.co/docs/transformers/model_doc/canine)** (来自 Google Research) 伴随论文 [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://arxiv.org/abs/2103.06874) 由 Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting 发布。
+1. **[CLIP](https://huggingface.co/docs/transformers/model_doc/clip)** (来自 OpenAI) 伴随论文 [Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/abs/2103.00020) 由 Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever 发布。
+1. **[ConvBERT](https://huggingface.co/docs/transformers/model_doc/convbert)** (来自 YituTech) 伴随论文 [ConvBERT: Improving BERT with Span-based Dynamic Convolution](https://arxiv.org/abs/2008.02496) 由 Zihang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan 发布。
+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. **[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. **[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 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。
+1. **[Hubert](https://huggingface.co/docs/transformers/model_doc/hubert)** (来自 Facebook) 伴随论文 [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://arxiv.org/abs/2106.07447) 由 Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed 发布。
+1. **[I-BERT](https://huggingface.co/docs/transformers/model_doc/ibert)** (来自 Berkeley) 伴随论文 [I-BERT: Integer-only BERT Quantization](https://arxiv.org/abs/2101.01321) 由 Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer 发布。
+1. **[ImageGPT](https://huggingface.co/docs/transformers/master/model_doc/imagegpt)** (来自 OpenAI) 伴随论文 [Generative Pretraining from Pixels](https://openai.com/blog/image-gpt/) 由 Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever 发布。
+1. **[LayoutLM](https://huggingface.co/docs/transformers/model_doc/layoutlm)** (来自 Microsoft Research Asia) 伴随论文 [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://arxiv.org/abs/1912.13318) 由 Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou 发布。
+1. **[LayoutLMv2](https://huggingface.co/docs/transformers/model_doc/layoutlmv2)** (来自 Microsoft Research Asia) 伴随论文 [LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding](https://arxiv.org/abs/2012.14740) 由 Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou 发布。
+1. **[LayoutXLM](https://huggingface.co/docs/transformers/model_doc/layoutlmv2)** (来自 Microsoft Research Asia) 伴随论文 [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://arxiv.org/abs/2104.08836) 由 Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei 发布。
+1. **[LED](https://huggingface.co/docs/transformers/model_doc/led)** (来自 AllenAI) 伴随论文 [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) 由 Iz Beltagy, Matthew E. Peters, Arman Cohan 发布。
+1. **[Longformer](https://huggingface.co/docs/transformers/model_doc/longformer)** (来自 AllenAI) 伴随论文 [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) 由 Iz Beltagy, Matthew E. Peters, Arman Cohan 发布。
+1. **[LUKE](https://huggingface.co/docs/transformers/model_doc/luke)** (来自 Studio Ousia) 伴随论文 [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://arxiv.org/abs/2010.01057) 由 Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yuji Matsumoto 发布。
+1. **[LXMERT](https://huggingface.co/docs/transformers/model_doc/lxmert)** (来自 UNC Chapel Hill) 伴随论文 [LXMERT: Learning Cross-Modality Encoder Representations from Transformers for Open-Domain Question Answering](https://arxiv.org/abs/1908.07490) 由 Hao Tan and Mohit Bansal 发布。
+1. **[M2M100](https://huggingface.co/docs/transformers/model_doc/m2m_100)** (来自 Facebook) 伴随论文 [Beyond English-Centric Multilingual Machine Translation](https://arxiv.org/abs/2010.11125) 由 Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin 发布。
+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-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. **[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. **[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。
+1. **[RoFormer](https://huggingface.co/docs/transformers/model_doc/roformer)** (来自 ZhuiyiTechnology), 伴随论文 [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://arxiv.org/pdf/2104.09864v1.pdf) 由 Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu 发布。
+1. **[SegFormer](https://huggingface.co/docs/transformers/model_doc/segformer)** (来自 NVIDIA) 伴随论文 [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) 由 Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo 发布。
+1. **[SEW](https://huggingface.co/docs/transformers/model_doc/sew)** (来自 ASAPP) 伴随论文 [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) 由 Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi 发布。
+1. **[SEW-D](https://huggingface.co/docs/transformers/model_doc/sew_d)** (来自 ASAPP) 伴随论文 [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) 由 Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi 发布。
+1. **[SpeechToTextTransformer](https://huggingface.co/docs/transformers/model_doc/speech_to_text)** (来自 Facebook), 伴随论文 [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171) 由 Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino 发布。
+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. **[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. **[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. **[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. **[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. **[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. **[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. **[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. 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/transformers/index.html#supported-frameworks)。
+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)。

-这些实现均已于多个数据集测试（请参看用例脚本）并应于原版实现表现相当。你可以在用例文档的[此节](https://huggingface.co/transformers/examples.html)中了解表现的细节。
+这些实现均已于多个数据集测试（请参看用例脚本）并应于原版实现表现相当。你可以在用例文档的[此节](https://huggingface.co/docs/transformers/examples)中了解表现的细节。


 ## 了解更多
@@ -329,12 +331,12 @@ conda install -c huggingface transformers
 | 章节 | 描述 |
 |-|-|
 | [文档](https://huggingface.co/transformers/) | 完整的 API 文档和教程 |
-| [任务总结](https://huggingface.co/transformers/task_summary.html) | 🤗 Transformers 支持的任务 |
-| [预处理教程](https://huggingface.co/transformers/preprocessing.html) | 使用 `Tokenizer` 来为模型准备数据 |
-| [训练和微调](https://huggingface.co/transformers/training.html) | 在 PyTorch/TensorFlow 的训练循环或 `Trainer` API 中使用 🤗 Transformers 提供的模型 |
+| [任务总结](https://huggingface.co/docs/transformers/task_summary) | 🤗 Transformers 支持的任务 |
+| [预处理教程](https://huggingface.co/docs/transformers/preprocessing) | 使用 `Tokenizer` 来为模型准备数据 |
+| [训练和微调](https://huggingface.co/docstransformers/training) | 在 PyTorch/TensorFlow 的训练循环或 `Trainer` API 中使用 🤗 Transformers 提供的模型 |
 | [快速上手：微调和用例脚本](https://github.com/huggingface/transformers/tree/master/examples) | 为各种任务提供的用例脚本 |
-| [模型分享和上传](https://huggingface.co/transformers/model_sharing.html) | 和社区上传和分享你微调的模型 |
-| [迁移](https://huggingface.co/transformers/migration.html) | 从 `pytorch-transformers` 或 `pytorch-pretrained-bert` 迁移到 🤗 Transformers |
+| [模型分享和上传](https://huggingface.co/docs/transformers/model_sharing) | 和社区上传和分享你微调的模型 |
+| [迁移](https://huggingface.co/docs/transformers/migration) | 从 `pytorch-transformers` 或 `pytorch-pretrained-bert` 迁移到 🤗 Transformers |

 ## 引用

--- a/README_zh-hant.md
+++ b/README_zh-hant.md
@@ -63,8 +63,8 @@ user: 使用者
    <a href="https://github.com/huggingface/transformers/blob/master/LICENSE">
        <img alt="GitHub" src="https://img.shields.io/github/license/huggingface/transformers.svg?color=blue">
    </a>
-    <a href="https://huggingface.co/transformers/index.html">
-        <img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/transformers/index.html.svg?down_color=red&down_message=offline&up_message=online">
+    <a href="https://huggingface.co/docs/transformers/index">
+        <img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/docs/transformers/index.svg?down_color=red&down_message=offline&up_message=online">
    </a>
    <a href="https://github.com/huggingface/transformers/releases">
        <img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/transformers.svg">
@@ -149,7 +149,7 @@ user: 使用者

 ```

-除了提供問題解答，預訓練模型還提供了對應的信賴度分數以及解答在 tokenized 後的文本中開始和結束的位置。你可以從[這個教學](https://huggingface.co/transformers/task_summary.html)了解更多 `pipeline` API支援的任務。
+除了提供問題解答，預訓練模型還提供了對應的信賴度分數以及解答在 tokenized 後的文本中開始和結束的位置。你可以從[這個教學](https://huggingface.co/docs/transformers/task_summary)了解更多 `pipeline` API支援的任務。

 要在你的任務中下載和使用任何預訓練模型很簡單，只需三行程式碼。這裡是 PyTorch 版的範例：
 ```python
@@ -223,7 +223,7 @@ Tokenizer 為所有的預訓練模型提供了預處理，並可以直接轉換
 pip install transformers
 ```

-如果你想要試試範例或者想在正式發布前使用最新開發中的程式碼，你必須[從原始碼安裝](https://huggingface.co/transformers/installation.html#installing-from-source)。
+如果你想要試試範例或者想在正式發布前使用最新開發中的程式碼，你必須[從原始碼安裝](https://huggingface.co/docs/transformers/installation#installing-from-source)。

 ### 使用 conda

@@ -243,97 +243,99 @@ conda install -c huggingface transformers

 目前的檢查點數量： ![](https://img.shields.io/endpoint?url=https://huggingface.co/api/shields/models&color=brightgreen)

-🤗 Transformers 目前支援以下的架構（模型概覽請參閱[這裡](https://huggingface.co/transformers/model_summary.html)）：
+🤗 Transformers 目前支援以下的架構（模型概覽請參閱[這裡](https://huggingface.co/docs/transformers/model_summary)）：

-1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.
-1. **[BART](https://huggingface.co/transformers/model_doc/bart.html)** (from Facebook) released with the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/pdf/1910.13461.pdf) by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer.
-1. **[BARThez](https://huggingface.co/transformers/model_doc/barthez.html)** (from École polytechnique) released with the paper [BARThez: a Skilled Pretrained French Sequence-to-Sequence Model](https://arxiv.org/abs/2010.12321) by Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis.
-1. **[BARTpho](https://huggingface.co/transformers/model_doc/bartpho.html)** (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/transformers/model_doc/beit.html)** (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/transformers/model_doc/bert.html)** (from Google) released with the paper [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.
-1. **[BERT For Sequence Generation](https://huggingface.co/transformers/model_doc/bertgeneration.html)** (from Google) released with the paper [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
-1. **[BERTweet](https://huggingface.co/transformers/model_doc/bertweet.html)** (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/transformers/model_doc/bigbird_pegasus.html)** (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/transformers/model_doc/bigbird.html)** (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/transformers/model_doc/blenderbot.html)** (from Facebook) released with the paper [Recipes for building an open-domain chatbot](https://arxiv.org/abs/2004.13637) by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
-1. **[BlenderbotSmall](https://huggingface.co/transformers/model_doc/blenderbot_small.html)** (from Facebook) released with the paper [Recipes for building an open-domain chatbot](https://arxiv.org/abs/2004.13637) by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
-1. **[BORT](https://huggingface.co/transformers/model_doc/bort.html)** (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/transformers/model_doc/byt5.html)** (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/transformers/model_doc/camembert.html)** (from Inria/Facebook/Sorbonne) released with the paper [CamemBERT: a Tasty French Language Model](https://arxiv.org/abs/1911.03894) by Louis Martin*, Benjamin Muller*, Pedro Javier Ortiz Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
-1. **[CANINE](https://huggingface.co/transformers/model_doc/canine.html)** (from Google Research) released with the paper [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://arxiv.org/abs/2103.06874) by Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting.
-1. **[CLIP](https://huggingface.co/transformers/model_doc/clip.html)** (from OpenAI) released with the paper [Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/abs/2103.00020) by Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever.
-1. **[ConvBERT](https://huggingface.co/transformers/model_doc/convbert.html)** (from YituTech) released with the paper [ConvBERT: Improving BERT with Span-based Dynamic Convolution](https://arxiv.org/abs/2008.02496) by Zihang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan.
-1. **[CPM](https://huggingface.co/transformers/model_doc/cpm.html)** (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/transformers/model_doc/ctrl.html)** (from Salesforce) released with the paper [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://arxiv.org/abs/1909.05858) by Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and Richard Socher.
-1. **[DeBERTa](https://huggingface.co/transformers/model_doc/deberta.html)** (from Microsoft) 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/transformers/model_doc/deberta_v2.html)** (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/transformers/model_doc/deit.html)** (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/transformers/model_doc/detr.html)** (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/transformers/model_doc/dialogpt.html)** (from Microsoft Research) released with the paper [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://arxiv.org/abs/1911.00536) by Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan.
-1. **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace), released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into [DistilGPT2](https://github.com/huggingface/transformers/tree/master/examples/distillation), RoBERTa into [DistilRoBERTa](https://github.com/huggingface/transformers/tree/master/examples/distillation), Multilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers/tree/master/examples/distillation) and a German version of DistilBERT.
-1. **[DPR](https://huggingface.co/transformers/model_doc/dpr.html)** (from Facebook) released with the paper [Dense Passage Retrieval for Open-Domain Question Answering](https://arxiv.org/abs/2004.04906) by Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
-1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning.
-1. **[EncoderDecoder](https://huggingface.co/transformers/model_doc/encoderdecoder.html)** (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/transformers/model_doc/flaubert.html)** (from CNRS) released with the paper [FlauBERT: Unsupervised Language Model Pre-training for French](https://arxiv.org/abs/1912.05372) by Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, Didier Schwab.
-1. **[FNet](https://huggingface.co/transformers/model_doc/fnet.html)** (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/transformers/model_doc/funnel.html)** (from CMU/Google Brain) released with the paper [Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing](https://arxiv.org/abs/2006.03236) by Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le.
-1. **[GPT](https://huggingface.co/transformers/model_doc/gpt.html)** (from OpenAI) released with the paper [Improving Language Understanding by Generative Pre-Training](https://blog.openai.com/language-unsupervised/) by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever.
-1. **[GPT Neo](https://huggingface.co/transformers/model_doc/gpt_neo.html)** (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/transformers/model_doc/gpt2.html)** (from OpenAI) released with the paper [Language Models are Unsupervised Multitask Learners](https://blog.openai.com/better-language-models/) by Alec Radford*, Jeffrey Wu*, Rewon Child, David Luan, Dario Amodei** and Ilya Sutskever**.
-1. **[GPT-J](https://huggingface.co/transformers/model_doc/gptj.html)** (from EleutherAI) released with the paper [kingoflolz/mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax/) by Ben Wang and Aran Komatsuzaki.
-1. **[Hubert](https://huggingface.co/transformers/model_doc/hubert.html)** (from Facebook) released with the paper [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://arxiv.org/abs/2106.07447) by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed.
-1. **[I-BERT](https://huggingface.co/transformers/model_doc/ibert.html)** (from Berkeley) released with the paper [I-BERT: Integer-only BERT Quantization](https://arxiv.org/abs/2101.01321) by Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer.
-1. **[LayoutLM](https://huggingface.co/transformers/model_doc/layoutlm.html)** (from Microsoft Research Asia) released with the paper [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://arxiv.org/abs/1912.13318) by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou.
-1. **[LayoutLMv2](https://huggingface.co/transformers/model_doc/layoutlmv2.html)** (from Microsoft Research Asia) released with the paper [LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding](https://arxiv.org/abs/2012.14740) by Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou.
-1. **[LayoutXLM](https://huggingface.co/transformers/model_doc/layoutlmv2.html)** (from Microsoft Research Asia) released with the paper [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://arxiv.org/abs/2104.08836) by Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei.
-1. **[LED](https://huggingface.co/transformers/model_doc/led.html)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
-1. **[Longformer](https://huggingface.co/transformers/model_doc/longformer.html)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
-1. **[LUKE](https://huggingface.co/transformers/model_doc/luke.html)** (from Studio Ousia) released with the paper [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://arxiv.org/abs/2010.01057) by Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yuji Matsumoto.
-1. **[LXMERT](https://huggingface.co/transformers/model_doc/lxmert.html)** (from UNC Chapel Hill) released with the paper [LXMERT: Learning Cross-Modality Encoder Representations from Transformers for Open-Domain Question Answering](https://arxiv.org/abs/1908.07490) by Hao Tan and Mohit Bansal.
-1. **[M2M100](https://huggingface.co/transformers/model_doc/m2m_100.html)** (from Facebook) released with the paper [Beyond English-Centric Multilingual Machine Translation](https://arxiv.org/abs/2010.11125) by Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin.
-1. **[MarianMT](https://huggingface.co/transformers/model_doc/marian.html)** Machine translation models trained using [OPUS](http://opus.nlpl.eu/) data by Jörg Tiedemann. The [Marian Framework](https://marian-nmt.github.io/) is being developed by the Microsoft Translator Team.
-1. **[MBart](https://huggingface.co/transformers/model_doc/mbart.html)** (from Facebook) released with the paper [Multilingual Denoising Pre-training for Neural Machine Translation](https://arxiv.org/abs/2001.08210) by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
-1. **[MBart-50](https://huggingface.co/transformers/model_doc/mbart.html)** (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/transformers/model_doc/megatron_bert.html)** (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/transformers/model_doc/megatron_gpt2.html)** (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/transformers/model_doc/mpnet.html)** (from Microsoft Research) released with the paper [MPNet: Masked and Permuted Pre-training for Language Understanding](https://arxiv.org/abs/2004.09297) by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu.
-1. **[MT5](https://huggingface.co/transformers/model_doc/mt5.html)** (from Google AI) released with the paper [mT5: A massively multilingual pre-trained text-to-text transformer](https://arxiv.org/abs/2010.11934) by Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel.
-1. **[Pegasus](https://huggingface.co/transformers/model_doc/pegasus.html)** (from Google) released with the paper [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/abs/1912.08777) by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu.
-1. **[PhoBERT](https://huggingface.co/transformers/model_doc/phobert.html)** (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/transformers/model_doc/prophetnet.html)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
-1. **[Reformer](https://huggingface.co/transformers/model_doc/reformer.html)** (from Google Research) released with the paper [Reformer: The Efficient Transformer](https://arxiv.org/abs/2001.04451) by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
-1. **[RemBERT](https://huggingface.co/transformers/model_doc/rembert.html)** (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/transformers/model_doc/roberta.html)** (from Facebook), released together with the paper a [Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692) by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
-1. **[RoFormer](https://huggingface.co/transformers/model_doc/roformer.html)** (from ZhuiyiTechnology), released together with the paper a [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://arxiv.org/pdf/2104.09864v1.pdf) by Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu.
-1. **[SegFormer](https://huggingface.co/transformers/model_doc/segformer.html)** (from NVIDIA) released with the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo.
-1. **[SEW](https://huggingface.co/transformers/model_doc/sew.html)** (from ASAPP) released with the paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
-1. **[SEW-D](https://huggingface.co/transformers/model_doc/sew_d.html)** (from ASAPP) released with the paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
-1. **[SpeechEncoderDecoder](https://huggingface.co/transformers/model_doc/speechencoderdecoder.html)** 
-1. **[SpeechToTextTransformer](https://huggingface.co/transformers/model_doc/speech_to_text.html)** (from Facebook), released together with the paper [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171) by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino.
-1. **[SpeechToTextTransformer2](https://huggingface.co/transformers/model_doc/speech_to_text_2.html)** (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/transformers/model_doc/splinter.html)** (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/transformers/model_doc/squeezebert.html)** (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. **[T5](https://huggingface.co/transformers/model_doc/t5.html)** (from Google AI) released with the paper [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
-1. **[T5v1.1](https://huggingface.co/transformers/model_doc/t5v1.1.html)** (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/transformers/model_doc/tapas.html)** (from Google AI) released with the paper [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://arxiv.org/abs/2004.02349) by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos.
-1. **[Transformer-XL](https://huggingface.co/transformers/model_doc/transformerxl.html)** (from Google/CMU) released with the paper [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
-1. **[TrOCR](https://huggingface.co/transformers/model_doc/trocr.html)** (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/transformers/model_doc/unispeech.html)** (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/transformers/model_doc/unispeech_sat.html)** (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. **[Vision Transformer (ViT)](https://huggingface.co/transformers/model_doc/vit.html)** (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. **[VisionEncoderDecoder](https://huggingface.co/transformers/model_doc/visionencoderdecoder.html)** 
-1. **[VisualBERT](https://huggingface.co/transformers/model_doc/visual_bert.html)** (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. **[Wav2Vec2](https://huggingface.co/transformers/model_doc/wav2vec2.html)** (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. **[XLM](https://huggingface.co/transformers/model_doc/xlm.html)** (from Facebook) released together with the paper [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample and Alexis Conneau.
-1. **[XLM-ProphetNet](https://huggingface.co/transformers/model_doc/xlmprophetnet.html)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
-1. **[XLM-RoBERTa](https://huggingface.co/transformers/model_doc/xlmroberta.html)** (from Facebook AI), released together with the paper [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Alexis Conneau*, Kartikay Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov.
-1. **[XLNet](https://huggingface.co/transformers/model_doc/xlnet.html)** (from Google/CMU) released with the paper [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
-1. **[XLSR-Wav2Vec2](https://huggingface.co/transformers/model_doc/xlsr_wav2vec2.html)** (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. **[ALBERT](https://huggingface.co/docs/transformers/model_doc/albert)** (from Google Research and the Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.
+1. **[BART](https://huggingface.co/docs/transformers/model_doc/bart)** (from Facebook) released with the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/pdf/1910.13461.pdf) by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer.
+1. **[BARThez](https://huggingface.co/docs/transformers/model_doc/barthez)** (from École polytechnique) released with the paper [BARThez: a Skilled Pretrained French Sequence-to-Sequence Model](https://arxiv.org/abs/2010.12321) by Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis.
+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. **[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. **[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. **[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.
+1. **[CANINE](https://huggingface.co/docs/transformers/model_doc/canine)** (from Google Research) released with the paper [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://arxiv.org/abs/2103.06874) by Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting.
+1. **[CLIP](https://huggingface.co/docs/transformers/model_doc/clip)** (from OpenAI) released with the paper [Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/abs/2103.00020) by Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever.
+1. **[ConvBERT](https://huggingface.co/docs/transformers/model_doc/convbert)** (from YituTech) released with the paper [ConvBERT: Improving BERT with Span-based Dynamic Convolution](https://arxiv.org/abs/2008.02496) by Zihang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan.
+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. **[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. **[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 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.
+1. **[Hubert](https://huggingface.co/docs/transformers/model_doc/hubert)** (from Facebook) released with the paper [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://arxiv.org/abs/2106.07447) by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed.
+1. **[I-BERT](https://huggingface.co/docs/transformers/model_doc/ibert)** (from Berkeley) released with the paper [I-BERT: Integer-only BERT Quantization](https://arxiv.org/abs/2101.01321) by Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer.
+1. **[ImageGPT](https://huggingface.co/docs/transformers/master/model_doc/imagegpt)** (from OpenAI) released with the paper [Generative Pretraining from Pixels](https://openai.com/blog/image-gpt/) by Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever.
+1. **[LayoutLM](https://huggingface.co/docs/transformers/model_doc/layoutlm)** (from Microsoft Research Asia) released with the paper [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://arxiv.org/abs/1912.13318) by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou.
+1. **[LayoutLMv2](https://huggingface.co/docs/transformers/model_doc/layoutlmv2)** (from Microsoft Research Asia) released with the paper [LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding](https://arxiv.org/abs/2012.14740) by Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou.
+1. **[LayoutXLM](https://huggingface.co/docs/transformers/model_doc/layoutlmv2)** (from Microsoft Research Asia) released with the paper [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://arxiv.org/abs/2104.08836) by Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei.
+1. **[LED](https://huggingface.co/docs/transformers/model_doc/led)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
+1. **[Longformer](https://huggingface.co/docs/transformers/model_doc/longformer)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
+1. **[LUKE](https://huggingface.co/docs/transformers/model_doc/luke)** (from Studio Ousia) released with the paper [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://arxiv.org/abs/2010.01057) by Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yuji Matsumoto.
+1. **[LXMERT](https://huggingface.co/docs/transformers/model_doc/lxmert)** (from UNC Chapel Hill) released with the paper [LXMERT: Learning Cross-Modality Encoder Representations from Transformers for Open-Domain Question Answering](https://arxiv.org/abs/1908.07490) by Hao Tan and Mohit Bansal.
+1. **[M2M100](https://huggingface.co/docs/transformers/model_doc/m2m_100)** (from Facebook) released with the paper [Beyond English-Centric Multilingual Machine Translation](https://arxiv.org/abs/2010.11125) by Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin.
+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-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. **[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. **[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.
+1. **[RoFormer](https://huggingface.co/docs/transformers/model_doc/roformer)** (from ZhuiyiTechnology), released together with the paper a [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://arxiv.org/pdf/2104.09864v1.pdf) by Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu.
+1. **[SegFormer](https://huggingface.co/docs/transformers/model_doc/segformer)** (from NVIDIA) released with the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo.
+1. **[SEW](https://huggingface.co/docs/transformers/model_doc/sew)** (from ASAPP) released with the paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
+1. **[SEW-D](https://huggingface.co/docs/transformers/model_doc/sew_d)** (from ASAPP) released with the paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
+1. **[SpeechToTextTransformer](https://huggingface.co/docs/transformers/model_doc/speech_to_text)** (from Facebook), released together with the paper [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171) by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino.
+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. **[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. **[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. **[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. **[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. **[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. **[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. 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/transformers/index.html#supported-frameworks)。
+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)。

-這些實作均已於多個資料集測試（請參閱範例腳本）並應與原版實作表現相當。你可以在範例文件的[此節](https://huggingface.co/transformers/examples.html)中了解實作的細節。
+這些實作均已於多個資料集測試（請參閱範例腳本）並應與原版實作表現相當。你可以在範例文件的[此節](https://huggingface.co/docs/transformers/examples)中了解實作的細節。


 ## 了解更多
@@ -341,12 +343,12 @@ conda install -c huggingface transformers
 | 章節 | 描述 |
 |-|-|
 | [文件](https://huggingface.co/transformers/) | 完整的 API 文件和教學 |
-| [任務概覽](https://huggingface.co/transformers/task_summary.html) | 🤗 Transformers 支援的任務 |
-| [預處理教學](https://huggingface.co/transformers/preprocessing.html) | 使用 `Tokenizer` 來為模型準備資料 |
-| [訓練和微調](https://huggingface.co/transformers/training.html) | 使用 PyTorch/TensorFlow 的內建的訓練方式或於 `Trainer` API 中使用 🤗 Transformers 提供的模型 |
+| [任務概覽](https://huggingface.co/docs/transformers/task_summary) | 🤗 Transformers 支援的任務 |
+| [預處理教學](https://huggingface.co/docs/transformers/preprocessing) | 使用 `Tokenizer` 來為模型準備資料 |
+| [訓練和微調](https://huggingface.co/docs/transformers/training) | 使用 PyTorch/TensorFlow 的內建的訓練方式或於 `Trainer` API 中使用 🤗 Transformers 提供的模型 |
 | [快速上手：微調和範例腳本](https://github.com/huggingface/transformers/tree/master/examples) | 為各種任務提供的範例腳本 |
-| [模型分享和上傳](https://huggingface.co/transformers/model_sharing.html) | 上傳並與社群分享你微調的模型 |
-| [遷移](https://huggingface.co/transformers/migration.html) | 從 `pytorch-transformers` 或 `pytorch-pretrained-bert` 遷移到 🤗 Transformers |
+| [模型分享和上傳](https://huggingface.co/docs/transformers/model_sharing) | 上傳並與社群分享你微調的模型 |
+| [遷移](https://huggingface.co/docs/transformers/migration) | 從 `pytorch-transformers` 或 `pytorch-pretrained-bert` 遷移到 🤗 Transformers |

 ## 引用

--- a/docs/source/_static/css/Calibre-Light.ttf
+++ b/docs/source/_static/css/Calibre-Light.ttf
--- a/docs/source/_static/css/Calibre-Medium.otf
+++ b/docs/source/_static/css/Calibre-Medium.otf
--- a/docs/source/_static/css/Calibre-Regular.otf
+++ b/docs/source/_static/css/Calibre-Regular.otf
--- a/docs/source/_static/css/Calibre-Thin.otf
+++ b/docs/source/_static/css/Calibre-Thin.otf
--- a/docs/source/_static/css/code-snippets.css
+++ b/docs/source/_static/css/code-snippets.css
@@ -1,16 +0,0 @@
-
-.highlight .c1, .highlight .sd{
-    color: #999
-}
-
-.highlight .nn, .highlight .k, .highlight .s1, .highlight .nb, .highlight .bp, .highlight .kc {
-    color: #FB8D68;
-}
-
-.highlight .kn, .highlight .nv, .highlight .s2, .highlight .ow {
-    color: #6670FF;
-}
-
-.highlight .gp {
-    color: #FB8D68;
-}
--- a/docs/source/_static/css/huggingface.css
+++ b/docs/source/_static/css/huggingface.css
@@ -1,350 +0,0 @@
-/* Our DOM objects */
-
-/* Colab dropdown */
-
-table.center-aligned-table td {
-    text-align: center;
-}
-
-table.center-aligned-table th {
-    text-align: center;
-    vertical-align: middle;
-}
-
-.colab-dropdown {
-    position: relative;
-    display: inline-block;
-}
-  
-.colab-dropdown-content {
-    display: none;
-    position: absolute;
-    background-color: #f9f9f9;
-    min-width: 117px;
-    box-shadow: 0px 8px 16px 0px rgba(0,0,0,0.2);
-    z-index: 1;
-}
-  
-.colab-dropdown-content button {
-    color: #6670FF;
-    background-color: #f9f9f9;
-    font-size: 12px;
-    border: none;
-    min-width: 117px;
-    padding: 5px 5px;
-    text-decoration: none;
-    display: block;
-}
-  
-.colab-dropdown-content button:hover {background-color: #eee;}
-  
-.colab-dropdown:hover .colab-dropdown-content {display: block;}
-
-/* Version control */
-
-.version-button {
-    background-color: #6670FF;
-    color: white;
-    border: none;
-    padding: 5px;
-    font-size: 15px;
-    cursor: pointer;
-}
-
-.version-button:hover, .version-button:focus {
-    background-color: #A6B0FF;
-}
- 
-.version-dropdown {
-    display: none;
-    background-color: #6670FF;
-    min-width: 160px;
-    overflow: auto;
-    font-size: 15px;
-}
-  
-.version-dropdown a {
-    color: white;
-    padding: 3px 4px;
-    text-decoration: none;
-    display: block;
-}
-  
-.version-dropdown a:hover {
-    background-color: #A6B0FF;
-}
-  
-.version-show {
-    display: block;
-}
-
-/* Framework selector */
-
-.framework-selector {
-    display: flex;
-    flex-direction: row;
-    justify-content: flex-end;
-    margin-right: 30px;
-}
-
-.framework-selector > button {
-    background-color: white;
-    color: #6670FF;
-    border: 1px solid #6670FF;
-    padding: 5px;
-}
-
-.framework-selector > button.selected{
-    background-color: #6670FF;
-    color: white;
-    border: 1px solid #6670FF;
-    padding: 5px;
-}
-
-/* Copy button */
-
-a.copybtn {
-    margin: 3px;
-}
-
-/* The literal code blocks */
-.rst-content tt.literal, .rst-content tt.literal, .rst-content code.literal {
-    color: #6670FF;
-}
-
-/* To keep the logo centered */
-.wy-side-scroll {
-    width: auto;
-    font-size: 20px;
-}
-
-/* The div that holds the Hugging Face logo */
-.HuggingFaceDiv {
-    width: 100%
-}
-
-/* The research field on top of the toc tree */
-.wy-side-nav-search{
-    padding-top: 0;
-    background-color: #6670FF;
-}
-
-/* The toc tree */
-.wy-nav-side{
-    background-color: #6670FF;
-}
-
-/* The section headers in the toc tree */
-.wy-menu-vertical p.caption{
-    background-color: #4d59ff;
-    line-height: 40px;
-}
-
-/* The selected items in the toc tree */
-.wy-menu-vertical li.current{
-    background-color: #A6B0FF;
-}
-
-/* When a list item that does belong to the selected block from the toc tree is hovered */
-.wy-menu-vertical li.current a:hover{
-    background-color: #B6C0FF;
-}
-
-/* When a list item that does NOT belong to the selected block from the toc tree is hovered. */
-.wy-menu-vertical li a:hover{
-    background-color: #A7AFFB;
-}
-
-/* The text items on the toc tree */
-.wy-menu-vertical a {
-    color: #FFFFDD;
-    font-family: Calibre-Light, sans-serif;
-}
-.wy-menu-vertical header, .wy-menu-vertical p.caption{
-    color: white;
-    font-family: Calibre-Light, sans-serif;
-}
-
-/* The color inside the selected toc tree block */
-.wy-menu-vertical li.toctree-l2 a, .wy-menu-vertical li.toctree-l3 a, .wy-menu-vertical li.toctree-l4 a {
-    color: black;
-}
-
-/* Inside the depth-2 selected toc tree block */
-.wy-menu-vertical li.toctree-l2.current>a {
-    background-color: #B6C0FF
-}
-.wy-menu-vertical li.toctree-l2.current li.toctree-l3>a {
-    background-color: #C6D0FF
-}
-
-/* Inside the depth-3 selected toc tree block */
-.wy-menu-vertical li.toctree-l3.current li.toctree-l4>a{
-    background-color: #D6E0FF
-}
-
-/* Inside code snippets */
-.rst-content dl:not(.docutils) dt{
-    font-size: 15px;
-}
-
-/* Links */
-a {
-    color: #6670FF;
-}
-
-/* Content bars */
-.rst-content dl:not(.docutils) dt {
-    background-color: rgba(251, 141, 104, 0.1);
-    border-right: solid 2px #FB8D68;
-    border-left: solid 2px #FB8D68;
-    color: #FB8D68;
-    font-family: Calibre-Light, sans-serif;
-    border-top: none;
-    font-style: normal !important;
-}
-
-/* Expand button */
-.wy-menu-vertical li.toctree-l2 span.toctree-expand,
-.wy-menu-vertical li.on a span.toctree-expand, .wy-menu-vertical li.current>a span.toctree-expand,
-.wy-menu-vertical li.toctree-l3 span.toctree-expand{
-    color: black;
-}
-
-/* Max window size */
-.wy-nav-content{
-    max-width: 1200px;
-}
-
-/* Mobile header */
-.wy-nav-top{
-    background-color: #6670FF;
-}
-
-
-/* Source spans */
-.rst-content .viewcode-link, .rst-content .viewcode-back{
-    color: #6670FF;
-    font-size: 110%;
-    letter-spacing: 2px;
-    text-transform: uppercase;
-}
-
-/* It would be better for table to be visible without horizontal scrolling */
-.wy-table-responsive table td, .wy-table-responsive table th{
-    white-space: normal;
-}
-
-.footer {
-    margin-top: 20px;
-}
-
-.footer__Social {
-    display: flex;
-    flex-direction: row;
-}
-
-.footer__CustomImage {
-    margin: 2px 5px 0 0;
-}
-
-/* class and method names in doc */
-.rst-content dl:not(.docutils) tt.descname, .rst-content dl:not(.docutils) tt.descclassname, .rst-content dl:not(.docutils) tt.descname, .rst-content dl:not(.docutils) code.descname, .rst-content dl:not(.docutils) tt.descclassname, .rst-content dl:not(.docutils) code.descclassname{
-    font-family: Calibre, sans-serif;
-    font-size: 20px !important;
-}
-
-/* class name in doc*/
-.rst-content dl:not(.docutils) tt.descname, .rst-content dl:not(.docutils) tt.descname, .rst-content dl:not(.docutils) code.descname{
-    margin-right: 10px;
-    font-family: Calibre-Medium, sans-serif;
-}
-
-/* Method and class parameters */
-.sig-param{
-    line-height: 23px;
-}
-
-/* Class introduction "class" string at beginning */
-.rst-content dl:not(.docutils) .property{
-    font-size: 18px;
-    color: black;
-}
-
-
-/* FONTS */
-body{
-    font-family: Calibre, sans-serif;
-    font-size: 16px;
-}
-
-h1 {
-    font-family: Calibre-Thin, sans-serif;
-    font-size: 70px;
-}
-
-h2, .rst-content .toctree-wrapper p.caption, h3, h4, h5, h6, legend{
-    font-family: Calibre-Medium, sans-serif;
-}
-
-@font-face {
-    font-family: Calibre-Medium;
-    src: url(./Calibre-Medium.otf);
-    font-weight:400;
-}
-
-@font-face {
-    font-family: Calibre;
-    src: url(./Calibre-Regular.otf);
-    font-weight:400;
-}
-
-@font-face {
-    font-family: Calibre-Light;
-    src: url(./Calibre-Light.ttf);
-    font-weight:400;
-}
-
-@font-face {
-    font-family: Calibre-Thin;
-    src: url(./Calibre-Thin.otf);
-    font-weight:400;
-}
-
-
-/**
- * Nav Links to other parts of huggingface.co
- */
- div.menu {
-    position: absolute;
-    top: 0;
-    right: 0;
-    padding-top: 20px;
-    padding-right: 20px;
-    z-index: 1000;
-}
-div.menu a {
-    font-size: 14px;
-    letter-spacing: 0.3px;
-    text-transform: uppercase;
-    color: white;
-    -webkit-font-smoothing: antialiased;
-    background: linear-gradient(0deg, #6671ffb8, #9a66ffb8 50%);
-    padding: 10px 16px 6px 16px;
-    border-radius: 3px;
-    margin-left: 12px;
-    position: relative;
-}
-div.menu a:active {
-    top: 1px;
-}
-@media (min-width: 768px) and (max-width: 1750px) {
-    .wy-breadcrumbs {
-        margin-top: 32px;
-    }
-}
-@media (max-width: 768px) {
-    div.menu {
-        display: none;
-    }
-}
--- a/docs/source/_static/js/custom.js
+++ b/docs/source/_static/js/custom.js
--- a/docs/source/_static/js/huggingface_logo.svg
+++ b/docs/source/_static/js/huggingface_logo.svg
--- a/docs/source/_toctree.yml
+++ b/docs/source/_toctree.yml
@@ -0,0 +1,306 @@
+- sections: 
+  - local: index
+    title: 🤗 Transformers
+  - local: quicktour
+    title: Quick tour
+  - local: installation
+    title: Installation
+  - local: philosophy
+    title: Philosophy
+  - local: glossary
+    title: Glossary
+  title: Get started
+- sections:
+  - local: task_summary
+    title: Summary of the tasks
+  - local: model_summary
+    title: Summary of the models
+  - local: preprocessing
+    title: Preprocessing data
+  - local: training
+    title: Fine-tuning a pretrained model
+  - local: model_sharing
+    title: Model sharing and uploading
+  - local: tokenizer_summary
+    title: Summary of the tokenizers
+  - local: multilingual
+    title: Multi-lingual models
+  title: "Using 🤗 Transformers"
+- sections:
+  - local: examples
+    title: Examples
+  - local: troubleshooting
+    title: Troubleshooting
+  - local: custom_datasets
+    title: Fine-tuning with custom datasets
+  - local: notebooks
+    title: "🤗 Transformers Notebooks"
+  - local: sagemaker
+    title: Run training on Amazon SageMaker
+  - local: community
+    title: Community
+  - local: converting_tensorflow_models
+    title: Converting Tensorflow Checkpoints
+  - local: migration
+    title: Migrating from previous packages
+  - local: contributing
+    title: How to contribute to transformers?
+  - local: add_new_model
+    title: "How to add a model to 🤗 Transformers?"
+  - local: add_new_pipeline
+    title: "How to add a pipeline to 🤗 Transformers?"
+  - local: fast_tokenizers
+    title: "Using tokenizers from 🤗 Tokenizers"
+  - local: performance
+    title: 'Performance and Scalability: How To Fit a Bigger Model and Train It Faster'
+  - local: parallelism
+    title: Model Parallelism
+  - local: testing
+    title: Testing
+  - local: debugging
+    title: Debugging
+  - local: serialization
+    title: Exporting transformers models
+  title: Advanced guides
+- sections:
+  - local: bertology
+    title: BERTology
+  - local: perplexity
+    title: Perplexity of fixed-length models
+  - local: benchmarks
+    title: Benchmarks
+  title: Research
+- sections:
+  - sections:
+    - local: main_classes/callback
+      title: Callbacks
+    - local: main_classes/configuration
+      title: Configuration
+    - local: main_classes/data_collator
+      title: Data Collator
+    - local: main_classes/keras_callbacks
+      title: Keras callbacks
+    - local: main_classes/logging
+      title: Logging
+    - local: main_classes/model
+      title: Models
+    - local: main_classes/optimizer_schedules
+      title: Optimization
+    - local: main_classes/output
+      title: Model outputs
+    - local: main_classes/pipelines
+      title: Pipelines
+    - local: main_classes/processors
+      title: Processors
+    - local: main_classes/tokenizer
+      title: Tokenizer
+    - local: main_classes/trainer
+      title: Trainer
+    - local: main_classes/deepspeed
+      title: DeepSpeed Integration
+    - local: main_classes/feature_extractor
+      title: Feature Extractor
+    title: Main Classes
+  - sections:
+    - local: model_doc/albert
+      title: ALBERT
+    - local: model_doc/auto
+      title: Auto Classes
+    - local: model_doc/bart
+      title: BART
+    - local: model_doc/barthez
+      title: BARThez
+    - local: model_doc/bartpho
+      title: BARTpho
+    - local: model_doc/beit
+      title: BEiT
+    - local: model_doc/bert
+      title: BERT
+    - local: model_doc/bertweet
+      title: Bertweet
+    - local: model_doc/bertgeneration
+      title: BertGeneration
+    - local: model_doc/bert_japanese
+      title: BertJapanese
+    - local: model_doc/bigbird
+      title: BigBird
+    - local: model_doc/bigbird_pegasus
+      title: BigBirdPegasus
+    - local: model_doc/blenderbot
+      title: Blenderbot
+    - local: model_doc/blenderbot_small
+      title: Blenderbot Small
+    - local: model_doc/bort
+      title: BORT
+    - local: model_doc/byt5
+      title: ByT5
+    - local: model_doc/camembert
+      title: CamemBERT
+    - local: model_doc/canine
+      title: CANINE
+    - local: model_doc/clip
+      title: CLIP
+    - local: model_doc/convbert
+      title: ConvBERT
+    - local: model_doc/cpm
+      title: CPM
+    - local: model_doc/ctrl
+      title: CTRL
+    - local: model_doc/deberta
+      title: DeBERTa
+    - local: model_doc/deberta_v2
+      title: DeBERTa-v2
+    - local: model_doc/deit
+      title: DeiT
+    - local: model_doc/detr
+      title: DETR
+    - local: model_doc/dialogpt
+      title: DialoGPT
+    - local: model_doc/distilbert
+      title: DistilBERT
+    - local: model_doc/dpr
+      title: DPR
+    - local: model_doc/electra
+      title: ELECTRA
+    - local: model_doc/encoderdecoder
+      title: Encoder Decoder Models
+    - local: model_doc/flaubert
+      title: FlauBERT
+    - local: model_doc/fnet
+      title: FNet
+    - local: model_doc/fsmt
+      title: FSMT
+    - local: model_doc/funnel
+      title: Funnel Transformer
+    - local: model_doc/herbert
+      title: herBERT
+    - local: model_doc/ibert
+      title: I-BERT
+    - local: model_doc/imagegpt
+      title: ImageGPT
+    - local: model_doc/layoutlm
+      title: LayoutLM
+    - local: model_doc/layoutlmv2
+      title: LayoutLMV2
+    - local: model_doc/layoutxlm
+      title: LayoutXLM
+    - local: model_doc/led
+      title: LED
+    - local: model_doc/longformer
+      title: Longformer
+    - local: model_doc/luke
+      title: LUKE
+    - local: model_doc/lxmert
+      title: LXMERT
+    - local: model_doc/marian
+      title: MarianMT
+    - local: model_doc/m2m_100
+      title: M2M100
+    - local: model_doc/mbart
+      title: MBart and MBart-50
+    - local: model_doc/megatron_bert
+      title: MegatronBERT
+    - local: model_doc/megatron_gpt2
+      title: MegatronGPT2
+    - local: model_doc/mobilebert
+      title: MobileBERT
+    - local: model_doc/mpnet
+      title: MPNet
+    - local: model_doc/mt5
+      title: MT5
+    - local: model_doc/gpt
+      title: OpenAI GPT
+    - local: model_doc/gpt2
+      title: OpenAI GPT2
+    - local: model_doc/gptj
+      title: GPT-J
+    - local: model_doc/gpt_neo
+      title: GPT Neo
+    - local: model_doc/hubert
+      title: Hubert
+    - local: model_doc/perceiver
+      title: Perceiver
+    - local: model_doc/pegasus
+      title: Pegasus
+    - local: model_doc/phobert
+      title: PhoBERT
+    - local: model_doc/prophetnet
+      title: ProphetNet
+    - local: model_doc/qdqbert
+      title: QDQBert
+    - local: model_doc/rag
+      title: RAG
+    - local: model_doc/reformer
+      title: Reformer
+    - local: model_doc/rembert
+      title: RemBERT
+    - local: model_doc/retribert
+      title: RetriBERT
+    - local: model_doc/roberta
+      title: RoBERTa
+    - local: model_doc/roformer
+      title: RoFormer
+    - local: model_doc/segformer
+      title: SegFormer
+    - local: model_doc/sew
+      title: SEW
+    - local: model_doc/sew_d
+      title: SEW-D
+    - local: model_doc/speechencoderdecoder
+      title: Speech Encoder Decoder Models
+    - local: model_doc/speech_to_text
+      title: Speech2Text
+    - local: model_doc/speech_to_text_2
+      title: Speech2Text2
+    - local: model_doc/splinter
+      title: Splinter
+    - local: model_doc/squeezebert
+      title: SqueezeBERT
+    - local: model_doc/t5
+      title: T5
+    - local: model_doc/t5v1.1
+      title: T5v1.1
+    - local: model_doc/tapas
+      title: TAPAS
+    - local: model_doc/transformerxl
+      title: Transformer XL
+    - local: model_doc/trocr
+      title: TrOCR
+    - local: model_doc/unispeech
+      title: UniSpeech
+    - local: model_doc/unispeech_sat
+      title: UniSpeech-SAT
+    - local: model_doc/visionencoderdecoder
+      title: Vision Encoder Decoder Models
+    - local: model_doc/vit
+      title: Vision Transformer (ViT)
+    - local: model_doc/visual_bert
+      title: VisualBERT
+    - local: model_doc/wav2vec2
+      title: Wav2Vec2
+    - local: model_doc/xlm
+      title: XLM
+    - local: model_doc/xlmprophetnet
+      title: XLM-ProphetNet
+    - local: model_doc/xlmroberta
+      title: XLM-RoBERTa
+    - local: model_doc/xlnet
+      title: XLNet
+    - local: model_doc/xlsr_wav2vec2
+      title: XLSR-Wav2Vec2
+    title: Models
+  - sections:
+    - local: internal/modeling_utils
+      title: Custom Layers and Utilities
+    - local: internal/pipelines_utils
+      title: Utilities for pipelines
+    - local: internal/tokenization_utils
+      title: Utilities for Tokenizers
+    - local: internal/trainer_utils
+      title: Utilities for Trainer
+    - local: internal/generation_utils
+      title: Utilities for Generation
+    - local: internal/file_utils
+      title: General Utilities
+    title: Internal Helpers
+  title: API
--- a/docs/source/add_new_model.rst
+++ b/docs/source/add_new_model.rst
@@ -72,7 +72,7 @@ call the model to be added to 🤗 Transformers ``BrandNewBert``.

 Let's take a look:

-.. image:: ./imgs/transformers_overview.png
+.. image:: /imgs/transformers_overview.png

 As you can see, we do make use of inheritance in 🤗 Transformers, but we keep the level of abstraction to an absolute
 minimum. There are never more than two levels of abstraction for any model in the library. :obj:`BrandNewBertModel`
--- a/docs/source/add_new_pipeline.rst
+++ b/docs/source/add_new_pipeline.rst
@@ -29,23 +29,23 @@ Start by inheriting the base class :obj:`Pipeline`. with the 4 methods needed to
    from transformers import Pipeline

    class MyPipeline(Pipeline):
-        def _sanitize_parameters(self, **kwargs)
+        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)
+        def preprocess(self, inputs, maybe_arg=2):
            model_input = Tensor(....)
            return {"model_input": model_input}

-        def _forward(self, model_inputs)
+        def _forward(self, model_inputs):
            # model_inputs == {"model_input": model_input}
-            oututs = self.model(**model_inputs)
+            outputs = self.model(**model_inputs)
            # Maybe {"logits": Tensor(...)}
            return outputs

-        def postprocess(self, model_outputs)
+        def postprocess(self, model_outputs):
            best_class = model_outputs["logits"].softmax(-1)
            return best_class

@@ -89,12 +89,12 @@ In order to achieve that, we'll update our :obj:`postprocess` method with a defa
 .. code-block::


-        def postprocess(self, model_outputs, top_k=5)
+        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)
+        def _sanitize_parameters(self, **kwargs):
            preprocess_kwargs = {}
            if "maybe_arg" in kwargs:
                preprocess_kwargs["maybe_arg"] = kwargs["maybe_arg"]
--- a/docs/source/benchmarks.mdx
+++ b/docs/source/benchmarks.mdx
@@ -0,0 +1,345 @@
+<!--Copyright 2020 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# Benchmarks
+
+Let's take a look at how 🤗 Transformer models can be benchmarked, best practices, and already available benchmarks.
+
+A notebook explaining in more detail how to benchmark 🤗 Transformer models can be found [here](https://github.com/huggingface/transformers/tree/master/notebooks/05-benchmark.ipynb).
+
+## How to benchmark 🤗 Transformer 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_.
+
+<Tip>
+
+Hereby, _inference_ is defined by a single forward pass, and _training_ is defined by a single forward pass and
+backward pass.
+
+</Tip>
+
+The benchmark classes [`PyTorchBenchmark`] and [`TensorFlowBenchmark`] expect an object of type [`PyTorchBenchmarkArguments`] and
+[`TensorFlowBenchmarkArguments`], respectively, for instantiation. [`PyTorchBenchmarkArguments`] and [`TensorFlowBenchmarkArguments`] are data classes and contain all relevant configurations for their corresponding benchmark class. In the following example, it is shown how a BERT model of type _bert-base-cased_ can be benchmarked.
+
+```py
+>>> from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
+
+>>> 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])
+>>> benchmark = TensorFlowBenchmark(args)
+```
+
+Here, three arguments are given to the benchmark argument data classes, namely `models`, `batch_sizes`, and
+`sequence_lengths`. The argument `models` is required and expects a `list` of model identifiers from the
+[model hub](https://huggingface.co/models) The `list` arguments `batch_sizes` and `sequence_lengths` define
+the size of the `input_ids` on which the model is benchmarked. There are many more parameters that can be configured
+via the benchmark argument data classes. For more detail on these one can either directly consult the files
+`src/transformers/benchmark/benchmark_args_utils.py`, `src/transformers/benchmark/benchmark_args.py` (for PyTorch)
+and `src/transformers/benchmark/benchmark_args_tf.py` (for Tensorflow). Alternatively, running the following shell
+commands from root will print out a descriptive list of all configurable parameters for PyTorch and Tensorflow
+respectively.
+
+```bash
+python examples/pytorch/benchmarking/run_benchmark.py --help
+
+===PT-TF-SPLIT===
+python examples/tensorflow/benchmarking/run_benchmark_tf.py --help
+```
+
+An instantiated benchmark object can then simply be run by calling `benchmark.run()`.
+
+```py
+>>> results = benchmark.run()
+>>> print(results)
+====================       INFERENCE - SPEED - RESULT       ====================
+--------------------------------------------------------------------------------
+Model Name             Batch Size     Seq Length     Time in s                  
+--------------------------------------------------------------------------------
+bert-base-uncased          8               8             0.006     
+bert-base-uncased          8               32            0.006     
+bert-base-uncased          8              128            0.018     
+bert-base-uncased          8              512            0.088     
+--------------------------------------------------------------------------------
+
+====================      INFERENCE - MEMORY - RESULT       ====================
+--------------------------------------------------------------------------------
+Model Name             Batch Size     Seq Length    Memory in MB 
+--------------------------------------------------------------------------------
+bert-base-uncased          8               8             1227
+bert-base-uncased          8               32            1281
+bert-base-uncased          8              128            1307
+bert-base-uncased          8              512            1539
+--------------------------------------------------------------------------------
+
+====================        ENVIRONMENT INFORMATION         ====================
+
+- transformers_version: 2.11.0
+- framework: PyTorch
+- use_torchscript: False
+- framework_version: 1.4.0
+- python_version: 3.6.10
+- system: Linux
+- cpu: x86_64
+- architecture: 64bit
+- date: 2020-06-29
+- time: 08:58:43.371351
+- fp16: False
+- use_multiprocessing: True
+- only_pretrain_model: False
+- cpu_ram_mb: 32088
+- use_gpu: True
+- num_gpus: 1
+- gpu: TITAN RTX
+- gpu_ram_mb: 24217
+- gpu_power_watts: 280.0
+- gpu_performance_state: 2
+- use_tpu: False
+
+===PT-TF-SPLIT===
+>>> results = benchmark.run()
+>>> print(results)
+====================       INFERENCE - SPEED - RESULT       ====================
+--------------------------------------------------------------------------------
+Model Name             Batch Size     Seq Length     Time in s                  
+--------------------------------------------------------------------------------
+bert-base-uncased          8               8             0.005
+bert-base-uncased          8               32            0.008
+bert-base-uncased          8              128            0.022
+bert-base-uncased          8              512            0.105
+--------------------------------------------------------------------------------
+
+====================      INFERENCE - MEMORY - RESULT       ====================
+--------------------------------------------------------------------------------
+Model Name             Batch Size     Seq Length    Memory in MB 
+--------------------------------------------------------------------------------
+bert-base-uncased          8               8             1330
+bert-base-uncased          8               32            1330
+bert-base-uncased          8              128            1330
+bert-base-uncased          8              512            1770
+--------------------------------------------------------------------------------
+
+====================        ENVIRONMENT INFORMATION         ====================
+
+- transformers_version: 2.11.0
+- framework: Tensorflow
+- use_xla: False
+- framework_version: 2.2.0
+- python_version: 3.6.10
+- system: Linux
+- cpu: x86_64
+- architecture: 64bit
+- date: 2020-06-29
+- time: 09:26:35.617317
+- fp16: False
+- use_multiprocessing: True
+- only_pretrain_model: False
+- cpu_ram_mb: 32088
+- use_gpu: True
+- num_gpus: 1
+- gpu: TITAN RTX
+- gpu_ram_mb: 24217
+- gpu_power_watts: 280.0
+- gpu_performance_state: 2
+- use_tpu: False
+```
+
+By default, the _time_ and the _required memory_ for _inference_ are benchmarked. In the example output above the first
+two sections show the result corresponding to _inference time_ and _inference memory_. In addition, all relevant
+information about the computing environment, _e.g._ the GPU type, the system, the library versions, etc... are printed
+out in the third section under _ENVIRONMENT INFORMATION_. This information can optionally be saved in a _.csv_ file
+when adding the argument `save_to_csv=True` to [`PyTorchBenchmarkArguments`] and
+[`TensorFlowBenchmarkArguments`] respectively. In this case, every section is saved in a separate
+_.csv_ file. The path to each _.csv_ file can optionally be defined via the argument data classes.
+
+Instead of benchmarking pre-trained models via their model identifier, _e.g._ `bert-base-uncased`, the user can
+alternatively benchmark an arbitrary configuration of any available model class. In this case, a `list` of
+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])
+>>> config_base = BertConfig()
+>>> config_384_hid = BertConfig(hidden_size=384)
+>>> config_6_lay = BertConfig(num_hidden_layers=6)
+
+>>> benchmark = PyTorchBenchmark(args, configs=[config_base, config_384_hid, config_6_lay])
+>>> benchmark.run()
+====================       INFERENCE - SPEED - RESULT       ====================
+--------------------------------------------------------------------------------
+Model Name             Batch Size     Seq Length       Time in s                  
+--------------------------------------------------------------------------------
+bert-base                  8              128            0.006
+bert-base                  8              512            0.006
+bert-base                  8              128            0.018     
+bert-base                  8              512            0.088     
+bert-384-hid              8               8             0.006     
+bert-384-hid              8               32            0.006     
+bert-384-hid              8              128            0.011     
+bert-384-hid              8              512            0.054     
+bert-6-lay                 8               8             0.003     
+bert-6-lay                 8               32            0.004     
+bert-6-lay                 8              128            0.009     
+bert-6-lay                 8              512            0.044
+--------------------------------------------------------------------------------
+
+====================      INFERENCE - MEMORY - RESULT       ====================
+--------------------------------------------------------------------------------
+Model Name             Batch Size     Seq Length      Memory in MB 
+--------------------------------------------------------------------------------
+bert-base                  8               8             1277
+bert-base                  8               32            1281
+bert-base                  8              128            1307     
+bert-base                  8              512            1539     
+bert-384-hid              8               8             1005     
+bert-384-hid              8               32            1027     
+bert-384-hid              8              128            1035     
+bert-384-hid              8              512            1255     
+bert-6-lay                 8               8             1097     
+bert-6-lay                 8               32            1101     
+bert-6-lay                 8              128            1127     
+bert-6-lay                 8              512            1359
+--------------------------------------------------------------------------------
+
+====================        ENVIRONMENT INFORMATION         ====================
+
+- transformers_version: 2.11.0
+- framework: PyTorch
+- use_torchscript: False
+- framework_version: 1.4.0
+- python_version: 3.6.10
+- system: Linux
+- cpu: x86_64
+- architecture: 64bit
+- date: 2020-06-29
+- time: 09:35:25.143267
+- fp16: False
+- use_multiprocessing: True
+- only_pretrain_model: False
+- cpu_ram_mb: 32088
+- use_gpu: True
+- num_gpus: 1
+- gpu: TITAN RTX
+- gpu_ram_mb: 24217
+- gpu_power_watts: 280.0
+- gpu_performance_state: 2
+- use_tpu: False
+
+===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])
+>>> config_base = BertConfig()
+>>> config_384_hid = BertConfig(hidden_size=384)
+>>> config_6_lay = BertConfig(num_hidden_layers=6)
+
+>>> benchmark = TensorFlowBenchmark(args, configs=[config_base, config_384_hid, config_6_lay])
+>>> benchmark.run()
+====================       INFERENCE - SPEED - RESULT       ====================
+--------------------------------------------------------------------------------
+Model Name             Batch Size     Seq Length       Time in s                  
+--------------------------------------------------------------------------------
+bert-base                  8               8             0.005
+bert-base                  8               32            0.008
+bert-base                  8              128            0.022
+bert-base                  8              512            0.106
+bert-384-hid              8               8             0.005
+bert-384-hid              8               32            0.007
+bert-384-hid              8              128            0.018
+bert-384-hid              8              512            0.064
+bert-6-lay                 8               8             0.002
+bert-6-lay                 8               32            0.003
+bert-6-lay                 8              128            0.0011
+bert-6-lay                 8              512            0.074
+--------------------------------------------------------------------------------
+
+====================      INFERENCE - MEMORY - RESULT       ====================
+--------------------------------------------------------------------------------
+Model Name             Batch Size     Seq Length      Memory in MB 
+--------------------------------------------------------------------------------
+bert-base                  8               8             1330
+bert-base                  8               32            1330
+bert-base                  8              128            1330
+bert-base                  8              512            1770
+bert-384-hid              8               8             1330
+bert-384-hid              8               32            1330
+bert-384-hid              8              128            1330
+bert-384-hid              8              512            1540
+bert-6-lay                 8               8             1330
+bert-6-lay                 8               32            1330
+bert-6-lay                 8              128            1330
+bert-6-lay                 8              512            1540
+--------------------------------------------------------------------------------
+
+====================        ENVIRONMENT INFORMATION         ====================
+
+- transformers_version: 2.11.0
+- framework: Tensorflow
+- use_xla: False
+- framework_version: 2.2.0
+- python_version: 3.6.10
+- system: Linux
+- cpu: x86_64
+- architecture: 64bit
+- date: 2020-06-29
+- time: 09:38:15.487125
+- fp16: False
+- use_multiprocessing: True
+- only_pretrain_model: False
+- cpu_ram_mb: 32088
+- use_gpu: True
+- num_gpus: 1
+- gpu: TITAN RTX
+- gpu_ram_mb: 24217
+- gpu_power_watts: 280.0
+- gpu_performance_state: 2
+- use_tpu: False
+```
+
+Again, _inference time_ and _required memory_ for _inference_ are measured, but this time for customized configurations
+of the `BertModel` class. This feature can especially be helpful when deciding for which configuration the model
+should be trained.
+
+
+## Benchmark best practices
+
+This section lists a couple of best practices one should be aware of when benchmarking a model.
+
+- Currently, only single device benchmarking is supported. When benchmarking on GPU, it is recommended that the user
+  specifies on which device the code should be run by setting the `CUDA_VISIBLE_DEVICES` environment variable in the
+  shell, _e.g._ `export CUDA_VISIBLE_DEVICES=0` before running the code.
+- The option `no_multi_processing` should only be set to `True` for testing and debugging. To ensure accurate
+  memory measurement it is recommended to run each memory benchmark in a separate process by making sure
+  `no_multi_processing` is set to `True`.
+- One should always state the environment information when sharing the results of a model benchmark. Results can vary
+  heavily between different GPU devices, library versions, etc., so that benchmark results on their own are not very
+  useful for the community.
+
+
+## Sharing your benchmark
+
+Previously all available core models (10 at the time) have been benchmarked for _inference time_, across many different
+settings: using PyTorch, with and without TorchScript, using TensorFlow, with and without XLA. All of those tests were
+done across CPUs (except for TensorFlow XLA) and GPUs.
+
+The approach is detailed in the [following blogpost](https://medium.com/huggingface/benchmarking-transformers-pytorch-and-tensorflow-e2917fb891c2) and the results are
+available [here](https://docs.google.com/spreadsheets/d/1sryqufw2D0XlUH4sq3e9Wnxu5EAQkaohzrJbd5HdQ_w/edit?usp=sharing).
+
+With the new _benchmark_ tools, it is easier than ever to share your benchmark results with the community
+
+- [PyTorch Benchmarking Results](https://github.com/huggingface/transformers/tree/master/examples/pytorch/benchmarking/README.md).
+- [TensorFlow Benchmarking Results](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/benchmarking/README.md).
--- a/docs/source/benchmarks.rst
+++ b/docs/source/benchmarks.rst
@@ -1,363 +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.
-
-Benchmarks
-=======================================================================================================================
-
-Let's take a look at how 🤗 Transformer models can be benchmarked, best practices, and already available benchmarks.
-
-A notebook explaining in more detail how to benchmark 🤗 Transformer models can be found :prefix_link:`here
-<notebooks/05-benchmark.ipynb>`.
-
-How to benchmark 🤗 Transformer models
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The classes :class:`~transformers.PyTorchBenchmark` and :class:`~transformers.TensorFlowBenchmark` allow to flexibly
-benchmark 🤗 Transformer models. The benchmark classes allow us to measure the `peak memory usage` and `required time`
-for both `inference` and `training`.
-
-.. note::
-
-  Hereby, `inference` is defined by a single forward pass, and `training` is defined by a single forward pass and
-  backward pass.
-
-The benchmark classes :class:`~transformers.PyTorchBenchmark` and :class:`~transformers.TensorFlowBenchmark` expect an
-object of type :class:`~transformers.PyTorchBenchmarkArguments` and
-:class:`~transformers.TensorFlowBenchmarkArguments`, respectively, for instantiation.
-:class:`~transformers.PyTorchBenchmarkArguments` and :class:`~transformers.TensorFlowBenchmarkArguments` are data
-classes and contain all relevant configurations for their corresponding benchmark class. In the following example, it
-is shown how a BERT model of type `bert-base-cased` can be benchmarked.
-
-.. code-block::
-
-    >>> ## PYTORCH CODE
-    >>> from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
-
-    >>> args = PyTorchBenchmarkArguments(models=["bert-base-uncased"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512])
-    >>> benchmark = PyTorchBenchmark(args)
-
-    >>> ## TENSORFLOW CODE
-    >>> from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
-
-    >>> args = TensorFlowBenchmarkArguments(models=["bert-base-uncased"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512])
-    >>> benchmark = TensorFlowBenchmark(args)
-
-
-Here, three arguments are given to the benchmark argument data classes, namely ``models``, ``batch_sizes``, and
-``sequence_lengths``. The argument ``models`` is required and expects a :obj:`list` of model identifiers from the
-`model hub <https://huggingface.co/models>`__ The :obj:`list` arguments ``batch_sizes`` and ``sequence_lengths`` define
-the size of the ``input_ids`` on which the model is benchmarked. There are many more parameters that can be configured
-via the benchmark argument data classes. For more detail on these one can either directly consult the files
-``src/transformers/benchmark/benchmark_args_utils.py``, ``src/transformers/benchmark/benchmark_args.py`` (for PyTorch)
-and ``src/transformers/benchmark/benchmark_args_tf.py`` (for Tensorflow). Alternatively, running the following shell
-commands from root will print out a descriptive list of all configurable parameters for PyTorch and Tensorflow
-respectively.
-
-.. code-block:: bash
-
-    ## PYTORCH CODE
-    python examples/pytorch/benchmarking/run_benchmark.py --help
-
-    ## TENSORFLOW CODE
-    python examples/tensorflow/benchmarking/run_benchmark_tf.py --help
-
-
-An instantiated benchmark object can then simply be run by calling ``benchmark.run()``.
-
-.. code-block::
-
-    >>> ## PYTORCH CODE
-    >>> results = benchmark.run()
-    >>> print(results)
-    ====================       INFERENCE - SPEED - RESULT       ====================
-    --------------------------------------------------------------------------------
-    Model Name             Batch Size     Seq Length     Time in s                  
-    --------------------------------------------------------------------------------
-    bert-base-uncased          8               8             0.006     
-    bert-base-uncased          8               32            0.006     
-    bert-base-uncased          8              128            0.018     
-    bert-base-uncased          8              512            0.088     
-    --------------------------------------------------------------------------------
-
-    ====================      INFERENCE - MEMORY - RESULT       ====================
-    --------------------------------------------------------------------------------
-    Model Name             Batch Size     Seq Length    Memory in MB 
-    --------------------------------------------------------------------------------
-    bert-base-uncased          8               8             1227
-    bert-base-uncased          8               32            1281
-    bert-base-uncased          8              128            1307
-    bert-base-uncased          8              512            1539
-    --------------------------------------------------------------------------------
-
-    ====================        ENVIRONMENT INFORMATION         ====================
-
-    - transformers_version: 2.11.0
-    - framework: PyTorch
-    - use_torchscript: False
-    - framework_version: 1.4.0
-    - python_version: 3.6.10
-    - system: Linux
-    - cpu: x86_64
-    - architecture: 64bit
-    - date: 2020-06-29
-    - time: 08:58:43.371351
-    - fp16: False
-    - use_multiprocessing: True
-    - only_pretrain_model: False
-    - cpu_ram_mb: 32088
-    - use_gpu: True
-    - num_gpus: 1
-    - gpu: TITAN RTX
-    - gpu_ram_mb: 24217
-    - gpu_power_watts: 280.0
-    - gpu_performance_state: 2
-    - use_tpu: False
-
-    >>> ## TENSORFLOW CODE
-    >>> results = benchmark.run()
-    >>> print(results)
-    ====================       INFERENCE - SPEED - RESULT       ====================
-    --------------------------------------------------------------------------------
-    Model Name             Batch Size     Seq Length     Time in s                  
-    --------------------------------------------------------------------------------
-    bert-base-uncased          8               8             0.005
-    bert-base-uncased          8               32            0.008
-    bert-base-uncased          8              128            0.022
-    bert-base-uncased          8              512            0.105
-    --------------------------------------------------------------------------------
-
-    ====================      INFERENCE - MEMORY - RESULT       ====================
-    --------------------------------------------------------------------------------
-    Model Name             Batch Size     Seq Length    Memory in MB 
-    --------------------------------------------------------------------------------
-    bert-base-uncased          8               8             1330
-    bert-base-uncased          8               32            1330
-    bert-base-uncased          8              128            1330
-    bert-base-uncased          8              512            1770
-    --------------------------------------------------------------------------------
-
-    ====================        ENVIRONMENT INFORMATION         ====================
-
-    - transformers_version: 2.11.0
-    - framework: Tensorflow
-    - use_xla: False
-    - framework_version: 2.2.0
-    - python_version: 3.6.10
-    - system: Linux
-    - cpu: x86_64
-    - architecture: 64bit
-    - date: 2020-06-29
-    - time: 09:26:35.617317
-    - fp16: False
-    - use_multiprocessing: True
-    - only_pretrain_model: False
-    - cpu_ram_mb: 32088
-    - use_gpu: True
-    - num_gpus: 1
-    - gpu: TITAN RTX
-    - gpu_ram_mb: 24217
-    - gpu_power_watts: 280.0
-    - gpu_performance_state: 2
-    - use_tpu: False
-
-By default, the `time` and the `required memory` for `inference` are benchmarked. In the example output above the first
-two sections show the result corresponding to `inference time` and `inference memory`. In addition, all relevant
-information about the computing environment, `e.g.` the GPU type, the system, the library versions, etc... are printed
-out in the third section under `ENVIRONMENT INFORMATION`. This information can optionally be saved in a `.csv` file
-when adding the argument :obj:`save_to_csv=True` to :class:`~transformers.PyTorchBenchmarkArguments` and
-:class:`~transformers.TensorFlowBenchmarkArguments` respectively. In this case, every section is saved in a separate
-`.csv` file. The path to each `.csv` file can optionally be defined via the argument data classes.
-
-Instead of benchmarking pre-trained models via their model identifier, `e.g.` `bert-base-uncased`, the user can
-alternatively benchmark an arbitrary configuration of any available model class. In this case, a :obj:`list` of
-configurations must be inserted with the benchmark args as follows.
-
-.. code-block::
-
-    >>> ## PYTORCH CODE
-    >>> 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])
-    >>> config_base = BertConfig()
-    >>> config_384_hid = BertConfig(hidden_size=384)
-    >>> config_6_lay = BertConfig(num_hidden_layers=6)
-
-    >>> benchmark = PyTorchBenchmark(args, configs=[config_base, config_384_hid, config_6_lay])
-    >>> benchmark.run()
-    ====================       INFERENCE - SPEED - RESULT       ====================
-    --------------------------------------------------------------------------------
-    Model Name             Batch Size     Seq Length       Time in s                  
-    --------------------------------------------------------------------------------
-    bert-base                  8              128            0.006
-    bert-base                  8              512            0.006
-    bert-base                  8              128            0.018     
-    bert-base                  8              512            0.088     
-    bert-384-hid              8               8             0.006     
-    bert-384-hid              8               32            0.006     
-    bert-384-hid              8              128            0.011     
-    bert-384-hid              8              512            0.054     
-    bert-6-lay                 8               8             0.003     
-    bert-6-lay                 8               32            0.004     
-    bert-6-lay                 8              128            0.009     
-    bert-6-lay                 8              512            0.044
-    --------------------------------------------------------------------------------
-
-    ====================      INFERENCE - MEMORY - RESULT       ====================
-    --------------------------------------------------------------------------------
-    Model Name             Batch Size     Seq Length      Memory in MB 
-    --------------------------------------------------------------------------------
-    bert-base                  8               8             1277
-    bert-base                  8               32            1281
-    bert-base                  8              128            1307     
-    bert-base                  8              512            1539     
-    bert-384-hid              8               8             1005     
-    bert-384-hid              8               32            1027     
-    bert-384-hid              8              128            1035     
-    bert-384-hid              8              512            1255     
-    bert-6-lay                 8               8             1097     
-    bert-6-lay                 8               32            1101     
-    bert-6-lay                 8              128            1127     
-    bert-6-lay                 8              512            1359
-    --------------------------------------------------------------------------------
-
-    ====================        ENVIRONMENT INFORMATION         ====================
-
-    - transformers_version: 2.11.0
-    - framework: PyTorch
-    - use_torchscript: False
-    - framework_version: 1.4.0
-    - python_version: 3.6.10
-    - system: Linux
-    - cpu: x86_64
-    - architecture: 64bit
-    - date: 2020-06-29
-    - time: 09:35:25.143267
-    - fp16: False
-    - use_multiprocessing: True
-    - only_pretrain_model: False
-    - cpu_ram_mb: 32088
-    - use_gpu: True
-    - num_gpus: 1
-    - gpu: TITAN RTX
-    - gpu_ram_mb: 24217
-    - gpu_power_watts: 280.0
-    - gpu_performance_state: 2
-    - use_tpu: False
-
-    >>> ## TENSORFLOW CODE
-    >>> 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])
-    >>> config_base = BertConfig()
-    >>> config_384_hid = BertConfig(hidden_size=384)
-    >>> config_6_lay = BertConfig(num_hidden_layers=6)
-
-    >>> benchmark = TensorFlowBenchmark(args, configs=[config_base, config_384_hid, config_6_lay])
-    >>> benchmark.run()
-    ====================       INFERENCE - SPEED - RESULT       ====================
-    --------------------------------------------------------------------------------
-    Model Name             Batch Size     Seq Length       Time in s                  
-    --------------------------------------------------------------------------------
-    bert-base                  8               8             0.005
-    bert-base                  8               32            0.008
-    bert-base                  8              128            0.022
-    bert-base                  8              512            0.106
-    bert-384-hid              8               8             0.005
-    bert-384-hid              8               32            0.007
-    bert-384-hid              8              128            0.018
-    bert-384-hid              8              512            0.064
-    bert-6-lay                 8               8             0.002
-    bert-6-lay                 8               32            0.003
-    bert-6-lay                 8              128            0.0011
-    bert-6-lay                 8              512            0.074
-    --------------------------------------------------------------------------------
-
-    ====================      INFERENCE - MEMORY - RESULT       ====================
-    --------------------------------------------------------------------------------
-    Model Name             Batch Size     Seq Length      Memory in MB 
-    --------------------------------------------------------------------------------
-    bert-base                  8               8             1330
-    bert-base                  8               32            1330
-    bert-base                  8              128            1330
-    bert-base                  8              512            1770
-    bert-384-hid              8               8             1330
-    bert-384-hid              8               32            1330
-    bert-384-hid              8              128            1330
-    bert-384-hid              8              512            1540
-    bert-6-lay                 8               8             1330
-    bert-6-lay                 8               32            1330
-    bert-6-lay                 8              128            1330
-    bert-6-lay                 8              512            1540
-    --------------------------------------------------------------------------------
-
-    ====================        ENVIRONMENT INFORMATION         ====================
-
-    - transformers_version: 2.11.0
-    - framework: Tensorflow
-    - use_xla: False
-    - framework_version: 2.2.0
-    - python_version: 3.6.10
-    - system: Linux
-    - cpu: x86_64
-    - architecture: 64bit
-    - date: 2020-06-29
-    - time: 09:38:15.487125
-    - fp16: False
-    - use_multiprocessing: True
-    - only_pretrain_model: False
-    - cpu_ram_mb: 32088
-    - use_gpu: True
-    - num_gpus: 1
-    - gpu: TITAN RTX
-    - gpu_ram_mb: 24217
-    - gpu_power_watts: 280.0
-    - gpu_performance_state: 2
-    - use_tpu: False
-
-
-Again, `inference time` and `required memory` for `inference` are measured, but this time for customized configurations
-of the :obj:`BertModel` class. This feature can especially be helpful when deciding for which configuration the model
-should be trained.
-
-
-Benchmark best practices
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-This section lists a couple of best practices one should be aware of when benchmarking a model.
-
- Currently, only single device benchmarking is supported. When benchmarking on GPU, it is recommended that the user
-  specifies on which device the code should be run by setting the ``CUDA_VISIBLE_DEVICES`` environment variable in the
-  shell, `e.g.` ``export CUDA_VISIBLE_DEVICES=0`` before running the code.
- The option :obj:`no_multi_processing` should only be set to :obj:`True` for testing and debugging. To ensure accurate
-  memory measurement it is recommended to run each memory benchmark in a separate process by making sure
-  :obj:`no_multi_processing` is set to :obj:`True`.
- One should always state the environment information when sharing the results of a model benchmark. Results can vary
-  heavily between different GPU devices, library versions, etc., so that benchmark results on their own are not very
-  useful for the community.
-
-
-Sharing your benchmark
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Previously all available core models (10 at the time) have been benchmarked for `inference time`, across many different
-settings: using PyTorch, with and without TorchScript, using TensorFlow, with and without XLA. All of those tests were
-done across CPUs (except for TensorFlow XLA) and GPUs.
-
-The approach is detailed in the `following blogpost
-<https://medium.com/huggingface/benchmarking-transformers-pytorch-and-tensorflow-e2917fb891c2>`__ and the results are
-available `here
-<https://docs.google.com/spreadsheets/d/1sryqufw2D0XlUH4sq3e9Wnxu5EAQkaohzrJbd5HdQ_w/edit?usp=sharing>`__.
-
-With the new `benchmark` tools, it is easier than ever to share your benchmark results with the community
-
- :prefix_link:`PyTorch Benchmarking Results<examples/pytorch/benchmarking/README.md>`.
- :prefix_link:`TensorFlow Benchmarking Results<examples/tensorflow/benchmarking/README.md>`.
--- a/docs/source/community.md
+++ b/docs/source/community.md
@@ -6,7 +6,7 @@ This page regroups resources around 🤗 Transformers developed by the community

 | Resource     |      Description      |      Author      |
 |:----------|:-------------|------:|
-| [Hugging Face Transformers Glossary Flashcards](https://www.darigovresearch.com/huggingface-transformers-glossary-flashcards) | A set of flashcards based on the [Transformers Docs Glossary](https://huggingface.co/transformers/master/glossary.html) that has been put into a form which can be easily learnt/revised using [Anki ](https://apps.ankiweb.net/) an open source, cross platform app specifically designed for long term knowledge retention. See this [Introductory video on how to use the flashcards](https://www.youtube.com/watch?v=Dji_h7PILrw). | [Darigov Research](https://www.darigovresearch.com/) |
+| [Hugging Face Transformers Glossary Flashcards](https://www.darigovresearch.com/huggingface-transformers-glossary-flashcards) | A set of flashcards based on the [Transformers Docs Glossary](glossary) that has been put into a form which can be easily learnt/revised using [Anki ](https://apps.ankiweb.net/) an open source, cross platform app specifically designed for long term knowledge retention. See this [Introductory video on how to use the flashcards](https://www.youtube.com/watch?v=Dji_h7PILrw). | [Darigov Research](https://www.darigovresearch.com/) |

 ## Community notebooks:

--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -1,226 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-# Configuration file for the Sphinx documentation builder.
-#
-# This file does only contain a selection of the most common options. For a
-# full list see the documentation:
-# http://www.sphinx-doc.org/en/master/config
-
-# -- Path setup --------------------------------------------------------------
-
-# If extensions (or modules to document with autodoc) are in another directory,
-# add these directories to sys.path here. If the directory is relative to the
-# documentation root, use os.path.abspath to make it absolute, like shown here.
-#
-import os
-import sys
-
-sys.path.insert(0, os.path.abspath("../../src"))
-
-
-# -- Project information -----------------------------------------------------
-
-project = "transformers"
-copyright = "2020, The Hugging Face Team, Licenced under the Apache License, Version 2.0"
-author = "huggingface"
-
-# The short X.Y version
-version = ""
-# The full version, including alpha/beta/rc tags
-release = "4.12.0"
-
-
-
-
-
-
-
-
-# Prefix link to point to master, comment this during version release and uncomment below line
-extlinks = {"prefix_link": ("https://github.com/huggingface/transformers/blob/master/%s", "")}
-# Prefix link to always point to corresponding version, uncomment this during version release
-# extlinks = {'prefix_link': ('https://github.com/huggingface/transformers/blob/v'+ release + '/%s', '')}
-
-# -- General configuration ---------------------------------------------------
-
-# If your documentation needs a minimal Sphinx version, state it here.
-#
-# needs_sphinx = '1.0'
-
-# Add any Sphinx extension module names here, as strings. They can be
-# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
-# ones.
-extensions = [
-    "sphinx.ext.autodoc",
-    "sphinx.ext.extlinks",
-    "sphinx.ext.coverage",
-    "sphinx.ext.napoleon",
-    "recommonmark",
-    "sphinx.ext.viewcode",
-    "sphinx_markdown_tables",
-    "sphinxext.opengraph",
-    "sphinx_copybutton",
-]
-
-# Add any paths that contain templates here, relative to this directory.
-templates_path = ["_templates"]
-
-# The suffix(es) of source filenames.
-# You can specify multiple suffix as a list of string:
-#
-source_suffix = [".rst", ".md"]
-# source_suffix = '.rst'
-
-# The master toctree document.
-master_doc = "index"
-
-# The language for content autogenerated by Sphinx. Refer to documentation
-# for a list of supported languages.
-#
-# This is also used if you do content translation via gettext catalogs.
-# Usually you set "language" from the command line for these cases.
-language = None
-
-# List of patterns, relative to source directory, that match files and
-# directories to ignore when looking for source files.
-# This pattern also affects html_static_path and html_extra_path.
-exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
-
-# The name of the Pygments (syntax highlighting) style to use.
-pygments_style = None
-
-# Remove the prompt when copying examples
-copybutton_prompt_text = r">>> |\.\.\. "
-copybutton_prompt_is_regexp = True
-
-# -- Options for HTML output -------------------------------------------------
-
-# The theme to use for HTML and HTML Help pages.  See the documentation for
-# a list of builtin themes.
-#
-html_theme = "sphinx_rtd_theme"
-
-# Theme options are theme-specific and customize the look and feel of a theme
-# further.  For a list of options available for each theme, see the
-# documentation.
-#
-html_theme_options = {"analytics_id": "UA-83738774-2", "navigation_with_keys": True}
-
-#  Configuration for OpenGraph and Twitter Card Tags.
-# These are responsible for creating nice shareable social images https://ahrefs.com/blog/open-graph-meta-tags/
-# https://ogp.me/#type_website
-ogp_image = "https://huggingface.co/front/thumbnails/transformers.png"
-ogp_description = "State-of-the-art Natural Language Processing for PyTorch and TensorFlow 2.0. Transformers provides thousands of pretrained models to perform tasks on texts such as classification, information extraction, question answering, summarization, translation, text generation, etc in 100+ languages. Its aim is to make cutting-edge NLP easier to use for everyone"
-ogp_description_length = 160
-
-ogp_custom_meta_tags = [
-    f'<meta name="twitter:image" content="{ogp_image}">',
-    f'<meta name="twitter:description" content="{ogp_description}">',
-]
-
-# Add any paths that contain custom static files (such as style sheets) here,
-# relative to this directory. They are copied after the builtin static files,
-# so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ["_static"]
-
-# Custom sidebar templates, must be a dictionary that maps document names
-# to template names.
-#
-# The default sidebars (for documents that don't match any pattern) are
-# defined by theme itself.  Builtin themes are using these templates by
-# default: ``['localtoc.html', 'relations.html', 'sourcelink.html',
-# 'searchbox.html']``.
-#
-# html_sidebars = {}
-
-# This must be the name of an image file (path relative to the configuration
-# directory) that is the favicon of the docs. Modern browsers use this as
-# the icon for tabs, windows and bookmarks. It should be a Windows-style
-# icon file (.ico).
-html_favicon = "favicon.ico"
-
-
-# -- Options for HTMLHelp output ---------------------------------------------
-
-# Output file base name for HTML help builder.
-htmlhelp_basename = "transformersdoc"
-
-
-# -- Options for LaTeX output ------------------------------------------------
-
-latex_elements = {
-    # The paper size ('letterpaper' or 'a4paper').
-    #
-    # 'papersize': 'letterpaper',
-    # The font size ('10pt', '11pt' or '12pt').
-    #
-    # 'pointsize': '10pt',
-    # Additional stuff for the LaTeX preamble.
-    #
-    # 'preamble': '',
-    # Latex figure (float) alignment
-    #
-    # 'figure_align': 'htbp',
-}
-
-# Grouping the document tree into LaTeX files. List of tuples
-# (source start file, target name, title,
-#  author, documentclass [howto, manual, or own class]).
-latex_documents = [
-    (master_doc, "transformers.tex", "transformers Documentation", "huggingface", "manual"),
-]
-
-
-# -- Options for manual page output ------------------------------------------
-
-# One entry per manual page. List of tuples
-# (source start file, name, description, authors, manual section).
-man_pages = [(master_doc, "transformers", "transformers Documentation", [author], 1)]
-
-
-# -- Options for Texinfo output ----------------------------------------------
-
-# Grouping the document tree into Texinfo files. List of tuples
-# (source start file, target name, title, author,
-#  dir menu entry, description, category)
-texinfo_documents = [
-    (
-        master_doc,
-        "transformers",
-        "transformers Documentation",
-        author,
-        "transformers",
-        "One line description of project.",
-        "Miscellaneous",
-    ),
-]
-
-
-# -- Options for Epub output -------------------------------------------------
-
-# Bibliographic Dublin Core info.
-epub_title = project
-
-# The unique identifier of the text. This can be a ISBN number
-# or the project homepage.
-#
-# epub_identifier = ''
-
-# A unique identification for the text.
-#
-# epub_uid = ''
-
-# A list of files that should not be packed into the epub file.
-epub_exclude_files = ["search.html"]
-
-# Localization
-locale_dirs = ['locale/']
-gettext_compact = False
-
-def setup(app):
-    app.add_css_file("css/huggingface.css")
-    app.add_css_file("css/code-snippets.css")
-    app.add_js_file("js/custom.js")
-
-
-# -- Extension configuration -------------------------------------------------
--- a/docs/source/converting_tensorflow_models.rst
+++ b/docs/source/converting_tensorflow_models.rst
@@ -26,22 +26,22 @@ 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
+<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>` \ ).
+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.
+checkpoint (the three files starting with ``bert_model.ckpt``) but be sure to keep the configuration file (\
+``bert_config.json``) and the vocabulary file (``vocab.txt``) as these are needed for the PyTorch model too.

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

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

@@ -64,9 +64,9 @@ 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.
+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:

@@ -104,7 +104,7 @@ 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>`__\ )
+<https://github.com/openai/gpt-2>`__)

 .. code-block:: shell

@@ -120,7 +120,7 @@ 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>`__\ )
+<https://github.com/kimiyoung/transformer-xl/tree/master/tf#obtain-and-evaluate-pretrained-sota-models>`__)

 .. code-block:: shell

--- a/docs/source/custom_datasets.mdx
+++ b/docs/source/custom_datasets.mdx
@@ -0,0 +1,679 @@
+<!--Copyright 2020 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# How to fine-tune a model for common downstream tasks
+
+This guide will show you how to fine-tune 🤗 Transformers models for common downstream tasks. You will use the 🤗
+Datasets library to quickly load and preprocess the datasets, getting them ready for training with PyTorch and
+TensorFlow.
+
+Before you begin, make sure you have the 🤗 Datasets library installed. For more detailed installation instructions,
+refer to the 🤗 Datasets [installation page](https://huggingface.co/docs/datasets/installation.html). All of the
+examples in this guide will use 🤗 Datasets to load and preprocess a dataset.
+
+```bash
+pip install datasets
+```
+
+Learn how to fine-tune a model for:
+
+- [seq_imdb](#seq_imdb)
+- [tok_ner](#tok_ner)
+- [qa_squad](#qa_squad)
+
+<a id='seq_imdb'></a>
+
+## Sequence classification with IMDb reviews
+
+Sequence classification refers to the task of classifying sequences of text according to a given number of classes. In
+this example, learn how to fine-tune a model on the [IMDb dataset](https://huggingface.co/datasets/imdb) to determine
+whether a review is positive or negative.
+
+<Tip>
+
+For a more in-depth example of how to fine-tune a model for text classification, take a look at the corresponding
+[PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/text_classification.ipynb)
+or [TensorFlow notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/text_classification-tf.ipynb).
+
+</Tip>
+
+### Load IMDb dataset
+
+The 🤗 Datasets library makes it simple to load a dataset:
+
+```python
+from datasets import load_dataset
+imdb = load_dataset("imdb")
+```
+
+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!'
+}
+```
+
+### Preprocess
+
+The next step is to tokenize the text into a readable format by the model. It is important to load the same tokenizer a
+model was trained with to ensure appropriately tokenized words. Load the DistilBERT tokenizer with the
+[`AutoTokenizer`] because we will eventually train a classifier using a pretrained [DistilBERT](https://huggingface.co/distilbert-base-uncased) model:
+
+```python
+from transformers import AutoTokenizer
+tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
+```
+
+Now that you have instantiated a tokenizer, create a function that will tokenize the text. You should also truncate
+longer sequences in the text to be no longer than the model's maximum input length:
+
+```python
+def preprocess_function(examples):
+    return tokenizer(examples["text"], truncation=True)
+```
+
+Use 🤗 Datasets `map` function to apply the preprocessing function to the entire dataset. You can also set
+`batched=True` to apply the preprocessing function to multiple elements of the dataset at once for faster
+preprocessing:
+
+```python
+tokenized_imdb = imdb.map(preprocess_function, batched=True)
+```
+
+Lastly, pad your text so they are a uniform length. While it is possible to pad your text in the `tokenizer` function
+by setting `padding=True`, it is more efficient to only pad the text to the length of the longest element in its
+batch. This is known as **dynamic padding**. You can do this with the `DataCollatorWithPadding` function:
+
+```python
+from transformers import DataCollatorWithPadding
+data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
+```
+
+### Fine-tune with the Trainer API
+
+Now load your model with the [`AutoModelForSequenceClassification`] class along with the number of expected labels:
+
+```python
+from transformers import AutoModelForSequenceClassification
+model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased", num_labels=2)
+```
+
+At this point, only three steps remain:
+
+1. Define your training hyperparameters in [`TrainingArguments`].
+2. Pass the training arguments to a [`Trainer`] along with the model, dataset, tokenizer, and data collator.
+3. Call [`Trainer.train()`] to fine-tune your model.
+
+```python
+from transformers import TrainingArguments, Trainer
+
+training_args = TrainingArguments(
+    output_dir='./results',
+    learning_rate=2e-5,
+    per_device_train_batch_size=16,
+    per_device_eval_batch_size=16,
+    num_train_epochs=5,
+    weight_decay=0.01,
+)
+
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized_imdb["train"],
+    eval_dataset=tokenized_imdb["test"],
+    tokenizer=tokenizer,
+    data_collator=data_collator,
+)
+
+trainer.train()
+```
+
+### Fine-tune with TensorFlow
+
+Fine-tuning with TensorFlow is just as easy, with only a few differences.
+
+Start by batching the processed examples together with dynamic padding using the [`DataCollatorWithPadding`] function.
+Make sure you set `return_tensors="tf"` to return `tf.Tensor` outputs instead of PyTorch tensors!
+
+```python
+from transformers import DataCollatorWithPadding
+data_collator = DataCollatorWithPadding(tokenizer, return_tensors="tf")
+```
+
+Next, convert your datasets to the `tf.data.Dataset` format with `to_tf_dataset`. Specify inputs and labels in the
+`columns` argument:
+
+```python
+tf_train_dataset = tokenized_imdb["train"].to_tf_dataset(
+    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'],
+    shuffle=False,
+    batch_size=16,
+    collate_fn=data_collator,
+)
+```
+
+Set up an optimizer function, learning rate schedule, and some training hyperparameters:
+
+```python
+from transformers import create_optimizer
+import tensorflow as tf
+
+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
+)
+```
+
+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)
+```
+
+Compile the model:
+
+```python
+import tensorflow as tf
+model.compile(optimizer=optimizer)
+```
+
+Finally, fine-tune the model by calling `model.fit`:
+
+```python
+model.fit(
+    tf_train_set,
+    validation_data=tf_validation_set,
+    epochs=num_train_epochs,
+)
+```
+
+<a id='tok_ner'></a>
+
+## Token classification with WNUT emerging entities
+
+Token classification refers to the task of classifying individual tokens in a sentence. One of the most common token
+classification tasks is Named Entity Recognition (NER). NER attempts to find a label for each entity in a sentence,
+such as a person, location, or organization. In this example, learn how to fine-tune a model on the [WNUT 17](https://huggingface.co/datasets/wnut_17) dataset to detect new entities.
+
+<Tip>
+
+For a more in-depth example of how to fine-tune a model for token classification, take a look at the corresponding
+[PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/token_classification.ipynb)
+or [TensorFlow notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/token_classification-tf.ipynb).
+
+</Tip>
+
+### Load WNUT 17 dataset
+
+Load the WNUT 17 dataset from the 🤗 Datasets library:
+
+```python
+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]
+{'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', '.']
+}
+```
+
+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'
+]
+```
+
+A letter prefixes each NER tag which can mean:
+
+- `B-` indicates the beginning of an entity.
+- `I-` indicates a token is contained inside the same entity (e.g., the `State` token is a part of an entity like
+  `Empire State Building`).
+- `0` indicates the token doesn't correspond to any entity.
+
+### Preprocess
+
+Now you need to tokenize the text. Load the DistilBERT tokenizer with an [`AutoTokenizer`]:
+
+```python
+from transformers import AutoTokenizer
+tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
+```
+
+Since the input has already been split into words, set `is_split_into_words=True` to tokenize the words into
+subwords:
+
+```python
+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]']
+```
+
+The addition of the special tokens `[CLS]` and `[SEP]` and subword tokenization creates a mismatch between the
+input and labels. Realign the labels and tokens by:
+
+1. Mapping all tokens to their corresponding word with the `word_ids` method.
+2. Assigning the label `-100` to the special tokens `[CLS]` and ``[SEP]``` so the PyTorch loss function ignores
+   them.
+3. Only labeling the first token of a given word. Assign `-100` to the other subtokens from the same word.
+
+Here is how you can create a function that will realign the labels and tokens:
+
+```python
+def tokenize_and_align_labels(examples):
+    tokenized_inputs = tokenizer(examples["tokens"], truncation=True, is_split_into_words=True)
+
+    labels = []
+    for i, label in enumerate(examples[f"ner_tags"]):
+        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.
+            if word_idx is None:
+                label_ids.append(-100)
+            elif word_idx != previous_word_idx:              # Only label the first token of a given word.
+                label_ids.append(label[word_idx])
+
+        labels.append(label_ids)
+
+    tokenized_inputs["labels"] = labels
+    return tokenized_inputs
+```
+
+Now tokenize and align the labels over the entire dataset with 🤗 Datasets `map` function:
+
+```python
+tokenized_wnut = wnut.map(tokenize_and_align_labels, batched=True)
+```
+
+Finally, pad your text and labels, so they are a uniform length:
+
+```python
+from transformers import DataCollatorForTokenClassification
+data_collator = DataCollatorForTokenClassification(tokenizer)
+```
+
+### Fine-tune with the Trainer API
+
+Load your model with the [`AutoModelForTokenClassification`] class along with the number of expected labels:
+
+```python
+from transformers import AutoModelForTokenClassification, TrainingArguments, Trainer
+model = AutoModelForTokenClassification.from_pretrained("distilbert-base-uncased", num_labels=len(label_list))
+```
+
+Gather your training arguments in [`TrainingArguments`]:
+
+```python
+training_args = TrainingArguments(
+    output_dir='./results',
+    evaluation_strategy="epoch",
+    learning_rate=2e-5,
+    per_device_train_batch_size=16,
+    per_device_eval_batch_size=16,
+    num_train_epochs=3,
+    weight_decay=0.01,
+)
+```
+
+Collect your model, training arguments, dataset, data collator, and tokenizer in [`Trainer`]:
+
+```python
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized_wnut["train"],
+    eval_dataset=tokenized_wnut["test"],
+    data_collator=data_collator,
+    tokenizer=tokenizer,
+)
+```
+
+Fine-tune your model:
+
+```python
+trainer.train()
+```
+
+### Fine-tune with TensorFlow
+
+Batch your examples together and pad your text and labels, so they are a uniform length:
+
+```python
+from transformers import DataCollatorForTokenClassification
+data_collator = DataCollatorForTokenClassification(tokenizer, return_tensors="tf")
+```
+
+Convert your datasets to the `tf.data.Dataset` format with `to_tf_dataset`:
+
+```python
+tf_train_set = tokenized_wnut["train"].to_tf_dataset(
+    columns=["attention_mask", "input_ids", "labels"],
+    shuffle=True,
+    batch_size=16,
+    collate_fn=data_collator,
+)
+
+tf_validation_set = tokenized_wnut["validation"].to_tf_dataset(
+    columns=["attention_mask", "input_ids", "labels"],
+    shuffle=False,
+    batch_size=16,
+    collate_fn=data_collator,
+)
+```
+
+Load the model with the [`TFAutoModelForTokenClassification`] class along with the number of expected labels:
+
+```python
+from transformers import TFAutoModelForTokenClassification
+model = TFAutoModelForTokenClassification.from_pretrained("distilbert-base-uncased", num_labels=len(label_list))
+```
+
+Set up an optimizer function, learning rate schedule, and some training hyperparameters:
+
+```python
+from transformers import create_optimizer
+
+batch_size = 16
+num_train_epochs = 3
+num_train_steps = (len(tokenized_datasets["train"]) // batch_size) * num_train_epochs
+optimizer, lr_schedule = create_optimizer(
+    init_lr=2e-5,
+    num_train_steps=num_train_steps,
+    weight_decay_rate=0.01,
+    num_warmup_steps=0,
+)
+```
+
+Compile the model:
+
+```python
+import tensorflow as tf
+model.compile(optimizer=optimizer)
+```
+
+Call `model.fit` to fine-tune your model:
+
+```python
+model.fit(
+    tf_train_set,
+    validation_data=tf_validation_set,
+    epochs=num_train_epochs,
+)
+```
+
+<a id='qa_squad'></a>
+
+## Question Answering with SQuAD
+
+There are many types of question answering (QA) tasks. Extractive QA focuses on identifying the answer from the text
+given a question. In this example, learn how to fine-tune a model on the [SQuAD](https://huggingface.co/datasets/squad) dataset.
+
+<Tip>
+
+For a more in-depth example of how to fine-tune a model for question answering, take a look at the corresponding
+[PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb)
+or [TensorFlow notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering-tf.ipynb).
+
+</Tip>
+
+### Load SQuAD dataset
+
+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]
+{'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',
+ 'question': 'To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?',
+ 'title': 'University_of_Notre_Dame'
+}
+```
+
+### Preprocess
+
+Load the DistilBERT tokenizer with an [`AutoTokenizer`]:
+
+```python
+from transformers import AutoTokenizer
+tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
+```
+
+There are a few things to be aware of when preprocessing text for question answering:
+
+1. Some examples in a dataset may have a very long `context` that exceeds the maximum input length of the model. You
+   can deal with this by truncating the `context` and set `truncation="only_second"`.
+2. Next, you need to map the start and end positions of the answer to the original context. Set
+   `return_offset_mapping=True` to handle this.
+3. With the mapping in hand, you can find the start and end tokens of the answer. Use the `sequence_ids` method to
+   find which part of the offset corresponds to the question, and which part of the offset corresponds to the context.
+
+Assemble everything in a preprocessing function as shown below:
+
+```python
+def preprocess_function(examples):
+    questions = [q.strip() for q in examples["question"]]
+    inputs = tokenizer(
+        questions,
+        examples["context"],
+        max_length=384,
+        truncation="only_second",
+        return_offsets_mapping=True,
+        padding="max_length",
+    )
+
+    offset_mapping = inputs.pop("offset_mapping")
+    answers = examples["answers"]
+    start_positions = []
+    end_positions = []
+
+    for i, offset in enumerate(offset_mapping):
+        answer = answers[i]
+        start_char = answer["answer_start"][0]
+        end_char = answer["answer_start"][0] + len(answer["text"][0])
+        sequence_ids = inputs.sequence_ids(i)
+
+        # Find the start and end of the context
+        idx = 0
+        while sequence_ids[idx] != 1:
+            idx += 1
+        context_start = idx
+        while sequence_ids[idx] == 1:
+            idx += 1
+        context_end = idx - 1
+
+        # If the answer is not fully inside the context, label it (0, 0)
+        if offset[context_start][0] > end_char or offset[context_end][1] < start_char:
+            start_positions.append(0)
+            end_positions.append(0)
+        else:
+            # Otherwise it's the start and end token positions
+            idx = context_start
+            while idx <= context_end and offset[idx][0] <= start_char:
+                idx += 1
+            start_positions.append(idx - 1)
+
+            idx = context_end
+            while idx >= context_start and offset[idx][1] >= end_char:
+                idx -= 1
+            end_positions.append(idx + 1)
+
+    inputs["start_positions"] = start_positions
+    inputs["end_positions"] = end_positions
+    return inputs
+```
+
+Apply the preprocessing function over the entire dataset with 🤗 Datasets `map` function:
+
+```python
+tokenized_squad = squad.map(preprocess_function, batched=True, remove_columns=squad["train"].column_names)
+```
+
+Batch the processed examples together:
+
+```python
+from transformers import default_data_collator
+data_collator = default_data_collator
+```
+
+### Fine-tune with the Trainer API
+
+Load your model with the [`AutoModelForQuestionAnswering`] class:
+
+```python
+from transformers import AutoModelForQuestionAnswering, TrainingArguments, Trainer
+model = AutoModelForQuestionAnswering.from_pretrained("distilbert-base-uncased")
+```
+
+Gather your training arguments in [`TrainingArguments`]:
+
+```python
+training_args = TrainingArguments(
+    output_dir='./results',
+    evaluation_strategy="epoch",
+    learning_rate=2e-5,
+    per_device_train_batch_size=16,
+    per_device_eval_batch_size=16,
+    num_train_epochs=3,
+    weight_decay=0.01,
+)
+```
+
+Collect your model, training arguments, dataset, data collator, and tokenizer in [`Trainer`]:
+
+```python
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized_squad["train"],
+    eval_dataset=tokenized_squad["validation"],
+    data_collator=data_collator,
+    tokenizer=tokenizer,
+)
+```
+
+Fine-tune your model:
+
+```python
+trainer.train()
+```
+
+### Fine-tune with TensorFlow
+
+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
+```
+
+Convert your datasets to the `tf.data.Dataset` format with the `to_tf_dataset` function:
+
+```python
+tf_train_set = tokenized_squad["train"].to_tf_dataset(
+    columns=["attention_mask", "input_ids", "start_positions", "end_positions"],
+    dummy_labels=True,
+    shuffle=True,
+    batch_size=16,
+    collate_fn=data_collator,
+)
+
+tf_validation_set = tokenized_squad["validation"].to_tf_dataset(
+    columns=["attention_mask", "input_ids", "start_positions", "end_positions"],
+    dummy_labels=True,
+    shuffle=False,
+    batch_size=16,
+    collate_fn=data_collator,
+)
+```
+
+Set up an optimizer function, learning rate schedule, and some training hyperparameters:
+
+```python
+from transformers import create_optimizer
+
+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, 
+    num_train_steps=total_train_steps,
+)
+```
+
+Load your model with the [`TFAutoModelForQuestionAnswering`] class:
+
+```python
+from transformers import TFAutoModelForQuestionAnswering
+model = TFAutoModelForQuestionAnswering("distilbert-base-uncased")
+```
+
+Compile the model:
+
+```python
+import tensorflow as tf
+model.compile(optimizer=optimizer)
+```
+
+Call `model.fit` to fine-tune the model:
+
+```python
+model.fit(
+    tf_train_set,
+    validation_data=tf_validation_set,
+    epochs=num_train_epochs,
+)
+```
--- a/docs/source/custom_datasets.rst
+++ b/docs/source/custom_datasets.rst
@@ -1,729 +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.
-
-Fine-tuning with custom datasets
-=======================================================================================================================
-
-.. note::
-
-    The datasets used in this tutorial are available and can be more easily accessed using the `🤗 Datasets library
-    <https://github.com/huggingface/datasets>`_. We do not use this library to access the datasets here since this
-    tutorial meant to illustrate how to work with your own data. A brief introduction can be found at the end of the
-    tutorial in the section ":ref:`datasetslib`".
-
-This tutorial will take you through several examples of using 🤗 Transformers models with your own datasets. The guide
-shows one of many valid workflows for using these models and is meant to be illustrative rather than definitive. We
-show examples of reading in several data formats, preprocessing the data for several types of tasks, and then preparing
-the data into PyTorch/TensorFlow ``Dataset`` objects which can easily be used either with
-:class:`~transformers.Trainer`/:class:`~transformers.TFTrainer` or with native PyTorch/TensorFlow.
-
-We include several examples, each of which demonstrates a different type of common downstream task:
-
-  - :ref:`seq_imdb`
-  - :ref:`tok_ner`
-  - :ref:`qa_squad`
-  - :ref:`resources`
-
-.. _seq_imdb:
-
-Sequence Classification with IMDb Reviews
-----------------------------------------------------------------------------------------------------------------------
-
-.. note::
-
-    This dataset can be explored in the Hugging Face model hub (`IMDb <https://huggingface.co/datasets/imdb>`_), and
-    can be alternatively downloaded with the 🤗 Datasets library with ``load_dataset("imdb")``.
-
-In this example, we'll show how to download, tokenize, and train a model on the IMDb reviews dataset. This task takes
-the text of a review and requires the model to predict whether the sentiment of the review is positive or negative.
-Let's start by downloading the dataset from the `Large Movie Review Dataset
-<http://ai.stanford.edu/~amaas/data/sentiment/>`_ webpage.
-
-.. code-block:: bash
-
-    wget http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz
-    tar -xf aclImdb_v1.tar.gz
-
-This data is organized into ``pos`` and ``neg`` folders with one text file per example. Let's write a function that can
-read this in.
-
-.. code-block:: python
-
-    from pathlib import Path
-
-    def read_imdb_split(split_dir):
-        split_dir = Path(split_dir)
-        texts = []
-        labels = []
-        for label_dir in ["pos", "neg"]:
-            for text_file in (split_dir/label_dir).iterdir():
-                texts.append(text_file.read_text())
-                labels.append(0 if label_dir is "neg" else 1)
-
-        return texts, labels
-
-    train_texts, train_labels = read_imdb_split('aclImdb/train')
-    test_texts, test_labels = read_imdb_split('aclImdb/test')
-
-We now have a train and test dataset, but let's also create a validation set which we can use for for evaluation and
-tuning without tainting our test set results. Sklearn has a convenient utility for creating such splits:
-
-.. code-block:: python
-
-    from sklearn.model_selection import train_test_split
-    train_texts, val_texts, train_labels, val_labels = train_test_split(train_texts, train_labels, test_size=.2)
-
-Alright, we've read in our dataset. Now let's tackle tokenization. We'll eventually train a classifier using
-pre-trained DistilBert, so let's use the DistilBert tokenizer.
-
-.. code-block:: python
-
-    from transformers import DistilBertTokenizerFast
-    tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-uncased')
-
-Now we can simply pass our texts to the tokenizer. We'll pass ``truncation=True`` and ``padding=True``, which will
-ensure that all of our sequences are padded to the same length and are truncated to be no longer than model's maximum
-input length. This will allow us to feed batches of sequences into the model at the same time.
-
-.. code-block:: python
-
-    train_encodings = tokenizer(train_texts, truncation=True, padding=True)
-    val_encodings = tokenizer(val_texts, truncation=True, padding=True)
-    test_encodings = tokenizer(test_texts, truncation=True, padding=True)
-
-Now, let's turn our labels and encodings into a Dataset object. In PyTorch, this is done by subclassing a
-``torch.utils.data.Dataset`` object and implementing ``__len__`` and ``__getitem__``. In TensorFlow, we pass our input
-encodings and labels to the ``from_tensor_slices`` constructor method. We put the data in this format so that the data
-can be easily batched such that each key in the batch encoding corresponds to a named parameter of the
-:meth:`~transformers.DistilBertForSequenceClassification.forward` method of the model we will train.
-
-.. code-block:: python
-
-    ## PYTORCH CODE
-    import torch
-
-    class IMDbDataset(torch.utils.data.Dataset):
-        def __init__(self, encodings, labels):
-            self.encodings = encodings
-            self.labels = labels
-
-        def __getitem__(self, idx):
-            item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
-            item['labels'] = torch.tensor(self.labels[idx])
-            return item
-
-        def __len__(self):
-            return len(self.labels)
-
-    train_dataset = IMDbDataset(train_encodings, train_labels)
-    val_dataset = IMDbDataset(val_encodings, val_labels)
-    test_dataset = IMDbDataset(test_encodings, test_labels)
-    ## TENSORFLOW CODE
-    import tensorflow as tf
-
-    train_dataset = tf.data.Dataset.from_tensor_slices((
-        dict(train_encodings),
-        train_labels
-    ))
-    val_dataset = tf.data.Dataset.from_tensor_slices((
-        dict(val_encodings),
-        val_labels
-    ))
-    test_dataset = tf.data.Dataset.from_tensor_slices((
-        dict(test_encodings),
-        test_labels
-    ))
-
-Now that our datasets are ready, we can fine-tune a model either with the 🤗
-:class:`~transformers.Trainer`/:class:`~transformers.TFTrainer` or with native PyTorch/TensorFlow. See :doc:`training
-<training>`.
-
-.. _ft_trainer:
-
-Fine-tuning with Trainer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The steps above prepared the datasets in the way that the trainer is expected. Now all we need to do is create a model
-to fine-tune, define the :class:`~transformers.TrainingArguments`/:class:`~transformers.TFTrainingArguments` and
-instantiate a :class:`~transformers.Trainer`/:class:`~transformers.TFTrainer`.
-
-.. code-block:: python
-
-    ## PYTORCH CODE
-    from transformers import DistilBertForSequenceClassification, Trainer, TrainingArguments
-
-    training_args = TrainingArguments(
-        output_dir='./results',          # output directory
-        num_train_epochs=3,              # total number of training epochs
-        per_device_train_batch_size=16,  # batch size per device during training
-        per_device_eval_batch_size=64,   # batch size for evaluation
-        warmup_steps=500,                # number of warmup steps for learning rate scheduler
-        weight_decay=0.01,               # strength of weight decay
-        logging_dir='./logs',            # directory for storing logs
-        logging_steps=10,
-    )
-
-    model = DistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased")
-
-    trainer = Trainer(
-        model=model,                         # the instantiated 🤗 Transformers model to be trained
-        args=training_args,                  # training arguments, defined above
-        train_dataset=train_dataset,         # training dataset
-        eval_dataset=val_dataset             # evaluation dataset
-    )
-
-    trainer.train()
-    ## TENSORFLOW CODE
-    from transformers import TFDistilBertForSequenceClassification, TFTrainer, TFTrainingArguments
-
-    training_args = TFTrainingArguments(
-        output_dir='./results',          # output directory
-        num_train_epochs=3,              # total number of training epochs
-        per_device_train_batch_size=16,  # batch size per device during training
-        per_device_eval_batch_size=64,   # batch size for evaluation
-        warmup_steps=500,                # number of warmup steps for learning rate scheduler
-        weight_decay=0.01,               # strength of weight decay
-        logging_dir='./logs',            # directory for storing logs
-        logging_steps=10,
-    )
-
-    with training_args.strategy.scope():
-        model = TFDistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased")
-
-    trainer = TFTrainer(
-        model=model,                         # the instantiated 🤗 Transformers model to be trained
-        args=training_args,                  # training arguments, defined above
-        train_dataset=train_dataset,         # training dataset
-        eval_dataset=val_dataset             # evaluation dataset
-    )
-
-    trainer.train()
-
-.. _ft_native:
-
-Fine-tuning with native PyTorch/TensorFlow
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-We can also train using native PyTorch or TensorFlow:
-
-.. code-block:: python
-
-    ## PYTORCH CODE
-    from torch.utils.data import DataLoader
-    from transformers import DistilBertForSequenceClassification, AdamW
-
-    device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
-
-    model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased')
-    model.to(device)
-    model.train()
-
-    train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)
-
-    optim = AdamW(model.parameters(), lr=5e-5)
-
-    for epoch in range(3):
-        for batch in train_loader:
-            optim.zero_grad()
-            input_ids = batch['input_ids'].to(device)
-            attention_mask = batch['attention_mask'].to(device)
-            labels = batch['labels'].to(device)
-            outputs = model(input_ids, attention_mask=attention_mask, labels=labels)
-            loss = outputs[0]
-            loss.backward()
-            optim.step()
-
-    model.eval()
-    ## TENSORFLOW CODE
-    from transformers import TFDistilBertForSequenceClassification
-
-    model = TFDistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased')
-
-    optimizer = tf.keras.optimizers.Adam(learning_rate=5e-5)
-    model.compile(optimizer=optimizer, loss=model.compute_loss) # can also use any keras loss fn
-    model.fit(train_dataset.shuffle(1000).batch(16), epochs=3, batch_size=16)
-
-.. _tok_ner:
-
-Token Classification with W-NUT Emerging Entities
-----------------------------------------------------------------------------------------------------------------------
-
-.. note::
-
-    This dataset can be explored in the Hugging Face model hub (`WNUT-17 <https://huggingface.co/datasets/wnut_17>`_),
-    and can be alternatively downloaded with the 🤗 Datasets library with ``load_dataset("wnut_17")``.
-
-Next we will look at token classification. Rather than classifying an entire sequence, this task classifies token by
-token. We'll demonstrate how to do this with `Named Entity Recognition
-<http://nlpprogress.com/english/named_entity_recognition.html>`_, which involves identifying tokens which correspond to
-a predefined set of "entities". Specifically, we'll use the `W-NUT Emerging and Rare entities
-<http://noisy-text.github.io/2017/emerging-rare-entities.html>`_ corpus. The data is given as a collection of
-pre-tokenized documents where each token is assigned a tag.
-
-Let's start by downloading the data.
-
-.. code-block:: bash
-
-    wget http://noisy-text.github.io/2017/files/wnut17train.conll
-
-In this case, we'll just download the train set, which is a single text file. Each line of the file contains either (1)
-a word and tag separated by a tab, or (2) a blank line indicating the end of a document. Let's write a function to read
-this in. We'll take in the file path and return ``token_docs`` which is a list of lists of token strings, and
-``token_tags`` which is a list of lists of tag strings.
-
-.. code-block:: python
-
-    from pathlib import Path
-    import re
-
-    def read_wnut(file_path):
-        file_path = Path(file_path)
-
-        raw_text = file_path.read_text().strip()
-        raw_docs = re.split(r'\n\t?\n', raw_text)
-        token_docs = []
-        tag_docs = []
-        for doc in raw_docs:
-            tokens = []
-            tags = []
-            for line in doc.split('\n'):
-                token, tag = line.split('\t')
-                tokens.append(token)
-                tags.append(tag)
-            token_docs.append(tokens)
-            tag_docs.append(tags)
-
-        return token_docs, tag_docs
-
-    texts, tags = read_wnut('wnut17train.conll')
-
-Just to see what this data looks like, let's take a look at a segment of the first document.
-
-.. code-block:: python
-
-    >>> print(texts[0][10:17], tags[0][10:17], sep='\n')
-    ['for', 'two', 'weeks', '.', 'Empire', 'State', 'Building']
-    ['O', 'O', 'O', 'O', 'B-location', 'I-location', 'I-location']
-
-``location`` is an entity type, ``B-`` indicates the beginning of an entity, and ``I-`` indicates consecutive positions
-of the same entity ("Empire State Building" is considered one entity). ``O`` indicates the token does not correspond to
-any entity.
-
-Now that we've read the data in, let's create a train/validation split:
-
-.. code-block:: python
-
-    from sklearn.model_selection import train_test_split
-    train_texts, val_texts, train_tags, val_tags = train_test_split(texts, tags, test_size=.2)
-
-Next, let's create encodings for our tokens and tags. For the tags, we can start by just create a simple mapping which
-we'll use in a moment:
-
-.. code-block:: python
-
-    unique_tags = set(tag for doc in tags for tag in doc)
-    tag2id = {tag: id for id, tag in enumerate(unique_tags)}
-    id2tag = {id: tag for tag, id in tag2id.items()}
-
-To encode the tokens, we'll use a pre-trained DistilBert tokenizer. We can tell the tokenizer that we're dealing with
-ready-split tokens rather than full sentence strings by passing ``is_split_into_words=True``. We'll also pass
-``padding=True`` and ``truncation=True`` to pad the sequences to be the same length. Lastly, we can tell the model to
-return information about the tokens which are split by the wordpiece tokenization process, which we will need in a
-moment.
-
-.. code-block:: python
-
-    from transformers import DistilBertTokenizerFast
-    tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-cased')
-    train_encodings = tokenizer(train_texts, is_split_into_words=True, return_offsets_mapping=True, padding=True, truncation=True)
-    val_encodings = tokenizer(val_texts, is_split_into_words=True, return_offsets_mapping=True, padding=True, truncation=True)
-
-Great, so now our tokens are nicely encoded in the format that they need to be in to feed them into our DistilBert
-model below.
-
-Now we arrive at a common obstacle with using pre-trained models for token-level classification: many of the tokens in
-the W-NUT corpus are not in DistilBert's vocabulary. Bert and many models like it use a method called WordPiece
-Tokenization, meaning that single words are split into multiple tokens such that each token is likely to be in the
-vocabulary. For example, DistilBert's tokenizer would split the Twitter handle ``@huggingface`` into the tokens ``['@',
-'hugging', '##face']``. This is a problem for us because we have exactly one tag per token. If the tokenizer splits a
-token into multiple sub-tokens, then we will end up with a mismatch between our tokens and our labels.
-
-One way to handle this is to only train on the tag labels for the first subtoken of a split token. We can do this in 🤗
-Transformers by setting the labels we wish to ignore to ``-100``. In the example above, if the label for
-``@HuggingFace`` is ``3`` (indexing ``B-corporation``), we would set the labels of ``['@', 'hugging', '##face']`` to
-``[3, -100, -100]``.
-
-Let's write a function to do this. This is where we will use the ``offset_mapping`` from the tokenizer as mentioned
-above. For each sub-token returned by the tokenizer, the offset mapping gives us a tuple indicating the sub-token's
-start position and end position relative to the original token it was split from. That means that if the first position
-in the tuple is anything other than ``0``, we will set its corresponding label to ``-100``. While we're at it, we can
-also set labels to ``-100`` if the second position of the offset mapping is ``0``, since this means it must be a
-special token like ``[PAD]`` or ``[CLS]``.
-
-.. note::
-
-    Due to a recently fixed bug, -1 must be used instead of -100 when using TensorFlow in 🤗 Transformers <= 3.02.
-
-.. code-block:: python
-
-    import numpy as np
-
-    def encode_tags(tags, encodings):
-        labels = [[tag2id[tag] for tag in doc] for doc in tags]
-        encoded_labels = []
-        for doc_labels, doc_offset in zip(labels, encodings.offset_mapping):
-            # create an empty array of -100
-            doc_enc_labels = np.ones(len(doc_offset),dtype=int) * -100
-            arr_offset = np.array(doc_offset)
-
-            # set labels whose first offset position is 0 and the second is not 0
-            doc_enc_labels[(arr_offset[:,0] == 0) & (arr_offset[:,1] != 0)] = doc_labels
-            encoded_labels.append(doc_enc_labels.tolist())
-
-        return encoded_labels
-
-    train_labels = encode_tags(train_tags, train_encodings)
-    val_labels = encode_tags(val_tags, val_encodings)
-
-The hard part is now done. Just as in the sequence classification example above, we can create a dataset object:
-
-.. code-block:: python
-
-    ## PYTORCH CODE
-    import torch
-
-    class WNUTDataset(torch.utils.data.Dataset):
-        def __init__(self, encodings, labels):
-            self.encodings = encodings
-            self.labels = labels
-
-        def __getitem__(self, idx):
-            item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
-            item['labels'] = torch.tensor(self.labels[idx])
-            return item
-
-        def __len__(self):
-            return len(self.labels)
-
-    train_encodings.pop("offset_mapping") # we don't want to pass this to the model
-    val_encodings.pop("offset_mapping")
-    train_dataset = WNUTDataset(train_encodings, train_labels)
-    val_dataset = WNUTDataset(val_encodings, val_labels)
-    ## TENSORFLOW CODE
-    import tensorflow as tf
-
-    train_encodings.pop("offset_mapping") # we don't want to pass this to the model
-    val_encodings.pop("offset_mapping")
-
-    train_dataset = tf.data.Dataset.from_tensor_slices((
-        dict(train_encodings),
-        train_labels
-    ))
-    val_dataset = tf.data.Dataset.from_tensor_slices((
-        dict(val_encodings),
-        val_labels
-    ))
-
-Now load in a token classification model and specify the number of labels:
-
-.. code-block:: python
-
-    ## PYTORCH CODE
-    from transformers import DistilBertForTokenClassification
-    model = DistilBertForTokenClassification.from_pretrained('distilbert-base-cased', num_labels=len(unique_tags))
-    ## TENSORFLOW CODE
-    from transformers import TFDistilBertForTokenClassification
-    model = TFDistilBertForTokenClassification.from_pretrained('distilbert-base-cased', num_labels=len(unique_tags))
-
-The data and model are both ready to go. You can train the model either with
-:class:`~transformers.Trainer`/:class:`~transformers.TFTrainer` or with native PyTorch/TensorFlow, exactly as in the
-sequence classification example above.
-
-  - :ref:`ft_trainer`
-  - :ref:`ft_native`
-
-.. _qa_squad:
-
-Question Answering with SQuAD 2.0
-----------------------------------------------------------------------------------------------------------------------
-
-.. note::
-
-    This dataset can be explored in the Hugging Face model hub (`SQuAD V2
-    <https://huggingface.co/datasets/squad_v2>`_), and can be alternatively downloaded with the 🤗 Datasets library with
-    ``load_dataset("squad_v2")``.
-
-Question answering comes in many forms. In this example, we'll look at the particular type of extractive QA that
-involves answering a question about a passage by highlighting the segment of the passage that answers the question.
-This involves fine-tuning a model which predicts a start position and an end position in the passage. We will use the
-`Stanford Question Answering Dataset (SQuAD) 2.0 <https://rajpurkar.github.io/SQuAD-explorer/>`_.
-
-We will start by downloading the data:
-
-.. code-block:: bash
-
-    mkdir squad
-    wget https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v2.0.json -O squad/train-v2.0.json
-    wget https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v2.0.json -O squad/dev-v2.0.json
-
-Each split is in a structured json file with a number of questions and answers for each passage (or context). We'll
-take this apart into parallel lists of contexts, questions, and answers (note that the contexts here are repeated since
-there are multiple questions per context):
-
-.. code-block:: python
-
-    import json
-    from pathlib import Path
-
-    def read_squad(path):
-        path = Path(path)
-        with open(path, 'rb') as f:
-            squad_dict = json.load(f)
-
-        contexts = []
-        questions = []
-        answers = []
-        for group in squad_dict['data']:
-            for passage in group['paragraphs']:
-                context = passage['context']
-                for qa in passage['qas']:
-                    question = qa['question']
-                    for answer in qa['answers']:
-                        contexts.append(context)
-                        questions.append(question)
-                        answers.append(answer)
-
-        return contexts, questions, answers
-
-    train_contexts, train_questions, train_answers = read_squad('squad/train-v2.0.json')
-    val_contexts, val_questions, val_answers = read_squad('squad/dev-v2.0.json')
-
-The contexts and questions are just strings. The answers are dicts containing the subsequence of the passage with the
-correct answer as well as an integer indicating the character at which the answer begins. In order to train a model on
-this data we need (1) the tokenized context/question pairs, and (2) integers indicating at which *token* positions the
-answer begins and ends.
-
-First, let's get the *character* position at which the answer ends in the passage (we are given the starting position).
-Sometimes SQuAD answers are off by one or two characters, so we will also adjust for that.
-
-.. code-block:: python
-
-    def add_end_idx(answers, contexts):
-        for answer, context in zip(answers, contexts):
-            gold_text = answer['text']
-            start_idx = answer['answer_start']
-            end_idx = start_idx + len(gold_text)
-
-            # sometimes squad answers are off by a character or two – fix this
-            if context[start_idx:end_idx] == gold_text:
-                answer['answer_end'] = end_idx
-            elif context[start_idx-1:end_idx-1] == gold_text:
-                answer['answer_start'] = start_idx - 1
-                answer['answer_end'] = end_idx - 1     # When the gold label is off by one character
-            elif context[start_idx-2:end_idx-2] == gold_text:
-                answer['answer_start'] = start_idx - 2
-                answer['answer_end'] = end_idx - 2     # When the gold label is off by two characters
-
-    add_end_idx(train_answers, train_contexts)
-    add_end_idx(val_answers, val_contexts)
-
-Now ``train_answers`` and ``val_answers`` include the character end positions and the corrected start positions. Next,
-let's tokenize our context/question pairs. 🤗 Tokenizers can accept parallel lists of sequences and encode them together
-as sequence pairs.
-
-.. code-block:: python
-
-    from transformers import DistilBertTokenizerFast
-    tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-uncased')
-
-    train_encodings = tokenizer(train_contexts, train_questions, truncation=True, padding=True)
-    val_encodings = tokenizer(val_contexts, val_questions, truncation=True, padding=True)
-
-Next we need to convert our character start/end positions to token start/end positions. When using 🤗 Fast Tokenizers,
-we can use the built in :func:`~transformers.BatchEncoding.char_to_token` method.
-
-.. code-block:: python
-
-    def add_token_positions(encodings, answers):
-        start_positions = []
-        end_positions = []
-        for i in range(len(answers)):
-            start_positions.append(encodings.char_to_token(i, answers[i]['answer_start']))
-            end_positions.append(encodings.char_to_token(i, answers[i]['answer_end'] - 1))
-
-            # if start position is None, the answer passage has been truncated
-            if start_positions[-1] is None:
-                start_positions[-1] = tokenizer.model_max_length
-            if end_positions[-1] is None:
-                end_positions[-1] = tokenizer.model_max_length
-
-        encodings.update({'start_positions': start_positions, 'end_positions': end_positions})
-
-    add_token_positions(train_encodings, train_answers)
-    add_token_positions(val_encodings, val_answers)
-
-Our data is ready. Let's just put it in a PyTorch/TensorFlow dataset so that we can easily use it for training. In
-PyTorch, we define a custom ``Dataset`` class. In TensorFlow, we pass a tuple of ``(inputs_dict, labels_dict)`` to the
-``from_tensor_slices`` method.
-
-.. code-block:: python
-
-    ## PYTORCH CODE
-    import torch
-
-    class SquadDataset(torch.utils.data.Dataset):
-        def __init__(self, encodings):
-            self.encodings = encodings
-
-        def __getitem__(self, idx):
-            return {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
-
-        def __len__(self):
-            return len(self.encodings.input_ids)
-
-    train_dataset = SquadDataset(train_encodings)
-    val_dataset = SquadDataset(val_encodings)
-    ## TENSORFLOW CODE
-    import tensorflow as tf
-
-    train_dataset = tf.data.Dataset.from_tensor_slices((
-        {key: train_encodings[key] for key in ['input_ids', 'attention_mask']},
-        {key: train_encodings[key] for key in ['start_positions', 'end_positions']}
-    ))
-    val_dataset = tf.data.Dataset.from_tensor_slices((
-        {key: val_encodings[key] for key in ['input_ids', 'attention_mask']},
-        {key: val_encodings[key] for key in ['start_positions', 'end_positions']}
-    ))
-
-Now we can use a DistilBert model with a QA head for training:
-
-.. code-block:: python
-
-    ## PYTORCH CODE
-    from transformers import DistilBertForQuestionAnswering
-    model = DistilBertForQuestionAnswering.from_pretrained("distilbert-base-uncased")
-    ## TENSORFLOW CODE
-    from transformers import TFDistilBertForQuestionAnswering
-    model = TFDistilBertForQuestionAnswering.from_pretrained("distilbert-base-uncased")
-
-
-The data and model are both ready to go. You can train the model with
-:class:`~transformers.Trainer`/:class:`~transformers.TFTrainer` exactly as in the sequence classification example
-above. If using native PyTorch, replace ``labels`` with ``start_positions`` and ``end_positions`` in the training
-example. If using Keras's ``fit``, we need to make a minor modification to handle this example since it involves
-multiple model outputs.
-
-  - :ref:`ft_trainer`
-
-.. code-block:: python
-
-    ## PYTORCH CODE
-    from torch.utils.data import DataLoader
-    from transformers import AdamW
-
-    device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
-
-    model.to(device)
-    model.train()
-
-    train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)
-
-    optim = AdamW(model.parameters(), lr=5e-5)
-
-    for epoch in range(3):
-        for batch in train_loader:
-            optim.zero_grad()
-            input_ids = batch['input_ids'].to(device)
-            attention_mask = batch['attention_mask'].to(device)
-            start_positions = batch['start_positions'].to(device)
-            end_positions = batch['end_positions'].to(device)
-            outputs = model(input_ids, attention_mask=attention_mask, start_positions=start_positions, end_positions=end_positions)
-            loss = outputs[0]
-            loss.backward()
-            optim.step()
-
-    model.eval()
-    ## TENSORFLOW CODE
-    # Keras will expect a tuple when dealing with labels
-    train_dataset = train_dataset.map(lambda x, y: (x, (y['start_positions'], y['end_positions'])))
-
-    # Keras will assign a separate loss for each output and add them together. So we'll just use the standard CE loss
-    # instead of using the built-in model.compute_loss, which expects a dict of outputs and averages the two terms.
-    # Note that this means the loss will be 2x of when using TFTrainer since we're adding instead of averaging them.
-    loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
-    model.distilbert.return_dict = False # if using 🤗 Transformers >3.02, make sure outputs are tuples
-
-    optimizer = tf.keras.optimizers.Adam(learning_rate=5e-5)
-    model.compile(optimizer=optimizer, loss=loss) # can also use any keras loss fn
-    model.fit(train_dataset.shuffle(1000).batch(16), epochs=3, batch_size=16)
-
-.. _resources:
-
-Additional Resources
-----------------------------------------------------------------------------------------------------------------------
-
-  - `How to train a new language model from scratch using Transformers and Tokenizers
-    <https://huggingface.co/blog/how-to-train>`_. Blog post showing the steps to load in Esperanto data and train a
-    masked language model from scratch.
-  - :doc:`Preprocessing <preprocessing>`. Docs page on data preprocessing.
-  - :doc:`Training <training>`. Docs page on training and fine-tuning.
-
-.. _datasetslib:
-
-Using the 🤗 Datasets & Metrics library
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-This tutorial demonstrates how to read in datasets from various raw text formats and prepare them for training with 🤗
-Transformers so that you can do the same thing with your own custom datasets. However, we recommend users use the `🤗
-Datasets library <https://github.com/huggingface/datasets>`_ for working with the 150+ datasets included in the `hub
-<https://huggingface.co/datasets>`_, including the three datasets used in this tutorial. As a very brief overview, we
-will show how to use the Datasets library to download and prepare the IMDb dataset from the first example,
-:ref:`seq_imdb`.
-
-Start by downloading the dataset:
-
-.. code-block:: python
-
-    from datasets import load_dataset
-    train = load_dataset("imdb", split="train")
-
-Each dataset has multiple columns corresponding to different features. Let's see what our columns are.
-
-.. code-block:: python
-
-    >>> print(train.column_names)
-    ['label', 'text']
-
-Great. Now let's tokenize the text. We can do this using the ``map`` method. We'll also rename the ``label`` column to
-``labels`` to match the model's input arguments.
-
-.. code-block:: python
-
-    train = train.map(lambda batch: tokenizer(batch["text"], truncation=True, padding=True), batched=True)
-    train.rename_column_("label", "labels")
-
-Lastly, we can use the ``set_format`` method to determine which columns and in what data format we want to access
-dataset elements.
-
-.. code-block:: python
-
-    ## PYTORCH CODE
-    >>> train.set_format("torch", columns=["input_ids", "attention_mask", "labels"])
-    >>> {key: val.shape for key, val in train[0].items()})
-    {'labels': torch.Size([]), 'input_ids': torch.Size([512]), 'attention_mask': torch.Size([512])}
-    ## TENSORFLOW CODE
-    >>> train.set_format("tensorflow", columns=["input_ids", "attention_mask", "labels"])
-    >>> {key: val.shape for key, val in train[0].items()})
-    {'labels': TensorShape([]), 'input_ids': TensorShape([512]), 'attention_mask': TensorShape([512])}
-
-We now have a fully-prepared dataset. Check out `the 🤗 Datasets docs
-<https://huggingface.co/docs/datasets/processing.html>`_ for a more thorough introduction.
--- a/docs/source/favicon.ico
+++ b/docs/source/favicon.ico
--- a/docs/source/imgs/tf32-bf16-fp16-fp32.png
+++ b/docs/source/imgs/tf32-bf16-fp16-fp32.png
--- a/docs/source/index.mdx
+++ b/docs/source/index.mdx
@@ -0,0 +1,271 @@
+<!--Copyright 2020 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# 🤗 Transformers
+
+State-of-the-art Machine Learning for Jax, Pytorch and TensorFlow
+
+🤗 Transformers (formerly known as _pytorch-transformers_ and _pytorch-pretrained-bert_) provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio.
+
+These models can applied on:
+
+* 📝 Text, for tasks like text classification, information extraction, question answering, summarization, translation, text generation, in over 100 languages.
+* 🖼️ Images, for tasks like image classification, object detection, and segmentation.
+* 🗣️ Audio, for tasks like speech recognition and audio classification.
+
+Transformer models can also perform tasks on **several modalities combined**, such as table question answering, optical character recognition, information extraction from scanned documents, video classification, and visual question answering.
+
+🤗 Transformers provides APIs to quickly download and use those pretrained models on a given text, fine-tune them on your own datasets and then share them with the community on our [model hub](https://huggingface.co/models). At the same time, each python module defining an architecture is fully standalone and can be modified to enable quick research experiments.
+
+🤗 Transformers is backed by the three most popular deep learning libraries — [Jax](https://jax.readthedocs.io/en/latest/), [PyTorch](https://pytorch.org/) and [TensorFlow](https://www.tensorflow.org/) — with a seamless integration between them. It's straightforward to train your models with one before loading them for inference with the other.
+
+This is the documentation of our repository [transformers](https://github.com/huggingface/transformers). You can
+also follow our [online course](https://huggingface.co/course) that teaches how to use this library, as well as the
+other libraries developed by Hugging Face and the Hub.
+
+## If you are looking for custom support from the Hugging Face team
+
+<a target="_blank" href="https://huggingface.co/support">
+<img alt="HuggingFace Expert Acceleration Program" src="https://huggingface.co/front/thumbnails/support.png" style="max-width: 600px; border: 1px solid #eee; border-radius: 4px; box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);">
+</a><br>
+
+## Features
+
+1. Easy-to-use state-of-the-art models:
+    - High performance on natural language understanding & generation, computer vision, and audio tasks.
+    - Low barrier to entry for educators and practitioners.
+    - Few user-facing abstractions with just three classes to learn.
+    - A unified API for using all our pretrained models.
+
+1. Lower compute costs, smaller carbon footprint:
+    - Researchers can share trained models instead of always retraining.
+    - Practitioners can reduce compute time and production costs.
+    - Dozens of architectures with over 20,000 pretrained models, some in more than 100 languages.
+
+1. Choose the right framework for every part of a model's lifetime:
+    - Train state-of-the-art models in 3 lines of code.
+    - Move a single model between TF2.0/PyTorch/JAX frameworks at will.
+    - Seamlessly pick the right framework for training, evaluation and production.
+
+1. Easily customize a model or an example to your needs:
+    - We provide examples for each architecture to reproduce the results published by its original authors.
+    - Model internals are exposed as consistently as possible.
+    - Model files can be used independently of the library for quick experiments.
+
+[All the model checkpoints](https://huggingface.co/models) are seamlessly integrated from the huggingface.co [model
+hub](https://huggingface.co) where they are uploaded directly by [users](https://huggingface.co/users) and
+[organizations](https://huggingface.co/organizations).
+
+Current number of checkpoints: <img src="https://img.shields.io/endpoint?url=https://huggingface.co/api/shields/models&color=brightgreen">
+
+## Contents
+
+The documentation is organized in five parts:
+
+- **GET STARTED** contains a quick tour, the installation instructions and some useful information about our philosophy
+  and a glossary.
+- **USING 🤗 TRANSFORMERS** contains general tutorials on how to use the library.
+- **ADVANCED GUIDES** contains more advanced guides that are more specific to a given script or part of the library.
+- **RESEARCH** focuses on tutorials that have less to do with how to use the library but more about general research in
+  transformers model
+- **API** contains the documentation of each public class and function, grouped in:
+
+  - **MAIN CLASSES** for the main classes exposing the important APIs of the library.
+  - **MODELS** for the classes and functions related to each model implemented in the library.
+  - **INTERNAL HELPERS** for the classes and functions we use internally.
+
+The library currently contains Jax, PyTorch and Tensorflow implementations, pretrained model weights, usage scripts and
+conversion utilities for the following models.
+
+### Supported models
+
+<!--This list is updated automatically from the README with _make fix-copies_. Do not update manually! -->
+
+1. **[ALBERT](model_doc/albert)** (from Google Research and the Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.
+1. **[BART](model_doc/bart)** (from Facebook) released with the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/pdf/1910.13461.pdf) by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer.
+1. **[BARThez](model_doc/barthez)** (from École polytechnique) released with the paper [BARThez: a Skilled Pretrained French Sequence-to-Sequence Model](https://arxiv.org/abs/2010.12321) by Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis.
+1. **[BARTpho](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](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. **[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. **[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.
+1. **[CANINE](model_doc/canine)** (from Google Research) released with the paper [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://arxiv.org/abs/2103.06874) by Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting.
+1. **[CLIP](model_doc/clip)** (from OpenAI) released with the paper [Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/abs/2103.00020) by Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever.
+1. **[ConvBERT](model_doc/convbert)** (from YituTech) released with the paper [ConvBERT: Improving BERT with Span-based Dynamic Convolution](https://arxiv.org/abs/2008.02496) by Zihang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan.
+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. **[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/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](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. **[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-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.
+1. **[Hubert](model_doc/hubert)** (from Facebook) released with the paper [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://arxiv.org/abs/2106.07447) by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed.
+1. **[I-BERT](model_doc/ibert)** (from Berkeley) released with the paper [I-BERT: Integer-only BERT Quantization](https://arxiv.org/abs/2101.01321) by Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer.
+1. **[ImageGPT](model_doc/imagegpt)** (from OpenAI) released with the paper [Generative Pretraining from Pixels](https://openai.com/blog/image-gpt/) by Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever.
+1. **[LayoutLM](model_doc/layoutlm)** (from Microsoft Research Asia) released with the paper [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://arxiv.org/abs/1912.13318) by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou.
+1. **[LayoutLMv2](model_doc/layoutlmv2)** (from Microsoft Research Asia) released with the paper [LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding](https://arxiv.org/abs/2012.14740) by Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou.
+1. **[LayoutXLM](model_doc/layoutlmv2)** (from Microsoft Research Asia) released with the paper [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://arxiv.org/abs/2104.08836) by Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei.
+1. **[LED](model_doc/led)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
+1. **[Longformer](model_doc/longformer)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
+1. **[LUKE](model_doc/luke)** (from Studio Ousia) released with the paper [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://arxiv.org/abs/2010.01057) by Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yuji Matsumoto.
+1. **[mLUKE](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. **[LXMERT](model_doc/lxmert)** (from UNC Chapel Hill) released with the paper [LXMERT: Learning Cross-Modality Encoder Representations from Transformers for Open-Domain Question Answering](https://arxiv.org/abs/1908.07490) by Hao Tan and Mohit Bansal.
+1. **[M2M100](model_doc/m2m_100)** (from Facebook) released with the paper [Beyond English-Centric Multilingual Machine Translation](https://arxiv.org/abs/2010.11125) by Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin.
+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-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. **[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. **[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.
+1. **[RoFormer](model_doc/roformer)** (from ZhuiyiTechnology), released together with the paper a [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://arxiv.org/pdf/2104.09864v1.pdf) by Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu.
+1. **[SegFormer](model_doc/segformer)** (from NVIDIA) released with the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo.
+1. **[SEW](model_doc/sew)** (from ASAPP) released with the paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
+1. **[SEW-D](model_doc/sew_d)** (from ASAPP) released with the paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
+1. **[SpeechToTextTransformer](model_doc/speech_to_text)** (from Facebook), released together with the paper [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171) by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino.
+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. **[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. **[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. **[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. **[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. **[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. **[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. **[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.
+
+
+### Supported frameworks
+
+The table below represents the current support in the library for each of those models, whether they have a Python
+tokenizer (called "slow"). A "fast" tokenizer backed by the 🤗 Tokenizers library, whether they have support in Jax (via
+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             |       ❌       |       ❌       |       ✅        |         ❌         |      ✅      |
+|            BERT             |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+|       Bert Generation       |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
+|           BigBird           |       ✅       |       ✅       |       ✅        |         ❌         |      ✅      |
+|       BigBirdPegasus        |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|         Blenderbot          |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+|       BlenderbotSmall       |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+|          CamemBERT          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+|           Canine            |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
+|            CLIP             |       ✅       |       ✅       |       ✅        |         ❌         |      ✅      |
+|          ConvBERT           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+|            CTRL             |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
+|           DeBERTa           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+|         DeBERTa-v2          |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
+|            DeiT             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|            DETR             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|         DistilBERT          |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+|             DPR             |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+|           ELECTRA           |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+|       Encoder decoder       |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |
+| FairSeq Machine-Translation |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
+|          FlauBERT           |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
+|            FNet             |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
+|     Funnel Transformer      |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+|           GPT Neo           |       ❌       |       ❌       |       ✅        |         ❌         |      ✅      |
+|            GPT-J            |       ❌       |       ❌       |       ✅        |         ❌         |      ✅      |
+|           Hubert            |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
+|           I-BERT            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|          ImageGPT           |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|          LayoutLM           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+|         LayoutLMv2          |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
+|             LED             |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+|         Longformer          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+|            LUKE             |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
+|           LXMERT            |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+|           M2M100            |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
+|           Marian            |       ✅       |       ❌       |       ✅        |         ✅         |      ✅      |
+|            mBART            |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+|        MegatronBert         |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|         MobileBERT          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+|            MPNet            |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+|             mT5             |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+|         OpenAI GPT          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+|        OpenAI GPT-2         |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+|           Pegasus           |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+|          Perceiver          |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
+|         ProphetNet          |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
+|           QDQBert           |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|             RAG             |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
+|          Reformer           |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
+|           RemBERT           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+|          RetriBERT          |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
+|           RoBERTa           |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+|          RoFormer           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+|          SegFormer          |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|             SEW             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|            SEW-D            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|   Speech Encoder decoder    |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|         Speech2Text         |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
+|        Speech2Text2         |       ✅       |       ❌       |       ❌        |         ❌         |      ❌      |
+|          Splinter           |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
+|         SqueezeBERT         |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
+|             T5              |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+|            TAPAS            |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
+|       Transformer-XL        |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
+|            TrOCR            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|          UniSpeech          |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|        UniSpeechSat         |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|   Vision Encoder decoder    |       ❌       |       ❌       |       ✅        |         ❌         |      ✅      |
+|    VisionTextDualEncoder    |       ❌       |       ❌       |       ✅        |         ❌         |      ✅      |
+|         VisualBert          |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|             ViT             |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |
+|          Wav2Vec2           |       ✅       |       ❌       |       ✅        |         ✅         |      ✅      |
+|             XLM             |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
+|         XLM-RoBERTa         |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+|        XLMProphetNet        |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
+|            XLNet            |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+
+<!-- End table-->
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -213,139 +213,150 @@ Supported models
    Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed.
 38. :doc:`I-BERT <model_doc/ibert>` (from Berkeley) released with the paper `I-BERT: Integer-only BERT Quantization
    <https://arxiv.org/abs/2101.01321>`__ by Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer.
-39. :doc:`LayoutLM <model_doc/layoutlm>` (from Microsoft Research Asia) released with the paper `LayoutLM: Pre-training
+39. `ImageGPT <https://huggingface.co/transformers/master/model_doc/imagegpt.html>`__ (from OpenAI) released with the
+    paper `Generative Pretraining from Pixels <https://openai.com/blog/image-gpt/>`__ by Mark Chen, Alec Radford, Rewon
+    Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever.
+40. :doc:`LayoutLM <model_doc/layoutlm>` (from Microsoft Research Asia) released with the paper `LayoutLM: Pre-training
    of Text and Layout for Document Image Understanding <https://arxiv.org/abs/1912.13318>`__ by Yiheng Xu, Minghao Li,
    Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou.
-40. :doc:`LayoutLMv2 <model_doc/layoutlmv2>` (from Microsoft Research Asia) released with the paper `LayoutLMv2:
+41. :doc:`LayoutLMv2 <model_doc/layoutlmv2>` (from Microsoft Research Asia) released with the paper `LayoutLMv2:
    Multi-modal Pre-training for Visually-Rich Document Understanding <https://arxiv.org/abs/2012.14740>`__ by Yang Xu,
    Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min
    Zhang, Lidong Zhou.
-41. :doc:`LayoutXLM <model_doc/layoutlmv2>` (from Microsoft Research Asia) released with the paper `LayoutXLM:
+42. :doc:`LayoutXLM <model_doc/layoutlmv2>` (from Microsoft Research Asia) released with the paper `LayoutXLM:
    Multimodal Pre-training for Multilingual Visually-rich Document Understanding <https://arxiv.org/abs/2104.08836>`__
    by Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei.
-42. :doc:`LED <model_doc/led>` (from AllenAI) released with the paper `Longformer: The Long-Document Transformer
+43. :doc:`LED <model_doc/led>` (from AllenAI) released with the paper `Longformer: The Long-Document Transformer
    <https://arxiv.org/abs/2004.05150>`__ by Iz Beltagy, Matthew E. Peters, Arman Cohan.
-43. :doc:`Longformer <model_doc/longformer>` (from AllenAI) released with the paper `Longformer: The Long-Document
+44. :doc:`Longformer <model_doc/longformer>` (from AllenAI) released with the paper `Longformer: The Long-Document
    Transformer <https://arxiv.org/abs/2004.05150>`__ by Iz Beltagy, Matthew E. Peters, Arman Cohan.
-44. :doc:`LUKE <model_doc/luke>` (from Studio Ousia) released with the paper `LUKE: Deep Contextualized Entity
+45. :doc:`LUKE <model_doc/luke>` (from Studio Ousia) released with the paper `LUKE: Deep Contextualized Entity
    Representations with Entity-aware Self-attention <https://arxiv.org/abs/2010.01057>`__ by Ikuya Yamada, Akari Asai,
    Hiroyuki Shindo, Hideaki Takeda, Yuji Matsumoto.
-45. :doc:`LXMERT <model_doc/lxmert>` (from UNC Chapel Hill) released with the paper `LXMERT: Learning Cross-Modality
+46. :doc:`LXMERT <model_doc/lxmert>` (from UNC Chapel Hill) released with the paper `LXMERT: Learning Cross-Modality
    Encoder Representations from Transformers for Open-Domain Question Answering <https://arxiv.org/abs/1908.07490>`__
    by Hao Tan and Mohit Bansal.
-46. :doc:`M2M100 <model_doc/m2m_100>` (from Facebook) released with the paper `Beyond English-Centric Multilingual
+47. :doc:`M2M100 <model_doc/m2m_100>` (from Facebook) released with the paper `Beyond English-Centric Multilingual
    Machine Translation <https://arxiv.org/abs/2010.11125>`__ by Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma,
    Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal,
    Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin.
-47. :doc:`MarianMT <model_doc/marian>` Machine translation models trained using `OPUS <http://opus.nlpl.eu/>`__ data by
+48. :doc:`MarianMT <model_doc/marian>` Machine translation models trained using `OPUS <http://opus.nlpl.eu/>`__ data by
    Jörg Tiedemann. The `Marian Framework <https://marian-nmt.github.io/>`__ is being developed by the Microsoft
    Translator Team.
-48. :doc:`MBart <model_doc/mbart>` (from Facebook) released with the paper `Multilingual Denoising Pre-training for
+49. :doc:`MBart <model_doc/mbart>` (from Facebook) released with the paper `Multilingual Denoising Pre-training for
    Neural Machine Translation <https://arxiv.org/abs/2001.08210>`__ by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li,
    Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
-49. :doc:`MBart-50 <model_doc/mbart>` (from Facebook) released with the paper `Multilingual Translation with Extensible
+50. :doc:`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.
-50. :doc:`Megatron-BERT <model_doc/megatron_bert>` (from NVIDIA) released with the paper `Megatron-LM: Training
+51. :doc:`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.
-51. :doc:`Megatron-GPT2 <model_doc/megatron_gpt2>` (from NVIDIA) released with the paper `Megatron-LM: Training
+52. :doc:`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.
-52. :doc:`MPNet <model_doc/mpnet>` (from Microsoft Research) released with the paper `MPNet: Masked and Permuted
+53. :doc:`MPNet <model_doc/mpnet>` (from Microsoft Research) released with the paper `MPNet: Masked and Permuted
    Pre-training for Language Understanding <https://arxiv.org/abs/2004.09297>`__ by Kaitao Song, Xu Tan, Tao Qin,
    Jianfeng Lu, Tie-Yan Liu.
-53. :doc:`MT5 <model_doc/mt5>` (from Google AI) released with the paper `mT5: A massively multilingual pre-trained
+54. :doc:`MT5 <model_doc/mt5>` (from Google AI) released with the paper `mT5: A massively multilingual pre-trained
    text-to-text transformer <https://arxiv.org/abs/2010.11934>`__ by Linting Xue, Noah Constant, Adam Roberts, Mihir
    Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel.
-54. :doc:`Pegasus <model_doc/pegasus>` (from Google) released with the paper `PEGASUS: Pre-training with Extracted
+55. :doc:`Pegasus <model_doc/pegasus>` (from Google) released with the paper `PEGASUS: Pre-training with Extracted
    Gap-sentences for Abstractive Summarization <https://arxiv.org/abs/1912.08777>`__ by Jingqing Zhang, Yao Zhao,
    Mohammad Saleh and Peter J. Liu.
-55. :doc:`PhoBERT <model_doc/phobert>` (from VinAI Research) released with the paper `PhoBERT: Pre-trained language
+56. `Perceiver IO <https://huggingface.co/transformers/model_doc/master/perceiver.html>`__ (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.
+57. :doc:`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.
-56. :doc:`ProphetNet <model_doc/prophetnet>` (from Microsoft Research) released with the paper `ProphetNet: Predicting
+58. :doc:`ProphetNet <model_doc/prophetnet>` (from Microsoft Research) released with the paper `ProphetNet: Predicting
    Future N-gram for Sequence-to-Sequence Pre-training <https://arxiv.org/abs/2001.04063>`__ by Yu Yan, Weizhen Qi,
    Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
-57. :doc:`Reformer <model_doc/reformer>` (from Google Research) released with the paper `Reformer: The Efficient
+59. :doc:`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.
+60. :doc:`Reformer <model_doc/reformer>` (from Google Research) released with the paper `Reformer: The Efficient
    Transformer <https://arxiv.org/abs/2001.04451>`__ by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
-58. :doc:`RemBERT <model_doc/rembert>` (from Google Research) released with the paper `Rethinking embedding coupling in
+61. :doc:`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.
-59. :doc:`RoBERTa <model_doc/roberta>` (from Facebook), released together with the paper a `Robustly Optimized BERT
+62. :doc:`RoBERTa <model_doc/roberta>` (from Facebook), released together with the paper a `Robustly Optimized BERT
    Pretraining Approach <https://arxiv.org/abs/1907.11692>`__ by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar
    Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
-60. :doc:`RoFormer <model_doc/roformer>` (from ZhuiyiTechnology), released together with the paper a `RoFormer:
+63. :doc:`RoFormer <model_doc/roformer>` (from ZhuiyiTechnology), released together with the paper a `RoFormer:
    Enhanced Transformer with Rotary Position Embedding <https://arxiv.org/pdf/2104.09864v1.pdf>`__ by Jianlin Su and
    Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu.
-61. :doc:`SegFormer <model_doc/segformer>` (from NVIDIA) released with the paper `SegFormer: Simple and Efficient
+64. :doc:`SegFormer <model_doc/segformer>` (from NVIDIA) released with the paper `SegFormer: Simple and Efficient
    Design for Semantic Segmentation with Transformers <https://arxiv.org/abs/2105.15203>`__ by Enze Xie, Wenhai Wang,
    Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo.
-62. :doc:`SEW <model_doc/sew>` (from ASAPP) released with the paper `Performance-Efficiency Trade-offs in Unsupervised
+65. :doc:`SEW <model_doc/sew>` (from ASAPP) released with the paper `Performance-Efficiency Trade-offs in Unsupervised
    Pre-training for Speech Recognition <https://arxiv.org/abs/2109.06870>`__ by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu
    Han, Kilian Q. Weinberger, Yoav Artzi.
-63. :doc:`SEW-D <model_doc/sew_d>` (from ASAPP) released with the paper `Performance-Efficiency Trade-offs in
+66. :doc:`SEW-D <model_doc/sew_d>` (from ASAPP) released with the paper `Performance-Efficiency Trade-offs in
    Unsupervised Pre-training for Speech Recognition <https://arxiv.org/abs/2109.06870>`__ by Felix Wu, Kwangyoun Kim,
    Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
-64. :doc:`SpeechToTextTransformer <model_doc/speech_to_text>` (from Facebook), released together with the paper
+67. :doc:`SpeechToTextTransformer <model_doc/speech_to_text>` (from Facebook), released together with the paper
    `fairseq S2T: Fast Speech-to-Text Modeling with fairseq <https://arxiv.org/abs/2010.05171>`__ by Changhan Wang, Yun
    Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino.
-65. :doc:`SpeechToTextTransformer2 <model_doc/speech_to_text_2>` (from Facebook), released together with the paper
+68. :doc:`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.
-66. :doc:`Splinter <model_doc/splinter>` (from Tel Aviv University), released together with the paper `Few-Shot
+69. :doc:`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.
-67. :doc:`SqueezeBert <model_doc/squeezebert>` (from Berkeley) released with the paper `SqueezeBERT: What can computer
+70. :doc:`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.
-68. :doc:`T5 <model_doc/t5>` (from Google AI) released with the paper `Exploring the Limits of Transfer Learning with a
+71. :doc:`T5 <model_doc/t5>` (from Google AI) released with the paper `Exploring the Limits of Transfer Learning with a
    Unified Text-to-Text Transformer <https://arxiv.org/abs/1910.10683>`__ by Colin Raffel and Noam Shazeer and Adam
    Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
-69. :doc:`T5v1.1 <model_doc/t5v1.1>` (from Google AI) released in the repository
+72. :doc:`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.
-70. :doc:`TAPAS <model_doc/tapas>` (from Google AI) released with the paper `TAPAS: Weakly Supervised Table Parsing via
+73. :doc:`TAPAS <model_doc/tapas>` (from Google AI) released with the paper `TAPAS: Weakly Supervised Table Parsing via
    Pre-training <https://arxiv.org/abs/2004.02349>`__ by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller,
    Francesco Piccinno and Julian Martin Eisenschlos.
-71. :doc:`Transformer-XL <model_doc/transformerxl>` (from Google/CMU) released with the paper `Transformer-XL:
+74. :doc:`Transformer-XL <model_doc/transformerxl>` (from Google/CMU) released with the paper `Transformer-XL:
    Attentive Language Models Beyond a Fixed-Length Context <https://arxiv.org/abs/1901.02860>`__ by Zihang Dai*,
    Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
-72. :doc:`TrOCR <model_doc/trocr>` (from Microsoft), released together with the paper `TrOCR: Transformer-based Optical
+75. :doc:`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.
-73. :doc:`UniSpeech <model_doc/unispeech>` (from Microsoft Research) released with the paper `UniSpeech: Unified Speech
+76. :doc:`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.
-74. :doc:`UniSpeechSat <model_doc/unispeech_sat>` (from Microsoft Research) released with the paper `UNISPEECH-SAT:
+77. :doc:`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.
-75. :doc:`Vision Transformer (ViT) <model_doc/vit>` (from Google AI) released with the paper `An Image is Worth 16x16
+78. :doc:`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.
-76. :doc:`VisualBERT <model_doc/visual_bert>` (from UCLA NLP) released with the paper `VisualBERT: A Simple and
+79. :doc:`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.
-77. :doc:`Wav2Vec2 <model_doc/wav2vec2>` (from Facebook AI) released with the paper `wav2vec 2.0: A Framework for
+80. :doc:`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.
-78. :doc:`XLM <model_doc/xlm>` (from Facebook) released together with the paper `Cross-lingual Language Model
+81. :doc:`XLM <model_doc/xlm>` (from Facebook) released together with the paper `Cross-lingual Language Model
    Pretraining <https://arxiv.org/abs/1901.07291>`__ by Guillaume Lample and Alexis Conneau.
-79. :doc:`XLM-ProphetNet <model_doc/xlmprophetnet>` (from Microsoft Research) released with the paper `ProphetNet:
+82. :doc:`XLM-ProphetNet <model_doc/xlmprophetnet>` (from Microsoft Research) released with the paper `ProphetNet:
    Predicting Future N-gram for Sequence-to-Sequence Pre-training <https://arxiv.org/abs/2001.04063>`__ by Yu Yan,
    Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
-80. :doc:`XLM-RoBERTa <model_doc/xlmroberta>` (from Facebook AI), released together with the paper `Unsupervised
+83. :doc:`XLM-RoBERTa <model_doc/xlmroberta>` (from Facebook AI), released together with the paper `Unsupervised
    Cross-lingual Representation Learning at Scale <https://arxiv.org/abs/1911.02116>`__ by Alexis Conneau*, Kartikay
    Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke
    Zettlemoyer and Veselin Stoyanov.
-81. :doc:`XLNet <model_doc/xlnet>` (from Google/CMU) released with the paper `XLNet: Generalized Autoregressive
+84. :doc:`XLNet <model_doc/xlnet>` (from Google/CMU) released with the paper `XLNet: Generalized Autoregressive
    Pretraining for Language Understanding <https://arxiv.org/abs/1906.08237>`__ by Zhilin Yang*, Zihang Dai*, Yiming
    Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
-82. :doc:`XLSR-Wav2Vec2 <model_doc/xlsr_wav2vec2>` (from Facebook AI) released with the paper `Unsupervised
+85. :doc:`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.

@@ -379,7 +390,7 @@ Flax), PyTorch, and/or TensorFlow.
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |       BigBirdPegasus        |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|         Blenderbot          |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
+|         Blenderbot          |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |       BlenderbotSmall       |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
@@ -419,12 +430,14 @@ Flax), PyTorch, and/or TensorFlow.
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |           GPT Neo           |       ❌       |       ❌       |       ✅        |         ❌         |      ✅      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|            GPT-J            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|            GPT-J            |       ❌       |       ❌       |       ✅        |         ❌         |      ✅      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |           Hubert            |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |           I-BERT            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|          ImageGPT           |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |          LayoutLM           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |         LayoutLMv2          |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
@@ -457,8 +470,12 @@ Flax), PyTorch, and/or TensorFlow.
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |           Pegasus           |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|          Perceiver          |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |         ProphetNet          |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|           QDQBert           |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |             RAG             |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |          Reformer           |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
@@ -489,7 +506,7 @@ Flax), PyTorch, and/or TensorFlow.
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |             T5              |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|            TAPAS            |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
+|            TAPAS            |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |       Transformer-XL        |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
@@ -499,11 +516,13 @@ Flax), PyTorch, and/or TensorFlow.
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |        UniSpeechSat         |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|   Vision Encoder decoder    |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|   Vision Encoder decoder    |       ❌       |       ❌       |       ✅        |         ❌         |      ✅      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|    VisionTextDualEncoder    |       ❌       |       ❌       |       ✅        |         ❌         |      ✅      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |         VisualBert          |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|             ViT             |       ❌       |       ❌       |       ✅        |         ❌         |      ✅      |
+|             ViT             |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |          Wav2Vec2           |       ✅       |       ❌       |       ✅        |         ✅         |      ✅      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
@@ -559,6 +578,7 @@ Flax), PyTorch, and/or TensorFlow.
    testing
    debugging
    serialization
+    pr_checks

 .. toctree::
    :maxdepth: 2
@@ -628,6 +648,7 @@ Flax), PyTorch, and/or TensorFlow.
    model_doc/funnel
    model_doc/herbert
    model_doc/ibert
+    model_doc/imagegpt
    model_doc/layoutlm
    model_doc/layoutlmv2
    model_doc/layoutxlm
@@ -649,8 +670,10 @@ Flax), PyTorch, and/or TensorFlow.
    model_doc/gpt_neo
    model_doc/hubert
    model_doc/pegasus
+    model_doc/perceiver
    model_doc/phobert
    model_doc/prophetnet
+    model_doc/qdqbert
    model_doc/rag
    model_doc/reformer
    model_doc/rembert
@@ -673,6 +696,7 @@ Flax), PyTorch, and/or TensorFlow.
    model_doc/unispeech
    model_doc/unispeech_sat
    model_doc/visionencoderdecoder
+    model_doc/vision_text_dual_encoder
    model_doc/vit
    model_doc/visual_bert
    model_doc/wav2vec2
--- a/docs/source/main_classes/data_collator.rst
+++ b/docs/source/main_classes/data_collator.rst
@@ -29,6 +29,13 @@ Default data collator
 .. autofunction:: transformers.data.data_collator.default_data_collator


+DefaultDataCollator
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.data.data_collator.DefaultDataCollator
+    :members:
+
+
 DataCollatorWithPadding
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

--- a/docs/source/main_classes/deepspeed.rst
+++ b/docs/source/main_classes/deepspeed.rst
@@ -46,6 +46,20 @@ won't be possible on a single GPU.
   parts of DeepSpeed like ``zero.Init`` for ZeRO stage 3 and higher. To tap into this feature read the docs on
   :ref:`deepspeed-non-trainer-integration`.

+What is integrated:
+
+Training:
+
+1. DeepSpeed ZeRO training supports the full ZeRO stages 1, 2 and 3 with ZeRO-Infinity (CPU and NVME offload).
+
+Inference:
+
+1. DeepSpeed ZeRO Inference supports ZeRO stage 3 with ZeRO-Infinity. It uses the same ZeRO protocol as training, but
+   it doesn't use an optimizer and a lr scheduler and only stage 3 is relevant. For more details see:
+   :ref:`deepspeed-zero-inference`.
+
+There is also DeepSpeed Inference - this is a totally different technology which uses Tensor Parallelism instead of
+ZeRO (coming soon).



@@ -1628,6 +1642,47 @@ larger multi-dimensional shape, this means that the parameter is partitioned and



+.. _deepspeed-zero-inference:
+
+
+ZeRO Inference
+=======================================================================================================================
+
+ZeRO Inference uses the same config as ZeRO-3 Training. You just don't need the optimizer and scheduler sections. In
+fact you can leave these in the config file if you want to share the same one with the training. They will just be
+ignored.
+
+Otherwise you just need to pass the usual :class:`~transformers.TrainingArguments` arguments. For example:
+
+.. code-block:: bash
+
+    deepspeed --num_gpus=2 your_program.py <normal cl args> --do_eval --deepspeed ds_config.json
+
+The only important thing is that you need to use a ZeRO-3 configuration, since ZeRO-2 provides no benefit whatsoever
+for the inference as only ZeRO-3 performs sharding of parameters, whereas ZeRO-1 shards gradients and optimizer states.
+
+Here is an example of running ``run_translation.py`` under DeepSpeed deploying all available GPUs:
+
+.. code-block:: bash
+
+    deepspeed examples/pytorch/translation/run_translation.py \
+    --deepspeed tests/deepspeed/ds_config_zero3.json \
+    --model_name_or_path t5-small --output_dir output_dir \
+    --do_eval --max_eval_samples 50 --warmup_steps 50  \
+    --max_source_length 128 --val_max_target_length 128 \
+    --overwrite_output_dir --per_device_eval_batch_size 4 \
+    --predict_with_generate --dataset_config "ro-en" --fp16 \
+    --source_lang en --target_lang ro --dataset_name wmt16 \
+    --source_prefix "translate English to Romanian: "
+
+Since for inference there is no need for additional large memory used by the optimizer states and the gradients you
+should be able to fit much larger batches and/or sequence length onto the same hardware.
+
+
+Additionally DeepSpeed is currently developing a related product called Deepspeed-Inference which has no relationship
+to the ZeRO technology, but instead uses tensor parallelism to scale models that can't fit onto a single GPU. This is a
+work in progress and we will provide the integration once that product is complete.
+

 Filing Issues
 =======================================================================================================================
--- a/docs/source/main_classes/pipelines.rst
+++ b/docs/source/main_classes/pipelines.rst
@@ -45,7 +45,7 @@ The pipeline abstraction
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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

 Simple call on one item:

@@ -55,6 +55,15 @@ Simple call on one item:
    >>> 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::
@@ -71,6 +80,11 @@ 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")

@@ -85,6 +99,170 @@ GPU. If it doesn't don't hesitate to create an issue.

 .. 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
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

--- a/docs/source/main_classes/tokenizer.rst
+++ b/docs/source/main_classes/tokenizer.rst
@@ -20,7 +20,7 @@ Rust library `tokenizers <https://github.com/huggingface/tokenizers>`__. The "Fa
 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, XLMRoBERTa
+   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`
@@ -39,7 +39,8 @@ methods for using all the tokenizers:
 - 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 tokenizer's encoding methods (``__call__``,
+: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
--- a/docs/source/migration.md
+++ b/docs/source/migration.md
@@ -31,7 +31,7 @@ This introduces two breaking changes:

 ##### How to obtain the same behavior as v3.x in v4.x

- The pipelines now contain additional features out of the box. See the [token-classification pipeline with the `grouped_entities` flag](https://huggingface.co/transformers/main_classes/pipelines.html?highlight=textclassification#tokenclassificationpipeline).
+- The pipelines now contain additional features out of the box. See the [token-classification pipeline with the `grouped_entities` flag](main_classes/pipelines#transformers.TokenClassificationPipeline).
 - The auto-tokenizers now return rust tokenizers. In order to obtain the python tokenizers instead, the user may use the `use_fast` flag by setting it to `False`:

 In version `v3.x`:
@@ -98,7 +98,7 @@ from transformers.models.bert.modeling_bert import BertLayer

 #### 4. Switching the `return_dict` argument to `True` by default

-The [`return_dict` argument](https://huggingface.co/transformers/main_classes/output.html) enables the return of dict-like python objects containing the model outputs, instead of the standard tuples. This object is self-documented as keys can be used to retrieve values, while also behaving as a tuple as users may retrieve objects by index or by slice.
+The [`return_dict` argument](main_classes/output) enables the return of dict-like python objects containing the model outputs, instead of the standard tuples. This object is self-documented as keys can be used to retrieve values, while also behaving as a tuple as users may retrieve objects by index or by slice.

 This is a breaking change as the limitation of that tuple is that it cannot be unpacked: `value0, value1 = outputs` will not work.

--- a/docs/source/model_doc/auto.rst
+++ b/docs/source/model_doc/auto.rst
@@ -76,6 +76,13 @@ AutoFeatureExtractor
    :members:


+AutoProcessor
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.AutoProcessor
+    :members:
+
+
 AutoModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -160,6 +167,13 @@ AutoModelForImageClassification
    :members:


+AutoModelForVision2Seq
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.AutoModelForVision2Seq
+    :members:
+
+
 AutoModelForAudioClassification
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -216,6 +230,13 @@ TFAutoModelForCausalLM
    :members:


+TFAutoModelForImageClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFAutoModelForImageClassification
+    :members:
+
+
 TFAutoModelForMaskedLM
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -244,6 +265,13 @@ TFAutoModelForMultipleChoice
    :members:


+TFAutoModelForTableQuestionAnswering
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFAutoModelForTableQuestionAnswering
+    :members:
+
+
 TFAutoModelForTokenClassification
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -333,3 +361,10 @@ FlaxAutoModelForImageClassification

 .. autoclass:: transformers.FlaxAutoModelForImageClassification
    :members:
+
+
+FlaxAutoModelForVision2Seq
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaxAutoModelForVision2Seq
+    :members:
--- a/docs/source/model_doc/bart.rst
+++ b/docs/source/model_doc/bart.rst
@@ -74,7 +74,7 @@ The :obj:`facebook/bart-base` and :obj:`facebook/bart-large` checkpoints can be
 .. code-block::

    from transformers import BartForConditionalGeneration, BartTokenizer
-    model = BartForConditionalGeneration.from_pretrained("facebook/bart-large", force_bos_token_to_be_generated=True)
+    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')
--- a/docs/source/model_doc/beit.rst
+++ b/docs/source/model_doc/beit.rst
@@ -63,6 +63,17 @@ This model was contributed by `nielsr <https://huggingface.co/nielsr>`__. The JA
 contributed by `kamalkraj <https://huggingface.co/kamalkraj>`__. The original code can be found `here
 <https://github.com/microsoft/unilm/tree/master/beit>`__.

+
+BEiT specific outputs
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.models.beit.modeling_beit.BeitModelOutputWithPooling
+    :members:
+
+.. autoclass:: transformers.models.beit.modeling_flax_beit.FlaxBeitModelOutputWithPooling
+    :members:
+
+
 BeitConfig
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -98,6 +109,13 @@ BeitForImageClassification
    :members: forward


+BeitForSemanticSegmentation
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.BeitForSemanticSegmentation
+    :members: forward
+
+
 FlaxBeitModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

--- a/docs/source/model_doc/blenderbot.rst
+++ b/docs/source/model_doc/blenderbot.rst
@@ -47,7 +47,7 @@ Implementation Notes
 - Available checkpoints can be found in the `model hub <https://huggingface.co/models?search=blenderbot>`__.
 - This is the `default` Blenderbot model class. However, some smaller checkpoints, such as
  ``facebook/blenderbot_small_90M``, have a different architecture and consequently should be used with
-  `BlenderbotSmall <https://huggingface.co/transformers/master/model_doc/blenderbot_small.html>`__.
+  `BlenderbotSmall <blenderbot_small>`__.


 Usage
@@ -81,6 +81,13 @@ BlenderbotTokenizer
    :members: build_inputs_with_special_tokens


+BlenderbotTokenizerFast
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.BlenderbotTokenizerFast
+    :members: build_inputs_with_special_tokens
+
+
 BlenderbotModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -118,3 +125,17 @@ TFBlenderbotForConditionalGeneration

 .. autoclass:: transformers.TFBlenderbotForConditionalGeneration
    :members: call
+
+
+FlaxBlenderbotModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaxBlenderbotModel
+    :members: __call__, encode, decode
+
+
+FlaxBlenderbotForConditionalGeneration
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaxBlenderbotForConditionalGeneration
+    :members: __call__, encode, decode
--- a/docs/source/model_doc/blenderbot_small.rst
+++ b/docs/source/model_doc/blenderbot_small.rst
@@ -97,3 +97,17 @@ TFBlenderbotSmallForConditionalGeneration

 .. autoclass:: transformers.TFBlenderbotSmallForConditionalGeneration
    :members: call
+
+
+FlaxBlenderbotSmallModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaxBlenderbotSmallModel
+    :members: __call__, encode, decode
+
+
+FlaxBlenderbotForConditionalGeneration
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaxBlenderbotSmallForConditionalGeneration
+    :members: __call__, encode, decode
--- a/docs/source/model_doc/deit.rst
+++ b/docs/source/model_doc/deit.rst
@@ -25,12 +25,12 @@ Overview

 The DeiT model was proposed in `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. The `Vision Transformer (ViT) <https://huggingface.co/transformers/model_doc/vit.html>`__
-introduced in `Dosovitskiy et al., 2020 <https://arxiv.org/abs/2010.11929>`__ has shown that one can match or even
-outperform existing convolutional neural networks using a Transformer encoder (BERT-like). However, the ViT models
-introduced in that paper required training on expensive infrastructure for multiple weeks, using external data. DeiT
-(data-efficient image transformers) are more efficiently trained transformers for image classification, requiring far
-less data and far less computing resources compared to the original ViT models.
+Sablayrolles, Hervé Jégou. The `Vision Transformer (ViT) <vit>`__ introduced in `Dosovitskiy et al., 2020
+<https://arxiv.org/abs/2010.11929>`__ has shown that one can match or even outperform existing convolutional neural
+networks using a Transformer encoder (BERT-like). However, the ViT models introduced in that paper required training on
+expensive infrastructure for multiple weeks, using external data. DeiT (data-efficient image transformers) are more
+efficiently trained transformers for image classification, requiring far less data and far less computing resources
+compared to the original ViT models.

 The abstract from the paper is the following:

--- a/docs/source/model_doc/detr.mdx
+++ b/docs/source/model_doc/detr.mdx
@@ -0,0 +1,169 @@
+<!--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.
+-->
+
+# DETR
+
+## Overview
+
+The DETR model was proposed in [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) by
+Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov and Sergey Zagoruyko. DETR
+consists of a convolutional backbone followed by an encoder-decoder Transformer which can be trained end-to-end for
+object detection. It greatly simplifies a lot of the complexity of models like Faster-R-CNN and Mask-R-CNN, which use
+things like region proposals, non-maximum suppression procedure and anchor generation. Moreover, DETR can also be
+naturally extended to perform panoptic segmentation, by simply adding a mask head on top of the decoder outputs.
+
+The abstract from the paper is the following:
+
+*We present a new method that views object detection as a direct set prediction problem. Our approach streamlines the
+detection pipeline, effectively removing the need for many hand-designed components like a non-maximum suppression
+procedure or anchor generation that explicitly encode our prior knowledge about the task. The main ingredients of the
+new framework, called DEtection TRansformer or DETR, are a set-based global loss that forces unique predictions via
+bipartite matching, and a transformer encoder-decoder architecture. Given a fixed small set of learned object queries,
+DETR reasons about the relations of the objects and the global image context to directly output the final set of
+predictions in parallel. The new model is conceptually simple and does not require a specialized library, unlike many
+other modern detectors. DETR demonstrates accuracy and run-time performance on par with the well-established and
+highly-optimized Faster RCNN baseline on the challenging COCO object detection dataset. Moreover, DETR can be easily
+generalized to produce panoptic segmentation in a unified manner. We show that it significantly outperforms competitive
+baselines.*
+
+This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found [here](https://github.com/facebookresearch/detr).
+
+The quickest way to get started with DETR is by checking the [example notebooks](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/DETR) (which showcase both inference and
+fine-tuning on custom data).
+
+Here's a TLDR explaining how [`~transformers.DetrForObjectDetection`] works:
+
+First, an image is sent through a pre-trained convolutional backbone (in the paper, the authors use
+ResNet-50/ResNet-101). Let's assume we also add a batch dimension. This means that the input to the backbone is a
+tensor of shape `(batch_size, 3, height, width)`, assuming the image has 3 color channels (RGB). The CNN backbone
+outputs a new lower-resolution feature map, typically of shape `(batch_size, 2048, height/32, width/32)`. This is
+then projected to match the hidden dimension of the Transformer of DETR, which is `256` by default, using a
+`nn.Conv2D` layer. So now, we have a tensor of shape `(batch_size, 256, height/32, width/32).` Next, the
+feature map is flattened and transposed to obtain a tensor of shape `(batch_size, seq_len, d_model)` =
+`(batch_size, width/32*height/32, 256)`. So a difference with NLP models is that the sequence length is actually
+longer than usual, but with a smaller `d_model` (which in NLP is typically 768 or higher).
+
+Next, this is sent through the encoder, outputting `encoder_hidden_states` of the same shape (you can consider
+these as image features). Next, so-called **object queries** are sent through the decoder. This is a tensor of shape
+`(batch_size, num_queries, d_model)`, with `num_queries` typically set to 100 and initialized with zeros.
+These input embeddings are learnt positional encodings that the authors refer to as object queries, and similarly to
+the encoder, they are added to the input of each attention layer. Each object query will look for a particular object
+in the image. The decoder updates these embeddings through multiple self-attention and encoder-decoder attention layers
+to output `decoder_hidden_states` of the same shape: `(batch_size, num_queries, d_model)`. Next, two heads
+are added on top for object detection: a linear layer for classifying each object query into one of the objects or "no
+object", and a MLP to predict bounding boxes for each query.
+
+The model is trained using a **bipartite matching loss**: so what we actually do is compare the predicted classes +
+bounding boxes of each of the N = 100 object queries to the ground truth annotations, padded up to the same length N
+(so if an image only contains 4 objects, 96 annotations will just have a "no object" as class and "no bounding box" as
+bounding box). The [Hungarian matching algorithm](https://en.wikipedia.org/wiki/Hungarian_algorithm) is used to find
+an optimal one-to-one mapping of each of the N queries to each of the N annotations. Next, standard cross-entropy (for
+the classes) and a linear combination of the L1 and [generalized IoU loss](https://giou.stanford.edu/) (for the
+bounding boxes) are used to optimize the parameters of the model.
+
+DETR can be naturally extended to perform panoptic segmentation (which unifies semantic segmentation and instance
+segmentation). [`~transformers.DetrForSegmentation`] adds a segmentation mask head on top of
+[`~transformers.DetrForObjectDetection`]. The mask head can be trained either jointly, or in a two steps process,
+where one first trains a [`~transformers.DetrForObjectDetection`] model to detect bounding boxes around both
+"things" (instances) and "stuff" (background things like trees, roads, sky), then freeze all the weights and train only
+the mask head for 25 epochs. Experimentally, these two approaches give similar results. Note that predicting boxes is
+required for the training to be possible, since the Hungarian matching is computed using distances between boxes.
+
+Tips:
+
+- DETR uses so-called **object queries** to detect objects in an image. The number of queries determines the maximum
+  number of objects that can be detected in a single image, and is set to 100 by default (see parameter
+  `num_queries` of [`~transformers.DetrConfig`]). Note that it's good to have some slack (in COCO, the
+  authors used 100, while the maximum number of objects in a COCO image is ~70).
+- The decoder of DETR updates the query embeddings in parallel. This is different from language models like GPT-2,
+  which use autoregressive decoding instead of parallel. Hence, no causal attention mask is used.
+- DETR adds position embeddings to the hidden states at each self-attention and cross-attention layer before projecting
+  to queries and keys. For the position embeddings of the image, one can choose between fixed sinusoidal or learned
+  absolute position embeddings. By default, the parameter `position_embedding_type` of
+  [`~transformers.DetrConfig`] is set to `"sine"`.
+- During training, the authors of DETR did find it helpful to use auxiliary losses in the decoder, especially to help
+  the model output the correct number of objects of each class. If you set the parameter `auxiliary_loss` of
+  [`~transformers.DetrConfig`] to `True`, then prediction feedforward neural networks and Hungarian losses
+  are added after each decoder layer (with the FFNs sharing parameters).
+- If you want to train the model in a distributed environment across multiple nodes, then one should update the
+  _num_boxes_ variable in the _DetrLoss_ class of _modeling_detr.py_. When training on multiple nodes, this should be
+  set to the average number of target boxes across all nodes, as can be seen in the original implementation [here](https://github.com/facebookresearch/detr/blob/a54b77800eb8e64e3ad0d8237789fcbf2f8350c5/models/detr.py#L227-L232).
+- [`~transformers.DetrForObjectDetection`] and [`~transformers.DetrForSegmentation`] can be initialized with
+  any convolutional backbone available in the [timm library](https://github.com/rwightman/pytorch-image-models).
+  Initializing with a MobileNet backbone for example can be done by setting the `backbone` attribute of
+  [`~transformers.DetrConfig`] to `"tf_mobilenetv3_small_075"`, and then initializing the model with that
+  config.
+- DETR resizes the input images such that the shortest side is at least a certain amount of pixels while the longest is
+  at most 1333 pixels. At training time, scale augmentation is used such that the shortest side is randomly set to at
+  least 480 and at most 800 pixels. At inference time, the shortest side is set to 800. One can use
+  [`~transformers.DetrFeatureExtractor`] to prepare images (and optional annotations in COCO format) for the
+  model. Due to this resizing, images in a batch can have different sizes. DETR solves this by padding images up to the
+  largest size in a batch, and by creating a pixel mask that indicates which pixels are real/which are padding.
+  Alternatively, one can also define a custom `collate_fn` in order to batch images together, using
+  [`~transformers.DetrFeatureExtractor.pad_and_create_pixel_mask`].
+- The size of the images will determine the amount of memory being used, and will thus determine the `batch_size`.
+  It is advised to use a batch size of 2 per GPU. See [this Github thread](https://github.com/facebookresearch/detr/issues/150) for more info.
+
+As a summary, consider the following table:
+
+| Task | Object detection | Instance segmentation | Panoptic segmentation |
+|------|------------------|-----------------------|-----------------------|
+| **Description** | Predicting bounding boxes and class labels around objects in an image | Predicting masks around objects (i.e. instances) in an image | Predicting masks around both objects (i.e. instances) as well as "stuff" (i.e. background things like trees and roads) in an image |
+| **Model** | [`~transformers.DetrForObjectDetection`] | [`~transformers.DetrForSegmentation`] | [`~transformers.DetrForSegmentation`] |
+| **Example dataset** | COCO detection | COCO detection, COCO panoptic | COCO panoptic  |                                                                        |
+| **Format of annotations to provide to**  [`~transformers.DetrFeatureExtractor`] | {'image_id': `int`, 'annotations': `List[Dict]`} each Dict being a COCO object annotation  | {'image_id': `int`, 'annotations': `List[Dict]`}  (in case of COCO detection) or {'file_name': `str`, 'image_id': `int`, 'segments_info': `List[Dict]`} (in case of COCO panoptic) | {'file_name': `str`, 'image_id': `int`, 'segments_info': `List[Dict]`} and masks_path (path to directory containing PNG files of the masks) |
+| **Postprocessing** (i.e. converting the output of the model to COCO API) | [`~transformers.DetrFeatureExtractor.post_process`] | [`~transformers.DetrFeatureExtractor.post_process_segmentation`] | [`~transformers.DetrFeatureExtractor.post_process_segmentation`], [`~transformers.DetrFeatureExtractor.post_process_panoptic`] |
+| **evaluators** | `CocoEvaluator` with `iou_types="bbox"` | `CocoEvaluator` with `iou_types="bbox"` or `"segm"` | `CocoEvaluator` with `iou_tupes="bbox"` or `"segm"`, `PanopticEvaluator` |
+
+In short, one should prepare the data either in COCO detection or COCO panoptic format, then use
+[`~transformers.DetrFeatureExtractor`] to create `pixel_values`, `pixel_mask` and optional
+`labels`, which can then be used to train (or fine-tune) a model. For evaluation, one should first convert the
+outputs of the model using one of the postprocessing methods of [`~transformers.DetrFeatureExtractor`]. These can
+be be provided to either `CocoEvaluator` or `PanopticEvaluator`, which allow you to calculate metrics like
+mean Average Precision (mAP) and Panoptic Quality (PQ). The latter objects are implemented in the [original repository](https://github.com/facebookresearch/detr). See the [example notebooks](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/DETR) for more info regarding evaluation.
+
+
+## DETR specific outputs
+
+[[autodoc]] models.detr.modeling_detr.DetrModelOutput
+
+[[autodoc]] models.detr.modeling_detr.DetrObjectDetectionOutput
+
+[[autodoc]] models.detr.modeling_detr.DetrSegmentationOutput
+
+## DetrConfig
+
+[[autodoc]] DetrConfig
+
+## DetrFeatureExtractor
+
+[[autodoc]] DetrFeatureExtractor
+    - __call__
+    - pad_and_create_pixel_mask
+    - post_process
+    - post_process_segmentation
+    - post_process_panoptic
+
+## DetrModel
+
+[[autodoc]] DetrModel
+    - forward
+
+## DetrForObjectDetection
+
+[[autodoc]] DetrForObjectDetection
+    - forward
+
+## DetrForSegmentation
+
+[[autodoc]] DetrForSegmentation
+    - forward
--- a/docs/source/model_doc/detr.rst
+++ b/docs/source/model_doc/detr.rst
@@ -1,207 +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.
-
-DETR
-----------------------------------------------------------------------------------------------------------------------
-
-Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The DETR model was proposed in `End-to-End Object Detection with Transformers <https://arxiv.org/abs/2005.12872>`__ by
-Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov and Sergey Zagoruyko. DETR
-consists of a convolutional backbone followed by an encoder-decoder Transformer which can be trained end-to-end for
-object detection. It greatly simplifies a lot of the complexity of models like Faster-R-CNN and Mask-R-CNN, which use
-things like region proposals, non-maximum suppression procedure and anchor generation. Moreover, DETR can also be
-naturally extended to perform panoptic segmentation, by simply adding a mask head on top of the decoder outputs.
-
-The abstract from the paper is the following:
-
-*We present a new method that views object detection as a direct set prediction problem. Our approach streamlines the
-detection pipeline, effectively removing the need for many hand-designed components like a non-maximum suppression
-procedure or anchor generation that explicitly encode our prior knowledge about the task. The main ingredients of the
-new framework, called DEtection TRansformer or DETR, are a set-based global loss that forces unique predictions via
-bipartite matching, and a transformer encoder-decoder architecture. Given a fixed small set of learned object queries,
-DETR reasons about the relations of the objects and the global image context to directly output the final set of
-predictions in parallel. The new model is conceptually simple and does not require a specialized library, unlike many
-other modern detectors. DETR demonstrates accuracy and run-time performance on par with the well-established and
-highly-optimized Faster RCNN baseline on the challenging COCO object detection dataset. Moreover, DETR can be easily
-generalized to produce panoptic segmentation in a unified manner. We show that it significantly outperforms competitive
-baselines.*
-
-This model was contributed by `nielsr <https://huggingface.co/nielsr>`__. The original code can be found `here
-<https://github.com/facebookresearch/detr>`__.
-
-The quickest way to get started with DETR is by checking the `example notebooks
-<https://github.com/NielsRogge/Transformers-Tutorials/tree/master/DETR>`__ (which showcase both inference and
-fine-tuning on custom data).
-
-Here's a TLDR explaining how :class:`~transformers.DetrForObjectDetection` works:
-
-First, an image is sent through a pre-trained convolutional backbone (in the paper, the authors use
-ResNet-50/ResNet-101). Let's assume we also add a batch dimension. This means that the input to the backbone is a
-tensor of shape :obj:`(batch_size, 3, height, width)`, assuming the image has 3 color channels (RGB). The CNN backbone
-outputs a new lower-resolution feature map, typically of shape :obj:`(batch_size, 2048, height/32, width/32)`. This is
-then projected to match the hidden dimension of the Transformer of DETR, which is :obj:`256` by default, using a
-:obj:`nn.Conv2D` layer. So now, we have a tensor of shape :obj:`(batch_size, 256, height/32, width/32).` Next, the
-feature map is flattened and transposed to obtain a tensor of shape :obj:`(batch_size, seq_len, d_model)` =
-:obj:`(batch_size, width/32*height/32, 256)`. So a difference with NLP models is that the sequence length is actually
-longer than usual, but with a smaller :obj:`d_model` (which in NLP is typically 768 or higher).
-
-Next, this is sent through the encoder, outputting :obj:`encoder_hidden_states` of the same shape (you can consider
-these as image features). Next, so-called **object queries** are sent through the decoder. This is a tensor of shape
-:obj:`(batch_size, num_queries, d_model)`, with :obj:`num_queries` typically set to 100 and initialized with zeros.
-These input embeddings are learnt positional encodings that the authors refer to as object queries, and similarly to
-the encoder, they are added to the input of each attention layer. Each object query will look for a particular object
-in the image. The decoder updates these embeddings through multiple self-attention and encoder-decoder attention layers
-to output :obj:`decoder_hidden_states` of the same shape: :obj:`(batch_size, num_queries, d_model)`. Next, two heads
-are added on top for object detection: a linear layer for classifying each object query into one of the objects or "no
-object", and a MLP to predict bounding boxes for each query.
-
-The model is trained using a **bipartite matching loss**: so what we actually do is compare the predicted classes +
-bounding boxes of each of the N = 100 object queries to the ground truth annotations, padded up to the same length N
-(so if an image only contains 4 objects, 96 annotations will just have a "no object" as class and "no bounding box" as
-bounding box). The `Hungarian matching algorithm <https://en.wikipedia.org/wiki/Hungarian_algorithm>`__ is used to find
-an optimal one-to-one mapping of each of the N queries to each of the N annotations. Next, standard cross-entropy (for
-the classes) and a linear combination of the L1 and `generalized IoU loss <https://giou.stanford.edu/>`__ (for the
-bounding boxes) are used to optimize the parameters of the model.
-
-DETR can be naturally extended to perform panoptic segmentation (which unifies semantic segmentation and instance
-segmentation). :class:`~transformers.DetrForSegmentation` adds a segmentation mask head on top of
-:class:`~transformers.DetrForObjectDetection`. The mask head can be trained either jointly, or in a two steps process,
-where one first trains a :class:`~transformers.DetrForObjectDetection` model to detect bounding boxes around both
-"things" (instances) and "stuff" (background things like trees, roads, sky), then freeze all the weights and train only
-the mask head for 25 epochs. Experimentally, these two approaches give similar results. Note that predicting boxes is
-required for the training to be possible, since the Hungarian matching is computed using distances between boxes.
-
-Tips:
-
- DETR uses so-called **object queries** to detect objects in an image. The number of queries determines the maximum
-  number of objects that can be detected in a single image, and is set to 100 by default (see parameter
-  :obj:`num_queries` of :class:`~transformers.DetrConfig`). Note that it's good to have some slack (in COCO, the
-  authors used 100, while the maximum number of objects in a COCO image is ~70).
- The decoder of DETR updates the query embeddings in parallel. This is different from language models like GPT-2,
-  which use autoregressive decoding instead of parallel. Hence, no causal attention mask is used.
- DETR adds position embeddings to the hidden states at each self-attention and cross-attention layer before projecting
-  to queries and keys. For the position embeddings of the image, one can choose between fixed sinusoidal or learned
-  absolute position embeddings. By default, the parameter :obj:`position_embedding_type` of
-  :class:`~transformers.DetrConfig` is set to :obj:`"sine"`.
- During training, the authors of DETR did find it helpful to use auxiliary losses in the decoder, especially to help
-  the model output the correct number of objects of each class. If you set the parameter :obj:`auxiliary_loss` of
-  :class:`~transformers.DetrConfig` to :obj:`True`, then prediction feedforward neural networks and Hungarian losses
-  are added after each decoder layer (with the FFNs sharing parameters).
- If you want to train the model in a distributed environment across multiple nodes, then one should update the
-  `num_boxes` variable in the `DetrLoss` class of `modeling_detr.py`. When training on multiple nodes, this should be
-  set to the average number of target boxes across all nodes, as can be seen in the original implementation `here
-  <https://github.com/facebookresearch/detr/blob/a54b77800eb8e64e3ad0d8237789fcbf2f8350c5/models/detr.py#L227-L232>`__.
- :class:`~transformers.DetrForObjectDetection` and :class:`~transformers.DetrForSegmentation` can be initialized with
-  any convolutional backbone available in the `timm library <https://github.com/rwightman/pytorch-image-models>`__.
-  Initializing with a MobileNet backbone for example can be done by setting the :obj:`backbone` attribute of
-  :class:`~transformers.DetrConfig` to :obj:`"tf_mobilenetv3_small_075"`, and then initializing the model with that
-  config.
- DETR resizes the input images such that the shortest side is at least a certain amount of pixels while the longest is
-  at most 1333 pixels. At training time, scale augmentation is used such that the shortest side is randomly set to at
-  least 480 and at most 800 pixels. At inference time, the shortest side is set to 800. One can use
-  :class:`~transformers.DetrFeatureExtractor` to prepare images (and optional annotations in COCO format) for the
-  model. Due to this resizing, images in a batch can have different sizes. DETR solves this by padding images up to the
-  largest size in a batch, and by creating a pixel mask that indicates which pixels are real/which are padding.
-  Alternatively, one can also define a custom :obj:`collate_fn` in order to batch images together, using
-  :meth:`~transformers.DetrFeatureExtractor.pad_and_create_pixel_mask`.
- The size of the images will determine the amount of memory being used, and will thus determine the :obj:`batch_size`.
-  It is advised to use a batch size of 2 per GPU. See `this Github thread
-  <https://github.com/facebookresearch/detr/issues/150>`__ for more info.
-
-As a summary, consider the following table:
-
-+---------------------------------------------+---------------------------------------------------------+----------------------------------------------------------------------+------------------------------------------------------------------------+
-| **Task**                                    | **Object detection**                                    | **Instance segmentation**                                            | **Panoptic segmentation**                                              |
-+---------------------------------------------+---------------------------------------------------------+----------------------------------------------------------------------+------------------------------------------------------------------------+
-| **Description**                             | Predicting bounding boxes and class labels around       | Predicting masks around objects (i.e. instances) in an image         | Predicting masks around both objects (i.e. instances) as well as       |
-|                                             | objects in an image                                     |                                                                      | "stuff" (i.e. background things like trees and roads) in an image      |
-+---------------------------------------------+---------------------------------------------------------+----------------------------------------------------------------------+------------------------------------------------------------------------+
-| **Model**                                   | :class:`~transformers.DetrForObjectDetection`           | :class:`~transformers.DetrForSegmentation`                           | :class:`~transformers.DetrForSegmentation`                             |
-+---------------------------------------------+---------------------------------------------------------+----------------------------------------------------------------------+------------------------------------------------------------------------+
-| **Example dataset**                         | COCO detection                                          | COCO detection,                                                      | COCO panoptic                                                          |
-|                                             |                                                         | COCO panoptic                                                        |                                                                        |
-+---------------------------------------------+---------------------------------------------------------+----------------------------------------------------------------------+------------------------------------------------------------------------+
-| **Format of annotations to provide to**     | {‘image_id’: int,                                       | {‘image_id’: int,                                                    | {‘file_name: str,                                                      |
-| :class:`~transformers.DetrFeatureExtractor` | ‘annotations’: List[Dict]}, each Dict being a COCO      | ‘annotations’: [List[Dict]] } (in case of COCO detection)            | ‘image_id: int,                                                        |
-|                                             | object annotation                                       |                                                                      | ‘segments_info’: List[Dict] }                                          |
-|                                             |                                                         | or                                                                   |                                                                        |
-|                                             |                                                         |                                                                      | and masks_path (path to directory containing PNG files of the masks)   |
-|                                             |                                                         | {‘file_name’: str,                                                   |                                                                        |
-|                                             |                                                         | ‘image_id’: int,                                                     |                                                                        |
-|                                             |                                                         | ‘segments_info’: List[Dict]} (in case of COCO panoptic)              |                                                                        |
-+---------------------------------------------+---------------------------------------------------------+----------------------------------------------------------------------+------------------------------------------------------------------------+
-| **Postprocessing** (i.e. converting the     | :meth:`~transformers.DetrFeatureExtractor.post_process` | :meth:`~transformers.DetrFeatureExtractor.post_process_segmentation` | :meth:`~transformers.DetrFeatureExtractor.post_process_segmentation`,  |
-| output of the model to COCO API)            |                                                         |                                                                      | :meth:`~transformers.DetrFeatureExtractor.post_process_panoptic`       |
-+---------------------------------------------+---------------------------------------------------------+----------------------------------------------------------------------+------------------------------------------------------------------------+
-| **evaluators**                              | :obj:`CocoEvaluator` with iou_types = “bbox”            | :obj:`CocoEvaluator` with iou_types = “bbox”, “segm”                 | :obj:`CocoEvaluator` with iou_tupes = “bbox, “segm”                    |
-|                                             |                                                         |                                                                      |                                                                        |
-|                                             |                                                         |                                                                      | :obj:`PanopticEvaluator`                                               |
-+---------------------------------------------+---------------------------------------------------------+----------------------------------------------------------------------+------------------------------------------------------------------------+
-
-In short, one should prepare the data either in COCO detection or COCO panoptic format, then use
-:class:`~transformers.DetrFeatureExtractor` to create :obj:`pixel_values`, :obj:`pixel_mask` and optional
-:obj:`labels`, which can then be used to train (or fine-tune) a model. For evaluation, one should first convert the
-outputs of the model using one of the postprocessing methods of :class:`~transformers.DetrFeatureExtractor`. These can
-be be provided to either :obj:`CocoEvaluator` or :obj:`PanopticEvaluator`, which allow you to calculate metrics like
-mean Average Precision (mAP) and Panoptic Quality (PQ). The latter objects are implemented in the `original repository
-<https://github.com/facebookresearch/detr>`__. See the `example notebooks
-<https://github.com/NielsRogge/Transformers-Tutorials/tree/master/DETR>`__ for more info regarding evaluation.
-
-
-DETR specific outputs
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.models.detr.modeling_detr.DetrModelOutput
-    :members:
-
-.. autoclass:: transformers.models.detr.modeling_detr.DetrObjectDetectionOutput
-    :members:
-
-.. autoclass:: transformers.models.detr.modeling_detr.DetrSegmentationOutput
-    :members:
-
-
-DetrConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.DetrConfig
-    :members:
-
-
-DetrFeatureExtractor
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.DetrFeatureExtractor
-    :members: __call__, pad_and_create_pixel_mask, post_process, post_process_segmentation, post_process_panoptic
-
-
-DetrModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.DetrModel
-    :members: forward
-
-
-DetrForObjectDetection
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.DetrForObjectDetection
-    :members: forward
-
-
-DetrForSegmentation
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.DetrForSegmentation
-    :members: forward
--- a/docs/source/model_doc/dpr.rst
+++ b/docs/source/model_doc/dpr.rst
@@ -41,13 +41,6 @@ DPRConfig
    :members:


-DPRPreTrainedModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.DPRPreTrainedModel
-    :members:
-
-
 DPRContextEncoderTokenizer
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

--- a/docs/source/model_doc/gptj.rst
+++ b/docs/source/model_doc/gptj.rst
@@ -119,3 +119,24 @@ GPTJForSequenceClassification

 .. autoclass:: transformers.GPTJForSequenceClassification
    :members: forward
+
+
+GPTJForQuestionAnswering
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.GPTJForQuestionAnswering
+    :members: forward
+
+
+FlaxGPTJModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaxGPTJModel
+    :members: __call__
+
+
+FlaxGPTJForCausalLM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaxGPTJForCausalLM
+    :members: __call__
--- a/docs/source/model_doc/imagegpt.rst
+++ b/docs/source/model_doc/imagegpt.rst
@@ -0,0 +1,110 @@
+.. 
+    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.
+
+ImageGPT
+-----------------------------------------------------------------------------------------------------------------------
+
+Overview
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The ImageGPT model was proposed in `Generative Pretraining from Pixels <https://openai.com/blog/image-gpt/>`__ by Mark
+Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever. ImageGPT (iGPT) is a GPT-2-like
+model trained to predict the next pixel value, allowing for both unconditional and conditional image generation.
+
+The abstract from the paper is the following:
+
+*Inspired by progress in unsupervised representation learning for natural language, we examine whether similar models
+can learn useful representations for images. We train a sequence Transformer to auto-regressively predict pixels,
+without incorporating knowledge of the 2D input structure. Despite training on low-resolution ImageNet without labels,
+we find that a GPT-2 scale model learns strong image representations as measured by linear probing, fine-tuning, and
+low-data classification. On CIFAR-10, we achieve 96.3% accuracy with a linear probe, outperforming a supervised Wide
+ResNet, and 99.0% accuracy with full fine-tuning, matching the top supervised pre-trained models. We are also
+competitive with self-supervised benchmarks on ImageNet when substituting pixels for a VQVAE encoding, achieving 69.0%
+top-1 accuracy on a linear probe of our features.*
+
+The figure below summarizes the approach (taken from the `original paper
+<https://cdn.openai.com/papers/Generative_Pretraining_from_Pixels_V2.pdf>`__):
+
+.. image:: https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/imagegpt_architecture.png
+  :width: 600
+
+Tips:
+
+- ImageGPT is almost exactly the same as :doc:`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
+  length, the authors pre-trained ImageGPT on smaller input resolutions, such as 32x32 and 64x64. However, feeding a
+  sequence of 32x32x3=3072 tokens from 0..255 into a Transformer is still prohibitively large. Therefore, the authors
+  applied k-means clustering to the (R,G,B) pixel values with k=512. This way, we only have a 32*32 = 1024-long
+  sequence, but now of integers in the range 0..511. So we are shrinking the sequence length at the cost of a bigger
+  embedding matrix. In other words, the vocabulary size of ImageGPT is 512, + 1 for a special "start of sentence" (SOS)
+  token, used at the beginning of every sequence. One can use :class:`~transformers.ImageGPTFeatureExtractor` to
+  prepare images for the model.
+- Despite being pre-trained entirely unsupervised (i.e. without the use of any labels), ImageGPT produces fairly
+  performant image features useful for downstream tasks, such as image classification. The authors showed that the
+  features in the middle of the network are the most performant, and can be used as-is to train a linear model (such as
+  a sklearn logistic regression model for example). This is also referred to as "linear probing". Features can be
+  easily obtained by first forwarding the image through the model, then specifying `output_hidden_states=True`, and
+  then average-pool the hidden states at whatever layer you like.
+- Alternatively, one can further fine-tune the entire model on a downstream dataset, similar to BERT. For this, you can
+  use :class:`~transformers.ImageGPTForImageClassification`.
+- ImageGPT comes in different sizes: there's ImageGPT-small, ImageGPT-medium and ImageGPT-large. The authors did also
+  train an XL variant, which they didn't release. The differences in size are summarized in the following table:
+
+-------------------+----------------------+-----------------+---------------------+--------------+
+| **Model variant** | **Number of layers** | **Hidden size** | **Number of heads** | **# params** |
+-------------------+----------------------+-----------------+---------------------+--------------+
+| iGPT-small        | 24                   | 512             | 8                   | 76 million   |
+-------------------+----------------------+-----------------+---------------------+--------------+
+| iGPT-medium       | 36                   | 1024            | 8                   | 455 million  |
+-------------------+----------------------+-----------------+---------------------+--------------+
+| iGPT-large        | 48                   | 1536            | 16                  | 1.4 million  |
+-------------------+----------------------+-----------------+---------------------+--------------+
+| iGPT-XL           | 60                   | 3072            | not specified       | 6.8 billion  |
+-------------------+----------------------+-----------------+---------------------+--------------+
+
+This model was contributed by `nielsr <https://huggingface.co/nielsr>`__, based on `this issue
+<https://github.com/openai/image-gpt/issues/7>`__. The original code can be found `here
+<https://github.com/openai/image-gpt>`__.
+
+ImageGPTConfig
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.ImageGPTConfig
+    :members:
+
+ImageGPTFeatureExtractor
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.ImageGPTFeatureExtractor
+    :members: __call__
+
+ImageGPTModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.ImageGPTModel
+    :members: forward
+
+
+ImageGPTForCausalImageModeling
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.ImageGPTForCausalImageModeling
+    :members: forward
+
+
+ImageGPTForImageClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.ImageGPTForImageClassification
+    :members: forward
--- a/docs/source/model_doc/layoutlmv2.rst
+++ b/docs/source/model_doc/layoutlmv2.rst
@@ -18,9 +18,8 @@ Overview

 The LayoutLMV2 model was proposed in `LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding
 <https://arxiv.org/abs/2012.14740>`__ by Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu,
-Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou. LayoutLMV2 improves `LayoutLM
-<https://huggingface.co/transformers/model_doc/layoutlm.html>`__ to obtain state-of-the-art results across several
-document image understanding benchmarks:
+Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou. LayoutLMV2 improves `LayoutLM <layoutlm>`__ to obtain
+state-of-the-art results across several document image understanding benchmarks:

 - information extraction from scanned documents: the `FUNSD <https://guillaumejaume.github.io/FUNSD/>`__ dataset (a
  collection of 199 annotated forms comprising more than 30,000 words), the `CORD <https://github.com/clovaai/cord>`__
--- a/docs/source/model_doc/layoutxlm.rst
+++ b/docs/source/model_doc/layoutxlm.rst
@@ -40,17 +40,45 @@ One can directly plug in the weights of LayoutXLM into a LayoutLMv2 model, like

    model = LayoutLMv2Model.from_pretrained('microsoft/layoutxlm-base') 

-Note that LayoutXLM requires a different tokenizer, based on :class:`~transformers.XLMRobertaTokenizer`. You can
-initialize it as follows:
+Note that LayoutXLM has its own tokenizer, based on
+:class:`~transformers.LayoutXLMTokenizer`/:class:`~transformers.LayoutXLMTokenizerFast`. You can initialize it as
+follows:

 .. code-block::

-    from transformers import AutoTokenizer
+    from transformers import LayoutXLMTokenizer

-    tokenizer = AutoTokenizer.from_pretrained('microsoft/layoutxlm-base') 
+    tokenizer = LayoutXLMTokenizer.from_pretrained('microsoft/layoutxlm-base') 
+
+Similar to LayoutLMv2, you can use :class:`~transformers.LayoutXLMProcessor` (which internally applies
+:class:`~transformers.LayoutLMv2FeatureExtractor` and
+:class:`~transformers.LayoutXLMTokenizer`/:class:`~transformers.LayoutXLMTokenizerFast` in sequence) to prepare all
+data for the model.

 As LayoutXLM's architecture is equivalent to that of LayoutLMv2, one can refer to :doc:`LayoutLMv2's documentation page
 <layoutlmv2>` for all tips, code examples and notebooks.

 This model was contributed by `nielsr <https://huggingface.co/nielsr>`__. The original code can be found `here
 <https://github.com/microsoft/unilm>`__.
+
+
+LayoutXLMTokenizer
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.LayoutXLMTokenizer
+    :members: __call__, build_inputs_with_special_tokens, get_special_tokens_mask,
+        create_token_type_ids_from_sequences, save_vocabulary
+
+
+LayoutXLMTokenizerFast
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.LayoutXLMTokenizerFast
+    :members: __call__
+
+
+LayoutXLMProcessor
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.LayoutXLMProcessor
+    :members: __call__
--- a/docs/source/model_doc/luke.rst
+++ b/docs/source/model_doc/luke.rst
@@ -137,6 +137,12 @@ LukeModel
 .. autoclass:: transformers.LukeModel
    :members: forward

+LukeForMaskedLM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.LukeForMaskedLM
+    :members: forward
+

 LukeForEntityClassification
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
--- a/docs/source/model_doc/marian.rst
+++ b/docs/source/model_doc/marian.rst
@@ -103,8 +103,8 @@ Here is the code to see all available pretrained models on the hub:

 .. code-block:: python

-    from huggingface_hub.hf_api import HfApi
-    model_list = HfApi().list_models()
+    from huggingface_hub import list_models
+    model_list = list_models()
    org = "Helsinki-NLP"
    model_ids = [x.modelId for x in model_list if x.modelId.startswith(org)]
    suffix = [x.split('/')[1] for x in model_ids]
--- a/docs/source/model_doc/mluke.rst
+++ b/docs/source/model_doc/mluke.rst
@@ -0,0 +1,66 @@
+..
+    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.
+
+mLUKE
+-----------------------------------------------------------------------------------------------------------------------
+
+Overview
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The mLUKE model was proposed in `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. It's a multilingual extension
+of the `LUKE model <https://arxiv.org/abs/2010.01057>`__ trained on the basis of XLM-RoBERTa.
+
+It is based on XLM-RoBERTa and adds entity embeddings, which helps improve performance on various downstream tasks
+involving reasoning about entities such as named entity recognition, extractive question answering, relation
+classification, cloze-style knowledge completion.
+
+The abstract from the paper is the following:
+
+*Recent studies have shown that multilingual pretrained language models can be effectively improved with cross-lingual
+alignment information from Wikipedia entities. However, existing methods only exploit entity information in pretraining
+and do not explicitly use entities in downstream tasks. In this study, we explore the effectiveness of leveraging
+entity representations for downstream cross-lingual tasks. We train a multilingual language model with 24 languages
+with entity representations and show the model consistently outperforms word-based pretrained models in various
+cross-lingual transfer tasks. We also analyze the model and the key insight is that incorporating entity
+representations into the input allows us to extract more language-agnostic features. We also evaluate the model with a
+multilingual cloze prompt task with the mLAMA dataset. We show that entity-based prompt elicits correct factual
+knowledge more likely than using only word representations.*
+
+One can directly plug in the weights of mLUKE into a LUKE model, like so:
+
+.. code-block::
+
+    from transformers import LukeModel
+
+    model = LukeModel.from_pretrained('studio-ousia/mluke-base')
+
+Note that mLUKE has its own tokenizer, :class:`~transformers.MLukeTokenizer`. You can initialize it as follows:
+
+.. code-block::
+
+    from transformers import MLukeTokenizer
+
+    tokenizer = MLukeTokenizer.from_pretrained('studio-ousia/mluke-base')
+
+
+As mLUKE's architecture is equivalent to that of LUKE, one can refer to :doc:`LUKE's documentation page <luke>` for all
+tips, code examples and notebooks.
+
+This model was contributed by `ryo0634 <https://huggingface.co/ryo0634>`__. The original code can be found `here
+<https://github.com/studio-ousia/luke>`__.
+
+MLukeTokenizer
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.MLukeTokenizer
+    :members: __call__, save_vocabulary
--- a/docs/source/model_doc/perceiver.rst
+++ b/docs/source/model_doc/perceiver.rst
@@ -0,0 +1,234 @@
+.. 
+    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.
+
+Perceiver
+-----------------------------------------------------------------------------------------------------------------------
+
+Overview
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The Perceiver IO model was proposed in `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.
+
+Perceiver IO is a generalization of `Perceiver <https://arxiv.org/abs/2103.03206>`__ to handle arbitrary outputs in
+addition to arbitrary inputs. The original Perceiver only produced a single classification label. In addition to
+classification labels, Perceiver IO can produce (for example) language, optical flow, and multimodal videos with audio.
+This is done using the same building blocks as the original Perceiver. The computational complexity of Perceiver IO is
+linear in the input and output size and the bulk of the processing occurs in the latent space, allowing us to process
+inputs and outputs that are much larger than can be handled by standard Transformers. This means, for example,
+Perceiver IO can do BERT-style masked language modeling directly using bytes instead of tokenized inputs.
+
+The abstract from the paper is the following:
+
+*The recently-proposed Perceiver model obtains good results on several domains (images, audio, multimodal, point
+clouds) while scaling linearly in compute and memory with the input size. While the Perceiver supports many kinds of
+inputs, it can only produce very simple outputs such as class scores. Perceiver IO overcomes this limitation without
+sacrificing the original's appealing properties by learning to flexibly query the model's latent space to produce
+outputs of arbitrary size and semantics. Perceiver IO still decouples model depth from data size and still scales
+linearly with data size, but now with respect to both input and output sizes. The full Perceiver IO model achieves
+strong results on tasks with highly structured output spaces, such as natural language and visual understanding,
+StarCraft II, and multi-task and multi-modal domains. As highlights, Perceiver IO matches a Transformer-based BERT
+baseline on the GLUE language benchmark without the need for input tokenization and achieves state-of-the-art
+performance on Sintel optical flow estimation.*
+
+Here's a TLDR explaining how Perceiver works:
+
+The main problem with the self-attention mechanism of the Transformer is that the time and memory requirements scale
+quadratically with the sequence length. Hence, models like BERT and RoBERTa are limited to a max sequence length of 512
+tokens. Perceiver aims to solve this issue by, instead of performing self-attention on the inputs, perform it on a set
+of latent variables, and only use the inputs for cross-attention. In this way, the time and memory requirements don't
+depend on the length of the inputs anymore, as one uses a fixed amount of latent variables, like 256 or 512. These are
+randomly initialized, after which they are trained end-to-end using backpropagation.
+
+Internally, :class:`~transformers.PerceiverModel` will create the latents, which is a tensor of shape
+:obj:`(batch_size, num_latents, d_latents)`. One must provide :obj:`inputs` (which could be text, images, audio, you
+name it!) to the model, which it will use to perform cross-attention with the latents. The output of the Perceiver
+encoder is a tensor of the same shape. One can then, similar to BERT, convert the last hidden states of the latents to
+classification logits by averaging along the sequence dimension, and placing a linear layer on top of that to project
+the :obj:`d_latents` to :obj:`num_labels`.
+
+This was the idea of the original Perceiver paper. However, it could only output classification logits. In a follow-up
+work, PerceiverIO, they generalized it to let the model also produce outputs of arbitrary size. How, you might ask? The
+idea is actually relatively simple: one defines outputs of an arbitrary size, and then applies cross-attention with the
+last hidden states of the latents, using the outputs as queries, and the latents as keys and values.
+
+So let's say one wants to perform masked language modeling (BERT-style) with the Perceiver. As the Perceiver's input
+length will not have an impact on the computation time of the self-attention layers, one can provide raw bytes,
+providing :obj:`inputs` of length 2048 to the model. If one now masks out certain of these 2048 tokens, one can define
+the :obj:`outputs` as being of shape: :obj:`(batch_size, 2048, 768)`. Next, one performs cross-attention with the final
+hidden states of the latents to update the :obj:`outputs` tensor. After cross-attention, one still has a tensor of
+shape :obj:`(batch_size, 2048, 768)`. One can then place a regular language modeling head on top, to project the last
+dimension to the vocabulary size of the model, i.e. creating logits of shape :obj:`(batch_size, 2048, 262)` (as
+Perceiver uses a vocabulary size of 262 byte IDs).
+
+
+This model was contributed by `<nielsr> <https://huggingface.co/nielsr>`__. The original code can be found `here
+<https://github.com/deepmind/deepmind-research/tree/master/perceiver>`__.
+
+
+Perceiver specific outputs
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.models.perceiver.modeling_perceiver.PerceiverModelOutput
+    :members:
+
+.. autoclass:: transformers.models.perceiver.modeling_perceiver.PerceiverDecoderOutput
+    :members:
+
+.. autoclass:: transformers.models.perceiver.modeling_perceiver.PerceiverMaskedLMOutput
+    :members:
+
+.. autoclass:: transformers.models.perceiver.modeling_perceiver.PerceiverClassifierOutput
+    :members:
+
+
+PerceiverConfig
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.PerceiverConfig
+    :members:
+
+
+PerceiverTokenizer
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.PerceiverTokenizer
+    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
+        create_token_type_ids_from_sequences, save_vocabulary
+
+
+PerceiverFeatureExtractor
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.PerceiverFeatureExtractor
+    :members: 
+
+
+PerceiverTextPreprocessor
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.models.perceiver.modeling_perceiver.PerceiverTextPreprocessor
+    :members: 
+
+
+PerceiverImagePreprocessor
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.models.perceiver.modeling_perceiver.PerceiverImagePreprocessor
+    :members: 
+
+
+PerceiverOneHotPreprocessor
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.models.perceiver.modeling_perceiver.PerceiverOneHotPreprocessor
+    :members: 
+
+
+PerceiverAudioPreprocessor
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.models.perceiver.modeling_perceiver.PerceiverAudioPreprocessor
+    :members: 
+
+
+PerceiverMultimodalPreprocessor
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.models.perceiver.modeling_perceiver.PerceiverMultimodalPreprocessor
+    :members: 
+
+
+PerceiverProjectionPostprocessor
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.models.perceiver.modeling_perceiver.PerceiverProjectionPostprocessor
+    :members: 
+
+
+PerceiverAudioPostprocessor
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.models.perceiver.modeling_perceiver.PerceiverAudioPostprocessor
+    :members: 
+
+
+PerceiverClassificationPostprocessor
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.models.perceiver.modeling_perceiver.PerceiverClassificationPostprocessor
+    :members: 
+
+
+PerceiverMultimodalPostprocessor
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.models.perceiver.modeling_perceiver.PerceiverMultimodalPostprocessor
+    :members: 
+
+
+PerceiverModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.PerceiverModel
+    :members: forward
+
+
+PerceiverForMaskedLM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.PerceiverForMaskedLM
+    :members: forward
+
+
+PerceiverForSequenceClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.PerceiverForSequenceClassification
+    :members: forward
+
+
+PerceiverForImageClassificationLearned
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.PerceiverForImageClassificationLearned
+    :members: forward
+
+
+PerceiverForImageClassificationFourier
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.PerceiverForImageClassificationFourier
+    :members: forward
+
+
+PerceiverForImageClassificationConvProcessing
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.PerceiverForImageClassificationConvProcessing
+    :members: forward
+
+
+PerceiverForOpticalFlow
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.PerceiverForOpticalFlow
+    :members: forward
+
+
+PerceiverForMultimodalAutoencoding
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.PerceiverForMultimodalAutoencoding
+    :members: forward
--- a/docs/source/model_doc/qdqbert.rst
+++ b/docs/source/model_doc/qdqbert.rst
@@ -0,0 +1,189 @@
+.. 
+    Copyright 2021 NVIDIA Corporation and The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
+QDQBERT
+-----------------------------------------------------------------------------------------------------------------------
+
+Overview
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The QDQBERT model can be referenced in `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.
+
+The abstract from the paper is the following:
+
+*Quantization techniques can reduce the size of Deep Neural Networks and improve inference latency and throughput by
+taking advantage of high throughput integer instructions. In this paper we review the mathematical aspects of
+quantization parameters and evaluate their choices on a wide range of neural network models for different application
+domains, including vision, speech, and language. We focus on quantization techniques that are amenable to acceleration
+by processors with high-throughput integer math pipelines. We also present a workflow for 8-bit quantization that is
+able to maintain accuracy within 1% of the floating-point baseline on all networks studied, including models that are
+more difficult to quantize, such as MobileNets and BERT-large.*
+
+Tips:
+
+- QDQBERT model adds fake quantization operations (pair of QuantizeLinear/DequantizeLinear ops) to (i) linear layer
+  inputs and weights, (ii) matmul inputs, (iii) residual add inputs, in BERT model.
+
+- QDQBERT requires the dependency of `Pytorch Quantization Toolkit
+  <https://github.com/NVIDIA/TensorRT/tree/master/tools/pytorch-quantization>`__. To install ``pip install
+  pytorch-quantization --extra-index-url https://pypi.ngc.nvidia.com``
+
+- QDQBERT model can be loaded from any checkpoint of HuggingFace BERT model (for example *bert-base-uncased*), and
+  perform Quantization Aware Training/Post Training Quantization.
+
+- A complete example of using QDQBERT model to perform Quatization Aware Training and Post Training Quantization for
+  SQUAD task can be found at `transformers/examples/research_projects/quantization-qdqbert/
+  </examples/research_projects/quantization-qdqbert/>`_.
+
+This model was contributed by `shangz <https://huggingface.co/shangz>`__.
+
+
+Set default quantizers
+_______________________________________________________________________________________________________________________
+
+QDQBERT model adds fake quantization operations (pair of QuantizeLinear/DequantizeLinear ops) to BERT by
+:obj:`TensorQuantizer` in `Pytorch Quantization Toolkit
+<https://github.com/NVIDIA/TensorRT/tree/master/tools/pytorch-quantization>`__. :obj:`TensorQuantizer` is the module
+for quantizing tensors, with :obj:`QuantDescriptor` defining how the tensor should be quantized. Refer to `Pytorch
+Quantization Toolkit userguide
+<https://docs.nvidia.com/deeplearning/tensorrt/pytorch-quantization-toolkit/docs/userguide.html>`__ for more details.
+
+Before creating QDQBERT model, one has to set the default :obj:`QuantDescriptor` defining default tensor quantizers.
+Example:
+
+.. code-block::
+
+    >>> import pytorch_quantization.nn as quant_nn
+    >>> from pytorch_quantization.tensor_quant import QuantDescriptor
+
+    >>> # The default tensor quantizer is set to use Max calibration method
+    >>> input_desc = QuantDescriptor(num_bits=8, calib_method="max")
+    >>> # The default tensor quantizer is set to be per-channel quantization for weights
+    >>> weight_desc = QuantDescriptor(num_bits=8, axis=((0,)))
+    >>> quant_nn.QuantLinear.set_default_quant_desc_input(input_desc)
+    >>> quant_nn.QuantLinear.set_default_quant_desc_weight(weight_desc)
+
+
+Calibration
+_______________________________________________________________________________________________________________________
+
+Calibration is the terminology of passing data samples to the quantizer and deciding the best scaling factors for
+tensors. After setting up the tensor quantizers, one can use the following example to calibrate the model:
+
+.. code-block::
+
+    >>> # Find the TensorQuantizer and enable calibration
+    >>> for name, module in model.named_modules():
+    >>>     if name.endswith('_input_quantizer'):
+    >>>         module.enable_calib()
+    >>>         module.disable_quant()  # Use full precision data to calibrate
+
+    >>> # Feeding data samples
+    >>> model(x)
+    >>> # ...
+
+    >>> # Finalize calibration
+    >>> for name, module in model.named_modules():
+    >>>     if name.endswith('_input_quantizer'):
+    >>>         module.load_calib_amax()
+    >>>         module.enable_quant()
+
+    >>> # If running on GPU, it needs to call .cuda() again because new tensors will be created by calibration process
+    >>> model.cuda()
+
+    >>> # Keep running the quantized model
+    >>> # ...
+
+
+Export to ONNX
+_______________________________________________________________________________________________________________________
+
+The goal of exporting to ONNX is to deploy inference by `TensorRT <https://developer.nvidia.com/tensorrt>`__. Fake
+quantization will be broken into a pair of QuantizeLinear/DequantizeLinear ONNX ops. After setting static member of
+TensorQuantizer to use Pytorch’s own fake quantization functions, fake quantized model can be exported to ONNX, follow
+the instructions in `torch.onnx <https://pytorch.org/docs/stable/onnx.html>`__. Example:
+
+.. code-block::
+
+    >>> from pytorch_quantization.nn import TensorQuantizer
+    >>> TensorQuantizer.use_fb_fake_quant = True
+
+    >>> # Load the calibrated model
+    >>> ...
+    >>> # ONNX export
+    >>> torch.onnx.export(...)
+
+
+QDQBertConfig
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.QDQBertConfig
+    :members:
+
+
+QDQBertModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.QDQBertModel
+    :members: forward
+
+
+QDQBertLMHeadModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.QDQBertLMHeadModel
+    :members: forward
+
+
+QDQBertForMaskedLM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.QDQBertForMaskedLM
+    :members: forward
+
+
+QDQBertForSequenceClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.QDQBertForSequenceClassification
+    :members: forward
+
+
+QDQBertForNextSentencePrediction
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.QDQBertForNextSentencePrediction
+    :members: forward
+
+
+QDQBertForMultipleChoice
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.QDQBertForMultipleChoice
+    :members: forward
+
+
+QDQBertForTokenClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.QDQBertForTokenClassification
+    :members: forward
+
+
+QDQBertForQuestionAnswering
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.QDQBertForQuestionAnswering
+    :members: forward
+
--- a/docs/source/model_doc/segformer.rst
+++ b/docs/source/model_doc/segformer.rst
@@ -38,6 +38,58 @@ Cityscapes validation set and shows excellent zero-shot robustness on Cityscapes
 This model was contributed by `nielsr <https://huggingface.co/nielsr>`__. The original code can be found `here
 <https://github.com/NVlabs/SegFormer>`__.

+The figure below illustrates the architecture of SegFormer. Taken from the `original paper
+<https://arxiv.org/abs/2105.15203>`__.
+
+.. image:: https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/segformer_architecture.png
+  :width: 600
+
+Tips:
+
+- SegFormer consists of a hierarchical Transformer encoder, and a lightweight all-MLP decode head.
+  :class:`~transformers.SegformerModel` is the hierarchical Transformer encoder (which in the paper is also referred to
+  as Mix Transformer or MiT). :class:`~transformers.SegformerForSemanticSegmentation` adds the all-MLP decode head on
+  top to perform semantic segmentation of images. In addition, there's
+  :class:`~transformers.SegformerForImageClassification` which can be used to - you guessed it - classify images. The
+  authors of SegFormer first pre-trained the Transformer encoder on ImageNet-1k to classify images. Next, they throw
+  away the classification head, and replace it by the all-MLP decode head. Next, they fine-tune the model altogether on
+  ADE20K, Cityscapes and COCO-stuff, which are important benchmarks for semantic segmentation. All checkpoints can be
+  found on the `hub <https://huggingface.co/models?other=segformer>`__.
+- The quickest way to get started with SegFormer is by checking the `example notebooks
+  <https://github.com/NielsRogge/Transformers-Tutorials/tree/master/SegFormer>`__ (which showcase both inference and
+  fine-tuning on custom data).
+- One can use :class:`~transformers.SegformerFeatureExtractor` to prepare images and corresponding segmentation maps
+  for the model. Note that this feature extractor is fairly basic and does not include all data augmentations used in
+  the original paper. The original preprocessing pipelines (for the ADE20k dataset for instance) can be found `here
+  <https://github.com/NVlabs/SegFormer/blob/master/local_configs/_base_/datasets/ade20k_repeat.py>`__. The most
+  important preprocessing step is that images and segmentation maps are randomly cropped and padded to the same size,
+  such as 512x512 or 640x640, after which they are normalized.
+- One additional thing to keep in mind is that one can initialize :class:`~transformers.SegformerFeatureExtractor` with
+  :obj:`reduce_labels` set to `True` or `False`. In some datasets (like ADE20k), the 0 index is used in the annotated
+  segmentation maps for background. However, ADE20k doesn't include the "background" class in its 150 labels.
+  Therefore, :obj:`reduce_labels` is used to reduce all labels by 1, and to make sure no loss is computed for the
+  background class (i.e. it replaces 0 in the annotated maps by 255, which is the `ignore_index` of the loss function
+  used by :class:`~transformers.SegformerForSemanticSegmentation`). However, other datasets use the 0 index as
+  background class and include this class as part of all labels. In that case, :obj:`reduce_labels` should be set to
+  `False`, as loss should also be computed for the background class.
+- As most models, SegFormer comes in different sizes, the details of which can be found in the table below.
+
+-------------------+---------------+---------------------+-------------------------+----------------+-----------------------+
+| **Model variant** | **Depths**    | **Hidden sizes**    | **Decoder hidden size** | **Params (M)** | **ImageNet-1k Top 1** |
+-------------------+---------------+---------------------+-------------------------+----------------+-----------------------+
+| MiT-b0            | [2, 2, 2, 2]  | [32, 64, 160, 256]  | 256                     | 3.7            | 70.5                  |
+-------------------+---------------+---------------------+-------------------------+----------------+-----------------------+
+| MiT-b1            | [2, 2, 2, 2]  | [64, 128, 320, 512] | 256                     | 14.0           | 78.7                  |
+-------------------+---------------+---------------------+-------------------------+----------------+-----------------------+
+| MiT-b2            | [3, 4, 6, 3]  | [64, 128, 320, 512] | 768                     | 25.4           | 81.6                  |
+-------------------+---------------+---------------------+-------------------------+----------------+-----------------------+
+| MiT-b3            | [3, 4, 18, 3] | [64, 128, 320, 512] | 768                     | 45.2           | 83.1                  |
+-------------------+---------------+---------------------+-------------------------+----------------+-----------------------+
+| MiT-b4            | [3, 8, 27, 3] | [64, 128, 320, 512] | 768                     | 62.6           | 83.6                  |
+-------------------+---------------+---------------------+-------------------------+----------------+-----------------------+
+| MiT-b5            | [3, 6, 40, 3] | [64, 128, 320, 512] | 768                     | 82.0           | 83.8                  |
+-------------------+---------------+---------------------+-------------------------+----------------+-----------------------+
+
 SegformerConfig
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

--- a/docs/source/model_doc/speech_to_text_2.rst
+++ b/docs/source/model_doc/speech_to_text_2.rst
@@ -36,7 +36,7 @@ Tips:
 - Speech2Text2 achieves state-of-the-art results on the CoVoST Speech Translation dataset. For more information, see
  the `official models <https://huggingface.co/models?other=speech2text2>`__ .
 - Speech2Text2 is always used within the :doc:`SpeechEncoderDecoder <speechencoderdecoder>` framework.
- Speech2Text2's tokenizer currently only supports inference, but not training.
+- Speech2Text2's tokenizer is based on `fastBPE <https://github.com/glample/fastBPE>`.

 Inference
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
--- a/docs/source/model_doc/tapas.rst
+++ b/docs/source/model_doc/tapas.rst
@@ -49,7 +49,8 @@ entailment (a binary classification task). For more details, see their follow-up
 intermediate pre-training <https://www.aclweb.org/anthology/2020.findings-emnlp.27/>`__ by Julian Martin Eisenschlos,
 Syrine Krichene and Thomas Müller.

-This model was contributed by `nielsr <https://huggingface.co/nielsr>`__. The original code can be found `here
+This model was contributed by `nielsr <https://huggingface.co/nielsr>`__. The Tensorflow version of this model was
+contributed by `kamalkraj <https://huggingface.co/kamalkraj>`__. The original code can be found `here
 <https://github.com/google-research/tapas>`__.

 Tips:
@@ -130,6 +131,24 @@ for your environment):
        >>> config = TapasConfig('google-base-finetuned-wikisql-supervised')
        >>> model = TapasForQuestionAnswering.from_pretrained('google/tapas-base', config=config)

+In TensorFlow, this can be done as follows (make sure to have installed the `tensorflow_probability dependency
+<https://github.com/tensorflow/probability`>__ for your environment):
+
+.. code-block::
+
+        >>> from transformers import TapasConfig, TFTapasForQuestionAnswering
+
+        >>> # for example, the base sized model with default SQA configuration
+        >>> model = TFTapasForQuestionAnswering.from_pretrained('google/tapas-base')
+
+        >>> # or, the base sized model with WTQ configuration
+        >>> config = TapasConfig.from_pretrained('google/tapas-base-finetuned-wtq')
+        >>> model = TFTapasForQuestionAnswering.from_pretrained('google/tapas-base', config=config)
+
+        >>> # or, the base sized model with WikiSQL configuration
+        >>> config = TapasConfig('google-base-finetuned-wikisql-supervised')
+        >>> model = TFTapasForQuestionAnswering.from_pretrained('google/tapas-base', config=config)
+

 Of course, you don't necessarily have to follow one of these three ways in which TAPAS was fine-tuned. You can also
 experiment by defining any hyperparameters you want when initializing :class:`~transformers.TapasConfig`, and then
@@ -142,10 +161,21 @@ way. Here's an example:
        >>> from transformers import TapasConfig, TapasForQuestionAnswering

        >>> # you can initialize the classification heads any way you want (see docs of TapasConfig)
-        >>> config = TapasConfig(num_aggregation_labels=3, average_logits_per_cell=True, select_one_column=False)
+        >>> config = TapasConfig(num_aggregation_labels=3, average_logits_per_cell=True)
        >>> # initializing the pre-trained base sized model with our custom classification heads
        >>> model = TapasForQuestionAnswering.from_pretrained('google/tapas-base', config=config)

+And here is the equivalent code for TensorFlow:
+
+.. code-block::
+
+        >>> from transformers import TapasConfig, TFTapasForQuestionAnswering
+
+        >>> # you can initialize the classification heads any way you want (see docs of TapasConfig)
+        >>> config = TapasConfig(num_aggregation_labels=3, average_logits_per_cell=True)
+        >>> # initializing the pre-trained base sized model with our custom classification heads
+        >>> model = TFTapasForQuestionAnswering.from_pretrained('google/tapas-base', config=config)
+
 What you can also do is start from an already fine-tuned checkpoint. A note here is that the already fine-tuned
 checkpoint on WTQ has some issues due to the L2-loss which is somewhat brittle. See `here
 <https://github.com/google-research/tapas/issues/91#issuecomment-735719340>`__ for more info.
@@ -180,12 +210,13 @@ SQA format. The author explains this `here
 are not perfect (the ``answer_coordinates`` and ``float_answer`` fields are populated based on the ``answer_text``),
 meaning that WTQ and WikiSQL results could actually be improved.

-**STEP 3: Convert your data into PyTorch tensors using TapasTokenizer**
+**STEP 3: Convert your data into PyTorch/TensorFlow tensors using TapasTokenizer**

 Third, given that you've prepared your data in this TSV/CSV format (and corresponding CSV files containing the tabular
 data), you can then use :class:`~transformers.TapasTokenizer` to convert table-question pairs into :obj:`input_ids`,
 :obj:`attention_mask`, :obj:`token_type_ids` and so on. Again, based on which of the three cases you picked above,
-:class:`~transformers.TapasForQuestionAnswering` requires different inputs to be fine-tuned:
+:class:`~transformers.TapasForQuestionAnswering`/:class:`~transformers.TFTapasForQuestionAnswering` requires different
+inputs to be fine-tuned:

 +------------------------------------+----------------------------------------------------------------------------------------------+
 | **Task**                           | **Required inputs**                                                                          |
@@ -220,6 +251,8 @@ are already in the TSV file of step 2. Here's an example:
        {'input_ids': tensor([[ ... ]]), 'attention_mask': tensor([[...]]), 'token_type_ids': tensor([[[...]]]),
        'numeric_values': tensor([[ ... ]]), 'numeric_values_scale: tensor([[ ... ]]), labels: tensor([[ ... ]])}

+Set `return_tensors='tf'` when calling the tokenizer to prepare data for the TF models.
+
 Note that :class:`~transformers.TapasTokenizer` expects the data of the table to be **text-only**. You can use
 ``.astype(str)`` on a dataframe to turn it into text-only data. Of course, this only shows how to encode a single
 training example. It is advised to create a PyTorch dataset and a corresponding dataloader:
@@ -261,15 +294,67 @@ training example. It is advised to create a PyTorch dataset and a corresponding
        >>> train_dataset = TableDataset(data, tokenizer)
        >>> train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=32)

+And here is the equivalent code for TensorFlow:
+
+.. code-block::
+
+        >>> import tensorflow as tf
+        >>> import pandas as pd
+
+        >>> tsv_path = "your_path_to_the_tsv_file"
+        >>> table_csv_path = "your_path_to_a_directory_containing_all_csv_files"
+
+        >>> class TableDataset:
+        ...     def __init__(self, data, tokenizer):
+        ...         self.data = data
+        ...         self.tokenizer = tokenizer
+        ...
+        ...     def __iter__(self):
+        ...         for idx in range(self.__len__()):
+        ...             item = self.data.iloc[idx]
+        ...             table = pd.read_csv(table_csv_path + item.table_file).astype(str) # be sure to make your table data text only
+        ...             encoding = self.tokenizer(table=table, 
+        ...                                   queries=item.question, 
+        ...                                   answer_coordinates=item.answer_coordinates, 
+        ...                                   answer_text=item.answer_text,
+        ...                                   truncation=True,
+        ...                                   padding="max_length",
+        ...                                   return_tensors="tf"
+        ...             )
+        ...             # remove the batch dimension which the tokenizer adds by default
+        ...             encoding = {key: tf.squeeze(val,0) for key, val in encoding.items()}
+        ...             # add the float_answer which is also required (weak supervision for aggregation case)
+        ...             encoding["float_answer"] = tf.convert_to_tensor(item.float_answer,dtype=tf.float32)
+        ...             yield encoding['input_ids'], encoding['attention_mask'], encoding['numeric_values'], \
+        ...                   encoding['numeric_values_scale'], encoding['token_type_ids'], encoding['labels'], \
+        ...                   encoding['float_answer']
+        ...
+        ...     def __len__(self):
+        ...        return len(self.data)
+
+        >>> data = pd.read_csv(tsv_path, sep='\t')
+        >>> train_dataset = TableDataset(data, tokenizer)
+        >>> output_signature = (
+        ... tf.TensorSpec(shape=(512,), dtype=tf.int32),
+        ... tf.TensorSpec(shape=(512,), dtype=tf.int32),
+        ... tf.TensorSpec(shape=(512,), dtype=tf.float32),
+        ... tf.TensorSpec(shape=(512,), dtype=tf.float32),
+        ... tf.TensorSpec(shape=(512,7), dtype=tf.int32),
+        ... tf.TensorSpec(shape=(512,), dtype=tf.int32),
+        ... tf.TensorSpec(shape=(512,), dtype=tf.float32))
+        >>> train_dataloader = tf.data.Dataset.from_generator(train_dataset, output_signature=output_signature).batch(32)
+
 Note that here, we encode each table-question pair independently. This is fine as long as your dataset is **not
 conversational**. In case your dataset involves conversational questions (such as in SQA), then you should first group
 together the ``queries``, ``answer_coordinates`` and ``answer_text`` per table (in the order of their ``position``
 index) and batch encode each table with its questions. This will make sure that the ``prev_labels`` token types (see
 docs of :class:`~transformers.TapasTokenizer`) are set correctly. See `this notebook
 <https://github.com/NielsRogge/Transformers-Tutorials/blob/master/TAPAS/Fine_tuning_TapasForQuestionAnswering_on_SQA.ipynb>`__
-for more info.
+for more info. See `this notebook
+<https://github.com/kamalkraj/Tapas-Tutorial/blob/master/TAPAS/Fine_tuning_TapasForQuestionAnswering_on_SQA.ipynb>`__
+for more info regarding using the TensorFlow model.

-**STEP 4: Train (fine-tune) TapasForQuestionAnswering**
+**STEP 4: Train (fine-tune) TapasForQuestionAnswering/TFTapasForQuestionAnswering**

 You can then fine-tune :class:`~transformers.TapasForQuestionAnswering` using native PyTorch as follows (shown here for
 the weak supervision for aggregation case):
@@ -316,6 +401,52 @@ the weak supervision for aggregation case):
        ...         loss.backward()
        ...         optimizer.step()

+
+Equivalently, fine-tuning :class:`~transformers.TFTapasForQuestionAnswering` in native TensorFlow can be done as
+follows (shown here for the weak supervision for aggregation case):
+
+.. code-block::
+
+        >>> import tensorflow as tf
+        >>> from transformers import TapasConfig, TFTapasForQuestionAnswering
+
+        >>> # this is the default WTQ configuration
+        >>> config = TapasConfig(
+        ...            num_aggregation_labels = 4,
+        ...            use_answer_as_supervision = True,
+        ...            answer_loss_cutoff = 0.664694,
+        ...            cell_selection_preference = 0.207951,
+        ...            huber_loss_delta = 0.121194,
+        ...            init_cell_selection_weights_to_zero = True,
+        ...            select_one_column = True,
+        ...            allow_empty_column_selection = False,
+        ...            temperature = 0.0352513,
+        ... )
+        >>> model = TFTapasForQuestionAnswering.from_pretrained("google/tapas-base", config=config)
+
+        >>> optimizer = tf.keras.optimizers.Adam(learning_rate=5e-5)
+
+        >>> for epoch in range(2):  # loop over the dataset multiple times
+        ...    for idx, batch in enumerate(train_dataloader):
+        ...         # get the inputs; 
+        ...         input_ids = batch[0]
+        ...         attention_mask = batch[1]
+        ...         token_type_ids = batch[4]
+        ...         labels = batch[-1]
+        ...         numeric_values = batch[2]
+        ...         numeric_values_scale = batch[3]
+        ...         float_answer = batch[6]
+
+        ...         # forward + backward + optimize
+        ...         with tf.GradientTape() as tape:
+        ...              outputs = model(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, 
+        ...                        labels=labels, numeric_values=numeric_values, numeric_values_scale=numeric_values_scale, 
+        ...                        float_answer=float_answer )
+        ...         grads = tape.gradient(outputs.loss, model.trainable_weights)
+        ...         optimizer.apply_gradients(zip(grads, model.trainable_weights))
+
+
+
 Usage: inference
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -380,10 +511,68 @@ of that:
        What is the total number of movies?
        Predicted answer: SUM > 87, 53, 69

+
+And here is the equivalent code for TensorFlow:
+
+.. code-block::
+
+        >>> from transformers import TapasTokenizer, TFTapasForQuestionAnswering
+        >>> import pandas as pd 
+
+        >>> model_name = 'google/tapas-base-finetuned-wtq'
+        >>> model = TFTapasForQuestionAnswering.from_pretrained(model_name)
+        >>> tokenizer = TapasTokenizer.from_pretrained(model_name)
+
+        >>> data = {'Actors': ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"], 'Number of movies': ["87", "53", "69"]}
+        >>> queries = ["What is the name of the first actor?", "How many movies has George Clooney played in?", "What is the total number of movies?"]
+        >>> table = pd.DataFrame.from_dict(data)
+        >>> inputs = tokenizer(table=table, queries=queries, padding='max_length', return_tensors="tf") 
+        >>> outputs = model(**inputs)
+        >>> predicted_answer_coordinates, predicted_aggregation_indices = tokenizer.convert_logits_to_predictions(
+        ...         inputs, 
+        ...         outputs.logits, 
+        ...         outputs.logits_aggregation
+        ... )
+
+        >>> # let's print out the results:
+        >>> id2aggregation = {0: "NONE", 1: "SUM", 2: "AVERAGE", 3:"COUNT"}
+        >>> aggregation_predictions_string = [id2aggregation[x] for x in predicted_aggregation_indices]
+
+        >>> answers = []
+        >>> for coordinates in predicted_answer_coordinates:
+        ...   if len(coordinates) == 1:
+        ...     # only a single cell:
+        ...     answers.append(table.iat[coordinates[0]])
+        ...   else:
+        ...     # multiple cells
+        ...     cell_values = []
+        ...     for coordinate in coordinates:
+        ...        cell_values.append(table.iat[coordinate])
+        ...     answers.append(", ".join(cell_values))
+
+        >>> display(table)
+        >>> print("")
+        >>> for query, answer, predicted_agg in zip(queries, answers, aggregation_predictions_string):
+        ...   print(query)
+        ...   if predicted_agg == "NONE":
+        ...     print("Predicted answer: " + answer)
+        ...   else:
+        ...     print("Predicted answer: " + predicted_agg + " > " + answer)    
+        What is the name of the first actor?
+        Predicted answer: Brad Pitt
+        How many movies has George Clooney played in?
+        Predicted answer: COUNT > 69
+        What is the total number of movies?
+        Predicted answer: SUM > 87, 53, 69
+
+
 In case of a conversational set-up, then each table-question pair must be provided **sequentially** to the model, such
 that the ``prev_labels`` token types can be overwritten by the predicted ``labels`` of the previous table-question
 pair. Again, more info can be found in `this notebook
-<https://github.com/NielsRogge/Transformers-Tutorials/blob/master/TAPAS/Fine_tuning_TapasForQuestionAnswering_on_SQA.ipynb>`__.
+<https://github.com/NielsRogge/Transformers-Tutorials/blob/master/TAPAS/Fine_tuning_TapasForQuestionAnswering_on_SQA.ipynb>`__
+(for PyTorch) and `this notebook
+<https://github.com/kamalkraj/Tapas-Tutorial/blob/master/TAPAS/Fine_tuning_TapasForQuestionAnswering_on_SQA.ipynb>`__
+(for TensorFlow).


 Tapas specific outputs
@@ -433,3 +622,31 @@ TapasForQuestionAnswering

 .. autoclass:: transformers.TapasForQuestionAnswering
    :members: forward
+
+
+TFTapasModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFTapasModel
+    :members: call
+
+
+TFTapasForMaskedLM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFTapasForMaskedLM
+    :members: call
+
+
+TFTapasForSequenceClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFTapasForSequenceClassification
+    :members: call
+
+
+TFTapasForQuestionAnswering
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFTapasForQuestionAnswering
+    :members: call
--- a/docs/source/model_doc/vision_text_dual_encoder.rst
+++ b/docs/source/model_doc/vision_text_dual_encoder.rst
@@ -0,0 +1,56 @@
+.. 
+    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.
+
+VisionTextDualEncoder
+-----------------------------------------------------------------------------------------------------------------------
+
+Overview
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The :class:`~transformers.VisionTextDualEncoderModel` can be used to initialize a vision-text dual encoder model with
+any pretrained vision autoencoding model as the vision encoder (*e.g.* :doc:`ViT <vit>`, :doc:`BEiT <beit>`, :doc:`DeiT
+<deit>`) and any pretrained text autoencoding model as the text encoder (*e.g.* :doc:`RoBERTa <roberta>`, :doc:`BERT
+<bert>`). Two projection layers are added on top of both the vision and text encoder to project the output embeddings
+to a shared latent space. The projection layers are randomly initialized so the model should be fine-tuned on a
+downstream task. This model can be used to align the vision-text embeddings using CLIP like contrastive image-text
+training and then can be used for zero-shot vision tasks such image-classification or retrieval.
+
+In `LiT: Zero-Shot Transfer with Locked-image Text Tuning <https://arxiv.org/abs/2111.07991>`__ it is shown how
+leveraging pre-trained (locked/frozen) image and text model for contrastive learning yields significant improvment on
+new zero-shot vision tasks such as image classification or retrieval.
+
+VisionTextDualEncoderConfig
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.VisionTextDualEncoderConfig
+    :members:
+
+
+VisionTextDualEncoderProcessor
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.VisionTextDualEncoderProcessor
+    :members:
+
+
+VisionTextDualEncoderModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.VisionTextDualEncoderModel
+    :members: forward
+
+
+FlaxVisionTextDualEncoderModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaxVisionTextDualEncoderModel
+    :members: __call__
--- a/docs/source/model_doc/visionencoderdecoder.rst
+++ b/docs/source/model_doc/visionencoderdecoder.rst
@@ -39,3 +39,10 @@ VisionEncoderDecoderModel

 .. autoclass:: transformers.VisionEncoderDecoderModel
    :members: forward, from_encoder_decoder_pretrained
+
+
+FlaxVisionEncoderDecoderModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaxVisionEncoderDecoderModel
+    :members: __call__, from_encoder_decoder_pretrained
--- a/docs/source/model_doc/vit.rst
+++ b/docs/source/model_doc/vit.rst
@@ -120,6 +120,20 @@ ViTForImageClassification
    :members: forward


+TFViTModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFViTModel
+    :members: call
+
+
+TFViTForImageClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFViTForImageClassification
+    :members: call
+
+
 FlaxVitModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

--- a/docs/source/model_doc/wav2vec2.rst
+++ b/docs/source/model_doc/wav2vec2.rst
@@ -67,9 +67,19 @@ Wav2Vec2Processor
    :members: __call__, pad, from_pretrained, save_pretrained, batch_decode, decode, as_target_processor


+Wav2Vec2ProcessorWithLM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.Wav2Vec2ProcessorWithLM
+    :members: __call__, pad, from_pretrained, save_pretrained, batch_decode, decode, as_target_processor
+
+
 Wav2Vec2 specific outputs
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

+.. autoclass:: transformers.models.wav2vec2_with_lm.processing_wav2vec2_with_lm.Wav2Vec2DecoderWithLMOutput
+    :members: 
+
 .. autoclass:: transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2BaseModelOutput
    :members: 

--- a/docs/source/model_summary.rst
+++ b/docs/source/model_summary.rst
@@ -80,7 +80,7 @@ Original GPT
   <a href="https://huggingface.co/models?filter=openai-gpt">
       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-openai--gpt-blueviolet">
   </a>
-   <a href="model_doc/gpt.html">
+   <a href="model_doc/gpt">
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-openai--gpt-blueviolet">
   </a>

@@ -100,7 +100,7 @@ GPT-2
   <a href="https://huggingface.co/models?filter=gpt2">
       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-gpt2-blueviolet">
   </a>
-   <a href="model_doc/gpt2.html">
+   <a href="model_doc/gpt2">
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-gpt2-blueviolet">
   </a>

@@ -122,7 +122,7 @@ CTRL
   <a href="https://huggingface.co/models?filter=ctrl">
       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-ctrl-blueviolet">
   </a>
-   <a href="model_doc/ctrl.html">
+   <a href="model_doc/ctrl">
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-ctrl-blueviolet">
   </a>

@@ -143,7 +143,7 @@ Transformer-XL
   <a href="https://huggingface.co/models?filter=transfo-xl">
       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-transfo--xl-blueviolet">
   </a>
-   <a href="model_doc/transformerxl.html">
+   <a href="model_doc/transformerxl">
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-transfo--xl-blueviolet">
   </a>

@@ -174,7 +174,7 @@ Reformer
   <a href="https://huggingface.co/models?filter=reformer">
       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-reformer-blueviolet">
   </a>
-   <a href="model_doc/reformer.html">
+   <a href="model_doc/reformer">
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-reformer-blueviolet">
   </a>

@@ -208,7 +208,7 @@ XLNet
   <a href="https://huggingface.co/models?filter=xlnet">
       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-xlnet-blueviolet">
   </a>
-   <a href="model_doc/xlnet.html">
+   <a href="model_doc/xlnet">
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-xlnet-blueviolet">
   </a>

@@ -248,7 +248,7 @@ BERT
   <a href="https://huggingface.co/models?filter=bert">
       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-bert-blueviolet">
   </a>
-   <a href="model_doc/bert.html">
+   <a href="model_doc/bert">
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-bert-blueviolet">
   </a>

@@ -277,7 +277,7 @@ ALBERT
   <a href="https://huggingface.co/models?filter=albert">
       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-albert-blueviolet">
   </a>
-   <a href="model_doc/albert.html">
+   <a href="model_doc/albert">
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-albert-blueviolet">
   </a>

@@ -306,7 +306,7 @@ RoBERTa
   <a href="https://huggingface.co/models?filter=roberta">
       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-roberta-blueviolet">
   </a>
-   <a href="model_doc/roberta.html">
+   <a href="model_doc/roberta">
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-roberta-blueviolet">
   </a>

@@ -331,7 +331,7 @@ DistilBERT
   <a href="https://huggingface.co/models?filter=distilbert">
       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-distilbert-blueviolet">
   </a>
-   <a href="model_doc/distilbert.html">
+   <a href="model_doc/distilbert">
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-distilbert-blueviolet">
   </a>

@@ -356,7 +356,7 @@ ConvBERT
   <a href="https://huggingface.co/models?filter=convbert">
       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-convbert-blueviolet">
   </a>
-   <a href="model_doc/convbert.html">
+   <a href="model_doc/convbert">
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-convbert-blueviolet">
   </a>

@@ -386,7 +386,7 @@ XLM
   <a href="https://huggingface.co/models?filter=xlm">
       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-xlm-blueviolet">
   </a>
-   <a href="model_doc/xlm.html">
+   <a href="model_doc/xlm">
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-xlm-blueviolet">
   </a>

@@ -420,7 +420,7 @@ XLM-RoBERTa
   <a href="https://huggingface.co/models?filter=xlm-roberta">
       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-xlm--roberta-blueviolet">
   </a>
-   <a href="model_doc/xlmroberta.html">
+   <a href="model_doc/xlmroberta">
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-xlm--roberta-blueviolet">
   </a>

@@ -442,7 +442,7 @@ FlauBERT
   <a href="https://huggingface.co/models?filter=flaubert">
       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-flaubert-blueviolet">
   </a>
-   <a href="model_doc/flaubert.html">
+   <a href="model_doc/flaubert">
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-flaubert-blueviolet">
   </a>

@@ -460,7 +460,7 @@ ELECTRA
   <a href="https://huggingface.co/models?filter=electra">
       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-electra-blueviolet">
   </a>
-   <a href="model_doc/electra.html">
+   <a href="model_doc/electra">
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-electra-blueviolet">
   </a>

@@ -484,7 +484,7 @@ Funnel Transformer
   <a href="https://huggingface.co/models?filter=funnel">
       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-funnel-blueviolet">
   </a>
-   <a href="model_doc/funnel.html">
+   <a href="model_doc/funnel">
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-funnel-blueviolet">
   </a>

@@ -518,7 +518,7 @@ Longformer
   <a href="https://huggingface.co/models?filter=longformer">
       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-longformer-blueviolet">
   </a>
-   <a href="model_doc/longformer.html">
+   <a href="model_doc/longformer">
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-longformer-blueviolet">
   </a>

@@ -558,7 +558,7 @@ BART
   <a href="https://huggingface.co/models?filter=bart">
       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-bart-blueviolet">
   </a>
-   <a href="model_doc/bart.html">
+   <a href="model_doc/bart">
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-bart-blueviolet">
   </a>

@@ -585,7 +585,7 @@ Pegasus
   <a href="https://huggingface.co/models?filter=pegasus">
       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-pegasus-blueviolet">
   </a>
-   <a href="model_doc/pegasus.html">
+   <a href="model_doc/pegasus">
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-pegasus-blueviolet">
   </a>

@@ -616,7 +616,7 @@ MarianMT
   <a href="https://huggingface.co/models?filter=marian">
       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-marian-blueviolet">
   </a>
-   <a href="model_doc/marian.html">
+   <a href="model_doc/marian">
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-marian-blueviolet">
   </a>

@@ -635,7 +635,7 @@ T5
   <a href="https://huggingface.co/models?filter=t5">
       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-t5-blueviolet">
   </a>
-   <a href="model_doc/t5.html">
+   <a href="model_doc/t5">
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-t5-blueviolet">
   </a>

@@ -668,7 +668,7 @@ MT5
   <a href="https://huggingface.co/models?filter=mt5">
       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-mt5-blueviolet">
   </a>
-   <a href="model_doc/mt5.html">
+   <a href="model_doc/mt5">
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-mt5-blueviolet">
   </a>

@@ -689,7 +689,7 @@ MBart
   <a href="https://huggingface.co/models?filter=mbart">
       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-mbart-blueviolet">
   </a>
-   <a href="model_doc/mbart.html">
+   <a href="model_doc/mbart">
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-mbart-blueviolet">
   </a>

@@ -718,7 +718,7 @@ ProphetNet
   <a href="https://huggingface.co/models?filter=prophetnet">
       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-prophetnet-blueviolet">
   </a>
-   <a href="model_doc/prophetnet.html">
+   <a href="model_doc/prophetnet">
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-prophetnet-blueviolet">
   </a>

@@ -743,7 +743,7 @@ XLM-ProphetNet
   <a href="https://huggingface.co/models?filter=xprophetnet">
       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-xprophetnet-blueviolet">
   </a>
-   <a href="model_doc/xlmprophetnet.html">
+   <a href="model_doc/xlmprophetnet">
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-xprophetnet-blueviolet">
   </a>

@@ -781,7 +781,7 @@ model know which part of the input vector corresponds to the text and which to t
 The pretrained model only works for classification.

 ..
-    More information in this :doc:`model documentation </model_doc/mmbt.html>`. TODO: write this page
+    More information in this :doc:`model documentation <model_doc/mmbt>`. TODO: write this page

 .. _retrieval-based-models:

@@ -799,7 +799,7 @@ DPR
   <a href="https://huggingface.co/models?filter=dpr">
       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-dpr-blueviolet">
   </a>
-   <a href="model_doc/dpr.html">
+   <a href="model_doc/dpr">
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-dpr-blueviolet">
   </a>

@@ -828,7 +828,7 @@ RAG
   <a href="https://huggingface.co/models?filter=rag">
       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-rag-blueviolet">
   </a>
-   <a href="model_doc/rag.html">
+   <a href="model_doc/rag">
       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-rag-blueviolet">
   </a>

@@ -877,7 +877,7 @@ Some preselected input tokens are also given global attention: for those few tok
 all tokens and this process is symmetric: all other tokens have access to those specific tokens (on top of the ones in
 their local window). This is shown in Figure 2d of the paper, see below for a sample attention mask:

-.. image:: imgs/local_attention_mask.png
+.. image:: /imgs/local_attention_mask.png
   :scale: 50 %
   :align: center

--- a/docs/source/multilingual.mdx
+++ b/docs/source/multilingual.mdx
@@ -0,0 +1,116 @@
+<!--Copyright 2020 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# Multi-lingual models
+
+Most of the models available in this library are mono-lingual models (English, Chinese and German). A few multi-lingual
+models are available and have a different mechanisms than mono-lingual models. This page details the usage of these
+models.
+
+## XLM
+
+XLM has a total of 10 different checkpoints, only one of which is mono-lingual. The 9 remaining model checkpoints can
+be split in two categories: the checkpoints that make use of language embeddings, and those that don't
+
+### XLM & Language Embeddings
+
+This section concerns the following checkpoints:
+
+- `xlm-mlm-ende-1024` (Masked language modeling, English-German)
+- `xlm-mlm-enfr-1024` (Masked language modeling, English-French)
+- `xlm-mlm-enro-1024` (Masked language modeling, English-Romanian)
+- `xlm-mlm-xnli15-1024` (Masked language modeling, XNLI languages)
+- `xlm-mlm-tlm-xnli15-1024` (Masked language modeling + Translation, XNLI languages)
+- `xlm-clm-enfr-1024` (Causal language modeling, English-French)
+- `xlm-clm-ende-1024` (Causal language modeling, English-German)
+
+These checkpoints require language embeddings that will specify the language used at inference time. These language
+embeddings are represented as a tensor that is of the same shape as the input ids passed to the model. The values in
+these tensors depend on the language used and are identifiable using the `lang2id` and `id2lang` attributes from
+the tokenizer.
+
+Here is an example using the `xlm-clm-enfr-1024` checkpoint (Causal language modeling, English-French):
+
+
+```py
+>>> import torch
+>>> from transformers import XLMTokenizer, XLMWithLMHeadModel
+
+>>> tokenizer = XLMTokenizer.from_pretrained("xlm-clm-enfr-1024")
+>>> model = XLMWithLMHeadModel.from_pretrained("xlm-clm-enfr-1024")
+```
+
+The different languages this model/tokenizer handles, as well as the ids of these languages are visible using the
+`lang2id` attribute:
+
+```py
+>>> print(tokenizer.lang2id)
+{'en': 0, 'fr': 1}
+```
+
+These ids should be used when passing a language parameter during a model pass. Let's define our inputs:
+
+```py
+>>> input_ids = torch.tensor([tokenizer.encode("Wikipedia was used to")]) # batch size of 1
+```
+
+We should now define the language embedding by using the previously defined language id. We want to create a tensor
+filled with the appropriate language ids, of the same size as input_ids. For english, the id is 0:
+
+```py
+>>> language_id = tokenizer.lang2id['en']  # 0
+>>> langs = torch.tensor([language_id] * input_ids.shape[1])  # torch.tensor([0, 0, 0, ..., 0])
+
+>>> # We reshape it to be of size (batch_size, sequence_length)
+>>> langs = langs.view(1, -1) # is now of shape [1, sequence_length] (we have a batch size of 1)
+```
+
+You can then feed it all as input to your model:
+
+```py
+>>> outputs = model(input_ids, langs=langs)
+```
+
+The example [run_generation.py](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-generation/run_generation.py) can generate text
+using the CLM checkpoints from XLM, using the language embeddings.
+
+### XLM without Language Embeddings
+
+This section concerns the following checkpoints:
+
+- `xlm-mlm-17-1280` (Masked language modeling, 17 languages)
+- `xlm-mlm-100-1280` (Masked language modeling, 100 languages)
+
+These checkpoints do not require language embeddings at inference time. These models are used to have generic sentence
+representations, differently from previously-mentioned XLM checkpoints.
+
+
+## BERT
+
+BERT has two checkpoints that can be used for multi-lingual tasks:
+
+- `bert-base-multilingual-uncased` (Masked language modeling + Next sentence prediction, 102 languages)
+- `bert-base-multilingual-cased` (Masked language modeling + Next sentence prediction, 104 languages)
+
+These checkpoints do not require language embeddings at inference time. They should identify the language used in the
+context and infer accordingly.
+
+## XLM-RoBERTa
+
+XLM-RoBERTa was trained on 2.5TB of newly created clean CommonCrawl data in 100 languages. It provides strong gains
+over previously released multi-lingual models like mBERT or XLM on downstream tasks like classification, sequence
+labeling and question answering.
+
+Two XLM-RoBERTa checkpoints can be used for multi-lingual tasks:
+
+- `xlm-roberta-base` (Masked language modeling, 100 languages)
+- `xlm-roberta-large` (Masked language modeling, 100 languages)
--- a/docs/source/multilingual.rst
+++ b/docs/source/multilingual.rst
@@ -1,129 +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.
-
-Multi-lingual models
-=======================================================================================================================
-
-Most of the models available in this library are mono-lingual models (English, Chinese and German). A few multi-lingual
-models are available and have a different mechanisms than mono-lingual models. This page details the usage of these
-models.
-
-The two models that currently support multiple languages are BERT and XLM.
-
-XLM
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-XLM has a total of 10 different checkpoints, only one of which is mono-lingual. The 9 remaining model checkpoints can
-be split in two categories: the checkpoints that make use of language embeddings, and those that don't
-
-XLM & Language Embeddings
-----------------------------------------------------------------------------------------------------------------------
-
-This section concerns the following checkpoints:
-
- ``xlm-mlm-ende-1024`` (Masked language modeling, English-German)
- ``xlm-mlm-enfr-1024`` (Masked language modeling, English-French)
- ``xlm-mlm-enro-1024`` (Masked language modeling, English-Romanian)
- ``xlm-mlm-xnli15-1024`` (Masked language modeling, XNLI languages)
- ``xlm-mlm-tlm-xnli15-1024`` (Masked language modeling + Translation, XNLI languages)
- ``xlm-clm-enfr-1024`` (Causal language modeling, English-French)
- ``xlm-clm-ende-1024`` (Causal language modeling, English-German)
-
-These checkpoints require language embeddings that will specify the language used at inference time. These language
-embeddings are represented as a tensor that is of the same shape as the input ids passed to the model. The values in
-these tensors depend on the language used and are identifiable using the ``lang2id`` and ``id2lang`` attributes from
-the tokenizer.
-
-Here is an example using the ``xlm-clm-enfr-1024`` checkpoint (Causal language modeling, English-French):
-
-
-.. code-block::
-
-    >>> import torch
-    >>> from transformers import XLMTokenizer, XLMWithLMHeadModel
-
-    >>> tokenizer = XLMTokenizer.from_pretrained("xlm-clm-enfr-1024")
-    >>> model = XLMWithLMHeadModel.from_pretrained("xlm-clm-enfr-1024")
-
-
-The different languages this model/tokenizer handles, as well as the ids of these languages are visible using the
-``lang2id`` attribute:
-
-.. code-block::
-
-    >>> print(tokenizer.lang2id)
-    {'en': 0, 'fr': 1}
-
-
-These ids should be used when passing a language parameter during a model pass. Let's define our inputs:
-
-.. code-block::
-
-    >>> input_ids = torch.tensor([tokenizer.encode("Wikipedia was used to")]) # batch size of 1
-
-
-We should now define the language embedding by using the previously defined language id. We want to create a tensor
-filled with the appropriate language ids, of the same size as input_ids. For english, the id is 0:
-
-.. code-block::
-
-    >>> language_id = tokenizer.lang2id['en']  # 0
-    >>> langs = torch.tensor([language_id] * input_ids.shape[1])  # torch.tensor([0, 0, 0, ..., 0])
-
-    >>> # We reshape it to be of size (batch_size, sequence_length)
-    >>> langs = langs.view(1, -1) # is now of shape [1, sequence_length] (we have a batch size of 1)
-
-
-You can then feed it all as input to your model:
-
-.. code-block::
-
-    >>> outputs = model(input_ids, langs=langs)
-
-
-The example :prefix_link:`run_generation.py <examples/pytorch/text-generation/run_generation.py>` can generate text
-using the CLM checkpoints from XLM, using the language embeddings.
-
-XLM without Language Embeddings
-----------------------------------------------------------------------------------------------------------------------
-
-This section concerns the following checkpoints:
-
- ``xlm-mlm-17-1280`` (Masked language modeling, 17 languages)
- ``xlm-mlm-100-1280`` (Masked language modeling, 100 languages)
-
-These checkpoints do not require language embeddings at inference time. These models are used to have generic sentence
-representations, differently from previously-mentioned XLM checkpoints.
-
-
-BERT
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-BERT has two checkpoints that can be used for multi-lingual tasks:
-
- ``bert-base-multilingual-uncased`` (Masked language modeling + Next sentence prediction, 102 languages)
- ``bert-base-multilingual-cased`` (Masked language modeling + Next sentence prediction, 104 languages)
-
-These checkpoints do not require language embeddings at inference time. They should identify the language used in the
-context and infer accordingly.
-
-XLM-RoBERTa
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-XLM-RoBERTa was trained on 2.5TB of newly created clean CommonCrawl data in 100 languages. It provides strong gains
-over previously released multi-lingual models like mBERT or XLM on downstream tasks like classification, sequence
-labeling and question answering.
-
-Two XLM-RoBERTa checkpoints can be used for multi-lingual tasks:
-
- ``xlm-roberta-base`` (Masked language modeling, 100 languages)
- ``xlm-roberta-large`` (Masked language modeling, 100 languages)
--- a/docs/source/parallelism.md
+++ b/docs/source/parallelism.md
@@ -46,7 +46,7 @@ Most users with just 2 GPUs already enjoy the increased training speed up thanks
 ## ZeRO Data Parallel

 ZeRO-powered data parallelism (ZeRO-DP) is described on the following diagram from this [blog post](https://www.microsoft.com/en-us/research/blog/zero-deepspeed-new-system-optimizations-enable-training-models-with-over-100-billion-parameters/)
-![DeepSpeed-Image-1](imgs/parallelism-zero.png)
+![DeepSpeed-Image-1](/imgs/parallelism-zero.png)

 It can be difficult to wrap one's head around it, but in reality the concept is quite simple. This is just the usual DataParallel (DP), except, instead of replicating the full model params, gradients and optimizer states, each GPU stores only a slice of it.  And then at run-time when the full layer params are needed just for the given layer, all GPUs synchronize to give each other parts that they miss - this is it.

@@ -122,7 +122,7 @@ Implementations:

 - [DeepSpeed](https://www.deepspeed.ai/features/#the-zero-redundancy-optimizer) ZeRO-DP stages 1+2+3
 - [Fairscale](https://github.com/facebookresearch/fairscale/#optimizer-state-sharding-zero) ZeRO-DP stages 1+2+3
- [`transformers` integration](https://huggingface.co/transformers/master/main_classes/trainer.html#trainer-integrations)
+- [`transformers` integration](main_classes/trainer#trainer-integrations)

 ## Naive Model Parallel (Vertical) and Pipeline Parallel

@@ -150,7 +150,7 @@ Pipeline Parallel (PP) is almost identical to a naive MP, but it solves the GPU

 The following illustration from the [GPipe paper](https://ai.googleblog.com/2019/03/introducing-gpipe-open-source-library.html) shows the naive MP on the top, and PP on the bottom:

-![mp-pp](imgs/parallelism-gpipe-bubble.png)
+![mp-pp](/imgs/parallelism-gpipe-bubble.png)

 It's easy to see from the bottom diagram how PP has less dead zones, where GPUs are idle. The idle parts are referred to as the "bubble".

@@ -170,27 +170,44 @@ With `chunks=1` you end up with the naive MP, which is very inefficient. With a

 While the diagram shows that there is a bubble of "dead" time that can't be parallelized because the last `forward` stage has to wait for `backward` to complete the pipeline, the purpose of finding the best value for `chunks` is to enable a high concurrent GPU utilization across all participating GPUs which translates to minimizing the size of the bubble.

-Problems:
+There are 2 groups of solutions - the traditional Pipeline API and the more modern solutions that make things much easier for the end user.
+
+Traditional Pipeline API solutions:
+- PyTorch
+- FairScale
+- DeepSpeed
+- Megatron-LM
+
+Modern solutions:
+- Varuna
+- Sagemaker
+
+Problems with traditional Pipeline API solutions:
 - have to modify the model quite heavily, because Pipeline requires one to rewrite the normal flow of modules into a `nn.Sequential` sequence of the same, which may require changes to the design of the model.
 - currently the Pipeline API is very restricted. If you had a bunch of python variables being passed in the very first stage of the Pipeline, you will have to find a way around it. Currently, the pipeline interface requires either a single Tensor or a tuple of Tensors as the only input and output. These tensors must have a batch size as the very first dimension, since pipeline is going to chunk the mini batch into micro-batches. Possible improvements are being discussed here https://github.com/pytorch/pytorch/pull/50693
- have to arrange each layer so that the output of one model becomes an input to the other model
+- conditional control flow at the level of pipe stages is not possible - e.g., Encoder-Decoder models like T5 require special workarounds to handle a conditional encoder stage.
+- have to arrange each layer so that the output of one model becomes an input to the other model.
+
+We are yet to experiment with Varuna and SageMaker but their papers report that they have overcome the list of problems mentioned above and that they require much smaller changes to the user's model.

 Implementations:
 - [Pytorch](https://pytorch.org/docs/stable/pipeline.html) (initial support in pytorch-1.8, and progressively getting improved in 1.9 and more so in 1.10). Some [examples](https://github.com/pytorch/pytorch/blob/master/benchmarks/distributed/pipeline/pipe.py)
 - [FairScale](https://fairscale.readthedocs.io/en/latest/tutorials/pipe.html)
 - [DeepSpeed](https://www.deepspeed.ai/tutorials/pipeline/)
 - [Megatron-LM](https://github.com/NVIDIA/Megatron-LM) has an internal implementation - no API.
+- [Varuna](https://github.com/microsoft/varuna)
+- [SageMaker](https://arxiv.org/abs/2111.05972) - this is a proprietary solution that can only be used on AWS.

 🤗 Transformers status: as of this writing none of the models supports full-PP. GPT2 and T5 models have naive PP support. The main obstacle is being unable to convert the models to `nn.Sequential` and have all the inputs to be Tensors. This is because currently the models include many features that make the conversion very complicated, and will need to be removed to accomplish that.

 Other approaches:

-DeepSpeed and SageMaker use the concept of an [Interleaved Pipeline](https://docs.aws.amazon.com/sagemaker/latest/dg/model-parallel-core-features.html)
-![interleaved-pipeline-execution](imgs/parallelism-sagemaker-interleaved-pipeline.png)
+DeepSpeed, Varuna and SageMaker use the concept of an [Interleaved Pipeline](https://docs.aws.amazon.com/sagemaker/latest/dg/model-parallel-core-features.html)
+![interleaved-pipeline-execution](/imgs/parallelism-sagemaker-interleaved-pipeline.png)

 Here the bubble (idle time) is further minimized by prioritizing backward passes.

-According to [the same document](https://docs.aws.amazon.com/sagemaker/latest/dg/model-parallel-core-features.html), it might be able to automate the non `nn.Sequential` model conversion to pipeline. The only problem is that this is currently only available at AWS, so you can't run it on your own hardware.
+Varuna further tries to improve the schedule by using simulations to discover the most efficient scheduling.


 ## Tensor Parallelism
@@ -204,28 +221,31 @@ The main building block of any transformer is a fully connected `nn.Linear` foll
 Following the Megatron's paper notation, we can write the dot-product part of it as `Y = GeLU(XA)`, where `X` and `Y` are the input and output vectors, and `A` is the weight matrix.

 If we look at the computation in matrix form, it's easy to see how the matrix multiplication can be split between multiple GPUs:
-![Parallel GEMM](imgs/parallelism-tp-parallel_gemm.png)
+![Parallel GEMM](/imgs/parallelism-tp-parallel_gemm.png)

 If we split the weight matrix `A` column-wise across `N` GPUs and perform matrix multiplications `XA_1` through `XA_n` in parallel, then we will end up with `N` output vectors `Y_1, Y_2, ..., Y_n` which can be fed into `GeLU` independently:
-![independent GeLU](imgs/parallelism-tp-independent-gelu.png)
+![independent GeLU](/imgs/parallelism-tp-independent-gelu.png)

 Using this principle, we can update an MLP of arbitrary depth, without the need for any synchronization between GPUs until the very end, where we need to reconstruct the output vector from shards. The Megatron-LM paper authors provide a helpful illustration for that:
-![parallel shard processing](imgs/parallelism-tp-parallel_shard_processing.png)
+![parallel shard processing](/imgs/parallelism-tp-parallel_shard_processing.png)

 Parallelizing the multi-headed attention layers is even simpler, since they are already inherently parallel, due to having multiple independent heads!
-![parallel self-attention](imgs/parallelism-tp-parallel_self_attention.png)
+![parallel self-attention](/imgs/parallelism-tp-parallel_self_attention.png)

 Special considerations: TP requires very fast network, and therefore it's not advisable to do TP across more than one node. Practically, if a node has 4 GPUs, the highest TP degree is therefore 4. If you need a TP degree of 8, you need to use nodes that have at least 8 GPUs.

 This section is based on the original much more [detailed TP overview](https://github.com/huggingface/transformers/issues/10321#issuecomment-783543530).
 by [@anton-l](https://github.com/anton-l).

+SageMaker combines TP with DP for a more efficient processing.
+
 Alternative names:
 - DeepSpeed calls it [tensor slicing](https://www.deepspeed.ai/features/#model-parallelism)

 Implementations:
 - [Megatron-LM](https://github.com/NVIDIA/Megatron-LM) has an internal implementation, as it's very model-specific
 - [parallelformers](https://github.com/tunib-ai/parallelformers) (only inference at the moment)
+- [SageMaker](https://arxiv.org/abs/2111.05972) - this is a proprietary solution that can only be used on AWS.

 🤗 Transformers status:
 - core: not yet implemented in the core
@@ -238,7 +258,7 @@ Implementations:

 The following diagram from the DeepSpeed [pipeline tutorial](https://www.deepspeed.ai/tutorials/pipeline/) demonstrates how one combines DP with PP.

-![dp-pp-2d](imgs/parallelism-zero-dp-pp.png)
+![dp-pp-2d](/imgs/parallelism-zero-dp-pp.png)

 Here it's important to see how DP rank 0 doesn't see GPU2 and DP rank 1 doesn't see GPU3. To DP there is just GPUs 0 and 1 where it feeds data as if there were just 2 GPUs. GPU0 "secretly" offloads some of its load to GPU2 using PP. And GPU1 does the same by enlisting GPU3 to its aid.

@@ -247,6 +267,8 @@ Since each dimension requires at least 2 GPUs, here you'd need at least 4 GPUs.
 Implementations:
 - [DeepSpeed](https://github.com/microsoft/DeepSpeed)
 - [Megatron-LM](https://github.com/NVIDIA/Megatron-LM)
+- [Varuna](https://github.com/microsoft/varuna)
+- [SageMaker](https://arxiv.org/abs/2111.05972)

 🤗 Transformers status: not yet implemented

@@ -255,7 +277,7 @@ Implementations:

 To get an even more efficient training a 3D parallelism is used where PP is combined with TP and DP. This can be seen in the following diagram.

-![dp-pp-tp-3d](imgs/parallelism-deepspeed-3d.png)
+![dp-pp-tp-3d](/imgs/parallelism-deepspeed-3d.png)

 This diagram is from a blog post [3D parallelism: Scaling to trillion-parameter models](https://www.microsoft.com/en-us/research/blog/deepspeed-extreme-scale-model-training-for-everyone/), which is a good read as well.

@@ -264,6 +286,8 @@ Since each dimension requires at least 2 GPUs, here you'd need at least 8 GPUs.
 Implementations:
 - [DeepSpeed](https://github.com/microsoft/DeepSpeed) - DeepSpeed also includes an even more efficient DP, which they call ZeRO-DP.
 - [Megatron-LM](https://github.com/NVIDIA/Megatron-LM)
+- [Varuna](https://github.com/microsoft/varuna)
+- [SageMaker](https://arxiv.org/abs/2111.05972)

 🤗 Transformers status: not yet implemented, since we have no PP and TP.

@@ -318,7 +342,7 @@ We have 10 batches of 512 length. If we parallelize them by attribute dimension

 It is similar with tensor model parallelism or naive layer-wise model parallelism.

-![flex-flow-soap](imgs/parallelism-flexflow.jpeg)
+![flex-flow-soap](/imgs/parallelism-flexflow.jpeg)

 The significance of this framework is that it takes resources like (1) GPU/TPU/CPU vs. (2) RAM/DRAM vs. (3) fast-intra-connect/slow-inter-connect and it automatically optimizes all these  algorithmically deciding which parallelisation to use where.

--- a/docs/source/performance.md
+++ b/docs/source/performance.md
@@ -52,7 +52,8 @@ Software:
 - Pipeline Parallelism
 - Tensor Parallelism
 - Low-memory Optimizers
- fp16/bf16 (smaller data)
+- fp16/bf16 (smaller data/faster throughput)
+- tf32 (faster throughput)
 - Gradient checkpointing


@@ -164,16 +165,74 @@ Software: `pytorch-1.8-to-be` + `cuda-11.0` / `transformers==4.3.0.dev0`
 ### Anatomy of Model's Memory

 The components on GPU memory are the following:
- the model weights
- the forward activations saved for gradient computation
- the gradients
- the optimizer state
+1. model weights
+2. optimizer states
+3. gradients
+4. forward activations saved for gradient computation
+5. temporary buffers
+6. functionality-specific memory
+
+A typical model trained in mixed precision with AdamW requires 18 bytes per model parameter plus activation memory.
+
+For inference there are no optimizer states and gradients, so we can subtract those. And thus we end up with 6 bytes per model parameter for mixed precision inference, plus activation memory.
+
+Let's look at the details.
+
+#### Model Weights
+
+- 4 bytes * number of parameters for fp32 training
+- 6 bytes * number of parameters for mixed precision training
+
+#### Optimizer States
+
+- 8 bytes * number of parameters for normal AdamW (maintains 2 states)
+- 2 bytes * number of parameters for 8-bit AdamW optimizers like [bitsandbytes](https://github.com/facebookresearch/bitsandbytes)
+- 4 bytes * number of parameters for optimizers like SGD (maintains only 1 state)
+
+#### Gradients
+
+- 4 bytes * number of parameters for either fp32 or mixed precision training
+
+#### Forward Activations
+
+- size depends on many factors, the key ones being sequence length, hidden size and batch size.
+
+There are the input and output that are being passed and returned by the forward and the backward functions and the forward activations saved for gradient computation.
+
+#### Temporary Memory
+
+Additionally there are all kinds of temporary variables which get released once the calculation is done, but in the moment these could require additional memory and could push to OOM. Therefore when coding it's crucial to think strategically about such temporary variables and sometimes to explicitly free those as soon as they are no longer needed.
+
+#### Functionality-specific memory
+
+Then your software could have special memory needs. For example, when generating text using beam search, the software needs to maintain multiple copies of inputs and outputs.
+
+

 ### `forward` vs `backward` Execution Speed

 For convolutions and linear layers there are 2x flops in the backward compared to the forward, which generally translates into ~2x slower (sometimes more, because sizes in the backward tend to be more awkward). Activations are usually bandwidth-limited, and it’s typical for an activation to have to read more data in the backward than in the forward (e.g. activation forward reads once, writes once, activation backward reads twice, gradOutput and output of the forward, and writes once, gradInput).

-### fp16
+
+### Floating Data Types
+
+Here are the commonly used floating point data types choice of which impacts both memory usage and throughput:
+
+- fp32 (`float32`)
+- fp16 (`float16`)
+- bf16 (`bfloat16`)
+- tf32 (CUDA internal data type)
+
+Here is a diagram that shows how these data types correlate to each other.
+
+![data types](/imgs/tf32-bf16-fp16-fp32.png)
+
+(source: [NVIDIA Blog](https://developer.nvidia.com/blog/accelerating-ai-training-with-tf32-tensor-cores/))
+
+While fp16 and fp32 have been around for quite some time, bf16 and tf32 are only available on the Ampere architecture GPUS. TPUs support bf16 as well.
+
+
+#### fp16

 AMP = Automatic Mixed Precision

@@ -185,6 +244,8 @@ If we look at what's happening with FP16 training (mixed precision) we have:

 So the savings only happen for the forward activations saved for the backward computation, and there is a slight overhead because the model weights are stored both in half- and full-precision.

+In 🤗 Transformers fp16 mixed precision is enabled by passing `--fp16` to the 🤗 Trainer.
+
 Now let's look at a simple text-classification fine-tuning on 2 GPUs (I'm giving the command for reference):
 ```
 export BS=16
@@ -217,15 +278,92 @@ Summary: FP16 with apex or AMP will only give you some memory savings with a rea

 Additionally, under mixed precision when possible, it's important that the batch size is a multiple of 8 to efficiently use tensor cores.

+Note that in some situations the speed up can be as big as 5x when using mixed precision. e.g. we have observed that while using [Megatron-Deepspeed](https://github.com/bigscience-workshop/Megatron-DeepSpeed).
+
 Some amazing tutorials to read on mixed precision:
 - @sgugger wrote a great explanation of mixed precision [here](https://docs.fast.ai/callback.fp16.html#A-little-bit-of-theory)
 - Aleksey Bilogur's [A developer-friendly guide to mixed precision training with PyTorch](https://spell.ml/blog/mixed-precision-training-with-pytorch-Xuk7YBEAACAASJam)

-### fp16 caching
+##### fp16 caching

 pytorch `autocast` which performs AMP include a caching feature, which speed things up by caching fp16-converted values. Here is the full description from this [comment](https://discuss.pytorch.org/t/autocast-and-torch-no-grad-unexpected-behaviour/93475/3):

-Autocast maintains a cache of the FP16 casts of model params (leaves). This helps streamline parameter reuse: if the same FP32 param is used in several different FP16list ops, like several matmuls, instead of re-casting the param to FP16 on entering each matmul, the cast will occur on the first matmul, the casted FP16 copy will be cached, and for all later matmuls the FP16 copy will be reused. The cache is maintained only within a particular outermost autocast context. When you exit the autocast context the cache is dropped. For recommended usage, in which autocast wraps the forward pass, and then you exit the context before calling backward(), this means the cache only lasts the duration of the forward pass each iteration, and will be rebuilt next iteration. (The cache of FP16-casted copies MUST be rebuilt each iteration. The FP32 params get updated by the optimizer, so the FP16 copies must be recreated, otherwise the FP16 values will be stale.)
+Autocast maintains a cache of the FP16 casts of model parameters (leaves). This helps streamline parameter reuse: if the same FP32 param is used in several different FP16list ops, like several matmuls, instead of re-casting the param to FP16 on entering each matmul, the cast will occur on the first matmul, the casted FP16 copy will be cached, and for all later matmuls the FP16 copy will be reused. The cache is maintained only within a particular outermost autocast context. When you exit the autocast context the cache is dropped. For recommended usage, in which autocast wraps the forward pass, and then you exit the context before calling backward(), this means the cache only lasts the duration of the forward pass each iteration, and will be rebuilt next iteration. (The cache of FP16-casted copies MUST be rebuilt each iteration. The FP32 parameters get updated by the optimizer, so the FP16 copies must be recreated, otherwise the FP16 values will be stale.)
+
+##### fp16 Inference
+
+While normally inference is done with fp16/amp as with training, it's also possible to use the full fp16 mode without using mixed precision. This is especially a good fit if the pretrained model weights are already in fp16. So a lot less memory is used: 2 bytes per parameter vs 6 bytes with mixed precision!
+
+How good the results this will deliver will depend on the model. If it can handle fp16 without overflows and accuracy issues, then it'll definitely better to use the full fp16 mode.
+
+For example, LayerNorm has to be done in fp32 and recent pytorch (1.10+) has been fixed to do that regardless of the input types, but earlier pytorch versions accumulate in the input type which can be an issue.
+
+In 🤗 Transformers the full fp16 inference is enabled by passing `--fp16_full_eval` to the 🤗 Trainer.
+
+
+#### bf16
+
+If you own Ampere or newer hardware you can start using bf16 for your training and evaluation. While bf16 has a worse precision than fp16, it has a much much bigger dynamic range. Therefore, if in the past you were experiencing overflow issues while training the model, bf16 will prevent this from happening most of the time. Remember that in fp16 the biggest number you can have is `65535` and any number above that will overflow. A bf16 number can be as large as `3.39e+38` (!) which is about the same as fp32 - because both have 8-bits used for the numerical range.
+
+Automatic Mixed Precision (AMP) is the same as with fp16, except it'll use bf16.
+
+Thanks to the fp32-like dynamic range with bf16 mixed precision loss scaling is no longer needed.
+
+If you have tried to finetune models pre-trained under bf16 mixed precision (e.g. T5) it's very likely that you have encountered overflow issues. Now you should be able to finetune those models without any issues.
+
+That said, also be aware that if you pre-trained a model in bf16, it's likely to have overflow issues if someone tries to finetune it in fp16 down the road. So once started on the bf16-mode path it's best to remain on it and not switch to fp16.
+
+In 🤗 Transformers bf16 mixed precision is enabled by passing `--bf16` to the 🤗 Trainer.
+
+If you use your own trainer, this is just:
+
+```
+from torch.cuda.amp import autocast
+with autocast(dtype=torch.bfloat16):
+    loss, outputs = ...
+```
+
+If you need to switch a tensor to bf16, it's just: `t.to(dtype=torch.bfloat16)`
+
+Here is how you can check if your setup supports bf16:
+
+```
+python -c 'import transformers; print(f"BF16 support is {transformers.file_utils.is_torch_bf16_available()}")'
+```
+
+On the other hand bf16 has a much worse precision than fp16, so there are certain situations where you'd still want to use fp16 and not bf16.
+
+
+##### bf16 Inference
+
+Same as with fp16, you can do inference in either the mixed precision bf16 or using the full bf16 mode. The same caveats apply. For details see [fp16 Inference](#fp16-inference).
+
+In 🤗 Transformers the full bf16 inference is enabled by passing `--bf16_full_eval` to the 🤗 Trainer.
+
+
+#### tf32
+
+The Ampere hardware uses a magical data type called tf32. It has the same numerical range as fp32 (8-bits), but instead of 23 bits precision it has only 10 bits (same as fp16). In total it uses only 19 bits.
+
+It's magical in the sense that you can use the normal fp32 training and/or inference code and by enabling tf32 support you can get up to 3x throughput improvement. All you need to do is to add this to your code:
+
+```
+import torch
+torch.backends.cuda.matmul.allow_tf32 = True
+```
+
+When this is done CUDA will automatically switch to using tf32 instead of fp32 where it's possible. This, of course, assumes that the used GPU is from the Ampere series.
+
+Like all cases with reduced precision this may or may not be satisfactory for your needs, so you have to experiment and see. According to [NVIDIA research](https://developer.nvidia.com/blog/accelerating-ai-training-with-tf32-tensor-cores/) the majority of machine learning training shouldn't be impacted and showed the same perplexity and convergence as the fp32 training.
+
+If you're already using fp16 or bf16 mixed precision it may help with the throughput as well.
+
+You can enable this mode in the 🤗 Trainer with `--tf32`, or disable it with `--tf32 0` or `--no_tf32`.
+By default the PyTorch default is used.
+
+Note: tf32 mode is internal to CUDA and can't be accessed directly via `tensor.to(dtype=torch.tf32)` as `torch.tf32` doesn't exit.
+
+Note: you need `torch>=1.7` to enjoy this feature.


 ### Gradient Checkpointing
--- a/docs/source/perplexity.mdx
+++ b/docs/source/perplexity.mdx
@@ -0,0 +1,126 @@
+<!--Copyright 2020 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# Perplexity of fixed-length models
+
+Perplexity (PPL) is one of the most common metrics for evaluating language models. Before diving in, we should note
+that the metric applies specifically to classical language models (sometimes called autoregressive or causal language
+models) and is not well defined for masked language models like BERT (see [summary of the models](model_summary)).
+
+Perplexity is defined as the exponentiated average negative log-likelihood of a sequence. If we have a tokenized
+sequence \\(X = (x_0, x_1, \dots, x_t)\\), then the perplexity of \\(X\\) is,
+
+$$\text{PPL}(X) = \exp \left\{ {-\frac{1}{t}\sum_i^t \log p_\theta (x_i|x_{<i}) } \right\}$$
+
+where \\(\log p_\theta (x_i|x_{<i})\\) is the log-likelihood of the ith token conditioned on the preceding tokens \\(x_{<i}\\) according to our model. Intuitively, it can be thought of as an evaluation of the model's ability to predict uniformly among the set of specified tokens in a corpus. Importantly, this means that the tokenization procedure has a direct impact on a model's perplexity which should always be taken into consideration when comparing different models.
+
+This is also equivalent to the exponentiation of the cross-entropy between the data and model predictions. For more
+intuition about perplexity and its relationship to Bits Per Character (BPC) and data compression, check out this
+[fantastic blog post on The Gradient](https://thegradient.pub/understanding-evaluation-metrics-for-language-models/).
+
+## Calculating PPL with fixed-length models
+
+If we weren't limited by a model's context size, we would evaluate the model's perplexity by autoregressively
+factorizing a sequence and conditioning on the entire preceding subsequence at each step, as shown below.
+
+<img width="600" alt="Full decomposition of a sequence with unlimited context length" src="/imgs/ppl_full.gif"/>
+
+When working with approximate models, however, we typically have a constraint on the number of tokens the model can
+process. The largest version of [GPT-2](model_doc/gpt2), for example, has a fixed length of 1024 tokens, so we
+cannot calculate \\(p_\theta(x_t|x_{<t})\\) directly when \\(t\\) is greater than 1024.
+
+Instead, the sequence is typically broken into subsequences equal to the model's maximum input size. If a model's max
+input size is \\(k\\), we then approximate the likelihood of a token \\(x_t\\) by conditioning only on the
+\\(k-1\\) tokens that precede it rather than the entire context. When evaluating the model's perplexity of a
+sequence, a tempting but suboptimal approach is to break the sequence into disjoint chunks and add up the decomposed
+log-likelihoods of each segment independently.
+
+<img width="600" alt="Suboptimal PPL not taking advantage of full available context" src="/imgs/ppl_chunked.gif"/>
+
+This is quick to compute since the perplexity of each segment can be computed in one forward pass, but serves as a poor
+approximation of the fully-factorized perplexity and will typically yield a higher (worse) PPL because the model will
+have less context at most of the prediction steps.
+
+Instead, the PPL of fixed-length models should be evaluated with a sliding-window strategy. This involves repeatedly
+sliding the context window so that the model has more context when making each prediction.
+
+<img width="600" alt="Sliding window PPL taking advantage of all available context" src="/imgs/ppl_sliding.gif"/>
+
+This is a closer approximation to the true decomposition of the sequence probability and will typically yield a more
+favorable score. The downside is that it requires a separate forward pass for each token in the corpus. A good
+practical compromise is to employ a strided sliding window, moving the context by larger strides rather than sliding by
+1 token a time. This allows computation to proceed much faster while still giving the model a large context to make
+predictions at each step.
+
+## Example: Calculating perplexity with GPT-2 in 🤗 Transformers
+
+Let's demonstrate this process with GPT-2.
+
+```python
+from transformers import GPT2LMHeadModel, GPT2TokenizerFast
+device = 'cuda'
+model_id = 'gpt2-large'
+model = GPT2LMHeadModel.from_pretrained(model_id).to(device)
+tokenizer = GPT2TokenizerFast.from_pretrained(model_id)
+```
+
+We'll load in the WikiText-2 dataset and evaluate the perplexity using a few different sliding-window strategies. Since
+this dataset is small and we're just doing one forward pass over the set, we can just load and encode the entire
+dataset in memory.
+
+```python
+from datasets import load_dataset
+test = load_dataset('wikitext', 'wikitext-2-raw-v1', split='test')
+encodings = tokenizer('\n\n'.join(test['text']), return_tensors='pt')
+```
+
+With 🤗 Transformers, we can simply pass the `input_ids` as the `labels` to our model, and the average negative
+log-likelihood for each token is returned as the loss. With our sliding window approach, however, there is overlap in
+the tokens we pass to the model at each iteration. We don't want the log-likelihood for the tokens we're just treating
+as context to be included in our loss, so we can set these targets to `-100` so that they are ignored. The following
+is an example of how we could do this with a stride of `512`. This means that the model will have at least 512 tokens
+for context when calculating the conditional likelihood of any one token (provided there are 512 preceding tokens
+available to condition on).
+
+```python
+import torch
+from tqdm import tqdm
+
+max_length = model.config.n_positions
+stride = 512
+
+nlls = []
+for i in tqdm(range(0, encodings.input_ids.size(1), stride)):
+    begin_loc = max(i + stride - max_length, 0)
+    end_loc = min(i + stride, encodings.input_ids.size(1))
+    trg_len = end_loc - i    # may be different from stride on last loop
+    input_ids = encodings.input_ids[:,begin_loc:end_loc].to(device)
+    target_ids = input_ids.clone()
+    target_ids[:,:-trg_len] = -100
+
+    with torch.no_grad():
+        outputs = model(input_ids, labels=target_ids)
+        neg_log_likelihood = outputs[0] * trg_len
+
+    nlls.append(neg_log_likelihood)
+
+ppl = torch.exp(torch.stack(nlls).sum() / end_loc)
+```
+
+Running this with the stride length equal to the max input length is equivalent to the suboptimal, non-sliding-window
+strategy we discussed above. The smaller the stride, the more context the model will have in making each prediction,
+and the better the reported perplexity will typically be.
+
+When we run the above with `stride = 1024`, i.e. no overlap, the resulting PPL is `19.64`, which is about the same
+as the `19.93` reported in the GPT-2 paper. By using `stride = 512` and thereby employing our striding window
+strategy, this jumps down to `16.53`. This is not only a more favorable score, but is calculated in a way that is
+closer to the true autoregressive decomposition of a sequence likelihood.
--- a/docs/source/perplexity.rst
+++ b/docs/source/perplexity.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
-    specific language governing permissions and limitations under the License.
-
-Perplexity of fixed-length models
-=======================================================================================================================
-
-Perplexity (PPL) is one of the most common metrics for evaluating language models. Before diving in, we should note
-that the metric applies specifically to classical language models (sometimes called autoregressive or causal language
-models) and is not well defined for masked language models like BERT (see :doc:`summary of the models
-<model_summary>`).
-
-Perplexity is defined as the exponentiated average negative log-likelihood of a sequence. If we have a tokenized
-sequence :math:`X = (x_0, x_1, \dots, x_t)`, then the perplexity of :math:`X` is,
-
-.. math::
-
-    \text{PPL}(X)
-    = \exp \left\{ {-\frac{1}{t}\sum_i^t \log p_\theta (x_i|x_{<i}) } \right\}
-
-where :math:`\log p_\theta (x_i|x_{<i})` is the log-likelihood of the ith token conditioned on the preceding tokens
-:math:`x_{<i}` according to our model. Intuitively, it can be thought of as an evaluation of the model's ability to
-predict uniformly among the set of specified tokens in a corpus. Importantly, this means that the tokenization
-procedure has a direct impact on a model's perplexity which should always be taken into consideration when comparing
-different models.
-
-This is also equivalent to the exponentiation of the cross-entropy between the data and model predictions. For more
-intuition about perplexity and its relationship to Bits Per Character (BPC) and data compression, check out this
-`fantastic blog post on The Gradient <https://thegradient.pub/understanding-evaluation-metrics-for-language-models/>`_.
-
-Calculating PPL with fixed-length models
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-If we weren't limited by a model's context size, we would evaluate the model's perplexity by autoregressively
-factorizing a sequence and conditioning on the entire preceding subsequence at each step, as shown below.
-
-.. image:: imgs/ppl_full.gif
-    :width: 600
-    :alt: Full decomposition of a sequence with unlimited context length
-
-When working with approximate models, however, we typically have a constraint on the number of tokens the model can
-process. The largest version of :doc:`GPT-2 <model_doc/gpt2>`, for example, has a fixed length of 1024 tokens, so we
-cannot calculate :math:`p_\theta(x_t|x_{<t})` directly when :math:`t` is greater than 1024.
-
-Instead, the sequence is typically broken into subsequences equal to the model's maximum input size. If a model's max
-input size is :math:`k`, we then approximate the likelihood of a token :math:`x_t` by conditioning only on the
-:math:`k-1` tokens that precede it rather than the entire context. When evaluating the model's perplexity of a
-sequence, a tempting but suboptimal approach is to break the sequence into disjoint chunks and add up the decomposed
-log-likelihoods of each segment independently.
-
-.. image:: imgs/ppl_chunked.gif
-    :width: 600
-    :alt: Suboptimal PPL not taking advantage of full available context
-
-This is quick to compute since the perplexity of each segment can be computed in one forward pass, but serves as a poor
-approximation of the fully-factorized perplexity and will typically yield a higher (worse) PPL because the model will
-have less context at most of the prediction steps.
-
-Instead, the PPL of fixed-length models should be evaluated with a sliding-window strategy. This involves repeatedly
-sliding the context window so that the model has more context when making each prediction.
-
-.. image:: imgs/ppl_sliding.gif
-    :width: 600
-    :alt: Sliding window PPL taking advantage of all available context
-
-This is a closer approximation to the true decomposition of the sequence probability and will typically yield a more
-favorable score. The downside is that it requires a separate forward pass for each token in the corpus. A good
-practical compromise is to employ a strided sliding window, moving the context by larger strides rather than sliding by
-1 token a time. This allows computation to proceed much faster while still giving the model a large context to make
-predictions at each step.
-
-Example: Calculating perplexity with GPT-2 in 🤗 Transformers
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Let's demonstrate this process with GPT-2.
-
-.. code-block:: python
-
-    from transformers import GPT2LMHeadModel, GPT2TokenizerFast
-    device = 'cuda'
-    model_id = 'gpt2-large'
-    model = GPT2LMHeadModel.from_pretrained(model_id).to(device)
-    tokenizer = GPT2TokenizerFast.from_pretrained(model_id)
-
-We'll load in the WikiText-2 dataset and evaluate the perplexity using a few different sliding-window strategies. Since
-this dataset is small and we're just doing one forward pass over the set, we can just load and encode the entire
-dataset in memory.
-
-.. code-block:: python
-
-    from datasets import load_dataset
-    test = load_dataset('wikitext', 'wikitext-2-raw-v1', split='test')
-    encodings = tokenizer('\n\n'.join(test['text']), return_tensors='pt')
-
-With 🤗 Transformers, we can simply pass the ``input_ids`` as the ``labels`` to our model, and the average negative
-log-likelihood for each token is returned as the loss. With our sliding window approach, however, there is overlap in
-the tokens we pass to the model at each iteration. We don't want the log-likelihood for the tokens we're just treating
-as context to be included in our loss, so we can set these targets to ``-100`` so that they are ignored. The following
-is an example of how we could do this with a stride of ``512``. This means that the model will have at least 512 tokens
-for context when calculating the conditional likelihood of any one token (provided there are 512 preceding tokens
-available to condition on).
-
-.. code-block:: python
-
-    import torch
-    from tqdm import tqdm
-
-    max_length = model.config.n_positions
-    stride = 512
-
-    nlls = []
-    for i in tqdm(range(0, encodings.input_ids.size(1), stride)):
-        begin_loc = max(i + stride - max_length, 0)
-        end_loc = min(i + stride, encodings.input_ids.size(1))
-        trg_len = end_loc - i    # may be different from stride on last loop
-        input_ids = encodings.input_ids[:,begin_loc:end_loc].to(device)
-        target_ids = input_ids.clone()
-        target_ids[:,:-trg_len] = -100
-
-        with torch.no_grad():
-            outputs = model(input_ids, labels=target_ids)
-            neg_log_likelihood = outputs[0] * trg_len
-
-        nlls.append(neg_log_likelihood)
-
-    ppl = torch.exp(torch.stack(nlls).sum() / end_loc)
-
-Running this with the stride length equal to the max input length is equivalent to the suboptimal, non-sliding-window
-strategy we discussed above. The smaller the stride, the more context the model will have in making each prediction,
-and the better the reported perplexity will typically be.
-
-When we run the above with ``stride = 1024``, i.e. no overlap, the resulting PPL is ``19.64``, which is about the same
-as the ``19.93`` reported in the GPT-2 paper. By using ``stride = 512`` and thereby employing our striding window
-strategy, this jumps down to ``16.53``. This is not only a more favorable score, but is calculated in a way that is
-closer to the true autoregressive decomposition of a sequence likelihood.
--- a/docs/source/pr_checks.md
+++ b/docs/source/pr_checks.md
@@ -0,0 +1,131 @@
+<!---
+Copyright 2020 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# Checks on a Pull Request
+
+When you open a pull request on 🤗 Transformers, a fair number of checks will be run to make sure the patch you are adding is not breaking anything existing. Those checks are of four types:
+- regular tests
+- documentation build
+- code and documentation style
+- general repository consistency
+
+In this document, we will take a stab at explaining what those various checks are and the reason behind them, as well as how to debug them locally if one of them fails on your PR.
+
+Note that they all require you to have a dev install:
+
+```bash
+pip install transformers[dev]
+```
+
+or for an editable install:
+
+```bash
+pip install -e .[dev]
+```
+
+inside the Transformers repo.
+
+## Tests
+
+All the jobs that begin with `ci/circleci: run_tests_` run parts of the Transformers testing suite. Each of those jobs focuses on a part of the library in a certain environment: for instance `ci/circleci: run_tests_pipelines_tf` runs the pipelines test in an environment where TensorFlow only is installed.
+
+Note that to avoid running tests when there is no real change in the modules they are testing, only part of the test suite is run each time: a utility is run to determine the differences in the library between before and after the PR (what GitHub shows you in the "Files changes" tab) and picks the tests impacted by that diff. That utility can be run locally with:
+
+```bash
+python utils/test_fetcher.py
+```
+
+from the root of the Transformers repo. It will:
+
+1. Check for each file in the diff if the changes are in the code or only in comments or docstrings. Only the files with real code changes are kept.
+2. Build an internal map that gives for each file of the source code of the library all the files it recursively impacts. Module A is said to impact module B if module B imports module A. For the recursive impact, we need a chain of modules going from module A to module B in which each module imports the previous one.
+3. Apply this map on the files gathered in step 1, which  gives us the list of model files impacted by the PR.
+4. Map each of those files to their corresponding test file(s) and get the list of tests to run.
+
+When executing the script locally, you should get the results of step 1, 3 and 4 printed and thus know which tests are run. The script will also create a file named `test_list.txt` which contains the list of tests to run, and you can run them locally with the following command:
+
+```bash
+python -m pytest -n 8 --dist=loadfile -rA -s $(cat test_list.txt)
+```
+
+Just in case anything slipped through the cracks, the full test suite is also run daily.
+
+## Documentation build
+
+The job `ci/circleci: build_doc` runs a build of the documentation just to make sure everything will be okay once your PR is merged. If that steps fails, you can inspect it locally by going into the `docs` folder of the Transformers repo and then typing
+
+```bash
+make html
+```
+
+Sphinx is not known for its helpful error messages, so you might have to try a few things to really find the source of the error.
+
+## Code and documentation style
+
+Code formatting is applied to all the source files, the examples and the tests using `black` and `isort`. We also have a custom tool taking care of the formatting of docstrings and `rst` files (`utils/style_doc.py`), as well as the order of the lazy imports performed in the Transformers `__init__.py` files (`utils/custom_init_isort.py`). All of this can be launched by executing
+
+```bash
+make style
+```
+
+The CI checks those have been applied inside the `ci/circleci: check_code_quality` check. It also runs `flake8`, that will have a basic look at your code and will complain if it finds an undefined variable, or one that is not used. To run that check locally, use
+
+```bash
+make quality
+```
+
+This can take a lot of time, so to run the same thing on only the files you modified in the current branch, run
+
+```bash
+make fixup
+```
+
+This last command will also run all the additional checks for the repository consistency. Let's have a look at them.
+
+## Repository consistency
+
+This regroups all the tests to make sure your PR leaves the repository in a good state, and is performed by the `ci/circleci: check_repository_consistency` check. You can locally run that check by executing the following:
+
+```bash
+make repo-consistency
+```
+
+This checks that:
+
+- All objects added to the init are documented (performed by `utils/check_repo.py`)
+- All `__init__.py` files have the same content in their two sections (performed by `utils/check_inits.py`)
+- All code identified as a copy from another module is consistent with the original (performed by `utils/check_copies.py`)
+- The translations of the READMEs and the index of the doc have the same model list as the main README (performed by `utils/check_copies.py`)
+- The auto-generated tables in the documentation are up to date (performed by `utils/check_table.py`)
+- The library has all objects available even if not all optional dependencies are installed (performed by `utils/check_dummies.py`)
+
+Should this check fail, the first two items require manual fixing, the last four can be fixed automatically for you by running the command
+
+```bash
+make fix-copies
+```
+
+Additional checks concern PRs that add new models, mainly that:
+
+- All models added are in an Auto-mapping (performed by `utils/check_repo.py`)
+<!-- TODO Sylvain, add a check that makes sure the common tests are implemented.-->
+- All models are properly tested (performed by `utils/check_repo.py`)
+
+<!-- TODO Sylvain, add the following
+- All models are added to the main README, inside the master doc
+- All checkpoints used actually exist on the Hub
+
+-->
--- a/docs/source/preprocessing.mdx
+++ b/docs/source/preprocessing.mdx
@@ -0,0 +1,339 @@
+<!--Copyright 2020 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# Preprocessing data
+
+In this tutorial, we'll explore how to preprocess your data using 🤗 Transformers. The main tool for this is what we
+call a [tokenizer](main_classes/tokenizer). You can build one using the tokenizer class associated to the model
+you would like to use, or directly with the [`AutoTokenizer`] class.
+
+As we saw in the [quick tour](quicktour), the tokenizer will first split a given text in words (or part of
+words, punctuation symbols, etc.) usually called _tokens_. Then it will convert those _tokens_ into numbers, to be able
+to build a tensor out of them and feed them to the model. It will also add any additional inputs the model might expect
+to work properly.
+
+<Tip>
+
+If you plan on using a pretrained model, it's important to use the associated pretrained tokenizer: it will split
+the text you give it in tokens the same way for the pretraining corpus, and it will use the same correspondence
+token to index (that we usually call a _vocab_) as during pretraining.
+
+</Tip>
+
+To automatically download the vocab used during pretraining or fine-tuning a given model, you can use the
+[`AutoTokenizer.from_pretrained`] method:
+
+```py
+from transformers import AutoTokenizer
+tokenizer = AutoTokenizer.from_pretrained('bert-base-cased')
+```
+
+## Base use
+
+
+<Youtube id="Yffk5aydLzg"/>
+
+A [`PreTrainedTokenizer`] has many methods, but the only one you need to remember for preprocessing
+is its `__call__`: you just need to feed your sentence to your tokenizer object.
+
+```py
+>>> encoded_input = tokenizer("Hello, I'm a single sentence!")
+>>> print(encoded_input)
+{'input_ids': [101, 138, 18696, 155, 1942, 3190, 1144, 1572, 13745, 1104, 159, 9664, 2107, 102], 
+ 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 
+ 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
+```
+
+This returns a dictionary string to list of ints. The [input_ids](glossary#input-ids) are the indices corresponding
+to each token in our sentence. We will see below what the [attention_mask](glossary#attention-mask) is used for and
+in [the next section](#preprocessing-pairs-of-sentences) the goal of [token_type_ids](glossary#token-type-ids).
+
+The tokenizer can decode a list of token ids in a proper sentence:
+
+```py
+>>> tokenizer.decode(encoded_input["input_ids"])
+"[CLS] Hello, I'm a single sentence! [SEP]"
+```
+
+As you can see, the tokenizer automatically added some special tokens that the model expects. Not all models need
+special tokens; for instance, if we had used _gpt2-medium_ instead of _bert-base-cased_ to create our tokenizer, we
+would have seen the same sentence as the original one here. You can disable this behavior (which is only advised if you
+have added those special tokens yourself) by passing `add_special_tokens=False`.
+
+If you have several sentences you want to process, you can do this efficiently by sending them as a list to the
+tokenizer:
+
+```py
+>>> batch_sentences = ["Hello I'm a single sentence",
+...                    "And another sentence",
+...                    "And the very very last one"]
+>>> encoded_inputs = tokenizer(batch_sentences)
+>>> print(encoded_inputs)
+{'input_ids': [[101, 8667, 146, 112, 182, 170, 1423, 5650, 102],
+               [101, 1262, 1330, 5650, 102],
+               [101, 1262, 1103, 1304, 1304, 1314, 1141, 102]],
+ 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0],
+                    [0, 0, 0, 0, 0],
+                    [0, 0, 0, 0, 0, 0, 0, 0]],
+ 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1],
+                    [1, 1, 1, 1, 1],
+                    [1, 1, 1, 1, 1, 1, 1, 1]]}
+```
+
+We get back a dictionary once again, this time with values being lists of lists of ints.
+
+If the purpose of sending several sentences at a time to the tokenizer is to build a batch to feed the model, you will
+probably want:
+
+- To pad each sentence to the maximum length there is in your batch.
+- To truncate each sentence to the maximum length the model can accept (if applicable).
+- To return tensors.
+
+You can do all of this by using the following options when feeding your list of sentences to the tokenizer:
+
+```py
+>>> batch = tokenizer(batch_sentences, padding=True, truncation=True, return_tensors="pt")
+>>> print(batch)
+{'input_ids': tensor([[ 101, 8667,  146,  112,  182,  170, 1423, 5650,  102],
+                      [ 101, 1262, 1330, 5650,  102,    0,    0,    0,    0],
+                      [ 101, 1262, 1103, 1304, 1304, 1314, 1141,  102,    0]]),
+ 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0],
+                           [0, 0, 0, 0, 0, 0, 0, 0, 0],
+                           [0, 0, 0, 0, 0, 0, 0, 0, 0]]), 
+ 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1],
+                           [1, 1, 1, 1, 1, 0, 0, 0, 0],
+                           [1, 1, 1, 1, 1, 1, 1, 1, 0]])}
+===PT-TF-SPLIT===
+>>> batch = tokenizer(batch_sentences, padding=True, truncation=True, return_tensors="tf")
+>>> print(batch)
+{'input_ids': tf.Tensor([[ 101, 8667,  146,  112,  182,  170, 1423, 5650,  102],
+                      [ 101, 1262, 1330, 5650,  102,    0,    0,    0,    0],
+                      [ 101, 1262, 1103, 1304, 1304, 1314, 1141,  102,    0]]),
+ 'token_type_ids': tf.Tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0],
+                           [0, 0, 0, 0, 0, 0, 0, 0, 0],
+                           [0, 0, 0, 0, 0, 0, 0, 0, 0]]), 
+ 'attention_mask': tf.Tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1],
+                           [1, 1, 1, 1, 1, 0, 0, 0, 0],
+                           [1, 1, 1, 1, 1, 1, 1, 1, 0]])}
+```
+
+It returns a dictionary with string keys and tensor values. We can now see what the [attention_mask](glossary#attention-mask) is all about: it points out which tokens the model should pay attention to and which ones
+it should not (because they represent padding in this case).
+
+
+Note that if your model does not have a maximum length associated to it, the command above will throw a warning. You
+can safely ignore it. You can also pass `verbose=False` to stop the tokenizer from throwing those kinds of warnings.
+
+<a id='sentence-pairs'></a>
+
+## Preprocessing pairs of sentences
+
+<Youtube id="0u3ioSwev3s"/>
+
+Sometimes you need to feed a pair of sentences to your model. For instance, if you want to classify if two sentences in
+a pair are similar, or for question-answering models, which take a context and a question. For BERT models, the input
+is then represented like this: `[CLS] Sequence A [SEP] Sequence B [SEP]`
+
+You can encode a pair of sentences in the format expected by your model by supplying the two sentences as two arguments
+(not a list since a list of two sentences will be interpreted as a batch of two single sentences, as we saw before).
+This will once again return a dict string to list of ints:
+
+```py
+>>> encoded_input = tokenizer("How old are you?", "I'm 6 years old")
+>>> print(encoded_input)
+{'input_ids': [101, 1731, 1385, 1132, 1128, 136, 102, 146, 112, 182, 127, 1201, 1385, 102], 
+ 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1], 
+ 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
+```
+
+This shows us what the [token_type_ids](glossary#token-type-ids) are for: they indicate to the model which part of
+the inputs correspond to the first sentence and which part corresponds to the second sentence. Note that
+_token_type_ids_ are not required or handled by all models. By default, a tokenizer will only return the inputs that
+its associated model expects. You can force the return (or the non-return) of any of those special arguments by using
+`return_input_ids` or `return_token_type_ids`.
+
+If we decode the token ids we obtained, we will see that the special tokens have been properly added.
+
+```py
+>>> tokenizer.decode(encoded_input["input_ids"])
+"[CLS] How old are you? [SEP] I'm 6 years old [SEP]"
+```
+
+If you have a list of pairs of sequences you want to process, you should feed them as two lists to your tokenizer: the
+list of first sentences and the list of second sentences:
+
+```py
+>>> batch_sentences = ["Hello I'm a single sentence",
+...                    "And another sentence",
+...                    "And the very very last one"]
+>>> batch_of_second_sentences = ["I'm a sentence that goes with the first sentence",
+...                              "And I should be encoded with the second sentence",
+...                              "And I go with the very last one"]
+>>> encoded_inputs = tokenizer(batch_sentences, batch_of_second_sentences)
+>>> print(encoded_inputs)
+{'input_ids': [[101, 8667, 146, 112, 182, 170, 1423, 5650, 102, 146, 112, 182, 170, 5650, 1115, 2947, 1114, 1103, 1148, 5650, 102], 
+               [101, 1262, 1330, 5650, 102, 1262, 146, 1431, 1129, 12544, 1114, 1103, 1248, 5650, 102], 
+               [101, 1262, 1103, 1304, 1304, 1314, 1141, 102, 1262, 146, 1301, 1114, 1103, 1304, 1314, 1141, 102]], 
+'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 
+                   [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 
+                   [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 
+'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 
+                   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 
+                   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
+```
+
+As we can see, it returns a dictionary where each value is a list of lists of ints.
+
+To double-check what is fed to the model, we can decode each list in _input_ids_ one by one:
+
+```py
+>>> for ids in encoded_inputs["input_ids"]:
+>>>     print(tokenizer.decode(ids))
+[CLS] Hello I'm a single sentence [SEP] I'm a sentence that goes with the first sentence [SEP]
+[CLS] And another sentence [SEP] And I should be encoded with the second sentence [SEP]
+[CLS] And the very very last one [SEP] And I go with the very last one [SEP]
+```
+
+Once again, you can automatically pad your inputs to the maximum sentence length in the batch, truncate to the maximum
+length the model can accept and return tensors directly with the following:
+
+```py
+batch = tokenizer(batch_sentences, batch_of_second_sentences, padding=True, truncation=True, return_tensors="pt")
+===PT-TF-SPLIT===
+batch = tokenizer(batch_sentences, batch_of_second_sentences, padding=True, truncation=True, return_tensors="tf")
+```
+
+## Everything you always wanted to know about padding and truncation
+
+We have seen the commands that will work for most cases (pad your batch to the length of the maximum sentence and
+truncate to the maximum length the model can accept). However, the API supports more strategies if you need them. The
+three arguments you need to know for this are `padding`, `truncation` and `max_length`.
+
+- `padding` controls the padding. It can be a boolean or a string which should be:
+
+  - `True` or `'longest'` to pad to the longest sequence in the batch (doing no padding if you only provide
+    a single sequence).
+  - `'max_length'` to pad to a length specified by the `max_length` argument or the maximum length accepted
+    by the model if no `max_length` is provided (`max_length=None`). If you only provide a single sequence,
+    padding will still be applied to it.
+  - `False` or `'do_not_pad'` to not pad the sequences. As we have seen before, this is the default
+    behavior.
+
+- `truncation` controls the truncation. It can be a boolean or a string which should be:
+
+  - `True` or `'longest_first'` truncate to a maximum length specified by the `max_length` argument or
+    the maximum length accepted by the model if no `max_length` is provided (`max_length=None`). This will
+    truncate token by token, removing a token from the longest sequence in the pair until the proper length is
+    reached.
+  - `'only_second'` truncate to a maximum length specified by the `max_length` argument or the maximum
+    length accepted by the model if no `max_length` is provided (`max_length=None`). This will only truncate
+    the second sentence of a pair if a pair of sequence (or a batch of pairs of sequences) is provided.
+  - `'only_first'` truncate to a maximum length specified by the `max_length` argument or the maximum
+    length accepted by the model if no `max_length` is provided (`max_length=None`). This will only truncate
+    the first sentence of a pair if a pair of sequence (or a batch of pairs of sequences) is provided.
+  - `False` or `'do_not_truncate'` to not truncate the sequences. As we have seen before, this is the
+    default behavior.
+
+- `max_length` to control the length of the padding/truncation. It can be an integer or `None`, in which case
+  it will default to the maximum length the model can accept. If the model has no specific maximum input length,
+  truncation/padding to `max_length` is deactivated.
+
+Here is a table summarizing the recommend way to setup padding and truncation. If you use pair of inputs sequence in
+any of the following examples, you can replace `truncation=True` by a `STRATEGY` selected in
+`['only_first', 'only_second', 'longest_first']`, i.e. `truncation='only_second'` or `truncation= 'longest_first'` to control how both sequence in the pair are truncated as detailed before.
+
+| Truncation                           | Padding                           | Instruction                                                                                 |
+|--------------------------------------|-----------------------------------|---------------------------------------------------------------------------------------------|
+| no truncation                        | no padding                        | `tokenizer(batch_sentences)`                                                           |
+|                                      | padding to max sequence in batch  | `tokenizer(batch_sentences, padding=True)` or                                          |
+|                                      |                                   | `tokenizer(batch_sentences, padding='longest')`                                        |
+|                                      | padding to max model input length | `tokenizer(batch_sentences, padding='max_length')`                                     |
+|                                      | padding to specific length        | `tokenizer(batch_sentences, padding='max_length', max_length=42)`                      |
+| truncation to max model input length | no padding                        | `tokenizer(batch_sentences, truncation=True)` or                                       |
+|                                      |                                   | `tokenizer(batch_sentences, truncation=STRATEGY)`                                      |
+|                                      | padding to max sequence in batch  | `tokenizer(batch_sentences, padding=True, truncation=True)` or                         |
+|                                      |                                   | `tokenizer(batch_sentences, padding=True, truncation=STRATEGY)`                        |
+|                                      | padding to max model input length | `tokenizer(batch_sentences, padding='max_length', truncation=True)` or                 |
+|                                      |                                   | `tokenizer(batch_sentences, padding='max_length', truncation=STRATEGY)`                |
+|                                      | padding to specific length        | Not possible                                                                                |
+| truncation to specific length        | no padding                        | `tokenizer(batch_sentences, truncation=True, max_length=42)` or                        |
+|                                      |                                   | `tokenizer(batch_sentences, truncation=STRATEGY, max_length=42)`                       |
+|                                      | padding to max sequence in batch  | `tokenizer(batch_sentences, padding=True, truncation=True, max_length=42)` or          |
+|                                      |                                   | `tokenizer(batch_sentences, padding=True, truncation=STRATEGY, max_length=42)`         |
+|                                      | padding to max model input length | Not possible                                                                                |
+|                                      | padding to specific length        | `tokenizer(batch_sentences, padding='max_length', truncation=True, max_length=42)` or  |
+|                                      |                                   | `tokenizer(batch_sentences, padding='max_length', truncation=STRATEGY, max_length=42)` |
+
+## Pre-tokenized inputs
+
+The tokenizer also accept pre-tokenized inputs. This is particularly useful when you want to compute labels and extract
+predictions in [named entity recognition (NER)](https://en.wikipedia.org/wiki/Named-entity_recognition) or
+[part-of-speech tagging (POS tagging)](https://en.wikipedia.org/wiki/Part-of-speech_tagging).
+
+<Tip warning={true}>
+
+Pre-tokenized does not mean your inputs are already tokenized (you wouldn't need to pass them through the tokenizer
+if that was the case) but just split into words (which is often the first step in subword tokenization algorithms
+like BPE).
+
+</Tip>
+
+If you want to use pre-tokenized inputs, just set `is_split_into_words=True` when passing your inputs to the
+tokenizer. For instance, we have:
+
+```py
+>>> encoded_input = tokenizer(["Hello", "I'm", "a", "single", "sentence"], is_split_into_words=True)
+>>> print(encoded_input)
+{'input_ids': [101, 8667, 146, 112, 182, 170, 1423, 5650, 102],
+ 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0], 
+ 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1]}
+```
+
+Note that the tokenizer still adds the ids of special tokens (if applicable) unless you pass
+`add_special_tokens=False`.
+
+This works exactly as before for batch of sentences or batch of pairs of sentences. You can encode a batch of sentences
+like this:
+
+```py
+batch_sentences = [["Hello", "I'm", "a", "single", "sentence"],
+                   ["And", "another", "sentence"],
+                   ["And", "the", "very", "very", "last", "one"]]
+encoded_inputs = tokenizer(batch_sentences, is_split_into_words=True)
+```
+
+or a batch of pair sentences like this:
+
+```py
+batch_of_second_sentences = [["I'm", "a", "sentence", "that", "goes", "with", "the", "first", "sentence"],
+                             ["And", "I", "should", "be", "encoded", "with", "the", "second", "sentence"],
+                             ["And", "I", "go", "with", "the", "very", "last", "one"]]
+encoded_inputs = tokenizer(batch_sentences, batch_of_second_sentences, is_split_into_words=True)
+```
+
+And you can add padding, truncation as well as directly return tensors like before:
+
+```py
+batch = tokenizer(batch_sentences,
+                  batch_of_second_sentences,
+                  is_split_into_words=True,
+                  padding=True,
+                  truncation=True,
+                  return_tensors="pt")
+===PT-TF-SPLIT===
+batch = tokenizer(batch_sentences,
+                  batch_of_second_sentences,
+                  is_split_into_words=True,
+                  padding=True,
+                  truncation=True,
+                  return_tensors="tf")
+```
--- a/docs/source/preprocessing.rst
+++ b/docs/source/preprocessing.rst
@@ -1,366 +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.
-
-Preprocessing data
-=======================================================================================================================
-
-In this tutorial, we'll explore how to preprocess your data using 🤗 Transformers. The main tool for this is what we
-call a :doc:`tokenizer <main_classes/tokenizer>`. You can build one using the tokenizer class associated to the model
-you would like to use, or directly with the :class:`~transformers.AutoTokenizer` class.
-
-As we saw in the :doc:`quick tour </quicktour>`, the tokenizer will first split a given text in words (or part of
-words, punctuation symbols, etc.) usually called `tokens`. Then it will convert those `tokens` into numbers, to be able
-to build a tensor out of them and feed them to the model. It will also add any additional inputs the model might expect
-to work properly.
-
-.. note::
-
-    If you plan on using a pretrained model, it's important to use the associated pretrained tokenizer: it will split
-    the text you give it in tokens the same way for the pretraining corpus, and it will use the same correspondence
-    token to index (that we usually call a `vocab`) as during pretraining.
-
-To automatically download the vocab used during pretraining or fine-tuning a given model, you can use the
-:func:`~transformers.AutoTokenizer.from_pretrained` method:
-
-.. code-block::
-
-    from transformers import AutoTokenizer
-    tokenizer = AutoTokenizer.from_pretrained('bert-base-cased')
-
-Base use
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. raw:: html
-
-   <iframe width="560" height="315" src="https://www.youtube.com/embed/Yffk5aydLzg" title="YouTube video player"
-   frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope;
-   picture-in-picture" allowfullscreen></iframe>
-
-A :class:`~transformers.PreTrainedTokenizer` has many methods, but the only one you need to remember for preprocessing
-is its ``__call__``: you just need to feed your sentence to your tokenizer object.
-
-.. code-block::
-
-    >>> encoded_input = tokenizer("Hello, I'm a single sentence!")
-    >>> print(encoded_input)
-    {'input_ids': [101, 138, 18696, 155, 1942, 3190, 1144, 1572, 13745, 1104, 159, 9664, 2107, 102], 
-     'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 
-     'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
-
-This returns a dictionary string to list of ints. The `input_ids <glossary.html#input-ids>`__ are the indices
-corresponding to each token in our sentence. We will see below what the `attention_mask
-<glossary.html#attention-mask>`__ is used for and in :ref:`the next section <sentence-pairs>` the goal of
-`token_type_ids <glossary.html#token-type-ids>`__.
-
-The tokenizer can decode a list of token ids in a proper sentence:
-
-.. code-block::
-
-    >>> tokenizer.decode(encoded_input["input_ids"])
-    "[CLS] Hello, I'm a single sentence! [SEP]"
-
-As you can see, the tokenizer automatically added some special tokens that the model expects. Not all models need
-special tokens; for instance, if we had used `gpt2-medium` instead of `bert-base-cased` to create our tokenizer, we
-would have seen the same sentence as the original one here. You can disable this behavior (which is only advised if you
-have added those special tokens yourself) by passing ``add_special_tokens=False``.
-
-If you have several sentences you want to process, you can do this efficiently by sending them as a list to the
-tokenizer:
-
-.. code-block::
-
-    >>> batch_sentences = ["Hello I'm a single sentence",
-    ...                    "And another sentence",
-    ...                    "And the very very last one"]
-    >>> encoded_inputs = tokenizer(batch_sentences)
-    >>> print(encoded_inputs)
-    {'input_ids': [[101, 8667, 146, 112, 182, 170, 1423, 5650, 102],
-                   [101, 1262, 1330, 5650, 102],
-                   [101, 1262, 1103, 1304, 1304, 1314, 1141, 102]],
-     'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0],
-                        [0, 0, 0, 0, 0],
-                        [0, 0, 0, 0, 0, 0, 0, 0]],
-     'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1],
-                        [1, 1, 1, 1, 1],
-                        [1, 1, 1, 1, 1, 1, 1, 1]]}
-
-We get back a dictionary once again, this time with values being lists of lists of ints.
-
-If the purpose of sending several sentences at a time to the tokenizer is to build a batch to feed the model, you will
-probably want:
-
- To pad each sentence to the maximum length there is in your batch.
- To truncate each sentence to the maximum length the model can accept (if applicable).
- To return tensors.
-
-You can do all of this by using the following options when feeding your list of sentences to the tokenizer:
-
-.. code-block::
-
-    >>> ## PYTORCH CODE
-    >>> batch = tokenizer(batch_sentences, padding=True, truncation=True, return_tensors="pt")
-    >>> print(batch)
-    {'input_ids': tensor([[ 101, 8667,  146,  112,  182,  170, 1423, 5650,  102],
-                          [ 101, 1262, 1330, 5650,  102,    0,    0,    0,    0],
-                          [ 101, 1262, 1103, 1304, 1304, 1314, 1141,  102,    0]]),
-     'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0],
-                               [0, 0, 0, 0, 0, 0, 0, 0, 0],
-                               [0, 0, 0, 0, 0, 0, 0, 0, 0]]), 
-     'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1],
-                               [1, 1, 1, 1, 1, 0, 0, 0, 0],
-                               [1, 1, 1, 1, 1, 1, 1, 1, 0]])}
-    >>> ## TENSORFLOW CODE
-    >>> batch = tokenizer(batch_sentences, padding=True, truncation=True, return_tensors="tf")
-    >>> print(batch)
-    {'input_ids': tf.Tensor([[ 101, 8667,  146,  112,  182,  170, 1423, 5650,  102],
-                          [ 101, 1262, 1330, 5650,  102,    0,    0,    0,    0],
-                          [ 101, 1262, 1103, 1304, 1304, 1314, 1141,  102,    0]]),
-     'token_type_ids': tf.Tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0],
-                               [0, 0, 0, 0, 0, 0, 0, 0, 0],
-                               [0, 0, 0, 0, 0, 0, 0, 0, 0]]), 
-     'attention_mask': tf.Tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1],
-                               [1, 1, 1, 1, 1, 0, 0, 0, 0],
-                               [1, 1, 1, 1, 1, 1, 1, 1, 0]])}
-
-It returns a dictionary with string keys and tensor values. We can now see what the `attention_mask
-<glossary.html#attention-mask>`__ is all about: it points out which tokens the model should pay attention to and which
-ones it should not (because they represent padding in this case).
-
-
-Note that if your model does not have a maximum length associated to it, the command above will throw a warning. You
-can safely ignore it. You can also pass ``verbose=False`` to stop the tokenizer from throwing those kinds of warnings.
-
-.. _sentence-pairs:
-
-Preprocessing pairs of sentences
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. 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>
-
-Sometimes you need to feed a pair of sentences to your model. For instance, if you want to classify if two sentences in
-a pair are similar, or for question-answering models, which take a context and a question. For BERT models, the input
-is then represented like this: :obj:`[CLS] Sequence A [SEP] Sequence B [SEP]`
-
-You can encode a pair of sentences in the format expected by your model by supplying the two sentences as two arguments
-(not a list since a list of two sentences will be interpreted as a batch of two single sentences, as we saw before).
-This will once again return a dict string to list of ints:
-
-.. code-block::
-
-    >>> encoded_input = tokenizer("How old are you?", "I'm 6 years old")
-    >>> print(encoded_input)
-    {'input_ids': [101, 1731, 1385, 1132, 1128, 136, 102, 146, 112, 182, 127, 1201, 1385, 102], 
-     'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1], 
-     'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
-
-This shows us what the `token_type_ids <glossary.html#token-type-ids>`__ are for: they indicate to the model which part
-of the inputs correspond to the first sentence and which part corresponds to the second sentence. Note that
-`token_type_ids` are not required or handled by all models. By default, a tokenizer will only return the inputs that
-its associated model expects. You can force the return (or the non-return) of any of those special arguments by using
-``return_input_ids`` or ``return_token_type_ids``.
-
-If we decode the token ids we obtained, we will see that the special tokens have been properly added.
-
-.. code-block::
-
-    >>> tokenizer.decode(encoded_input["input_ids"])
-    "[CLS] How old are you? [SEP] I'm 6 years old [SEP]"
-
-If you have a list of pairs of sequences you want to process, you should feed them as two lists to your tokenizer: the
-list of first sentences and the list of second sentences:
-
-.. code-block::
-
-    >>> batch_sentences = ["Hello I'm a single sentence",
-    ...                    "And another sentence",
-    ...                    "And the very very last one"]
-    >>> batch_of_second_sentences = ["I'm a sentence that goes with the first sentence",
-    ...                              "And I should be encoded with the second sentence",
-    ...                              "And I go with the very last one"]
-    >>> encoded_inputs = tokenizer(batch_sentences, batch_of_second_sentences)
-    >>> print(encoded_inputs)
-    {'input_ids': [[101, 8667, 146, 112, 182, 170, 1423, 5650, 102, 146, 112, 182, 170, 5650, 1115, 2947, 1114, 1103, 1148, 5650, 102], 
-                   [101, 1262, 1330, 5650, 102, 1262, 146, 1431, 1129, 12544, 1114, 1103, 1248, 5650, 102], 
-                   [101, 1262, 1103, 1304, 1304, 1314, 1141, 102, 1262, 146, 1301, 1114, 1103, 1304, 1314, 1141, 102]], 
-    'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 
-                       [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 
-                       [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 
-    'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 
-                       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 
-                       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
-
-As we can see, it returns a dictionary where each value is a list of lists of ints.
-
-To double-check what is fed to the model, we can decode each list in `input_ids` one by one:
-
-.. code-block::
-
-    >>> for ids in encoded_inputs["input_ids"]:
-    >>>     print(tokenizer.decode(ids))
-    [CLS] Hello I'm a single sentence [SEP] I'm a sentence that goes with the first sentence [SEP]
-    [CLS] And another sentence [SEP] And I should be encoded with the second sentence [SEP]
-    [CLS] And the very very last one [SEP] And I go with the very last one [SEP]
-
-Once again, you can automatically pad your inputs to the maximum sentence length in the batch, truncate to the maximum
-length the model can accept and return tensors directly with the following:
-
-.. code-block::
-
-    ## PYTORCH CODE
-    batch = tokenizer(batch_sentences, batch_of_second_sentences, padding=True, truncation=True, return_tensors="pt")
-    ## TENSORFLOW CODE
-    batch = tokenizer(batch_sentences, batch_of_second_sentences, padding=True, truncation=True, return_tensors="tf")
-
-Everything you always wanted to know about padding and truncation
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-We have seen the commands that will work for most cases (pad your batch to the length of the maximum sentence and
-truncate to the maximum length the model can accept). However, the API supports more strategies if you need them. The
-three arguments you need to know for this are :obj:`padding`, :obj:`truncation` and :obj:`max_length`.
-
- :obj:`padding` controls the padding. It can be a boolean or a string which should be:
-
-    - :obj:`True` or :obj:`'longest'` to pad to the longest sequence in the batch (doing no padding if you only provide
-      a single sequence).
-    - :obj:`'max_length'` to pad to a length specified by the :obj:`max_length` argument or the maximum length accepted
-      by the model if no :obj:`max_length` is provided (``max_length=None``). If you only provide a single sequence,
-      padding will still be applied to it.
-    - :obj:`False` or :obj:`'do_not_pad'` to not pad the sequences. As we have seen before, this is the default
-      behavior.
-
- :obj:`truncation` controls the truncation. It can be a boolean or a string which should be:
-
-    - :obj:`True` or :obj:`'longest_first'` truncate to a maximum length specified by the :obj:`max_length` argument or
-      the maximum length accepted by the model if no :obj:`max_length` is provided (``max_length=None``). This will
-      truncate token by token, removing a token from the longest sequence in the pair until the proper length is
-      reached.
-    - :obj:`'only_second'` truncate to a maximum length specified by the :obj:`max_length` argument or the maximum
-      length accepted by the model if no :obj:`max_length` is provided (``max_length=None``). This will only truncate
-      the second sentence of a pair if a pair of sequence (or a batch of pairs of sequences) is provided.
-    - :obj:`'only_first'` truncate to a maximum length specified by the :obj:`max_length` argument or the maximum
-      length accepted by the model if no :obj:`max_length` is provided (``max_length=None``). This will only truncate
-      the first sentence of a pair if a pair of sequence (or a batch of pairs of sequences) is provided.
-    - :obj:`False` or :obj:`'do_not_truncate'` to not truncate the sequences. As we have seen before, this is the
-      default behavior.
-
- :obj:`max_length` to control the length of the padding/truncation. It can be an integer or :obj:`None`, in which case
-  it will default to the maximum length the model can accept. If the model has no specific maximum input length,
-  truncation/padding to :obj:`max_length` is deactivated.
-
-Here is a table summarizing the recommend way to setup padding and truncation. If you use pair of inputs sequence in
-any of the following examples, you can replace :obj:`truncation=True` by a :obj:`STRATEGY` selected in
-:obj:`['only_first', 'only_second', 'longest_first']`, i.e. :obj:`truncation='only_second'` or :obj:`truncation=
-'longest_first'` to control how both sequence in the pair are truncated as detailed before.
-
-+--------------------------------------+-----------------------------------+---------------------------------------------------------------------------------------------+
-| Truncation                           | Padding                           | Instruction                                                                                 |
-+======================================+===================================+=============================================================================================+
-| no truncation                        | no padding                        | :obj:`tokenizer(batch_sentences)`                                                           |
-|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
-|                                      | padding to max sequence in batch  | :obj:`tokenizer(batch_sentences, padding=True)` or                                          |
-|                                      |                                   | :obj:`tokenizer(batch_sentences, padding='longest')`                                        |
-|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
-|                                      | padding to max model input length | :obj:`tokenizer(batch_sentences, padding='max_length')`                                     |
-|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
-|                                      | padding to specific length        | :obj:`tokenizer(batch_sentences, padding='max_length', max_length=42)`                      |
-+--------------------------------------+-----------------------------------+---------------------------------------------------------------------------------------------+
-| truncation to max model input length | no padding                        | :obj:`tokenizer(batch_sentences, truncation=True)` or                                       |
-|                                      |                                   | :obj:`tokenizer(batch_sentences, truncation=STRATEGY)`                                      |
-|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
-|                                      | padding to max sequence in batch  | :obj:`tokenizer(batch_sentences, padding=True, truncation=True)` or                         |
-|                                      |                                   | :obj:`tokenizer(batch_sentences, padding=True, truncation=STRATEGY)`                        |
-|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
-|                                      | padding to max model input length | :obj:`tokenizer(batch_sentences, padding='max_length', truncation=True)` or                 |
-|                                      |                                   | :obj:`tokenizer(batch_sentences, padding='max_length', truncation=STRATEGY)`                |
-|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
-|                                      | padding to specific length        | Not possible                                                                                |
-+--------------------------------------+-----------------------------------+---------------------------------------------------------------------------------------------+
-| truncation to specific length        | no padding                        | :obj:`tokenizer(batch_sentences, truncation=True, max_length=42)` or                        |
-|                                      |                                   | :obj:`tokenizer(batch_sentences, truncation=STRATEGY, max_length=42)`                       |
-|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
-|                                      | padding to max sequence in batch  | :obj:`tokenizer(batch_sentences, padding=True, truncation=True, max_length=42)` or          |
-|                                      |                                   | :obj:`tokenizer(batch_sentences, padding=True, truncation=STRATEGY, max_length=42)`         |
-|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
-|                                      | padding to max model input length | Not possible                                                                                |
-|                                      +-----------------------------------+---------------------------------------------------------------------------------------------+
-|                                      | padding to specific length        | :obj:`tokenizer(batch_sentences, padding='max_length', truncation=True, max_length=42)` or  |
-|                                      |                                   | :obj:`tokenizer(batch_sentences, padding='max_length', truncation=STRATEGY, max_length=42)` |
-+--------------------------------------+-----------------------------------+---------------------------------------------------------------------------------------------+
-
-Pre-tokenized inputs
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The tokenizer also accept pre-tokenized inputs. This is particularly useful when you want to compute labels and extract
-predictions in `named entity recognition (NER) <https://en.wikipedia.org/wiki/Named-entity_recognition>`__ or
-`part-of-speech tagging (POS tagging) <https://en.wikipedia.org/wiki/Part-of-speech_tagging>`__.
-
-.. warning::
-
-    Pre-tokenized does not mean your inputs are already tokenized (you wouldn't need to pass them through the tokenizer
-    if that was the case) but just split into words (which is often the first step in subword tokenization algorithms
-    like BPE).
-
-If you want to use pre-tokenized inputs, just set :obj:`is_split_into_words=True` when passing your inputs to the
-tokenizer. For instance, we have:
-
-.. code-block::
-
-    >>> encoded_input = tokenizer(["Hello", "I'm", "a", "single", "sentence"], is_split_into_words=True)
-    >>> print(encoded_input)
-    {'input_ids': [101, 8667, 146, 112, 182, 170, 1423, 5650, 102],
-     'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0], 
-     'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1]}
-
-Note that the tokenizer still adds the ids of special tokens (if applicable) unless you pass
-``add_special_tokens=False``.
-
-This works exactly as before for batch of sentences or batch of pairs of sentences. You can encode a batch of sentences
-like this:
-
-.. code-block::
-
-    batch_sentences = [["Hello", "I'm", "a", "single", "sentence"],
-                       ["And", "another", "sentence"],
-                       ["And", "the", "very", "very", "last", "one"]]
-    encoded_inputs = tokenizer(batch_sentences, is_split_into_words=True)
-
-or a batch of pair sentences like this:
-
-.. code-block::
-
-    batch_of_second_sentences = [["I'm", "a", "sentence", "that", "goes", "with", "the", "first", "sentence"],
-                                 ["And", "I", "should", "be", "encoded", "with", "the", "second", "sentence"],
-                                 ["And", "I", "go", "with", "the", "very", "last", "one"]]
-    encoded_inputs = tokenizer(batch_sentences, batch_of_second_sentences, is_split_into_words=True)
-
-And you can add padding, truncation as well as directly return tensors like before:
-
-.. code-block::
-
-    ## PYTORCH CODE
-    batch = tokenizer(batch_sentences,
-                      batch_of_second_sentences,
-                      is_split_into_words=True,
-                      padding=True,
-                      truncation=True,
-                      return_tensors="pt")
-    ## TENSORFLOW CODE
-    batch = tokenizer(batch_sentences,
-                      batch_of_second_sentences,
-                      is_split_into_words=True,
-                      padding=True,
-                      truncation=True,
-                      return_tensors="tf")
--- a/docs/source/pretrained_models.rst
+++ b/docs/source/pretrained_models.rst
@@ -1,492 +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.
-
-Pretrained models
-=======================================================================================================================
-
-Here is a partial list of some of the available pretrained models together with a short presentation of each model.
-
-For the full list, refer to `https://huggingface.co/models <https://huggingface.co/models>`__.
-
-+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| Architecture       | Model id                                                   | Details of the model                                                                                                                  |
-+====================+============================================================+=======================================================================================================================================+
-| BERT               | ``bert-base-uncased``                                      | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
-|                    |                                                            | | Trained on lower-cased English text.                                                                                                |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``bert-large-uncased``                                     | | 24-layer, 1024-hidden, 16-heads, 336M parameters.                                                                                   |
-|                    |                                                            | | Trained on lower-cased English text.                                                                                                |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``bert-base-cased``                                        | | 12-layer, 768-hidden, 12-heads, 109M parameters.                                                                                    |
-|                    |                                                            | | Trained on cased English text.                                                                                                      |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``bert-large-cased``                                       | | 24-layer, 1024-hidden, 16-heads, 335M parameters.                                                                                   |
-|                    |                                                            | | Trained on cased English text.                                                                                                      |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``bert-base-multilingual-uncased``                         | | (Original, not recommended) 12-layer, 768-hidden, 12-heads, 168M parameters.                                                        |
-|                    |                                                            | | Trained on lower-cased text in the top 102 languages with the largest Wikipedias                                                    |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/google-research/bert/blob/master/multilingual.md>`__).                                              |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``bert-base-multilingual-cased``                           | | (New, **recommended**) 12-layer, 768-hidden, 12-heads, 179M parameters.                                                             |
-|                    |                                                            | | Trained on cased text in the top 104 languages with the largest Wikipedias                                                          |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/google-research/bert/blob/master/multilingual.md>`__).                                              |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``bert-base-chinese``                                      | | 12-layer, 768-hidden, 12-heads, 103M parameters.                                                                                    |
-|                    |                                                            | | Trained on cased Chinese Simplified and Traditional text.                                                                           |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``bert-base-german-cased``                                 | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
-|                    |                                                            | | Trained on cased German text by Deepset.ai                                                                                          |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details on deepset.ai website <https://deepset.ai/german-bert>`__).                                                             |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``bert-large-uncased-whole-word-masking``                  | | 24-layer, 1024-hidden, 16-heads, 336M parameters.                                                                                   |
-|                    |                                                            | | Trained on lower-cased English text using Whole-Word-Masking                                                                        |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/google-research/bert/#bert>`__).                                                                    |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``bert-large-cased-whole-word-masking``                    | | 24-layer, 1024-hidden, 16-heads, 335M parameters.                                                                                   |
-|                    |                                                            | | Trained on cased English text using Whole-Word-Masking                                                                              |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/google-research/bert/#bert>`__).                                                                    |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``bert-large-uncased-whole-word-masking-finetuned-squad``  | | 24-layer, 1024-hidden, 16-heads, 336M parameters.                                                                                   |
-|                    |                                                            | | The ``bert-large-uncased-whole-word-masking`` model fine-tuned on SQuAD                                                             |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see details of fine-tuning in the `example section <https://github.com/huggingface/transformers/tree/master/examples>`__).           |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``bert-large-cased-whole-word-masking-finetuned-squad``    | | 24-layer, 1024-hidden, 16-heads, 335M parameters                                                                                    |
-|                    |                                                            | | The ``bert-large-cased-whole-word-masking`` model fine-tuned on SQuAD                                                               |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details of fine-tuning in the example section <https://huggingface.co/transformers/examples.html>`__)                           |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``bert-base-cased-finetuned-mrpc``                         | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
-|                    |                                                            | | The ``bert-base-cased`` model fine-tuned on MRPC                                                                                    |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details of fine-tuning in the example section <https://huggingface.co/transformers/examples.html>`__)                           |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``bert-base-german-dbmdz-cased``                           | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
-|                    |                                                            | | Trained on cased German text by DBMDZ                                                                                               |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details on dbmdz repository <https://github.com/dbmdz/german-bert>`__).                                                         |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``bert-base-german-dbmdz-uncased``                         | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
-|                    |                                                            | | Trained on uncased German text by DBMDZ                                                                                             |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details on dbmdz repository <https://github.com/dbmdz/german-bert>`__).                                                         |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``cl-tohoku/bert-base-japanese``                           | | 12-layer, 768-hidden, 12-heads, 111M parameters.                                                                                    |
-|                    |                                                            | | Trained on Japanese text. Text is tokenized with MeCab and WordPiece and this requires some extra dependencies,                     |
-|                    |                                                            | | `fugashi <https://github.com/polm/fugashi>`__ which is a wrapper around `MeCab <https://taku910.github.io/mecab/>`__.               |
-|                    |                                                            | | Use ``pip install transformers["ja"]`` (or ``pip install -e .["ja"]`` if you install from source) to install them.                  |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__).                                               |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``cl-tohoku/bert-base-japanese-whole-word-masking``        | | 12-layer, 768-hidden, 12-heads, 111M parameters.                                                                                    |
-|                    |                                                            | | Trained on Japanese text. Text is tokenized with MeCab and WordPiece and this requires some extra dependencies,                     |
-|                    |                                                            | | `fugashi <https://github.com/polm/fugashi>`__ which is a wrapper around `MeCab <https://taku910.github.io/mecab/>`__.               |
-|                    |                                                            | | Use ``pip install transformers["ja"]`` (or ``pip install -e .["ja"]`` if you install from source) to install them.                  |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__).                                               |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``cl-tohoku/bert-base-japanese-char``                      | | 12-layer, 768-hidden, 12-heads, 90M parameters.                                                                                     |
-|                    |                                                            | | Trained on Japanese text. Text is tokenized into characters.                                                                        |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__).                                               |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``cl-tohoku/bert-base-japanese-char-whole-word-masking``   | | 12-layer, 768-hidden, 12-heads, 90M parameters.                                                                                     |
-|                    |                                                            | | Trained on Japanese text using Whole-Word-Masking. Text is tokenized into characters.                                               |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__).                                               |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``TurkuNLP/bert-base-finnish-cased-v1``                    | | 12-layer, 768-hidden, 12-heads, 125M parameters.                                                                                    |
-|                    |                                                            | | Trained on cased Finnish text.                                                                                                      |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details on turkunlp.org <http://turkunlp.org/FinBERT/>`__).                                                                     |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``TurkuNLP/bert-base-finnish-uncased-v1``                  | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
-|                    |                                                            | | Trained on uncased Finnish text.                                                                                                    |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details on turkunlp.org <http://turkunlp.org/FinBERT/>`__).                                                                     |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``wietsedv/bert-base-dutch-cased``                         | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
-|                    |                                                            | | Trained on cased Dutch text.                                                                                                        |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details on wietsedv repository <https://github.com/wietsedv/bertje/>`__).                                                       |
-+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| GPT                | ``openai-gpt``                                             | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
-|                    |                                                            | | OpenAI GPT English model                                                                                                            |
-+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| GPT-2              | ``gpt2``                                                   | | 12-layer, 768-hidden, 12-heads, 117M parameters.                                                                                    |
-|                    |                                                            | | OpenAI GPT-2 English model                                                                                                          |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``gpt2-medium``                                            | | 24-layer, 1024-hidden, 16-heads, 345M parameters.                                                                                   |
-|                    |                                                            | | OpenAI's Medium-sized GPT-2 English model                                                                                           |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``gpt2-large``                                             | | 36-layer, 1280-hidden, 20-heads, 774M parameters.                                                                                   |
-|                    |                                                            | | OpenAI's Large-sized GPT-2 English model                                                                                            |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``gpt2-xl``                                                | | 48-layer, 1600-hidden, 25-heads, 1558M parameters.                                                                                  |
-|                    |                                                            | | OpenAI's XL-sized GPT-2 English model                                                                                               |
-+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| GPTNeo             | ``EleutherAI/gpt-neo-1.3B``                                | | 24-layer, 2048-hidden, 16-heads, 1.3B parameters.                                                                                   |
-|                    |                                                            | | EleutherAI's GPT-3 like language model.                                                                                             |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``EleutherAI/gpt-neo-2.7B``                                | | 32-layer, 2560-hidden, 20-heads, 2.7B parameters.                                                                                   |
-|                    |                                                            | | EleutherAI's GPT-3 like language model.                                                                                             |
-+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| Transformer-XL     | ``transfo-xl-wt103``                                       | | 18-layer, 1024-hidden, 16-heads, 257M parameters.                                                                                   |
-|                    |                                                            | | English model trained on wikitext-103                                                                                               |
-+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| XLNet              | ``xlnet-base-cased``                                       | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
-|                    |                                                            | | XLNet English model                                                                                                                 |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``xlnet-large-cased``                                      | | 24-layer, 1024-hidden, 16-heads, 340M parameters.                                                                                   |
-|                    |                                                            | | XLNet Large English model                                                                                                           |
-+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| XLM                | ``xlm-mlm-en-2048``                                        | | 12-layer, 2048-hidden, 16-heads                                                                                                     |
-|                    |                                                            | | XLM English model                                                                                                                   |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``xlm-mlm-ende-1024``                                      | | 6-layer, 1024-hidden, 8-heads                                                                                                       |
-|                    |                                                            | | XLM English-German model trained on the concatenation of English and German wikipedia                                               |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``xlm-mlm-enfr-1024``                                      | | 6-layer, 1024-hidden, 8-heads                                                                                                       |
-|                    |                                                            | | XLM English-French model trained on the concatenation of English and French wikipedia                                               |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``xlm-mlm-enro-1024``                                      | | 6-layer, 1024-hidden, 8-heads                                                                                                       |
-|                    |                                                            | | XLM English-Romanian Multi-language model                                                                                           |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``xlm-mlm-xnli15-1024``                                    | | 12-layer, 1024-hidden, 8-heads                                                                                                      |
-|                    |                                                            | | XLM Model pre-trained with MLM on the `15 XNLI languages <https://github.com/facebookresearch/XNLI>`__.                             |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``xlm-mlm-tlm-xnli15-1024``                                | | 12-layer, 1024-hidden, 8-heads                                                                                                      |
-|                    |                                                            | | XLM Model pre-trained with MLM + TLM on the `15 XNLI languages <https://github.com/facebookresearch/XNLI>`__.                       |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``xlm-clm-enfr-1024``                                      | | 6-layer, 1024-hidden, 8-heads                                                                                                       |
-|                    |                                                            | | XLM English-French model trained with CLM (Causal Language Modeling) on the concatenation of English and French wikipedia           |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``xlm-clm-ende-1024``                                      | | 6-layer, 1024-hidden, 8-heads                                                                                                       |
-|                    |                                                            | | XLM English-German model trained with CLM (Causal Language Modeling) on the concatenation of English and German wikipedia           |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``xlm-mlm-17-1280``                                        | | 16-layer, 1280-hidden, 16-heads                                                                                                     |
-|                    |                                                            | | XLM model trained with MLM (Masked Language Modeling) on 17 languages.                                                              |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``xlm-mlm-100-1280``                                       | | 16-layer, 1280-hidden, 16-heads                                                                                                     |
-|                    |                                                            | | XLM model trained with MLM (Masked Language Modeling) on 100 languages.                                                             |
-+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| RoBERTa            | ``roberta-base``                                           | | 12-layer, 768-hidden, 12-heads, 125M parameters                                                                                     |
-|                    |                                                            | | RoBERTa using the BERT-base architecture                                                                                            |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/roberta>`__)                                                   |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``roberta-large``                                          | | 24-layer, 1024-hidden, 16-heads, 355M parameters                                                                                    |
-|                    |                                                            | | RoBERTa using the BERT-large architecture                                                                                           |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/roberta>`__)                                                   |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``roberta-large-mnli``                                     | | 24-layer, 1024-hidden, 16-heads, 355M parameters                                                                                    |
-|                    |                                                            | | ``roberta-large`` fine-tuned on `MNLI <http://www.nyu.edu/projects/bowman/multinli/>`__.                                            |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/roberta>`__)                                                   |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``distilroberta-base``                                     | | 6-layer, 768-hidden, 12-heads, 82M parameters                                                                                       |
-|                    |                                                            | | The DistilRoBERTa model distilled from the RoBERTa model `roberta-base` checkpoint.                                                 |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/huggingface/transformers/tree/master/examples/research_projects/distillation>`__)                   |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``roberta-base-openai-detector``                           | | 12-layer, 768-hidden, 12-heads, 125M parameters                                                                                     |
-|                    |                                                            | | ``roberta-base`` fine-tuned by OpenAI on the outputs of the 1.5B-parameter GPT-2 model.                                             |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/openai/gpt-2-output-dataset/tree/master/detector>`__)                                               |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``roberta-large-openai-detector``                          | | 24-layer, 1024-hidden, 16-heads, 355M parameters                                                                                    |
-|                    |                                                            | | ``roberta-large`` fine-tuned by OpenAI on the outputs of the 1.5B-parameter GPT-2 model.                                            |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/openai/gpt-2-output-dataset/tree/master/detector>`__)                                               |
-+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| DistilBERT         | ``distilbert-base-uncased``                                | | 6-layer, 768-hidden, 12-heads, 66M parameters                                                                                       |
-|                    |                                                            | | The DistilBERT model distilled from the BERT model `bert-base-uncased` checkpoint                                                   |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/huggingface/transformers/tree/master/examples/research_projects/distillation>`__)                   |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``distilbert-base-uncased-distilled-squad``                | | 6-layer, 768-hidden, 12-heads, 66M parameters                                                                                       |
-|                    |                                                            | | The DistilBERT model distilled from the BERT model `bert-base-uncased` checkpoint, with an additional linear layer.                 |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/huggingface/transformers/tree/master/examples/research_projects/distillation>`__)                   |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``distilbert-base-cased``                                  | | 6-layer, 768-hidden, 12-heads, 65M parameters                                                                                       |
-|                    |                                                            | | The DistilBERT model distilled from the BERT model `bert-base-cased` checkpoint                                                     |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/huggingface/transformers/tree/master/examples/research_projects/distillation>`__)                   |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``distilbert-base-cased-distilled-squad``                  | | 6-layer, 768-hidden, 12-heads, 65M parameters                                                                                       |
-|                    |                                                            | | The DistilBERT model distilled from the BERT model `bert-base-cased` checkpoint, with an additional question answering layer.       |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/huggingface/transformers/tree/master/examples/research_projects/distillation>`__)                   |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``distilgpt2``                                             | | 6-layer, 768-hidden, 12-heads, 82M parameters                                                                                       |
-|                    |                                                            | | The DistilGPT2 model distilled from the GPT2 model `gpt2` checkpoint.                                                               |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/huggingface/transformers/tree/master/examples/research_projects/distillation>`__)                   |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``distilbert-base-german-cased``                           | | 6-layer, 768-hidden, 12-heads, 66M parameters                                                                                       |
-|                    |                                                            | | The German DistilBERT model distilled from the German DBMDZ BERT model `bert-base-german-dbmdz-cased` checkpoint.                   |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/huggingface/transformers/tree/master/examples/research_projects/distillation>`__)                   |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``distilbert-base-multilingual-cased``                     | | 6-layer, 768-hidden, 12-heads, 134M parameters                                                                                      |
-|                    |                                                            | | The multilingual DistilBERT model distilled from the Multilingual BERT model `bert-base-multilingual-cased` checkpoint.             |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/huggingface/transformers/tree/master/examples/research_projects/distillation>`__)                   |
-+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| CTRL               | ``ctrl``                                                   | | 48-layer, 1280-hidden, 16-heads, 1.6B parameters                                                                                    |
-|                    |                                                            | | Salesforce's Large-sized CTRL English model                                                                                         |
-+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| CamemBERT          | ``camembert-base``                                         | | 12-layer, 768-hidden, 12-heads, 110M parameters                                                                                     |
-|                    |                                                            | | CamemBERT using the BERT-base architecture                                                                                          |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/camembert>`__)                                                 |
-+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| ALBERT             | ``albert-base-v1``                                         | | 12 repeating layers, 128 embedding, 768-hidden, 12-heads, 11M parameters                                                            |
-|                    |                                                            | | ALBERT base model                                                                                                                   |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/google-research/ALBERT>`__)                                                                         |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``albert-large-v1``                                        | | 24 repeating layers, 128 embedding, 1024-hidden, 16-heads, 17M parameters                                                           |
-|                    |                                                            | | ALBERT large model                                                                                                                  |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/google-research/ALBERT>`__)                                                                         |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``albert-xlarge-v1``                                       | | 24 repeating layers, 128 embedding, 2048-hidden, 16-heads, 58M parameters                                                           |
-|                    |                                                            | | ALBERT xlarge model                                                                                                                 |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/google-research/ALBERT>`__)                                                                         |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``albert-xxlarge-v1``                                      | | 12 repeating layer, 128 embedding, 4096-hidden, 64-heads, 223M parameters                                                           |
-|                    |                                                            | | ALBERT xxlarge model                                                                                                                |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/google-research/ALBERT>`__)                                                                         |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``albert-base-v2``                                         | | 12 repeating layers, 128 embedding, 768-hidden, 12-heads, 11M parameters                                                            |
-|                    |                                                            | | ALBERT base model with no dropout, additional training data and longer training                                                     |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/google-research/ALBERT>`__)                                                                         |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``albert-large-v2``                                        | | 24 repeating layers, 128 embedding, 1024-hidden, 16-heads, 17M parameters                                                           |
-|                    |                                                            | | ALBERT large model with no dropout, additional training data and longer training                                                    |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/google-research/ALBERT>`__)                                                                         |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``albert-xlarge-v2``                                       | | 24 repeating layers, 128 embedding, 2048-hidden, 16-heads, 58M parameters                                                           |
-|                    |                                                            | | ALBERT xlarge model with no dropout, additional training data and longer training                                                   |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/google-research/ALBERT>`__)                                                                         |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``albert-xxlarge-v2``                                      | | 12 repeating layer, 128 embedding, 4096-hidden, 64-heads, 223M parameters                                                           |
-|                    |                                                            | | ALBERT xxlarge model with no dropout, additional training data and longer training                                                  |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/google-research/ALBERT>`__)                                                                         |
-+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| T5                 | ``t5-small``                                               | | ~60M parameters with 6-layers, 512-hidden-state, 2048 feed-forward hidden-state, 8-heads,                                           |
-|                    |                                                            | | Trained on English text: the Colossal Clean Crawled Corpus (C4)                                                                     |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``t5-base``                                                | | ~220M parameters with 12-layers, 768-hidden-state, 3072 feed-forward hidden-state, 12-heads,                                        |
-|                    |                                                            | | Trained on English text: the Colossal Clean Crawled Corpus (C4)                                                                     |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``t5-large``                                               | | ~770M parameters with 24-layers, 1024-hidden-state, 4096 feed-forward hidden-state, 16-heads,                                       |
-|                    |                                                            | | Trained on English text: the Colossal Clean Crawled Corpus (C4)                                                                     |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``t5-3B``                                                  | | ~2.8B parameters with 24-layers, 1024-hidden-state, 16384 feed-forward hidden-state, 32-heads,                                      |
-|                    |                                                            | | Trained on English text: the Colossal Clean Crawled Corpus (C4)                                                                     |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``t5-11B``                                                 | | ~11B parameters with 24-layers, 1024-hidden-state, 65536 feed-forward hidden-state, 128-heads,                                      |
-|                    |                                                            | | Trained on English text: the Colossal Clean Crawled Corpus (C4)                                                                     |
-+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| XLM-RoBERTa        | ``xlm-roberta-base``                                       | | ~270M parameters with 12-layers, 768-hidden-state, 3072 feed-forward hidden-state, 8-heads,                                         |
-|                    |                                                            | | Trained on on 2.5 TB of newly created clean CommonCrawl data in 100 languages                                                       |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``xlm-roberta-large``                                      | | ~550M parameters with 24-layers, 1024-hidden-state, 4096 feed-forward hidden-state, 16-heads,                                       |
-|                    |                                                            | | Trained on 2.5 TB of newly created clean CommonCrawl data in 100 languages                                                          |
-+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| FlauBERT           | ``flaubert/flaubert_small_cased``                          | | 6-layer, 512-hidden, 8-heads, 54M parameters                                                                                        |
-|                    |                                                            | | FlauBERT small architecture                                                                                                         |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/getalp/Flaubert>`__)                                                                                |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``flaubert/flaubert_base_uncased``                         | | 12-layer, 768-hidden, 12-heads, 137M parameters                                                                                     |
-|                    |                                                            | | FlauBERT base architecture with uncased vocabulary                                                                                  |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/getalp/Flaubert>`__)                                                                                |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``flaubert/flaubert_base_cased``                           | | 12-layer, 768-hidden, 12-heads, 138M parameters                                                                                     |
-|                    |                                                            | | FlauBERT base architecture with cased vocabulary                                                                                    |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/getalp/Flaubert>`__)                                                                                |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``flaubert/flaubert_large_cased``                          | | 24-layer, 1024-hidden, 16-heads, 373M parameters                                                                                    |
-|                    |                                                            | | FlauBERT large architecture                                                                                                         |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/getalp/Flaubert>`__)                                                                                |
-+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| Bart               | ``facebook/bart-large``                                    | | 24-layer, 1024-hidden, 16-heads, 406M parameters                                                                                    |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/bart>`_)                                                       |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``facebook/bart-base``                                     | | 12-layer, 768-hidden, 16-heads, 139M parameters                                                                                     |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``facebook/bart-large-mnli``                               | | Adds a 2 layer classification head with 1 million parameters                                                                        |
-|                    |                                                            | | bart-large base architecture with a classification head, finetuned on MNLI                                                          |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``facebook/bart-large-cnn``                                | | 24-layer, 1024-hidden, 16-heads, 406M parameters       (same as large)                                                              |
-|                    |                                                            | | bart-large base architecture finetuned on cnn summarization task                                                                    |
-+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| BARThez            | ``moussaKam/barthez``                                      | | 12-layer,  768-hidden, 12-heads, 216M parameters                                                                                    |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/moussaKam/BARThez>`__)                                                                              |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``moussaKam/mbarthez``                                     | | 24-layer, 1024-hidden, 16-heads, 561M parameters                                                                                    |
-+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| DialoGPT           | ``DialoGPT-small``                                         | | 12-layer, 768-hidden, 12-heads, 124M parameters                                                                                     |
-|                    |                                                            | | Trained on English text: 147M conversation-like exchanges extracted from Reddit.                                                    |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``DialoGPT-medium``                                        | | 24-layer, 1024-hidden, 16-heads, 355M parameters                                                                                    |
-|                    |                                                            | | Trained on English text: 147M conversation-like exchanges extracted from Reddit.                                                    |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``DialoGPT-large``                                         | | 36-layer, 1280-hidden, 20-heads, 774M parameters                                                                                    |
-|                    |                                                            | | Trained on English text: 147M conversation-like exchanges extracted from Reddit.                                                    |
-+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| Reformer           | ``reformer-enwik8``                                        | | 12-layer, 1024-hidden, 8-heads, 149M parameters                                                                                     |
-|                    |                                                            | | Trained on English Wikipedia data - enwik8.                                                                                         |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``reformer-crime-and-punishment``                          | | 6-layer, 256-hidden, 2-heads, 3M parameters                                                                                         |
-|                    |                                                            | | Trained on English text: Crime and Punishment novel by Fyodor Dostoyevsky.                                                          |
-+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| M2M100             | ``facebook/m2m100_418M``                                   | | 24-layer, 1024-hidden, 16-heads, 418M parameters                                                                                    |
-|                    |                                                            | | multilingual machine translation model for 100 languages                                                                            |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``facebook/m2m100_1.2B``                                   | | 48-layer, 1024-hidden, 16-heads, 1.2B parameters                                                                                    |
-|                    |                                                            | | multilingual machine translation model for 100 languages                                                                            |
-+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| MarianMT           | ``Helsinki-NLP/opus-mt-{src}-{tgt}``                       | | 12-layer, 512-hidden, 8-heads, ~74M parameter Machine translation models. Parameter counts vary depending on vocab size.            |
-|                    |                                                            | | (see `model list <https://huggingface.co/Helsinki-NLP>`_)                                                                           |
-+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| Pegasus            | ``google/pegasus-{dataset}``                               | | 16-layer, 1024-hidden, 16-heads, ~568M parameter, 2.2 GB for summary. `model list <https://huggingface.co/models?search=pegasus>`__ |
-+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| Longformer         | ``allenai/longformer-base-4096``                           | | 12-layer, 768-hidden, 12-heads, ~149M parameters                                                                                    |
-|                    |                                                            | | Starting from RoBERTa-base checkpoint, trained on documents of max length 4,096                                                     |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``allenai/longformer-large-4096``                          | | 24-layer, 1024-hidden, 16-heads, ~435M parameters                                                                                   |
-|                    |                                                            | | Starting from RoBERTa-large checkpoint, trained on documents of max length 4,096                                                    |
-+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| MBart              | ``facebook/mbart-large-cc25``                              | | 24-layer, 1024-hidden, 16-heads, 610M parameters                                                                                    |
-|                    |                                                            | | mBART (bart-large architecture) model trained on 25 languages' monolingual corpus                                                   |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``facebook/mbart-large-en-ro``                             | | 24-layer, 1024-hidden, 16-heads, 610M parameters                                                                                    |
-|                    |                                                            | | mbart-large-cc25 model finetuned on WMT english romanian translation.                                                               |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``facebook/mbart-large-50``                                | | 24-layer, 1024-hidden, 16-heads,                                                                                                    |
-|                    |                                                            | | mBART model trained on 50 languages' monolingual corpus.                                                                            |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``facebook/mbart-large-50-one-to-many-mmt``                | | 24-layer, 1024-hidden, 16-heads,                                                                                                    |
-|                    |                                                            | | mbart-50-large model finetuned for one (English) to many multilingual machine translation covering 50 languages.                    |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``facebook/mbart-large-50-many-to-many-mmt``               | | 24-layer, 1024-hidden, 16-heads,                                                                                                    |
-|                    |                                                            | | mbart-50-large model finetuned for many to many multilingual machine translation covering 50 languages.                             |
-+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| Lxmert             | ``lxmert-base-uncased``                                    | | 9-language layers, 9-relationship layers, and 12-cross-modality layers                                                              |
-|                    |                                                            | | 768-hidden, 12-heads (for each layer) ~ 228M parameters                                                                             |
-|                    |                                                            | | Starting from lxmert-base checkpoint, trained on over 9 million image-text couplets from COCO, VisualGenome, GQA, VQA               |
-+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| Funnel Transformer | ``funnel-transformer/small``                               | | 14 layers: 3 blocks of 4 layers then 2 layers decoder, 768-hidden, 12-heads, 130M parameters                                        |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/laiguokun/Funnel-Transformer>`__)                                                                   |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``funnel-transformer/small-base``                          | | 12 layers: 3 blocks of 4 layers (no decoder), 768-hidden, 12-heads, 115M parameters                                                 |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/laiguokun/Funnel-Transformer>`__)                                                                   |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``funnel-transformer/medium``                              | | 14 layers: 3 blocks 6, 3x2, 3x2 layers then 2 layers decoder, 768-hidden, 12-heads, 130M parameters                                 |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/laiguokun/Funnel-Transformer>`__)                                                                   |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``funnel-transformer/medium-base``                         | | 12 layers: 3 blocks 6, 3x2, 3x2 layers(no decoder), 768-hidden, 12-heads, 115M parameters                                           |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/laiguokun/Funnel-Transformer>`__)                                                                   |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``funnel-transformer/intermediate``                        | | 20 layers: 3 blocks of 6 layers then 2 layers decoder, 768-hidden, 12-heads, 177M parameters                                        |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/laiguokun/Funnel-Transformer>`__)                                                                   |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``funnel-transformer/intermediate-base``                   | | 18 layers: 3 blocks of 6 layers (no decoder), 768-hidden, 12-heads, 161M parameters                                                 |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/laiguokun/Funnel-Transformer>`__)                                                                   |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``funnel-transformer/large``                               | | 26 layers: 3 blocks of 8 layers then 2 layers decoder, 1024-hidden, 12-heads, 386M parameters                                       |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/laiguokun/Funnel-Transformer>`__)                                                                   |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``funnel-transformer/large-base``                          | | 24 layers: 3 blocks of 8 layers (no decoder), 1024-hidden, 12-heads, 358M parameters                                                |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/laiguokun/Funnel-Transformer>`__)                                                                   |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``funnel-transformer/xlarge``                              | | 32 layers: 3 blocks of 10 layers then 2 layers decoder, 1024-hidden, 12-heads, 468M parameters                                      |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/laiguokun/Funnel-Transformer>`__)                                                                   |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``funnel-transformer/xlarge-base``                         | | 30 layers: 3 blocks of 10 layers (no decoder), 1024-hidden, 12-heads, 440M parameters                                               |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/laiguokun/Funnel-Transformer>`__)                                                                   |
-+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| LayoutLM           | ``microsoft/layoutlm-base-uncased``                        | | 12 layers, 768-hidden, 12-heads, 113M parameters                                                                                    |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/microsoft/unilm/tree/master/layoutlm>`__)                                                           |
-+                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``microsoft/layoutlm-large-uncased``                       | | 24 layers, 1024-hidden, 16-heads, 343M parameters                                                                                   |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/microsoft/unilm/tree/master/layoutlm>`__)                                                           |
-+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| DeBERTa            | ``microsoft/deberta-base``                                 | | 12-layer, 768-hidden, 12-heads, ~140M parameters                                                                                    |
-|                    |                                                            | | DeBERTa using the BERT-base architecture                                                                                            |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/microsoft/DeBERTa>`__)                                                                              |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``microsoft/deberta-large``                                | | 24-layer, 1024-hidden, 16-heads, ~400M parameters                                                                                   |
-|                    |                                                            | | DeBERTa using the BERT-large architecture                                                                                           |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/microsoft/DeBERTa>`__)                                                                              |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``microsoft/deberta-xlarge``                               | | 48-layer, 1024-hidden, 16-heads, ~750M parameters                                                                                   |
-|                    |                                                            | | DeBERTa XLarge with similar BERT architecture                                                                                       |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/microsoft/DeBERTa>`__)                                                                              |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``microsoft/deberta-xlarge-v2``                            | | 24-layer, 1536-hidden, 24-heads, ~900M parameters                                                                                   |
-|                    |                                                            | | DeBERTa XLarge V2 with similar BERT architecture                                                                                    |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/microsoft/DeBERTa>`__)                                                                              |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``microsoft/deberta-xxlarge-v2``                           | | 48-layer, 1536-hidden, 24-heads, ~1.5B parameters                                                                                   |
-|                    |                                                            | | DeBERTa XXLarge V2 with similar BERT architecture                                                                                   |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/microsoft/DeBERTa>`__)                                                                              |
-+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| SqueezeBERT        | ``squeezebert/squeezebert-uncased``                        | | 12-layer, 768-hidden, 12-heads, 51M parameters, 4.3x faster than bert-base-uncased on a smartphone.                                 |
-|                    |                                                            | | SqueezeBERT architecture pretrained from scratch on masked language model (MLM) and sentence order prediction (SOP) tasks.          |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``squeezebert/squeezebert-mnli``                           | | 12-layer, 768-hidden, 12-heads, 51M parameters, 4.3x faster than bert-base-uncased on a smartphone.                                 |
-|                    |                                                            | | This is the squeezebert-uncased model finetuned on MNLI sentence pair classification task with distillation from electra-base.      |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``squeezebert/squeezebert-mnli-headless``                  | | 12-layer, 768-hidden, 12-heads, 51M parameters, 4.3x faster than bert-base-uncased on a smartphone.                                 |
-|                    |                                                            | | This is the squeezebert-uncased model finetuned on MNLI sentence pair classification task with distillation from electra-base.      |
-|                    |                                                            | | The final classification layer is removed, so when you finetune, the final layer will be reinitialized.                             |
-+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
--- a/docs/source/quicktour.mdx
+++ b/docs/source/quicktour.mdx
@@ -0,0 +1,428 @@
+<!--Copyright 2020 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# Quick tour
+
+Let's have a quick look at the 🤗 Transformers library features. The library downloads pretrained models for Natural
+Language Understanding (NLU) tasks, such as analyzing the sentiment of a text, and Natural Language Generation (NLG),
+such as completing a prompt with new text or translating in another language.
+
+First we will see how to easily leverage the pipeline API to quickly use those pretrained models at inference. Then, we
+will dig a little bit more and see how the library gives you access to those models and helps you preprocess your data.
+
+<Tip>
+
+All code examples presented in the documentation have a switch on the top left for Pytorch versus TensorFlow. If
+not, the code is expected to work for both backends without any change needed.
+
+</Tip>
+
+## Getting started on a task with a pipeline
+
+The easiest way to use a pretrained model on a given task is to use [`~transformers.pipeline`].
+
+<Youtube id="tiZFewofSLM"/>
+
+🤗 Transformers provides the following tasks out of the box:
+
+- Sentiment analysis: is a text positive or negative?
+- Text generation (in English): provide a prompt and the model will generate what follows.
+- Name entity recognition (NER): in an input sentence, label each word with the entity it represents (person, place, etc.)
+- Question answering: provide the model with some context and a question, extract the answer from the context.
+- Filling masked text: given a text with masked words (e.g., replaced by `[MASK]`), fill the blanks.
+- Summarization: generate a summary of a long text.
+- Translation: translate a text in another language.
+- Feature extraction: return a tensor representation of the text.
+
+Let's see how this work for sentiment analysis (the other tasks are all covered in the [task summary](task_summary)):
+
+Install the following dependencies (if not already installed):
+
+```bash
+pip install torch
+===PT-TF-SPLIT===
+pip install tensorflow
+```
+
+```py
+>>> from transformers import pipeline
+>>> classifier = pipeline('sentiment-analysis')
+```
+
+When typing this command for the first time, a pretrained model and its tokenizer are downloaded and cached. We will
+look at both later on, but as an introduction the tokenizer's job is to preprocess the text for the model, which is
+then responsible for making predictions. The pipeline groups all of that together, and post-process the predictions to
+make them readable. For instance:
+
+
+```py
+>>> classifier('We are very happy to show you the 🤗 Transformers library.')
+[{'label': 'POSITIVE', 'score': 0.9998}]
+```
+
+That's encouraging! You can use it on a list of sentences, which will be preprocessed then fed to the model, returning
+a list of dictionaries like this one:
+
+```py
+>>> results = classifier(["We are very happy to show you the 🤗 Transformers library.",
+...            "We hope you don't hate it."])
+>>> for result in results:
+...     print(f"label: {result['label']}, with score: {round(result['score'], 4)}")
+label: POSITIVE, with score: 0.9998
+label: NEGATIVE, with score: 0.5309
+```
+
+To use with a large dataset, look at [iterating over a pipeline](main_classes/pipelines)
+
+You can see the second sentence has been classified as negative (it needs to be positive or negative) but its score is
+fairly neutral.
+
+By default, the model downloaded for this pipeline is called "distilbert-base-uncased-finetuned-sst-2-english". We can
+look at its [model page](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) to get more
+information about it. It uses the [DistilBERT architecture](model_doc/distilbert) and has been fine-tuned on a
+dataset called SST-2 for the sentiment analysis task.
+
+Let's say we want to use another model; for instance, one that has been trained on French data. We can search through
+the [model hub](https://huggingface.co/models) that gathers models pretrained on a lot of data by research labs, but
+also community models (usually fine-tuned versions of those big models on a specific dataset). Applying the tags
+"French" and "text-classification" gives back a suggestion "nlptown/bert-base-multilingual-uncased-sentiment". Let's
+see how we can use it.
+
+You can directly pass the name of the model to use to [`pipeline`]:
+
+```py
+>>> classifier = pipeline('sentiment-analysis', model="nlptown/bert-base-multilingual-uncased-sentiment")
+```
+
+This classifier can now deal with texts in English, French, but also Dutch, German, Italian and Spanish! You can also
+replace that name by a local folder where you have saved a pretrained model (see below). You can also pass a model
+object and its associated tokenizer.
+
+We will need two classes for this. The first is [`AutoTokenizer`], which we will use to download the tokenizer associated to the model we picked and instantiate it. The second is [`AutoModelForSequenceClassification`] (or [`TFAutoModelForSequenceClassification`] if you are using TensorFlow), which we will use to download the model itself. Note that if we were using the library on an other task, the class of the model would change. The [task summary](task_summary) tutorial summarizes which class is used for which task.
+
+```py
+>>> from transformers import AutoTokenizer, AutoModelForSequenceClassification
+===PT-TF-SPLIT===
+>>> from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
+```
+
+Now, to download the models and tokenizer we found previously, we just have to use the
+[`~transformers.AutoModelForSequenceClassification.from_pretrained`] method (feel free to replace `model_name` by
+any other model from the model hub):
+
+```py
+>>> model_name = "nlptown/bert-base-multilingual-uncased-sentiment"
+>>> model = AutoModelForSequenceClassification.from_pretrained(model_name)
+>>> tokenizer = AutoTokenizer.from_pretrained(model_name)
+>>> classifier = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
+===PT-TF-SPLIT===
+>>> model_name = "nlptown/bert-base-multilingual-uncased-sentiment"
+>>> # This model only exists in PyTorch, so we use the _from_pt_ flag to import that model in TensorFlow.
+>>> model = TFAutoModelForSequenceClassification.from_pretrained(model_name, from_pt=True)
+>>> tokenizer = AutoTokenizer.from_pretrained(model_name)
+>>> classifier = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
+```
+
+If you don't find a model that has been pretrained on some data similar to yours, you will need to fine-tune a
+pretrained model on your data. We provide [example scripts](examples) to do so. Once you're done, don't forget
+to share your fine-tuned model on the hub with the community, using [this tutorial](model_sharing).
+
+<a id='pretrained-model'></a>
+
+## Under the hood: pretrained models
+
+Let's now see what happens beneath the hood when using those pipelines.
+
+<Youtube id="AhChOFRegn4"/>
+
+As we saw, the model and tokenizer are created using the `from_pretrained` method:
+
+```py
+>>> from transformers import AutoTokenizer, AutoModelForSequenceClassification
+>>> model_name = "distilbert-base-uncased-finetuned-sst-2-english"
+>>> pt_model = AutoModelForSequenceClassification.from_pretrained(model_name)
+>>> tokenizer = AutoTokenizer.from_pretrained(model_name)
+===PT-TF-SPLIT===
+>>> from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
+>>> model_name = "distilbert-base-uncased-finetuned-sst-2-english"
+>>> tf_model = TFAutoModelForSequenceClassification.from_pretrained(model_name)
+>>> tokenizer = AutoTokenizer.from_pretrained(model_name)
+```
+
+### Using the tokenizer
+
+We mentioned the tokenizer is responsible for the preprocessing of your texts. First, it will split a given text in
+words (or part of words, punctuation symbols, etc.) usually called _tokens_. There are multiple rules that can govern
+that process (you can learn more about them in the [tokenizer summary](tokenizer_summary)), which is why we need
+to instantiate the tokenizer using the name of the model, to make sure we use the same rules as when the model was
+pretrained.
+
+The second step is to convert those _tokens_ into numbers, to be able to build a tensor out of them and feed them to
+the model. To do this, the tokenizer has a _vocab_, which is the part we download when we instantiate it with the
+`from_pretrained` method, since we need to use the same _vocab_ as when the model was pretrained.
+
+To apply these steps on a given text, we can just feed it to our tokenizer:
+
+```py
+>>> inputs = tokenizer("We are very happy to show you the 🤗 Transformers library.")
+```
+
+This returns a dictionary string to list of ints. It contains the [ids of the tokens](glossary#input-ids), as
+mentioned before, but also additional arguments that will be useful to the model. Here for instance, we also have an
+[attention mask](glossary#attention-mask) that the model will use to have a better understanding of the sequence:
+
+
+```py
+>>> print(inputs)
+{'input_ids': [101, 2057, 2024, 2200, 3407, 2000, 2265, 2017, 1996, 100, 19081, 3075, 1012, 102],
+ 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
+```
+
+You can pass a list of sentences directly to your tokenizer. If your goal is to send them through your model as a
+batch, you probably want to pad them all to the same length, truncate them to the maximum length the model can accept
+and get tensors back. You can specify all of that to the tokenizer:
+
+```py
+>>> pt_batch = tokenizer(
+...     ["We are very happy to show you the 🤗 Transformers library.", "We hope you don't hate it."],
+...     padding=True,
+...     truncation=True,
+...     max_length=512,
+...     return_tensors="pt"
+... )
+===PT-TF-SPLIT===
+>>> tf_batch = tokenizer(
+...     ["We are very happy to show you the 🤗 Transformers library.", "We hope you don't hate it."],
+...     padding=True,
+...     truncation=True,
+...     max_length=512,
+...     return_tensors="tf"
+... )
+```
+
+The padding is automatically applied on the side expected by the model (in this case, on the right), with the padding
+token the model was pretrained with. The attention mask is also adapted to take the padding into account:
+
+```py
+>>> for key, value in pt_batch.items():
+...     print(f"{key}: {value.numpy().tolist()}")
+input_ids: [[101, 2057, 2024, 2200, 3407, 2000, 2265, 2017, 1996, 100, 19081, 3075, 1012, 102], [101, 2057, 3246, 2017, 2123, 1005, 1056, 5223, 2009, 1012, 102, 0, 0, 0]]
+attention_mask: [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0]]
+===PT-TF-SPLIT===
+>>> for key, value in tf_batch.items():
+...     print(f"{key}: {value.numpy().tolist()}")
+input_ids: [[101, 2057, 2024, 2200, 3407, 2000, 2265, 2017, 1996, 100, 19081, 3075, 1012, 102], [101, 2057, 3246, 2017, 2123, 1005, 1056, 5223, 2009, 1012, 102, 0, 0, 0]]
+attention_mask: [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0]]
+```
+
+You can learn more about tokenizers [here](preprocessing).
+
+### Using the model
+
+Once your input has been preprocessed by the tokenizer, you can send it directly to the model. As we mentioned, it will
+contain all the relevant information the model needs. If you're using a TensorFlow model, you can pass the dictionary
+keys directly to tensors, for a PyTorch model, you need to unpack the dictionary by adding `**`.
+
+```py
+>>> pt_outputs = pt_model(**pt_batch)
+===PT-TF-SPLIT===
+>>> tf_outputs = tf_model(tf_batch)
+```
+
+In 🤗 Transformers, all outputs are objects that contain the model's final activations along with other metadata. These
+objects are described in greater detail [here](main_classes/output). For now, let's inspect the output ourselves:
+
+```py
+>>> print(pt_outputs)
+SequenceClassifierOutput(loss=None, logits=tensor([[-4.0833,  4.3364],
+        [ 0.0818, -0.0418]], grad_fn=&amp;lt;AddmmBackward>), hidden_states=None, attentions=None)
+===PT-TF-SPLIT===
+>>> print(tf_outputs)
+TFSequenceClassifierOutput(loss=None, logits=&amp;lt;tf.Tensor: shape=(2, 2), dtype=float32, numpy=
+array([[-4.0833 ,  4.3364  ],
+       [ 0.0818, -0.0418]], dtype=float32)>, hidden_states=None, attentions=None)
+```
+
+Notice how the output object has a `logits` attribute. You can use this to access the model's final activations.
+
+<Tip>
+
+All 🤗 Transformers models (PyTorch or TensorFlow) return the activations of the model *before* the final activation
+function (like SoftMax) since this final activation function is often fused with the loss.
+
+</Tip>
+
+Let's apply the SoftMax activation to get predictions.
+
+```py
+>>> from torch import nn
+>>> pt_predictions = nn.functional.softmax(pt_outputs.logits, dim=-1)
+===PT-TF-SPLIT===
+>>> import tensorflow as tf
+>>> tf_predictions = tf.nn.softmax(tf_outputs.logits, axis=-1)
+```
+
+We can see we get the numbers from before:
+
+```py
+>>> print(pt_predictions)
+tensor([[2.2043e-04, 9.9978e-01],
+        [5.3086e-01, 4.6914e-01]], grad_fn=&amp;lt;SoftmaxBackward>)
+===PT-TF-SPLIT===
+>>> print(tf_predictions)
+tf.Tensor(
+[[2.2043e-04 9.9978e-01]
+ [5.3086e-01 4.6914e-01]], shape=(2, 2), dtype=float32)
+```
+
+If you provide the model with labels in addition to inputs, the model output object will also contain a `loss`
+attribute:
+
+```py
+>>> import torch
+>>> pt_outputs = pt_model(**pt_batch, labels = torch.tensor([1, 0]))
+>>> print(pt_outputs)
+SequenceClassifierOutput(loss=tensor(0.3167, grad_fn=&amp;lt;NllLossBackward>), logits=tensor([[-4.0833,  4.3364],
+        [ 0.0818, -0.0418]], grad_fn=&amp;lt;AddmmBackward>), hidden_states=None, attentions=None)
+===PT-TF-SPLIT===
+>>> import tensorflow as tf
+>>> tf_outputs = tf_model(tf_batch, labels = tf.constant([1, 0]))
+>>> print(tf_outputs)
+TFSequenceClassifierOutput(loss=&amp;lt;tf.Tensor: shape=(2,), dtype=float32, numpy=array([2.2051e-04, 6.3326e-01], dtype=float32)>, logits=&amp;lt;tf.Tensor: shape=(2, 2), dtype=float32, numpy=
+array([[-4.0833 ,  4.3364  ],
+       [ 0.0818, -0.0418]], dtype=float32)>, hidden_states=None, attentions=None)
+```
+
+Models are standard [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) or [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) so you can use them in your usual training loop. 🤗 Transformers also provides a [`Trainer`] class to help with your training in PyTorch (taking care of things such as distributed training, mixed precision, etc.) whereas you can leverage the `fit()` method in Keras. See the [training tutorial](training) for more details.
+
+<Tip>
+
+Pytorch model outputs are special dataclasses so that you can get autocompletion for their attributes in an IDE.
+They also behave like a tuple or a dictionary (e.g., you can index with an integer, a slice or a string) in which
+case the attributes not set (that have `None` values) are ignored.
+
+</Tip>
+
+Once your model is fine-tuned, you can save it with its tokenizer in the following way:
+
+```py
+>>> pt_save_directory = './pt_save_pretrained'
+>>> tokenizer.save_pretrained(pt_save_directory)
+>>> pt_model.save_pretrained(pt_save_directory)
+===PT-TF-SPLIT===
+>>> tf_save_directory = './tf_save_pretrained'
+>>> tokenizer.save_pretrained(tf_save_directory)
+>>> tf_model.save_pretrained(tf_save_directory)
+```
+
+You can then load this model back using the [`AutoModel.from_pretrained`] method by passing the
+directory name instead of the model name. One cool feature of 🤗 Transformers is that you can easily switch between
+PyTorch and TensorFlow: any model saved as before can be loaded back either in PyTorch or TensorFlow.
+
+
+If you would like to load your saved model in the other framework, first make sure it is installed:
+
+```bash
+pip install tensorflow
+===PT-TF-SPLIT===
+pip install torch
+```
+
+Then, use the corresponding Auto class to load it like this:
+
+```py
+>>> from transformers import TFAutoModel
+>>> tokenizer = AutoTokenizer.from_pretrained(pt_save_directory)
+>>> tf_model = TFAutoModel.from_pretrained(pt_save_directory, from_pt=True)
+===PT-TF-SPLIT===
+>>> from transformers import AutoModel
+>>> tokenizer = AutoTokenizer.from_pretrained(tf_save_directory)
+>>> pt_model = AutoModel.from_pretrained(tf_save_directory, from_tf=True)
+```
+
+Lastly, you can also ask the model to return all hidden states and all attention weights if you need them:
+
+
+```py
+>>> pt_outputs = pt_model(**pt_batch, output_hidden_states=True, output_attentions=True)
+>>> all_hidden_states  = pt_outputs.hidden_states 
+>>> all_attentions = pt_outputs.attentions
+===PT-TF-SPLIT===
+>>> tf_outputs = tf_model(tf_batch, output_hidden_states=True, output_attentions=True)
+>>> all_hidden_states =  tf_outputs.hidden_states
+>>> all_attentions = tf_outputs.attentions
+```
+
+### Accessing the code
+
+The [`AutoModel`] and [`AutoTokenizer`] classes are just shortcuts that will automatically work with any
+pretrained model. Behind the scenes, the library has one model class per combination of architecture plus class, so the
+code is easy to access and tweak if you need to.
+
+In our previous example, the model was called "distilbert-base-uncased-finetuned-sst-2-english", which means it's using
+the [DistilBERT](model_doc/distilbert) architecture. As [`AutoModelForSequenceClassification`] (or [`TFAutoModelForSequenceClassification`] if you are using TensorFlow) was used, the model automatically created is then a [`DistilBertForSequenceClassification`]. You can look at its documentation for all details relevant to that specific model, or browse the source code. This is how you would
+directly instantiate model and tokenizer without the auto magic:
+
+```py
+>>> from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
+>>> model_name = "distilbert-base-uncased-finetuned-sst-2-english"
+>>> model = DistilBertForSequenceClassification.from_pretrained(model_name)
+>>> tokenizer = DistilBertTokenizer.from_pretrained(model_name)
+===PT-TF-SPLIT===
+>>> from transformers import DistilBertTokenizer, TFDistilBertForSequenceClassification
+>>> model_name = "distilbert-base-uncased-finetuned-sst-2-english"
+>>> model = TFDistilBertForSequenceClassification.from_pretrained(model_name)
+>>> tokenizer = DistilBertTokenizer.from_pretrained(model_name)
+```
+
+### Customizing the model
+
+If you want to change how the model itself is built, you can define a custom configuration class. Each architecture
+comes with its own relevant configuration. For example, [`DistilBertConfig`] allows you to specify
+parameters such as the hidden dimension, dropout rate, etc for DistilBERT. If you do core modifications, like changing
+the hidden size, you won't be able to use a pretrained model anymore and will need to train from scratch. You would
+then instantiate the model directly from this configuration.
+
+Below, we load a predefined vocabulary for a tokenizer with the
+[`~DistilBertTokenizer.from_pretrained`] method. However, unlike the tokenizer, we wish to initialize
+the model from scratch. Therefore, we instantiate the model from a configuration instead of using the
+[`DistilBertForSequenceClassification.from_pretrained`] method.
+
+```py
+>>> from transformers import DistilBertConfig, DistilBertTokenizer, DistilBertForSequenceClassification
+>>> config = DistilBertConfig(n_heads=8, dim=512, hidden_dim=4*512)
+>>> tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
+>>> model = DistilBertForSequenceClassification(config)
+===PT-TF-SPLIT===
+>>> from transformers import DistilBertConfig, DistilBertTokenizer, TFDistilBertForSequenceClassification
+>>> config = DistilBertConfig(n_heads=8, dim=512, hidden_dim=4*512)
+>>> tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
+>>> model = TFDistilBertForSequenceClassification(config)
+```
+
+For something that only changes the head of the model (for instance, the number of labels), you can still use a
+pretrained model for the body. For instance, let's define a classifier for 10 different labels using a pretrained body.
+Instead of creating a new configuration with all the default values just to change the number of labels, we can instead
+pass any argument a configuration would take to the [`from_pretrained`] method and it will update the default
+configuration appropriately:
+
+```py
+>>> from transformers import DistilBertConfig, DistilBertTokenizer, DistilBertForSequenceClassification
+>>> model_name = "distilbert-base-uncased"
+>>> model = DistilBertForSequenceClassification.from_pretrained(model_name, num_labels=10)
+>>> tokenizer = DistilBertTokenizer.from_pretrained(model_name)
+===PT-TF-SPLIT===
+>>> from transformers import DistilBertConfig, DistilBertTokenizer, TFDistilBertForSequenceClassification
+>>> model_name = "distilbert-base-uncased"
+>>> model = TFDistilBertForSequenceClassification.from_pretrained(model_name, num_labels=10)
+>>> tokenizer = DistilBertTokenizer.from_pretrained(model_name)
+```
--- a/docs/source/quicktour.rst
+++ b/docs/source/quicktour.rst
@@ -1,448 +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.
-
-Quick tour
-=======================================================================================================================
-
-Let's have a quick look at the 🤗 Transformers library features. The library downloads pretrained models for Natural
-Language Understanding (NLU) tasks, such as analyzing the sentiment of a text, and Natural Language Generation (NLG),
-such as completing a prompt with new text or translating in another language.
-
-First we will see how to easily leverage the pipeline API to quickly use those pretrained models at inference. Then, we
-will dig a little bit more and see how the library gives you access to those models and helps you preprocess your data.
-
-.. note::
-
-    All code examples presented in the documentation have a switch on the top left for Pytorch versus TensorFlow. If
-    not, the code is expected to work for both backends without any change needed.
-
-Getting started on a task with a pipeline
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The easiest way to use a pretrained model on a given task is to use :func:`~transformers.pipeline`.
-
-.. raw:: html
-
-   <iframe width="560" height="315" src="https://www.youtube.com/embed/tiZFewofSLM" title="YouTube video player"
-   frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope;
-   picture-in-picture" allowfullscreen></iframe>
-
-🤗 Transformers provides the following tasks out of the box:
-
- Sentiment analysis: is a text positive or negative?
- Text generation (in English): provide a prompt and the model will generate what follows.
- Name entity recognition (NER): in an input sentence, label each word with the entity it represents (person, place,
-  etc.)
- Question answering: provide the model with some context and a question, extract the answer from the context.
- Filling masked text: given a text with masked words (e.g., replaced by ``[MASK]``), fill the blanks.
- Summarization: generate a summary of a long text.
- Translation: translate a text in another language.
- Feature extraction: return a tensor representation of the text.
-
-Let's see how this work for sentiment analysis (the other tasks are all covered in the :doc:`task summary
-</task_summary>`):
-
-.. code-block::
-
-    >>> from transformers import pipeline
-    >>> classifier = pipeline('sentiment-analysis')
-
-When typing this command for the first time, a pretrained model and its tokenizer are downloaded and cached. We will
-look at both later on, but as an introduction the tokenizer's job is to preprocess the text for the model, which is
-then responsible for making predictions. The pipeline groups all of that together, and post-process the predictions to
-make them readable. For instance:
-
-
-.. code-block::
-
-    >>> classifier('We are very happy to show you the 🤗 Transformers library.')
-    [{'label': 'POSITIVE', 'score': 0.9998}]
-
-That's encouraging! You can use it on a list of sentences, which will be preprocessed then fed to the model, returning
-a list of dictionaries like this one:
-
-.. code-block::
-
-    >>> results = classifier(["We are very happy to show you the 🤗 Transformers library.",
-    ...            "We hope you don't hate it."])
-    >>> for result in results:
-    ...     print(f"label: {result['label']}, with score: {round(result['score'], 4)}")
-    label: POSITIVE, with score: 0.9998
-    label: NEGATIVE, with score: 0.5309
-
-To use with a large dataset, look at :doc:`iterating over a pipeline <./main_classes/pipelines>`
-
-You can see the second sentence has been classified as negative (it needs to be positive or negative) but its score is
-fairly neutral.
-
-By default, the model downloaded for this pipeline is called "distilbert-base-uncased-finetuned-sst-2-english". We can
-look at its `model page <https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english>`__ to get more
-information about it. It uses the :doc:`DistilBERT architecture </model_doc/distilbert>` and has been fine-tuned on a
-dataset called SST-2 for the sentiment analysis task.
-
-Let's say we want to use another model; for instance, one that has been trained on French data. We can search through
-the `model hub <https://huggingface.co/models>`__ that gathers models pretrained on a lot of data by research labs, but
-also community models (usually fine-tuned versions of those big models on a specific dataset). Applying the tags
-"French" and "text-classification" gives back a suggestion "nlptown/bert-base-multilingual-uncased-sentiment". Let's
-see how we can use it.
-
-You can directly pass the name of the model to use to :func:`~transformers.pipeline`:
-
-.. code-block::
-
-    >>> classifier = pipeline('sentiment-analysis', model="nlptown/bert-base-multilingual-uncased-sentiment")
-
-This classifier can now deal with texts in English, French, but also Dutch, German, Italian and Spanish! You can also
-replace that name by a local folder where you have saved a pretrained model (see below). You can also pass a model
-object and its associated tokenizer.
-
-We will need two classes for this. The first is :class:`~transformers.AutoTokenizer`, which we will use to download the
-tokenizer associated to the model we picked and instantiate it. The second is
-:class:`~transformers.AutoModelForSequenceClassification` (or
-:class:`~transformers.TFAutoModelForSequenceClassification` if you are using TensorFlow), which we will use to download
-the model itself. Note that if we were using the library on an other task, the class of the model would change. The
-:doc:`task summary </task_summary>` tutorial summarizes which class is used for which task.
-
-.. code-block::
-
-    >>> ## PYTORCH CODE
-    >>> from transformers import AutoTokenizer, AutoModelForSequenceClassification
-    >>> ## TENSORFLOW CODE
-    >>> from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
-
-Now, to download the models and tokenizer we found previously, we just have to use the
-:func:`~transformers.AutoModelForSequenceClassification.from_pretrained` method (feel free to replace ``model_name`` by
-any other model from the model hub):
-
-.. code-block::
-
-    >>> ## PYTORCH CODE
-    >>> model_name = "nlptown/bert-base-multilingual-uncased-sentiment"
-    >>> model = AutoModelForSequenceClassification.from_pretrained(model_name)
-    >>> tokenizer = AutoTokenizer.from_pretrained(model_name)
-    >>> classifier = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
-    >>> ## TENSORFLOW CODE
-    >>> model_name = "nlptown/bert-base-multilingual-uncased-sentiment"
-    >>> # This model only exists in PyTorch, so we use the `from_pt` flag to import that model in TensorFlow.
-    >>> model = TFAutoModelForSequenceClassification.from_pretrained(model_name, from_pt=True)
-    >>> tokenizer = AutoTokenizer.from_pretrained(model_name)
-    >>> classifier = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
-
-If you don't find a model that has been pretrained on some data similar to yours, you will need to fine-tune a
-pretrained model on your data. We provide :doc:`example scripts </examples>` to do so. Once you're done, don't forget
-to share your fine-tuned model on the hub with the community, using :doc:`this tutorial </model_sharing>`.
-
-.. _pretrained-model:
-
-Under the hood: pretrained models
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Let's now see what happens beneath the hood when using those pipelines.
-
-.. raw:: html
-
-   <iframe width="560" height="315" src="https://www.youtube.com/embed/AhChOFRegn4" title="YouTube video player"
-   frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope;
-   picture-in-picture" allowfullscreen></iframe>
-
-As we saw, the model and tokenizer are created using the :obj:`from_pretrained` method:
-
-.. code-block::
-
-    >>> ## PYTORCH CODE
-    >>> from transformers import AutoTokenizer, AutoModelForSequenceClassification
-    >>> model_name = "distilbert-base-uncased-finetuned-sst-2-english"
-    >>> pt_model = AutoModelForSequenceClassification.from_pretrained(model_name)
-    >>> tokenizer = AutoTokenizer.from_pretrained(model_name)
-    >>> ## TENSORFLOW CODE
-    >>> from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
-    >>> model_name = "distilbert-base-uncased-finetuned-sst-2-english"
-    >>> tf_model = TFAutoModelForSequenceClassification.from_pretrained(model_name)
-    >>> tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-Using the tokenizer
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-We mentioned the tokenizer is responsible for the preprocessing of your texts. First, it will split a given text in
-words (or part of words, punctuation symbols, etc.) usually called `tokens`. There are multiple rules that can govern
-that process (you can learn more about them in the :doc:`tokenizer summary <tokenizer_summary>`), which is why we need
-to instantiate the tokenizer using the name of the model, to make sure we use the same rules as when the model was
-pretrained.
-
-The second step is to convert those `tokens` into numbers, to be able to build a tensor out of them and feed them to
-the model. To do this, the tokenizer has a `vocab`, which is the part we download when we instantiate it with the
-:obj:`from_pretrained` method, since we need to use the same `vocab` as when the model was pretrained.
-
-To apply these steps on a given text, we can just feed it to our tokenizer:
-
-.. code-block::
-
-    >>> inputs = tokenizer("We are very happy to show you the 🤗 Transformers library.")
-
-This returns a dictionary string to list of ints. It contains the `ids of the tokens <glossary.html#input-ids>`__, as
-mentioned before, but also additional arguments that will be useful to the model. Here for instance, we also have an
-`attention mask <glossary.html#attention-mask>`__ that the model will use to have a better understanding of the
-sequence:
-
-
-.. code-block::
-
-    >>> print(inputs)
-    {'input_ids': [101, 2057, 2024, 2200, 3407, 2000, 2265, 2017, 1996, 100, 19081, 3075, 1012, 102],
-     'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
-
-You can pass a list of sentences directly to your tokenizer. If your goal is to send them through your model as a
-batch, you probably want to pad them all to the same length, truncate them to the maximum length the model can accept
-and get tensors back. You can specify all of that to the tokenizer:
-
-.. code-block::
-
-    >>> ## PYTORCH CODE
-    >>> pt_batch = tokenizer(
-    ...     ["We are very happy to show you the 🤗 Transformers library.", "We hope you don't hate it."],
-    ...     padding=True,
-    ...     truncation=True,
-    ...     max_length=512,
-    ...     return_tensors="pt"
-    ... )
-    >>> ## TENSORFLOW CODE
-    >>> tf_batch = tokenizer(
-    ...     ["We are very happy to show you the 🤗 Transformers library.", "We hope you don't hate it."],
-    ...     padding=True,
-    ...     truncation=True,
-    ...     max_length=512,
-    ...     return_tensors="tf"
-    ... )
-
-The padding is automatically applied on the side expected by the model (in this case, on the right), with the padding
-token the model was pretrained with. The attention mask is also adapted to take the padding into account:
-
-.. code-block::
-
-    >>> ## PYTORCH CODE
-    >>> for key, value in pt_batch.items():
-    ...     print(f"{key}: {value.numpy().tolist()}")
-    input_ids: [[101, 2057, 2024, 2200, 3407, 2000, 2265, 2017, 1996, 100, 19081, 3075, 1012, 102], [101, 2057, 3246, 2017, 2123, 1005, 1056, 5223, 2009, 1012, 102, 0, 0, 0]]
-    attention_mask: [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0]]
-    >>> ## TENSORFLOW CODE
-    >>> for key, value in tf_batch.items():
-    ...     print(f"{key}: {value.numpy().tolist()}")
-    input_ids: [[101, 2057, 2024, 2200, 3407, 2000, 2265, 2017, 1996, 100, 19081, 3075, 1012, 102], [101, 2057, 3246, 2017, 2123, 1005, 1056, 5223, 2009, 1012, 102, 0, 0, 0]]
-    attention_mask: [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0]]
-
-You can learn more about tokenizers :doc:`here <preprocessing>`.
-
-Using the model
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Once your input has been preprocessed by the tokenizer, you can send it directly to the model. As we mentioned, it will
-contain all the relevant information the model needs. If you're using a TensorFlow model, you can pass the dictionary
-keys directly to tensors, for a PyTorch model, you need to unpack the dictionary by adding :obj:`**`.
-
-.. code-block::
-
-    >>> ## PYTORCH CODE
-    >>> pt_outputs = pt_model(**pt_batch)
-    >>> ## TENSORFLOW CODE
-    >>> tf_outputs = tf_model(tf_batch)
-
-In 🤗 Transformers, all outputs are objects that contain the model's final activations along with other metadata. These
-objects are described in greater detail :doc:`here <main_classes/output>`. For now, let's inspect the output ourselves:
-
-.. code-block::
-
-    >>> ## PYTORCH CODE
-    >>> print(pt_outputs)
-    SequenceClassifierOutput(loss=None, logits=tensor([[-4.0833,  4.3364],
-            [ 0.0818, -0.0418]], grad_fn=<AddmmBackward>), hidden_states=None, attentions=None)
-    >>> ## TENSORFLOW CODE
-    >>> print(tf_outputs)
-    TFSequenceClassifierOutput(loss=None, logits=<tf.Tensor: shape=(2, 2), dtype=float32, numpy=
-    array([[-4.0833 ,  4.3364  ],
-           [ 0.0818, -0.0418]], dtype=float32)>, hidden_states=None, attentions=None)
-
-Notice how the output object has a ``logits`` attribute. You can use this to access the model's final activations.
-
-.. note::
-
-    All 🤗 Transformers models (PyTorch or TensorFlow) return the activations of the model *before* the final activation
-    function (like SoftMax) since this final activation function is often fused with the loss.
-
-Let's apply the SoftMax activation to get predictions.
-
-.. code-block::
-
-    >>> ## PYTORCH CODE
-    >>> from torch import nn
-    >>> pt_predictions = nn.functional.softmax(pt_outputs.logits, dim=-1)
-    >>> ## TENSORFLOW CODE
-    >>> import tensorflow as tf
-    >>> tf_predictions = tf.nn.softmax(tf_outputs.logits, axis=-1)
-
-We can see we get the numbers from before:
-
-.. code-block::
-
-    >>> ## TENSORFLOW CODE
-    >>> print(tf_predictions)
-    tf.Tensor(
-    [[2.2043e-04 9.9978e-01]
-     [5.3086e-01 4.6914e-01]], shape=(2, 2), dtype=float32)
-    >>> ## PYTORCH CODE
-    >>> print(pt_predictions)
-    tensor([[2.2043e-04, 9.9978e-01],
-            [5.3086e-01, 4.6914e-01]], grad_fn=<SoftmaxBackward>)
-
-If you provide the model with labels in addition to inputs, the model output object will also contain a ``loss``
-attribute:
-
-.. code-block::
-
-    >>> ## PYTORCH CODE
-    >>> import torch
-    >>> pt_outputs = pt_model(**pt_batch, labels = torch.tensor([1, 0]))
-    >>> print(pt_outputs)
-    SequenceClassifierOutput(loss=tensor(0.3167, grad_fn=<NllLossBackward>), logits=tensor([[-4.0833,  4.3364],
-            [ 0.0818, -0.0418]], grad_fn=<AddmmBackward>), hidden_states=None, attentions=None)
-    >>> ## TENSORFLOW CODE
-    >>> import tensorflow as tf
-    >>> tf_outputs = tf_model(tf_batch, labels = tf.constant([1, 0]))
-    >>> print(tf_outputs)
-    TFSequenceClassifierOutput(loss=<tf.Tensor: shape=(2,), dtype=float32, numpy=array([2.2051e-04, 6.3326e-01], dtype=float32)>, logits=<tf.Tensor: shape=(2, 2), dtype=float32, numpy=
-    array([[-4.0833 ,  4.3364  ],
-           [ 0.0818, -0.0418]], dtype=float32)>, hidden_states=None, attentions=None)
-
-Models are standard `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__ or `tf.keras.Model
-<https://www.tensorflow.org/api_docs/python/tf/keras/Model>`__ so you can use them in your usual training loop. 🤗
-Transformers also provides a :class:`~transformers.Trainer` (or :class:`~transformers.TFTrainer` if you are using
-TensorFlow) class to help with your training (taking care of things such as distributed training, mixed precision,
-etc.). See the :doc:`training tutorial <training>` for more details.
-
-.. note::
-
-    Pytorch model outputs are special dataclasses so that you can get autocompletion for their attributes in an IDE.
-    They also behave like a tuple or a dictionary (e.g., you can index with an integer, a slice or a string) in which
-    case the attributes not set (that have :obj:`None` values) are ignored.
-
-Once your model is fine-tuned, you can save it with its tokenizer in the following way:
-
-.. code-block::
-
-    tokenizer.save_pretrained(save_directory)
-    model.save_pretrained(save_directory)
-
-You can then load this model back using the :func:`~transformers.AutoModel.from_pretrained` method by passing the
-directory name instead of the model name. One cool feature of 🤗 Transformers is that you can easily switch between
-PyTorch and TensorFlow: any model saved as before can be loaded back either in PyTorch or TensorFlow. If you are
-loading a saved PyTorch model in a TensorFlow model, use :func:`~transformers.TFAutoModel.from_pretrained` like this:
-
-.. code-block::
-
-    from transformers import TFAutoModel
-    tokenizer = AutoTokenizer.from_pretrained(save_directory)
-    model = TFAutoModel.from_pretrained(save_directory, from_pt=True)
-
-and if you are loading a saved TensorFlow model in a PyTorch model, you should use the following code:
-
-.. code-block::
-
-    from transformers import AutoModel
-    tokenizer = AutoTokenizer.from_pretrained(save_directory)
-    model = AutoModel.from_pretrained(save_directory, from_tf=True)
-
-Lastly, you can also ask the model to return all hidden states and all attention weights if you need them:
-
-
-.. code-block::
-
-    >>> ## PYTORCH CODE
-    >>> pt_outputs = pt_model(**pt_batch, output_hidden_states=True, output_attentions=True)
-    >>> all_hidden_states  = pt_outputs.hidden_states 
-    >>> all_attentions = pt_outputs.attentions
-    >>> ## TENSORFLOW CODE
-    >>> tf_outputs = tf_model(tf_batch, output_hidden_states=True, output_attentions=True)
-    >>> all_hidden_states =  tf_outputs.hidden_states
-    >>> all_attentions = tf_outputs.attentions
-
-Accessing the code
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-The :obj:`AutoModel` and :obj:`AutoTokenizer` classes are just shortcuts that will automatically work with any
-pretrained model. Behind the scenes, the library has one model class per combination of architecture plus class, so the
-code is easy to access and tweak if you need to.
-
-In our previous example, the model was called "distilbert-base-uncased-finetuned-sst-2-english", which means it's using
-the :doc:`DistilBERT </model_doc/distilbert>` architecture. As
-:class:`~transformers.AutoModelForSequenceClassification` (or
-:class:`~transformers.TFAutoModelForSequenceClassification` if you are using TensorFlow) was used, the model
-automatically created is then a :class:`~transformers.DistilBertForSequenceClassification`. You can look at its
-documentation for all details relevant to that specific model, or browse the source code. This is how you would
-directly instantiate model and tokenizer without the auto magic:
-
-.. code-block::
-
-    >>> ## PYTORCH CODE
-    >>> from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
-    >>> model_name = "distilbert-base-uncased-finetuned-sst-2-english"
-    >>> model = DistilBertForSequenceClassification.from_pretrained(model_name)
-    >>> tokenizer = DistilBertTokenizer.from_pretrained(model_name)
-    >>> ## TENSORFLOW CODE
-    >>> from transformers import DistilBertTokenizer, TFDistilBertForSequenceClassification
-    >>> model_name = "distilbert-base-uncased-finetuned-sst-2-english"
-    >>> model = TFDistilBertForSequenceClassification.from_pretrained(model_name)
-    >>> tokenizer = DistilBertTokenizer.from_pretrained(model_name)
-
-Customizing the model
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-If you want to change how the model itself is built, you can define a custom configuration class. Each architecture
-comes with its own relevant configuration. For example, :class:`~transformers.DistilBertConfig` allows you to specify
-parameters such as the hidden dimension, dropout rate, etc for DistilBERT. If you do core modifications, like changing
-the hidden size, you won't be able to use a pretrained model anymore and will need to train from scratch. You would
-then instantiate the model directly from this configuration.
-
-Below, we load a predefined vocabulary for a tokenizer with the
-:func:`~transformers.DistilBertTokenizer.from_pretrained` method. However, unlike the tokenizer, we wish to initialize
-the model from scratch. Therefore, we instantiate the model from a configuration instead of using the
-:func:`~transformers.DistilBertForSequenceClassification.from_pretrained` method.
-
-.. code-block::
-
-    >>> ## PYTORCH CODE
-    >>> from transformers import DistilBertConfig, DistilBertTokenizer, DistilBertForSequenceClassification
-    >>> config = DistilBertConfig(n_heads=8, dim=512, hidden_dim=4*512)
-    >>> tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
-    >>> model = DistilBertForSequenceClassification(config)
-    >>> ## TENSORFLOW CODE
-    >>> from transformers import DistilBertConfig, DistilBertTokenizer, TFDistilBertForSequenceClassification
-    >>> config = DistilBertConfig(n_heads=8, dim=512, hidden_dim=4*512)
-    >>> tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
-    >>> model = TFDistilBertForSequenceClassification(config)
-
-For something that only changes the head of the model (for instance, the number of labels), you can still use a
-pretrained model for the body. For instance, let's define a classifier for 10 different labels using a pretrained body.
-Instead of creating a new configuration with all the default values just to change the number of labels, we can instead
-pass any argument a configuration would take to the :func:`from_pretrained` method and it will update the default
-configuration appropriately:
-
-.. code-block::
-
-    >>> ## PYTORCH CODE
-    >>> from transformers import DistilBertConfig, DistilBertTokenizer, DistilBertForSequenceClassification
-    >>> model_name = "distilbert-base-uncased"
-    >>> model = DistilBertForSequenceClassification.from_pretrained(model_name, num_labels=10)
-    >>> tokenizer = DistilBertTokenizer.from_pretrained(model_name)
-    >>> ## TENSORFLOW CODE
-    >>> from transformers import DistilBertConfig, DistilBertTokenizer, TFDistilBertForSequenceClassification
-    >>> model_name = "distilbert-base-uncased"
-    >>> model = TFDistilBertForSequenceClassification.from_pretrained(model_name, num_labels=10)
-    >>> tokenizer = DistilBertTokenizer.from_pretrained(model_name)
--- a/docs/source/task_summary.mdx
+++ b/docs/source/task_summary.mdx
@@ -0,0 +1,886 @@
+<!--Copyright 2020 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# Summary of the tasks
+
+This page shows the most frequent use-cases when using the library. The models available allow for many different
+configurations and a great versatility in use-cases. The most simple ones are presented here, showcasing usage for
+tasks such as question answering, sequence classification, named entity recognition and others.
+
+These examples leverage auto-models, which are classes that will instantiate a model according to a given checkpoint,
+automatically selecting the correct model architecture. Please check the [`AutoModel`] documentation
+for more information. Feel free to modify the code to be more specific and adapt it to your specific use-case.
+
+In order for a model to perform well on a task, it must be loaded from a checkpoint corresponding to that task. These
+checkpoints are usually pre-trained on a large corpus of data and fine-tuned on a specific task. This means the
+following:
+
+- Not all models were fine-tuned on all tasks. If you want to fine-tune a model on a specific task, you can leverage
+  one of the *run_$TASK.py* scripts in the [examples](https://github.com/huggingface/transformers/tree/master/examples) directory.
+- Fine-tuned models were fine-tuned on a specific dataset. This dataset may or may not overlap with your use-case and
+  domain. As mentioned previously, you may leverage the [examples](https://github.com/huggingface/transformers/tree/master/examples) scripts to fine-tune your model, or you may
+  create your own training script.
+
+In order to do an inference on a task, several mechanisms are made available by the library:
+
+- Pipelines: very easy-to-use abstractions, which require as little as two lines of code.
+- Direct model use: Less abstractions, but more flexibility and power via a direct access to a tokenizer
+  (PyTorch/TensorFlow) and full inference capacity.
+
+Both approaches are showcased here.
+
+<Tip>
+
+All tasks presented here leverage pre-trained checkpoints that were fine-tuned on specific tasks. Loading a
+checkpoint that was not fine-tuned on a specific task would load only the base transformer layers and not the
+additional head that is used for the task, initializing the weights of that head randomly.
+
+This would produce random output.
+
+</Tip>
+
+## Sequence Classification
+
+Sequence classification is the task of classifying sequences according to a given number of classes. An example of
+sequence classification is the GLUE dataset, which is entirely based on that task. If you would like to fine-tune a
+model on a GLUE sequence classification task, you may leverage the [run_glue.py](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification/run_glue.py), [run_tf_glue.py](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/text-classification/run_tf_glue.py), [run_tf_text_classification.py](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/text-classification/run_tf_text_classification.py) or [run_xnli.py](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification/run_xnli.py) scripts.
+
+Here is an example of using pipelines to do sentiment analysis: identifying if a sequence is positive or negative. It
+leverages a fine-tuned model on sst2, which is a GLUE task.
+
+This returns a label ("POSITIVE" or "NEGATIVE") alongside a score, as follows:
+
+```py
+>>> from transformers import pipeline
+
+>>> classifier = pipeline("sentiment-analysis")
+
+>>> result = classifier("I hate you")[0]
+>>> print(f"label: {result['label']}, with score: {round(result['score'], 4)}")
+label: NEGATIVE, with score: 0.9991
+
+>>> result = classifier("I love you")[0]
+>>> print(f"label: {result['label']}, with score: {round(result['score'], 4)}")
+label: POSITIVE, with score: 0.9999
+```
+
+Here is an example of doing a sequence classification using a model to determine if two sequences are paraphrases of
+each other. The process is the following:
+
+1. Instantiate a tokenizer and a model from the checkpoint name. The model is identified as a BERT model and loads it
+   with the weights stored in the checkpoint.
+2. Build a sequence from the two sentences, with the correct model-specific separators, token type ids and attention
+   masks (which will be created automatically by the tokenizer).
+3. Pass this sequence through the model so that it is classified in one of the two available classes: 0 (not a
+   paraphrase) and 1 (is a paraphrase).
+4. Compute the softmax of the result to get probabilities over the classes.
+5. Print the results.
+
+```py
+>>> from transformers import AutoTokenizer, AutoModelForSequenceClassification
+>>> import torch
+
+>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-cased-finetuned-mrpc")
+>>> model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased-finetuned-mrpc")
+
+>>> classes = ["not paraphrase", "is paraphrase"]
+
+>>> sequence_0 = "The company HuggingFace is based in New York City"
+>>> sequence_1 = "Apples are especially bad for your health"
+>>> sequence_2 = "HuggingFace's headquarters are situated in Manhattan"
+
+>>> # The tokenizer will automatically add any model specific separators (i.e. <CLS> and <SEP>) and tokens to
+>>> # the sequence, as well as compute the attention masks.
+>>> paraphrase = tokenizer(sequence_0, sequence_2, return_tensors="pt")
+>>> not_paraphrase = tokenizer(sequence_0, sequence_1, return_tensors="pt")
+
+>>> paraphrase_classification_logits = model(**paraphrase).logits
+>>> not_paraphrase_classification_logits = model(**not_paraphrase).logits
+
+>>> paraphrase_results = torch.softmax(paraphrase_classification_logits, dim=1).tolist()[0]
+>>> not_paraphrase_results = torch.softmax(not_paraphrase_classification_logits, dim=1).tolist()[0]
+
+>>> # Should be paraphrase
+>>> for i in range(len(classes)):
+...     print(f"{classes[i]}: {int(round(paraphrase_results[i] * 100))}%")
+not paraphrase: 10%
+is paraphrase: 90%
+
+>>> # Should not be paraphrase
+>>> for i in range(len(classes)):
+...     print(f"{classes[i]}: {int(round(not_paraphrase_results[i] * 100))}%")
+not paraphrase: 94%
+is paraphrase: 6%
+===PT-TF-SPLIT===
+>>> from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
+>>> import tensorflow as tf
+
+>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-cased-finetuned-mrpc")
+>>> model = TFAutoModelForSequenceClassification.from_pretrained("bert-base-cased-finetuned-mrpc")
+
+>>> classes = ["not paraphrase", "is paraphrase"]
+
+>>> sequence_0 = "The company HuggingFace is based in New York City"
+>>> sequence_1 = "Apples are especially bad for your health"
+>>> sequence_2 = "HuggingFace's headquarters are situated in Manhattan"
+
+>>> # The tokenizer will automatically add any model specific separators (i.e. <CLS> and <SEP>) and tokens to
+>>> # the sequence, as well as compute the attention masks.
+>>> paraphrase = tokenizer(sequence_0, sequence_2, return_tensors="tf")
+>>> not_paraphrase = tokenizer(sequence_0, sequence_1, return_tensors="tf")
+
+>>> paraphrase_classification_logits = model(paraphrase).logits
+>>> not_paraphrase_classification_logits = model(not_paraphrase).logits
+
+>>> paraphrase_results = tf.nn.softmax(paraphrase_classification_logits, axis=1).numpy()[0]
+>>> not_paraphrase_results = tf.nn.softmax(not_paraphrase_classification_logits, axis=1).numpy()[0]
+
+>>> # Should be paraphrase
+>>> for i in range(len(classes)):
+...     print(f"{classes[i]}: {int(round(paraphrase_results[i] * 100))}%")
+not paraphrase: 10%
+is paraphrase: 90%
+
+>>> # Should not be paraphrase
+>>> for i in range(len(classes)):
+...     print(f"{classes[i]}: {int(round(not_paraphrase_results[i] * 100))}%")
+not paraphrase: 94%
+is paraphrase: 6%
+```
+
+## Extractive Question Answering
+
+Extractive Question Answering is the task of extracting an answer from a text given a question. An example of a
+question answering dataset is the SQuAD dataset, which is entirely based on that task. If you would like to fine-tune a
+model on a SQuAD task, you may leverage the [run_qa.py](https://github.com/huggingface/transformers/tree/master/examples/pytorch/question-answering/run_qa.py) and
+[run_tf_squad.py](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/question-answering/run_tf_squad.py)
+scripts.
+
+
+Here is an example of using pipelines to do question answering: extracting an answer from a text given a question. It
+leverages a fine-tuned model on SQuAD.
+
+```py
+>>> from transformers import pipeline
+
+>>> question_answerer = pipeline("question-answering")
+
+>>> context = r"""
+... Extractive Question Answering is the task of extracting an answer from a text given a question. An example of a
+... question answering dataset is the SQuAD dataset, which is entirely based on that task. If you would like to fine-tune
+... a model on a SQuAD task, you may leverage the examples/pytorch/question-answering/run_squad.py script.
+... """
+```
+
+This returns an answer extracted from the text, a confidence score, alongside "start" and "end" values, which are the
+positions of the extracted answer in the text.
+
+```py
+>>> result = question_answerer(question="What is extractive question answering?", context=context)
+>>> print(f"Answer: '{result['answer']}', score: {round(result['score'], 4)}, start: {result['start']}, end: {result['end']}")
+Answer: 'the task of extracting an answer from a text given a question', score: 0.6177, start: 34, end: 95
+
+>>> result = question_answerer(question="What is a good example of a question answering dataset?", context=context)
+>>> print(f"Answer: '{result['answer']}', score: {round(result['score'], 4)}, start: {result['start']}, end: {result['end']}")
+Answer: 'SQuAD dataset', score: 0.5152, start: 147, end: 160
+```
+
+Here is an example of question answering using a model and a tokenizer. The process is the following:
+
+1. Instantiate a tokenizer and a model from the checkpoint name. The model is identified as a BERT model and loads it
+   with the weights stored in the checkpoint.
+2. Define a text and a few questions.
+3. Iterate over the questions and build a sequence from the text and the current question, with the correct
+   model-specific separators token type ids and attention masks.
+4. Pass this sequence through the model. This outputs a range of scores across the entire sequence tokens (question and
+   text), for both the start and end positions.
+5. Compute the softmax of the result to get probabilities over the tokens.
+6. Fetch the tokens from the identified start and stop values, convert those tokens to a string.
+7. Print the results.
+
+```py
+>>> from transformers import AutoTokenizer, AutoModelForQuestionAnswering
+>>> import torch
+
+>>> tokenizer = AutoTokenizer.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad")
+>>> model = AutoModelForQuestionAnswering.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad")
+
+>>> text = r"""
+... 🤗 Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides general-purpose
+... architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet…) for Natural Language Understanding (NLU) and Natural
+... Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between
+... TensorFlow 2.0 and PyTorch.
+... """
+
+>>> questions = [
+...     "How many pretrained models are available in 🤗 Transformers?",
+...     "What does 🤗 Transformers provide?",
+...     "🤗 Transformers provides interoperability between which frameworks?",
+... ]
+
+>>> for question in questions:
+...     inputs = tokenizer(question, text, add_special_tokens=True, return_tensors="pt")
+...     input_ids = inputs["input_ids"].tolist()[0]
+...
+...     outputs = model(**inputs)
+...     answer_start_scores = outputs.start_logits
+...     answer_end_scores = outputs.end_logits
+...
+...     # Get the most likely beginning of answer with the argmax of the score
+...     answer_start = torch.argmax(answer_start_scores)
+...     # Get the most likely end of answer with the argmax of the score 
+...     answer_end = torch.argmax(answer_end_scores) + 1
+...
+...     answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(input_ids[answer_start:answer_end]))
+...
+...     print(f"Question: {question}")
+...     print(f"Answer: {answer}")
+Question: How many pretrained models are available in 🤗 Transformers?
+Answer: over 32 +
+Question: What does 🤗 Transformers provide?
+Answer: general - purpose architectures
+Question: 🤗 Transformers provides interoperability between which frameworks?
+Answer: tensorflow 2. 0 and pytorch
+===PT-TF-SPLIT===
+>>> from transformers import AutoTokenizer, TFAutoModelForQuestionAnswering
+>>> import tensorflow as tf
+
+>>> tokenizer = AutoTokenizer.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad")
+>>> model = TFAutoModelForQuestionAnswering.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad")
+
+>>> text = r"""
+... 🤗 Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides general-purpose
+... architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet…) for Natural Language Understanding (NLU) and Natural
+... Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between
+... TensorFlow 2.0 and PyTorch.
+... """
+
+>>> questions = [
+...     "How many pretrained models are available in 🤗 Transformers?",
+...     "What does 🤗 Transformers provide?",
+...     "🤗 Transformers provides interoperability between which frameworks?",
+... ]
+
+>>> for question in questions:
+...     inputs = tokenizer(question, text, add_special_tokens=True, return_tensors="tf")
+...     input_ids = inputs["input_ids"].numpy()[0]
+...
+...     outputs = model(inputs)
+...     answer_start_scores = outputs.start_logits
+...     answer_end_scores = outputs.end_logits
+...
+...     # Get the most likely beginning of answer with the argmax of the score
+...     answer_start = tf.argmax(answer_start_scores, axis=1).numpy()[0]
+...     # Get the most likely end of answer with the argmax of the score
+...     answer_end = tf.argmax(answer_end_scores, axis=1).numpy()[0] + 1
+...
+...     answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(input_ids[answer_start:answer_end]))
+...
+...     print(f"Question: {question}")
+...     print(f"Answer: {answer}")
+Question: How many pretrained models are available in 🤗 Transformers?
+Answer: over 32 +
+Question: What does 🤗 Transformers provide?
+Answer: general - purpose architectures
+Question: 🤗 Transformers provides interoperability between which frameworks?
+Answer: tensorflow 2. 0 and pytorch
+```
+
+## Language Modeling
+
+Language modeling is the task of fitting a model to a corpus, which can be domain specific. All popular
+transformer-based models are trained using a variant of language modeling, e.g. BERT with masked language modeling,
+GPT-2 with causal language modeling.
+
+Language modeling can be useful outside of pretraining as well, for example to shift the model distribution to be
+domain-specific: using a language model trained over a very large corpus, and then fine-tuning it to a news dataset or
+on scientific papers e.g. [LysandreJik/arxiv-nlp](https://huggingface.co/lysandre/arxiv-nlp).
+
+### Masked Language Modeling
+
+Masked language modeling is the task of masking tokens in a sequence with a masking token, and prompting the model to
+fill that mask with an appropriate token. This allows the model to attend to both the right context (tokens on the
+right of the mask) and the left context (tokens on the left of the mask). Such a training creates a strong basis for
+downstream tasks requiring bi-directional context, such as SQuAD (question answering, see [Lewis, Lui, Goyal et al.](https://arxiv.org/abs/1910.13461), part 4.2). If you would like to fine-tune a model on a masked language modeling
+task, you may leverage the [run_mlm.py](https://github.com/huggingface/transformers/tree/master/examples/pytorch/language-modeling/run_mlm.py) script.
+
+Here is an example of using pipelines to replace a mask from a sequence:
+
+```py
+>>> from transformers import pipeline
+
+>>> unmasker = pipeline("fill-mask")
+```
+
+This outputs the sequences with the mask filled, the confidence score, and the token id in the tokenizer vocabulary:
+
+```py
+>>> from pprint import pprint
+>>> pprint(unmasker(f"HuggingFace is creating a {unmasker.tokenizer.mask_token} that the community uses to solve NLP tasks."))
+[{'score': 0.1793,
+  'sequence': 'HuggingFace is creating a tool that the community uses to solve '
+              'NLP tasks.',
+  'token': 3944,
+  'token_str': ' tool'},
+ {'score': 0.1135,
+  'sequence': 'HuggingFace is creating a framework that the community uses to '
+              'solve NLP tasks.',
+  'token': 7208,
+  'token_str': ' framework'},
+ {'score': 0.0524,
+  'sequence': 'HuggingFace is creating a library that the community uses to '
+              'solve NLP tasks.',
+  'token': 5560,
+  'token_str': ' library'},
+ {'score': 0.0349,
+  'sequence': 'HuggingFace is creating a database that the community uses to '
+              'solve NLP tasks.',
+  'token': 8503,
+  'token_str': ' database'},
+ {'score': 0.0286,
+  'sequence': 'HuggingFace is creating a prototype that the community uses to '
+              'solve NLP tasks.',
+  'token': 17715,
+  'token_str': ' prototype'}]
+```
+
+Here is an example of doing masked language modeling using a model and a tokenizer. The process is the following:
+
+1. Instantiate a tokenizer and a model from the checkpoint name. The model is identified as a DistilBERT model and
+   loads it with the weights stored in the checkpoint.
+2. Define a sequence with a masked token, placing the `tokenizer.mask_token` instead of a word.
+3. Encode that sequence into a list of IDs and find the position of the masked token in that list.
+4. Retrieve the predictions at the index of the mask token: this tensor has the same size as the vocabulary, and the
+   values are the scores attributed to each token. The model gives higher score to tokens it deems probable in that
+   context.
+5. Retrieve the top 5 tokens using the PyTorch `topk` or TensorFlow `top_k` methods.
+6. Replace the mask token by the tokens and print the results
+
+```py
+>>> from transformers import AutoModelForMaskedLM, AutoTokenizer
+>>> import torch
+
+>>> tokenizer = AutoTokenizer.from_pretrained("distilbert-base-cased")
+>>> model = AutoModelForMaskedLM.from_pretrained("distilbert-base-cased")
+
+>>> sequence = "Distilled models are smaller than the models they mimic. Using them instead of the large " \
+...     f"versions would help {tokenizer.mask_token} our carbon footprint."
+
+>>> inputs = tokenizer(sequence, return_tensors="pt")
+>>> mask_token_index = torch.where(inputs["input_ids"] == tokenizer.mask_token_id)[1]
+
+>>> token_logits = model(**inputs).logits
+>>> mask_token_logits = token_logits[0, mask_token_index, :]
+
+>>> top_5_tokens = torch.topk(mask_token_logits, 5, dim=1).indices[0].tolist()
+
+>>> for token in top_5_tokens:
+...     print(sequence.replace(tokenizer.mask_token, tokenizer.decode([token])))
+Distilled models are smaller than the models they mimic. Using them instead of the large versions would help reduce our carbon footprint.
+Distilled models are smaller than the models they mimic. Using them instead of the large versions would help increase our carbon footprint.
+Distilled models are smaller than the models they mimic. Using them instead of the large versions would help decrease our carbon footprint.
+Distilled models are smaller than the models they mimic. Using them instead of the large versions would help offset our carbon footprint.
+Distilled models are smaller than the models they mimic. Using them instead of the large versions would help improve our carbon footprint.
+===PT-TF-SPLIT===
+>>> from transformers import TFAutoModelForMaskedLM, AutoTokenizer
+>>> import tensorflow as tf
+
+>>> tokenizer = AutoTokenizer.from_pretrained("distilbert-base-cased")
+>>> model = TFAutoModelForMaskedLM.from_pretrained("distilbert-base-cased")
+
+>>> sequence = "Distilled models are smaller than the models they mimic. Using them instead of the large " \
+...     f"versions would help {tokenizer.mask_token} our carbon footprint."
+
+>>> inputs = tokenizer(sequence, return_tensors="tf")
+>>> mask_token_index = tf.where(inputs["input_ids"] == tokenizer.mask_token_id)[0, 1]
+
+>>> token_logits = model(**inputs).logits
+>>> mask_token_logits = token_logits[0, mask_token_index, :]
+
+>>> top_5_tokens = tf.math.top_k(mask_token_logits, 5).indices.numpy()
+
+>>> for token in top_5_tokens:
+...     print(sequence.replace(tokenizer.mask_token, tokenizer.decode([token])))
+Distilled models are smaller than the models they mimic. Using them instead of the large versions would help reduce our carbon footprint.
+Distilled models are smaller than the models they mimic. Using them instead of the large versions would help increase our carbon footprint.
+Distilled models are smaller than the models they mimic. Using them instead of the large versions would help decrease our carbon footprint.
+Distilled models are smaller than the models they mimic. Using them instead of the large versions would help offset our carbon footprint.
+Distilled models are smaller than the models they mimic. Using them instead of the large versions would help improve our carbon footprint.
+```
+
+This prints five sequences, with the top 5 tokens predicted by the model.
+
+
+### Causal Language Modeling
+
+Causal language modeling is the task of predicting the token following a sequence of tokens. In this situation, the
+model only attends to the left context (tokens on the left of the mask). Such a training is particularly interesting
+for generation tasks. If you would like to fine-tune a model on a causal language modeling task, you may leverage the
+[run_clm.py](https://github.com/huggingface/transformers/tree/master/examples/pytorch/language-modeling/run_clm.py) script.
+
+Usually, the next token is predicted by sampling from the logits of the last hidden state the model produces from the
+input sequence.
+
+Here is an example of using the tokenizer and model and leveraging the
+[`PreTrainedModel.top_k_top_p_filtering`] method to sample the next token following an input sequence
+of tokens.
+
+```py
+>>> from transformers import AutoModelForCausalLM, AutoTokenizer, top_k_top_p_filtering
+>>> import torch
+>>> from torch import nn
+
+>>> tokenizer = AutoTokenizer.from_pretrained("gpt2")
+>>> model = AutoModelForCausalLM.from_pretrained("gpt2")
+
+>>> sequence = f"Hugging Face is based in DUMBO, New York City, and"
+
+>>> inputs = tokenizer(sequence, return_tensors="pt")
+>>> input_ids = inputs["input_ids"]
+
+>>> # get logits of last hidden state
+>>> next_token_logits = model(**inputs).logits[:, -1, :]
+
+>>> # filter
+>>> filtered_next_token_logits = top_k_top_p_filtering(next_token_logits, top_k=50, top_p=1.0)
+
+>>> # sample
+>>> probs = nn.functional.softmax(filtered_next_token_logits, dim=-1)
+>>> next_token = torch.multinomial(probs, num_samples=1)
+
+>>> generated = torch.cat([input_ids, next_token], dim=-1)
+
+>>> resulting_string = tokenizer.decode(generated.tolist()[0])
+>>> print(resulting_string)
+Hugging Face is based in DUMBO, New York City, and ...
+===PT-TF-SPLIT===
+>>> from transformers import TFAutoModelForCausalLM, AutoTokenizer, tf_top_k_top_p_filtering
+>>> import tensorflow as tf
+
+>>> tokenizer = AutoTokenizer.from_pretrained("gpt2")
+>>> model = TFAutoModelForCausalLM.from_pretrained("gpt2")
+
+>>> sequence = f"Hugging Face is based in DUMBO, New York City, and"
+
+>>> inputs = tokenizer(sequence, return_tensors="tf")
+>>> input_ids = inputs["input_ids"]
+
+>>> # get logits of last hidden state
+>>> next_token_logits = model(**inputs).logits[:, -1, :]
+
+>>> # filter
+>>> filtered_next_token_logits = tf_top_k_top_p_filtering(next_token_logits, top_k=50, top_p=1.0)
+
+>>> # sample
+>>> next_token = tf.random.categorical(filtered_next_token_logits, dtype=tf.int32, num_samples=1)
+
+>>> generated = tf.concat([input_ids, next_token], axis=1)
+
+>>> resulting_string = tokenizer.decode(generated.numpy().tolist()[0])
+>>> print(resulting_string)
+Hugging Face is based in DUMBO, New York City, and ...
+```
+
+This outputs a (hopefully) coherent next token following the original sequence, which in our case is the word *is* or
+*features*.
+
+In the next section, we show how [`generation_utils.GenerationMixin.generate`] can be used to
+generate multiple tokens up to a specified length instead of one token at a time.
+
+### Text Generation
+
+In text generation (*a.k.a* *open-ended text generation*) the goal is to create a coherent portion of text that is a
+continuation from the given context. The following example shows how *GPT-2* can be used in pipelines to generate text.
+As a default all models apply *Top-K* sampling when used in pipelines, as configured in their respective configurations
+(see [gpt-2 config](https://huggingface.co/gpt2/blob/main/config.json) for example).
+
+```py
+>>> from transformers import pipeline
+
+>>> text_generator = pipeline("text-generation")
+>>> print(text_generator("As far as I am concerned, I will", max_length=50, do_sample=False))
+[{'generated_text': 'As far as I am concerned, I will be the first to admit that I am not a fan of the idea of a
+"free market." I think that the idea of a free market is a bit of a stretch. I think that the idea'}]
+```
+
+Here, the model generates a random text with a total maximal length of *50* tokens from context *"As far as I am
+concerned, I will"*. Behind the scenes, the pipeline object calls the method
+[`PreTrainedModel.generate`] to generate text. The default arguments for this method can be
+overridden in the pipeline, as is shown above for the arguments `max_length` and `do_sample`.
+
+Below is an example of text generation using `XLNet` and its tokenizer, which includes calling `generate()` directly:
+
+```py
+>>> from transformers import AutoModelForCausalLM, AutoTokenizer
+
+>>> model = AutoModelForCausalLM.from_pretrained("xlnet-base-cased")
+>>> tokenizer = AutoTokenizer.from_pretrained("xlnet-base-cased")
+
+>>> # Padding text helps XLNet with short prompts - proposed by Aman Rusia in https://github.com/rusiaaman/XLNet-gen#methodology
+>>> PADDING_TEXT = """In 1991, the remains of Russian Tsar Nicholas II and his family
+... (except for Alexei and Maria) are discovered.
+... The voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the
+... remainder of the story. 1883 Western Siberia,
+... a young Grigori Rasputin is asked by his father and a group of men to perform magic.
+... Rasputin has a vision and denounces one of the men as a horse thief. Although his
+... father initially slaps him for making such an accusation, Rasputin watches as the
+... man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
+... the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,
+... with people, even a bishop, begging for his blessing. <eod> </s> <eos>"""
+
+>>> prompt = "Today the weather is really nice and I am planning on "
+>>> inputs = tokenizer(PADDING_TEXT + prompt, add_special_tokens=False, return_tensors="pt")["input_ids"]
+
+>>> prompt_length = len(tokenizer.decode(inputs[0]))
+>>> outputs = model.generate(inputs, max_length=250, do_sample=True, top_p=0.95, top_k=60)
+>>> generated = prompt + tokenizer.decode(outputs[0])[prompt_length+1:]
+
+>>> print(generated)
+Today the weather is really nice and I am planning ...
+===PT-TF-SPLIT===
+>>> from transformers import TFAutoModelForCausalLM, AutoTokenizer
+
+>>> model = TFAutoModelForCausalLM.from_pretrained("xlnet-base-cased")
+>>> tokenizer = AutoTokenizer.from_pretrained("xlnet-base-cased")
+
+>>> # Padding text helps XLNet with short prompts - proposed by Aman Rusia in https://github.com/rusiaaman/XLNet-gen#methodology
+>>> PADDING_TEXT = """In 1991, the remains of Russian Tsar Nicholas II and his family
+... (except for Alexei and Maria) are discovered.
+... The voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the
+... remainder of the story. 1883 Western Siberia,
+... a young Grigori Rasputin is asked by his father and a group of men to perform magic.
+... Rasputin has a vision and denounces one of the men as a horse thief. Although his
+... father initially slaps him for making such an accusation, Rasputin watches as the
+... man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
+... the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,
+... with people, even a bishop, begging for his blessing. <eod> </s> <eos>"""
+
+>>> prompt = "Today the weather is really nice and I am planning on "
+>>> inputs = tokenizer(PADDING_TEXT + prompt, add_special_tokens=False, return_tensors="tf")["input_ids"]
+
+>>> prompt_length = len(tokenizer.decode(inputs[0]))
+>>> outputs = model.generate(inputs, max_length=250, do_sample=True, top_p=0.95, top_k=60)
+>>> generated = prompt + tokenizer.decode(outputs[0])[prompt_length+1:]
+
+>>> print(generated)
+Today the weather is really nice and I am planning ...
+```
+
+Text generation is currently possible with *GPT-2*, *OpenAi-GPT*, *CTRL*, *XLNet*, *Transfo-XL* and *Reformer* in
+PyTorch and for most models in Tensorflow as well. As can be seen in the example above *XLNet* and *Transfo-XL* often
+need to be padded to work well. GPT-2 is usually a good choice for *open-ended text generation* because it was trained
+on millions of webpages with a causal language modeling objective.
+
+For more information on how to apply different decoding strategies for text generation, please also refer to our text
+generation blog post [here](https://huggingface.co/blog/how-to-generate).
+
+
+## Named Entity Recognition
+
+Named Entity Recognition (NER) is the task of classifying tokens according to a class, for example, identifying a token
+as a person, an organisation or a location. An example of a named entity recognition dataset is the CoNLL-2003 dataset,
+which is entirely based on that task. If you would like to fine-tune a model on an NER task, you may leverage the
+[run_ner.py](https://github.com/huggingface/transformers/tree/master/examples/pytorch/token-classification/run_ner.py) script.
+
+Here is an example of using pipelines to do named entity recognition, specifically, trying to identify tokens as
+belonging to one of 9 classes:
+
+- O, Outside of a named entity
+- B-MIS, Beginning of a miscellaneous entity right after another miscellaneous entity
+- I-MIS, Miscellaneous entity
+- B-PER, Beginning of a person's name right after another person's name
+- I-PER, Person's name
+- B-ORG, Beginning of an organisation right after another organisation
+- I-ORG, Organisation
+- B-LOC, Beginning of a location right after another location
+- I-LOC, Location
+
+It leverages a fine-tuned model on CoNLL-2003, fine-tuned by [@stefan-it](https://github.com/stefan-it) from [dbmdz](https://github.com/dbmdz).
+
+```py
+>>> from transformers import pipeline
+
+>>> ner_pipe = pipeline("ner")
+
+>>> sequence = """Hugging Face Inc. is a company based in New York City. Its headquarters are in DUMBO,
+... therefore very close to the Manhattan Bridge which is visible from the window."""
+```
+
+This outputs a list of all words that have been identified as one of the entities from the 9 classes defined above.
+Here are the expected results:
+
+```py
+>>> for entity in ner_pipe(sequence):
+...     print(entity)
+{'entity': 'I-ORG', 'score': 0.9996, 'index': 1, 'word': 'Hu', 'start': 0, 'end': 2}
+{'entity': 'I-ORG', 'score': 0.9910, 'index': 2, 'word': '##gging', 'start': 2, 'end': 7}
+{'entity': 'I-ORG', 'score': 0.9982, 'index': 3, 'word': 'Face', 'start': 8, 'end': 12}
+{'entity': 'I-ORG', 'score': 0.9995, 'index': 4, 'word': 'Inc', 'start': 13, 'end': 16}
+{'entity': 'I-LOC', 'score': 0.9994, 'index': 11, 'word': 'New', 'start': 40, 'end': 43}
+{'entity': 'I-LOC', 'score': 0.9993, 'index': 12, 'word': 'York', 'start': 44, 'end': 48}
+{'entity': 'I-LOC', 'score': 0.9994, 'index': 13, 'word': 'City', 'start': 49, 'end': 53}
+{'entity': 'I-LOC', 'score': 0.9863, 'index': 19, 'word': 'D', 'start': 79, 'end': 80}
+{'entity': 'I-LOC', 'score': 0.9514, 'index': 20, 'word': '##UM', 'start': 80, 'end': 82}
+{'entity': 'I-LOC', 'score': 0.9337, 'index': 21, 'word': '##BO', 'start': 82, 'end': 84}
+{'entity': 'I-LOC', 'score': 0.9762, 'index': 28, 'word': 'Manhattan', 'start': 114, 'end': 123}
+{'entity': 'I-LOC', 'score': 0.9915, 'index': 29, 'word': 'Bridge', 'start': 124, 'end': 130}
+```
+
+Note how the tokens of the sequence "Hugging Face" have been identified as an organisation, and "New York City",
+"DUMBO" and "Manhattan Bridge" have been identified as locations.
+
+Here is an example of doing named entity recognition, using a model and a tokenizer. The process is the following:
+
+1. Instantiate a tokenizer and a model from the checkpoint name. The model is identified as a BERT model and loads it
+   with the weights stored in the checkpoint.
+2. Define a sequence with known entities, such as "Hugging Face" as an organisation and "New York City" as a location.
+3. Split words into tokens so that they can be mapped to predictions. We use a small hack by, first, completely
+   encoding and decoding the sequence, so that we're left with a string that contains the special tokens.
+4. Encode that sequence into IDs (special tokens are added automatically).
+5. Retrieve the predictions by passing the input to the model and getting the first output. This results in a
+   distribution over the 9 possible classes for each token. We take the argmax to retrieve the most likely class for
+   each token.
+6. Zip together each token with its prediction and print it.
+
+```py
+>>> from transformers import AutoModelForTokenClassification, AutoTokenizer
+>>> import torch
+
+>>> model = AutoModelForTokenClassification.from_pretrained("dbmdz/bert-large-cased-finetuned-conll03-english")
+>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
+
+>>> sequence = "Hugging Face Inc. is a company based in New York City. Its headquarters are in DUMBO, " \
+...            "therefore very close to the Manhattan Bridge."
+
+>>> inputs = tokenizer(sequence, return_tensors="pt")
+>>> tokens = inputs.tokens()
+
+>>> outputs = model(**inputs).logits
+>>> predictions = torch.argmax(outputs, dim=2)
+===PT-TF-SPLIT===
+>>> from transformers import TFAutoModelForTokenClassification, AutoTokenizer
+>>> import tensorflow as tf
+
+>>> model = TFAutoModelForTokenClassification.from_pretrained("dbmdz/bert-large-cased-finetuned-conll03-english")
+>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
+
+>>> sequence = "Hugging Face Inc. is a company based in New York City. Its headquarters are in DUMBO, " \
+...            "therefore very close to the Manhattan Bridge."
+
+>>> inputs = tokenizer(sequence, return_tensors="tf")
+>>> tokens = inputs.tokens()
+
+>>> outputs = model(**inputs)[0]
+>>> predictions = tf.argmax(outputs, axis=2)
+```
+
+This outputs a list of each token mapped to its corresponding prediction. Differently from the pipeline, here every
+token has a prediction as we didn't remove the "0"th class, which means that no particular entity was found on that
+token.
+
+In the above example, `predictions` is an integer that corresponds to the predicted class. We can use the
+`model.config.id2label` property in order to recover the class name corresponding to the class number, which is
+illustrated below:
+
+```py
+>>> for token, prediction in zip(tokens, predictions[0].numpy()):
+...     print((token, model.config.id2label[prediction]))
+('[CLS]', 'O')
+('Hu', 'I-ORG')
+('##gging', 'I-ORG')
+('Face', 'I-ORG')
+('Inc', 'I-ORG')
+('.', 'O')
+('is', 'O')
+('a', 'O')
+('company', 'O')
+('based', 'O')
+('in', 'O')
+('New', 'I-LOC')
+('York', 'I-LOC')
+('City', 'I-LOC')
+('.', 'O')
+('Its', 'O')
+('headquarters', 'O')
+('are', 'O')
+('in', 'O')
+('D', 'I-LOC')
+('##UM', 'I-LOC')
+('##BO', 'I-LOC')
+(',', 'O')
+('therefore', 'O')
+('very', 'O')
+('close', 'O')
+('to', 'O')
+('the', 'O')
+('Manhattan', 'I-LOC')
+('Bridge', 'I-LOC')
+('.', 'O')
+('[SEP]', 'O')
+```
+
+## Summarization
+
+Summarization is the task of summarizing a document or an article into a shorter text. If you would like to fine-tune a
+model on a summarization task, you may leverage the [run_summarization.py](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization/run_summarization.py)
+script.
+
+An example of a summarization dataset is the CNN / Daily Mail dataset, which consists of long news articles and was
+created for the task of summarization. If you would like to fine-tune a model on a summarization task, various
+approaches are described in this [document](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization/README.md).
+
+Here is an example of using the pipelines to do summarization. It leverages a Bart model that was fine-tuned on the CNN
+/ Daily Mail data set.
+
+```py
+>>> from transformers import pipeline
+
+>>> summarizer = pipeline("summarization")
+
+>>> ARTICLE = """ New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County, New York.
+... A year later, she got married again in Westchester County, but to a different man and without divorcing her first husband.
+... Only 18 days after that marriage, she got hitched yet again. Then, Barrientos declared "I do" five more times, sometimes only within two weeks of each other.
+... In 2010, she married once more, this time in the Bronx. In an application for a marriage license, she stated it was her "first and only" marriage.
+... Barrientos, now 39, is facing two criminal counts of "offering a false instrument for filing in the first degree," referring to her false statements on the
+... 2010 marriage license application, according to court documents.
+... Prosecutors said the marriages were part of an immigration scam.
+... On Friday, she pleaded not guilty at State Supreme Court in the Bronx, according to her attorney, Christopher Wright, who declined to comment further.
+... After leaving court, Barrientos was arrested and charged with theft of service and criminal trespass for allegedly sneaking into the New York subway through an emergency exit, said Detective
+... Annette Markowski, a police spokeswoman. In total, Barrientos has been married 10 times, with nine of her marriages occurring between 1999 and 2002.
+... All occurred either in Westchester County, Long Island, New Jersey or the Bronx. She is believed to still be married to four men, and at one time, she was married to eight men at once, prosecutors say.
+... Prosecutors said the immigration scam involved some of her husbands, who filed for permanent residence status shortly after the marriages.
+... Any divorces happened only after such filings were approved. It was unclear whether any of the men will be prosecuted.
+... The case was referred to the Bronx District Attorney\'s Office by Immigration and Customs Enforcement and the Department of Homeland Security\'s
+... Investigation Division. Seven of the men are from so-called "red-flagged" countries, including Egypt, Turkey, Georgia, Pakistan and Mali.
+... Her eighth husband, Rashid Rajput, was deported in 2006 to his native Pakistan after an investigation by the Joint Terrorism Task Force.
+... If convicted, Barrientos faces up to four years in prison.  Her next court appearance is scheduled for May 18.
+... """
+```
+
+Because the summarization pipeline depends on the `PreTrainedModel.generate()` method, we can override the default
+arguments of `PreTrainedModel.generate()` directly in the pipeline for `max_length` and `min_length` as shown
+below. This outputs the following summary:
+
+```py
+>>> print(summarizer(ARTICLE, max_length=130, min_length=30, do_sample=False))
+[{'summary_text': ' Liana Barrientos, 39, is charged with two counts of "offering a false instrument for filing in
+the first degree" In total, she has been married 10 times, with nine of her marriages occurring between 1999 and
+2002 . At one time, she was married to eight men at once, prosecutors say .'}]
+```
+
+Here is an example of doing summarization using a model and a tokenizer. The process is the following:
+
+1. Instantiate a tokenizer and a model from the checkpoint name. Summarization is usually done using an encoder-decoder
+   model, such as `Bart` or `T5`.
+2. Define the article that should be summarized.
+3. Add the T5 specific prefix "summarize: ".
+4. Use the `PreTrainedModel.generate()` method to generate the summary.
+
+In this example we use Google's T5 model. Even though it was pre-trained only on a multi-task mixed dataset (including
+CNN / Daily Mail), it yields very good results.
+
+```py
+>>> from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+>>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")
+>>> tokenizer = AutoTokenizer.from_pretrained("t5-base")
+
+>>> # T5 uses a max_length of 512 so we cut the article to 512 tokens.
+>>> inputs = tokenizer("summarize: " + ARTICLE, return_tensors="pt", max_length=512, truncation=True)
+>>> outputs = model.generate(
+...     inputs["input_ids"], max_length=150, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True
+... )
+
+>>> print(tokenizer.decode(outputs[0]))
+<pad> prosecutors say the marriages were part of an immigration scam. if convicted, barrientos faces two criminal
+counts of "offering a false instrument for filing in the first degree" she has been married 10 times, nine of them
+between 1999 and 2002.</s>
+===PT-TF-SPLIT===
+>>> from transformers import TFAutoModelForSeq2SeqLM, AutoTokenizer
+
+>>> model = TFAutoModelForSeq2SeqLM.from_pretrained("t5-base")
+>>> tokenizer = AutoTokenizer.from_pretrained("t5-base")
+
+>>> # T5 uses a max_length of 512 so we cut the article to 512 tokens.
+>>> inputs = tokenizer("summarize: " + ARTICLE, return_tensors="tf", max_length=512)
+>>> outputs = model.generate(
+...     inputs["input_ids"], max_length=150, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True
+... )
+
+>>> print(tokenizer.decode(outputs[0]))
+<pad> prosecutors say the marriages were part of an immigration scam. if convicted, barrientos faces two criminal
+counts of "offering a false instrument for filing in the first degree" she has been married 10 times, nine of them
+between 1999 and 2002.
+```
+
+## Translation
+
+Translation is the task of translating a text from one language to another. If you would like to fine-tune a model on a
+translation task, you may leverage the [run_translation.py](https://github.com/huggingface/transformers/tree/master/examples/pytorch/translation/run_translation.py) script.
+
+An example of a translation dataset is the WMT English to German dataset, which has sentences in English as the input
+data and the corresponding sentences in German as the target data. If you would like to fine-tune a model on a
+translation task, various approaches are described in this [document](https://github.com/huggingface/transformers/tree/master/examples/pytorch/translation/README.md).
+
+Here is an example of using the pipelines to do translation. It leverages a T5 model that was only pre-trained on a
+multi-task mixture dataset (including WMT), yet, yielding impressive translation results.
+
+```py
+>>> from transformers import pipeline
+
+>>> translator = pipeline("translation_en_to_de")
+>>> print(translator("Hugging Face is a technology company based in New York and Paris", max_length=40))
+[{'translation_text': 'Hugging Face ist ein Technologieunternehmen mit Sitz in New York und Paris.'}]
+```
+
+Because the translation pipeline depends on the `PreTrainedModel.generate()` method, we can override the default
+arguments of `PreTrainedModel.generate()` directly in the pipeline as is shown for `max_length` above.
+
+Here is an example of doing translation using a model and a tokenizer. The process is the following:
+
+1. Instantiate a tokenizer and a model from the checkpoint name. Summarization is usually done using an encoder-decoder
+   model, such as `Bart` or `T5`.
+2. Define the article that should be summarized.
+3. Add the T5 specific prefix "translate English to German: "
+4. Use the `PreTrainedModel.generate()` method to perform the translation.
+
+```py
+>>> from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+>>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")
+>>> tokenizer = AutoTokenizer.from_pretrained("t5-base")
+
+>>> inputs = tokenizer(
+...     "translate English to German: Hugging Face is a technology company based in New York and Paris",
+...     return_tensors="pt"
+... )
+>>> outputs = model.generate(inputs["input_ids"], max_length=40, num_beams=4, early_stopping=True)
+
+>>> print(tokenizer.decode(outputs[0]))
+<pad> Hugging Face ist ein Technologieunternehmen mit Sitz in New York und Paris.</s>
+===PT-TF-SPLIT===
+>>> from transformers import TFAutoModelForSeq2SeqLM, AutoTokenizer
+
+>>> model = TFAutoModelForSeq2SeqLM.from_pretrained("t5-base")
+>>> tokenizer = AutoTokenizer.from_pretrained("t5-base")
+
+>>> inputs = tokenizer(
+...     "translate English to German: Hugging Face is a technology company based in New York and Paris",
+...     return_tensors="tf"
+... )
+>>> outputs = model.generate(inputs["input_ids"], max_length=40, num_beams=4, early_stopping=True)
+
+>>> print(tokenizer.decode(outputs[0]))
+<pad> Hugging Face ist ein Technologieunternehmen mit Sitz in New York und Paris.
+```
+
+We get the same translation as with the pipeline example.
--- a/docs/source/task_summary.rst
+++ b/docs/source/task_summary.rst
@@ -1,927 +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.
-
-Summary of the tasks
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-This page shows the most frequent use-cases when using the library. The models available allow for many different
-configurations and a great versatility in use-cases. The most simple ones are presented here, showcasing usage for
-tasks such as question answering, sequence classification, named entity recognition and others.
-
-These examples leverage auto-models, which are classes that will instantiate a model according to a given checkpoint,
-automatically selecting the correct model architecture. Please check the :class:`~transformers.AutoModel` documentation
-for more information. Feel free to modify the code to be more specific and adapt it to your specific use-case.
-
-In order for a model to perform well on a task, it must be loaded from a checkpoint corresponding to that task. These
-checkpoints are usually pre-trained on a large corpus of data and fine-tuned on a specific task. This means the
-following:
-
- Not all models were fine-tuned on all tasks. If you want to fine-tune a model on a specific task, you can leverage
-  one of the `run_$TASK.py` scripts in the `examples
-  <https://github.com/huggingface/transformers/tree/master/examples>`__ directory.
- Fine-tuned models were fine-tuned on a specific dataset. This dataset may or may not overlap with your use-case and
-  domain. As mentioned previously, you may leverage the `examples
-  <https://github.com/huggingface/transformers/tree/master/examples>`__ scripts to fine-tune your model, or you may
-  create your own training script.
-
-In order to do an inference on a task, several mechanisms are made available by the library:
-
- Pipelines: very easy-to-use abstractions, which require as little as two lines of code.
- Direct model use: Less abstractions, but more flexibility and power via a direct access to a tokenizer
-  (PyTorch/TensorFlow) and full inference capacity.
-
-Both approaches are showcased here.
-
-.. note::
-
-    All tasks presented here leverage pre-trained checkpoints that were fine-tuned on specific tasks. Loading a
-    checkpoint that was not fine-tuned on a specific task would load only the base transformer layers and not the
-    additional head that is used for the task, initializing the weights of that head randomly.
-
-    This would produce random output.
-
-Sequence Classification
-----------------------------------------------------------------------------------------------------------------------
-
-Sequence classification is the task of classifying sequences according to a given number of classes. An example of
-sequence classification is the GLUE dataset, which is entirely based on that task. If you would like to fine-tune a
-model on a GLUE sequence classification task, you may leverage the :prefix_link:`run_glue.py
-<examples/pytorch/text-classification/run_glue.py>`, :prefix_link:`run_tf_glue.py
-<examples/tensorflow/text-classification/run_tf_glue.py>`, :prefix_link:`run_tf_text_classification.py
-<examples/tensorflow/text-classification/run_tf_text_classification.py>` or :prefix_link:`run_xnli.py
-<examples/pytorch/text-classification/run_xnli.py>` scripts.
-
-Here is an example of using pipelines to do sentiment analysis: identifying if a sequence is positive or negative. It
-leverages a fine-tuned model on sst2, which is a GLUE task.
-
-This returns a label ("POSITIVE" or "NEGATIVE") alongside a score, as follows:
-
-.. code-block::
-
-    >>> from transformers import pipeline
-
-    >>> classifier = pipeline("sentiment-analysis")
-
-    >>> result = classifier("I hate you")[0]
-    >>> print(f"label: {result['label']}, with score: {round(result['score'], 4)}")
-    label: NEGATIVE, with score: 0.9991
-
-    >>> result = classifier("I love you")[0]
-    >>> print(f"label: {result['label']}, with score: {round(result['score'], 4)}")
-    label: POSITIVE, with score: 0.9999
-
-
-Here is an example of doing a sequence classification using a model to determine if two sequences are paraphrases of
-each other. The process is the following:
-
-1. Instantiate a tokenizer and a model from the checkpoint name. The model is identified as a BERT model and loads it
-   with the weights stored in the checkpoint.
-2. Build a sequence from the two sentences, with the correct model-specific separators, token type ids and attention
-   masks (which will be created automatically by the tokenizer).
-3. Pass this sequence through the model so that it is classified in one of the two available classes: 0 (not a
-   paraphrase) and 1 (is a paraphrase).
-4. Compute the softmax of the result to get probabilities over the classes.
-5. Print the results.
-
-.. code-block::
-
-    >>> ## PYTORCH CODE
-    >>> from transformers import AutoTokenizer, AutoModelForSequenceClassification
-    >>> import torch
-
-    >>> tokenizer = AutoTokenizer.from_pretrained("bert-base-cased-finetuned-mrpc")
-    >>> model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased-finetuned-mrpc")
-
-    >>> classes = ["not paraphrase", "is paraphrase"]
-
-    >>> sequence_0 = "The company HuggingFace is based in New York City"
-    >>> sequence_1 = "Apples are especially bad for your health"
-    >>> sequence_2 = "HuggingFace's headquarters are situated in Manhattan"
-
-    >>> # The tokenizer will automatically add any model specific separators (i.e. <CLS> and <SEP>) and tokens to
-    >>> # the sequence, as well as compute the attention masks.
-    >>> paraphrase = tokenizer(sequence_0, sequence_2, return_tensors="pt")
-    >>> not_paraphrase = tokenizer(sequence_0, sequence_1, return_tensors="pt")
-
-    >>> paraphrase_classification_logits = model(**paraphrase).logits
-    >>> not_paraphrase_classification_logits = model(**not_paraphrase).logits
-
-    >>> paraphrase_results = torch.softmax(paraphrase_classification_logits, dim=1).tolist()[0]
-    >>> not_paraphrase_results = torch.softmax(not_paraphrase_classification_logits, dim=1).tolist()[0]
-
-    >>> # Should be paraphrase
-    >>> for i in range(len(classes)):
-    ...     print(f"{classes[i]}: {int(round(paraphrase_results[i] * 100))}%")
-    not paraphrase: 10%
-    is paraphrase: 90%
-
-    >>> # Should not be paraphrase
-    >>> for i in range(len(classes)):
-    ...     print(f"{classes[i]}: {int(round(not_paraphrase_results[i] * 100))}%")
-    not paraphrase: 94%
-    is paraphrase: 6%
-    >>> ## TENSORFLOW CODE
-    >>> from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
-    >>> import tensorflow as tf
-
-    >>> tokenizer = AutoTokenizer.from_pretrained("bert-base-cased-finetuned-mrpc")
-    >>> model = TFAutoModelForSequenceClassification.from_pretrained("bert-base-cased-finetuned-mrpc")
-
-    >>> classes = ["not paraphrase", "is paraphrase"]
-
-    >>> sequence_0 = "The company HuggingFace is based in New York City"
-    >>> sequence_1 = "Apples are especially bad for your health"
-    >>> sequence_2 = "HuggingFace's headquarters are situated in Manhattan"
-
-    >>> # The tokenizer will automatically add any model specific separators (i.e. <CLS> and <SEP>) and tokens to
-    >>> # the sequence, as well as compute the attention masks.
-    >>> paraphrase = tokenizer(sequence_0, sequence_2, return_tensors="tf")
-    >>> not_paraphrase = tokenizer(sequence_0, sequence_1, return_tensors="tf")
-
-    >>> paraphrase_classification_logits = model(paraphrase).logits
-    >>> not_paraphrase_classification_logits = model(not_paraphrase).logits
-
-    >>> paraphrase_results = tf.nn.softmax(paraphrase_classification_logits, axis=1).numpy()[0]
-    >>> not_paraphrase_results = tf.nn.softmax(not_paraphrase_classification_logits, axis=1).numpy()[0]
-
-    >>> # Should be paraphrase
-    >>> for i in range(len(classes)):
-    ...     print(f"{classes[i]}: {int(round(paraphrase_results[i] * 100))}%")
-    not paraphrase: 10%
-    is paraphrase: 90%
-
-    >>> # Should not be paraphrase
-    >>> for i in range(len(classes)):
-    ...     print(f"{classes[i]}: {int(round(not_paraphrase_results[i] * 100))}%")
-    not paraphrase: 94%
-    is paraphrase: 6%
-
-Extractive Question Answering
-----------------------------------------------------------------------------------------------------------------------
-
-Extractive Question Answering is the task of extracting an answer from a text given a question. An example of a
-question answering dataset is the SQuAD dataset, which is entirely based on that task. If you would like to fine-tune a
-model on a SQuAD task, you may leverage the `run_qa.py
-<https://github.com/huggingface/transformers/tree/master/examples/pytorch/question-answering/run_qa.py>`__ and
-`run_tf_squad.py
-<https://github.com/huggingface/transformers/tree/master/examples/tensorflow/question-answering/run_tf_squad.py>`__
-scripts.
-
-
-Here is an example of using pipelines to do question answering: extracting an answer from a text given a question. It
-leverages a fine-tuned model on SQuAD.
-
-.. code-block::
-
-    >>> from transformers import pipeline
-
-    >>> question_answerer = pipeline("question-answering")
-
-    >>> context = r"""
-    ... Extractive Question Answering is the task of extracting an answer from a text given a question. An example of a
-    ... question answering dataset is the SQuAD dataset, which is entirely based on that task. If you would like to fine-tune
-    ... a model on a SQuAD task, you may leverage the examples/pytorch/question-answering/run_squad.py script.
-    ... """
-
-This returns an answer extracted from the text, a confidence score, alongside "start" and "end" values, which are the
-positions of the extracted answer in the text.
-
-.. code-block::
-
-    >>> result = question_answerer(question="What is extractive question answering?", context=context)
-    >>> print(f"Answer: '{result['answer']}', score: {round(result['score'], 4)}, start: {result['start']}, end: {result['end']}")
-    Answer: 'the task of extracting an answer from a text given a question', score: 0.6177, start: 34, end: 95
-
-    >>> result = question_answerer(question="What is a good example of a question answering dataset?", context=context)
-    >>> print(f"Answer: '{result['answer']}', score: {round(result['score'], 4)}, start: {result['start']}, end: {result['end']}")
-    Answer: 'SQuAD dataset', score: 0.5152, start: 147, end: 160
-
-
-Here is an example of question answering using a model and a tokenizer. The process is the following:
-
-1. Instantiate a tokenizer and a model from the checkpoint name. The model is identified as a BERT model and loads it
-   with the weights stored in the checkpoint.
-2. Define a text and a few questions.
-3. Iterate over the questions and build a sequence from the text and the current question, with the correct
-   model-specific separators token type ids and attention masks.
-4. Pass this sequence through the model. This outputs a range of scores across the entire sequence tokens (question and
-   text), for both the start and end positions.
-5. Compute the softmax of the result to get probabilities over the tokens.
-6. Fetch the tokens from the identified start and stop values, convert those tokens to a string.
-7. Print the results.
-
-.. code-block::
-
-    >>> ## PYTORCH CODE
-    >>> from transformers import AutoTokenizer, AutoModelForQuestionAnswering
-    >>> import torch
-
-    >>> tokenizer = AutoTokenizer.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad")
-    >>> model = AutoModelForQuestionAnswering.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad")
-
-    >>> text = r"""
-    ... 🤗 Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides general-purpose
-    ... architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet…) for Natural Language Understanding (NLU) and Natural
-    ... Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between
-    ... TensorFlow 2.0 and PyTorch.
-    ... """
-
-    >>> questions = [
-    ...     "How many pretrained models are available in 🤗 Transformers?",
-    ...     "What does 🤗 Transformers provide?",
-    ...     "🤗 Transformers provides interoperability between which frameworks?",
-    ... ]
-
-    >>> for question in questions:
-    ...     inputs = tokenizer(question, text, add_special_tokens=True, return_tensors="pt")
-    ...     input_ids = inputs["input_ids"].tolist()[0]
-    ...
-    ...     outputs = model(**inputs)
-    ...     answer_start_scores = outputs.start_logits
-    ...     answer_end_scores = outputs.end_logits
-    ...
-    ...     # Get the most likely beginning of answer with the argmax of the score
-    ...     answer_start = torch.argmax(answer_start_scores)
-    ...     # Get the most likely end of answer with the argmax of the score 
-    ...     answer_end = torch.argmax(answer_end_scores) + 1
-    ...
-    ...     answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(input_ids[answer_start:answer_end]))
-    ...
-    ...     print(f"Question: {question}")
-    ...     print(f"Answer: {answer}")
-    Question: How many pretrained models are available in 🤗 Transformers?
-    Answer: over 32 +
-    Question: What does 🤗 Transformers provide?
-    Answer: general - purpose architectures
-    Question: 🤗 Transformers provides interoperability between which frameworks?
-    Answer: tensorflow 2. 0 and pytorch
-    >>> ## TENSORFLOW CODE
-    >>> from transformers import AutoTokenizer, TFAutoModelForQuestionAnswering
-    >>> import tensorflow as tf
-
-    >>> tokenizer = AutoTokenizer.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad")
-    >>> model = TFAutoModelForQuestionAnswering.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad")
-
-    >>> text = r"""
-    ... 🤗 Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides general-purpose
-    ... architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet…) for Natural Language Understanding (NLU) and Natural
-    ... Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between
-    ... TensorFlow 2.0 and PyTorch.
-    ... """
-
-    >>> questions = [
-    ...     "How many pretrained models are available in 🤗 Transformers?",
-    ...     "What does 🤗 Transformers provide?",
-    ...     "🤗 Transformers provides interoperability between which frameworks?",
-    ... ]
-
-    >>> for question in questions:
-    ...     inputs = tokenizer(question, text, add_special_tokens=True, return_tensors="tf")
-    ...     input_ids = inputs["input_ids"].numpy()[0]
-    ...
-    ...     outputs = model(inputs)
-    ...     answer_start_scores = outputs.start_logits
-    ...     answer_end_scores = outputs.end_logits
-    ...
-    ...     # Get the most likely beginning of answer with the argmax of the score
-    ...     answer_start = tf.argmax(answer_start_scores, axis=1).numpy()[0]
-    ...     # Get the most likely end of answer with the argmax of the score
-    ...     answer_end = tf.argmax(answer_end_scores, axis=1).numpy()[0] + 1
-    ...
-    ...     answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(input_ids[answer_start:answer_end]))
-    ...
-    ...     print(f"Question: {question}")
-    ...     print(f"Answer: {answer}")
-    Question: How many pretrained models are available in 🤗 Transformers?
-    Answer: over 32 +
-    Question: What does 🤗 Transformers provide?
-    Answer: general - purpose architectures
-    Question: 🤗 Transformers provides interoperability between which frameworks?
-    Answer: tensorflow 2. 0 and pytorch
-
-
-
-Language Modeling
-----------------------------------------------------------------------------------------------------------------------
-
-Language modeling is the task of fitting a model to a corpus, which can be domain specific. All popular
-transformer-based models are trained using a variant of language modeling, e.g. BERT with masked language modeling,
-GPT-2 with causal language modeling.
-
-Language modeling can be useful outside of pretraining as well, for example to shift the model distribution to be
-domain-specific: using a language model trained over a very large corpus, and then fine-tuning it to a news dataset or
-on scientific papers e.g. `LysandreJik/arxiv-nlp <https://huggingface.co/lysandre/arxiv-nlp>`__.
-
-Masked Language Modeling
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Masked language modeling is the task of masking tokens in a sequence with a masking token, and prompting the model to
-fill that mask with an appropriate token. This allows the model to attend to both the right context (tokens on the
-right of the mask) and the left context (tokens on the left of the mask). Such a training creates a strong basis for
-downstream tasks requiring bi-directional context, such as SQuAD (question answering, see `Lewis, Lui, Goyal et al.
-<https://arxiv.org/abs/1910.13461>`__, part 4.2). If you would like to fine-tune a model on a masked language modeling
-task, you may leverage the :prefix_link:`run_mlm.py <examples/pytorch/language-modeling/run_mlm.py>` script.
-
-Here is an example of using pipelines to replace a mask from a sequence:
-
-.. code-block::
-
-    >>> from transformers import pipeline
-
-    >>> unmasker = pipeline("fill-mask")
-
-This outputs the sequences with the mask filled, the confidence score, and the token id in the tokenizer vocabulary:
-
-.. code-block::
-
-    >>> from pprint import pprint
-    >>> pprint(unmasker(f"HuggingFace is creating a {unmasker.tokenizer.mask_token} that the community uses to solve NLP tasks."))
-    [{'score': 0.1793,
-      'sequence': 'HuggingFace is creating a tool that the community uses to solve '
-                  'NLP tasks.',
-      'token': 3944,
-      'token_str': ' tool'},
-     {'score': 0.1135,
-      'sequence': 'HuggingFace is creating a framework that the community uses to '
-                  'solve NLP tasks.',
-      'token': 7208,
-      'token_str': ' framework'},
-     {'score': 0.0524,
-      'sequence': 'HuggingFace is creating a library that the community uses to '
-                  'solve NLP tasks.',
-      'token': 5560,
-      'token_str': ' library'},
-     {'score': 0.0349,
-      'sequence': 'HuggingFace is creating a database that the community uses to '
-                  'solve NLP tasks.',
-      'token': 8503,
-      'token_str': ' database'},
-     {'score': 0.0286,
-      'sequence': 'HuggingFace is creating a prototype that the community uses to '
-                  'solve NLP tasks.',
-      'token': 17715,
-      'token_str': ' prototype'}]
-
-Here is an example of doing masked language modeling using a model and a tokenizer. The process is the following:
-
-1. Instantiate a tokenizer and a model from the checkpoint name. The model is identified as a DistilBERT model and
-   loads it with the weights stored in the checkpoint.
-2. Define a sequence with a masked token, placing the :obj:`tokenizer.mask_token` instead of a word.
-3. Encode that sequence into a list of IDs and find the position of the masked token in that list.
-4. Retrieve the predictions at the index of the mask token: this tensor has the same size as the vocabulary, and the
-   values are the scores attributed to each token. The model gives higher score to tokens it deems probable in that
-   context.
-5. Retrieve the top 5 tokens using the PyTorch :obj:`topk` or TensorFlow :obj:`top_k` methods.
-6. Replace the mask token by the tokens and print the results
-
-.. code-block::
-
-    >>> ## PYTORCH CODE
-    >>> from transformers import AutoModelForMaskedLM, AutoTokenizer
-    >>> import torch
-
-    >>> tokenizer = AutoTokenizer.from_pretrained("distilbert-base-cased")
-    >>> model = AutoModelForMaskedLM.from_pretrained("distilbert-base-cased")
-
-    >>> sequence = "Distilled models are smaller than the models they mimic. Using them instead of the large " \
-    ...     f"versions would help {tokenizer.mask_token} our carbon footprint."
-
-    >>> inputs = tokenizer(sequence, return_tensors="pt")
-    >>> mask_token_index = torch.where(inputs["input_ids"] == tokenizer.mask_token_id)[1]
-
-    >>> token_logits = model(**inputs).logits
-    >>> mask_token_logits = token_logits[0, mask_token_index, :]
-
-    >>> top_5_tokens = torch.topk(mask_token_logits, 5, dim=1).indices[0].tolist()
-
-    >>> for token in top_5_tokens:
-    ...     print(sequence.replace(tokenizer.mask_token, tokenizer.decode([token])))
-    Distilled models are smaller than the models they mimic. Using them instead of the large versions would help reduce our carbon footprint.
-    Distilled models are smaller than the models they mimic. Using them instead of the large versions would help increase our carbon footprint.
-    Distilled models are smaller than the models they mimic. Using them instead of the large versions would help decrease our carbon footprint.
-    Distilled models are smaller than the models they mimic. Using them instead of the large versions would help offset our carbon footprint.
-    Distilled models are smaller than the models they mimic. Using them instead of the large versions would help improve our carbon footprint.
-    >>> ## TENSORFLOW CODE
-    >>> from transformers import TFAutoModelForMaskedLM, AutoTokenizer
-    >>> import tensorflow as tf
-
-    >>> tokenizer = AutoTokenizer.from_pretrained("distilbert-base-cased")
-    >>> model = TFAutoModelForMaskedLM.from_pretrained("distilbert-base-cased")
-
-    >>> sequence = "Distilled models are smaller than the models they mimic. Using them instead of the large " \
-    ...     f"versions would help {tokenizer.mask_token} our carbon footprint."
-
-    >>> inputs = tokenizer(sequence, return_tensors="tf")
-    >>> mask_token_index = tf.where(inputs["input_ids"] == tokenizer.mask_token_id)[0, 1]
-
-    >>> token_logits = model(**inputs).logits
-    >>> mask_token_logits = token_logits[0, mask_token_index, :]
-
-    >>> top_5_tokens = tf.math.top_k(mask_token_logits, 5).indices.numpy()
-
-    >>> for token in top_5_tokens:
-    ...     print(sequence.replace(tokenizer.mask_token, tokenizer.decode([token])))
-    Distilled models are smaller than the models they mimic. Using them instead of the large versions would help reduce our carbon footprint.
-    Distilled models are smaller than the models they mimic. Using them instead of the large versions would help increase our carbon footprint.
-    Distilled models are smaller than the models they mimic. Using them instead of the large versions would help decrease our carbon footprint.
-    Distilled models are smaller than the models they mimic. Using them instead of the large versions would help offset our carbon footprint.
-    Distilled models are smaller than the models they mimic. Using them instead of the large versions would help improve our carbon footprint.
-
-
-This prints five sequences, with the top 5 tokens predicted by the model.
-
-
-Causal Language Modeling
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Causal language modeling is the task of predicting the token following a sequence of tokens. In this situation, the
-model only attends to the left context (tokens on the left of the mask). Such a training is particularly interesting
-for generation tasks. If you would like to fine-tune a model on a causal language modeling task, you may leverage the
-:prefix_link:`run_clm.py <examples/pytorch/language-modeling/run_clm.py>` script.
-
-Usually, the next token is predicted by sampling from the logits of the last hidden state the model produces from the
-input sequence.
-
-Here is an example of using the tokenizer and model and leveraging the
-:func:`~transformers.PreTrainedModel.top_k_top_p_filtering` method to sample the next token following an input sequence
-of tokens.
-
-.. code-block::
-
-    >>> ## PYTORCH CODE
-    >>> from transformers import AutoModelForCausalLM, AutoTokenizer, top_k_top_p_filtering
-    >>> import torch
-    >>> from torch import nn
-
-    >>> tokenizer = AutoTokenizer.from_pretrained("gpt2")
-    >>> model = AutoModelForCausalLM.from_pretrained("gpt2")
-
-    >>> sequence = f"Hugging Face is based in DUMBO, New York City, and"
-
-    >>> inputs = tokenizer(sequence, return_tensors="pt")
-    >>> input_ids = inputs["input_ids"]
-
-    >>> # get logits of last hidden state
-    >>> next_token_logits = model(**inputs).logits[:, -1, :]
-
-    >>> # filter
-    >>> filtered_next_token_logits = top_k_top_p_filtering(next_token_logits, top_k=50, top_p=1.0)
-
-    >>> # sample
-    >>> probs = nn.functional.softmax(filtered_next_token_logits, dim=-1)
-    >>> next_token = torch.multinomial(probs, num_samples=1)
-
-    >>> generated = torch.cat([input_ids, next_token], dim=-1)
-
-    >>> resulting_string = tokenizer.decode(generated.tolist()[0])
-    >>> print(resulting_string)
-    Hugging Face is based in DUMBO, New York City, and ...
-    >>> ## TENSORFLOW CODE
-    >>> from transformers import TFAutoModelForCausalLM, AutoTokenizer, tf_top_k_top_p_filtering
-    >>> import tensorflow as tf
-
-    >>> tokenizer = AutoTokenizer.from_pretrained("gpt2")
-    >>> model = TFAutoModelForCausalLM.from_pretrained("gpt2")
-
-    >>> sequence = f"Hugging Face is based in DUMBO, New York City, and"
-
-    >>> inputs = tokenizer(sequence, return_tensors="tf")
-    >>> input_ids = inputs["input_ids"]
-
-    >>> # get logits of last hidden state
-    >>> next_token_logits = model(**inputs).logits[:, -1, :]
-
-    >>> # filter
-    >>> filtered_next_token_logits = tf_top_k_top_p_filtering(next_token_logits, top_k=50, top_p=1.0)
-
-    >>> # sample
-    >>> next_token = tf.random.categorical(filtered_next_token_logits, dtype=tf.int32, num_samples=1)
-
-    >>> generated = tf.concat([input_ids, next_token], axis=1)
-
-    >>> resulting_string = tokenizer.decode(generated.numpy().tolist()[0])
-    >>> print(resulting_string)
-    Hugging Face is based in DUMBO, New York City, and ...
-
-This outputs a (hopefully) coherent next token following the original sequence, which in our case is the word *is* or
-*features*.
-
-In the next section, we show how :func:`~transformers.generation_utils.GenerationMixin.generate` can be used to
-generate multiple tokens up to a specified length instead of one token at a time.
-
-Text Generation
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-In text generation (*a.k.a* *open-ended text generation*) the goal is to create a coherent portion of text that is a
-continuation from the given context. The following example shows how *GPT-2* can be used in pipelines to generate text.
-As a default all models apply *Top-K* sampling when used in pipelines, as configured in their respective configurations
-(see `gpt-2 config <https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-config.json>`__ for example).
-
-.. code-block::
-
-    >>> from transformers import pipeline
-
-    >>> text_generator = pipeline("text-generation")
-    >>> print(text_generator("As far as I am concerned, I will", max_length=50, do_sample=False))
-    [{'generated_text': 'As far as I am concerned, I will be the first to admit that I am not a fan of the idea of a
-    "free market." I think that the idea of a free market is a bit of a stretch. I think that the idea'}]
-
-
-
-Here, the model generates a random text with a total maximal length of *50* tokens from context *"As far as I am
-concerned, I will"*. Behind the scenes, the pipeline object calls the method
-:func:`~transformers.PreTrainedModel.generate` to generate text. The default arguments for this method can be
-overridden in the pipeline, as is shown above for the arguments ``max_length`` and ``do_sample``.
-
-Below is an example of text generation using ``XLNet`` and its tokenizer, which includes calling ``generate`` directly:
-
-.. code-block::
-
-    >>> ## PYTORCH CODE
-    >>> from transformers import AutoModelForCausalLM, AutoTokenizer
-
-    >>> model = AutoModelForCausalLM.from_pretrained("xlnet-base-cased")
-    >>> tokenizer = AutoTokenizer.from_pretrained("xlnet-base-cased")
-
-    >>> # Padding text helps XLNet with short prompts - proposed by Aman Rusia in https://github.com/rusiaaman/XLNet-gen#methodology
-    >>> PADDING_TEXT = """In 1991, the remains of Russian Tsar Nicholas II and his family
-    ... (except for Alexei and Maria) are discovered.
-    ... The voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the
-    ... remainder of the story. 1883 Western Siberia,
-    ... a young Grigori Rasputin is asked by his father and a group of men to perform magic.
-    ... Rasputin has a vision and denounces one of the men as a horse thief. Although his
-    ... father initially slaps him for making such an accusation, Rasputin watches as the
-    ... man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
-    ... the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,
-    ... with people, even a bishop, begging for his blessing. <eod> </s> <eos>"""
-
-    >>> prompt = "Today the weather is really nice and I am planning on "
-    >>> inputs = tokenizer(PADDING_TEXT + prompt, add_special_tokens=False, return_tensors="pt")["input_ids"]
-
-    >>> prompt_length = len(tokenizer.decode(inputs[0]))
-    >>> outputs = model.generate(inputs, max_length=250, do_sample=True, top_p=0.95, top_k=60)
-    >>> generated = prompt + tokenizer.decode(outputs[0])[prompt_length+1:]
-
-    >>> print(generated)
-    Today the weather is really nice and I am planning ...
-    >>> ## TENSORFLOW CODE
-    >>> from transformers import TFAutoModelForCausalLM, AutoTokenizer
-
-    >>> model = TFAutoModelForCausalLM.from_pretrained("xlnet-base-cased")
-    >>> tokenizer = AutoTokenizer.from_pretrained("xlnet-base-cased")
-
-    >>> # Padding text helps XLNet with short prompts - proposed by Aman Rusia in https://github.com/rusiaaman/XLNet-gen#methodology
-    >>> PADDING_TEXT = """In 1991, the remains of Russian Tsar Nicholas II and his family
-    ... (except for Alexei and Maria) are discovered.
-    ... The voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the
-    ... remainder of the story. 1883 Western Siberia,
-    ... a young Grigori Rasputin is asked by his father and a group of men to perform magic.
-    ... Rasputin has a vision and denounces one of the men as a horse thief. Although his
-    ... father initially slaps him for making such an accusation, Rasputin watches as the
-    ... man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
-    ... the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,
-    ... with people, even a bishop, begging for his blessing. <eod> </s> <eos>"""
-
-    >>> prompt = "Today the weather is really nice and I am planning on "
-    >>> inputs = tokenizer(PADDING_TEXT + prompt, add_special_tokens=False, return_tensors="tf")["input_ids"]
-
-    >>> prompt_length = len(tokenizer.decode(inputs[0]))
-    >>> outputs = model.generate(inputs, max_length=250, do_sample=True, top_p=0.95, top_k=60)
-    >>> generated = prompt + tokenizer.decode(outputs[0])[prompt_length+1:]
-
-    >>> print(generated)
-    Today the weather is really nice and I am planning ...
-
-
-Text generation is currently possible with *GPT-2*, *OpenAi-GPT*, *CTRL*, *XLNet*, *Transfo-XL* and *Reformer* in
-PyTorch and for most models in Tensorflow as well. As can be seen in the example above *XLNet* and *Transfo-XL* often
-need to be padded to work well. GPT-2 is usually a good choice for *open-ended text generation* because it was trained
-on millions of webpages with a causal language modeling objective.
-
-For more information on how to apply different decoding strategies for text generation, please also refer to our text
-generation blog post `here <https://huggingface.co/blog/how-to-generate>`__.
-
-
-Named Entity Recognition
-----------------------------------------------------------------------------------------------------------------------
-
-Named Entity Recognition (NER) is the task of classifying tokens according to a class, for example, identifying a token
-as a person, an organisation or a location. An example of a named entity recognition dataset is the CoNLL-2003 dataset,
-which is entirely based on that task. If you would like to fine-tune a model on an NER task, you may leverage the
-:prefix_link:`run_ner.py <examples/pytorch/token-classification/run_ner.py>` script.
-
-Here is an example of using pipelines to do named entity recognition, specifically, trying to identify tokens as
-belonging to one of 9 classes:
-
- O, Outside of a named entity
- B-MIS, Beginning of a miscellaneous entity right after another miscellaneous entity
- I-MIS, Miscellaneous entity
- B-PER, Beginning of a person's name right after another person's name
- I-PER, Person's name
- B-ORG, Beginning of an organisation right after another organisation
- I-ORG, Organisation
- B-LOC, Beginning of a location right after another location
- I-LOC, Location
-
-It leverages a fine-tuned model on CoNLL-2003, fine-tuned by `@stefan-it <https://github.com/stefan-it>`__ from `dbmdz
-<https://github.com/dbmdz>`__.
-
-.. code-block::
-
-    >>> from transformers import pipeline
-
-    >>> ner_pipe = pipeline("ner")
-
-    >>> sequence = """Hugging Face Inc. is a company based in New York City. Its headquarters are in DUMBO,
-    ... therefore very close to the Manhattan Bridge which is visible from the window."""
-
-
-This outputs a list of all words that have been identified as one of the entities from the 9 classes defined above.
-Here are the expected results:
-
-.. code-block::
-
-    >>> for entity in ner_pipe(sequence):
-    ...     print(entity)
-    {'entity': 'I-ORG', 'score': 0.9996, 'index': 1, 'word': 'Hu', 'start': 0, 'end': 2}
-    {'entity': 'I-ORG', 'score': 0.9910, 'index': 2, 'word': '##gging', 'start': 2, 'end': 7}
-    {'entity': 'I-ORG', 'score': 0.9982, 'index': 3, 'word': 'Face', 'start': 8, 'end': 12}
-    {'entity': 'I-ORG', 'score': 0.9995, 'index': 4, 'word': 'Inc', 'start': 13, 'end': 16}
-    {'entity': 'I-LOC', 'score': 0.9994, 'index': 11, 'word': 'New', 'start': 40, 'end': 43}
-    {'entity': 'I-LOC', 'score': 0.9993, 'index': 12, 'word': 'York', 'start': 44, 'end': 48}
-    {'entity': 'I-LOC', 'score': 0.9994, 'index': 13, 'word': 'City', 'start': 49, 'end': 53}
-    {'entity': 'I-LOC', 'score': 0.9863, 'index': 19, 'word': 'D', 'start': 79, 'end': 80}
-    {'entity': 'I-LOC', 'score': 0.9514, 'index': 20, 'word': '##UM', 'start': 80, 'end': 82}
-    {'entity': 'I-LOC', 'score': 0.9337, 'index': 21, 'word': '##BO', 'start': 82, 'end': 84}
-    {'entity': 'I-LOC', 'score': 0.9762, 'index': 28, 'word': 'Manhattan', 'start': 114, 'end': 123}
-    {'entity': 'I-LOC', 'score': 0.9915, 'index': 29, 'word': 'Bridge', 'start': 124, 'end': 130}
-
-Note how the tokens of the sequence "Hugging Face" have been identified as an organisation, and "New York City",
-"DUMBO" and "Manhattan Bridge" have been identified as locations.
-
-Here is an example of doing named entity recognition, using a model and a tokenizer. The process is the following:
-
-1. Instantiate a tokenizer and a model from the checkpoint name. The model is identified as a BERT model and loads it
-   with the weights stored in the checkpoint.
-2. Define a sequence with known entities, such as "Hugging Face" as an organisation and "New York City" as a location.
-3. Split words into tokens so that they can be mapped to predictions. We use a small hack by, first, completely
-   encoding and decoding the sequence, so that we're left with a string that contains the special tokens.
-4. Encode that sequence into IDs (special tokens are added automatically).
-5. Retrieve the predictions by passing the input to the model and getting the first output. This results in a
-   distribution over the 9 possible classes for each token. We take the argmax to retrieve the most likely class for
-   each token.
-6. Zip together each token with its prediction and print it.
-
-.. code-block::
-
-    >>> ## PYTORCH CODE
-    >>> from transformers import AutoModelForTokenClassification, AutoTokenizer
-    >>> import torch
-
-    >>> model = AutoModelForTokenClassification.from_pretrained("dbmdz/bert-large-cased-finetuned-conll03-english")
-    >>> tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
-
-    >>> sequence = "Hugging Face Inc. is a company based in New York City. Its headquarters are in DUMBO, " \
-    ...            "therefore very close to the Manhattan Bridge."
-
-    >>> inputs = tokenizer(sequence, return_tensors="pt")
-    >>> tokens = inputs.tokens()
-
-    >>> outputs = model(**inputs).logits
-    >>> predictions = torch.argmax(outputs, dim=2)
-    >>> ## TENSORFLOW CODE
-    >>> from transformers import TFAutoModelForTokenClassification, AutoTokenizer
-    >>> import tensorflow as tf
-
-    >>> model = TFAutoModelForTokenClassification.from_pretrained("dbmdz/bert-large-cased-finetuned-conll03-english")
-    >>> tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
-
-    >>> sequence = "Hugging Face Inc. is a company based in New York City. Its headquarters are in DUMBO, " \
-    ...            "therefore very close to the Manhattan Bridge."
-
-    >>> inputs = tokenizer(sequence, return_tensors="tf")
-    >>> tokens = inputs.tokens()
-
-    >>> outputs = model(**inputs)[0]
-    >>> predictions = tf.argmax(outputs, axis=2)
-
-
-This outputs a list of each token mapped to its corresponding prediction. Differently from the pipeline, here every
-token has a prediction as we didn't remove the "0"th class, which means that no particular entity was found on that
-token.
-
-In the above example, ``predictions`` is an integer that corresponds to the predicted class. We can use the
-``model.config.id2label`` property in order to recover the class name corresponding to the class number, which is
-illustrated below:
-
-.. code-block::
-
-    >>> for token, prediction in zip(tokens, predictions[0].numpy()):
-    ...     print((token, model.config.id2label[prediction]))
-    ('[CLS]', 'O')
-    ('Hu', 'I-ORG')
-    ('##gging', 'I-ORG')
-    ('Face', 'I-ORG')
-    ('Inc', 'I-ORG')
-    ('.', 'O')
-    ('is', 'O')
-    ('a', 'O')
-    ('company', 'O')
-    ('based', 'O')
-    ('in', 'O')
-    ('New', 'I-LOC')
-    ('York', 'I-LOC')
-    ('City', 'I-LOC')
-    ('.', 'O')
-    ('Its', 'O')
-    ('headquarters', 'O')
-    ('are', 'O')
-    ('in', 'O')
-    ('D', 'I-LOC')
-    ('##UM', 'I-LOC')
-    ('##BO', 'I-LOC')
-    (',', 'O')
-    ('therefore', 'O')
-    ('very', 'O')
-    ('close', 'O')
-    ('to', 'O')
-    ('the', 'O')
-    ('Manhattan', 'I-LOC')
-    ('Bridge', 'I-LOC')
-    ('.', 'O')
-    ('[SEP]', 'O')
-
-
-Summarization
-----------------------------------------------------------------------------------------------------------------------
-
-Summarization is the task of summarizing a document or an article into a shorter text. If you would like to fine-tune a
-model on a summarization task, you may leverage the `run_summarization.py
-<https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization/run_summarization.py>`__
-script.
-
-An example of a summarization dataset is the CNN / Daily Mail dataset, which consists of long news articles and was
-created for the task of summarization. If you would like to fine-tune a model on a summarization task, various
-approaches are described in this :prefix_link:`document <examples/pytorch/summarization/README.md>`.
-
-Here is an example of using the pipelines to do summarization. It leverages a Bart model that was fine-tuned on the CNN
-/ Daily Mail data set.
-
-.. code-block::
-
-    >>> from transformers import pipeline
-
-    >>> summarizer = pipeline("summarization")
-
-    >>> ARTICLE = """ New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County, New York.
-    ... A year later, she got married again in Westchester County, but to a different man and without divorcing her first husband.
-    ... Only 18 days after that marriage, she got hitched yet again. Then, Barrientos declared "I do" five more times, sometimes only within two weeks of each other.
-    ... In 2010, she married once more, this time in the Bronx. In an application for a marriage license, she stated it was her "first and only" marriage.
-    ... Barrientos, now 39, is facing two criminal counts of "offering a false instrument for filing in the first degree," referring to her false statements on the
-    ... 2010 marriage license application, according to court documents.
-    ... Prosecutors said the marriages were part of an immigration scam.
-    ... On Friday, she pleaded not guilty at State Supreme Court in the Bronx, according to her attorney, Christopher Wright, who declined to comment further.
-    ... After leaving court, Barrientos was arrested and charged with theft of service and criminal trespass for allegedly sneaking into the New York subway through an emergency exit, said Detective
-    ... Annette Markowski, a police spokeswoman. In total, Barrientos has been married 10 times, with nine of her marriages occurring between 1999 and 2002.
-    ... All occurred either in Westchester County, Long Island, New Jersey or the Bronx. She is believed to still be married to four men, and at one time, she was married to eight men at once, prosecutors say.
-    ... Prosecutors said the immigration scam involved some of her husbands, who filed for permanent residence status shortly after the marriages.
-    ... Any divorces happened only after such filings were approved. It was unclear whether any of the men will be prosecuted.
-    ... The case was referred to the Bronx District Attorney\'s Office by Immigration and Customs Enforcement and the Department of Homeland Security\'s
-    ... Investigation Division. Seven of the men are from so-called "red-flagged" countries, including Egypt, Turkey, Georgia, Pakistan and Mali.
-    ... Her eighth husband, Rashid Rajput, was deported in 2006 to his native Pakistan after an investigation by the Joint Terrorism Task Force.
-    ... If convicted, Barrientos faces up to four years in prison.  Her next court appearance is scheduled for May 18.
-    ... """
-
-Because the summarization pipeline depends on the ``PreTrainedModel.generate()`` method, we can override the default
-arguments of ``PreTrainedModel.generate()`` directly in the pipeline for ``max_length`` and ``min_length`` as shown
-below. This outputs the following summary:
-
-.. code-block::
-
-    >>> print(summarizer(ARTICLE, max_length=130, min_length=30, do_sample=False))
-    [{'summary_text': ' Liana Barrientos, 39, is charged with two counts of "offering a false instrument for filing in
-    the first degree" In total, she has been married 10 times, with nine of her marriages occurring between 1999 and
-    2002 . At one time, she was married to eight men at once, prosecutors say .'}]
-
-Here is an example of doing summarization using a model and a tokenizer. The process is the following:
-
-1. Instantiate a tokenizer and a model from the checkpoint name. Summarization is usually done using an encoder-decoder
-   model, such as ``Bart`` or ``T5``.
-2. Define the article that should be summarized.
-3. Add the T5 specific prefix "summarize: ".
-4. Use the ``PreTrainedModel.generate()`` method to generate the summary.
-
-In this example we use Google's T5 model. Even though it was pre-trained only on a multi-task mixed dataset (including
-CNN / Daily Mail), it yields very good results.
-
-.. code-block::
-
-    >>> ## PYTORCH CODE
-    >>> from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
-
-    >>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")
-    >>> tokenizer = AutoTokenizer.from_pretrained("t5-base")
-
-    >>> # T5 uses a max_length of 512 so we cut the article to 512 tokens.
-    >>> inputs = tokenizer("summarize: " + ARTICLE, return_tensors="pt", max_length=512, truncation=True)
-    >>> outputs = model.generate(
-    ...     inputs["input_ids"], max_length=150, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True
-    ... )
-
-    >>> print(tokenizer.decode(outputs[0]))
-    <pad> prosecutors say the marriages were part of an immigration scam. if convicted, barrientos faces two criminal
-    counts of "offering a false instrument for filing in the first degree" she has been married 10 times, nine of them
-    between 1999 and 2002.</s>
-    >>> ## TENSORFLOW CODE
-    >>> from transformers import TFAutoModelForSeq2SeqLM, AutoTokenizer
-
-    >>> model = TFAutoModelForSeq2SeqLM.from_pretrained("t5-base")
-    >>> tokenizer = AutoTokenizer.from_pretrained("t5-base")
-
-    >>> # T5 uses a max_length of 512 so we cut the article to 512 tokens.
-    >>> inputs = tokenizer("summarize: " + ARTICLE, return_tensors="tf", max_length=512)
-    >>> outputs = model.generate(
-    ...     inputs["input_ids"], max_length=150, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True
-    ... )
-
-    >>> print(tokenizer.decode(outputs[0]))
-    <pad> prosecutors say the marriages were part of an immigration scam. if convicted, barrientos faces two criminal
-    counts of "offering a false instrument for filing in the first degree" she has been married 10 times, nine of them
-    between 1999 and 2002.
-
-
-Translation
-----------------------------------------------------------------------------------------------------------------------
-
-Translation is the task of translating a text from one language to another. If you would like to fine-tune a model on a
-translation task, you may leverage the `run_translation.py
-<https://github.com/huggingface/transformers/tree/master/examples/pytorch/translation/run_translation.py>`__ script.
-
-An example of a translation dataset is the WMT English to German dataset, which has sentences in English as the input
-data and the corresponding sentences in German as the target data. If you would like to fine-tune a model on a
-translation task, various approaches are described in this :prefix_link:`document
-<examples/pytorch/translation/README.md>`.
-
-Here is an example of using the pipelines to do translation. It leverages a T5 model that was only pre-trained on a
-multi-task mixture dataset (including WMT), yet, yielding impressive translation results.
-
-.. code-block::
-
-    >>> from transformers import pipeline
-
-    >>> translator = pipeline("translation_en_to_de")
-    >>> print(translator("Hugging Face is a technology company based in New York and Paris", max_length=40))
-    [{'translation_text': 'Hugging Face ist ein Technologieunternehmen mit Sitz in New York und Paris.'}]
-
-Because the translation pipeline depends on the ``PreTrainedModel.generate()`` method, we can override the default
-arguments of ``PreTrainedModel.generate()`` directly in the pipeline as is shown for ``max_length`` above.
-
-Here is an example of doing translation using a model and a tokenizer. The process is the following:
-
-1. Instantiate a tokenizer and a model from the checkpoint name. Summarization is usually done using an encoder-decoder
-   model, such as ``Bart`` or ``T5``.
-2. Define the article that should be summarized.
-3. Add the T5 specific prefix "translate English to German: "
-4. Use the ``PreTrainedModel.generate()`` method to perform the translation.
-
-.. code-block::
-
-    >>> ## PYTORCH CODE
-    >>> from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
-
-    >>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")
-    >>> tokenizer = AutoTokenizer.from_pretrained("t5-base")
-
-    >>> inputs = tokenizer(
-    ...     "translate English to German: Hugging Face is a technology company based in New York and Paris",
-    ...     return_tensors="pt"
-    ... )
-    >>> outputs = model.generate(inputs["input_ids"], max_length=40, num_beams=4, early_stopping=True)
-
-    >>> print(tokenizer.decode(outputs[0]))
-    <pad> Hugging Face ist ein Technologieunternehmen mit Sitz in New York und Paris.</s>
-    >>> ## TENSORFLOW CODE
-    >>> from transformers import TFAutoModelForSeq2SeqLM, AutoTokenizer
-
-    >>> model = TFAutoModelForSeq2SeqLM.from_pretrained("t5-base")
-    >>> tokenizer = AutoTokenizer.from_pretrained("t5-base")
-
-    >>> inputs = tokenizer(
-    ...     "translate English to German: Hugging Face is a technology company based in New York and Paris",
-    ...     return_tensors="tf"
-    ... )
-    >>> outputs = model.generate(inputs["input_ids"], max_length=40, num_beams=4, early_stopping=True)
-
-    >>> print(tokenizer.decode(outputs[0]))
-    <pad> Hugging Face ist ein Technologieunternehmen mit Sitz in New York und Paris.
-
-We get the same translation as with the pipeline example.
--- a/docs/source/tokenizer_summary.mdx
+++ b/docs/source/tokenizer_summary.mdx
@@ -0,0 +1,274 @@
+<!--Copyright 2020 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# Summary of the tokenizers
+
+On this page, we will have a closer look at tokenization.
+
+<Youtube id="VFp38yj8h3A"/>
+
+As we saw in [the preprocessing tutorial](preprocessing), tokenizing a text is splitting it into words or
+subwords, which then are converted to ids through a look-up table. Converting words or subwords to ids is
+straightforward, so in this summary, we will focus on splitting a text into words or subwords (i.e. tokenizing a text).
+More specifically, we will look at the three main types of tokenizers used in 🤗 Transformers: [Byte-Pair Encoding
+(BPE)](#byte-pair-encoding), [WordPiece](#wordpiece), and [SentencePiece](#sentencepiece), and show examples
+of which tokenizer type is used by which model.
+
+Note that on each model page, you can look at the documentation of the associated tokenizer to know which tokenizer
+type was used by the pretrained model. For instance, if we look at [`BertTokenizer`], we can see
+that the model uses [WordPiece](#wordpiece).
+
+## Introduction
+
+Splitting a text into smaller chunks is a task that is harder than it looks, and there are multiple ways of doing so.
+For instance, let's look at the sentence `"Don't you love 🤗 Transformers? We sure do."`
+
+<Youtube id="nhJxYji1aho"/>
+
+A simple way of tokenizing this text is to split it by spaces, which would give:
+
+```
+["Don't", "you", "love", "🤗", "Transformers?", "We", "sure", "do."]
+```
+
+This is a sensible first step, but if we look at the tokens `"Transformers?"` and `"do."`, we notice that the
+punctuation is attached to the words `"Transformer"` and `"do"`, which is suboptimal. We should take the
+punctuation into account so that a model does not have to learn a different representation of a word and every possible
+punctuation symbol that could follow it, which would explode the number of representations the model has to learn.
+Taking punctuation into account, tokenizing our exemplary text would give:
+
+```
+["Don", "'", "t", "you", "love", "🤗", "Transformers", "?", "We", "sure", "do", "."]
+```
+
+Better. However, it is disadvantageous, how the tokenization dealt with the word `"Don't"`. `"Don't"` stands for
+`"do not"`, so it would be better tokenized as `["Do", "n't"]`. This is where things start getting complicated, and
+part of the reason each model has its own tokenizer type. Depending on the rules we apply for tokenizing a text, a
+different tokenized output is generated for the same text. A pretrained model only performs properly if you feed it an
+input that was tokenized with the same rules that were used to tokenize its training data.
+
+[spaCy](https://spacy.io/) and [Moses](http://www.statmt.org/moses/?n=Development.GetStarted) are two popular
+rule-based tokenizers. Applying them on our example, *spaCy* and *Moses* would output something like:
+
+```
+["Do", "n't", "you", "love", "🤗", "Transformers", "?", "We", "sure", "do", "."]
+```
+
+As can be seen space and punctuation tokenization, as well as rule-based tokenization, is used here. Space and
+punctuation tokenization and rule-based tokenization are both examples of word tokenization, which is loosely defined
+as splitting sentences into words. While it's the most intuitive way to split texts into smaller chunks, this
+tokenization method can lead to problems for massive text corpora. In this case, space and punctuation tokenization
+usually generates a very big vocabulary (the set of all unique words and tokens used). *E.g.*, [Transformer XL](model_doc/transformerxl) uses space and punctuation tokenization, resulting in a vocabulary size of 267,735!
+
+Such a big vocabulary size forces the model to have an enormous embedding matrix as the input and output layer, which
+causes both an increased memory and time complexity. In general, transformers models rarely have a vocabulary size
+greater than 50,000, especially if they are pretrained only on a single language.
+
+So if simple space and punctuation tokenization is unsatisfactory, why not simply tokenize on characters?
+
+<Youtube id="ssLq_EK2jLE"/>
+
+While character tokenization is very simple and would greatly reduce memory and time complexity it makes it much harder
+for the model to learn meaningful input representations. *E.g.* learning a meaningful context-independent
+representation for the letter `"t"` is much harder than learning a context-independent representation for the word
+`"today"`. Therefore, character tokenization is often accompanied by a loss of performance. So to get the best of
+both worlds, transformers models use a hybrid between word-level and character-level tokenization called **subword**
+tokenization.
+
+### Subword tokenization
+
+<Youtube id="zHvTiHr506c"/>
+
+Subword tokenization algorithms rely on the principle that frequently used words should not be split into smaller
+subwords, but rare words should be decomposed into meaningful subwords. For instance `"annoyingly"` might be
+considered a rare word and could be decomposed into `"annoying"` and `"ly"`. Both `"annoying"` and `"ly"` as
+stand-alone subwords would appear more frequently while at the same time the meaning of `"annoyingly"` is kept by the
+composite meaning of `"annoying"` and `"ly"`. This is especially useful in agglutinative languages such as Turkish,
+where you can form (almost) arbitrarily long complex words by stringing together subwords.
+
+Subword tokenization allows the model to have a reasonable vocabulary size while being able to learn meaningful
+context-independent representations. In addition, subword tokenization enables the model to process words it has never
+seen before, by decomposing them into known subwords. For instance, the [`~transformers.BertTokenizer`] tokenizes
+`"I have a new GPU!"` as follows:
+
+```py
+>>> from transformers import BertTokenizer
+>>> tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
+>>> tokenizer.tokenize("I have a new GPU!")
+["i", "have", "a", "new", "gp", "##u", "!"]
+```
+
+Because we are considering the uncased model, the sentence was lowercased first. We can see that the words `["i", "have", "a", "new"]` are present in the tokenizer's vocabulary, but the word `"gpu"` is not. Consequently, the
+tokenizer splits `"gpu"` into known subwords: `["gp" and "##u"]`. `"##"` means that the rest of the token should
+be attached to the previous one, without space (for decoding or reversal of the tokenization).
+
+As another example, [`~transformers.XLNetTokenizer`] tokenizes our previously exemplary text as follows:
+
+```py
+>>> from transformers import XLNetTokenizer
+>>> tokenizer = XLNetTokenizer.from_pretrained("xlnet-base-cased")
+>>> tokenizer.tokenize("Don't you love 🤗 Transformers? We sure do.")
+["▁Don", "'", "t", "▁you", "▁love", "▁", "🤗", "▁", "Transform", "ers", "?", "▁We", "▁sure", "▁do", "."]
+```
+
+We'll get back to the meaning of those `"▁"` when we look at [SentencePiece](#sentencepiece). As one can see,
+the rare word `"Transformers"` has been split into the more frequent subwords `"Transform"` and `"ers"`.
+
+Let's now look at how the different subword tokenization algorithms work. Note that all of those tokenization
+algorithms rely on some form of training which is usually done on the corpus the corresponding model will be trained
+on.
+
+<a id='byte-pair-encoding'></a>
+
+## Byte-Pair Encoding (BPE)
+
+Byte-Pair Encoding (BPE) was introduced in [Neural Machine Translation of Rare Words with Subword Units (Sennrich et
+al., 2015)](https://arxiv.org/abs/1508.07909). BPE relies on a pre-tokenizer that splits the training data into
+words. Pretokenization can be as simple as space tokenization, e.g. [GPT-2](model_doc/gpt2), [Roberta](model_doc/roberta). More advanced pre-tokenization include rule-based tokenization, e.g. [XLM](model_doc/xlm),
+[FlauBERT](model_doc/flaubert) which uses Moses for most languages, or [GPT](model_doc/gpt) which uses
+Spacy and ftfy, to count the frequency of each word in the training corpus.
+
+After pre-tokenization, a set of unique words has been created and the frequency of each word it occurred in the
+training data has been determined. Next, BPE creates a base vocabulary consisting of all symbols that occur in the set
+of unique words and learns merge rules to form a new symbol from two symbols of the base vocabulary. It does so until
+the vocabulary has attained the desired vocabulary size. Note that the desired vocabulary size is a hyperparameter to
+define before training the tokenizer.
+
+As an example, let's assume that after pre-tokenization, the following set of words including their frequency has been
+determined:
+
+```
+("hug", 10), ("pug", 5), ("pun", 12), ("bun", 4), ("hugs", 5)
+```
+
+Consequently, the base vocabulary is `["b", "g", "h", "n", "p", "s", "u"]`. Splitting all words into symbols of the
+base vocabulary, we obtain:
+
+```
+("h" "u" "g", 10), ("p" "u" "g", 5), ("p" "u" "n", 12), ("b" "u" "n", 4), ("h" "u" "g" "s", 5)
+```
+
+BPE then counts the frequency of each possible symbol pair and picks the symbol pair that occurs most frequently. In
+the example above `"h"` followed by `"u"` is present _10 + 5 = 15_ times (10 times in the 10 occurrences of
+`"hug"`, 5 times in the 5 occurrences of `"hugs"`). However, the most frequent symbol pair is `"u"` followed by
+`"g"`, occurring _10 + 5 + 5 = 20_ times in total. Thus, the first merge rule the tokenizer learns is to group all
+`"u"` symbols followed by a `"g"` symbol together. Next, `"ug"` is added to the vocabulary. The set of words then
+becomes
+
+```
+("h" "ug", 10), ("p" "ug", 5), ("p" "u" "n", 12), ("b" "u" "n", 4), ("h" "ug" "s", 5)
+```
+
+BPE then identifies the next most common symbol pair. It's `"u"` followed by `"n"`, which occurs 16 times. `"u"`,
+`"n"` is merged to `"un"` and added to the vocabulary. The next most frequent symbol pair is `"h"` followed by
+`"ug"`, occurring 15 times. Again the pair is merged and `"hug"` can be added to the vocabulary.
+
+At this stage, the vocabulary is `["b", "g", "h", "n", "p", "s", "u", "ug", "un", "hug"]` and our set of unique words
+is represented as
+
+```
+("hug", 10), ("p" "ug", 5), ("p" "un", 12), ("b" "un", 4), ("hug" "s", 5)
+```
+
+Assuming, that the Byte-Pair Encoding training would stop at this point, the learned merge rules would then be applied
+to new words (as long as those new words do not include symbols that were not in the base vocabulary). For instance,
+the word `"bug"` would be tokenized to `["b", "ug"]` but `"mug"` would be tokenized as `["<unk>", "ug"]` since
+the symbol `"m"` is not in the base vocabulary. In general, single letters such as `"m"` are not replaced by the
+`"<unk>"` symbol because the training data usually includes at least one occurrence of each letter, but it is likely
+to happen for very special characters like emojis.
+
+As mentioned earlier, the vocabulary size, *i.e.* the base vocabulary size + the number of merges, is a hyperparameter
+to choose. For instance [GPT](model_doc/gpt) has a vocabulary size of 40,478 since they have 478 base characters
+and chose to stop training after 40,000 merges.
+
+### Byte-level BPE
+
+A base vocabulary that includes all possible base characters can be quite large if *e.g.* all unicode characters are
+considered as base characters. To have a better base vocabulary, [GPT-2](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) uses bytes
+as the base vocabulary, which is a clever trick to force the base vocabulary to be of size 256 while ensuring that
+every base character is included in the vocabulary. With some additional rules to deal with punctuation, the GPT2's
+tokenizer can tokenize every text without the need for the <unk> symbol. [GPT-2](model_doc/gpt) has a vocabulary
+size of 50,257, which corresponds to the 256 bytes base tokens, a special end-of-text token and the symbols learned
+with 50,000 merges.
+
+<a id='wordpiece'></a>
+
+#### WordPiece
+
+WordPiece is the subword tokenization algorithm used for [BERT](model_doc/bert), [DistilBERT](model_doc/distilbert), and [Electra](model_doc/electra). The algorithm was outlined in [Japanese and Korean
+Voice Search (Schuster et al., 2012)](https://static.googleusercontent.com/media/research.google.com/ja//pubs/archive/37842.pdf) and is very similar to
+BPE. WordPiece first initializes the vocabulary to include every character present in the training data and
+progressively learns a given number of merge rules. In contrast to BPE, WordPiece does not choose the most frequent
+symbol pair, but the one that maximizes the likelihood of the training data once added to the vocabulary.
+
+So what does this mean exactly? Referring to the previous example, maximizing the likelihood of the training data is
+equivalent to finding the symbol pair, whose probability divided by the probabilities of its first symbol followed by
+its second symbol is the greatest among all symbol pairs. *E.g.* `"u"`, followed by `"g"` would have only been
+merged if the probability of `"ug"` divided by `"u"`, `"g"` would have been greater than for any other symbol
+pair. Intuitively, WordPiece is slightly different to BPE in that it evaluates what it _loses_ by merging two symbols
+to make ensure it's _worth it_.
+
+<a id='unigram'></a>
+
+#### Unigram
+
+Unigram is a subword tokenization algorithm introduced in [Subword Regularization: Improving Neural Network Translation
+Models with Multiple Subword Candidates (Kudo, 2018)](https://arxiv.org/pdf/1804.10959.pdf). In contrast to BPE or
+WordPiece, Unigram initializes its base vocabulary to a large number of symbols and progressively trims down each
+symbol to obtain a smaller vocabulary. The base vocabulary could for instance correspond to all pre-tokenized words and
+the most common substrings. Unigram is not used directly for any of the models in the transformers, but it's used in
+conjunction with [SentencePiece](#sentencepiece).
+
+At each training step, the Unigram algorithm defines a loss (often defined as the log-likelihood) over the training
+data given the current vocabulary and a unigram language model. Then, for each symbol in the vocabulary, the algorithm
+computes how much the overall loss would increase if the symbol was to be removed from the vocabulary. Unigram then
+removes p (with p usually being 10% or 20%) percent of the symbols whose loss increase is the lowest, *i.e.* those
+symbols that least affect the overall loss over the training data. This process is repeated until the vocabulary has
+reached the desired size. The Unigram algorithm always keeps the base characters so that any word can be tokenized.
+
+Because Unigram is not based on merge rules (in contrast to BPE and WordPiece), the algorithm has several ways of
+tokenizing new text after training. As an example, if a trained Unigram tokenizer exhibits the vocabulary:
+
+```
+["b", "g", "h", "n", "p", "s", "u", "ug", "un", "hug"],
+```
+
+`"hugs"` could be tokenized both as `["hug", "s"]`, `["h", "ug", "s"]` or `["h", "u", "g", "s"]`. So which one
+to choose? Unigram saves the probability of each token in the training corpus on top of saving the vocabulary so that
+the probability of each possible tokenization can be computed after training. The algorithm simply picks the most
+likely tokenization in practice, but also offers the possibility to sample a possible tokenization according to their
+probabilities.
+
+Those probabilities are defined by the loss the tokenizer is trained on. Assuming that the training data consists of
+the words \\(x_{1}, \dots, x_{N}\\) and that the set of all possible tokenizations for a word \\(x_{i}\\) is
+defined as \\(S(x_{i})\\), then the overall loss is defined as
+
+$$\mathcal{L} = -\sum_{i=1}^{N} \log \left ( \sum_{x \in S(x_{i})} p(x) \right )$$
+
+<a id='sentencepiece'></a>
+
+#### SentencePiece
+
+All tokenization algorithms described so far have the same problem: It is assumed that the input text uses spaces to
+separate words. However, not all languages use spaces to separate words. One possible solution is to use language
+specific pre-tokenizers, *e.g.* [XLM](model_doc/xlm) uses a specific Chinese, Japanese, and Thai pre-tokenizer).
+To solve this problem more generally, [SentencePiece: A simple and language independent subword tokenizer and
+detokenizer for Neural Text Processing (Kudo et al., 2018)](https://arxiv.org/pdf/1808.06226.pdf) treats the input
+as a raw input stream, thus including the space in the set of characters to use. It then uses the BPE or unigram
+algorithm to construct the appropriate vocabulary.
+
+The [`XLNetTokenizer`] uses SentencePiece for example, which is also why in the example earlier the
+`"▁"` character was included in the vocabulary. Decoding with SentencePiece is very easy since all tokens can just be
+concatenated and `"▁"` is replaced by a space.
+
+All transformers models in the library that use SentencePiece use it in combination with unigram. Examples of models
+using SentencePiece are [ALBERT](model_doc/albert), [XLNet](model_doc/xlnet), [Marian](model_doc/marian), and [T5](model_doc/t5).
--- a/docs/source/tokenizer_summary.rst
+++ b/docs/source/tokenizer_summary.rst
@@ -1,306 +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.
-
-Summary of the tokenizers
-----------------------------------------------------------------------------------------------------------------------
-
-On this page, we will have a closer look at tokenization.
-
-.. 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>
-
-As we saw in :doc:`the preprocessing tutorial <preprocessing>`, tokenizing a text is splitting it into words or
-subwords, which then are converted to ids through a look-up table. Converting words or subwords to ids is
-straightforward, so in this summary, we will focus on splitting a text into words or subwords (i.e. tokenizing a text).
-More specifically, we will look at the three main types of tokenizers used in 🤗 Transformers: :ref:`Byte-Pair Encoding
-(BPE) <byte-pair-encoding>`, :ref:`WordPiece <wordpiece>`, and :ref:`SentencePiece <sentencepiece>`, and show examples
-of which tokenizer type is used by which model.
-
-Note that on each model page, you can look at the documentation of the associated tokenizer to know which tokenizer
-type was used by the pretrained model. For instance, if we look at :class:`~transformers.BertTokenizer`, we can see
-that the model uses :ref:`WordPiece <wordpiece>`.
-
-Introduction
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Splitting a text into smaller chunks is a task that is harder than it looks, and there are multiple ways of doing so.
-For instance, let's look at the sentence ``"Don't you love 🤗 Transformers? We sure do."``
-
-.. raw:: html
-
-   <iframe width="560" height="315" src="https://www.youtube.com/embed/nhJxYji1aho" title="YouTube video player"
-   frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope;
-   picture-in-picture" allowfullscreen></iframe>
-
-A simple way of tokenizing this text is to split it by spaces, which would give:
-
-.. code-block::
-
-    ["Don't", "you", "love", "🤗", "Transformers?", "We", "sure", "do."]
-
-This is a sensible first step, but if we look at the tokens ``"Transformers?"`` and ``"do."``, we notice that the
-punctuation is attached to the words ``"Transformer"`` and ``"do"``, which is suboptimal. We should take the
-punctuation into account so that a model does not have to learn a different representation of a word and every possible
-punctuation symbol that could follow it, which would explode the number of representations the model has to learn.
-Taking punctuation into account, tokenizing our exemplary text would give:
-
-.. code-block::
-
-    ["Don", "'", "t", "you", "love", "🤗", "Transformers", "?", "We", "sure", "do", "."]
-
-Better. However, it is disadvantageous, how the tokenization dealt with the word ``"Don't"``. ``"Don't"`` stands for
-``"do not"``, so it would be better tokenized as ``["Do", "n't"]``. This is where things start getting complicated, and
-part of the reason each model has its own tokenizer type. Depending on the rules we apply for tokenizing a text, a
-different tokenized output is generated for the same text. A pretrained model only performs properly if you feed it an
-input that was tokenized with the same rules that were used to tokenize its training data.
-
-`spaCy <https://spacy.io/>`__ and `Moses <http://www.statmt.org/moses/?n=Development.GetStarted>`__ are two popular
-rule-based tokenizers. Applying them on our example, *spaCy* and *Moses* would output something like:
-
-.. code-block::
-
-    ["Do", "n't", "you", "love", "🤗", "Transformers", "?", "We", "sure", "do", "."]
-
-As can be seen space and punctuation tokenization, as well as rule-based tokenization, is used here. Space and
-punctuation tokenization and rule-based tokenization are both examples of word tokenization, which is loosely defined
-as splitting sentences into words. While it's the most intuitive way to split texts into smaller chunks, this
-tokenization method can lead to problems for massive text corpora. In this case, space and punctuation tokenization
-usually generates a very big vocabulary (the set of all unique words and tokens used). *E.g.*, :doc:`Transformer XL
-<model_doc/transformerxl>` uses space and punctuation tokenization, resulting in a vocabulary size of 267,735!
-
-Such a big vocabulary size forces the model to have an enormous embedding matrix as the input and output layer, which
-causes both an increased memory and time complexity. In general, transformers models rarely have a vocabulary size
-greater than 50,000, especially if they are pretrained only on a single language.
-
-So if simple space and punctuation tokenization is unsatisfactory, why not simply tokenize on characters?
-
-.. raw:: html
-
-   <iframe width="560" height="315" src="https://www.youtube.com/embed/ssLq_EK2jLE" title="YouTube video player"
-   frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope;
-   picture-in-picture" allowfullscreen></iframe>
-
-While character tokenization is very simple and would greatly reduce memory and time complexity it makes it much harder
-for the model to learn meaningful input representations. *E.g.* learning a meaningful context-independent
-representation for the letter ``"t"`` is much harder than learning a context-independent representation for the word
-``"today"``. Therefore, character tokenization is often accompanied by a loss of performance. So to get the best of
-both worlds, transformers models use a hybrid between word-level and character-level tokenization called **subword**
-tokenization.
-
-Subword tokenization
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. raw:: html
-
-   <iframe width="560" height="315" src="https://www.youtube.com/embed/zHvTiHr506c" title="YouTube video player"
-   frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope;
-   picture-in-picture" allowfullscreen></iframe>
-
-Subword tokenization algorithms rely on the principle that frequently used words should not be split into smaller
-subwords, but rare words should be decomposed into meaningful subwords. For instance ``"annoyingly"`` might be
-considered a rare word and could be decomposed into ``"annoying"`` and ``"ly"``. Both ``"annoying"`` and ``"ly"`` as
-stand-alone subwords would appear more frequently while at the same time the meaning of ``"annoyingly"`` is kept by the
-composite meaning of ``"annoying"`` and ``"ly"``. This is especially useful in agglutinative languages such as Turkish,
-where you can form (almost) arbitrarily long complex words by stringing together subwords.
-
-Subword tokenization allows the model to have a reasonable vocabulary size while being able to learn meaningful
-context-independent representations. In addition, subword tokenization enables the model to process words it has never
-seen before, by decomposing them into known subwords. For instance, the :class:`~transformers.BertTokenizer` tokenizes
-``"I have a new GPU!"`` as follows:
-
-.. code-block::
-
-    >>> from transformers import BertTokenizer
-    >>> tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
-    >>> tokenizer.tokenize("I have a new GPU!")
-    ["i", "have", "a", "new", "gp", "##u", "!"]
-
-Because we are considering the uncased model, the sentence was lowercased first. We can see that the words ``["i",
-"have", "a", "new"]`` are present in the tokenizer's vocabulary, but the word ``"gpu"`` is not. Consequently, the
-tokenizer splits ``"gpu"`` into known subwords: ``["gp" and "##u"]``. ``"##"`` means that the rest of the token should
-be attached to the previous one, without space (for decoding or reversal of the tokenization).
-
-As another example, :class:`~transformers.XLNetTokenizer` tokenizes our previously exemplary text as follows:
-
-.. code-block::
-
-    >>> from transformers import XLNetTokenizer
-    >>> tokenizer = XLNetTokenizer.from_pretrained("xlnet-base-cased")
-    >>> tokenizer.tokenize("Don't you love 🤗 Transformers? We sure do.")
-    ["▁Don", "'", "t", "▁you", "▁love", "▁", "🤗", "▁", "Transform", "ers", "?", "▁We", "▁sure", "▁do", "."]
-
-We'll get back to the meaning of those ``"▁"`` when we look at :ref:`SentencePiece <sentencepiece>`. As one can see,
-the rare word ``"Transformers"`` has been split into the more frequent subwords ``"Transform"`` and ``"ers"``.
-
-Let's now look at how the different subword tokenization algorithms work. Note that all of those tokenization
-algorithms rely on some form of training which is usually done on the corpus the corresponding model will be trained
-on.
-
-.. _byte-pair-encoding:
-
-Byte-Pair Encoding (BPE)
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Byte-Pair Encoding (BPE) was introduced in `Neural Machine Translation of Rare Words with Subword Units (Sennrich et
-al., 2015) <https://arxiv.org/abs/1508.07909>`__. BPE relies on a pre-tokenizer that splits the training data into
-words. Pretokenization can be as simple as space tokenization, e.g. :doc:`GPT-2 <model_doc/gpt2>`, :doc:`Roberta
-<model_doc/roberta>`. More advanced pre-tokenization include rule-based tokenization, e.g. :doc:`XLM <model_doc/xlm>`,
-:doc:`FlauBERT <model_doc/flaubert>` which uses Moses for most languages, or :doc:`GPT <model_doc/gpt>` which uses
-Spacy and ftfy, to count the frequency of each word in the training corpus.
-
-After pre-tokenization, a set of unique words has been created and the frequency of each word it occurred in the
-training data has been determined. Next, BPE creates a base vocabulary consisting of all symbols that occur in the set
-of unique words and learns merge rules to form a new symbol from two symbols of the base vocabulary. It does so until
-the vocabulary has attained the desired vocabulary size. Note that the desired vocabulary size is a hyperparameter to
-define before training the tokenizer.
-
-As an example, let's assume that after pre-tokenization, the following set of words including their frequency has been
-determined:
-
-.. code-block::
-
-    ("hug", 10), ("pug", 5), ("pun", 12), ("bun", 4), ("hugs", 5)
-
-Consequently, the base vocabulary is ``["b", "g", "h", "n", "p", "s", "u"]``. Splitting all words into symbols of the
-base vocabulary, we obtain:
-
-.. code-block::
-
-    ("h" "u" "g", 10), ("p" "u" "g", 5), ("p" "u" "n", 12), ("b" "u" "n", 4), ("h" "u" "g" "s", 5)
-
-BPE then counts the frequency of each possible symbol pair and picks the symbol pair that occurs most frequently. In
-the example above ``"h"`` followed by ``"u"`` is present `10 + 5 = 15` times (10 times in the 10 occurrences of
-``"hug"``, 5 times in the 5 occurrences of ``"hugs"``). However, the most frequent symbol pair is ``"u"`` followed by
-``"g"``, occurring `10 + 5 + 5 = 20` times in total. Thus, the first merge rule the tokenizer learns is to group all
-``"u"`` symbols followed by a ``"g"`` symbol together. Next, ``"ug"`` is added to the vocabulary. The set of words then
-becomes
-
-.. code-block::
-
-    ("h" "ug", 10), ("p" "ug", 5), ("p" "u" "n", 12), ("b" "u" "n", 4), ("h" "ug" "s", 5)
-
-BPE then identifies the next most common symbol pair. It's ``"u"`` followed by ``"n"``, which occurs 16 times. ``"u"``,
-``"n"`` is merged to ``"un"`` and added to the vocabulary. The next most frequent symbol pair is ``"h"`` followed by
-``"ug"``, occurring 15 times. Again the pair is merged and ``"hug"`` can be added to the vocabulary.
-
-At this stage, the vocabulary is ``["b", "g", "h", "n", "p", "s", "u", "ug", "un", "hug"]`` and our set of unique words
-is represented as
-
-.. code-block::
-
-    ("hug", 10), ("p" "ug", 5), ("p" "un", 12), ("b" "un", 4), ("hug" "s", 5)
-
-Assuming, that the Byte-Pair Encoding training would stop at this point, the learned merge rules would then be applied
-to new words (as long as those new words do not include symbols that were not in the base vocabulary). For instance,
-the word ``"bug"`` would be tokenized to ``["b", "ug"]`` but ``"mug"`` would be tokenized as ``["<unk>", "ug"]`` since
-the symbol ``"m"`` is not in the base vocabulary. In general, single letters such as ``"m"`` are not replaced by the
-``"<unk>"`` symbol because the training data usually includes at least one occurrence of each letter, but it is likely
-to happen for very special characters like emojis.
-
-As mentioned earlier, the vocabulary size, *i.e.* the base vocabulary size + the number of merges, is a hyperparameter
-to choose. For instance :doc:`GPT <model_doc/gpt>` has a vocabulary size of 40,478 since they have 478 base characters
-and chose to stop training after 40,000 merges.
-
-Byte-level BPE
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-A base vocabulary that includes all possible base characters can be quite large if *e.g.* all unicode characters are
-considered as base characters. To have a better base vocabulary, `GPT-2
-<https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf>`__ uses bytes
-as the base vocabulary, which is a clever trick to force the base vocabulary to be of size 256 while ensuring that
-every base character is included in the vocabulary. With some additional rules to deal with punctuation, the GPT2's
-tokenizer can tokenize every text without the need for the <unk> symbol. :doc:`GPT-2 <model_doc/gpt>` has a vocabulary
-size of 50,257, which corresponds to the 256 bytes base tokens, a special end-of-text token and the symbols learned
-with 50,000 merges.
-
-.. _wordpiece:
-
-WordPiece
-=======================================================================================================================
-
-WordPiece is the subword tokenization algorithm used for :doc:`BERT <model_doc/bert>`, :doc:`DistilBERT
-<model_doc/distilbert>`, and :doc:`Electra <model_doc/electra>`. The algorithm was outlined in `Japanese and Korean
-Voice Search (Schuster et al., 2012)
-<https://static.googleusercontent.com/media/research.google.com/ja//pubs/archive/37842.pdf>`__ and is very similar to
-BPE. WordPiece first initializes the vocabulary to include every character present in the training data and
-progressively learns a given number of merge rules. In contrast to BPE, WordPiece does not choose the most frequent
-symbol pair, but the one that maximizes the likelihood of the training data once added to the vocabulary.
-
-So what does this mean exactly? Referring to the previous example, maximizing the likelihood of the training data is
-equivalent to finding the symbol pair, whose probability divided by the probabilities of its first symbol followed by
-its second symbol is the greatest among all symbol pairs. *E.g.* ``"u"``, followed by ``"g"`` would have only been
-merged if the probability of ``"ug"`` divided by ``"u"``, ``"g"`` would have been greater than for any other symbol
-pair. Intuitively, WordPiece is slightly different to BPE in that it evaluates what it `loses` by merging two symbols
-to make ensure it's `worth it`.
-
-.. _unigram:
-
-Unigram
-=======================================================================================================================
-
-Unigram is a subword tokenization algorithm introduced in `Subword Regularization: Improving Neural Network Translation
-Models with Multiple Subword Candidates (Kudo, 2018) <https://arxiv.org/pdf/1804.10959.pdf>`__. In contrast to BPE or
-WordPiece, Unigram initializes its base vocabulary to a large number of symbols and progressively trims down each
-symbol to obtain a smaller vocabulary. The base vocabulary could for instance correspond to all pre-tokenized words and
-the most common substrings. Unigram is not used directly for any of the models in the transformers, but it's used in
-conjunction with :ref:`SentencePiece <sentencepiece>`.
-
-At each training step, the Unigram algorithm defines a loss (often defined as the log-likelihood) over the training
-data given the current vocabulary and a unigram language model. Then, for each symbol in the vocabulary, the algorithm
-computes how much the overall loss would increase if the symbol was to be removed from the vocabulary. Unigram then
-removes p (with p usually being 10% or 20%) percent of the symbols whose loss increase is the lowest, *i.e.* those
-symbols that least affect the overall loss over the training data. This process is repeated until the vocabulary has
-reached the desired size. The Unigram algorithm always keeps the base characters so that any word can be tokenized.
-
-Because Unigram is not based on merge rules (in contrast to BPE and WordPiece), the algorithm has several ways of
-tokenizing new text after training. As an example, if a trained Unigram tokenizer exhibits the vocabulary:
-
-.. code-block::
-
-    ["b", "g", "h", "n", "p", "s", "u", "ug", "un", "hug"],
-
-``"hugs"`` could be tokenized both as ``["hug", "s"]``, ``["h", "ug", "s"]`` or ``["h", "u", "g", "s"]``. So which one
-to choose? Unigram saves the probability of each token in the training corpus on top of saving the vocabulary so that
-the probability of each possible tokenization can be computed after training. The algorithm simply picks the most
-likely tokenization in practice, but also offers the possibility to sample a possible tokenization according to their
-probabilities.
-
-Those probabilities are defined by the loss the tokenizer is trained on. Assuming that the training data consists of
-the words :math:`x_{1}, \dots, x_{N}` and that the set of all possible tokenizations for a word :math:`x_{i}` is
-defined as :math:`S(x_{i})`, then the overall loss is defined as
-
-.. math::
-    \mathcal{L} = -\sum_{i=1}^{N} \log \left ( \sum_{x \in S(x_{i})} p(x) \right )
-
-.. _sentencepiece:
-
-SentencePiece
-=======================================================================================================================
-
-All tokenization algorithms described so far have the same problem: It is assumed that the input text uses spaces to
-separate words. However, not all languages use spaces to separate words. One possible solution is to use language
-specific pre-tokenizers, *e.g.* :doc:`XLM <model_doc/xlm>` uses a specific Chinese, Japanese, and Thai pre-tokenizer).
-To solve this problem more generally, `SentencePiece: A simple and language independent subword tokenizer and
-detokenizer for Neural Text Processing (Kudo et al., 2018) <https://arxiv.org/pdf/1808.06226.pdf>`__ treats the input
-as a raw input stream, thus including the space in the set of characters to use. It then uses the BPE or unigram
-algorithm to construct the appropriate vocabulary.
-
-The :class:`~transformers.XLNetTokenizer` uses SentencePiece for example, which is also why in the example earlier the
-``"▁"`` character was included in the vocabulary. Decoding with SentencePiece is very easy since all tokens can just be
-concatenated and ``"▁"`` is replaced by a space.
-
-All transformers models in the library that use SentencePiece use it in combination with unigram. Examples of models
-using SentencePiece are :doc:`ALBERT <model_doc/albert>`, :doc:`XLNet <model_doc/xlnet>`, :doc:`Marian
-<model_doc/marian>`, and :doc:`T5 <model_doc/t5>`.
--- a/docs/source/training.mdx
+++ b/docs/source/training.mdx
@@ -0,0 +1,397 @@
+<!--Copyright 2020 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# Fine-tuning a pretrained model
+
+In this tutorial, we will show you how to fine-tune a pretrained model from the Transformers library. In TensorFlow,
+models can be directly trained using Keras and the `fit` method. In PyTorch, there is no generic training loop so
+the 🤗 Transformers library provides an API with the class [`Trainer`] to let you fine-tune or train
+a model from scratch easily. Then we will show you how to alternatively write the whole training loop in PyTorch.
+
+Before we can fine-tune a model, we need a dataset. In this tutorial, we will show you how to fine-tune BERT on the
+[IMDB dataset](https://www.imdb.com/interfaces/): the task is to classify whether movie reviews are positive or
+negative. For examples of other tasks, refer to the [additional-resources](#additional-resources) section!
+
+<a id='data-processing'></a>
+
+## Preparing the datasets
+
+<Youtube id="_BZearw7f0w"/>
+
+We will use the [🤗 Datasets](https://github.com/huggingface/datasets/) library to download and preprocess the IMDB
+datasets. We will go over this part pretty quickly. Since the focus of this tutorial is on training, you should refer
+to the 🤗 Datasets [documentation](https://huggingface.co/docs/datasets/) or the [preprocessing](preprocessing) tutorial for
+more information.
+
+First, we can use the `load_dataset` function to download and cache the dataset:
+
+```python
+from datasets import load_dataset
+
+raw_datasets = load_dataset("imdb")
+```
+
+This works like the `from_pretrained` method we saw for the models and tokenizers (except the cache directory is
+_~/.cache/huggingface/dataset_ by default).
+
+The `raw_datasets` object is a dictionary with three keys: `"train"`, `"test"` and `"unsupervised"`
+(which correspond to the three splits of that dataset). We will use the `"train"` split for training and the
+`"test"` split for validation.
+
+To preprocess our data, we will need a tokenizer:
+
+```python
+from transformers import AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
+```
+
+As we saw in [preprocessing](preprocessing), we can prepare the text inputs for the model with the following command (this is an
+example, not a command you can execute):
+
+```python
+inputs = tokenizer(sentences, padding="max_length", truncation=True)
+```
+
+This will make all the samples have the maximum length the model can accept (here 512), either by padding or truncating
+them.
+
+However, we can instead apply these preprocessing steps to all the splits of our dataset at once by using the
+`map` method:
+
+```python
+def tokenize_function(examples):
+    return tokenizer(examples["text"], padding="max_length", truncation=True)
+
+tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)
+```
+
+You can learn more about the map method or the other ways to preprocess the data in the 🤗 Datasets [documentation](https://huggingface.co/docs/datasets/).
+
+Next we will generate a small subset of the training and validation set, to enable faster training:
+
+```python
+small_train_dataset = tokenized_datasets["train"].shuffle(seed=42).select(range(1000)) 
+small_eval_dataset = tokenized_datasets["test"].shuffle(seed=42).select(range(1000)) 
+full_train_dataset = tokenized_datasets["train"]
+full_eval_dataset = tokenized_datasets["test"]
+```
+
+In all the examples below, we will always use `small_train_dataset` and `small_eval_dataset`. Just replace
+them by their _full_ equivalent to train or evaluate on the full dataset.
+
+<a id='trainer'></a>
+
+## Fine-tuning in PyTorch with the Trainer API
+
+<Youtube id="nvBXf7s7vTI"/>
+
+Since PyTorch does not provide a training loop, the 🤗 Transformers library provides a [`Trainer`]
+API that is optimized for 🤗 Transformers models, with a wide range of training options and with built-in features like
+logging, gradient accumulation, and mixed precision.
+
+First, let's define our model:
+
+```python
+from transformers import AutoModelForSequenceClassification
+
+model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=2)
+```
+
+This will issue a warning about some of the pretrained weights not being used and some weights being randomly
+initialized. That's because we are throwing away the pretraining head of the BERT model to replace it with a
+classification head which is randomly initialized. We will fine-tune this model on our task, transferring the knowledge
+of the pretrained model to it (which is why doing this is called transfer learning).
+
+Then, to define our [`Trainer`], we will need to instantiate a
+[`TrainingArguments`]. This class contains all the hyperparameters we can tune for the
+[`Trainer`] or the flags to activate the different training options it supports. Let's begin by
+using all the defaults, the only thing we then have to provide is a directory in which the checkpoints will be saved:
+
+```python
+from transformers import TrainingArguments
+
+training_args = TrainingArguments("test_trainer")
+```
+
+Then we can instantiate a [`Trainer`] like this:
+
+```python
+from transformers import Trainer
+
+trainer = Trainer(
+    model=model, args=training_args, train_dataset=small_train_dataset, eval_dataset=small_eval_dataset
+)
+```
+
+To fine-tune our model, we just need to call
+
+```python
+trainer.train()
+```
+
+which will start a training that you can follow with a progress bar, which should take a couple of minutes to complete
+(as long as you have access to a GPU). It won't actually tell you anything useful about how well (or badly) your model
+is performing however as by default, there is no evaluation during training, and we didn't tell the
+[`Trainer`] to compute any metrics. Let's have a look on how to do that now!
+
+To have the [`Trainer`] compute and report metrics, we need to give it a `compute_metrics`
+function that takes predictions and labels (grouped in a namedtuple called [`EvalPrediction`]) and
+return a dictionary with string items (the metric names) and float values (the metric values).
+
+The 🤗 Datasets library provides an easy way to get the common metrics used in NLP with the `load_metric` function.
+here we simply use accuracy. Then we define the `compute_metrics` function that just convert logits to predictions
+(remember that all 🤗 Transformers models return the logits) and feed them to `compute` method of this metric.
+
+```python
+import numpy as np
+from datasets import load_metric
+
+metric = load_metric("accuracy")
+
+def compute_metrics(eval_pred):
+    logits, labels = eval_pred
+    predictions = np.argmax(logits, axis=-1)
+    return metric.compute(predictions=predictions, references=labels)
+```
+
+The compute function needs to receive a tuple (with logits and labels) and has to return a dictionary with string keys
+(the name of the metric) and float values. It will be called at the end of each evaluation phase on the whole arrays of
+predictions/labels.
+
+To check if this works on practice, let's create a new [`Trainer`] with our fine-tuned model:
+
+```python
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=small_train_dataset,
+    eval_dataset=small_eval_dataset,
+    compute_metrics=compute_metrics,
+)
+trainer.evaluate()
+```
+
+which showed an accuracy of 87.5% in our case.
+
+If you want to fine-tune your model and regularly report the evaluation metrics (for instance at the end of each
+epoch), here is how you should define your training arguments:
+
+```python
+from transformers import TrainingArguments
+
+training_args = TrainingArguments("test_trainer", evaluation_strategy="epoch")
+```
+
+See the documentation of [`TrainingArguments`] for more options.
+
+
+<a id='keras'></a>
+
+## Fine-tuning with Keras
+
+<Youtube id="rnTGBy2ax1c"/>
+
+Models can also be trained natively in TensorFlow using the Keras API. First, let's define our model:
+
+```python
+import tensorflow as tf
+from transformers import TFAutoModelForSequenceClassification
+
+model = TFAutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=2)
+```
+
+Then we will need to convert our datasets from before in standard `tf.data.Dataset`. Since we have fixed shapes,
+it can easily be done like this. First we remove the _"text"_ column from our datasets and set them in TensorFlow
+format:
+
+```python
+tf_train_dataset = small_train_dataset.remove_columns(["text"]).with_format("tensorflow")
+tf_eval_dataset = small_eval_dataset.remove_columns(["text"]).with_format("tensorflow")
+```
+
+Then we convert everything in big tensors and use the `tf.data.Dataset.from_tensor_slices` method:
+
+```python
+train_features = {x: tf_train_dataset[x] for x in tokenizer.model_input_names}
+train_tf_dataset = tf.data.Dataset.from_tensor_slices((train_features, tf_train_dataset["label"]))
+train_tf_dataset = train_tf_dataset.shuffle(len(tf_train_dataset)).batch(8)
+
+eval_features = {x: tf_eval_dataset[x] for x in tokenizer.model_input_names}
+eval_tf_dataset = tf.data.Dataset.from_tensor_slices((eval_features, tf_eval_dataset["label"]))
+eval_tf_dataset = eval_tf_dataset.batch(8)
+```
+
+With this done, the model can then be compiled and trained as any Keras model:
+
+```python
+model.compile(
+    optimizer=tf.keras.optimizers.Adam(learning_rate=5e-5),
+    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+    metrics=tf.metrics.SparseCategoricalAccuracy(),
+)
+
+model.fit(train_tf_dataset, validation_data=eval_tf_dataset, epochs=3)
+```
+
+With the tight interoperability between TensorFlow and PyTorch models, you can even save the model and then reload it
+as a PyTorch model (or vice-versa):
+
+```python
+from transformers import AutoModelForSequenceClassification
+
+model.save_pretrained("my_imdb_model")
+pytorch_model = AutoModelForSequenceClassification.from_pretrained("my_imdb_model", from_tf=True)
+```
+
+<a id='pytorch_native'></a>
+
+## Fine-tuning in native PyTorch
+
+<Youtube id="Dh9CL8fyG80"/>
+
+You might need to restart your notebook at this stage to free some memory, or execute the following code:
+
+```python
+del model
+del pytorch_model
+del trainer
+torch.cuda.empty_cache()
+```
+
+Let's now see how to achieve the same results as in [trainer section](#trainer) in PyTorch. First we need to
+define the dataloaders, which we will use to iterate over batches. We just need to apply a bit of post-processing to
+our `tokenized_datasets` before doing that to:
+
+- remove the columns corresponding to values the model does not expect (here the `"text"` column)
+- rename the column `"label"` to `"labels"` (because the model expect the argument to be named `labels`)
+- set the format of the datasets so they return PyTorch Tensors instead of lists.
+
+Our _tokenized_datasets_ has one method for each of those steps:
+
+```python
+tokenized_datasets = tokenized_datasets.remove_columns(["text"])
+tokenized_datasets = tokenized_datasets.rename_column("label", "labels")
+tokenized_datasets.set_format("torch")
+
+small_train_dataset = tokenized_datasets["train"].shuffle(seed=42).select(range(1000))
+small_eval_dataset = tokenized_datasets["test"].shuffle(seed=42).select(range(1000))
+```
+
+Now that this is done, we can easily define our dataloaders:
+
+```python
+from torch.utils.data import DataLoader
+
+train_dataloader = DataLoader(small_train_dataset, shuffle=True, batch_size=8)
+eval_dataloader = DataLoader(small_eval_dataset, batch_size=8)
+```
+
+Next, we define our model:
+
+```python
+from transformers import AutoModelForSequenceClassification
+
+model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=2)
+```
+
+We are almost ready to write our training loop, the only two things are missing are an optimizer and a learning rate
+scheduler. The default optimizer used by the [`Trainer`] is [`AdamW`]:
+
+```python
+from transformers import AdamW
+
+optimizer = AdamW(model.parameters(), lr=5e-5)
+```
+
+Finally, the learning rate scheduler used by default is just a linear decay from the maximum value (5e-5 here) to 0:
+
+```python
+from transformers import get_scheduler
+
+num_epochs = 3
+num_training_steps = num_epochs * len(train_dataloader)
+lr_scheduler = get_scheduler(
+    "linear",
+    optimizer=optimizer,
+    num_warmup_steps=0,
+    num_training_steps=num_training_steps
+)
+```
+
+One last thing, we will want to use the GPU if we have access to one (otherwise training might take several hours
+instead of a couple of minutes). To do this, we define a `device` we will put our model and our batches on.
+
+```python
+import torch
+
+device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+model.to(device)
+```
+
+We now are ready to train! To get some sense of when it will be finished, we add a progress bar over our number of
+training steps, using the _tqdm_ library.
+
+```python
+from tqdm.auto import tqdm
+
+progress_bar = tqdm(range(num_training_steps))
+
+model.train()
+for epoch in range(num_epochs):
+    for batch in train_dataloader:
+        batch = {k: v.to(device) for k, v in batch.items()}
+        outputs = model(**batch)
+        loss = outputs.loss
+        loss.backward()
+
+        optimizer.step()
+        lr_scheduler.step()
+        optimizer.zero_grad()
+        progress_bar.update(1)
+```
+
+Note that if you are used to freezing the body of your pretrained model (like in computer vision) the above may seem a
+bit strange, as we are directly fine-tuning the whole model without taking any precaution. It actually works better
+this way for Transformers model (so this is not an oversight on our side). If you're not familiar with what "freezing
+the body" of the model means, forget you read this paragraph.
+
+Now to check the results, we need to write the evaluation loop. Like in the [trainer section](#trainer) we will
+use a metric from the datasets library. Here we accumulate the predictions at each batch before computing the final
+result when the loop is finished.
+
+```python
+metric= load_metric("accuracy")
+model.eval()
+for batch in eval_dataloader:
+    batch = {k: v.to(device) for k, v in batch.items()}
+    with torch.no_grad():
+        outputs = model(**batch)
+
+    logits = outputs.logits
+    predictions = torch.argmax(logits, dim=-1)
+    metric.add_batch(predictions=predictions, references=batch["labels"])
+
+metric.compute()
+```
+
+<a id='additional-resources'></a>
+
+## Additional resources
+
+To look at more fine-tuning examples you can refer to:
+
+- [🤗 Transformers Examples](https://github.com/huggingface/transformers/tree/master/examples) which includes scripts
+  to train on all common NLP tasks in PyTorch and TensorFlow.
+
+- [🤗 Transformers Notebooks](notebooks) which contains various notebooks and in particular one per task (look for
+  the _how to finetune a model on xxx_).
--- a/docs/source/training.rst
+++ b/docs/source/training.rst
@@ -1,421 +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.
-
-Fine-tuning a pretrained model
-=======================================================================================================================
-
-In this tutorial, we will show you how to fine-tune a pretrained model from the Transformers library. In TensorFlow,
-models can be directly trained using Keras and the :obj:`fit` method. In PyTorch, there is no generic training loop so
-the 🤗 Transformers library provides an API with the class :class:`~transformers.Trainer` to let you fine-tune or train
-a model from scratch easily. Then we will show you how to alternatively write the whole training loop in PyTorch.
-
-Before we can fine-tune a model, we need a dataset. In this tutorial, we will show you how to fine-tune BERT on the
-`IMDB dataset <https://www.imdb.com/interfaces/>`__: the task is to classify whether movie reviews are positive or
-negative. For examples of other tasks, refer to the :ref:`additional-resources` section!
-
-.. _data-processing:
-
-Preparing the datasets
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. raw:: html
-
-   <iframe width="560" height="315" src="https://www.youtube.com/embed/_BZearw7f0w" title="YouTube video player"
-   frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope;
-   picture-in-picture" allowfullscreen></iframe>
-
-We will use the `🤗 Datasets <https://github.com/huggingface/datasets/>`__ library to download and preprocess the IMDB
-datasets. We will go over this part pretty quickly. Since the focus of this tutorial is on training, you should refer
-to the 🤗 Datasets `documentation <https://huggingface.co/docs/datasets/>`__ or the :doc:`preprocessing` tutorial for
-more information.
-
-First, we can use the :obj:`load_dataset` function to download and cache the dataset:
-
-.. code-block:: python
-
-    from datasets import load_dataset
-
-    raw_datasets = load_dataset("imdb")
-
-This works like the :obj:`from_pretrained` method we saw for the models and tokenizers (except the cache directory is
-`~/.cache/huggingface/dataset` by default).
-
-The :obj:`raw_datasets` object is a dictionary with three keys: :obj:`"train"`, :obj:`"test"` and :obj:`"unsupervised"`
-(which correspond to the three splits of that dataset). We will use the :obj:`"train"` split for training and the
-:obj:`"test"` split for validation.
-
-To preprocess our data, we will need a tokenizer:
-
-.. code-block:: python
-
-    from transformers import AutoTokenizer
-
-    tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
-
-As we saw in :doc:`preprocessing`, we can prepare the text inputs for the model with the following command (this is an
-example, not a command you can execute):
-
-.. code-block:: python
-
-    inputs = tokenizer(sentences, padding="max_length", truncation=True)
-
-This will make all the samples have the maximum length the model can accept (here 512), either by padding or truncating
-them.
-
-However, we can instead apply these preprocessing steps to all the splits of our dataset at once by using the
-:obj:`map` method:
-
-.. code-block:: python
-
-    def tokenize_function(examples):
-        return tokenizer(examples["text"], padding="max_length", truncation=True)
-
-    tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)
-
-You can learn more about the map method or the other ways to preprocess the data in the 🤗 Datasets `documentation
-<https://huggingface.co/docs/datasets/>`__.
-
-Next we will generate a small subset of the training and validation set, to enable faster training:
-
-.. code-block:: python
-
-    small_train_dataset = tokenized_datasets["train"].shuffle(seed=42).select(range(1000)) 
-    small_eval_dataset = tokenized_datasets["test"].shuffle(seed=42).select(range(1000)) 
-    full_train_dataset = tokenized_datasets["train"]
-    full_eval_dataset = tokenized_datasets["test"]
-
-In all the examples below, we will always use :obj:`small_train_dataset` and :obj:`small_eval_dataset`. Just replace
-them by their `full` equivalent to train or evaluate on the full dataset.
-
-.. _trainer:
-
-Fine-tuning in PyTorch with the Trainer API
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. raw:: html
-
-   <iframe width="560" height="315" src="https://www.youtube.com/embed/nvBXf7s7vTI" title="YouTube video player"
-   frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope;
-   picture-in-picture" allowfullscreen></iframe>
-
-Since PyTorch does not provide a training loop, the 🤗 Transformers library provides a :class:`~transformers.Trainer`
-API that is optimized for 🤗 Transformers models, with a wide range of training options and with built-in features like
-logging, gradient accumulation, and mixed precision.
-
-First, let's define our model:
-
-.. code-block:: python
-
-    from transformers import AutoModelForSequenceClassification
-
-    model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=2)
-
-This will issue a warning about some of the pretrained weights not being used and some weights being randomly
-initialized. That's because we are throwing away the pretraining head of the BERT model to replace it with a
-classification head which is randomly initialized. We will fine-tune this model on our task, transferring the knowledge
-of the pretrained model to it (which is why doing this is called transfer learning).
-
-Then, to define our :class:`~transformers.Trainer`, we will need to instantiate a
-:class:`~transformers.TrainingArguments`. This class contains all the hyperparameters we can tune for the
-:class:`~transformers.Trainer` or the flags to activate the different training options it supports. Let's begin by
-using all the defaults, the only thing we then have to provide is a directory in which the checkpoints will be saved:
-
-.. code-block:: python
-
-    from transformers import TrainingArguments
-
-    training_args = TrainingArguments("test_trainer")
-
-Then we can instantiate a :class:`~transformers.Trainer` like this:
-
-.. code-block:: python
-
-    from transformers import Trainer
-
-    trainer = Trainer(
-        model=model, args=training_args, train_dataset=small_train_dataset, eval_dataset=small_eval_dataset
-    )
-
-To fine-tune our model, we just need to call
-
-.. code-block:: python
-
-    trainer.train()
-
-which will start a training that you can follow with a progress bar, which should take a couple of minutes to complete
-(as long as you have access to a GPU). It won't actually tell you anything useful about how well (or badly) your model
-is performing however as by default, there is no evaluation during training, and we didn't tell the
-:class:`~transformers.Trainer` to compute any metrics. Let's have a look on how to do that now!
-
-To have the :class:`~transformers.Trainer` compute and report metrics, we need to give it a :obj:`compute_metrics`
-function that takes predictions and labels (grouped in a namedtuple called :class:`~transformers.EvalPrediction`) and
-return a dictionary with string items (the metric names) and float values (the metric values).
-
-The 🤗 Datasets library provides an easy way to get the common metrics used in NLP with the :obj:`load_metric` function.
-here we simply use accuracy. Then we define the :obj:`compute_metrics` function that just convert logits to predictions
-(remember that all 🤗 Transformers models return the logits) and feed them to :obj:`compute` method of this metric.
-
-.. code-block:: python
-
-    import numpy as np
-    from datasets import load_metric
-
-    metric = load_metric("accuracy")
-
-    def compute_metrics(eval_pred):
-        logits, labels = eval_pred
-        predictions = np.argmax(logits, axis=-1)
-        return metric.compute(predictions=predictions, references=labels)
-
-The compute function needs to receive a tuple (with logits and labels) and has to return a dictionary with string keys
-(the name of the metric) and float values. It will be called at the end of each evaluation phase on the whole arrays of
-predictions/labels.
-
-To check if this works on practice, let's create a new :class:`~transformers.Trainer` with our fine-tuned model:
-
-.. code-block:: python
-
-    trainer = Trainer(
-        model=model,
-        args=training_args,
-        train_dataset=small_train_dataset,
-        eval_dataset=small_eval_dataset,
-        compute_metrics=compute_metrics,
-    )
-    trainer.evaluate()
-
-which showed an accuracy of 87.5% in our case.
-
-If you want to fine-tune your model and regularly report the evaluation metrics (for instance at the end of each
-epoch), here is how you should define your training arguments:
-
-.. code-block:: python
-
-    from transformers import TrainingArguments
-
-    training_args = TrainingArguments("test_trainer", evaluation_strategy="epoch")
-
-See the documentation of :class:`~transformers.TrainingArguments` for more options.
-
-
-.. _keras:
-
-Fine-tuning with Keras
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. raw:: html
-
-   <iframe width="560" height="315" src="https://www.youtube.com/embed/rnTGBy2ax1c" title="YouTube video player"
-   frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope;
-   picture-in-picture" allowfullscreen></iframe>
-
-Models can also be trained natively in TensorFlow using the Keras API. First, let's define our model:
-
-.. code-block:: python
-
-    import tensorflow as tf
-    from transformers import TFAutoModelForSequenceClassification
-
-    model = TFAutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=2)
-
-Then we will need to convert our datasets from before in standard :obj:`tf.data.Dataset`. Since we have fixed shapes,
-it can easily be done like this. First we remove the `"text"` column from our datasets and set them in TensorFlow
-format:
-
-.. code-block:: python
-
-    tf_train_dataset = small_train_dataset.remove_columns(["text"]).with_format("tensorflow")
-    tf_eval_dataset = small_eval_dataset.remove_columns(["text"]).with_format("tensorflow")
-
-Then we convert everything in big tensors and use the :obj:`tf.data.Dataset.from_tensor_slices` method:
-
-.. code-block:: python
-
-    train_features = {x: tf_train_dataset[x] for x in tokenizer.model_input_names}
-    train_tf_dataset = tf.data.Dataset.from_tensor_slices((train_features, tf_train_dataset["label"]))
-    train_tf_dataset = train_tf_dataset.shuffle(len(tf_train_dataset)).batch(8)
-
-    eval_features = {x: tf_eval_dataset[x] for x in tokenizer.model_input_names}
-    eval_tf_dataset = tf.data.Dataset.from_tensor_slices((eval_features, tf_eval_dataset["label"]))
-    eval_tf_dataset = eval_tf_dataset.batch(8)
-
-With this done, the model can then be compiled and trained as any Keras model:
-
-.. code-block:: python
-
-    model.compile(
-        optimizer=tf.keras.optimizers.Adam(learning_rate=5e-5),
-        loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
-        metrics=tf.metrics.SparseCategoricalAccuracy(),
-    )
-
-    model.fit(train_tf_dataset, validation_data=eval_tf_dataset, epochs=3)
-
-With the tight interoperability between TensorFlow and PyTorch models, you can even save the model and then reload it
-as a PyTorch model (or vice-versa):
-
-.. code-block:: python
-
-    from transformers import AutoModelForSequenceClassification
-
-    model.save_pretrained("my_imdb_model")
-    pytorch_model = AutoModelForSequenceClassification.from_pretrained("my_imdb_model", from_tf=True)
-
-.. _pytorch_native:
-
-Fine-tuning in native PyTorch
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. raw:: html
-
-   <iframe width="560" height="315" src="https://www.youtube.com/embed/Dh9CL8fyG80" title="YouTube video player"
-   frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope;
-   picture-in-picture" allowfullscreen></iframe>
-
-You might need to restart your notebook at this stage to free some memory, or execute the following code:
-
-.. code-block:: python
-
-    del model
-    del pytorch_model
-    del trainer
-    torch.cuda.empty_cache()
-
-Let's now see how to achieve the same results as in :ref:`trainer section <trainer>` in PyTorch. First we need to
-define the dataloaders, which we will use to iterate over batches. We just need to apply a bit of post-processing to
-our :obj:`tokenized_datasets` before doing that to:
-
- remove the columns corresponding to values the model does not expect (here the :obj:`"text"` column)
- rename the column :obj:`"label"` to :obj:`"labels"` (because the model expect the argument to be named :obj:`labels`)
- set the format of the datasets so they return PyTorch Tensors instead of lists.
-
-Our `tokenized_datasets` has one method for each of those steps:
-
-.. code-block:: python
-
-    tokenized_datasets = tokenized_datasets.remove_columns(["text"])
-    tokenized_datasets = tokenized_datasets.rename_column("label", "labels")
-    tokenized_datasets.set_format("torch")
-
-    small_train_dataset = tokenized_datasets["train"].shuffle(seed=42).select(range(1000))
-    small_eval_dataset = tokenized_datasets["test"].shuffle(seed=42).select(range(1000))
-
-Now that this is done, we can easily define our dataloaders:
-
-.. code-block:: python
-
-    from torch.utils.data import DataLoader
-
-    train_dataloader = DataLoader(small_train_dataset, shuffle=True, batch_size=8)
-    eval_dataloader = DataLoader(small_eval_dataset, batch_size=8)
-
-Next, we define our model:
-
-.. code-block:: python
-
-    from transformers import AutoModelForSequenceClassification
-
-    model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=2)
-
-We are almost ready to write our training loop, the only two things are missing are an optimizer and a learning rate
-scheduler. The default optimizer used by the :class:`~transformers.Trainer` is :class:`~transformers.AdamW`:
-
-.. code-block:: python
-
-    from transformers import AdamW
-
-    optimizer = AdamW(model.parameters(), lr=5e-5)
-
-Finally, the learning rate scheduler used by default is just a linear decay from the maximum value (5e-5 here) to 0:
-
-.. code-block:: python
-
-    from transformers import get_scheduler
-
-    num_epochs = 3
-    num_training_steps = num_epochs * len(train_dataloader)
-    lr_scheduler = get_scheduler(
-        "linear",
-        optimizer=optimizer,
-        num_warmup_steps=0,
-        num_training_steps=num_training_steps
-    )
-
-One last thing, we will want to use the GPU if we have access to one (otherwise training might take several hours
-instead of a couple of minutes). To do this, we define a :obj:`device` we will put our model and our batches on.
-
-.. code-block:: python
-
-    import torch
-
-    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
-    model.to(device)
-
-We now are ready to train! To get some sense of when it will be finished, we add a progress bar over our number of
-training steps, using the `tqdm` library.
-
-.. code-block:: python
-
-    from tqdm.auto import tqdm
-
-    progress_bar = tqdm(range(num_training_steps))
-
-    model.train()
-    for epoch in range(num_epochs):
-        for batch in train_dataloader:
-            batch = {k: v.to(device) for k, v in batch.items()}
-            outputs = model(**batch)
-            loss = outputs.loss
-            loss.backward()
-
-            optimizer.step()
-            lr_scheduler.step()
-            optimizer.zero_grad()
-            progress_bar.update(1)
-
-Note that if you are used to freezing the body of your pretrained model (like in computer vision) the above may seem a
-bit strange, as we are directly fine-tuning the whole model without taking any precaution. It actually works better
-this way for Transformers model (so this is not an oversight on our side). If you're not familiar with what "freezing
-the body" of the model means, forget you read this paragraph.
-
-Now to check the results, we need to write the evaluation loop. Like in the :ref:`trainer section <trainer>` we will
-use a metric from the datasets library. Here we accumulate the predictions at each batch before computing the final
-result when the loop is finished.
-
-.. code-block:: python
-
-    metric= load_metric("accuracy")
-    model.eval()
-    for batch in eval_dataloader:
-        batch = {k: v.to(device) for k, v in batch.items()}
-        with torch.no_grad():
-            outputs = model(**batch)
-
-        logits = outputs.logits
-        predictions = torch.argmax(logits, dim=-1)
-        metric.add_batch(predictions=predictions, references=batch["labels"])
-
-    metric.compute()
-
-
-.. _additional-resources:
-
-Additional resources
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-To look at more fine-tuning examples you can refer to:
-
- `🤗 Transformers Examples <https://github.com/huggingface/transformers/tree/master/examples>`__ which includes scripts
-  to train on all common NLP tasks in PyTorch and TensorFlow.
-
- `🤗 Transformers Notebooks <notebooks.html>`__ which contains various notebooks and in particular one per task (look
-  for the `how to finetune a model on xxx`).
--- a/docs/source/troubleshooting.md
+++ b/docs/source/troubleshooting.md
@@ -27,4 +27,4 @@ ValueError: Connection error, and we cannot find the requested files in the cach
 Please try again or make sure your Internet connection is on.
 ```

-One possible solution in this situation is to use the ["offline-mode"](https://huggingface.co/transformers/installation.html#offline-mode).
+One possible solution in this situation is to use the ["offline-mode"](installation#offline-mode).
--- a/examples/README.md
+++ b/examples/README.md
@@ -42,34 +42,35 @@ To browse the examples corresponding to released versions of 🤗 Transformers,

 <details>
  <summary>Examples for older versions of 🤗 Transformers</summary>
-
-  - [v4.5.1](https://github.com/huggingface/transformers/tree/v4.5.1/examples)
-  - [v4.4.2](https://github.com/huggingface/transformers/tree/v4.4.2/examples)
-  - [v4.3.3](https://github.com/huggingface/transformers/tree/v4.3.3/examples)
-  - [v4.2.2](https://github.com/huggingface/transformers/tree/v4.2.2/examples)
-  - [v4.1.1](https://github.com/huggingface/transformers/tree/v4.1.1/examples)
-  - [v4.0.1](https://github.com/huggingface/transformers/tree/v4.0.1/examples)
-  - [v3.5.1](https://github.com/huggingface/transformers/tree/v3.5.1/examples)
-  - [v3.4.0](https://github.com/huggingface/transformers/tree/v3.4.0/examples)
-  - [v3.3.1](https://github.com/huggingface/transformers/tree/v3.3.1/examples)
-  - [v3.2.0](https://github.com/huggingface/transformers/tree/v3.2.0/examples)
-  - [v3.1.0](https://github.com/huggingface/transformers/tree/v3.1.0/examples)
-  - [v3.0.2](https://github.com/huggingface/transformers/tree/v3.0.2/examples)
-  - [v2.11.0](https://github.com/huggingface/transformers/tree/v2.11.0/examples)
-  - [v2.10.0](https://github.com/huggingface/transformers/tree/v2.10.0/examples)
-  - [v2.9.1](https://github.com/huggingface/transformers/tree/v2.9.1/examples)
-  - [v2.8.0](https://github.com/huggingface/transformers/tree/v2.8.0/examples)
-  - [v2.7.0](https://github.com/huggingface/transformers/tree/v2.7.0/examples)
-  - [v2.6.0](https://github.com/huggingface/transformers/tree/v2.6.0/examples)
-  - [v2.5.1](https://github.com/huggingface/transformers/tree/v2.5.1/examples)
-  - [v2.4.0](https://github.com/huggingface/transformers/tree/v2.4.0/examples)
-  - [v2.3.0](https://github.com/huggingface/transformers/tree/v2.3.0/examples)
-  - [v2.2.0](https://github.com/huggingface/transformers/tree/v2.2.0/examples)
-  - [v2.1.1](https://github.com/huggingface/transformers/tree/v2.1.0/examples)
-  - [v2.0.0](https://github.com/huggingface/transformers/tree/v2.0.0/examples)
-  - [v1.2.0](https://github.com/huggingface/transformers/tree/v1.2.0/examples)
-  - [v1.1.0](https://github.com/huggingface/transformers/tree/v1.1.0/examples)
-  - [v1.0.0](https://github.com/huggingface/transformers/tree/v1.0.0/examples)
+	<ul>
+		<li><a href="https://github.com/huggingface/transformers/tree/v4.5.1/examples">v4.5.1</a></li>
+		<li><a href="https://github.com/huggingface/transformers/tree/v4.4.2/examples">v4.4.2</a></li>
+		<li><a href="https://github.com/huggingface/transformers/tree/v4.3.3/examples">v4.3.3</a></li>
+		<li><a href="https://github.com/huggingface/transformers/tree/v4.2.2/examples">v4.2.2</a></li>
+		<li><a href="https://github.com/huggingface/transformers/tree/v4.1.1/examples">v4.1.1</a></li>
+		<li><a href="https://github.com/huggingface/transformers/tree/v4.0.1/examples">v4.0.1</a></li>
+		<li><a href="https://github.com/huggingface/transformers/tree/v3.5.1/examples">v3.5.1</a></li>
+		<li><a href="https://github.com/huggingface/transformers/tree/v3.4.0/examples">v3.4.0</a></li>
+		<li><a href="https://github.com/huggingface/transformers/tree/v3.3.1/examples">v3.3.1</a></li>
+		<li><a href="https://github.com/huggingface/transformers/tree/v3.2.0/examples">v3.2.0</a></li>
+		<li><a href="https://github.com/huggingface/transformers/tree/v3.1.0/examples">v3.1.0</a></li>
+		<li><a href="https://github.com/huggingface/transformers/tree/v3.0.2/examples">v3.0.2</a></li>
+		<li><a href="https://github.com/huggingface/transformers/tree/v2.11.0/examples">v2.11.0</a></li>
+		<li><a href="https://github.com/huggingface/transformers/tree/v2.10.0/examples">v2.10.0</a></li>
+		<li><a href="https://github.com/huggingface/transformers/tree/v2.9.1/examples">v2.9.1</a></li>
+		<li><a href="https://github.com/huggingface/transformers/tree/v2.8.0/examples">v2.8.0</a></li>
+		<li><a href="https://github.com/huggingface/transformers/tree/v2.7.0/examples">v2.7.0</a></li>
+		<li><a href="https://github.com/huggingface/transformers/tree/v2.6.0/examples">v2.6.0</a></li>
+		<li><a href="https://github.com/huggingface/transformers/tree/v2.5.1/examples">v2.5.1</a></li>
+		<li><a href="https://github.com/huggingface/transformers/tree/v2.4.0/examples">v2.4.0</a></li>
+		<li><a href="https://github.com/huggingface/transformers/tree/v2.3.0/examples">v2.3.0</a></li>
+		<li><a href="https://github.com/huggingface/transformers/tree/v2.2.0/examples">v2.2.0</a></li>
+		<li><a href="https://github.com/huggingface/transformers/tree/v2.1.0/examples">v2.1.1</a></li>
+		<li><a href="https://github.com/huggingface/transformers/tree/v2.0.0/examples">v2.0.0</a></li>
+		<li><a href="https://github.com/huggingface/transformers/tree/v1.2.0/examples">v1.2.0</a></li>
+		<li><a href="https://github.com/huggingface/transformers/tree/v1.1.0/examples">v1.1.0</a></li>
+		<li><a href="https://github.com/huggingface/transformers/tree/v1.0.0/examples">v1.0.0</a></li>
+	</ul>
 </details>

 Alternatively, you can switch your cloned 🤗 Transformers to a specific version (for instance with v3.5.1) with
--- a/examples/flax/_tests_requirements.txt
+++ b/examples/flax/_tests_requirements.txt
@@ -0,0 +1,7 @@
+datasets >= 1.1.3
+pytest
+conllu
+nltk
+rouge-score
+seqeval
+tensorboard
--- a/examples/flax/conftest.py
+++ b/examples/flax/conftest.py
@@ -0,0 +1,45 @@
+# 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.
+
+# tests directory-specific settings - this file is run automatically
+# by pytest before any tests are run
+
+import sys
+import warnings
+from os.path import abspath, dirname, join
+
+
+# allow having multiple repository checkouts and not needing to remember to rerun
+# 'pip install -e .[dev]' when switching between checkouts and running tests.
+git_repo_path = abspath(join(dirname(dirname(dirname(__file__))), "src"))
+sys.path.insert(1, git_repo_path)
+
+
+# silence FutureWarning warnings in tests since often we can't act on them until
+# they become normal warnings - i.e. the tests still need to test the current functionality
+warnings.simplefilter(action="ignore", category=FutureWarning)
+
+
+def pytest_addoption(parser):
+    from transformers.testing_utils import pytest_addoption_shared
+
+    pytest_addoption_shared(parser)
+
+
+def pytest_terminal_summary(terminalreporter):
+    from transformers.testing_utils import pytest_terminal_summary_main
+
+    make_reports = terminalreporter.config.getoption("--make-reports")
+    if make_reports:
+        pytest_terminal_summary_main(terminalreporter, id=make_reports)
--- a/examples/flax/language-modeling/requirements.txt
+++ b/examples/flax/language-modeling/requirements.txt
@@ -1,5 +1,5 @@
 datasets >= 1.1.3
 jax>=0.2.8
 jaxlib>=0.1.59
-flax>=0.3.4
+flax>=0.3.5
 optax>=0.0.9
--- a/examples/flax/language-modeling/run_clm_flax.py
+++ b/examples/flax/language-modeling/run_clm_flax.py
@@ -17,16 +17,18 @@
 Pre-training/Fine-tuning the library models for causal language modeling (GPT, GPT-2, CTRL, ...) on a text file or a dataset.

 Here is the full list of checkpoints on the hub that can be fine-tuned by this script:
-https://huggingface.co/models?filter=causal-lm
+https://huggingface.co/models?filter=text-generation
 """
 # You can also adapt this script on your own causal language modeling task. Pointers for this are left as comments.

+import json
 import logging
 import math
 import os
 import sys
 import time
 from dataclasses import dataclass, field
+from itertools import chain
 from pathlib import Path
 from typing import Callable, Optional

@@ -430,7 +432,7 @@ def main():
    # Main data processing function that will concatenate all texts from our dataset and generate chunks of block_size.
    def group_texts(examples):
        # Concatenate all texts.
-        concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
+        concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()}
        total_length = len(concatenated_examples[list(examples.keys())[0]])
        # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
        # customize this part to your needs.
@@ -671,6 +673,32 @@ def main():
                    if training_args.push_to_hub:
                        repo.push_to_hub(commit_message=f"Saving weights and logs of step {cur_step}", blocking=False)

+    # Eval after training
+    if training_args.do_eval:
+        eval_metrics = []
+        eval_loader = data_loader(input_rng, eval_dataset, eval_batch_size)
+        eval_steps = len(eval_dataset) // eval_batch_size
+        for _ in tqdm(range(eval_steps), desc="Evaluating...", position=2, leave=False):
+            # Model forward
+            batch = shard(next(eval_loader))
+            metrics = p_eval_step(state.params, batch)
+            eval_metrics.append(metrics)
+
+        # normalize eval metrics
+        eval_metrics = get_metrics(eval_metrics)
+        eval_metrics = jax.tree_map(lambda x: jnp.mean(x).item(), eval_metrics)
+
+        try:
+            eval_metrics["perplexity"] = math.exp(eval_metrics["loss"])
+        except OverflowError:
+            eval_metrics["perplexity"] = float("inf")
+
+        if jax.process_index() == 0:
+            eval_metrics = {f"eval_{metric_name}": value for metric_name, value in eval_metrics.items()}
+            path = os.path.join(training_args.output_dir, "eval_results.json")
+            with open(path, "w") as f:
+                json.dump(eval_metrics, f, indent=4, sort_keys=True)
+

 if __name__ == "__main__":
    main()
--- a/examples/flax/language-modeling/run_mlm_flax.py
+++ b/examples/flax/language-modeling/run_mlm_flax.py
@@ -18,13 +18,16 @@ Fine-tuning the library models for masked language modeling (BERT, ALBERT, RoBER
 text file or a dataset.

 Here is the full list of checkpoints on the hub that can be fine-tuned by this script:
-https://huggingface.co/models?filter=masked-lm
+https://huggingface.co/models?filter=fill-mask
 """
+import json
 import logging
+import math
 import os
 import sys
 import time
 from dataclasses import dataclass, field
+from itertools import chain

 # You can also adapt this script on your own masked language modeling task. Pointers for this are left as comments.
 from pathlib import Path
@@ -270,7 +273,7 @@ def write_eval_metric(summary_writer, eval_metrics, step):
        summary_writer.scalar(f"eval_{metric_name}", value, step)


-if __name__ == "__main__":
+def main():
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.
@@ -453,7 +456,7 @@ if __name__ == "__main__":
        # max_seq_length.
        def group_texts(examples):
            # Concatenate all texts.
-            concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
+            concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()}
            total_length = len(concatenated_examples[list(examples.keys())[0]])
            # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
            # customize this part to your needs.
@@ -699,3 +702,41 @@ if __name__ == "__main__":
                    tokenizer.save_pretrained(training_args.output_dir)
                    if training_args.push_to_hub:
                        repo.push_to_hub(commit_message=f"Saving weights and logs of step {cur_step}", blocking=False)
+
+    # Eval after training
+    if training_args.do_eval:
+        num_eval_samples = len(tokenized_datasets["validation"])
+        eval_samples_idx = jnp.arange(num_eval_samples)
+        eval_batch_idx = generate_batch_splits(eval_samples_idx, eval_batch_size)
+
+        eval_metrics = []
+        for _, batch_idx in enumerate(tqdm(eval_batch_idx, desc="Evaluating ...", position=2)):
+            samples = [tokenized_datasets["validation"][int(idx)] for idx in batch_idx]
+            model_inputs = data_collator(samples, pad_to_multiple_of=16)
+
+            # Model forward
+            model_inputs = shard(model_inputs.data)
+            metrics = p_eval_step(state.params, model_inputs)
+            eval_metrics.append(metrics)
+
+        # normalize eval metrics
+        eval_metrics = get_metrics(eval_metrics)
+        eval_metrics = jax.tree_map(lambda metric: jnp.sum(metric).item(), eval_metrics)
+        eval_normalizer = eval_metrics.pop("normalizer")
+        eval_metrics = jax.tree_map(lambda x: x / eval_normalizer, eval_metrics)
+
+        try:
+            perplexity = math.exp(eval_metrics["loss"])
+        except OverflowError:
+            perplexity = float("inf")
+        eval_metrics["perplexity"] = perplexity
+
+        if jax.process_index() == 0:
+            eval_metrics = {f"eval_{metric_name}": value for metric_name, value in eval_metrics.items()}
+            path = os.path.join(training_args.output_dir, "eval_results.json")
+            with open(path, "w") as f:
+                json.dump(eval_metrics, f, indent=4, sort_keys=True)
+
+
+if __name__ == "__main__":
+    main()
--- a/examples/flax/language-modeling/run_t5_mlm_flax.py
+++ b/examples/flax/language-modeling/run_t5_mlm_flax.py
@@ -20,11 +20,13 @@ Here is the full list of checkpoints on the hub that can be pretrained by this s
 https://huggingface.co/models?filter=t5
 """
 # You can also adapt this script on your own masked language modeling task. Pointers for this are left as comments.
+import json
 import logging
 import os
 import sys
 import time
 from dataclasses import dataclass, field
+from itertools import chain
 from pathlib import Path
 from typing import Dict, List, Optional

@@ -290,7 +292,7 @@ class FlaxDataCollatorForT5MLM:
        start_indices[:, 0] = mask_indices[:, 0]

        sentinel_ids = np.where(start_indices != 0, np.cumsum(start_indices, axis=-1), start_indices)
-        sentinel_ids = np.where(sentinel_ids != 0, (sentinel_ids + self.tokenizer.vocab_size - 1), 0)
+        sentinel_ids = np.where(sentinel_ids != 0, (len(self.tokenizer) - sentinel_ids), 0)
        sentinel_ids -= mask_indices - start_indices

        return sentinel_ids
@@ -400,7 +402,7 @@ def write_eval_metric(summary_writer, eval_metrics, step):
        summary_writer.scalar(f"eval_{metric_name}", value, step)


-if __name__ == "__main__":
+def main():
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.
@@ -521,9 +523,7 @@ if __name__ == "__main__":
            model_args.config_name, cache_dir=model_args.cache_dir, vocab_size=len(tokenizer)
        )
    elif model_args.model_name_or_path:
-        config = T5Config.from_pretrained(
-            model_args.model_name_or_path, cache_dir=model_args.cache_dir, vocab_size=len(tokenizer)
-        )
+        config = T5Config.from_pretrained(model_args.model_name_or_path, cache_dir=model_args.cache_dir)
    else:
        config = CONFIG_MAPPING[model_args.model_type]()
        logger.warning("You are instantiating a new config instance from scratch.")
@@ -563,7 +563,7 @@ if __name__ == "__main__":
    # Main data processing function that will concatenate all texts from our dataset and generate chunks of expanded_inputs_length.
    def group_texts(examples):
        # Concatenate all texts.
-        concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
+        concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()}
        total_length = len(concatenated_examples[list(examples.keys())[0]])
        # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
        # customize this part to your needs.
@@ -616,6 +616,7 @@ if __name__ == "__main__":
            model_args.model_name_or_path, config=config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype)
        )
    else:
+        config.vocab_size = len(tokenizer)
        model = FlaxT5ForConditionalGeneration(config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype))

    # Data collator
@@ -807,3 +808,33 @@ if __name__ == "__main__":
                    tokenizer.save_pretrained(training_args.output_dir)
                    if training_args.push_to_hub:
                        repo.push_to_hub(commit_message=f"Saving weights and logs of step {cur_step}", blocking=False)
+
+    # Eval after training
+    if training_args.do_eval:
+        num_eval_samples = len(tokenized_datasets["validation"])
+        eval_samples_idx = jnp.arange(num_eval_samples)
+        eval_batch_idx = generate_batch_splits(eval_samples_idx, eval_batch_size)
+
+        eval_metrics = []
+        for i, batch_idx in enumerate(tqdm(eval_batch_idx, desc="Evaluating ...", position=2)):
+            samples = [tokenized_datasets["validation"][int(idx)] for idx in batch_idx]
+            model_inputs = data_collator(samples)
+
+            # Model forward
+            model_inputs = shard(model_inputs.data)
+            metrics = p_eval_step(state.params, model_inputs)
+            eval_metrics.append(metrics)
+
+        # get eval metrics
+        eval_metrics = get_metrics(eval_metrics)
+        eval_metrics = jax.tree_map(lambda metric: jnp.mean(metric).item(), eval_metrics)
+
+        if jax.process_index() == 0:
+            eval_metrics = {f"eval_{metric_name}": value for metric_name, value in eval_metrics.items()}
+            path = os.path.join(training_args.output_dir, "eval_results.json")
+            with open(path, "w") as f:
+                json.dump(eval_metrics, f, indent=4, sort_keys=True)
+
+
+if __name__ == "__main__":
+    main()
--- a/examples/flax/question-answering/requirements.txt
+++ b/examples/flax/question-answering/requirements.txt
@@ -1,5 +1,5 @@
 datasets >= 1.8.0
 jax>=0.2.17
 jaxlib>=0.1.68
-flax>=0.3.4
+flax>=0.3.5
 optax>=0.0.8
--- a/examples/flax/question-answering/run_qa.py
+++ b/examples/flax/question-answering/run_qa.py
@@ -18,6 +18,7 @@ Fine-tuning the library models for question answering.
 """
 # You can also adapt this script on your own question answering task. Pointers for this are left as comments.

+import json
 import logging
 import os
 import random
@@ -39,7 +40,6 @@ import optax
 import transformers
 from flax import struct, traverse_util
 from flax.jax_utils import replicate, unreplicate
-from flax.metrics import tensorboard
 from flax.training import train_state
 from flax.training.common_utils import get_metrics, onehot, shard
 from huggingface_hub import Repository
@@ -51,6 +51,7 @@ from transformers import (
    HfArgumentParser,
    PreTrainedTokenizerFast,
    TrainingArguments,
+    is_tensorboard_available,
 )
 from transformers.file_utils import get_full_repo_name
 from transformers.utils import check_min_version
@@ -60,7 +61,7 @@ from utils_qa import postprocess_qa_predictions
 logger = logging.getLogger(__name__)

 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.12.0")
+check_min_version("4.13.0")

 Array = Any
 Dataset = datasets.arrow_dataset.Dataset
@@ -715,8 +716,23 @@ def main():
        logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")

    # Define a summary writer
-    summary_writer = tensorboard.SummaryWriter(training_args.output_dir)
-    summary_writer.hparams({**training_args.to_dict(), **vars(model_args), **vars(data_args)})
+    has_tensorboard = is_tensorboard_available()
+    if has_tensorboard and jax.process_index() == 0:
+        try:
+            from flax.metrics.tensorboard import SummaryWriter
+
+            summary_writer = SummaryWriter(training_args.output_dir)
+            summary_writer.hparams({**training_args.to_dict(), **vars(model_args), **vars(data_args)})
+        except ImportError as ie:
+            has_tensorboard = False
+            logger.warning(
+                f"Unable to display metrics through TensorBoard because some package are not installed: {ie}"
+            )
+    else:
+        logger.warning(
+            "Unable to display metrics through TensorBoard because the package is not installed: "
+            "Please run pip install tensorboard to enable."
+        )

    def write_train_metric(summary_writer, train_metrics, train_time, step):
        summary_writer.scalar("train_time", train_time, step)
@@ -832,7 +848,7 @@ def main():
                # Save metrics
                train_metric = unreplicate(train_metric)
                train_time += time.time() - train_start
-                if jax.process_index() == 0:
+                if has_tensorboard and jax.process_index() == 0:
                    write_train_metric(summary_writer, train_metrics, train_time, cur_step)

                epochs.write(
@@ -897,7 +913,7 @@ def main():

                logger.info(f"Step... ({cur_step}/{total_steps} | Evaluation metrics: {eval_metrics})")

-                if jax.process_index() == 0:
+                if has_tensorboard and jax.process_index() == 0:
                    write_eval_metric(summary_writer, eval_metrics, cur_step)

            if (cur_step % training_args.save_steps == 0 and cur_step > 0) or (cur_step == total_steps):
@@ -911,6 +927,58 @@ def main():
        epochs.desc = f"Epoch ... {epoch + 1}/{num_epochs}"
    # endregion

+    # Eval after training
+    if training_args.do_eval:
+        eval_metrics = {}
+        all_start_logits = []
+        all_end_logits = []
+
+        eva_loader = eval_data_collator(eval_dataset, eval_batch_size)
+        for batch in tqdm(eva_loader, total=len(eval_dataset) // eval_batch_size, desc="Evaluating ...", position=2):
+            _ = batch.pop("example_id")
+            _ = batch.pop("offset_mapping")
+            predictions = p_eval_step(state, batch)
+            start_logits = np.array([pred for pred in chain(*predictions[0])])
+            end_logits = np.array([pred for pred in chain(*predictions[1])])
+            all_start_logits.append(start_logits)
+            all_end_logits.append(end_logits)
+
+        # evaluate also on leftover examples (not divisible by batch_size)
+        num_leftover_samples = len(eval_dataset) % eval_batch_size
+
+        # make sure leftover batch is evaluated on one device
+        if num_leftover_samples > 0 and jax.process_index() == 0:
+            # take leftover samples
+            batch = eval_dataset[-num_leftover_samples:]
+            batch = {k: np.array(v) for k, v in batch.items()}
+            _ = batch.pop("example_id")
+            _ = batch.pop("offset_mapping")
+
+            predictions = eval_step(unreplicate(state), batch)
+            start_logits = np.array([pred for pred in predictions[0]])
+            end_logits = np.array([pred for pred in predictions[1]])
+            all_start_logits.append(start_logits)
+            all_end_logits.append(end_logits)
+
+        max_len = max([x.shape[1] for x in all_start_logits])  # Get the max_length of the tensor
+
+        # concatenate the numpy array
+        start_logits_concat = create_and_fill_np_array(all_start_logits, eval_dataset, max_len)
+        end_logits_concat = create_and_fill_np_array(all_end_logits, eval_dataset, max_len)
+
+        # delete the list of numpy arrays
+        del all_start_logits
+        del all_end_logits
+        outputs_numpy = (start_logits_concat, end_logits_concat)
+        prediction = post_processing_function(eval_examples, eval_dataset, outputs_numpy)
+        eval_metrics = compute_metrics(prediction)
+
+        if jax.process_index() == 0:
+            eval_metrics = {f"eval_{metric_name}": value for metric_name, value in eval_metrics.items()}
+            path = os.path.join(training_args.output_dir, "eval_results.json")
+            with open(path, "w") as f:
+                json.dump(eval_metrics, f, indent=4, sort_keys=True)
+

 if __name__ == "__main__":
    main()
--- a/examples/flax/summarization/requirements.txt
+++ b/examples/flax/summarization/requirements.txt
@@ -1,5 +1,5 @@
 datasets >= 1.1.3
 jax>=0.2.8
 jaxlib>=0.1.59
-flax>=0.3.4
+flax>=0.3.5
 optax>=0.0.8
--- a/examples/flax/summarization/run_summarization_flax.py
+++ b/examples/flax/summarization/run_summarization_flax.py
@@ -18,6 +18,7 @@ Fine-tuning the library models for summarization.
 """
 # You can also adapt this script on your own sequence to sequence task. Pointers for this are left as comments.

+import json
 import logging
 import os
 import sys
@@ -816,6 +817,13 @@ def main():
        desc = f"Predict Loss: {pred_metrics['loss']} | {rouge_desc})"
        logger.info(desc)

+        # save final metrics in json
+        if jax.process_index() == 0:
+            rouge_metrics = {f"test_{metric_name}": value for metric_name, value in rouge_metrics.items()}
+            path = os.path.join(training_args.output_dir, "test_results.json")
+            with open(path, "w") as f:
+                json.dump(rouge_metrics, f, indent=4, sort_keys=True)
+

 if __name__ == "__main__":
    main()
--- a/examples/flax/test_examples.py
+++ b/examples/flax/test_examples.py
@@ -0,0 +1,274 @@
+# coding=utf-8
+# Copyright 2021 HuggingFace Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import argparse
+import json
+import logging
+import os
+import sys
+from unittest.mock import patch
+
+from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
+
+
+SRC_DIRS = [
+    os.path.join(os.path.dirname(__file__), dirname)
+    for dirname in [
+        "text-classification",
+        "language-modeling",
+        "summarization",
+        "token-classification",
+        "question-answering",
+    ]
+]
+sys.path.extend(SRC_DIRS)
+
+
+if SRC_DIRS is not None:
+    import run_clm_flax
+    import run_flax_glue
+    import run_flax_ner
+    import run_mlm_flax
+    import run_qa
+    import run_summarization_flax
+    import run_t5_mlm_flax
+
+
+logging.basicConfig(level=logging.DEBUG)
+
+logger = logging.getLogger()
+
+
+def get_setup_file():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-f")
+    args = parser.parse_args()
+    return args.f
+
+
+def get_results(output_dir, split="eval"):
+    results = {}
+    path = os.path.join(output_dir, f"{split}_results.json")
+    if os.path.exists(path):
+        with open(path, "r") as f:
+            results = json.load(f)
+    else:
+        raise ValueError(f"can't find {path}")
+    return results
+
+
+class ExamplesTests(TestCasePlus):
+    def test_run_glue(self):
+        stream_handler = logging.StreamHandler(sys.stdout)
+        logger.addHandler(stream_handler)
+
+        tmp_dir = self.get_auto_remove_tmp_dir()
+        testargs = f"""
+            run_glue.py
+            --model_name_or_path distilbert-base-uncased
+            --output_dir {tmp_dir}
+            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
+            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
+            --per_device_train_batch_size=2
+            --per_device_eval_batch_size=1
+            --learning_rate=1e-4
+            --max_train_steps=10
+            --num_warmup_steps=2
+            --seed=42
+            --max_length=128
+            """.split()
+
+        with patch.object(sys, "argv", testargs):
+            run_flax_glue.main()
+            result = get_results(tmp_dir)
+            self.assertGreaterEqual(result["eval_accuracy"], 0.75)
+
+    @slow
+    def test_run_clm(self):
+        stream_handler = logging.StreamHandler(sys.stdout)
+        logger.addHandler(stream_handler)
+
+        tmp_dir = self.get_auto_remove_tmp_dir()
+        testargs = f"""
+            run_clm_flax.py
+            --model_name_or_path distilgpt2
+            --train_file ./tests/fixtures/sample_text.txt
+            --validation_file ./tests/fixtures/sample_text.txt
+            --do_train
+            --do_eval
+            --block_size 128
+            --per_device_train_batch_size 4
+            --per_device_eval_batch_size 4
+            --num_train_epochs 2
+            --logging_steps 2 --eval_steps 2
+            --output_dir {tmp_dir}
+            --overwrite_output_dir
+            """.split()
+
+        with patch.object(sys, "argv", testargs):
+            run_clm_flax.main()
+            result = get_results(tmp_dir)
+            self.assertLess(result["eval_perplexity"], 100)
+
+    @slow
+    def test_run_summarization(self):
+        stream_handler = logging.StreamHandler(sys.stdout)
+        logger.addHandler(stream_handler)
+
+        tmp_dir = self.get_auto_remove_tmp_dir()
+        testargs = f"""
+            run_summarization.py
+            --model_name_or_path t5-small
+            --train_file tests/fixtures/tests_samples/xsum/sample.json
+            --validation_file tests/fixtures/tests_samples/xsum/sample.json
+            --test_file tests/fixtures/tests_samples/xsum/sample.json
+            --output_dir {tmp_dir}
+            --overwrite_output_dir
+            --max_steps=50
+            --warmup_steps=8
+            --do_train
+            --do_eval
+            --do_predict
+            --learning_rate=2e-4
+            --per_device_train_batch_size=2
+            --per_device_eval_batch_size=1
+            --predict_with_generate
+        """.split()
+
+        with patch.object(sys, "argv", testargs):
+            run_summarization_flax.main()
+            result = get_results(tmp_dir, split="test")
+            self.assertGreaterEqual(result["test_rouge1"], 10)
+            self.assertGreaterEqual(result["test_rouge2"], 2)
+            self.assertGreaterEqual(result["test_rougeL"], 7)
+            self.assertGreaterEqual(result["test_rougeLsum"], 7)
+
+    @slow
+    def test_run_mlm(self):
+        stream_handler = logging.StreamHandler(sys.stdout)
+        logger.addHandler(stream_handler)
+
+        tmp_dir = self.get_auto_remove_tmp_dir()
+        testargs = f"""
+            run_mlm.py
+            --model_name_or_path distilroberta-base
+            --train_file ./tests/fixtures/sample_text.txt
+            --validation_file ./tests/fixtures/sample_text.txt
+            --output_dir {tmp_dir}
+            --overwrite_output_dir
+            --max_seq_length 128
+            --per_device_train_batch_size 4
+            --per_device_eval_batch_size 4
+            --logging_steps 2 --eval_steps 2
+            --do_train
+            --do_eval
+            --num_train_epochs=1
+        """.split()
+
+        with patch.object(sys, "argv", testargs):
+            run_mlm_flax.main()
+            result = get_results(tmp_dir)
+            self.assertLess(result["eval_perplexity"], 42)
+
+    @slow
+    def test_run_t5_mlm(self):
+        stream_handler = logging.StreamHandler(sys.stdout)
+        logger.addHandler(stream_handler)
+
+        tmp_dir = self.get_auto_remove_tmp_dir()
+        testargs = f"""
+            run_t5_mlm_flax.py
+            --model_name_or_path t5-small
+            --train_file ./tests/fixtures/sample_text.txt
+            --validation_file ./tests/fixtures/sample_text.txt
+            --do_train
+            --do_eval
+            --max_seq_length 128
+            --per_device_train_batch_size 4
+            --per_device_eval_batch_size 4
+            --num_train_epochs 2
+            --logging_steps 2 --eval_steps 2
+            --output_dir {tmp_dir}
+            --overwrite_output_dir
+            """.split()
+
+        with patch.object(sys, "argv", testargs):
+            run_t5_mlm_flax.main()
+            result = get_results(tmp_dir)
+            self.assertGreaterEqual(result["eval_accuracy"], 0.42)
+
+    @slow
+    def test_run_ner(self):
+        stream_handler = logging.StreamHandler(sys.stdout)
+        logger.addHandler(stream_handler)
+
+        # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
+        epochs = 7 if get_gpu_count() > 1 else 2
+
+        tmp_dir = self.get_auto_remove_tmp_dir()
+        testargs = f"""
+            run_flax_ner.py
+            --model_name_or_path bert-base-uncased
+            --train_file tests/fixtures/tests_samples/conll/sample.json
+            --validation_file tests/fixtures/tests_samples/conll/sample.json
+            --output_dir {tmp_dir}
+            --overwrite_output_dir
+            --do_train
+            --do_eval
+            --warmup_steps=2
+            --learning_rate=2e-4
+            --logging_steps 2 --eval_steps 2
+            --per_device_train_batch_size=2
+            --per_device_eval_batch_size=2
+            --num_train_epochs={epochs}
+            --seed 7
+        """.split()
+
+        with patch.object(sys, "argv", testargs):
+            run_flax_ner.main()
+            result = get_results(tmp_dir)
+            self.assertGreaterEqual(result["eval_accuracy"], 0.75)
+            self.assertGreaterEqual(result["eval_f1"], 0.3)
+
+    @slow
+    def test_run_qa(self):
+        stream_handler = logging.StreamHandler(sys.stdout)
+        logger.addHandler(stream_handler)
+
+        tmp_dir = self.get_auto_remove_tmp_dir()
+        testargs = f"""
+            run_qa.py
+            --model_name_or_path bert-base-uncased
+            --version_2_with_negative
+            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
+            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
+            --output_dir {tmp_dir}
+            --overwrite_output_dir
+            --max_steps=10
+            --warmup_steps=2
+            --do_train
+            --do_eval
+            --logging_steps 2 --eval_steps 2
+            --learning_rate=2e-4
+            --per_device_train_batch_size=2
+            --per_device_eval_batch_size=1
+        """.split()
+
+        with patch.object(sys, "argv", testargs):
+            run_qa.main()
+            result = get_results(tmp_dir)
+            self.assertGreaterEqual(result["eval_f1"], 30)
+            self.assertGreaterEqual(result["eval_exact"], 30)
--- a/examples/flax/text-classification/requirements.txt
+++ b/examples/flax/text-classification/requirements.txt
@@ -1,5 +1,5 @@
 datasets >= 1.1.3
 jax>=0.2.8
 jaxlib>=0.1.59
-flax>=0.3.4
+flax>=0.3.5
 optax>=0.0.8
--- a/examples/flax/text-classification/run_flax_glue.py
+++ b/examples/flax/text-classification/run_flax_glue.py
@@ -15,6 +15,7 @@
 # limitations under the License.
 """ Finetuning a 🤗 Flax Transformers model for sequence classification on GLUE."""
 import argparse
+import json
 import logging
 import os
 import random
@@ -32,11 +33,16 @@ import optax
 import transformers
 from flax import struct, traverse_util
 from flax.jax_utils import replicate, unreplicate
-from flax.metrics import tensorboard
 from flax.training import train_state
 from flax.training.common_utils import get_metrics, onehot, shard
 from huggingface_hub import Repository
-from transformers import AutoConfig, AutoTokenizer, FlaxAutoModelForSequenceClassification, PretrainedConfig
+from transformers import (
+    AutoConfig,
+    AutoTokenizer,
+    FlaxAutoModelForSequenceClassification,
+    PretrainedConfig,
+    is_tensorboard_available,
+)
 from transformers.file_utils import get_full_repo_name


@@ -403,8 +409,23 @@ def main():
        logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")

    # Define a summary writer
-    summary_writer = tensorboard.SummaryWriter(args.output_dir)
-    summary_writer.hparams(vars(args))
+    has_tensorboard = is_tensorboard_available()
+    if has_tensorboard and jax.process_index() == 0:
+        try:
+            from flax.metrics.tensorboard import SummaryWriter
+
+            summary_writer = SummaryWriter(args.output_dir)
+            summary_writer.hparams(vars(args))
+        except ImportError as ie:
+            has_tensorboard = False
+            logger.warning(
+                f"Unable to display metrics through TensorBoard because some package are not installed: {ie}"
+            )
+    else:
+        logger.warning(
+            "Unable to display metrics through TensorBoard because the package is not installed: "
+            "Please run pip install tensorboard to enable."
+        )

    def write_metric(train_metrics, eval_metrics, train_time, step):
        summary_writer.scalar("train_time", train_time, step)
@@ -512,7 +533,10 @@ def main():
        logger.info(f"    Done! Eval metrics: {eval_metric}")

        cur_step = epoch * (len(train_dataset) // train_batch_size)
-        write_metric(train_metrics, eval_metric, train_time, cur_step)
+
+        # Save metrics
+        if has_tensorboard and jax.process_index() == 0:
+            write_metric(train_metrics, eval_metric, train_time, cur_step)

        # save checkpoint after each epoch and push checkpoint to the hub
        if jax.process_index() == 0:
@@ -522,6 +546,13 @@ def main():
            if args.push_to_hub:
                repo.push_to_hub(commit_message=f"Saving weights and logs of epoch {epoch}", blocking=False)

+    # save the eval metrics in json
+    if jax.process_index() == 0:
+        eval_metric = {f"eval_{metric_name}": value for metric_name, value in eval_metric.items()}
+        path = os.path.join(args.output_dir, "eval_results.json")
+        with open(path, "w") as f:
+            json.dump(eval_metric, f, indent=4, sort_keys=True)
+

 if __name__ == "__main__":
    main()
--- a/examples/flax/token-classification/requirements.txt
+++ b/examples/flax/token-classification/requirements.txt
@@ -1,6 +1,6 @@
 datasets >= 1.8.0
 jax>=0.2.8
 jaxlib>=0.1.59
-flax>=0.3.4
+flax>=0.3.5
 optax>=0.0.8
 seqeval
--- a/Show More
+++ b/Show More