Release: v4.2.2

* fix tf led

* remove loop file

.circleci/TROUBLESHOOT.md

@@ -1,7 +0,0 @@
-# Troubleshooting
-
-This is a document explaining how to deal with various issues on Circle-CI. The entries may include actually solutions or pointers to Issues that cover those.
-
-## Circle CI
-
-* pytest worker runs out of resident RAM and gets killed by `cgroups`: https://github.com/huggingface/transformers/issues/11408

.circleci/config.yml

@@ -3,6 +3,7 @@ orbs:
     gcp-gke: circleci/gcp-gke@1.0.4
     go: circleci/go@1.3.0
 
+
 # TPU REFERENCES
 references:
     checkout_ml_testing: &checkout_ml_testing
@@ -68,8 +69,6 @@ jobs:
             - image: circleci/python:3.6
         environment:
             OMP_NUM_THREADS: 1
-            RUN_PT_TF_CROSS_TESTS: yes
-            TRANSFORMERS_IS_CI: yes
         resource_class: xlarge
         parallelism: 1
         steps:
@@ -78,119 +77,14 @@ jobs:
                   keys:
                       - v0.4-torch_and_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,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,tf-cpu,torch,testing,sentencepiece]
+            - run: pip install tapas torch-scatter -f https://pytorch-geometric.com/whl/torch-1.7.0+cpu.html
             - save_cache:
                 key: v0.4-{{ checksum "setup.py" }}
                 paths:
                     - '~/.cache/pip'
-            - run: python utils/tests_fetcher.py | 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 -rA -s --make-reports=tests_torch_and_tf $(cat test_list.txt) -m is_pt_tf_cross_test --durations=0 | tee tests_output.txt
-                  fi
-            - store_artifacts:
-                  path: ~/transformers/tests_output.txt
-            - store_artifacts:
-                  path: ~/transformers/reports
-    
-    run_tests_torch_and_tf_all:
-        working_directory: ~/transformers
-        docker:
-            - image: circleci/python:3.6
-        environment:
-            OMP_NUM_THREADS: 1
-            RUN_PT_TF_CROSS_TESTS: yes
-            TRANSFORMERS_IS_CI: yes
-        resource_class: xlarge
-        parallelism: 1
-        steps:
-            - checkout
-            - restore_cache:
-                  keys:
-                      - v0.4-torch_and_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,torch,testing,sentencepiece,torch-speech,vision]
-            - run: pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.9.0+cpu.html
-            - save_cache:
-                key: v0.4-{{ checksum "setup.py" }}
-                paths:
-                    - '~/.cache/pip'
-            - run: |
-                  python -m pytest -n 8 --dist=loadfile -rA -s --make-reports=tests_torch_and_tf tests -m is_pt_tf_cross_test --durations=0 | tee tests_output.txt
-            - store_artifacts:
-                  path: ~/transformers/tests_output.txt
-            - store_artifacts:
-                  path: ~/transformers/reports
-
-    run_tests_torch_and_flax:
-        working_directory: ~/transformers
-        docker:
-            - image: circleci/python:3.6
-        environment:
-            OMP_NUM_THREADS: 1
-            RUN_PT_FLAX_CROSS_TESTS: yes
-            TRANSFORMERS_IS_CI: yes
-        resource_class: xlarge
-        parallelism: 1
-        steps:
-            - checkout
-            - restore_cache:
-                  keys:
-                      - v0.4-torch_and_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: 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
-            - save_cache:
-                key: v0.4-{{ checksum "setup.py" }}
-                paths:
-                    - '~/.cache/pip'
-            - run: python utils/tests_fetcher.py | 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 -rA -s --make-reports=tests_torch_and_flax $(cat test_list.txt) -m is_pt_flax_cross_test --durations=0 | tee tests_output.txt
-                  fi
-            - store_artifacts:
-                  path: ~/transformers/tests_output.txt
-            - store_artifacts:
-                  path: ~/transformers/reports
-    
-    run_tests_torch_and_flax_all:
-        working_directory: ~/transformers
-        docker:
-            - image: circleci/python:3.6
-        environment:
-            OMP_NUM_THREADS: 1
-            RUN_PT_FLAX_CROSS_TESTS: yes
-            TRANSFORMERS_IS_CI: yes
-        resource_class: xlarge
-        parallelism: 1
-        steps:
-            - checkout
-            - restore_cache:
-                  keys:
-                      - v0.4-torch_and_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: 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
-            - save_cache:
-                key: v0.4-{{ checksum "setup.py" }}
-                paths:
-                    - '~/.cache/pip'
-            - run: |
-                  python -m pytest -n 8 --dist=loadfile -rA -s --make-reports=tests_torch_and_flax tests -m is_pt_flax_cross_test --durations=0 | tee tests_output.txt
+            - run: RUN_PT_TF_CROSS_TESTS=1 python -m pytest -n 8 --dist=loadfile -rA -s --make-reports=tests_torch_and_tf ./tests/ -m is_pt_tf_cross_test --durations=0 | tee tests_output.txt
             - store_artifacts:
                   path: ~/transformers/tests_output.txt
             - store_artifacts:
@@ -202,7 +96,6 @@ jobs:
             - image: circleci/python:3.7
         environment:
             OMP_NUM_THREADS: 1
-            TRANSFORMERS_IS_CI: yes
         resource_class: xlarge
         parallelism: 1
         steps:
@@ -211,51 +104,14 @@ jobs:
                   keys:
                       - v0.4-torch-{{ 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,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 .[sklearn,torch,testing,sentencepiece]
+            - run: pip install tapas torch-scatter -f https://pytorch-geometric.com/whl/torch-1.7.0+cpu.html
             - save_cache:
                   key: v0.4-torch-{{ checksum "setup.py" }}
                   paths:
                       - '~/.cache/pip'
-            - run: python utils/tests_fetcher.py | tee test_preparation.txt
-            - store_artifacts:
-                  path: ~/transformers/test_preparation.txt
-            - run: |
-                  if [ -f test_list.txt ]; then
-                    python -m pytest -n 3 --dist=loadfile -s --make-reports=tests_torch $(cat test_list.txt) | tee tests_output.txt
-                  fi
-            - store_artifacts:
-                  path: ~/transformers/tests_output.txt
-            - store_artifacts:
-                  path: ~/transformers/reports
-    
-    run_tests_torch_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-torch-{{ 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,torch,testing,sentencepiece,torch-speech,vision,timm]
-            - run: pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.9.0+cpu.html
-            - save_cache:
-                  key: v0.4-torch-{{ checksum "setup.py" }}
-                  paths:
-                      - '~/.cache/pip'
-            - run: |
-                  python -m pytest -n 3 --dist=loadfile -s --make-reports=tests_torch tests | tee tests_output.txt
+            - run: python -m pytest -n 8 --dist=loadfile -s --make-reports=tests_torch ./tests/ | tee tests_output.txt
             - store_artifacts:
                   path: ~/transformers/tests_output.txt
             - store_artifacts:
@@ -267,7 +123,6 @@ jobs:
             - image: circleci/python:3.7
         environment:
             OMP_NUM_THREADS: 1
-            TRANSFORMERS_IS_CI: yes
         resource_class: xlarge
         parallelism: 1
         steps:
@@ -277,46 +132,12 @@ jobs:
                       - v0.4-tf-{{ checksum "setup.py" }}
                       - v0.4-{{ checksum "setup.py" }}
             - run: pip install --upgrade pip
-            - run: pip install .[sklearn,tf-cpu,testing,sentencepiece,tf-speech]
+            - run: pip install .[sklearn,tf-cpu,testing,sentencepiece]
             - save_cache:
                   key: v0.4-tf-{{ checksum "setup.py" }}
                   paths:
                       - '~/.cache/pip'
-            - run: python utils/tests_fetcher.py | 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 -rA -s --make-reports=tests_tf $(cat test_list.txt) | tee tests_output.txt
-                  fi
-            - store_artifacts:
-                  path: ~/transformers/tests_output.txt
-            - store_artifacts:
-                  path: ~/transformers/reports
-    
-    run_tests_tf_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-tf-{{ checksum "setup.py" }}
-                      - v0.4-{{ checksum "setup.py" }}
-            - run: pip install --upgrade pip
-            - run: pip install .[sklearn,tf-cpu,testing,sentencepiece,tf-speech]
-            - save_cache:
-                  key: v0.4-tf-{{ checksum "setup.py" }}
-                  paths:
-                      - '~/.cache/pip'
-            - run: |
-                  python -m pytest -n 8 --dist=loadfile -rA -s --make-reports=tests_tf tests | tee tests_output.txt
+            - run: python -m pytest -n 8 --dist=loadfile -rA -s --make-reports=tests_tf ./tests/ | tee tests_output.txt
             - store_artifacts:
                   path: ~/transformers/tests_output.txt
             - store_artifacts:
@@ -328,7 +149,6 @@ jobs:
             - image: circleci/python:3.7
         environment:
             OMP_NUM_THREADS: 1
-            TRANSFORMERS_IS_CI: yes
         resource_class: xlarge
         parallelism: 1
         steps:
@@ -338,46 +158,12 @@ jobs:
                     - v0.4-flax-{{ checksum "setup.py" }}
                     - v0.4-{{ checksum "setup.py" }}
             - run: pip install --upgrade pip
-            - run: sudo pip install .[flax,testing,sentencepiece,flax-speech,vision]
+            - run: sudo pip install .[flax,sklearn,torch,testing,sentencepiece]
             - save_cache:
                   key: v0.4-flax-{{ checksum "setup.py" }}
                   paths:
                       - '~/.cache/pip'
-            - run: python utils/tests_fetcher.py | 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 -rA -s --make-reports=tests_flax $(cat test_list.txt) | tee tests_output.txt
-                  fi
-            - store_artifacts:
-                  path: ~/transformers/tests_output.txt
-            - store_artifacts:
-                  path: ~/transformers/reports
-    
-    run_tests_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-{{ checksum "setup.py" }}
-                    - v0.4-{{ checksum "setup.py" }}
-            - run: pip install --upgrade pip
-            - run: sudo pip install .[flax,testing,sentencepiece,vision,flax-speech]
-            - save_cache:
-                  key: v0.4-flax-{{ checksum "setup.py" }}
-                  paths:
-                      - '~/.cache/pip'
-            - run: |
-                  python -m pytest -n 8 --dist=loadfile -rA -s --make-reports=tests_flax tests | tee tests_output.txt
+            - run: python -m pytest -n 8 --dist=loadfile -rA -s --make-reports=tests_flax ./tests/ | tee tests_output.txt
             - store_artifacts:
                   path: ~/transformers/tests_output.txt
             - store_artifacts:
@@ -389,8 +175,6 @@ jobs:
             - image: circleci/python:3.7
         environment:
             OMP_NUM_THREADS: 1
-            RUN_PIPELINE_TESTS: yes
-            TRANSFORMERS_IS_CI: yes
         resource_class: xlarge
         parallelism: 1
         steps:
@@ -399,52 +183,14 @@ jobs:
                   keys:
                       - v0.4-torch-{{ 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,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]
+            - run: pip install tapas torch-scatter -f https://pytorch-geometric.com/whl/torch-1.7.0+cpu.html
             - save_cache:
                   key: v0.4-torch-{{ checksum "setup.py" }}
                   paths:
                       - '~/.cache/pip'
-            - run: python utils/tests_fetcher.py | 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 -rA -s --make-reports=tests_pipelines_torch -m is_pipeline_test $(cat test_list.txt) | tee tests_output.txt
-                  fi
-            - store_artifacts:
-                  path: ~/transformers/tests_output.txt
-            - store_artifacts:
-                  path: ~/transformers/reports
-    
-    run_tests_pipelines_torch_all:
-        working_directory: ~/transformers
-        docker:
-            - image: circleci/python:3.7
-        environment:
-            OMP_NUM_THREADS: 1
-            RUN_PIPELINE_TESTS: yes
-            TRANSFORMERS_IS_CI: yes
-        resource_class: xlarge
-        parallelism: 1
-        steps:
-            - checkout
-            - restore_cache:
-                  keys:
-                      - v0.4-torch-{{ 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,torch,testing,sentencepiece,torch-speech,vision]
-            - run: pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.9.0+cpu.html
-            - save_cache:
-                  key: v0.4-torch-{{ checksum "setup.py" }}
-                  paths:
-                      - '~/.cache/pip'
-            - run: |
-                  python -m pytest -n 8 --dist=loadfile -rA -s --make-reports=tests_pipelines_torch -m is_pipeline_test tests | tee tests_output.txt
+            - run: RUN_PIPELINE_TESTS=1 python -m pytest -n 8 --dist=loadfile -rA -s --make-reports=tests_pipelines_torch -m is_pipeline_test ./tests/ | tee tests_output.txt
             - store_artifacts:
                   path: ~/transformers/tests_output.txt
             - store_artifacts:
@@ -456,8 +202,6 @@ jobs:
             - image: circleci/python:3.7
         environment:
             OMP_NUM_THREADS: 1
-            RUN_PIPELINE_TESTS: yes
-            TRANSFORMERS_IS_CI: yes
         resource_class: xlarge
         parallelism: 1
         steps:
@@ -472,42 +216,7 @@ jobs:
                   key: v0.4-tf-{{ checksum "setup.py" }}
                   paths:
                       - '~/.cache/pip'
-            - run: python utils/tests_fetcher.py | 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 -rA -s --make-reports=tests_pipelines_tf $(cat test_list.txt) -m is_pipeline_test | tee tests_output.txt
-                  fi
-            - store_artifacts:
-                  path: ~/transformers/tests_output.txt
-            - store_artifacts:
-                  path: ~/transformers/reports
-
-    run_tests_pipelines_tf_all:
-        working_directory: ~/transformers
-        docker:
-            - image: circleci/python:3.7
-        environment:
-            OMP_NUM_THREADS: 1
-            RUN_PIPELINE_TESTS: yes
-            TRANSFORMERS_IS_CI: yes
-        resource_class: xlarge
-        parallelism: 1
-        steps:
-            - checkout
-            - restore_cache:
-                  keys:
-                      - v0.4-tf-{{ checksum "setup.py" }}
-                      - v0.4-{{ checksum "setup.py" }}
-            - run: pip install --upgrade pip
-            - run: pip install .[sklearn,tf-cpu,testing,sentencepiece]
-            - save_cache:
-                  key: v0.4-tf-{{ checksum "setup.py" }}
-                  paths:
-                      - '~/.cache/pip'
-            - run: |
-                  python -m pytest -n 8 --dist=loadfile -rA -s --make-reports=tests_pipelines_tf tests -m is_pipeline_test | tee tests_output.txt
+            - run: RUN_PIPELINE_TESTS=1 python -m pytest -n 8 --dist=loadfile -rA -s --make-reports=tests_pipelines_tf ./tests/ -m is_pipeline_test | tee tests_output.txt
             - store_artifacts:
                   path: ~/transformers/tests_output.txt
             - store_artifacts:
@@ -519,7 +228,6 @@ jobs:
             - image: circleci/python:3.7
         environment:
             RUN_CUSTOM_TOKENIZERS: yes
-            TRANSFORMERS_IS_CI: yes
         steps:
             - checkout
             - restore_cache:
@@ -527,16 +235,13 @@ jobs:
                       - v0.4-custom_tokenizers-{{ checksum "setup.py" }}
                       - v0.4-{{ checksum "setup.py" }}
             - run: pip install --upgrade pip
-            - run: pip install .[ja,testing,sentencepiece,jieba]
+            - run: pip install .[ja,testing,sentencepiece]
             - run: python -m unidic download
             - save_cache:
                   key: v0.4-custom_tokenizers-{{ checksum "setup.py" }}
                   paths:
                       - '~/.cache/pip'
-            - run: |
-                  if [ -f test_list.txt ]; then
-                    python -m pytest -s --make-reports=tests_custom_tokenizers ./tests/test_tokenization_bert_japanese.py | tee tests_output.txt
-                  fi
+            - run: python -m pytest -s --make-reports=tests_custom_tokenizers ./tests/test_tokenization_bert_japanese.py | tee tests_output.txt
             - store_artifacts:
                   path: ~/transformers/tests_output.txt
             - store_artifacts:
@@ -548,7 +253,6 @@ jobs:
             - image: circleci/python:3.6
         environment:
             OMP_NUM_THREADS: 1
-            TRANSFORMERS_IS_CI: yes
         resource_class: xlarge
         parallelism: 1
         steps:
@@ -557,207 +261,52 @@ jobs:
                   keys:
                       - v0.4-torch_examples-{{ 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,torch,sentencepiece,testing,torch-speech]
-            - run: pip install -r examples/pytorch/_tests_requirements.txt
+            - run: pip install .[sklearn,torch,sentencepiece,testing]
+            - run: pip install -r examples/_tests_requirements.txt
             - save_cache:
                   key: v0.4-torch_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_torch ./examples/pytorch/ | tee tests_output.txt
-                  fi
-            - store_artifacts:
-                  path: ~/transformers/examples_output.txt
-            - store_artifacts:
-                  path: ~/transformers/reports
-    
-    run_examples_torch_all:
-        working_directory: ~/transformers
-        docker:
-            - image: circleci/python:3.6
-        environment:
-            OMP_NUM_THREADS: 1
-            TRANSFORMERS_IS_CI: yes
-        resource_class: xlarge
-        parallelism: 1
-        steps:
-            - checkout
-            - restore_cache:
-                  keys:
-                      - v0.4-torch_examples-{{ 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,torch,sentencepiece,testing,torch-speech]
-            - run: pip install -r examples/pytorch/_tests_requirements.txt
-            - save_cache:
-                  key: v0.4-torch_examples-{{ checksum "setup.py" }}
-                  paths:
-                      - '~/.cache/pip'
-            - run: |
-                  TRANSFORMERS_IS_CI=1 python -m pytest -n 8 --dist=loadfile -s --make-reports=examples_torch ./examples/pytorch/ | tee examples_output.txt
+            - run: python -m pytest -n 8 --dist=loadfile -s --make-reports=examples_torch ./examples/ | tee examples_output.txt
             - store_artifacts:
                   path: ~/transformers/examples_output.txt
             - store_artifacts:
                   path: ~/transformers/reports
 
-    run_tests_hub:
+    run_tests_git_lfs:
         working_directory: ~/transformers
         docker:
             - image: circleci/python:3.7
-        environment:
-            HUGGINGFACE_CO_STAGING: yes
-            RUN_GIT_LFS_TESTS: yes
-            TRANSFORMERS_IS_CI: yes
         resource_class: xlarge
         parallelism: 1
         steps:
             - checkout
-            - restore_cache:
-                  keys:
-                      - v0.4-hub-{{ checksum "setup.py" }}
-                      - v0.4-{{ checksum "setup.py" }}
             - run: sudo apt-get install git-lfs
             - run: |
                 git config --global user.email "ci@dummy.com"
                 git config --global user.name "ci"
             - run: pip install --upgrade pip
-            - run: pip install .[torch,sentencepiece,testing]
-            - save_cache:
-                  key: v0.4-hub-{{ checksum "setup.py" }}
-                  paths:
-                      - '~/.cache/pip'
-            - run: python utils/tests_fetcher.py | tee test_preparation.txt
-            - store_artifacts:
-                  path: ~/transformers/test_preparation.txt
-            - run: |
-                  if [ -f test_list.txt ]; then
-                    python -m pytest -sv --make-reports=tests_hub $(cat test_list.txt) -m is_staging_test | tee tests_output.txt
-                  fi
-            - store_artifacts:
-                  path: ~/transformers/tests_output.txt
-            - store_artifacts:
-                  path: ~/transformers/reports
-    
-    run_tests_hub_all:
-        working_directory: ~/transformers
-        docker:
-            - image: circleci/python:3.7
-        environment:
-            HUGGINGFACE_CO_STAGING: yes
-            RUN_GIT_LFS_TESTS: yes
-            TRANSFORMERS_IS_CI: yes
-        resource_class: xlarge
-        parallelism: 1
-        steps:
-            - checkout
-            - restore_cache:
-                  keys:
-                      - v0.4-hub-{{ checksum "setup.py" }}
-                      - v0.4-{{ checksum "setup.py" }}
-            - run: sudo apt-get install git-lfs
-            - run: |
-                git config --global user.email "ci@dummy.com"
-                git config --global user.name "ci"
-            - run: pip install --upgrade pip
-            - run: pip install .[torch,sentencepiece,testing]
-            - save_cache:
-                  key: v0.4-hub-{{ checksum "setup.py" }}
-                  paths:
-                      - '~/.cache/pip'
-            - run: |
-                  python -m pytest -sv --make-reports=tests_hub tests -m is_staging_test | tee tests_output.txt
-            - store_artifacts:
-                  path: ~/transformers/tests_output.txt
-            - store_artifacts:
-                  path: ~/transformers/reports
-
-    run_tests_onnxruntime:
-        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-torch-{{ checksum "setup.py" }}
-                      - v0.4-{{ checksum "setup.py" }}
-            - run: pip install --upgrade pip
-            - run: pip install .[torch,testing,sentencepiece,onnxruntime]
-            - save_cache:
-                  key: v0.4-onnx-{{ checksum "setup.py" }}
-                  paths:
-                      - '~/.cache/pip'
-            - run: python utils/tests_fetcher.py | tee test_preparation.txt
-            - store_artifacts:
-                  path: ~/transformers/test_preparation.txt
-            - run: |
-                  if [ -f test_list.txt ]; then
-                    python -m pytest -n 1 --dist=loadfile -s --make-reports=tests_onnx $(cat test_list.txt) -k onnx | tee tests_output.txt
-                  fi
-            - store_artifacts:
-                  path: ~/transformers/tests_output.txt
-            - store_artifacts:
-                  path: ~/transformers/reports
-    
-    run_tests_onnxruntime_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-torch-{{ checksum "setup.py" }}
-                      - v0.4-{{ checksum "setup.py" }}
-            - run: pip install --upgrade pip
-            - run: pip install .[torch,testing,sentencepiece,onnxruntime]
-            - save_cache:
-                  key: v0.4-onnx-{{ checksum "setup.py" }}
-                  paths:
-                      - '~/.cache/pip'
-            - run: |
-                  python -m pytest -n 1 --dist=loadfile -s --make-reports=tests_onnx tests -k onnx | tee tests_output.txt
-            - store_artifacts:
-                  path: ~/transformers/tests_output.txt
-            - store_artifacts:
-                  path: ~/transformers/reports
+            - run: pip install .[testing]
+            - run: RUN_GIT_LFS_TESTS=1 python -m pytest -sv ./tests/test_hf_api.py -k "HfLargefilesTest"
 
     build_doc:
         working_directory: ~/transformers
         docker:
             - 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 ."[all, docs]"
             - save_cache:
                   key: v0.4-build_doc-{{ checksum "setup.py" }}
                   paths:
                       - '~/.cache/pip'
-            - run: cd docs && make html SPHINXOPTS="-W -j 4"
+            - run: cd docs && make html SPHINXOPTS="-W"
             - store_artifacts:
                 path: ./docs/_build
 
@@ -765,7 +314,6 @@ jobs:
         working_directory: ~/transformers
         docker:
             - image: circleci/python:3.6
-        resource_class: large
         steps:
             - add_ssh_keys:
                 fingerprints:
@@ -775,9 +323,7 @@ jobs:
                   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]"
+            - run: pip install ."[all,docs]"
             - save_cache:
                   key: v0.4-deploy_doc-{{ checksum "setup.py" }}
                   paths:
@@ -788,9 +334,7 @@ jobs:
         working_directory: ~/transformers
         docker:
             - image: circleci/python:3.6
-        resource_class: large
-        environment:
-            TRANSFORMERS_IS_CI: yes
+        resource_class: medium
         parallelism: 1
         steps:
             - checkout
@@ -799,7 +343,7 @@ 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 isort
             - run: pip install .[all,quality]
             - save_cache:
                   key: v0.4-code_quality-{{ checksum "setup.py" }}
@@ -807,16 +351,12 @@ jobs:
                       - '~/.cache/pip'
             - run: black --check examples tests src utils
             - run: isort --check-only examples tests src utils
-            - run: python utils/custom_init_isort.py --check_only
             - run: flake8 examples tests src utils
             - run: python utils/style_doc.py src/transformers docs/source --max_len 119 --check_only
             - run: python utils/check_copies.py
             - run: python utils/check_table.py
             - run: python utils/check_dummies.py
             - run: python utils/check_repo.py
-            - run: python utils/check_inits.py
-            - run: make deps_table_check_updated
-            - run: python utils/tests_fetcher.py --sanity_check
 
     check_repository_consistency:
         working_directory: ~/transformers
@@ -829,51 +369,12 @@ jobs:
             - run: pip install requests
             - run: python ./utils/link_tester.py
 
-    run_tests_layoutlmv2:
-        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-torch-{{ 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 .[torch,testing,vision]
-            - run: pip install torchvision
-            - run: python -m pip install 'git+https://github.com/facebookresearch/detectron2.git'
-            - run: sudo apt install tesseract-ocr
-            - run: pip install pytesseract
-            - save_cache:
-                  key: v0.4-torch-{{ checksum "setup.py" }}
-                  paths:
-                      - '~/.cache/pip'
-            - run: python utils/tests_fetcher.py | tee test_preparation.txt
-            - store_artifacts:
-                  path: ~/transformers/test_preparation.txt
-            - run: |
-                  if [ -f test_list.txt ]; then
-                    python -m pytest -n 1 tests/*layoutlmv2* --dist=loadfile -s --make-reports=tests_layoutlmv2 --durations=100
-                  fi
-            - store_artifacts:
-                  path: ~/transformers/tests_output.txt
-            - store_artifacts:
-                  path: ~/transformers/reports
-
 # TPU JOBS
     run_examples_tpu:
         docker:
             - image: circleci/python:3.6
         environment:
             OMP_NUM_THREADS: 1
-            TRANSFORMERS_IS_CI: yes
         resource_class: xlarge
         parallelism: 1
         steps:
@@ -912,46 +413,23 @@ workflows:
             - run_examples_torch
             - run_tests_custom_tokenizers
             - run_tests_torch_and_tf
-            - run_tests_torch_and_flax
             - run_tests_torch
             - run_tests_tf
             - run_tests_flax
             - run_tests_pipelines_torch
             - run_tests_pipelines_tf
-            - run_tests_onnxruntime
-            - run_tests_hub
+            - run_tests_git_lfs
             - build_doc
-            - run_tests_layoutlmv2
             - deploy_doc: *workflow_filters
-    nightly:
+    tpu_testing_jobs:
         triggers:
             - schedule:
-                cron: "0 0 * * *"
+                # Set to run at the first minute of every hour.
+                cron: "0 8 * * *"
                 filters:
                     branches:
                         only:
                             - master
         jobs:
-            - run_examples_torch_all
-            - run_tests_torch_and_tf_all
-            - run_tests_torch_and_flax_all
-            - run_tests_torch_all
-            - run_tests_tf_all
-            - run_tests_flax_all
-            - run_tests_pipelines_torch_all
-            - run_tests_pipelines_tf_all
-            - run_tests_onnxruntime_all
-            - run_tests_hub_all
-
-#    tpu_testing_jobs:
-#        triggers:
-#            - schedule:
-#                # Set to run at the first minute of every hour.
-#                cron: "0 8 * * *"
-#                filters:
-#                    branches:
-#                        only:
-#                            - master
-#        jobs:
-#            - cleanup-gke-jobs
-#            - run_examples_tpu
+            - cleanup-gke-jobs
+            - run_examples_tpu

.circleci/deploy.sh

@@ -3,7 +3,6 @@ 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
@@ -46,7 +45,7 @@ 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 "10d7239" v2.10.0 
 deploy_doc "b42586e" v2.11.0
 deploy_doc "7fb8bdf" v3.0.2
 deploy_doc "4b3ee9c" v3.1.0
@@ -54,22 +53,5 @@ 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 Latest stable release
+deploy_doc "c781171" v4.0.0
+deploy_doc "bfa4ccf" # v4.1.1 Latest stable release

.gitattributes

@@ -1,3 +0,0 @@
-*.py	eol=lf
-*.rst	eol=lf
-*.md	eol=lf

.github/ISSUE_TEMPLATE/bug-report.md

@@ -25,44 +25,32 @@ assignees: ''
  If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
  Please tag fewer than 3 people.
 
-Models:
-
-- albert, bert, xlm: @LysandreJik
-- blenderbot, bart, marian, pegasus, encoderdecoder,  t5: @patrickvonplaten, @patil-suraj
-- longformer, reformer, transfoxl, xlnet: @patrickvonplaten
-- fsmt: @stas00
-- funnel: @sgugger
-- gpt2: @patrickvonplaten, @LysandreJik
-- rag: @patrickvonplaten, @lhoestq
-- tensorflow: @Rocketknight1
-
-Library:
-
-- benchmarks: @patrickvonplaten
-- deepspeed: @stas00
-- ray/raytune: @richardliaw, @amogkam
-- text generation: @patrickvonplaten
-- tokenizers: @LysandreJik
-- trainer: @sgugger
-- pipelines: @LysandreJik
-
-Documentation: @sgugger
-
-Model hub:
-
-- for issues with a model report at https://discuss.huggingface.co/ and tag the model's creator.
-
-HF projects:
-
-- datasets: [different repo](https://github.com/huggingface/datasets)
-- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)
-
-Examples:
-
-- maintained examples (not research project or legacy): @sgugger, @patil-suraj
-- research_projects/bert-loses-patience: @JetRunner
-- research_projects/distillation: @VictorSanh
-
+ albert, bert, GPT2, XLM: @LysandreJik
+ tokenizers: @mfuntowicz
+ Trainer: @sgugger
+ Speed and Memory Benchmarks: @patrickvonplaten
+ Model Cards: @julien-c
+ TextGeneration: @TevenLeScao
+ examples/distillation: @VictorSanh
+ nlp datasets: [different repo](https://github.com/huggingface/nlp)
+ rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)
+ Text Generation: @patrickvonplaten @TevenLeScao
+ Blenderbot: @patrickvonplaten
+ Bart: @patrickvonplaten
+ Marian: @patrickvonplaten
+ Pegasus: @patrickvonplaten
+ mBART: @patrickvonplaten
+ T5: @patrickvonplaten
+ Longformer/Reformer: @patrickvonplaten
+ TransfoXL/XLNet: @TevenLeScao
+ RAG: @patrickvonplaten, @lhoestq
+ FSMT: @stas00
+ examples/seq2seq: @patil-suraj
+ examples/bert-loses-patience: @JetRunner
+ ray/raytune: @richardliaw @amogkam
+ tensorflow: @jplu
+ examples/token-classification: @stefan-it
+ documentation: @sgugger
  -->
 
 ## Information

.github/PULL_REQUEST_TEMPLATE.md

@@ -30,45 +30,33 @@ Fixes # (issue)
 ## Who can review?
 
 Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
-members/contributors who may be interested in your PR.
+members/contributors which may be interested in your PR.
 
 <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @
 
  If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
  Please tag fewer than 3 people.
 
-Models:
-
-- albert, bert, xlm: @LysandreJik
-- blenderbot, bart, marian, pegasus, encoderdecoder,  t5: @patrickvonplaten, @patil-suraj
-- longformer, reformer, transfoxl, xlnet: @patrickvonplaten
-- fsmt: @stas00
-- funnel: @sgugger
-- gpt2: @patrickvonplaten, @LysandreJik
-- rag: @patrickvonplaten, @lhoestq
-- tensorflow: @LysandreJik
-
-Library:
-
-- benchmarks: @patrickvonplaten
-- deepspeed: @stas00
-- ray/raytune: @richardliaw, @amogkam
-- text generation: @patrickvonplaten
-- tokenizers: @n1t0, @LysandreJik
-- trainer: @sgugger
-- pipelines: @LysandreJik
-
-Documentation: @sgugger
-
-HF projects:
-
-- datasets: [different repo](https://github.com/huggingface/datasets)
-- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)
-
-Examples:
-
-- maintained examples (not research project or legacy): @sgugger, @patil-suraj
-- research_projects/bert-loses-patience: @JetRunner
-- research_projects/distillation: @VictorSanh
-
+ albert, bert, XLM: @LysandreJik
+ GPT2: @LysandreJik, @patrickvonplaten
+ tokenizers: @mfuntowicz
+ Trainer: @sgugger
+ Benchmarks: @patrickvonplaten
+ Model Cards: @julien-c
+ examples/distillation: @VictorSanh
+ nlp datasets: [different repo](https://github.com/huggingface/nlp)
+ rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)
+ Text Generation: @patrickvonplaten, @TevenLeScao
+ Blenderbot, Bart, Marian, Pegasus: @patrickvonplaten
+ T5: @patrickvonplaten
+ Rag: @patrickvonplaten, @lhoestq
+ EncoderDecoder: @patrickvonplaten
+ Longformer, Reformer: @patrickvonplaten
+ TransfoXL, XLNet: @TevenLeScao, @patrickvonplaten
+ examples/seq2seq: @patil-suraj
+ examples/bert-loses-patience: @JetRunner
+ tensorflow: @jplu
+ examples/token-classification: @stefan-it
+ documentation: @sgugger
+ FSMT: @stas00
  -->

.github/conda/meta.yaml

@@ -14,10 +14,8 @@ requirements:
   host:
     - python
     - pip
-    - numpy >=1.17
+    - numpy
     - dataclasses
-    - importlib_metadata
-    - huggingface_hub
     - packaging
     - filelock
     - requests
@@ -25,14 +23,11 @@ requirements:
     - sacremoses
     - regex !=2019.12.17
     - protobuf
-    - tokenizers >=0.10.1,<0.11.0
-    - pyyaml >=5.1
+    - tokenizers ==0.9.4
   run:
     - python
-    - numpy >=1.17
+    - numpy
     - dataclasses
-    - importlib_metadata
-    - huggingface_hub
     - packaging
     - filelock
     - requests
@@ -40,8 +35,7 @@ requirements:
     - sacremoses
     - regex !=2019.12.17
     - protobuf
-    - tokenizers >=0.10.1,<0.11.0
-    - pyyaml >=5.1
+    - tokenizers ==0.9.4
 
 test:
   imports:

.github/stale.yml

@@ -0,0 +1,18 @@
+# Number of days of inactivity before an issue becomes stale
+daysUntilStale: 60
+# Number of days of inactivity before a stale issue is closed
+daysUntilClose: 7
+# Issues with these labels will never be considered stale
+exemptLabels:
+  - pinned
+  - security
+  - Feature request
+# Label to use when marking an issue as stale
+staleLabel: wontfix
+# Comment to post when marking an issue as stale. Set to `false` to disable
+markComment: >
+  This issue has been automatically marked as stale because it has not had
+  recent activity. It will be closed if no further activity occurs. Thank you
+  for your contributions.
+# Comment to post when closing a stale issue. Set to `false` to disable
+closeComment: false

.github/workflows/TROUBLESHOOT.md

@@ -1,9 +0,0 @@
-# Troubleshooting
-
-This is a document explaining how to deal with various issues on github-actions self-hosted CI. The entries may include actually solutions or pointers to Issues that cover those.
-
-## GitHub Actions (self-hosted CI)
-
-* Deepspeed
-
-  - if jit build hangs, clear out `rm -rf ~/.cache/torch_extensions/` reference: https://github.com/huggingface/transformers/pull/12723

.github/workflows/doctests.yml

@@ -1,42 +0,0 @@
-name: Doctests
-
-on:
-  push:
-    branches:
-      - doctest*
-  repository_dispatch:
-  schedule:
-    - cron: "0 0 * * *"
-
-
-env:
-  HF_HOME: /mnt/cache
-  TRANSFORMERS_IS_CI: yes
-  RUN_SLOW: yes
-  OMP_NUM_THREADS: 16
-  MKL_NUM_THREADS: 16
-  PYTEST_TIMEOUT: 600
-
-jobs:
-  run_doctests:
-    runs-on: [self-hosted, docker-gpu, single-gpu]
-    container:
-      image: pytorch/pytorch:1.9.0-cuda11.1-cudnn8-runtime
-      options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
-    steps:
-      - name: Launcher docker
-        uses: actions/checkout@v2
-
-      - name: NVIDIA-SMI
-        run: |
-          nvidia-smi
-
-      - name: Install dependencies
-        run: |
-          apt -y update && apt install -y libsndfile1-dev
-          pip install --upgrade pip
-          pip install .[dev]
-
-      - name: Run doctests
-        run: |
-          pytest --doctest-modules $(cat utils/documentation_tests.txt) -sv --doctest-continue-on-failure

.github/workflows/github-torch-hub.yml

@@ -37,10 +37,10 @@ jobs:
         # no longer needed
         pip uninstall -y transformers
 
-    #- name: Torch hub list
-    #  run: |
-    #    python -c "import torch; print(torch.hub.list('huggingface/transformers:$BRANCH'))"
+    - name: Torch hub list
+      run: |
+        python -c "import torch; print(torch.hub.list('huggingface/transformers:$BRANCH'))"
 
-    #- name: Torch hub help
-    #  run: |
-    #    python -c "import torch; print(torch.hub.help('huggingface/transformers:$BRANCH', 'modelForSequenceClassification'))"
+    - name: Torch hub help
+      run: |
+        python -c "import torch; print(torch.hub.help('huggingface/transformers:$BRANCH', 'modelForSequenceClassification'))"

.github/workflows/model-templates.yml

@@ -2,15 +2,11 @@ name: Model templates runner
 
 on:
   push:
-    branches:
-      - master
-  pull_request:
     paths:
       - "src/**"
       - "tests/**"
       - ".github/**"
       - "templates/**"
-    types: [assigned, opened, synchronize, reopened]
 
 jobs:
   run_tests_templates:
@@ -37,7 +33,6 @@ jobs:
       - name: Install dependencies
         run: |
           pip install --upgrade pip
-          sudo apt -y update && sudo apt install -y libsndfile1-dev
           pip install .[dev]
       - name: Create model files
         run: |
@@ -47,12 +42,9 @@ jobs:
           transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/tf-encoder-bert-tokenizer.json --path=templates/adding_a_new_model
           transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/tf-seq-2-seq-bart-tokenizer.json --path=templates/adding_a_new_model
           transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/pt-seq-2-seq-bart-tokenizer.json --path=templates/adding_a_new_model
-          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/flax-encoder-bert-tokenizer.json --path=templates/adding_a_new_model
-          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/flax-seq-2-seq-bart-tokenizer.json --path=templates/adding_a_new_model
           make style
           python utils/check_table.py --fix_and_overwrite
           python utils/check_dummies.py --fix_and_overwrite
-          python utils/check_copies.py --fix_and_overwrite
 
       - name: Run all non-slow tests
         run: |
@@ -61,7 +53,7 @@ jobs:
       - name: Run style changes
         run: |
           git fetch origin master:master
-          make style && make quality
+          make fixup
 
       - name: Failure short reports
         if: ${{ always() }}

.github/workflows/release-conda.yml

@@ -4,8 +4,6 @@ on:
   push:
     tags:
       - v*
-    branches:
-      - conda_*
 
 env:
   ANACONDA_API_TOKEN: ${{ secrets.ANACONDA_API_TOKEN }}
@@ -26,7 +24,6 @@ jobs:
         with:
           auto-update-conda: true
           auto-activate-base: false
-          python-version: 3.8
           activate-environment: "build-transformers"
           channels: huggingface
 

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

@@ -1,257 +0,0 @@
-name: Self-hosted runner; Nightly (scheduled)
-
-on:
-    push:
-        branches:
-            - nightly_ci*
-    repository_dispatch:
-    schedule:
-        - cron: "0 0 */3 * *"
-
-env:
-    HF_HOME: /mnt/cache
-    TRANSFORMERS_IS_CI: yes
-    RUN_SLOW: yes
-    OMP_NUM_THREADS: 16
-    MKL_NUM_THREADS: 16
-    PYTEST_TIMEOUT: 600
-    SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }}
-
-jobs:
-    run_all_tests_torch_gpu:
-        runs-on: [self-hosted, docker-gpu, single-gpu]
-        container:
-            image: pytorch/pytorch:1.9.0-cuda11.1-cudnn8-runtime
-            options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
-        steps:
-            - name: Launcher docker
-              uses: actions/checkout@v2
-
-            - name: NVIDIA-SMI
-              run: |
-                  nvidia-smi
-
-            - name: Install dependencies
-              run: |
-                  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 --pre torch torchvision torchaudio -f https://download.pytorch.org/whl/nightly/cu111/torch_nightly.html -U
-
-            - 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())"
-
-            - name: Run all tests on GPU
-              run: |
-                  python -m pytest -n 1 -v --dist=loadfile --make-reports=tests_torch_gpu tests
-
-            - name: Failure short reports
-              if: ${{ always() }}
-              run: cat reports/tests_torch_gpu_failures_short.txt
-
-            - name: Run examples tests on GPU
-              if: ${{ always() }}
-              env:
-                  OMP_NUM_THREADS: 16
-                  MKL_NUM_THREADS: 16
-                  RUN_SLOW: yes
-                  HF_HOME: /mnt/cache
-                  TRANSFORMERS_IS_CI: yes
-              run: |
-                  pip install -r examples/pytorch/_tests_requirements.txt
-                  python -m pytest -n 1 -v --dist=loadfile --make-reports=examples_torch_gpu examples
-
-            - name: Failure short reports
-              if: ${{ always() }}
-              run: cat reports/examples_torch_gpu_failures_short.txt
-
-            - name: Run all pipeline tests on GPU
-              if: ${{ always() }}
-              env:
-                  RUN_PIPELINE_TESTS: yes
-              run: |
-                  python -m pytest -n 1 -v --dist=loadfile -m is_pipeline_test --make-reports=tests_torch_pipeline_gpu tests
-
-            - name: Failure short reports
-              if: ${{ always() }}
-              run: cat reports/tests_torch_pipeline_gpu_failures_short.txt
-
-            - name: Test suite reports artifacts
-              if: ${{ always() }}
-              uses: actions/upload-artifact@v2
-              with:
-                  name: run_all_tests_torch_gpu_test_reports
-                  path: reports
-
-    run_all_tests_torch_multi_gpu:
-        runs-on: [self-hosted, docker-gpu, multi-gpu]
-        container:
-            image: pytorch/pytorch:1.9.0-cuda11.1-cudnn8-runtime
-            options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
-        steps:
-            - name: Launcher docker
-              uses: actions/checkout@v2
-
-            - name: NVIDIA-SMI
-              continue-on-error: true
-              run: |
-                  nvidia-smi
-
-            - name: Install dependencies
-              run: |
-                  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 --pre torch torchvision torchaudio -f https://download.pytorch.org/whl/nightly/cu111/torch_nightly.html -U
-
-            - 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())"
-
-            - name: Run all tests on GPU
-              env:
-                  MKL_SERVICE_FORCE_INTEL: 1
-              run: |
-                  python -m pytest -n 1 -v --dist=loadfile --make-reports=tests_torch_multi_gpu tests
-
-            - name: Failure short reports
-              if: ${{ always() }}
-              run: cat reports/tests_torch_multi_gpu_failures_short.txt
-
-            - name: Run all pipeline tests on GPU
-              if: ${{ always() }}
-              env:
-                  RUN_PIPELINE_TESTS: yes
-              run: |
-                  python -m pytest -n 1 -v --dist=loadfile -m is_pipeline_test --make-reports=tests_torch_pipeline_multi_gpu tests
-
-            - name: Failure short reports
-              if: ${{ always() }}
-              run: cat reports/tests_torch_pipeline_multi_gpu_failures_short.txt
-
-            - name: Test suite reports artifacts
-              if: ${{ always() }}
-              uses: actions/upload-artifact@v2
-              with:
-                  name: run_all_tests_torch_multi_gpu_test_reports
-                  path: reports
-
-    run_all_tests_torch_cuda_extensions_gpu:
-        runs-on: [self-hosted, docker-gpu, single-gpu]
-        container:
-            image: nvcr.io/nvidia/pytorch:21.03-py3
-            options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
-        steps:
-            - name: Launcher docker
-              uses: actions/checkout@v2
-
-            - name: NVIDIA-SMI
-              run: |
-                  nvidia-smi
-
-            - name: Install dependencies
-              run: |
-                  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]
-                  pip install git+https://github.com/microsoft/DeepSpeed
-
-            - 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())"
-
-            - name: Run all tests on GPU
-              run: |
-                  python -m pytest -n 1 -v --dist=loadfile --make-reports=tests_torch_cuda_extensions_gpu tests/deepspeed tests/extended
-
-            - name: Failure short reports
-              if: ${{ always() }}
-              run: cat reports/tests_torch_cuda_extensions_gpu_failures_short.txt
-
-            - name: Test suite reports artifacts
-              if: ${{ always() }}
-              uses: actions/upload-artifact@v2
-              with:
-                  name: run_tests_torch_cuda_extensions_gpu_test_reports
-                  path: reports
-
-    run_all_tests_torch_cuda_extensions_multi_gpu:
-        runs-on: [self-hosted, docker-gpu, multi-gpu]
-        container:
-            image: nvcr.io/nvidia/pytorch:21.03-py3
-            options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
-        steps:
-            - name: Launcher docker
-              uses: actions/checkout@v2
-
-            - name: NVIDIA-SMI
-              continue-on-error: true
-              run: |
-                  nvidia-smi
-
-            - name: Install dependencies
-              run: |
-                  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
-
-            - 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())"
-
-            - 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
-
-            - name: Test suite reports artifacts
-              if: ${{ always() }}
-              uses: actions/upload-artifact@v2
-              with:
-                  name: run_tests_torch_cuda_extensions_multi_gpu_test_reports
-                  path: reports
-
-    send_results:
-        name: Send results to webhook
-        runs-on: ubuntu-latest
-        if: always()
-        needs: [
-                run_all_tests_torch_gpu,
-                run_all_tests_torch_multi_gpu,
-                run_all_tests_torch_cuda_extensions_gpu,
-                run_all_tests_torch_cuda_extensions_multi_gpu
-        ]
-        steps:
-            - uses: actions/checkout@v2
-
-            - uses: actions/download-artifact@v2
-
-            - name: Send message to Slack
-              env:
-                  CI_SLACK_BOT_TOKEN: ${{ secrets.CI_SLACK_BOT_TOKEN }}
-                  CI_SLACK_CHANNEL_ID: ${{ secrets.CI_SLACK_CHANNEL_ID }}
-                  CI_SLACK_CHANNEL_ID_DAILY: ${{ secrets.CI_SLACK_CHANNEL_ID_DAILY }}
-                  CI_SLACK_CHANNEL_ID_PAST_FUTURE: ${{ secrets.CI_SLACK_CHANNEL_ID_PAST_FUTURE }}
-
-              run: |
-                  pip install slack_sdk
-                  python utils/notification_service.py scheduled nightly-torch

.github/workflows/self-push.yml

@@ -5,243 +5,207 @@ on:
     branches:
       - master
       - ci_*
-      - ci-*
     paths:
       - "src/**"
       - "tests/**"
       - ".github/**"
       - "templates/**"
-      - "utils/**"
+  # pull_request:
   repository_dispatch:
 
-env:
-  HF_HOME: /mnt/cache
-  TRANSFORMERS_IS_CI: yes
-  OMP_NUM_THREADS: 8
-  MKL_NUM_THREADS: 8
-  PYTEST_TIMEOUT: 60
 
 jobs:
   run_tests_torch_gpu:
-    runs-on: [self-hosted, docker-gpu, single-gpu]
-    container:
-      image: pytorch/pytorch:1.9.0-cuda11.1-cudnn8-runtime
-      options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+    runs-on: [self-hosted, gpu, single-gpu]
     steps:
+      - uses: actions/checkout@v2
+      - name: Python version
+        run: |
+          which python
+          python --version
+          pip --version
+
+      - name: Current dir
+        run: pwd
+      - run: nvidia-smi
+
+      - name: Loading cache.
+        uses: actions/cache@v2
+        id: cache
+        with:
+          path: .env
+          key: v1.1-tests_torch_gpu-${{ hashFiles('setup.py') }}
+
+      - name: Create new python env (on self-hosted runners we have to handle isolation ourselves)
+        run: |
+          python -m venv .env
+          source .env/bin/activate
+          which python
+          python --version
+          pip --version
+
       - name: Install dependencies
         run: |
-          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
-          apt install -y libsndfile1-dev
+          source .env/bin/activate
           pip install --upgrade pip
-          pip install .[sklearn,testing,onnxruntime,sentencepiece,torch-speech,vision,timm]
-
-      - name: Launcher docker
-        uses: actions/checkout@v2
-        with:
-          fetch-depth: 2
-
-      - name: NVIDIA-SMI
-        run: |
-          nvidia-smi
+          pip install .[torch,sklearn,testing,onnxruntime,sentencepiece]
+          pip install git+https://github.com/huggingface/datasets
+          pip install pandas torch-scatter -f https://pytorch-geometric.com/whl/torch-1.7.0+cu102.html
 
       - name: Are GPUs recognized by our DL frameworks
         run: |
+          source .env/bin/activate
           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())"
-      
-      - name: Fetch the tests to run
-        run: |
-          python utils/tests_fetcher.py --diff_with_last_commit | tee test_preparation.txt
 
-      - name: Report fetched tests
-        uses: actions/upload-artifact@v2
-        with:
-          name: test_fetched
-          path: test_preparation.txt
+#      - name: Create model files
+#        run: |
+#          source .env/bin/activate
+#          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/encoder-bert-tokenizer.json --path=templates/adding_a_new_model
+#          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/pt-encoder-bert-tokenizer.json --path=templates/adding_a_new_model
+#          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/standalone.json --path=templates/adding_a_new_model
+#          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/tf-encoder-bert-tokenizer.json --path=templates/adding_a_new_model
 
       - name: Run all non-slow tests on GPU
+        env:
+          OMP_NUM_THREADS: 1
+          CUDA_VISIBLE_DEVICES: 0
         run: |
-          if [ -f test_list.txt ]; then
-            python -m pytest -n 2 --dist=loadfile -v --make-reports=tests_torch_gpu $(cat test_list.txt)
-          fi
+          source .env/bin/activate
+          python -m pytest -n 2 --dist=loadfile -s --make-reports=tests_torch_gpu tests
 
       - name: Failure short reports
-        if: ${{ failure() }}
+        if: ${{ always() }}
         run: cat reports/tests_torch_gpu_failures_short.txt
-
+        
       - name: Test suite reports artifacts
         if: ${{ always() }}
         uses: actions/upload-artifact@v2
         with:
           name: run_all_tests_torch_gpu_test_reports
           path: reports
+                  
 
-  run_tests_flax_gpu:
-    runs-on: [self-hosted, docker-gpu-test, single-gpu]
-    container:
-      image: tensorflow/tensorflow:2.4.1-gpu
-      options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+  run_tests_tf_gpu:
+    runs-on: [self-hosted, gpu, single-gpu]
     steps:
+      - uses: actions/checkout@v2
+      - name: Python version
+        run: |
+          which python
+          python --version
+          pip --version
+      - name: Current dir
+        run: pwd
+      - run: nvidia-smi
+
+      - name: Loading cache.
+        uses: actions/cache@v2
+        id: cache
+        with:
+          path: .env
+          key: v1.1-tests_tf_gpu-${{ hashFiles('setup.py') }}
+
+      - name: Create new python env (on self-hosted runners we have to handle isolation ourselves)
+        run: |
+          python -m venv .env
+          source .env/bin/activate
+          which python
+          python --version
+          pip --version
+
       - name: Install dependencies
         run: |
-          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 "jax[cuda111]" -f https://storage.googleapis.com/jax-releases/jax_releases.html
+          source .env/bin/activate
           pip install --upgrade pip
-          pip install .[sklearn,testing,sentencepiece,flax,flax-speech,vision]
-
-      - name: Launcher docker
-        uses: actions/checkout@v2
-        with:
-          fetch-depth: 2
-
-      - name: NVIDIA-SMI
-        continue-on-error: true
-        run: |
-          nvidia-smi
+          pip install .[tf,sklearn,testing,onnxruntime,sentencepiece]
+          pip install git+https://github.com/huggingface/datasets
 
       - name: Are GPUs recognized by our DL frameworks
         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
+          source .env/bin/activate
+          TF_CPP_MIN_LOG_LEVEL=3 python -c "import tensorflow as tf; print('TF GPUs available:', bool(tf.config.list_physical_devices('GPU')))"
+          TF_CPP_MIN_LOG_LEVEL=3 python -c "import tensorflow as tf; print('Number of TF GPUs available:', len(tf.config.list_physical_devices('GPU')))"
 
-      - name: Report fetched tests
-        uses: actions/upload-artifact@v2
-        with:
-          name: test_fetched
-          path: test_preparation.txt
+      - name: Create model files
+        run: |
+          source .env/bin/activate
+#          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/encoder-bert-tokenizer.json --path=templates/adding_a_new_model
+#          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/pt-encoder-bert-tokenizer.json --path=templates/adding_a_new_model
+#          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/standalone.json --path=templates/adding_a_new_model
+#          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/tf-encoder-bert-tokenizer.json --path=templates/adding_a_new_model
 
       - name: Run all non-slow tests on GPU
+        env:
+          OMP_NUM_THREADS: 1
+          CUDA_VISIBLE_DEVICES: 0
         run: |
-          if [ -f test_list.txt ]; then
-            python -m pytest -n 2 --dist=loadfile -v --make-reports=tests_flax_gpu $(cat test_list.txt)
-          fi
+          source .env/bin/activate
+          python -m pytest -n 2 --dist=loadfile -s --make-reports=tests_tf_gpu tests
 
       - name: Failure short reports
-        if: ${{ failure() }}
-        run: cat reports/tests_flax_gpu_failures_short.txt
-
+        if: ${{ always() }}
+        run: cat reports/tests_tf_gpu_failures_short.txt
+        
       - name: Test suite reports artifacts
         if: ${{ always() }}
         uses: actions/upload-artifact@v2
         with:
-          name: run_all_tests_flax_gpu_test_reports
+          name: run_all_tests_tf_gpu_test_reports
           path: reports
 
-#  run_tests_tf_gpu:
-#    runs-on: [self-hosted, docker-gpu, single-gpu]
-#    timeout-minutes: 120
-#    container:
-#      image: tensorflow/tensorflow:2.4.1-gpu
-#      options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
-#    steps:
-#      - name: Install dependencies
-#        run: |
-#          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]
-#
-#      - name: Launcher docker
-#        uses: actions/checkout@v2
-#        with:
-#          fetch-depth: 2
-#
-#      - name: NVIDIA-SMI
-#        run: |
-#          nvidia-smi
-#
-#      - name: Are GPUs recognized by our DL frameworks
-#        run: |
-#          TF_CPP_MIN_LOG_LEVEL=3 python -c "import tensorflow as tf; print('TF GPUs available:', bool(tf.config.list_physical_devices('GPU')))"
-#          TF_CPP_MIN_LOG_LEVEL=3 python -c "import tensorflow as tf; print('Number of TF GPUs available:', len(tf.config.list_physical_devices('GPU')))"
-#
-#      - name: Fetch the tests to run
-#        run: |
-#          python utils/tests_fetcher.py --diff_with_last_commit | tee test_preparation.txt
-#
-#      - name: Report fetched tests
-#        uses: actions/upload-artifact@v2
-#        with:
-#          name: test_fetched
-#          path: test_preparation.txt
-#
-#      - name: Run all non-slow tests on GPU
-#        env:
-#          TF_NUM_INTRAOP_THREADS: 8
-#          TF_NUM_INTEROP_THREADS: 1
-#        run: |
-#          if [ -f test_list.txt ]; then
-#            python -m pytest -n 1 --dist=loadfile --make-reports=tests_tf_gpu $(cat test_list.txt)
-#          fi
-#
-#      - name: Failure short reports
-#        if: ${{ failure() }}
-#        run: cat reports/tests_tf_gpu_failures_short.txt
-#
-#      - name: Test suite reports artifacts
-#        if: ${{ always() }}
-#        uses: actions/upload-artifact@v2
-#        with:
-#          name: run_all_tests_tf_gpu_test_reports
-#          path: reports
-
-
   run_tests_torch_multi_gpu:
-    runs-on: [self-hosted, docker-gpu, multi-gpu]
-    container:
-      image: pytorch/pytorch:1.9.0-cuda11.1-cudnn8-runtime
-      options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+    runs-on: [self-hosted, gpu, multi-gpu]
     steps:
+      - uses: actions/checkout@v2
+      - name: Python version
+        run: |
+          which python
+          python --version
+          pip --version
+
+      - name: Current dir
+        run: pwd
+      - run: nvidia-smi
+
+      - name: Loading cache.
+        uses: actions/cache@v2
+        id: cache
+        with:
+          path: .env
+          key: v1.1-tests_torch_multi_gpu-${{ hashFiles('setup.py') }}
+
+      - name: Create new python env (on self-hosted runners we have to handle isolation ourselves)
+        run: |
+          python -m venv .env
+          source .env/bin/activate
+          which python
+          python --version
+          pip --version
       - name: Install dependencies
         run: |
-          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
-          apt install -y libsndfile1-dev
+          source .env/bin/activate
           pip install --upgrade pip
-          pip install .[sklearn,testing,onnxruntime,sentencepiece,torch-speech,vision,timm]
-      
-      - name: Launcher docker
-        uses: actions/checkout@v2
-        with:
-          fetch-depth: 2
-
-      - name: NVIDIA-SMI
-        continue-on-error: true
-        run: |
-          nvidia-smi
+          pip install .[torch,sklearn,testing,onnxruntime,sentencepiece]
+          pip install git+https://github.com/huggingface/datasets
+          pip install pandas torch-scatter -f https://pytorch-geometric.com/whl/torch-1.7.0+cu102.html
 
       - name: Are GPUs recognized by our DL frameworks
         run: |
+          source .env/bin/activate
           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())"
 
-      - name: Fetch the tests to run
-        run: |
-          python utils/tests_fetcher.py --diff_with_last_commit | tee test_preparation.txt
-
-      - name: Report fetched tests
-        uses: actions/upload-artifact@v2
-        with:
-          name: test_fetched
-          path: test_preparation.txt
-
       - name: Run all non-slow tests on GPU
         env:
-          MKL_SERVICE_FORCE_INTEL: 1
+          OMP_NUM_THREADS: 1
         run: |
-          if [ -f test_list.txt ]; then
-            python -m pytest -n 2 --dist=loadfile -v --make-reports=tests_torch_multi_gpu $(cat test_list.txt)
-          fi
+          source .env/bin/activate
+          python -m pytest -n 2 --dist=loadfile -s --make-reports=tests_torch_multi_gpu tests
 
       - name: Failure short reports
-        if: ${{ failure() }}
-        run: cat reports/tests_torch_multi_gpu_failures_short.txt
+        if: ${{ always() }}
+        run: cat reports/tests_torch_multi_gpu_failures_short.txt          
 
       - name: Test suite reports artifacts
         if: ${{ always() }}
@@ -250,252 +214,62 @@ jobs:
           name: run_all_tests_torch_multi_gpu_test_reports
           path: reports
 
-#  run_tests_flax_multi_gpu:
-#    runs-on: [self-hosted, docker-gpu, multi-gpu]
-#    container:
-#      image: tensorflow/tensorflow:2.4.1-gpu
-#      options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
-#    steps:
-#      - name: Install dependencies
-#        run: |
-#          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 "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]
-#
-#      - name: Launcher docker
-#        uses: actions/checkout@v2
-#        with:
-#          fetch-depth: 2
-#
-#      - name: NVIDIA-SMI
-#        continue-on-error: true
-#        run: |
-#          nvidia-smi
-#
-#      - name: Are GPUs recognized by our DL frameworks
-#        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
-#
-#      - name: Report fetched tests
-#        uses: actions/upload-artifact@v2
-#        with:
-#          name: test_fetched
-#          path: test_preparation.txt
-#
-#      - name: Run all non-slow tests on GPU
-#        run: |
-#          if [ -f test_list.txt ]; then
-#            python -m pytest -n 2 --dist=loadfile -v --make-reports=tests_flax_multi_gpu $(cat test_list.txt)
-#          fi
-#
-#      - name: Failure short reports
-#        if: ${{ failure() }}
-#        run: cat reports/tests_flax_multi_gpu_failures_short.txt
-#
-#      - name: Test suite reports artifacts
-#        if: ${{ always() }}
-#        uses: actions/upload-artifact@v2
-#        with:
-#          name: run_all_tests_flax_multi_gpu_test_reports
-#          path: reports
-
-#  run_tests_tf_multi_gpu:
-#    runs-on: [self-hosted, docker-gpu, multi-gpu]
-#    timeout-minutes: 120
-#    container:
-#      image: tensorflow/tensorflow:2.4.1-gpu
-#      options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
-#    steps:
-#      - name: Install dependencies
-#        run: |
-#          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]
-#
-#      - name: Launcher docker
-#        uses: actions/checkout@v2
-#        with:
-#          fetch-depth: 2
-#
-#      - name: NVIDIA-SMI
-#        run: |
-#          nvidia-smi
-#
-#      - name: Are GPUs recognized by our DL frameworks
-#        run: |
-#          TF_CPP_MIN_LOG_LEVEL=3 python -c "import tensorflow as tf; print('TF GPUs available:', bool(tf.config.list_physical_devices('GPU')))"
-#          TF_CPP_MIN_LOG_LEVEL=3 python -c "import tensorflow as tf; print('Number of TF GPUs available:', len(tf.config.list_physical_devices('GPU')))"
-#
-#      - name: Fetch the tests to run
-#        run: |
-#          python utils/tests_fetcher.py --diff_with_last_commit | tee test_preparation.txt
-#
-#      - name: Report fetched tests
-#        uses: actions/upload-artifact@v2
-#        with:
-#          name: test_fetched
-#          path: test_preparation.txt
-#
-#      - name: Run all non-slow tests on GPU
-#        env:
-#          TF_NUM_INTRAOP_THREADS: 8
-#          TF_NUM_INTEROP_THREADS: 1
-#        run: |
-#          if [ -f test_list.txt ]; then
-#            python -m pytest -n 1 --dist=loadfile --make-reports=tests_tf_multi_gpu $(cat test_list.txt)
-#          fi
-#
-#      - name: Failure short reports
-#        if: ${{ failure() }}
-#        run: cat reports/tests_tf_multi_gpu_failures_short.txt
-#
-#      - name: Test suite reports artifacts
-#        if: ${{ always() }}
-#        uses: actions/upload-artifact@v2
-#        with:
-#          name: run_all_tests_tf_multi_gpu_test_reports
-#          path: reports
-
-  run_tests_torch_cuda_extensions_gpu:
-    runs-on: [self-hosted, docker-gpu, single-gpu]
-    container:
-      image: nvcr.io/nvidia/pytorch:21.03-py3
-      options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
-    steps:
-      - name: Launcher docker
-        uses: actions/checkout@v2
-        with:
-          fetch-depth: 2
-
-      - name: NVIDIA-SMI
-        run: |
-          nvidia-smi
-
-      - name: Install dependencies
-        run: |
-          apt -y update && apt install -y libaio-dev
-          pip install --upgrade pip
-          pip install .[testing,deepspeed]
-
-      - 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())"
-      
-      - 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:
-          name: test_fetched
-          path: test_preparation.txt
-
-      - name: Run all tests on GPU
-        run: |
-          if [ -f test_list.txt ]; then
-            python -m pytest -n 1 --dist=loadfile -v --make-reports=tests_torch_cuda_extensions_gpu $(cat test_list.txt)
-          fi
-
-      - name: Failure short reports
-        if: ${{ failure() }}
-        run: cat reports/tests_torch_cuda_extensions_gpu_failures_short.txt
-
-      - name: Test suite reports artifacts
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v2
-        with:
-          name: run_tests_torch_cuda_extensions_gpu_test_reports
-          path: reports
-
-  run_tests_torch_cuda_extensions_multi_gpu:
-    runs-on: [self-hosted, docker-gpu, multi-gpu]
-    container:
-      image: nvcr.io/nvidia/pytorch:21.03-py3
-      options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
-    steps:
-      - name: Launcher docker
-        uses: actions/checkout@v2
-        with:
-          fetch-depth: 2
-
-      - name: NVIDIA-SMI
-        continue-on-error: true
-        run: |
-          nvidia-smi
-
-      - name: Install dependencies
-        run: |
-          apt -y update && apt install -y libaio-dev
-          pip install --upgrade pip
-          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())"
-
-      - 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:
-          name: test_fetched
-          path: test_preparation.txt
-
-      - name: Run all tests on GPU
-        run: |
-          if [ -f test_list.txt ]; then
-            python -m pytest -n 1 --dist=loadfile -v --make-reports=tests_torch_cuda_extensions_multi_gpu $(cat test_list.txt)
-          fi
-
-      - name: Failure short reports
-        if: ${{ failure() }}
-        run: cat reports/tests_torch_cuda_extensions_multi_gpu_failures_short.txt
-
-      - name: Test suite reports artifacts
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v2
-        with:
-          name: run_tests_torch_cuda_extensions_multi_gpu_test_reports
-          path: reports
-
-
-  send_results:
-    name: Send results to webhook
-    runs-on: ubuntu-latest
-    if: always()
-    needs: [
-        run_tests_torch_gpu,
-#        run_tests_tf_gpu,
-        run_tests_torch_multi_gpu,
-#        run_tests_tf_multi_gpu,
-        run_tests_torch_cuda_extensions_gpu,
-        run_tests_torch_cuda_extensions_multi_gpu
-    ]
+  run_tests_tf_multi_gpu:
+    runs-on: [self-hosted, gpu, multi-gpu]
     steps:
       - uses: actions/checkout@v2
-
-      - uses: actions/download-artifact@v2
-
-      - name: Send message to Slack
-        env:
-          CI_SLACK_BOT_TOKEN: ${{ secrets.CI_SLACK_BOT_TOKEN }}
-          CI_SLACK_CHANNEL_ID: ${{ secrets.CI_SLACK_CHANNEL_ID }}
-
+      - name: Python version
         run: |
-          pip install slack_sdk
-          python utils/notification_service.py push
+          which python
+          python --version
+          pip --version
+
+      - name: Current dir
+        run: pwd
+      - run: nvidia-smi
+
+      - name: Loading cache.
+        uses: actions/cache@v2
+        id: cache
+        with:
+          path: .env
+          key: v1.1-tests_tf_multi_gpu-${{ hashFiles('setup.py') }}
+
+      - name: Create new python env (on self-hosted runners we have to handle isolation ourselves)
+        run: |
+          python -m venv .env
+          source .env/bin/activate
+          which python
+          python --version
+          pip --version
+      - name: Install dependencies
+        run: |
+          source .env/bin/activate
+          pip install --upgrade pip
+          pip install .[tf,sklearn,testing,onnxruntime,sentencepiece]
+          pip install git+https://github.com/huggingface/datasets
+
+      - name: Are GPUs recognized by our DL frameworks
+        run: |
+          source .env/bin/activate
+          TF_CPP_MIN_LOG_LEVEL=3 python -c "import tensorflow as tf; print('TF GPUs available:', bool(tf.config.list_physical_devices('GPU')))"
+          TF_CPP_MIN_LOG_LEVEL=3 python -c "import tensorflow as tf; print('Number of TF GPUs available:', len(tf.config.list_physical_devices('GPU')))"
+
+      - name: Run all non-slow tests on GPU
+        env:
+          OMP_NUM_THREADS: 1
+        run: |
+          source .env/bin/activate
+          python -m pytest -n 2 --dist=loadfile -s --make-reports=tests_tf_multi_gpu tests
+
+      - name: Failure short reports
+        if: ${{ always() }}
+        run: cat reports/tests_tf_multi_gpu_failures_short.txt
+
+      - name: Test suite reports artifacts
+        if: ${{ always() }}
+        uses: actions/upload-artifact@v2
+        with:
+          name: run_all_tests_tf_multi_gpu_test_reports
+          path: reports
+          

.github/workflows/self-scheduled.yml

@@ -1,68 +1,82 @@
+# configuration notes:
+#
+# - `source .env/bin/activate` is currently needed to be run first thing first in each step. Otherwise
+# the step uses the system-wide python interpreter.
+
 name: Self-hosted runner (scheduled)
 
 on:
-  push:
-    branches:
-      - multi_ci_*
   repository_dispatch:
   schedule:
     - cron: "0 0 * * *"
 
-env:
-  HF_HOME: /mnt/cache
-  TRANSFORMERS_IS_CI: yes
-  RUN_SLOW: yes
-  OMP_NUM_THREADS: 16
-  MKL_NUM_THREADS: 16
-  PYTEST_TIMEOUT: 600
-  SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }}
-
 jobs:
   run_all_tests_torch_gpu:
-    runs-on: [self-hosted, docker-gpu, single-gpu]
-    container:
-      image: pytorch/pytorch:1.9.0-cuda11.1-cudnn8-runtime
-      options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+    runs-on: [self-hosted, gpu, single-gpu]
     steps:
-      - name: Launcher docker
-        uses: actions/checkout@v2
+      - uses: actions/checkout@v2
 
-      - name: NVIDIA-SMI
+      - name: Loading cache.
+        uses: actions/cache@v2
+        id: cache
+        with:
+          path: .env
+          key: v  1.1-slow_tests_torch_gpu-${{ hashFiles('setup.py') }}
+
+      - name: Python version
         run: |
-          nvidia-smi
+          which python
+          python --version
+          pip --version
+
+      - name: Current dir
+        run: pwd
+      - run: nvidia-smi
+
+      - name: Create new python env (on self-hosted runners we have to handle isolation ourselves)
+        if: steps.cache.outputs.cache-hit != 'true'
+        run: |
+          python -m venv .env
+          source .env/bin/activate
+          which python
+          python --version
+          pip --version
 
       - name: Install dependencies
         run: |
-          apt -y update && apt install -y libsndfile1-dev git
+          source .env/bin/activate
           pip install --upgrade pip
-          pip install .[integrations,sklearn,testing,onnxruntime,sentencepiece,torch-speech,vision,timm]
+          pip install .[torch,sklearn,testing,onnxruntime,sentencepiece]
+          pip install git+https://github.com/huggingface/datasets
+          pip list
 
       - name: Are GPUs recognized by our DL frameworks
         run: |
+          source .env/bin/activate
           python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
-          python -c "import torch; print('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())"
 
       - name: Run all tests on GPU
+        env:
+          OMP_NUM_THREADS: 1
+          RUN_SLOW: yes
         run: |
-          python -m pytest -n 1 -v --dist=loadfile --make-reports=tests_torch_gpu tests
+          source .env/bin/activate
+          python -m pytest -n 1 --dist=loadfile -s --make-reports=tests_torch_gpu tests
 
       - name: Failure short reports
         if: ${{ always() }}
         run: cat reports/tests_torch_gpu_failures_short.txt
-
+        
       - name: Run examples tests on GPU
         if: ${{ always() }}
         env:
-          OMP_NUM_THREADS: 16
-          MKL_NUM_THREADS: 16
+          OMP_NUM_THREADS: 1
           RUN_SLOW: yes
-          HF_HOME: /mnt/cache
-          TRANSFORMERS_IS_CI: yes
         run: |
-          pip install -r examples/pytorch/_tests_requirements.txt
-          python -m pytest -n 1 -v --dist=loadfile --make-reports=examples_torch_gpu examples
+          source .env/bin/activate
+          pip install -r examples/_tests_requirements.txt
+          python -m pytest -n 1 --dist=loadfile -s --make-reports=examples_torch_gpu examples
 
       - name: Failure short reports
         if: ${{ always() }}
@@ -71,9 +85,13 @@ jobs:
       - name: Run all pipeline tests on GPU
         if: ${{ always() }}
         env:
+          TF_FORCE_GPU_ALLOW_GROWTH: "true"
+          OMP_NUM_THREADS: 1
+          RUN_SLOW: yes
           RUN_PIPELINE_TESTS: yes
         run: |
-          python -m pytest -n 1 -v --dist=loadfile -m is_pipeline_test --make-reports=tests_torch_pipeline_gpu tests
+          source .env/bin/activate
+          python -m pytest -n 1 --dist=loadfile -s -m is_pipeline_test --make-reports=tests_torch_pipeline_gpu tests
 
       - name: Failure short reports
         if: ${{ always() }}
@@ -86,77 +104,60 @@ jobs:
           name: run_all_tests_torch_gpu_test_reports
           path: reports
 
-  run_all_tests_flax_gpu:
-    runs-on: [self-hosted, docker-gpu-test, single-gpu]
-    container:
-      image: tensorflow/tensorflow:2.4.1-gpu
-      options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
-    steps:
-      - name: Launcher docker
-        uses: actions/checkout@v2
-
-      - name: NVIDIA-SMI
-        continue-on-error: true
-        run: |
-          nvidia-smi
-
-      - name: Install dependencies
-        run: |
-          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]
-
-      - name: Are GPUs recognized by our DL frameworks
-        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: Run all tests on GPU
-        run: |
-          python -m pytest -n 1 -v --dist=loadfile --make-reports=tests_flax_gpu tests
-
-      - name: Failure short reports
-        if: ${{ always() }}
-        run: cat reports/tests_flax_gpu_failures_short.txt
-
-      - name: Test suite reports artifacts
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v2
-        with:
-          name: run_all_tests_flax_gpu_test_reports
-          path: reports
 
   run_all_tests_tf_gpu:
-    runs-on: [self-hosted, docker-gpu, single-gpu]
-    container:
-      image: tensorflow/tensorflow:2.4.1-gpu
-      options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+    runs-on: [self-hosted, gpu, single-gpu]
     steps:
-      - name: Launcher docker
-        uses: actions/checkout@v2
+      - uses: actions/checkout@v2
 
-      - name: NVIDIA-SMI
+      - name: Loading cache.
+        uses: actions/cache@v2
+        id: cache
+        with:
+          path: .env
+          key: v1.1-slow_tests_tf_gpu-${{ hashFiles('setup.py') }}
+
+      - name: Python version
         run: |
-          nvidia-smi
+          which python
+          python --version
+          pip --version
+
+      - name: Current dir
+        run: pwd
+      - run: nvidia-smi
+
+      - name: Create new python env (on self-hosted runners we have to handle isolation ourselves)
+        if: steps.cache.outputs.cache-hit != 'true'
+        run: |
+          python -m venv .env
+          source .env/bin/activate
+          which python
+          python --version
+          pip --version
 
       - name: Install dependencies
         run: |
-          apt -y update && apt install -y libsndfile1-dev git
+          source .env/bin/activate
           pip install --upgrade pip
-          pip install .[sklearn,testing,onnx,sentencepiece,tf-speech]
+          pip install .[tf,sklearn,testing,onnxruntime,sentencepiece]
+          pip install git+https://github.com/huggingface/datasets
+          pip list
 
       - name: Are GPUs recognized by our DL frameworks
         run: |
+          source .env/bin/activate
           TF_CPP_MIN_LOG_LEVEL=3 python -c "import tensorflow as tf; print('TF GPUs available:', bool(tf.config.list_physical_devices('GPU')))"
           TF_CPP_MIN_LOG_LEVEL=3 python -c "import tensorflow as tf; print('Number of TF GPUs available:', len(tf.config.list_physical_devices('GPU')))"
 
       - name: Run all tests on GPU
         env:
-          TF_NUM_INTEROP_THREADS: 1
-          TF_NUM_INTRAOP_THREADS: 16
+          OMP_NUM_THREADS: 1
+          RUN_SLOW: yes
         run: |
-          python -m pytest -n 1 -v --dist=loadfile --make-reports=tests_tf_gpu tests
-
+          source .env/bin/activate
+          python -m pytest -n 1 --dist=loadfile -s --make-reports=tests_tf_gpu tests
+          
       - name: Failure short reports
         if: ${{ always() }}
         run: cat reports/tests_tf_gpu_failures_short.txt
@@ -164,15 +165,17 @@ jobs:
       - name: Run all pipeline tests on GPU
         if: ${{ always() }}
         env:
+          TF_FORCE_GPU_ALLOW_GROWTH: "true"
+          OMP_NUM_THREADS: 1
+          RUN_SLOW: yes
           RUN_PIPELINE_TESTS: yes
-          TF_NUM_INTEROP_THREADS: 1
-          TF_NUM_INTRAOP_THREADS: 16
         run: |
-          python -m pytest -n 1 -v --dist=loadfile -m is_pipeline_test --make-reports=tests_tf_pipeline_gpu tests
+          source .env/bin/activate
+          python -m pytest -n 1 --dist=loadfile -s -m is_pipeline_test --make-reports=tests_tf_pipelines_gpu tests
 
       - name: Failure short reports
         if: ${{ always() }}
-        run: cat reports/tests_tf_pipeline_gpu_failures_short.txt
+        run: cat reports/tests_tf_pipelines_gpu_failures_short.txt
 
       - name: Test suite reports artifacts
         if: ${{ always() }}
@@ -180,50 +183,86 @@ jobs:
         with:
           name: run_all_tests_tf_gpu_test_reports
           path: reports
-
+          
   run_all_tests_torch_multi_gpu:
-    runs-on: [self-hosted, docker-gpu, multi-gpu]
-    container:
-      image: pytorch/pytorch:1.9.0-cuda11.1-cudnn8-runtime
-      options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+    runs-on: [self-hosted, gpu, multi-gpu]
     steps:
-      - name: Launcher docker
-        uses: actions/checkout@v2
+      - uses: actions/checkout@v2
 
-      - name: NVIDIA-SMI
-        continue-on-error: true
+      - name: Loading cache.
+        uses: actions/cache@v2
+        id: cache
+        with:
+          path: .env
+          key: v1.1-slow_tests_torch_multi_gpu-${{ hashFiles('setup.py') }}
+
+      - name: Python version
         run: |
-          nvidia-smi
+          which python
+          python --version
+          pip --version
+
+      - name: Current dir
+        run: pwd
+      - run: nvidia-smi
+
+      - name: Create new python env (on self-hosted runners we have to handle isolation ourselves)
+        if: steps.cache.outputs.cache-hit != 'true'
+        run: |
+          python -m venv .env
+          source .env/bin/activate
+          which python
+          python --version
+          pip --version
 
       - name: Install dependencies
         run: |
-          apt -y update && apt install -y libsndfile1-dev git
+          source .env/bin/activate
           pip install --upgrade pip
-          pip install .[integrations,sklearn,testing,onnxruntime,sentencepiece,torch-speech,vision,timm]
+          pip install .[torch,sklearn,testing,onnxruntime,sentencepiece]
+          pip install git+https://github.com/huggingface/datasets
+          pip list
 
       - name: Are GPUs recognized by our DL frameworks
         run: |
+          source .env/bin/activate
           python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
-          python -c "import torch; print('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())"
 
-      - name: Run all tests on GPU
+      - name: Run all tests on multi-GPU
         env:
-          MKL_SERVICE_FORCE_INTEL: 1
+          OMP_NUM_THREADS: 1
+          RUN_SLOW: yes
         run: |
-          python -m pytest -n 1 -v --dist=loadfile --make-reports=tests_torch_multi_gpu tests
+          source .env/bin/activate
+          python -m pytest -n 1 --dist=loadfile -s --make-reports=tests_torch_multi_gpu tests
 
       - name: Failure short reports
         if: ${{ always() }}
         run: cat reports/tests_torch_multi_gpu_failures_short.txt
 
-      - name: Run all pipeline tests on GPU
+      - name: Run examples tests on multi-GPU
+        env:
+          OMP_NUM_THREADS: 1
+          RUN_SLOW: yes
+        run: |
+          source .env/bin/activate
+          python -m pytest -n 1 --dist=loadfile -s --make-reports=tests_torch_examples_multi_gpu examples
+
+      - name: Failure short reports
+        if: ${{ always() }}
+        run: cat reports/tests_torch_examples_multi_gpu_failures_short.txt
+
+      - name: Run all pipeline tests on multi-GPU
         if: ${{ always() }}
         env:
+          TF_FORCE_GPU_ALLOW_GROWTH: "true"
+          OMP_NUM_THREADS: 1
+          RUN_SLOW: yes
           RUN_PIPELINE_TESTS: yes
         run: |
-          python -m pytest -n 1 -v --dist=loadfile -m is_pipeline_test --make-reports=tests_torch_pipeline_multi_gpu tests
+          source .env/bin/activate
+          python -m pytest -n 1 --dist=loadfile -s -m is_pipeline_test --make-reports=tests_torch_pipeline_multi_gpu tests
 
       - name: Failure short reports
         if: ${{ always() }}
@@ -237,50 +276,73 @@ jobs:
           path: reports
 
   run_all_tests_tf_multi_gpu:
-    runs-on: [self-hosted, docker-gpu, multi-gpu]
-    container:
-      image: tensorflow/tensorflow:2.4.1-gpu
-      options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+    runs-on: [self-hosted, gpu, multi-gpu]
     steps:
-      - name: Launcher docker
-        uses: actions/checkout@v2
+      - uses: actions/checkout@v2
 
-      - name: NVIDIA-SMI
-        continue-on-error: true
+      - name: Loading cache.
+        uses: actions/cache@v2
+        id: cache
+        with:
+          path: .env
+          key: v1.1-slow_tests_tf_multi_gpu-${{ hashFiles('setup.py') }}
+
+      - name: Python version
         run: |
-          nvidia-smi
+          which python
+          python --version
+          pip --version
+
+      - name: Current dir
+        run: pwd
+      - run: nvidia-smi
+
+      - name: Create new python env (on self-hosted runners we have to handle isolation ourselves)
+        if: steps.cache.outputs.cache-hit != 'true'
+        run: |
+          python -m venv .env
+          source .env/bin/activate
+          which python
+          python --version
+          pip --version
 
       - name: Install dependencies
         run: |
-          apt -y update && apt install -y libsndfile1-dev git
+          source .env/bin/activate
           pip install --upgrade pip
-          pip install .[sklearn,testing,onnx,sentencepiece,tf-speech]
+          pip install .[tf,sklearn,testing,onnxruntime,sentencepiece]
+          pip install git+https://github.com/huggingface/datasets
+          pip list
 
       - name: Are GPUs recognized by our DL frameworks
         run: |
+          source .env/bin/activate
           TF_CPP_MIN_LOG_LEVEL=3 python -c "import tensorflow as tf; print('TF GPUs available:', bool(tf.config.list_physical_devices('GPU')))"
           TF_CPP_MIN_LOG_LEVEL=3 python -c "import tensorflow as tf; print('Number of TF GPUs available:', len(tf.config.list_physical_devices('GPU')))"
 
-      - name: Run all tests on GPU
+      - name: Run all tests on multi-GPU
         env:
-          TF_NUM_INTEROP_THREADS: 1
-          TF_NUM_INTRAOP_THREADS: 16
+          OMP_NUM_THREADS: 1
+          RUN_SLOW: yes
         run: |
-          python -m pytest -n 1 -v --dist=loadfile --make-reports=tests_tf_multi_gpu tests
+          source .env/bin/activate
+          python -m pytest -n 1 --dist=loadfile -s --make-reports=tests_tf_multi_gpu tests
 
       - name: Failure short reports
         if: ${{ always() }}
         run: cat reports/tests_tf_multi_gpu_failures_short.txt
 
-      - name: Run all pipeline tests on GPU
+      - name: Run all pipeline tests on multi-GPU
         if: ${{ always() }}
         env:
+          TF_FORCE_GPU_ALLOW_GROWTH: "true"
+          OMP_NUM_THREADS: 1
+          RUN_SLOW: yes
           RUN_PIPELINE_TESTS: yes
-          TF_NUM_INTEROP_THREADS: 1
-          TF_NUM_INTRAOP_THREADS: 16
         run: |
-          python -m pytest -n 1 -v --dist=loadfile -m is_pipeline_test --make-reports=tests_tf_pipeline_multi_gpu tests
-
+          source .env/bin/activate
+          python -m pytest -n 1 --dist=loadfile -s -m is_pipeline_test --make-reports=tests_tf_pipeline_multi_gpu tests
+          
       - name: Failure short reports
         if: ${{ always() }}
         run: cat reports/tests_tf_pipeline_multi_gpu_failures_short.txt
@@ -291,153 +353,4 @@ jobs:
         with:
           name: run_all_tests_tf_multi_gpu_test_reports
           path: reports
-
-#  run_all_tests_flax_multi_gpu:
-#    runs-on: [self-hosted, docker-gpu, multi-gpu]
-#    container:
-#      image: tensorflow/tensorflow:2.4.1-gpu
-#      options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
-#    steps:
-#      - name: Launcher docker
-#        uses: actions/checkout@v2
-#
-#      - name: NVIDIA-SMI
-#        run: |
-#          nvidia-smi
-#
-#      - name: Install dependencies
-#        run: |
-#          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]
-#
-#      - name: Are GPUs recognized by our DL frameworks
-#        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: Run all tests on GPU
-#        run: |
-#          python -m pytest -n 1 -v --dist=loadfile --make-reports=tests_flax_gpu tests
-#
-#      - name: Failure short reports
-#        if: ${{ always() }}
-#        run: cat reports/tests_flax_gpu_failures_short.txt
-#
-#      - name: Test suite reports artifacts
-#        if: ${{ always() }}
-#        uses: actions/upload-artifact@v2
-#        with:
-#          name: run_all_tests_flax_gpu_test_reports
-#          path: reports
-
-  run_all_tests_torch_cuda_extensions_gpu:
-    runs-on: [self-hosted, docker-gpu, single-gpu]
-    container:
-      image: nvcr.io/nvidia/pytorch:21.03-py3
-      options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
-    steps:
-      - name: Launcher docker
-        uses: actions/checkout@v2
-
-      - name: NVIDIA-SMI
-        run: |
-          nvidia-smi
-
-      - name: Install dependencies
-        run: |
-          apt -y update && apt install -y libaio-dev
-          pip install --upgrade pip
-          pip install .[testing,deepspeed]
-
-      - 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())"
-
-      - name: Run all tests on GPU
-        run: |
-          python -m pytest -n 1 -v --dist=loadfile --make-reports=tests_torch_cuda_extensions_gpu tests/deepspeed tests/extended
-
-      - name: Failure short reports
-        if: ${{ always() }}
-        run: cat reports/tests_torch_cuda_extensions_gpu_failures_short.txt
-
-      - name: Test suite reports artifacts
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v2
-        with:
-          name: run_tests_torch_cuda_extensions_gpu_test_reports
-          path: reports
-
-  run_all_tests_torch_cuda_extensions_multi_gpu:
-    runs-on: [self-hosted, docker-gpu, multi-gpu]
-    container:
-      image: nvcr.io/nvidia/pytorch:21.03-py3
-      options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
-    steps:
-      - name: Launcher docker
-        uses: actions/checkout@v2
-
-      - name: NVIDIA-SMI
-        continue-on-error: true
-        run: |
-          nvidia-smi
-
-      - name: Install dependencies
-        run: |
-          apt -y update && apt install -y libaio-dev
-          pip install --upgrade pip
-          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())"
-
-      - 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
-
-      - name: Test suite reports artifacts
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v2
-        with:
-          name: run_tests_torch_cuda_extensions_multi_gpu_test_reports
-          path: reports
-
-  send_results:
-    name: Send results to webhook
-    runs-on: ubuntu-latest
-    if: always()
-    needs: [
-        run_all_tests_torch_gpu,
-        run_all_tests_tf_gpu,
-        run_all_tests_torch_multi_gpu,
-        run_all_tests_tf_multi_gpu,
-        run_all_tests_torch_cuda_extensions_gpu,
-        run_all_tests_torch_cuda_extensions_multi_gpu
-    ]
-    steps:
-      - uses: actions/checkout@v2
-
-      - uses: actions/download-artifact@v2
-
-      - name: Send message to Slack
-        env:
-          CI_SLACK_BOT_TOKEN: ${{ secrets.CI_SLACK_BOT_TOKEN }}
-          CI_SLACK_CHANNEL_ID: ${{ secrets.CI_SLACK_CHANNEL_ID }}
-          CI_SLACK_CHANNEL_ID_DAILY: ${{ secrets.CI_SLACK_CHANNEL_ID_DAILY }}
-
-
-        run: |
-          pip install slack_sdk
-          python utils/notification_service.py scheduled
+          

.github/workflows/stale.yml

@@ -1,27 +0,0 @@
-name: Stale Bot
-
-on:
-  schedule:
-    - cron: "0 15 * * *"
-
-jobs:
-  close_stale_issues:
-    name: Close Stale Issues
-    if: github.repository == 'huggingface/transformers'
-    runs-on: ubuntu-latest
-    env:
-      GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-    steps:
-    - uses: actions/checkout@v2
-
-    - name: Setup Python
-      uses: actions/setup-python@v1
-      with:
-        python-version: 3.7
-
-    - name: Install requirements
-      run: |
-        pip install PyGithub
-    - name: Close stale issues
-      run: |
-        python scripts/stale.py

.gitignore

@@ -9,7 +9,8 @@ __pycache__/
 *.so
 
 # tests and logs
-tests/fixtures/cached_*_text.txt
+tests/fixtures/*
+!tests/fixtures/sample_text_no_unicode.txt
 logs/
 lightning_logs/
 lang_code_data/

CITATION.cff

@@ -1,82 +0,0 @@
-cff-version: "1.2.0"
-date-released: 2020-10
-message: "If you use this software, please cite it using these metadata."
-title: "Transformers: State-of-the-Art Natural Language Processing"
-url: "https://github.com/huggingface/transformers"
-authors: 
-  - family-names: Wolf
-    given-names: Thomas
-  - family-names: Debut
-    given-names: Lysandre
-  - family-names: Sanh
-    given-names: Victor
-  - family-names: Chaumond
-    given-names: Julien
-  - family-names: Delangue
-    given-names: Clement
-  - family-names: Moi
-    given-names: Anthony
-  - family-names: Cistac
-    given-names: Perric
-  - family-names: Ma
-    given-names: Clara
-  - family-names: Jernite
-    given-names: Yacine
-  - family-names: Plu
-    given-names: Julien
-  - family-names: Xu
-    given-names: Canwen
-  - family-names: "Le Scao"
-    given-names: Teven
-  - family-names: Gugger
-    given-names: Sylvain
-  - family-names: Drame
-    given-names: Mariama
-  - family-names: Lhoest
-    given-names: Quentin
-  - family-names: Rush
-    given-names: "Alexander M."
-preferred-citation:
-  type: inproceedings
-  authors:
-  - family-names: Wolf
-    given-names: Thomas
-  - family-names: Debut
-    given-names: Lysandre
-  - family-names: Sanh
-    given-names: Victor
-  - family-names: Chaumond
-    given-names: Julien
-  - family-names: Delangue
-    given-names: Clement
-  - family-names: Moi
-    given-names: Anthony
-  - family-names: Cistac
-    given-names: Perric
-  - family-names: Ma
-    given-names: Clara
-  - family-names: Jernite
-    given-names: Yacine
-  - family-names: Plu
-    given-names: Julien
-  - family-names: Xu
-    given-names: Canwen
-  - family-names: "Le Scao"
-    given-names: Teven
-  - family-names: Gugger
-    given-names: Sylvain
-  - family-names: Drame
-    given-names: Mariama
-  - family-names: Lhoest
-    given-names: Quentin
-  - family-names: Rush
-    given-names: "Alexander M."
-  booktitle: "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations"
-  month: 10
-  start: 38
-  end: 45
-  title: "Transformers: State-of-the-Art Natural Language Processing"
-  year: 2020
-  publisher: "Association for Computational Linguistics"
-  url: "https://www.aclweb.org/anthology/2020.emnlp-demos.6"
-  address: "Online"

CONTRIBUTING.md

@@ -36,13 +36,6 @@ There are 4 ways you can contribute to transformers:
 * Contributing to the examples or to the documentation;
 * Submitting issues related to bugs or desired new features.
 
-In particular there is a special [Good First
-Issue](https://github.com/huggingface/transformers/contribute) listing. It will give you a list of
-open Issues that are open to anybody to work on. Just comment in the issue that you'd like to work
-on it. In that same listing you will also find some Issues with `Good Second Issue` label. These are
-typically slightly more complicated than the Issues with just `Good First Issue` label. But if you
-feel you know what you're doing, go for it.
-
 *All are equally valuable to the community.*
 
 ## Submitting a new issue or feature request
@@ -53,7 +46,7 @@ feedback.
 
 ### Did you find a bug?
 
-The 🤗 Transformers library is robust and reliable thanks to the users who notify us of
+The transformers are robust and reliable thanks to the users who notify us of
 the problems they encounter. So thank you for reporting an issue.
 
 First, we would really appreciate it if you could **make sure the bug was not
@@ -292,7 +285,7 @@ $ python -m pytest -n auto --dist=loadfile -s -v ./tests/
 and for the examples:
 
 ```bash
-$ pip install -r examples/xxx/requirements.txt  # only needed the first time
+$ pip install -r examples/requirements.txt  # only needed the first time
 $ python -m pytest -n auto --dist=loadfile -s -v ./examples/
 ```
 In fact, that's how `make test` and `make test-examples` are implemented (sans the `pip install` line)!
@@ -350,7 +343,7 @@ You can now use `make` from any terminal (Powershell, cmd.exe, etc) 🎉
 
 ### Syncing forked master with upstream (HuggingFace) master
 
-To avoid pinging the upstream repository which adds reference notes to each upstream PR and sends unnessary notifications to the developers involved in these PRs,
+To avoid pinging the upstream repository which adds reference notes to each upstream PR and sends unnessary notifications to the developers involved in these PRs, 
 when syncing the master branch of a forked repository, please, follow these steps:
 1. When possible, avoid syncing with the upstream using a branch and PR on the forked repository. Instead merge directly into the forked master.
 2. If a PR is absolutely necessary, use the following steps after checking out your branch:

ISSUES.md

@@ -207,8 +207,6 @@ You are not required to read the following guidelines before opening an issue. H
 
    Do not dispair if you can't figure it out from the begining, just share what you can and perhaps someone else will be able to help you at the forums.
 
-   If your setup involves any custom datasets, the best way to help us reproduce the problem is to create a [Google Colab notebook](https://colab.research.google.com/) that demonstrates the issue and once you verify that the issue still exists, include a link to that notebook in the Issue. Just make sure that you don't copy and paste the location bar url of the open notebook - as this is private and we won't be able to open it. Instead, you need to click on `Share` in the right upper corner of the notebook, select `Get Link` and then copy and paste the public link it will give to you.
-
 7. If you forked off some of this project's code or example applications, please, do not ask us to go into your code repository and figure out what you may have done. The code is already very complex and unless there is an easy way to do a diff and it's a small diff, it won't be possible to find someone with time on their hands to make a lengthy investigation. Albeit, you might find someone at the forums who will be generous to do this for you.
 
 8. Before reporting an issue, first, always try to update your environment to the latest official version of this library. We have no resources to go and debug older revisions, which could easily have bugs that have been fixed in the latest released version.

Makefile

@@ -1,7 +1,5 @@
 .PHONY: deps_table_update modified_only_fixup extra_quality_checks quality style fixup fix-copies test test-examples docs
 
-# make sure to test the local checkout in scripts and not the pre-installed one (don't use quotes!)
-export PYTHONPATH = src
 
 check_dirs := examples tests src utils
 
@@ -21,50 +19,33 @@ modified_only_fixup:
 deps_table_update:
 	@python setup.py deps_table_update
 
-deps_table_check_updated:
-	@md5sum src/transformers/dependency_versions_table.py > md5sum.saved
-	@python setup.py deps_table_update
-	@md5sum -c --quiet md5sum.saved || (printf "\nError: the version dependency table is outdated.\nPlease run 'make fixup' or 'make style' and commit the changes.\n\n" && exit 1)
-	@rm md5sum.saved
-
-# autogenerating code
-
-autogenerate_code: deps_table_update
-
 # Check that source code meets quality standards
 
-extra_quality_checks:
+extra_quality_checks: deps_table_update
 	python utils/check_copies.py
 	python utils/check_table.py
 	python utils/check_dummies.py
 	python utils/check_repo.py
-	python utils/check_inits.py
-	python utils/tests_fetcher.py --sanity_check
+	python utils/style_doc.py src/transformers docs/source --max_len 119
 
 # this target runs checks on all files
 quality:
 	black --check $(check_dirs)
 	isort --check-only $(check_dirs)
-	python utils/custom_init_isort.py --check_only
 	flake8 $(check_dirs)
+	python utils/style_doc.py src/transformers docs/source --max_len 119 --check_only
 	${MAKE} extra_quality_checks
 
 # Format source code automatically and check is there are any problems left that need manual fixing
 
-extra_style_checks:
-	python utils/custom_init_isort.py
-	python utils/style_doc.py src/transformers docs/source --max_len 119
-
-# this target runs checks on all files and potentially modifies some of them
-style:
+style: deps_table_update
 	black $(check_dirs)
 	isort $(check_dirs)
-	${MAKE} autogenerate_code
-	${MAKE} extra_style_checks
+	python utils/style_doc.py src/transformers docs/source --max_len 119
 
 # Super fast fix and check target that only works on relevant modified files since the branch was made
 
-fixup: modified_only_fixup extra_style_checks autogenerate_code extra_quality_checks
+fixup: modified_only_fixup extra_quality_checks
 
 # Make marked copies of snippets of codes conform to the original
 
@@ -81,29 +62,9 @@ test:
 # Run tests for examples
 
 test-examples:
-	python -m pytest -n auto --dist=loadfile -s -v ./examples/pytorch/
-
-# Run tests for SageMaker DLC release
-
-test-sagemaker: # install sagemaker dependencies in advance with pip install .[sagemaker]
-	TEST_SAGEMAKER=True python -m pytest -n auto  -s -v ./tests/sagemaker
-
+	python -m pytest -n auto --dist=loadfile -s -v ./examples/
 
 # Check that docs can build
 
 docs:
 	cd docs && make html SPHINXOPTS="-W -j 4"
-
-# Release stuff
-
-pre-release:
-	python utils/release.py
-
-pre-patch:
-	python utils/release.py --patch
-
-post-release:
-	python utils/release.py --post_release
-
-post-patch:
-	python utils/release.py --post_release --patch

README.md

@@ -35,68 +35,49 @@ limitations under the License.
     <a href="https://github.com/huggingface/transformers/blob/master/CODE_OF_CONDUCT.md">
         <img alt="Contributor Covenant" src="https://img.shields.io/badge/Contributor%20Covenant-v2.0%20adopted-ff69b4.svg">
     </a>
-    <a href="https://zenodo.org/badge/latestdoi/155220641"><img src="https://zenodo.org/badge/155220641.svg" alt="DOI"></a>
 </p>
 
-<h4 align="center">
-    <p>
-        <b>English</b> |
-        <a href="https://github.com/huggingface/transformers/blob/master/README_zh-hans.md">简体中文</a> |
-        <a href="https://github.com/huggingface/transformers/blob/master/README_zh-hant.md">繁體中文</a>
-    <p>
-</h4>
-
 <h3 align="center">
-    <p>State-of-the-art Natural Language Processing for Jax, PyTorch and TensorFlow</p>
+<p>State-of-the-art Natural Language Processing for PyTorch and TensorFlow 2.0
 </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, etc in 100+ languages. Its aim is to make cutting-edge NLP easier to use for everyone.
 
-🤗 Transformers provides thousands of pretrained models to perform tasks on texts such as classification, information extraction, question answering, summarization, translation, text generation and more in over 100 languages. Its aim is to make cutting-edge NLP easier to use for everyone.
+🤗 Transformers provides APIs to quickly download and use those pretrained models on a given text, fine-tune them on your own datasets then share them with the community on our [model hub](https://huggingface.co/models). At the same time, each python module defining an architecture can be used as a standalone and modified to enable quick research experiments.
 
-🤗 Transformers provides APIs to quickly download and use those pretrained models on a given text, fine-tune them on your own datasets 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.
+🤗 Transformers is backed by the two most popular deep learning libraries, [PyTorch](https://pytorch.org/) and [TensorFlow](https://www.tensorflow.org/), with a seamless integration between them, allowing you to train your models with one then load it for inference with the other.
 
 ## Online demos
 
-You can test most of our models directly on their pages from the [model hub](https://huggingface.co/models). We also offer [private model hosting, versioning, & an inference API](https://huggingface.co/pricing) for public and private models.
+You can test most of our models directly on their pages from the [model hub](https://huggingface.co/models). We also offer [private model hosting, versioning, & an inference API](https://huggingface.co/pricing) to use those models.
 
 Here are a few examples:
 - [Masked word completion with BERT](https://huggingface.co/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
 - [Name Entity Recognition with Electra](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city)
 - [Text generation with GPT-2](https://huggingface.co/gpt2?text=A+long+time+ago%2C+)
-- [Natural Language Inference with RoBERTa](https://huggingface.co/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal)
+- [Natural Langugage Inference with RoBERTa](https://huggingface.co/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal)
 - [Summarization with BART](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct)
 - [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)
 
 **[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
-
-<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>
-
 ## 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 text, we provide the `pipeline` API. Pipelines group together a pretrained model with the preprocessing that was used during that model training. Here is how to quickly use a pipeline to classify positive versus negative texts
 
 ```python
 >>> from transformers import pipeline
 
 # Allocate a pipeline for sentiment-analysis
 >>> classifier = pipeline('sentiment-analysis')
->>> classifier('We are very happy to introduce pipeline to the transformers repository.')
-[{'label': 'POSITIVE', 'score': 0.9996980428695679}]
+>>> classifier('We are very happy to include pipeline into the transformers repository.')
+[{'label': 'POSITIVE', 'score': 0.9978193640708923}]
 ```
 
-The second line of code downloads and caches the pretrained model used by the pipeline, while the third evaluates it on the given text. Here the answer is "positive" with a confidence of 99.97%.
+The second line of code downloads and caches the pretrained model used by the pipeline, the third line evaluates it on the given text. Here the answer is "positive" with a confidence of 99.8%.
 
-Many NLP tasks have a pre-trained `pipeline` ready to go. For example, we can easily extract question answers given context:
+This is another example of pipeline used for that can extract question answers from some context:
 
 ``` python
 >>> from transformers import pipeline
@@ -105,15 +86,15 @@ Many NLP tasks have a pre-trained `pipeline` ready to go. For example, we can ea
 >>> question_answerer = pipeline('question-answering')
 >>> question_answerer({
 ...     'question': 'What is the name of the repository ?',
-...     'context': 'Pipeline has been included in the huggingface/transformers repository'
+...     'context': 'Pipeline have been included in the huggingface/transformers repository'
 ... })
-{'score': 0.30970096588134766, 'start': 34, 'end': 58, 'answer': 'huggingface/transformers'}
+{'score': 0.5135612454720828, 'start': 35, 'end': 59, 'answer': 'huggingface/transformers'}
 
 ```
 
-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).
+On top of the answer, the pretrained model used here returned its confidence score, along with the start position and its end position in the tokenized sentence. You can learn more about the tasks supported by the `pipeline` API in [this tutorial](https://huggingface.co/transformers/task_summary.html).
 
-To download and use any of the pretrained models on your given task, all it takes is three lines of code. Here is the PyTorch version:
+To download and use any of the pretrained models on your given task, you just need to use those three lines of codes (PyTorch version):
 ```python
 >>> from transformers import AutoTokenizer, AutoModel
 
@@ -123,7 +104,7 @@ To download and use any of the pretrained models on your given task, all it take
 >>> inputs = tokenizer("Hello world!", return_tensors="pt")
 >>> outputs = model(**inputs)
 ```
-And here is the equivalent code for TensorFlow:
+or for TensorFlow:
 ```python
 >>> from transformers import AutoTokenizer, TFAutoModel
 
@@ -134,9 +115,9 @@ And here is the equivalent code for TensorFlow:
 >>> outputs = model(**inputs)
 ```
 
-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 tokenizer is responsible for all the preprocessing the pretrained model expects, and can be called directly on one (or list) of texts (as we can see on the fourth line of both code examples). It will output a dictionary you can directly pass to your model (which is done on the fifth line).
 
-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. For instance, [this tutorial](https://huggingface.co/transformers/training.html) explains how to integrate such a model in classic PyTorch or TensorFlow training loop, or how to use our `Trainer` API to quickly fine-tune the on a new dataset.
 
 ## Why should I use transformers?
 
@@ -154,16 +135,16 @@ The model itself is a regular [Pytorch `nn.Module`](https://pytorch.org/docs/sta
 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.
-    - Seamlessly pick the right framework for training, evaluation and production.
+    - Seamlessly pick the right framework for training, evaluation, 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.
+    - Examples for each architecture to reproduce the results by the official authors of said architecture.
+    - Expose the models internal as consistently as possible.
     - Model files can be used independently of the library for quick experiments.
 
 ## Why shouldn't I use transformers?
 
-- This library is not a modular toolbox of building blocks for neural nets. The code in the model files is not refactored with additional abstractions on purpose, so that researchers can quickly iterate on each of the models without diving into additional abstractions/files.
+- This library is not a modular toolbox of building blocks for neural nets. The code in the model files is not refactored with additional abstractions on purpose, so that researchers can quickly iterate on each of the models without diving in additional abstractions/files.
 - The training API is not intended to work on any model but is optimized to work with the models provided by the library. For generic machine learning loops, you should use another library.
 - While we strive to present as many use cases as possible, the scripts in our [examples folder](https://github.com/huggingface/transformers/tree/master/examples) are just that: examples. It is expected that they won't work out-of-the box on your specific problem and that you will be required to change a few lines of code to adapt them to your needs.
 
@@ -171,22 +152,22 @@ The model itself is a regular [Pytorch `nn.Module`](https://pytorch.org/docs/sta
 
 ### With pip
 
-This repository is tested on Python 3.6+, Flax 0.3.2+, PyTorch 1.3.1+ and TensorFlow 2.3+.
+This repository is tested on Python 3.6+, PyTorch 1.0.0+ (PyTorch 1.3.1+ for [examples](https://github.com/huggingface/transformers/tree/master/examples)) and TensorFlow 2.0.
 
 You should install 🤗 Transformers in a [virtual environment](https://docs.python.org/3/library/venv.html). If you're unfamiliar with Python virtual environments, check out the [user guide](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/).
 
 First, create a virtual environment with the version of Python you're going to use and activate it.
 
-Then, you will need to install at least one of Flax, PyTorch or TensorFlow.
-Please refer to [TensorFlow installation page](https://www.tensorflow.org/install/), [PyTorch installation page](https://pytorch.org/get-started/locally/#start-locally) and/or [Flax installation page](https://github.com/google/flax#quick-install) regarding the specific install command for your platform.
+Then, you will need to install at least one of TensorFlow 2.0, PyTorch or Flax.
+Please refer to [TensorFlow installation page](https://www.tensorflow.org/install/pip#tensorflow-2.0-rc-is-available), [PyTorch installation page](https://pytorch.org/get-started/locally/#start-locally) regarding the specific install command for your platform and/or [Flax installation page](https://github.com/google/flax#quick-install).
 
-When one of those backends has been installed, 🤗 Transformers can be installed using pip as follows:
+When TensorFlow 2.0 and/or PyTorch has been installed, 🤗 Transformers can be installed using pip as follows:
 
 ```bash
 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, you must [install the library from source](https://huggingface.co/transformers/installation.html#installing-from-source).
 
 ### With conda
 
@@ -198,9 +179,9 @@ Since Transformers version v4.0.0, we now have a conda channel: `huggingface`.
 conda install -c huggingface transformers
 ```
 
-Follow the installation pages of Flax, PyTorch or TensorFlow to see how to install them with conda.
+Follow the installation pages of TensorFlow, PyTorch or Flax to see how to install them with conda. 
 
-## Model architectures
+## Models architectures
 
 **[All the model checkpoints](https://huggingface.co/models)** provided by 🤗 Transformers are seamlessly integrated from the huggingface.co [model hub](https://huggingface.co) where they are uploaded directly by [users](https://huggingface.co/users) and [organizations](https://huggingface.co/organizations).
 
@@ -211,84 +192,49 @@ Current number of checkpoints: ![](https://img.shields.io/endpoint?url=https://h
 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. **[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. **[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. **[DeBERTa](https://huggingface.co/transformers/model_doc/deberta.html)** (from Microsoft Research) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
 1. **[DialoGPT](https://huggingface.co/transformers/model_doc/dialogpt.html)** (from Microsoft Research) released with the paper [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://arxiv.org/abs/1911.00536) by Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan.
 1. **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace), released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into [DistilGPT2](https://github.com/huggingface/transformers/tree/master/examples/distillation), RoBERTa into [DistilRoBERTa](https://github.com/huggingface/transformers/tree/master/examples/distillation), Multilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers/tree/master/examples/distillation) and a German version of DistilBERT.
 1. **[DPR](https://huggingface.co/transformers/model_doc/dpr.html)** (from Facebook) released with the paper [Dense Passage Retrieval
 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. **[Pegasus](https://huggingface.co/transformers/model_doc/pegasus.html)** (from Google) released with the paper [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/abs/1912.08777)> by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu.
 1. **[ProphetNet](https://huggingface.co/transformers/model_doc/prophetnet.html)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
 1. **[Reformer](https://huggingface.co/transformers/model_doc/reformer.html)** (from Google Research) released with the paper [Reformer: The Efficient Transformer](https://arxiv.org/abs/2001.04451) by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
-1. **[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. **[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 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.
+ultilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers/tree/master/examples/distillation) and a German version of DistilBERT.
+1. **[SqueezeBert](https://huggingface.co/transformers/model_doc/squeezebert.html)** released with the paper [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer.
 1. **[T5](https://huggingface.co/transformers/model_doc/t5.html)** (from Google AI) released with the paper [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
-1. **[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. **[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. 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 PyTorch/TensorFlow/Flax or has an associated tokenizer backed by the 🤗 Tokenizers library, refer to [this table](https://huggingface.co/transformers/index.html#bigtable)
 
-These implementations have been tested on several datasets (see the example scripts) and should match the 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 performances of the original implementations. You can find more details on the performances in the Examples section of the [documentation](https://huggingface.co/transformers/examples.html).
 
 
 ## Learn more

README_zh-hans.md

@@ -1,343 +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.
--->
-
-<!---
-A useful guide for English-Chinese translation of Hugging Face documentation
-- Add space around English words and numbers when they appear between Chinese characters. E.g., 共 100 多种语言; 使用 transformers 库。
-- Use square quotes, e.g.,「引用」
-
-Dictionary
-
-Hugging Face: 抱抱脸
-token: 词符（并用括号标注原英文）
-tokenize: 词符化（并用括号标注原英文）
-tokenizer: 词符化器（并用括号标注原英文）
-transformer: transformer（不翻译）
-pipeline: 流水线
-API: API (不翻译）
-inference: 推理
-Trainer: 训练器。当作为类名出现时不翻译。
-pretrained/pretrain: 预训练
-finetune: 微调
-community: 社区
-example: 当特指仓库中 example 目录时翻译为「用例」
-Python data structures (e.g., list, set, dict): 翻译为列表，集合，词典，并用括号标注原英文
-NLP/Natural Language Processing: 以 NLP 出现时不翻译，以 Natural Language Processing 出现时翻译为自然语言处理
-checkpoint: 检查点
--->
-
-<p align="center">
-    <br>
-    <img src="https://raw.githubusercontent.com/huggingface/transformers/master/docs/source/imgs/transformers_logo_name.png" width="400"/>
-    <br>
-<p>
-<p align="center">
-    <a href="https://circleci.com/gh/huggingface/transformers">
-        <img alt="Build" src="https://img.shields.io/circleci/build/github/huggingface/transformers/master">
-    </a>
-    <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>
-    <a href="https://github.com/huggingface/transformers/releases">
-        <img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/transformers.svg">
-    </a>
-    <a href="https://github.com/huggingface/transformers/blob/master/CODE_OF_CONDUCT.md">
-        <img alt="Contributor Covenant" src="https://img.shields.io/badge/Contributor%20Covenant-v2.0%20adopted-ff69b4.svg">
-    </a>
-    <a href="https://zenodo.org/badge/latestdoi/155220641"><img src="https://zenodo.org/badge/155220641.svg" alt="DOI"></a>
-</p>
-
-<h4 align="center">
-    <p>
-        <a href="https://github.com/huggingface/transformers/">English</a> |
-        <b>简体中文</b> |
-        <a href="https://github.com/huggingface/transformers/blob/master/README_zh-hant.md">繁體中文</a>
-    <p>
-</h4>
-
-<h3 align="center">
-    <p>为 Jax、PyTorch 和 TensorFlow 打造的先进的自然语言处理</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 提供了数以千计的预训练模型，支持 100 多种语言的文本分类、信息抽取、问答、摘要、翻译、文本生成。它的宗旨让最先进的 NLP 技术人人易用。
-
-🤗 Transformers 提供了便于快速下载和使用的API，让你可以把预训练模型用在给定文本、在你的数据集上微调然后通过 [model hub](https://huggingface.co/models) 与社区共享。同时，每个定义的 Python 模块均完全独立，方便修改和快速研究实验。
-
-🤗 Transformers 支持三个最热门的深度学习库： [Jax](https://jax.readthedocs.io/en/latest/), [PyTorch](https://pytorch.org/) and [TensorFlow](https://www.tensorflow.org/) — 并与之无缝整合。你可以直接使用一个框架训练你的模型然后用另一个加载和推理。
-
-## 在线演示
-
-你可以直接在模型页面上测试大多数 [model hub](https://huggingface.co/models) 上的模型。 我们也提供了 [私有模型托管、模型版本管理以及推理API](https://huggingface.co/pricing)。
-
-这里是一些例子：
-- [用 BERT 做掩码填词](https://huggingface.co/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
-- [用 Electra 做命名实体识别](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city)
-- [用 GPT-2 做文本生成](https://huggingface.co/gpt2?text=A+long+time+ago%2C+)
-- [用 RoBERTa 做自然语言推理](https://huggingface.co/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal)
-- [用 BART 做文本摘要](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct)
-- [用 DistilBERT 做问答](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)
-- [用 T5 做翻译](https://huggingface.co/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
-
-**[Write With Transformer](https://transformer.huggingface.co)**，由抱抱脸团队打造，是一个文本生成的官方 demo。
-
-## 如果你在寻找由抱抱脸团队提供的定制化支持服务
-
-<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>
-
-## 快速上手
-
-我们为快速使用模型提供了 `pipeline` （流水线）API。流水线聚合了预训练模型和对应的文本预处理。下面是一个快速使用流水线去判断正负面情绪的例子：
-
-```python
->>> from transformers import pipeline
-
-# 使用情绪分析流水线
->>> classifier = pipeline('sentiment-analysis')
->>> classifier('We are very happy to introduce pipeline to the transformers repository.')
-[{'label': 'POSITIVE', 'score': 0.9996980428695679}]
-```
-
-第二行代码下载并缓存了流水线使用的预训练模型，而第三行代码则在给定的文本上进行了评估。这里的答案“正面” (positive) 具有 99 的置信度。
-
-许多的 NLP 任务都有开箱即用的预训练流水线。比如说，我们可以轻松的从给定文本中抽取问题答案：
-
-``` python
->>> from transformers import pipeline
-
-# 使用问答流水线
->>> question_answerer = pipeline('question-answering')
->>> question_answerer({
-...     'question': 'What is the name of the repository ?',
-...     'context': 'Pipeline has been included in the huggingface/transformers repository'
-... })
-{'score': 0.30970096588134766, 'start': 34, 'end': 58, 'answer': 'huggingface/transformers'}
-
-```
-
-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。
-
-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
->>> from transformers import AutoTokenizer, AutoModel
-
->>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
->>> model = AutoModel.from_pretrained("bert-base-uncased")
-
->>> inputs = tokenizer("Hello world!", return_tensors="pt")
->>> outputs = model(**inputs)
-```
-这里是等效的 TensorFlow 代码：
-```python
->>> from transformers import AutoTokenizer, TFAutoModel
-
->>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
->>> model = TFAutoModel.from_pretrained("bert-base-uncased")
-
->>> inputs = tokenizer("Hello world!", return_tensors="tf")
->>> outputs = model(**inputs)
-```
-
-词符化器 (tokenizer) 为所有的预训练模型提供了预处理，并可以直接对单个字符串进行调用（比如上面的例子）或对列表 (list) 调用。它会输出一个你可以在下游代码里使用或直接通过 `**` 解包表达式传给模型的词典 (dict)。
-
-模型本身是一个常规的 [Pytorch `nn.Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) 或 [TensorFlow `tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model)（取决于你的后端），可以常规方式使用。 [这个教程](https://huggingface.co/transformers/training.html)解释了如何将这样的模型整合到经典的 PyTorch 或 TensorFlow 训练循环中，或是如何使用我们的 `Trainer` 训练器）API 来在一个新的数据集上快速微调。
-
-## 为什么要用 transformers？
-
-1. 便于使用的先进模型：
-    - NLU 和 NLG 上表现优越
-    - 对教学和实践友好且低门槛
-    - 高级抽象，只需了解三个类
-    - 对所有模型统一的API
-
-1. 更低计算开销，更少的碳排放：
-    - 研究人员可以分享亿训练的模型而非次次从头开始训练
-    - 工程师可以减少计算用时和生产环境开销
-    - 数十种模型架构、两千多个预训练模型、100多种语言支持
-
-1. 对于模型生命周期的每一个部分都面面俱到：
-    - 训练先进的模型，只需 3 行代码
-    - 模型在不同深度学习框架间任意转移，随你心意
-    - 为训练、评估和生产选择最适合的框架，衔接无缝
-
-1. 为你的需求轻松定制专属模型和用例：
-    - 我们为每种模型架构提供了多个用例来复现原论文结果
-    - 模型内部结构保持透明一致
-    - 模型文件可单独使用，方便魔改和快速实验
-
-## 什么情况下我不该用 transformers？
-
-- 本库并不是模块化的神经网络工具箱。模型文件中的代码特意呈若璞玉，未经额外抽象封装，以便研究人员快速迭代魔改而不致溺于抽象和文件跳转之中。
-- `Trainer` API 并非兼容任何模型，只为本库之模型优化。若是在寻找适用于通用机器学习的训练循环实现，请另觅他库。
-- 尽管我们已尽力而为，[examples 目录](https://github.com/huggingface/transformers/tree/master/examples)中的脚本也仅为用例而已。对于你的特定问题，它们并不一定开箱即用，可能需要改几行代码以适之。
-
-## 安装
-
-### 使用 pip
-
-这个仓库已在 Python 3.6+、Flax 0.3.2+、PyTorch 1.3.1+ 和 TensorFlow 2.3+ 下经过测试。
-
-你可以在[虚拟环境](https://docs.python.org/3/library/venv.html)中安装 🤗 Transformers。如果你还不熟悉 Python 的虚拟环境，请阅此[用户说明](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/)。
-
-首先，用你打算使用的版本的 Python 创建一个虚拟环境并激活。
-
-然后，你需要安装 Flax、PyTorch 或 TensorFlow 其中之一。关于在你使用的平台上安装这些框架，请参阅 [TensorFlow 安装页](https://www.tensorflow.org/install/), [PyTorch 安装页](https://pytorch.org/get-started/locally/#start-locally) 或 [Flax 安装页](https://github.com/google/flax#quick-install)。
-
-当这些后端之一安装成功后， 🤗 Transformers 可依此安装：
-
-```bash
-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)。
-
-### 使用 conda
-
-自 Transformers 4.0.0 版始，我们有了一个 conda 频道： `huggingface`。
-
-🤗 Transformers 可以通过 conda 依此安装：
-
-```shell script
-conda install -c huggingface transformers
-```
-
-要通过 conda 安装 Flax、PyTorch 或 TensorFlow 其中之一，请参阅它们各自安装页的说明。
-
-## 模型架构
-
-**🤗 Transformers 支持的[所有的模型检查点](https://huggingface.co/models)** 由[用户](https://huggingface.co/users)和[组织](https://huggingface.co/organizations)上传，均与 huggingface.co [model hub](https://huggingface.co) 无缝整合。
-
-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)）：
-
-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. **[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. **[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/master/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. **[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. **[SpeechEncoderDecoder](https://huggingface.co/transformers/master/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/master/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. **[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. **[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. 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)。
-
-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。
-
-
-## 了解更多
-
-| 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 |
-| [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 |
-
-## 引用
-
-我们已将此库的[论文](https://www.aclweb.org/anthology/2020.emnlp-demos.6/)正式发表，如果你使用了 🤗 Transformers 库，请引用:
-```bibtex
-@inproceedings{wolf-etal-2020-transformers,
-    title = "Transformers: State-of-the-Art Natural Language Processing",
-    author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush",
-    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
-    month = oct,
-    year = "2020",
-    address = "Online",
-    publisher = "Association for Computational Linguistics",
-    url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6",
-    pages = "38--45"
-}
-```

README_zh-hant.md

@@ -1,355 +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.
--->
-
-<!---
-A useful guide for English-Traditional Chinese translation of Hugging Face documentation
-- Add space around English words and numbers when they appear between Chinese characters. E.g., 共 100 多種語言; 使用 transformers 函式庫。
-- Use square quotes, e.g.,「引用」
-- Some of terms in the file can be found at National Academy for Educational Research (https://terms.naer.edu.tw/), an official website providing bilingual translations between English and Traditional Chinese.
-
-Dictionary
-
-API: API (不翻譯）
-add: 加入
-checkpoint: 檢查點
-code: 程式碼
-community: 社群
-confidence: 信賴度
-dataset: 資料集
-documentation: 文件
-example: 基本翻譯為「範例」，或依語意翻為「例子」
-finetune: 微調
-Hugging Face: Hugging Face（不翻譯）
-implementation: 實作
-inference: 推論
-library: 函式庫
-module: 模組
-NLP/Natural Language Processing: 以 NLP 出現時不翻譯，以 Natural Language Processing 出現時翻譯為自然語言處理
-online demos: 線上Demo
-pipeline: pipeline（不翻譯）
-pretrained/pretrain: 預訓練
-Python data structures (e.g., list, set, dict): 翻譯為串列，集合，字典，並用括號標註原英文
-repository: repository（不翻譯）
-summary: 概覽
-token-: token-（不翻譯）
-Trainer: Trainer（不翻譯）
-transformer: transformer（不翻譯）
-tutorial: 教學
-user: 使用者
--->
-
-<p align="center">
-    <br>
-    <img src="https://raw.githubusercontent.com/huggingface/transformers/master/docs/source/imgs/transformers_logo_name.png" width="400"/>
-    <br>
-<p>
-<p align="center">
-    <a href="https://circleci.com/gh/huggingface/transformers">
-        <img alt="Build" src="https://img.shields.io/circleci/build/github/huggingface/transformers/master">
-    </a>
-    <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>
-    <a href="https://github.com/huggingface/transformers/releases">
-        <img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/transformers.svg">
-    </a>
-    <a href="https://github.com/huggingface/transformers/blob/master/CODE_OF_CONDUCT.md">
-        <img alt="Contributor Covenant" src="https://img.shields.io/badge/Contributor%20Covenant-v2.0%20adopted-ff69b4.svg">
-    </a>
-    <a href="https://zenodo.org/badge/latestdoi/155220641"><img src="https://zenodo.org/badge/155220641.svg" alt="DOI"></a>
-</p>
-
-<h4 align="center">
-    <p>
-        <a href="https://github.com/huggingface/transformers/">English</a> |
-        <a href="https://github.com/huggingface/transformers/blob/master/README_zh-hans.md">简体中文</a> |
-        <b>繁體中文</b>
-    <p>
-</h4>
-
-<h3 align="center">
-    <p>為 Jax、PyTorch 以及 TensorFlow 打造的先進自然語言處理函式庫</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 提供了數以千計的預訓練模型，支援 100 多種語言的文本分類、資訊擷取、問答、摘要、翻譯、文本生成。它的宗旨是讓最先進的 NLP 技術人人易用。
-
-🤗 Transformers 提供了便於快速下載和使用的API，讓你可以將預訓練模型用在給定文本、在你的資料集上微調然後經由 [model hub](https://huggingface.co/models) 與社群共享。同時，每個定義的 Python 模組架構均完全獨立，方便修改和快速研究實驗。
-
-🤗 Transformers 支援三個最熱門的深度學習函式庫： [Jax](https://jax.readthedocs.io/en/latest/), [PyTorch](https://pytorch.org/) 以及 [TensorFlow](https://www.tensorflow.org/) — 並與之完美整合。你可以直接使用其中一個框架訓練你的模型，然後用另一個載入和推論。
-
-## 線上Demo
-
-你可以直接在 [model hub](https://huggingface.co/models) 上測試大多數的模型。我們也提供了 [私有模型託管、模型版本管理以及推論API](https://huggingface.co/pricing)。
-
-這裡是一些範例：
-- [用 BERT 做遮蓋填詞](https://huggingface.co/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
-- [用 Electra 做專有名詞辨識](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city)
-- [用 GPT-2 做文本生成](https://huggingface.co/gpt2?text=A+long+time+ago%2C+)
-- [用 RoBERTa 做自然語言推論](https://huggingface.co/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal)
-- [用 BART 做文本摘要](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct)
-- [用 DistilBERT 做問答](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)
-- [用 T5 做翻譯](https://huggingface.co/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
-
-**[Write With Transformer](https://transformer.huggingface.co)**，由 Hugging Face 團隊所打造，是一個文本生成的官方 demo。
-
-## 如果你在尋找由 Hugging Face 團隊所提供的客製化支援服務
-
-<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>
-
-## 快速上手
-
-我們為快速使用模型提供了 `pipeline` API。 Pipeline 包含了預訓練模型和對應的文本預處理。下面是一個快速使用 pipeline 去判斷正負面情緒的例子：
-
-```python
->>> from transformers import pipeline
-
-# 使用情緒分析 pipeline
->>> classifier = pipeline('sentiment-analysis')
->>> classifier('We are very happy to introduce pipeline to the transformers repository.')
-[{'label': 'POSITIVE', 'score': 0.9996980428695679}]
-```
-
-第二行程式碼下載並快取 pipeline 使用的預訓練模型，而第三行程式碼則在給定的文本上進行了評估。這裡的答案“正面” (positive) 具有 99.97% 的信賴度。
-
-許多的 NLP 任務都有隨選即用的預訓練 `pipeline`。例如，我們可以輕鬆地從給定文本中擷取問題答案：
-
-``` python
->>> from transformers import pipeline
-
-# 使用問答 pipeline
->>> question_answerer = pipeline('question-answering')
->>> question_answerer({
-...     'question': 'What is the name of the repository ?',
-...     'context': 'Pipeline has been included in the huggingface/transformers repository'
-... })
-{'score': 0.30970096588134766, 'start': 34, 'end': 58, 'answer': 'huggingface/transformers'}
-
-```
-
-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。
-
-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
->>> from transformers import AutoTokenizer, AutoModel
-
->>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
->>> model = AutoModel.from_pretrained("bert-base-uncased")
-
->>> inputs = tokenizer("Hello world!", return_tensors="pt")
->>> outputs = model(**inputs)
-```
-這裡是對應的 TensorFlow 程式碼：
-```python
->>> from transformers import AutoTokenizer, TFAutoModel
-
->>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
->>> model = TFAutoModel.from_pretrained("bert-base-uncased")
-
->>> inputs = tokenizer("Hello world!", return_tensors="tf")
->>> outputs = model(**inputs)
-```
-
-Tokenizer 為所有的預訓練模型提供了預處理，並可以直接轉換單一字串（比如上面的例子）或串列 (list)。它會輸出一個的字典 (dict) 讓你可以在下游程式碼裡使用或直接藉由 `**` 運算式傳給模型。
-
-模型本身是一個常規的 [Pytorch `nn.Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) 或 [TensorFlow `tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model)（取決於你的後端），可依常規方式使用。 [這個教學](https://huggingface.co/transformers/training.html)解釋了如何將這樣的模型整合到一般的 PyTorch 或 TensorFlow 訓練迴圈中，或是如何使用我們的 `Trainer` API 在一個新的資料集上快速進行微調。
-
-## 為什麼要用 transformers？
-
-1. 便於使用的先進模型：
-    - NLU 和 NLG 上性能卓越
-    - 對教學和實作友好且低門檻
-    - 高度抽象，使用者只須學習 3 個類別
-    - 對所有模型使用的制式化API
-
-1. 更低的運算成本，更少的碳排放：
-    - 研究人員可以分享預訓練的模型而非從頭開始訓練
-    - 工程師可以減少計算時間以及生產成本
-    - 數十種模型架構、兩千多個預訓練模型、100多種語言支援
-
-1. 對於模型生命週期的每一個部分都面面俱到：
-    - 訓練先進的模型，只需 3 行程式碼
-    - 模型可以在不同深度學習框架之間任意轉換
-    - 為訓練、評估和生產選擇最適合的框架，並完美銜接
-
-1. 為你的需求輕鬆客製化專屬模型和範例：
-    - 我們為每種模型架構提供了多個範例來重現原論文結果
-    - 一致的模型內部架構
-    - 模型檔案可單獨使用，便於修改和快速實驗
-
-## 什麼情況下我不該用 transformers？
-
-- 本函式庫並不是模組化的神經網絡工具箱。模型文件中的程式碼並未做額外的抽象封裝，以便研究人員快速地翻閱及修改程式碼，而不會深陷複雜的類別包裝之中。
-- `Trainer` API 並非相容任何模型，它只為本函式庫中的模型最佳化。對於一般的機器學習用途，請使用其他函式庫。
-- 儘管我們已盡力而為，[examples 目錄](https://github.com/huggingface/transformers/tree/master/examples)中的腳本也僅為範例而已。對於特定問題，它們並不一定隨選即用，可能需要修改幾行程式碼以符合需求。
-
-## 安裝
-
-### 使用 pip
-
-這個 Repository 已在 Python 3.6+、Flax 0.3.2+、PyTorch 1.3.1+ 和 TensorFlow 2.3+ 下經過測試。
-
-你可以在[虛擬環境](https://docs.python.org/3/library/venv.html)中安裝 🤗 Transformers。如果你還不熟悉 Python 的虛擬環境，請閱此[使用者指引](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/)。
-
-首先，用你打算使用的版本的 Python 創建一個虛擬環境並進入。
-
-然後，你需要安裝 Flax、PyTorch 或 TensorFlow 其中之一。對於該如何在你使用的平台上安裝這些框架，請參閱 [TensorFlow 安裝頁面](https://www.tensorflow.org/install/), [PyTorch 安裝頁面](https://pytorch.org/get-started/locally/#start-locally) 或 [Flax 安裝頁面](https://github.com/google/flax#quick-install)。
-
-當其中一個後端安裝成功後，🤗 Transformers 可依此安裝：
-
-```bash
-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)。
-
-### 使用 conda
-
-自 Transformers 4.0.0 版始，我們有了一個 conda channel： `huggingface`。
-
-🤗 Transformers 可以藉由 conda 依此安裝：
-
-```shell script
-conda install -c huggingface transformers
-```
-
-要藉由 conda 安裝 Flax、PyTorch 或 TensorFlow 其中之一，請參閱它們各自安裝頁面的說明。
-
-## 模型架構
-
-**🤗 Transformers 支援的[所有的模型檢查點](https://huggingface.co/models)**，由[使用者](https://huggingface.co/users)和[組織](https://huggingface.co/organizations)上傳，均與 huggingface.co [model hub](https://huggingface.co) 完美結合。
-
-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)）：
-
-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. **[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. **[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/master/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. **[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. **[SpeechEncoderDecoder](https://huggingface.co/transformers/master/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/master/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. **[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. 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)。
-
-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。
-
-
-## 了解更多
-
-| 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 |
-| [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 |
-
-## 引用
-
-我們已將此函式庫的[論文](https://www.aclweb.org/anthology/2020.emnlp-demos.6/)正式發表。如果你使用了 🤗 Transformers 函式庫，可以引用：
-```bibtex
-@inproceedings{wolf-etal-2020-transformers,
-    title = "Transformers: State-of-the-Art Natural Language Processing",
-    author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush",
-    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
-    month = oct,
-    year = "2020",
-    address = "Online",
-    publisher = "Association for Computational Linguistics",
-    url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6",
-    pages = "38--45"
-}
-```

docker/transformers-pytorch-tpu/Dockerfile

@@ -53,7 +53,7 @@ RUN git clone https://github.com/huggingface/transformers.git && \
     git checkout CI && \
     cd .. && \
     pip install ./transformers && \
-    pip install -r ./transformers/examples/pytorch/_test_requirements.txt && \
+    pip install -r ./transformers/examples/requirements.txt && \
     pip install pytest
 
 RUN python -c "import torch_xla; print(torch_xla.__version__)"

docker/transformers-pytorch-tpu/bert-base-cased.jsonnet

@@ -27,7 +27,7 @@ local bertBaseCased = base.BaseTest {
   },
   command: utils.scriptCommand(
     |||
-      python -m pytest -s transformers/examples/pytorch/test_xla_examples.py -v
+      python -m pytest -s transformers/examples/test_xla_examples.py -v
       test_exit_code=$?
       echo "\nFinished running commands.\n"
       test $test_exit_code -eq 0

docs/README.md

@@ -26,7 +26,7 @@ pip install -e ".[docs]"
 ---
 **NOTE**
 
-You only need to generate the documentation to inspect it locally (if you're planning changes and want to
+You only need to generate the documentation to inspect it locally (if you're planning changes and want to 
 check how they look like before committing for instance). You don't have to commit the built documentation.
 
 ---
@@ -65,7 +65,7 @@ make html
 ```
 
 A folder called ``_build/html`` should have been created. You can now open the file ``_build/html/index.html`` in your
-browser.
+browser. 
 
 ---
 **NOTE**
@@ -95,15 +95,15 @@ following these steps:
   expand them).
 - Click on "details" next to the `ci/circleci: build_doc` check.
 - In the new window, click on the "Artifacts" tab.
-- Locate the file "docs/_build/html/index.html" (or any specific page you want to check) and click on it to get a
+- Locate the file "docs/_build/html/index.html" (or any specific page you want to check) and click on it to get a 
   preview.
 
 ## Writing Documentation - Specification
 
 The `huggingface/transformers` documentation follows the
 [Google documentation](https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html) style. It is
-mostly written in ReStructuredText
-([Sphinx simple documentation](https://www.sphinx-doc.org/en/master/usage/restructuredtext/index.html),
+mostly written in ReStructuredText 
+([Sphinx simple documentation](https://www.sphinx-doc.org/en/master/usage/restructuredtext/index.html), 
 [Sourceforge complete documentation](https://docutils.sourceforge.io/docs/ref/rst/restructuredtext.html)).
 
 
@@ -121,8 +121,8 @@ four.
 ### Adding a new model
 
 When adding a new model:
-
-- Create a file `xxx.rst` under `./source/model_doc` (don't hesitate to copy an existing file as template).
+ 
+- Create a file `xxx.rst` under `./source/model_doc` (don't hesitate to copy an existing file as template). 
 - Link that file in `./source/index.rst` on the `model_doc` toc-tree.
 - Write a short overview of the model:
     - Overview with paper & authors
@@ -130,8 +130,8 @@ When adding a new model:
     - Tips and tricks and how to use it best
 - Add the classes that should be linked in the model. This generally includes the configuration, the tokenizer, and
   every model of that class (the base model, alongside models with additional heads), both in PyTorch and TensorFlow.
-  The order is generally:
-    - Configuration,
+  The order is generally: 
+    - Configuration, 
     - Tokenizer
     - PyTorch base model
     - PyTorch head models
@@ -179,7 +179,7 @@ Links should be done as so (note the double underscore at the end): \`text for t
 
 #### Defining arguments in a method
 
-Arguments should be defined with the `Args:` prefix, followed by a line return and an indentation.
+Arguments should be defined with the `Args:` prefix, followed by a line return and an indentation. 
 The argument should be followed by its type, with its shape if it is a tensor, and a line return.
 Another indentation is necessary before writing the description of the argument.
 
@@ -216,9 +216,9 @@ then its documentation should look like this:
 
 Note that we always omit the "defaults to :obj:\`None\`" when None is the default for any argument. Also note that even
 if the first line describing your argument type and its default gets long, you can't break it on several lines. You can
-however write as many lines as you want in the indented description (see the example above with `input_ids`).
+however write as many lines as you want in the indented description (see the example above with `input_ids`). 
 
-#### Writing a multi-line code block
+#### Writing a multi-line code block 
 
 Multi-line code blocks can be useful for displaying examples. They are done like so:
 
@@ -237,7 +237,7 @@ the results stay consistent with the library.
 
 #### Writing a return block
 
-Arguments should be defined with the `Args:` prefix, followed by a line return and an indentation.
+Arguments should be defined with the `Args:` prefix, followed by a line return and an indentation. 
 The first line should be the type of the return, followed by a line return. No need to indent further for the elements
 building the return.
 
@@ -258,43 +258,3 @@ Here's an example for a single value return:
     Returns:
         :obj:`List[int]`: A list of integers in the range [0, 1] --- 1 for a special token, 0 for a sequence token.
 ```
-
-#### Adding a new section
-
-In ReST section headers are designated as such with the help of a line of underlying characters, e.g.,:
-
-```
-Section 1
-^^^^^^^^^^^^^^^^^^
-
-Sub-section 1
-~~~~~~~~~~~~~~~~~~
-```
-
-ReST allows the use of any characters to designate different section levels, as long as they are used consistently within the same document. For details see [sections doc](https://www.sphinx-doc.org/en/master/usage/restructuredtext/basics.html#sections). Because there is no standard different documents often end up using different characters for the same levels which makes it very difficult to know which character to use when creating a new section.
-
-Specifically, if when running `make docs` you get an error like:
-```
-docs/source/main_classes/trainer.rst:127:Title level inconsistent:
-```
-you picked an inconsistent character for some of the levels.
-
-But how do you know which characters you must use for an already existing level or when adding a new level?
-
-You can use this helper script:
-```
-perl -ne '/^(.)\1{100,}/ && do { $h{$1}=++$c if !$h{$1} }; END { %h = reverse %h ; print "$_ $h{$_}\n" for sort keys %h}' docs/source/main_classes/trainer.rst
-1 -
-2 ~
-3 ^
-4 =
-5 "
-```
-
-This tells you which characters have already been assigned for each level.
-
-So using this particular example's output -- if your current section's header uses `=` as its underline character, you now know you're at level 4, and if you want to add a sub-section header you know you want `"` as it'd level 5.
-
-If you needed to add yet another sub-level, then pick a character that is not used already. That is you must pick a character that is not in the output of that script.
-
-Here is the full list of characters that can be used in this context: `= - ` : ' " ~ ^ _ * + # < >`

docs/source/_static/js/custom.js

@@ -1,19 +1,10 @@
 // These two things need to be updated at each release for the version selector.
 // Last stable version
-const stableVersion = "v4.10.1"
+const stableVersion = "v4.1.1"
 // Dictionary doc folder to label. The last stable version should have an empty key.
 const versionMapping = {
     "master": "master",
-    "": "v4.10.0/v4.10.1 (stable)",
-    "v4.9.2": "v4.9.0/v4.9.1/v4.9.2",
-    "v4.8.2": "v4.8.0/v4.8.1/v4.8.2",
-    "v4.7.0": "v4.7.0",
-    "v4.6.0": "v4.6.0",
-    "v4.5.1": "v4.5.0/v4.5.1",
-    "v4.4.2": "v4.4.0/v4.4.1/v4.4.2",
-    "v4.3.3": "v4.3.0/v4.3.1/v4.3.2/v4.3.3",
-    "v4.2.2": "v4.2.0/v4.2.1/v4.2.2",
-    "v4.1.1": "v4.1.0/v4.1.1",
+    "": "v4.1.1 (stable)",
     "v4.0.1": "v4.0.0/v4.0.1",
     "v3.5.1": "v3.5.0/v3.5.1",
     "v3.4.0": "v3.4.0",
@@ -68,7 +59,7 @@ function addIcon() {
 function addCustomFooter() {
     const customFooter = document.createElement("div");
     const questionOrIssue = document.createElement("div");
-    questionOrIssue.innerHTML = "Stuck? Read our <a href='https://huggingface.co/blog'>Blog posts</a> or <a href='https://github.com/huggingface/transformers'>Create an issue</a>";
+    questionOrIssue.innerHTML = "Stuck? Read our <a href='https://medium.com/huggingface'>Blog posts</a> or <a href='https://github.com/huggingface/transformers'>Create an issue</a>";
     customFooter.appendChild(questionOrIssue);
     customFooter.classList.add("footer");
 
@@ -135,11 +126,11 @@ function addVersionControl() {
     const parts = location.toString().split('/');
     let versionIndex = parts.length - 2;
     // Index page may not have a last part with filename.html so we need to go up
-    if (parts[parts.length - 1] != "" && ! parts[parts.length - 1].match(/\.html/)) {
+    if (parts[parts.length - 1] != "" && ! parts[parts.length - 1].match(/\.html$|^search.html?/)) {
         versionIndex = parts.length - 1;
     }
     // Main classes and models are nested so we need to go deeper
-    else if (parts[versionIndex] == "main_classes" || parts[versionIndex] == "model_doc" || parts[versionIndex] == "internal") {
+    else if (parts[versionIndex] == "main_classes" || parts[versionIndex] == "model_doc") {
         versionIndex = versionIndex - 1;
     } 
     const version = parts[versionIndex];

docs/source/add_new_model.rst

@@ -1,844 +0,0 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-
-How to add a model to 🤗 Transformers?
-=======================================================================================================================
-
-Adding a new model is often difficult and requires an in-depth knowledge of the 🤗 Transformers library and ideally also
-of the model's original repository. At Hugging Face, we are trying to empower the community more and more to add models
-independently. Thus, for some new models that the community wants to be added to 🤗 Transformers, we create a customized
-*call-for-model-addition* that explains step-by-step how to add the requested model. With this
-*call-for-model-addition*, we want to teach a motivated and experienced contributor of the community how to port a
-model to 🤗 Transformers.
-
-If this sounds like something you would be interested in, feel free to check out the currently open
-“calls-for-model-addition” `here
-<https://github.com/huggingface/transformers/tree/master/templates/adding_a_new_model/open_model_proposals/README.md>`__
-and to contact us.
-
-If selected, you will then work closely with one member of the Hugging Face team to integrate the model into 🤗
-Transformers. By doing so, you will both gain a theoretical and deep practical understanding of the proposed model. But
-more importantly, you will have made a major open-source contribution to 🤗 Transformers. Along the way, you will:
-
--  get insights into open-source best practices
--  understand the design principles of one of the most popular NLP libraries
--  learn how to do efficiently test large NLP models
--  learn how to integrate Python utilities like ``black``, ``isort``, ``make fix-copies`` into a library to always
-   ensure clean and readable code
-
-We are also more than happy if you want to add a model that cannot be found in the “calls-for-model-addition” folder.
-The following sections explain in detail how to add a new model. It might also be very helpful to check out already
-added models to see if those resemble the model you would like to add `here
-<https://github.com/huggingface/transformers/pulls?q=is%3Apr+label%3A%22PR+for+Model+Addition%22+is%3Aclosed>`__.
-
-To start, let's try to get a general overview of the Transformers library.
-
-General overview of 🤗 Transformers
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-First, you should get a general overview of 🤗 Transformers. 🤗 Transformers is a very opinionated library, so there is a
-chance that you don't agree with some of the library's philosophies or design choices. From our experience, however, we
-found that the fundamental design choices and philosophies of the library are crucial to efficiently scale 🤗
-Transformers while keeping maintenance costs at a reasonable level.
-
-A good first starting point to better understand the library is to read the :doc:`documentation of our philosophy
-<philosophy>`. As a result of our way of working, there are some choices that we try to apply to all models:
-
--  Composition is generally favored over-abstraction
--  Duplicating code is not always bad if it strongly improves the readability or accessibility of a model
--  Model files are as self-contained as possible so that when you read the code of a specific model, you ideally only
-   have to look into the respective ``modeling_....py`` file.
-
-In our opinion, the library's code is not just a means to provide a product, *e.g.* the ability to use BERT for
-inference, but also as the very product that we want to improve. Hence, when adding a model, the user is not only the
-person that will use your model, but also everybody that will read, try to understand, and possibly tweak your code.
-
-With this in mind, let's go a bit deeper into the general library design.
-
-Overview of models
------------------------------------------------------------------------------------------------------------------------
-
-To successfully add a model, it is important to understand the interaction between your model and its config,
-:class:`~transformers.PreTrainedModel`, and :class:`~transformers.PretrainedConfig`. For exemplary purposes, we will
-call the model to be added to 🤗 Transformers ``BrandNewBert``.
-
-Let's take a look:
-
-.. 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`
-inherits from :obj:`BrandNewBertPreTrainedModel` which in turn inherits from :class:`~transformres.PreTrainedModel` and
-that's it. As a general rule, we want to make sure that a new model only depends on
-:class:`~transformers.PreTrainedModel`. The important functionalities that are automatically provided to every new
-model are :meth:`~transformers.PreTrainedModel.from_pretrained` and
-:meth:`~transformers.PreTrainedModel.save_pretrained`, which are used for serialization and deserialization. All of the
-other important functionalities, such as :meth:`BrandNewBertModel.forward` should be completely defined in the new
-``modeling_brand_new_bert.py`` script. Next, we want to make sure that a model with a specific head layer, such as
-:obj:`BrandNewBertForMaskedLM` does not inherit from :obj:`BrandNewBertModel`, but rather uses :obj:`BrandNewBertModel`
-as a component that can be called in its forward pass to keep the level of abstraction low. Every new model requires a
-configuration class, called :obj:`BrandNewBertConfig`. This configuration is always stored as an attribute in
-:class:`~transformers.PreTrainedModel`, and thus can be accessed via the ``config`` attribute for all classes
-inheriting from :obj:`BrandNewBertPreTrainedModel`:
-
-   .. code:: python
-
-      model = BrandNewBertModel.from_pretrained("brandy/brand_new_bert")
-      model.config  # model has access to its config
-
-Similar to the model, the configuration inherits basic serialization and deserialization functionalities from
-:class:`~transformers.PretrainedConfig`. Note that the configuration and the model are always serialized into two
-different formats - the model to a `pytorch_model.bin` file and the configuration to a `config.json` file. Calling
-:meth:`~transformers.PreTrainedModel.save_pretrained` will automatically call
-:meth:`~transformers.PretrainedConfig.save_pretrained`, so that both model and configuration are saved.
-
-
-Overview of tokenizers
------------------------------------------------------------------------------------------------------------------------
-
-Not quite ready yet :-( This section will be added soon!
-
-Step-by-step recipe to add a model to 🤗 Transformers
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Everyone has different preferences of how to port a model so it can be very helpful for you to take a look at summaries
-of how other contributors ported models to Hugging Face. Here is a list of community blog posts on how to port a model:
-
-1. `Porting GPT2 Model <https://medium.com/huggingface/from-tensorflow-to-pytorch-265f40ef2a28>`__ by `Thomas
-   <https://huggingface.co/thomwolf>`__
-2. `Porting WMT19 MT Model <https://huggingface.co/blog/porting-fsmt>`__ by `Stas <https://huggingface.co/stas>`__
-
-From experience, we can tell you that the most important things to keep in mind when adding a model are:
-
--  Don't reinvent the wheel! Most parts of the code you will add for the new 🤗 Transformers model already exist
-   somewhere in 🤗 Transformers. Take some time to find similar, already existing models and tokenizers you can copy
-   from. `grep <https://www.gnu.org/software/grep/>`__ and `rg <https://github.com/BurntSushi/ripgrep>`__ are your
-   friends. Note that it might very well happen that your model's tokenizer is based on one model implementation, and
-   your model's modeling code on another one. *E.g.* FSMT's modeling code is based on BART, while FSMT's tokenizer code
-   is based on XLM.
--  It's more of an engineering challenge than a scientific challenge. You should spend more time on creating an
-   efficient debugging environment than trying to understand all theoretical aspects of the model in the paper.
--  Ask for help, when you're stuck! Models are the core component of 🤗 Transformers so that we at Hugging Face are more
-   than happy to help you at every step to add your model. Don't hesitate to ask if you notice you are not making
-   progress.
-
-In the following, we try to give you a general recipe that we found most useful when porting a model to 🤗 Transformers.
-
-The following list is a summary of everything that has to be done to add a model and can be used by you as a To-Do
-List:
-
--  1. ☐ (Optional) Understood theoretical aspects
--  2. ☐ Prepared transformers dev environment
--  3. ☐ Set up debugging environment of the original repository
--  4. ☐ Created script that successfully runs forward pass using original repository and checkpoint
--  5. ☐ Successfully added the model skeleton to Transformers
--  6. ☐ Successfully converted original checkpoint to Transformers checkpoint
--  7. ☐ Successfully ran forward pass in Transformers that gives identical output to original checkpoint
--  8. ☐ Finished model tests in Transformers
--  9. ☐ Successfully added Tokenizer in Transformers
--  10. ☐ Run end-to-end integration tests
--  11. ☐ Finished docs
--  12. ☐ Uploaded model weights to the hub
--  13. ☐ Submitted the pull request
--  14. ☐ (Optional) Added a demo notebook
-
-To begin with, we usually recommend to start by getting a good theoretical understanding of ``BrandNewBert``. However,
-if you prefer to understand the theoretical aspects of the model *on-the-job*, then it is totally fine to directly dive
-into the ``BrandNewBert``'s code-base. This option might suit you better, if your engineering skills are better than
-your theoretical skill, if you have trouble understanding ``BrandNewBert``'s paper, or if you just enjoy programming
-much more than reading scientific papers.
-
-1. (Optional) Theoretical aspects of BrandNewBert
------------------------------------------------------------------------------------------------------------------------
-
-You should take some time to read *BrandNewBert's* paper, if such descriptive work exists. There might be large
-sections of the paper that are difficult to understand. If this is the case, this is fine - don't worry! The goal is
-not to get a deep theoretical understanding of the paper, but to extract the necessary information required to
-effectively re-implement the model in 🤗 Transformers. That being said, you don't have to spend too much time on the
-theoretical aspects, but rather focus on the practical ones, namely:
-
--  What type of model is *brand_new_bert*? BERT-like encoder-only model? GPT2-like decoder-only model? BART-like
-   encoder-decoder model? Look at the :doc:`model_summary` if you're not familiar with the differences between those.
--  What are the applications of *brand_new_bert*? Text classification? Text generation? Seq2Seq tasks, *e.g.,*
-   summarization?
--  What is the novel feature of the model making it different from BERT/GPT-2/BART?
--  Which of the already existing `🤗 Transformers models <https://huggingface.co/transformers/#contents>`__ is most
-   similar to *brand_new_bert*?
--  What type of tokenizer is used? A sentencepiece tokenizer? Word piece tokenizer? Is it the same tokenizer as used
-   for BERT or BART?
-
-After you feel like you have gotten a good overview of the architecture of the model, you might want to write to the
-Hugging Face team with any questions you might have. This might include questions regarding the model's architecture,
-its attention layer, etc. We will be more than happy to help you.
-
-2. Next prepare your environment
------------------------------------------------------------------------------------------------------------------------
-
-1. Fork the `repository <https://github.com/huggingface/transformers>`__ by clicking on the ‘Fork' button on the
-   repository's page. This creates a copy of the code under your GitHub user account.
-
-2. Clone your ``transformers`` fork to your local disk, and add the base repository as a remote:
-
-   .. code:: bash
-
-      git clone https://github.com/[your Github handle]/transformers.git
-      cd transformers
-      git remote add upstream https://github.com/huggingface/transformers.git
-
-3. Set up a development environment, for instance by running the following command:
-
-   .. code:: bash
-
-      python -m venv .env
-      source .env/bin/activate
-      pip install -e ".[dev]"
-
-and return to the parent directory
-
-.. code:: bash
-
-   cd ..
-
-4. We recommend adding the PyTorch version of *brand_new_bert* to Transformers. To install PyTorch, please follow the
-   instructions on https://pytorch.org/get-started/locally/.
-
-**Note:** You don't need to have CUDA installed. Making the new model work on CPU is sufficient.
-
-5. To port *brand_new_bert*, you will also need access to its original repository:
-
-.. code:: bash
-
-   git clone https://github.com/org_that_created_brand_new_bert_org/brand_new_bert.git 
-   cd brand_new_bert
-   pip install -e .
-
-Now you have set up a development environment to port *brand_new_bert* to 🤗 Transformers.
-
-3.-4. Run a pretrained checkpoint using the original repository
------------------------------------------------------------------------------------------------------------------------
-
-At first, you will work on the original *brand_new_bert* repository. Often, the original implementation is very
-“researchy”. Meaning that documentation might be lacking and the code can be difficult to understand. But this should
-be exactly your motivation to reimplement *brand_new_bert*. At Hugging Face, one of our main goals is to *make people
-stand on the shoulders of giants* which translates here very well into taking a working model and rewriting it to make
-it as **accessible, user-friendly, and beautiful** as possible. This is the number-one motivation to re-implement
-models into 🤗 Transformers - trying to make complex new NLP technology accessible to **everybody**.
-
-You should start thereby by diving into the original repository.
-
-Successfully running the official pretrained model in the original repository is often **the most difficult** step.
-From our experience, it is very important to spend some time getting familiar with the original code-base. You need to
-figure out the following:
-
--  Where to find the pretrained weights?
--  How to load the pretrained weights into the corresponding model?
--  How to run the tokenizer independently from the model?
--  Trace one forward pass so that you know which classes and functions are required for a simple forward pass. Usually,
-   you only have to reimplement those functions.
--  Be able to locate the important components of the model: Where is the model's class? Are there model sub-classes,
-   *e.g.* EncoderModel, DecoderModel? Where is the self-attention layer? Are there multiple different attention layers,
-   *e.g.* *self-attention*, *cross-attention*...?
--  How can you debug the model in the original environment of the repo? Do you have to add `print` statements, can you
-   work with an interactive debugger like `ipdb`, or should you use an efficient IDE to debug the model, like PyCharm?
-
-It is very important that before you start the porting process, that you can **efficiently** debug code in the original
-repository! Also, remember that you are working with an open-source library, so do not hesitate to open an issue, or
-even a pull request in the original repository. The maintainers of this repository are most likely very happy about
-someone looking into their code!
-
-At this point, it is really up to you which debugging environment and strategy you prefer to use to debug the original
-model. We strongly advise against setting up a costly GPU environment, but simply work on a CPU both when starting to
-dive into the original repository and also when starting to write the 🤗 Transformers implementation of the model. Only
-at the very end, when the model has already been successfully ported to 🤗 Transformers, one should verify that the
-model also works as expected on GPU.
-
-In general, there are two possible debugging environments for running the original model
-
--  `Jupyter notebooks <https://jupyter.org/>`__ / `google colab
-   <https://colab.research.google.com/notebooks/intro.ipynb>`__
--  Local python scripts.
-
-Jupyter notebooks have the advantage that they allow for cell-by-cell execution which can be helpful to better split
-logical components from one another and to have faster debugging cycles as intermediate results can be stored. Also,
-notebooks are often easier to share with other contributors, which might be very helpful if you want to ask the Hugging
-Face team for help. If you are familiar with Jupiter notebooks, we strongly recommend you to work with them.
-
-The obvious disadvantage of Jupyther notebooks is that if you are not used to working with them you will have to spend
-some time adjusting to the new programming environment and that you might not be able to use your known debugging tools
-anymore, like ``ipdb``.
-
-For each code-base, a good first step is always to load a **small** pretrained checkpoint and to be able to reproduce a
-single forward pass using a dummy integer vector of input IDs as an input. Such a script could look like this (in
-pseudocode):
-
-.. code:: bash
-
-   model = BrandNewBertModel.load_pretrained_checkpoint(/path/to/checkpoint/)
-   input_ids = [0, 4, 5, 2, 3, 7, 9]  # vector of input ids
-   original_output = model.predict(input_ids)
-
-Next, regarding the debugging strategy, there are generally a few from which to choose from:
-
--  Decompose the original model into many small testable components and run a forward pass on each of those for
-   verification
--  Decompose the original model only into the original *tokenizer* and the original *model*, run a forward pass on
-   those, and use intermediate print statements or breakpoints for verification
-
-Again, it is up to you which strategy to choose. Often, one or the other is advantageous depending on the original code
-base.
-
-If the original code-base allows you to decompose the model into smaller sub-components, *e.g.* if the original
-code-base can easily be run in eager mode, it is usually worth the effort to do so. There are some important advantages
-to taking the more difficult road in the beginning:
-
-- at a later stage when comparing the original model to the Hugging Face implementation, you can verify automatically
-  for each component individually that the corresponding component of the 🤗 Transformers implementation matches instead
-  of relying on visual comparison via print statements
-- it can give you some rope to decompose the big problem of porting a model into smaller problems of just porting
-  individual components and thus structure your work better
-- separating the model into logical meaningful components will help you to get a better overview of the model's design
-  and thus to better understand the model
-- at a later stage those component-by-component tests help you to ensure that no regression occurs as you continue
-  changing your code
-
-`Lysandre's <https://gist.github.com/LysandreJik/db4c948f6b4483960de5cbac598ad4ed>`__ integration checks for ELECTRA
-gives a nice example of how this can be done.
-
-However, if the original code-base is very complex or only allows intermediate components to be run in a compiled mode,
-it might be too time-consuming or even impossible to separate the model into smaller testable sub-components. A good
-example is `T5's MeshTensorFlow <https://github.com/tensorflow/mesh/tree/master/mesh_tensorflow>`__ library which is
-very complex and does not offer a simple way to decompose the model into its sub-components. For such libraries, one
-often relies on verifying print statements.
-
-No matter which strategy you choose, the recommended procedure is often the same in that you should start to debug the
-starting layers first and the ending layers last.
-
-It is recommended that you retrieve the output, either by print statements or sub-component functions, of the following
-layers in the following order:
-
-1.  Retrieve the input IDs passed to the model
-2.  Retrieve the word embeddings
-3.  Retrieve the input of the first Transformer layer
-4.  Retrieve the output of the first Transformer layer
-5.  Retrieve the output of the following n - 1 Transformer layers
-6.  Retrieve the output of the whole BrandNewBert Model
-
-Input IDs should thereby consists of an array of integers, *e.g.* ``input_ids = [0, 4, 4, 3, 2, 4, 1, 7, 19]``
-
-The outputs of the following layers often consist of multi-dimensional float arrays and can look like this:
-
-.. code:: bash
-
-   [[
-    [-0.1465, -0.6501,  0.1993,  ...,  0.1451,  0.3430,  0.6024],
-    [-0.4417, -0.5920,  0.3450,  ..., -0.3062,  0.6182,  0.7132],
-    [-0.5009, -0.7122,  0.4548,  ..., -0.3662,  0.6091,  0.7648],
-    ...,
-    [-0.5613, -0.6332,  0.4324,  ..., -0.3792,  0.7372,  0.9288],
-    [-0.5416, -0.6345,  0.4180,  ..., -0.3564,  0.6992,  0.9191],
-    [-0.5334, -0.6403,  0.4271,  ..., -0.3339,  0.6533,  0.8694]]],
-
-We expect that every model added to 🤗 Transformers passes a couple of integration tests, meaning that the original
-model and the reimplemented version in 🤗 Transformers have to give the exact same output up to a precision of 0.001!
-Since it is normal that the exact same model written in different libraries can give a slightly different output
-depending on the library framework, we accept an error tolerance of 1e-3 (0.001). It is not enough if the model gives
-nearly the same output, they have to be the almost identical. Therefore, you will certainly compare the intermediate
-outputs of the 🤗 Transformers version multiple times against the intermediate outputs of the original implementation of
-*brand_new_bert* in which case an **efficient** debugging environment of the original repository is absolutely
-important. Here is some advice is to make your debugging environment as efficient as possible.
-
--  Find the best way of debugging intermediate results. Is the original repository written in PyTorch? Then you should
-   probably take the time to write a longer script that decomposes the original model into smaller sub-components to
-   retrieve intermediate values. Is the original repository written in Tensorflow 1? Then you might have to rely on
-   TensorFlow print operations like `tf.print <https://www.tensorflow.org/api_docs/python/tf/print>`__ to output
-   intermediate values. Is the original repository written in Jax? Then make sure that the model is **not jitted** when
-   running the forward pass, *e.g.* check-out `this link <https://github.com/google/jax/issues/196>`__.
--  Use the smallest pretrained checkpoint you can find. The smaller the checkpoint, the faster your debug cycle
-   becomes. It is not efficient if your pretrained model is so big that your forward pass takes more than 10 seconds.
-   In case only very large checkpoints are available, it might make more sense to create a dummy model in the new
-   environment with randomly initialized weights and save those weights for comparison with the 🤗 Transformers version
-   of your model
--  Make sure you are using the easiest way of calling a forward pass in the original repository. Ideally, you want to
-   find the function in the original repository that **only** calls a single forward pass, *i.e.* that is often called
-   ``predict``, ``evaluate``, ``forward`` or ``__call__``. You don't want to debug a function that calls ``forward``
-   multiple times, *e.g.* to generate text, like ``autoregressive_sample``, ``generate``.
--  Try to separate the tokenization from the model's `forward` pass. If the original repository shows examples where
-   you have to input a string, then try to find out where in the forward call the string input is changed to input ids
-   and start from this point. This might mean that you have to possibly write a small script yourself or change the
-   original code so that you can directly input the ids instead of an input string.
--  Make sure that the model in your debugging setup is **not** in training mode, which often causes the model to yield
-   random outputs due to multiple dropout layers in the model. Make sure that the forward pass in your debugging
-   environment is **deterministic** so that the dropout layers are not used. Or use `transformers.file_utils.set_seed`
-   if the old and new implementations are in the same framework.
-
-The following section gives you more specific details/tips on how you can do this for *brand_new_bert*.
-
-5.-14. Port BrandNewBert to 🤗 Transformers
------------------------------------------------------------------------------------------------------------------------
-
-Next, you can finally start adding new code to 🤗 Transformers. Go into the clone of your 🤗 Transformers' fork:
-
-::
-
-    cd transformers
-
-In the special case that you are adding a model whose architecture exactly matches the model architecture of an
-existing model you only have to add a conversion script as described in `this section <#write-a-conversion-script>`__.
-In this case, you can just re-use the whole model architecture of the already existing model.
-
-Otherwise, let's start generating a new model with the amazing Cookiecutter!
-
-**Use the Cookiecutter to automatically generate the model's code**
-
-To begin with head over to the `🤗 Transformers templates
-<https://github.com/huggingface/transformers/tree/master/templates/adding_a_new_model>`__ to make use of our
-``cookiecutter`` implementation to automatically generate all the relevant files for your model. Again, we recommend
-only adding the PyTorch version of the model at first. Make sure you follow the instructions of the ``README.md`` on
-the `🤗 Transformers templates <https://github.com/huggingface/transformers/tree/master/templates/adding_a_new_model>`__
-carefully.
-
-**Open a Pull Request on the main huggingface/transformers repo**
-
-Before starting to adapt the automatically generated code, now is the time to open a “Work in progress (WIP)” pull
-request, *e.g.* “[WIP] Add *brand_new_bert*”, in 🤗 Transformers so that you and the Hugging Face team can work
-side-by-side on integrating the model into 🤗 Transformers.
-
-You should do the following:
-
-1. Create a branch with a descriptive name from your master branch
-
-::
-
-    git checkout -b add_brand_new_bert
-
-2. Commit the automatically generated code:
-
-::
-
-    git add .
-    git commit
-
-3. Fetch and rebase to current master
-
-::
-
-    git fetch upstream
-    git rebase upstream/master
-
-4. Push the changes to your account using:
-
-::
-
-    git push -u origin a-descriptive-name-for-my-changes
-
-5. Once you are satisfied, go to the webpage of your fork on GitHub. Click on “Pull request”. Make sure to add the
-   GitHub handle of some members of the Hugging Face team as reviewers, so that the Hugging Face team gets notified for
-   future changes.
-
-6. Change the PR into a draft by clicking on “Convert to draft” on the right of the GitHub pull request web page.
-
-In the following, whenever you have done some progress, don't forget to commit your work and push it to your account so
-that it shows in the pull request. Additionally, you should make sure to update your work with the current master from
-time to time by doing:
-
-::
-
-    git fetch upstream
-    git merge upstream/master
-
-In general, all questions you might have regarding the model or your implementation should be asked in your PR and
-discussed/solved in the PR. This way, the Hugging Face team will always be notified when you are committing new code or
-if you have a question. It is often very helpful to point the Hugging Face team to your added code so that the Hugging
-Face team can efficiently understand your problem or question.
-
-To do so, you can go to the “Files changed” tab where you see all of your changes, go to a line regarding which you
-want to ask a question, and click on the “+” symbol to add a comment. Whenever a question or problem has been solved,
-you can click on the “Resolve” button of the created comment.
-
-In the same way, the Hugging Face team will open comments when reviewing your code. We recommend asking most questions
-on GitHub on your PR. For some very general questions that are not very useful for the public, feel free to ping the
-Hugging Face team by Slack or email.
-
-**5. Adapt the generated models code for brand_new_bert**
-
-At first, we will focus only on the model itself and not care about the tokenizer. All the relevant code should be
-found in the generated files ``src/transformers/models/brand_new_bert/modeling_brand_new_bert.py`` and
-``src/transformers/models/brand_new_bert/configuration_brand_new_bert.py``.
-
-Now you can finally start coding :). The generated code in
-``src/transformers/models/brand_new_bert/modeling_brand_new_bert.py`` will either have the same architecture as BERT if
-it's an encoder-only model or BART if it's an encoder-decoder model. At this point, you should remind yourself what
-you've learned in the beginning about the theoretical aspects of the model: *How is the model different from BERT or
-BART?*". Implement those changes which often means to change the *self-attention* layer, the order of the normalization
-layer, etc… Again, it is often useful to look at the similar architecture of already existing models in Transformers to
-get a better feeling of how your model should be implemented.
-
-**Note** that at this point, you don't have to be very sure that your code is fully correct or clean. Rather, it is
-advised to add a first *unclean*, copy-pasted version of the original code to
-``src/transformers/models/brand_new_bert/modeling_brand_new_bert.py`` until you feel like all the necessary code is
-added. From our experience, it is much more efficient to quickly add a first version of the required code and
-improve/correct the code iteratively with the conversion script as described in the next section. The only thing that
-has to work at this point is that you can instantiate the 🤗 Transformers implementation of *brand_new_bert*, *i.e.* the
-following command should work:
-
-.. code:: python
-
-   from transformers import BrandNewBertModel, BrandNewBertConfig
-   model = BrandNewBertModel(BrandNewBertConfig())
-
-The above command will create a model according to the default parameters as defined in ``BrandNewBertConfig()`` with
-random weights, thus making sure that the ``init()`` methods of all components works.
-
-**6. Write a conversion script**
-
-Next, you should write a conversion script that lets you convert the checkpoint you used to debug *brand_new_bert* in
-the original repository to a checkpoint compatible with your just created 🤗 Transformers implementation of
-*brand_new_bert*. It is not advised to write the conversion script from scratch, but rather to look through already
-existing conversion scripts in 🤗 Transformers for one that has been used to convert a similar model that was written in
-the same framework as *brand_new_bert*. Usually, it is enough to copy an already existing conversion script and
-slightly adapt it for your use case. Don't hesitate to ask the Hugging Face team to point you to a similar already
-existing conversion script for your model.
-
--  If you are porting a model from TensorFlow to PyTorch, a good starting point might be BERT's conversion script `here
-   <https://github.com/huggingface/transformers/blob/7acfa95afb8194f8f9c1f4d2c6028224dbed35a2/src/transformers/models/bert/modeling_bert.py#L91>`__
--  If you are porting a model from PyTorch to PyTorch, a good starting point might be BART's conversion script `here
-   <https://github.com/huggingface/transformers/blob/master/src/transformers/models/bart/convert_bart_original_pytorch_checkpoint_to_pytorch.py>`__
-
-In the following, we'll quickly explain how PyTorch models store layer weights and define layer names. In PyTorch, the
-name of a layer is defined by the name of the class attribute you give the layer. Let's define a dummy model in
-PyTorch, called ``SimpleModel`` as follows:
-
-.. code:: python
-
-   from torch import nn
-
-   class SimpleModel(nn.Module):
-       def __init__(self):
-               super().__init__()
-               self.dense = nn.Linear(10, 10)
-               self.intermediate = nn.Linear(10, 10)
-               self.layer_norm = nn.LayerNorm(10)
-
-Now we can create an instance of this model definition which will fill all weights: ``dense``, ``intermediate``,
-``layer_norm`` with random weights. We can print the model to see its architecture
-
-.. code:: python
-
-   model = SimpleModel()
-
-   print(model)
-
-This will print out the following:
-
-.. code:: bash
-
-   SimpleModel(
-     (dense): Linear(in_features=10, out_features=10, bias=True)
-     (intermediate): Linear(in_features=10, out_features=10, bias=True)
-     (layer_norm): LayerNorm((10,), eps=1e-05, elementwise_affine=True)
-   )
-
-We can see that the layer names are defined by the name of the class attribute in PyTorch. You can print out the weight
-values of a specific layer:
-
-.. code:: python
-
-   print(model.dense.weight.data)
-
-to see that the weights were randomly initialized
-
-.. code:: bash
-
-   tensor([[-0.0818,  0.2207, -0.0749, -0.0030,  0.0045, -0.1569, -0.1598,  0.0212,
-            -0.2077,  0.2157],
-           [ 0.1044,  0.0201,  0.0990,  0.2482,  0.3116,  0.2509,  0.2866, -0.2190,
-             0.2166, -0.0212],
-           [-0.2000,  0.1107, -0.1999, -0.3119,  0.1559,  0.0993,  0.1776, -0.1950,
-            -0.1023, -0.0447],
-           [-0.0888, -0.1092,  0.2281,  0.0336,  0.1817, -0.0115,  0.2096,  0.1415,
-            -0.1876, -0.2467],
-           [ 0.2208, -0.2352, -0.1426, -0.2636, -0.2889, -0.2061, -0.2849, -0.0465,
-             0.2577,  0.0402],
-           [ 0.1502,  0.2465,  0.2566,  0.0693,  0.2352, -0.0530,  0.1859, -0.0604,
-             0.2132,  0.1680],
-           [ 0.1733, -0.2407, -0.1721,  0.1484,  0.0358, -0.0633, -0.0721, -0.0090,
-             0.2707, -0.2509],
-           [-0.1173,  0.1561,  0.2945,  0.0595, -0.1996,  0.2988, -0.0802,  0.0407,
-             0.1829, -0.1568],
-           [-0.1164, -0.2228, -0.0403,  0.0428,  0.1339,  0.0047,  0.1967,  0.2923,
-             0.0333, -0.0536],
-           [-0.1492, -0.1616,  0.1057,  0.1950, -0.2807, -0.2710, -0.1586,  0.0739,
-             0.2220,  0.2358]]).
-
-In the conversion script, you should fill those randomly initialized weights with the exact weights of the
-corresponding layer in the checkpoint. *E.g.*
-
-.. code:: python
-
-   # retrieve matching layer weights, e.g. by 
-   # recursive algorithm
-   layer_name = "dense"
-   pretrained_weight = array_of_dense_layer
-
-   model_pointer = getattr(model, "dense")
-
-   model_pointer.weight.data = torch.from_numpy(pretrained_weight)
-
-While doing so, you must verify that each randomly initialized weight of your PyTorch model and its corresponding
-pretrained checkpoint weight exactly match in both **shape and name**. To do so, it is **necessary** to add assert
-statements for the shape and print out the names of the checkpoints weights. E.g. you should add statements like:
-
-.. code:: python
-
-   assert (
-        model_pointer.weight.shape == pretrained_weight.shape
-   ), f"Pointer shape of random weight {model_pointer.shape} and array shape of checkpoint weight {pretrained_weight.shape} mismatched"
-
-Besides, you should also print out the names of both weights to make sure they match, *e.g.*
-
-.. code:: python
-
-   logger.info(f"Initialize PyTorch weight {layer_name} from {pretrained_weight.name}")
-
-If either the shape or the name doesn't match, you probably assigned the wrong checkpoint weight to a randomly
-initialized layer of the 🤗 Transformers implementation.
-
-An incorrect shape is most likely due to an incorrect setting of the config parameters in ``BrandNewBertConfig()`` that
-do not exactly match those that were used for the checkpoint you want to convert. However, it could also be that
-PyTorch's implementation of a layer requires the weight to be transposed beforehand.
-
-Finally, you should also check that **all** required weights are initialized and print out all checkpoint weights that
-were not used for initialization to make sure the model is correctly converted. It is completely normal, that the
-conversion trials fail with either a wrong shape statement or wrong name assignment. This is most likely because either
-you used incorrect parameters in ``BrandNewBertConfig()``, have a wrong architecture in the 🤗 Transformers
-implementation, you have a bug in the ``init()`` functions of one of the components of the 🤗 Transformers
-implementation or you need to transpose one of the checkpoint weights.
-
-This step should be iterated with the previous step until all weights of the checkpoint are correctly loaded in the
-Transformers model. Having correctly loaded the checkpoint into the 🤗 Transformers implementation, you can then save
-the model under a folder of your choice ``/path/to/converted/checkpoint/folder`` that should then contain both a
-``pytorch_model.bin`` file and a ``config.json`` file:
-
-.. code:: python
-
-   model.save_pretrained("/path/to/converted/checkpoint/folder")
-
-**7. Implement the forward pass**
-
-Having managed to correctly load the pretrained weights into the 🤗 Transformers implementation, you should now make
-sure that the forward pass is correctly implemented. In `Get familiar with the original repository
-<#run-a-pretrained-checkpoint-using-the-original-repository>`__, you have already created a script that runs a forward
-pass of the model using the original repository. Now you should write an analogous script using the 🤗 Transformers
-implementation instead of the original one. It should look as follows:
-
-.. code:: python
-
-   model = BrandNewBertModel.from_pretrained(/path/to/converted/checkpoint/folder)
-   input_ids = [0, 4, 4, 3, 2, 4, 1, 7, 19]
-   output = model(input_ids).last_hidden_states
-
-It is very likely that the 🤗 Transformers implementation and the original model implementation don't give the exact
-same output the very first time or that the forward pass throws an error. Don't be disappointed - it's expected! First,
-you should make sure that the forward pass doesn't throw any errors. It often happens that the wrong dimensions are
-used leading to a `Dimensionality mismatch` error or that the wrong data type object is used, *e.g.* ``torch.long``
-instead of ``torch.float32``. Don't hesitate to ask the Hugging Face team for help, if you don't manage to solve
-certain errors.
-
-The final part to make sure the 🤗 Transformers implementation works correctly is to ensure that the outputs are
-equivalent to a precision of ``1e-3``. First, you should ensure that the output shapes are identical, *i.e.*
-``outputs.shape`` should yield the same value for the script of the 🤗 Transformers implementation and the original
-implementation. Next, you should make sure that the output values are identical as well. This one of the most difficult
-parts of adding a new model. Common mistakes why the outputs are not identical are:
-
--  Some layers were not added, *i.e.* an `activation` layer was not added, or the residual connection was forgotten
--  The word embedding matrix was not tied
--  The wrong positional embeddings are used because the original implementation uses on offset
--  Dropout is applied during the forward pass. To fix this make sure `model.training is False` and that no dropout
-   layer is falsely activated during the forward pass, *i.e.* pass `self.training` to `PyTorch's functional dropout
-   <https://pytorch.org/docs/stable/nn.functional.html?highlight=dropout#torch.nn.functional.dropout>`_
-
-The best way to fix the problem is usually to look at the forward pass of the original implementation and the 🤗
-Transformers implementation side-by-side and check if there are any differences. Ideally, you should debug/print out
-intermediate outputs of both implementations of the forward pass to find the exact position in the network where the 🤗
-Transformers implementation shows a different output than the original implementation. First, make sure that the
-hard-coded ``input_ids`` in both scripts are identical. Next, verify that the outputs of the first transformation of
-the ``input_ids`` (usually the word embeddings) are identical. And then work your way up to the very last layer of the
-network. At some point, you will notice a difference between the two implementations, which should point you to the bug
-in the 🤗 Transformers implementation. From our experience, a simple and efficient way is to add many print statements
-in both the original implementation and 🤗 Transformers implementation, at the same positions in the network
-respectively, and to successively remove print statements showing the same values for intermediate presentions.
-
-When you're confident that both implementations yield the same output, verifying the outputs with
-``torch.allclose(original_output, output, atol=1e-3)``, you're done with the most difficult part! Congratulations - the
-work left to be done should be a cakewalk 😊.
-
-**8. Adding all necessary model tests**
-
-At this point, you have successfully added a new model. However, it is very much possible that the model does not yet
-fully comply with the required design. To make sure, the implementation is fully compatible with 🤗 Transformers, all
-common tests should pass. The Cookiecutter should have automatically added a test file for your model, probably under
-the same ``tests/test_modeling_brand_new_bert.py``. Run this test file to verify that all common tests pass:
-
-.. code:: python
-
-   pytest tests/test_modeling_brand_new_bert.py
-
-Having fixed all common tests, it is now crucial to ensure that all the nice work you have done is well tested, so that
-
--  
-
-   a) The community can easily understand your work by looking at specific tests of *brand_new_bert*
-
--  
-
-   b) Future changes to your model will not break any important feature of the model.
-
-At first, integration tests should be added. Those integration tests essentially do the same as the debugging scripts
-you used earlier to implement the model to 🤗 Transformers. A template of those model tests is already added by the
-Cookiecutter, called ``BrandNewBertModelIntegrationTests`` and only has to be filled out by you. To ensure that those
-tests are passing, run
-
-.. code:: python
-
-   RUN_SLOW=1 pytest -sv tests/test_modeling_brand_new_bert.py::BrandNewBertModelIntegrationTests
-
-.. note::
-
-  In case you are using Windows, you should replace ``RUN_SLOW=1`` with ``SET RUN_SLOW=1``
-
-Second, all features that are special to *brand_new_bert* should be tested additionally in a separate test under
-``BrandNewBertModelTester``/``BrandNewBertModelTest``. This part is often forgotten but is extremely useful in two
-ways:
-
--  It helps to transfer the knowledge you have acquired during the model addition to the community by showing how the
-   special features of *brand_new_bert* should work.
--  Future contributors can quickly test changes to the model by running those special tests.
-
-
-**9. Implement the tokenizer**
-
-Next, we should add the tokenizer of *brand_new_bert*. Usually, the tokenizer is equivalent or very similar to an
-already existing tokenizer of 🤗 Transformers.
-
-It is very important to find/extract the original tokenizer file and to manage to load this file into the 🤗
-Transformers' implementation of the tokenizer.
-
-To ensure that the tokenizer works correctly, it is recommended to first create a script in the original repository
-that inputs a string and returns the ``input_ids``. It could look similar to this (in pseudo-code):
-
-.. code:: bash
-
-   input_str = "This is a long example input string containing special characters .$?-, numbers 2872 234 12 and words."
-   model = BrandNewBertModel.load_pretrained_checkpoint(/path/to/checkpoint/)
-   input_ids = model.tokenize(input_str)
-
-You might have to take a deeper look again into the original repository to find the correct tokenizer function or you
-might even have to do changes to your clone of the original repository to only output the ``input_ids``. Having written
-a functional tokenization script that uses the original repository, an analogous script for 🤗 Transformers should be
-created. It should look similar to this:
-
-.. code:: python
-
-   from transformers import BrandNewBertTokenizer
-   input_str = "This is a long example input string containing special characters .$?-, numbers 2872 234 12 and words."
-
-   tokenizer = BrandNewBertTokenizer.from_pretrained(/path/to/tokenizer/folder/)
-
-   input_ids = tokenizer(input_str).input_ids
-
-When both ``input_ids`` yield the same values, as a final step a tokenizer test file should also be added.
-
-Analogous to the modeling test files of *brand_new_bert*, the tokenization test files of *brand_new_bert* should
-contain a couple of hard-coded integration tests.
-
-**10. Run End-to-end integration tests**
-
-Having added the tokenizer, you should also add a couple of end-to-end integration tests using both the model and the
-tokenizer to ``tests/test_modeling_brand_new_bert.py`` in 🤗 Transformers. Such a test should show on a meaningful
-text-to-text sample that the 🤗 Transformers implementation works as expected. A meaningful text-to-text sample can
-include *e.g.* a source-to-target-translation pair, an article-to-summary pair, a question-to-answer pair, etc… If none
-of the ported checkpoints has been fine-tuned on a downstream task it is enough to simply rely on the model tests. In a
-final step to ensure that the model is fully functional, it is advised that you also run all tests on GPU. It can
-happen that you forgot to add some ``.to(self.device)`` statements to internal tensors of the model, which in such a
-test would show in an error. In case you have no access to a GPU, the Hugging Face team can take care of running those
-tests for you.
-
-**11. Add Docstring**
-
-Now, all the necessary functionality for *brand_new_bert* is added - you're almost done! The only thing left to add is
-a nice docstring and a doc page. The Cookiecutter should have added a template file called
-``docs/source/model_doc/brand_new_bert.rst`` that you should fill out. Users of your model will usually first look at
-this page before using your model. Hence, the documentation must be understandable and concise. It is very useful for
-the community to add some *Tips* to show how the model should be used. Don't hesitate to ping the Hugging Face team
-regarding the docstrings.
-
-Next, make sure that the docstring added to ``src/transformers/models/brand_new_bert/modeling_brand_new_bert.py`` is
-correct and included all necessary inputs and outputs. It is always to good to remind oneself that documentation should
-be treated at least as carefully as the code in 🤗 Transformers since the documentation is usually the first contact
-point of the community with the model.
-
-**Code refactor**
-
-Great, now you have added all the necessary code for *brand_new_bert*. At this point, you should correct some potential
-incorrect code style by running:
-
-.. code:: bash
-
-   make style
-
-and verify that your coding style passes the quality check:
-
-.. code:: bash
-
-   make quality
-
-There are a couple of other very strict design tests in 🤗 Transformers that might still be failing, which shows up in
-the tests of your pull request. This is often because of some missing information in the docstring or some incorrect
-naming. The Hugging Face team will surely help you if you're stuck here.
-
-Lastly, it is always a good idea to refactor one's code after having ensured that the code works correctly. With all
-tests passing, now it's a good time to go over the added code again and do some refactoring.
-
-You have now finished the coding part, congratulation! 🎉 You are Awesome! 😎
-
-**12. Upload the models to the model hub**
-
-In this final part, you should convert and upload all checkpoints to the model hub and add a model card for each
-uploaded model checkpoint. You should work alongside the Hugging Face team here to decide on a fitting name for each
-checkpoint and to get the required access rights to be able to upload the model under the author's organization of
-*brand_new_bert*.
-
-It is worth spending some time to create fitting model cards for each checkpoint. The model cards should highlight the
-specific characteristics of this particular checkpoint, *e.g.* On which dataset was the checkpoint
-pretrained/fine-tuned on? On what down-stream task should the model be used? And also include some code on how to
-correctly use the model.
-
-**13. (Optional) Add notebook**
-
-It is very helpful to add a notebook that showcases in-detail how *brand_new_bert* can be used for inference and/or
-fine-tuned on a downstream task. This is not mandatory to merge your PR, but very useful for the community.
-
-**14. Submit your finished PR**
-
-You're done programming now and can move to the last step, which is getting your PR merged into master. Usually, the
-Hugging Face team should have helped you already at this point, but it is worth taking some time to give your finished
-PR a nice description and eventually add comments to your code, if you want to point out certain design choices to your
-reviewer.
-
-Share your work!!
------------------------------------------------------------------------------------------------------------------------
-
-Now, it's time to get some credit from the community for your work! Having completed a model addition is a major
-contribution to Transformers and the whole NLP community. Your code and the ported pre-trained models will certainly be
-used by hundreds and possibly even thousands of developers and researchers. You should be proud of your work and share
-your achievement with the community.
-
-**You have made another model that is super easy to access for everyone in the community! 🤯**

docs/source/add_new_pipeline.rst

@@ -1,143 +0,0 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-
-How to add a pipeline to 🤗 Transformers?
-=======================================================================================================================
-
-First and foremost, you need to decide the raw entries the pipeline will be able to take. It can be strings, raw bytes,
-dictionnaries or whatever seems to be the most likely desired input. Try to keep these inputs as pure Python as
-possible as it makes compatibility easier (even through other languages via JSON). Those will be the :obj:`inputs` of
-the pipeline (:obj:`preprocess`).
-
-Then define the :obj:`outputs`. Same policy as the :obj:`inputs`. The simpler, the better. Those will be the outputs of
-:obj:`postprocess` method.
-
-Start by inheriting the base class :obj:`Pipeline`. with the 4 methods needed to implement :obj:`preprocess`,
-:obj:`_forward`, :obj:`postprocess` and :obj:`_sanitize_parameters`.
-
-
-.. code-block::
-
-    from transformers import Pipeline
-
-    class MyPipeline(Pipeline):
-        def _sanitize_parameters(self, **kwargs)
-            preprocess_kwargs = {}
-            if "maybe_arg" in kwargs:
-                preprocess_kwargs["maybe_arg"] = kwargs["maybe_arg"]
-            return preprocess_kwargs, {}, {}
-
-        def preprocess(self, inputs, maybe_arg=2)
-            model_input = Tensor(....)
-            return {"model_input": model_input}
-
-        def _forward(self, model_inputs)
-            # model_inputs == {"model_input": model_input}
-            oututs = self.model(**model_inputs)
-            # Maybe {"logits": Tensor(...)}
-            return outputs
-
-        def postprocess(self, model_outputs)
-            best_class = model_outputs["logits"].softmax(-1)
-            return best_class
-
-
-The structure of this breakdown is to support relatively seemless support for CPU/GPU, while supporting doing
-pre/postprocessing on the CPU on different threads
-
-:obj:`preprocess` will take the original defined inputs, and turn them something feedable to the model. It might
-contain more information and is usally a :obj:`Dict`.
-
-:obj:`_forward` is the implementation detail and is not meant to be called directly :obj:`forward` is the preferred
-called method as it contains safeguards to make sure everything is working on the expected device. If anything is
-linked to a real model it belongs in the :obj:`_forward` method, anything else is in the preprocess/postrocess.
-
-:obj:`postprocess` methods will take the output of :obj:`_forward` and turn it into the final output that were decided
-earlier.
-
-:obj:`_sanitize_parameters` exists to allow users to pass any parameters whenever they wish, be it at initialization
-time ``pipeline(...., maybe_arg=4)`` or at call time ``pipe = pipeline(...); output = pipe(...., maybe_arg=4)``.
-
-The returns of :obj:`_sanitize_parameters` are the 3 dicts of kwargs that will be passed directly to :obj:`preprocess`,
-:obj:`_forward` and :obj:`postprocess`. Don't fill anything if the caller didn't call with any extra parameter. That
-allows to keep the default arguments in the function definition which is always more "natural".
-
-A classic example would be a :obj:`top_k` argument in the post processing in classification tasks.
-
-.. code-block::
-
-    >>> pipe = pipeline("my-new-task")
-    >>> pipe("This is a test")
-    [{"label": "1-star", "score": 0.8}, {"label": "2-star", "score": 0.1}, {"label": "3-star", "score": 0.05}
-    {"label": "4-star", "score": 0.025}, {"label": "5-star", "score": 0.025}]
-
-    >>> pipe("This is a test", top_k=2)
-    [{"label": "1-star", "score": 0.8}, {"label": "2-star", "score": 0.1}]
-
-In order to achieve that, we'll update our :obj:`postprocess` method with a default parameter to :obj:`5`. and edit
-:obj:`_sanitize_parameters` to allow this new parameter.
-
-
-.. code-block::
-
-
-        def postprocess(self, model_outputs, top_k=5)
-            best_class = model_outputs["logits"].softmax(-1)
-            # Add logic to handle top_k
-            return best_class
-
-        def _sanitize_parameters(self, **kwargs)
-            preprocess_kwargs = {}
-            if "maybe_arg" in kwargs:
-                preprocess_kwargs["maybe_arg"] = kwargs["maybe_arg"]
-
-            postprocess_kwargs = {}
-            if "top_k" in kwargs:
-                preprocess_kwargs["top_k"] = kwargs["top_k"]
-            return preprocess_kwargs, {}, postprocess_kwargs
-
-Try to keep the inputs/outputs very simple and ideally JSON-serializable as it makes the pipeline usage very easy
-without requiring users to understand new kind of objects. It's also relatively common to support many different types
-of arguments for ease of use (audio files, can be filenames, URLs or pure bytes)
-
-
-
-Adding it to the list of supported tasks
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Go to ``src/transformers/pipelines/__init__.py`` and fill in :obj:`SUPPORTED_TASKS` with your newly created pipeline.
-If possible it should provide a default model.
-
-Adding tests
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Create a new file ``tests/test_pipelines_MY_PIPELINE.py`` with example with the other tests.
-
-The :obj:`run_pipeline_test` function will be very generic and run on small random models on every possible
-architecture as defined by :obj:`model_mapping` and :obj:`tf_model_mapping`.
-
-This is very important to test future compatibilty, meaning if someone adds a new model for
-:obj:`XXXForQuestionAnswering` then the pipeline test will attempt to run on it. Because the models are random it's
-impossible to check for actual values, that's why There is a helper :obj:`ANY` that will simply attempt to match the
-output of the pipeline TYPE.
-
-You also *need* to implement 2 (ideally 4) tests.
-
-- :obj:`test_small_model_pt` : Define 1 small model for this pipeline (doesn't matter if the results don't make sense)
-  and test the pipeline outputs. The results should be the same as :obj:`test_small_model_tf`.
-- :obj:`test_small_model_tf` : Define 1 small model for this pipeline (doesn't matter if the results don't make sense)
-  and test the pipeline outputs. The results should be the same as :obj:`test_small_model_pt`.
-- :obj:`test_large_model_pt` (:obj:`optional`): Tests the pipeline on a real pipeline where the results are supposed to
-  make sense. These tests are slow and should be marked as such. Here the goal is to showcase the pipeline and to make
-  sure there is no drift in future releases
-- :obj:`test_large_model_tf` (:obj:`optional`): Tests the pipeline on a real pipeline where the results are supposed to
-  make sense. These tests are slow and should be marked as such. Here the goal is to showcase the pipeline and to make
-  sure there is no drift in future releases

docs/source/benchmarks.rst

@@ -65,10 +65,10 @@ respectively.
 .. code-block:: bash
 
     ## PYTORCH CODE
-    python examples/pytorch/benchmarking/run_benchmark.py --help
+    python examples/benchmarking/run_benchmark.py --help
 
     ## TENSORFLOW CODE
-    python examples/tensorflow/benchmarking/run_benchmark_tf.py --help
+    python examples/benchmarking/run_benchmark_tf.py --help
 
 
 An instantiated benchmark object can then simply be run by calling ``benchmark.run()``.
@@ -358,6 +358,4 @@ 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>`.
+:prefix_link:`here <examples/benchmarking/README.md>`.

docs/source/community.md

@@ -1,64 +0,0 @@
-# Community
-
-This page regroups resources around 🤗 Transformers developed by the community.
-
-## Community resources:
-
-| 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/) |
-
-## Community notebooks:
-
-| Notebook     |      Description      |      Author      |      |
-|:----------|:-------------|:-------------|------:|
-| [Fine-tune a pre-trained Transformer to generate lyrics](https://github.com/AlekseyKorshuk/huggingartists) | How to generate lyrics in the style of your favorite artist by fine-tuning a GPT-2 model |  [Aleksey Korshuk](https://github.com/AlekseyKorshuk) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb) |
-| [Train T5 in Tensorflow 2 ](https://github.com/snapthat/TF-T5-text-to-text) | How to train T5 for any task using Tensorflow 2. This notebook demonstrates a Question & Answer task implemented in Tensorflow 2 using SQUAD | [Muhammad Harris](https://github.com/HarrisDePerceptron) |[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/snapthat/TF-T5-text-to-text/blob/master/snapthatT5/notebooks/TF-T5-Datasets%20Training.ipynb) |
-| [Train T5 on TPU](https://github.com/patil-suraj/exploring-T5/blob/master/T5_on_TPU.ipynb)  | How to train T5 on SQUAD with Transformers and Nlp | [Suraj Patil](https://github.com/patil-suraj) |[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/patil-suraj/exploring-T5/blob/master/T5_on_TPU.ipynb#scrollTo=QLGiFCDqvuil) |
-| [Fine-tune T5 for Classification and Multiple Choice](https://github.com/patil-suraj/exploring-T5/blob/master/t5_fine_tuning.ipynb)  | How to fine-tune T5 for classification and multiple choice tasks using a text-to-text format with PyTorch Lightning |  [Suraj Patil](https://github.com/patil-suraj) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/patil-suraj/exploring-T5/blob/master/t5_fine_tuning.ipynb) |
-| [Fine-tune DialoGPT on New Datasets and Languages](https://github.com/ncoop57/i-am-a-nerd/blob/master/_notebooks/2020-05-12-chatbot-part-1.ipynb)  | How to fine-tune the DialoGPT model on a new dataset for open-dialog conversational chatbots |  [Nathan Cooper](https://github.com/ncoop57) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ncoop57/i-am-a-nerd/blob/master/_notebooks/2020-05-12-chatbot-part-1.ipynb) |
-| [Long Sequence Modeling with Reformer](https://github.com/patrickvonplaten/notebooks/blob/master/PyTorch_Reformer.ipynb)  | How to train on sequences as long as 500,000 tokens with Reformer |  [Patrick von Platen](https://github.com/patrickvonplaten) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/PyTorch_Reformer.ipynb)  |
-| [Fine-tune BART for Summarization](https://github.com/ohmeow/ohmeow_website/blob/master/_notebooks/2020-05-23-text-generation-with-blurr.ipynb) | How to fine-tune BART for summarization with fastai using blurr | [Wayde Gilliam](https://ohmeow.com/) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ohmeow/ohmeow_website/blob/master/_notebooks/2020-05-23-text-generation-with-blurr.ipynb) |
-| [Fine-tune a pre-trained Transformer on anyone's tweets](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb) | How to generate tweets in the style of your favorite Twitter account by fine-tuning a GPT-2 model |  [Boris Dayma](https://github.com/borisdayma) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb) |
-| [Optimize 🤗 Hugging Face models with Weights & Biases](https://colab.research.google.com/github/wandb/examples/blob/master/colabs/huggingface/Optimize_Hugging_Face_models_with_Weights_%26_Biases.ipynb) | A complete tutorial showcasing W&B integration with Hugging Face | [Boris Dayma](https://github.com/borisdayma) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/wandb/examples/blob/master/colabs/huggingface/Optimize_Hugging_Face_models_with_Weights_%26_Biases.ipynb) |
-| [Pretrain Longformer](https://github.com/allenai/longformer/blob/master/scripts/convert_model_to_long.ipynb)  | How to build a "long" version of existing pretrained models |  [Iz Beltagy](https://beltagy.net) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/allenai/longformer/blob/master/scripts/convert_model_to_long.ipynb) |
-| [Fine-tune Longformer for QA](https://github.com/patil-suraj/Notebooks/blob/master/longformer_qa_training.ipynb) | How to fine-tune longformer model for QA task | [Suraj Patil](https://github.com/patil-suraj) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/patil-suraj/Notebooks/blob/master/longformer_qa_training.ipynb) |
-| [Evaluate Model with 🤗nlp](https://github.com/patrickvonplaten/notebooks/blob/master/How_to_evaluate_Longformer_on_TriviaQA_using_NLP.ipynb) | How to evaluate longformer on TriviaQA with `nlp` | [Patrick von Platen](https://github.com/patrickvonplaten) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1m7eTGlPmLRgoPkkA7rkhQdZ9ydpmsdLE?usp=sharing) |
-| [Fine-tune T5 for Sentiment Span Extraction](https://github.com/enzoampil/t5-intro/blob/master/t5_qa_training_pytorch_span_extraction.ipynb)  | How to fine-tune T5 for sentiment span extraction using a text-to-text format with PyTorch Lightning |  [Lorenzo Ampil](https://github.com/enzoampil) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/enzoampil/t5-intro/blob/master/t5_qa_training_pytorch_span_extraction.ipynb) |
-| [Fine-tune DistilBert for Multiclass Classification](https://github.com/abhimishra91/transformers-tutorials/blob/master/transformers_multiclass_classification.ipynb) | How to fine-tune DistilBert for multiclass classification with PyTorch | [Abhishek Kumar Mishra](https://github.com/abhimishra91) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/abhimishra91/transformers-tutorials/blob/master/transformers_multiclass_classification.ipynb)|
-|[Fine-tune BERT for Multi-label Classification](https://github.com/abhimishra91/transformers-tutorials/blob/master/transformers_multi_label_classification.ipynb)|How to fine-tune BERT for multi-label classification using PyTorch|[Abhishek Kumar Mishra](https://github.com/abhimishra91) |[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/abhimishra91/transformers-tutorials/blob/master/transformers_multi_label_classification.ipynb)|
-|[Fine-tune T5 for Summarization](https://github.com/abhimishra91/transformers-tutorials/blob/master/transformers_summarization_wandb.ipynb)|How to fine-tune T5 for summarization in PyTorch and track experiments with WandB|[Abhishek Kumar Mishra](https://github.com/abhimishra91) |[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/abhimishra91/transformers-tutorials/blob/master/transformers_summarization_wandb.ipynb)|
-|[Speed up Fine-Tuning in Transformers with Dynamic Padding / Bucketing](https://github.com/ELS-RD/transformers-notebook/blob/master/Divide_Hugging_Face_Transformers_training_time_by_2_or_more.ipynb)|How to speed up fine-tuning by a factor of 2 using dynamic padding / bucketing|[Michael Benesty](https://github.com/pommedeterresautee) |[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1CBfRU1zbfu7-ijiOqAAQUA-RJaxfcJoO?usp=sharing)|
-|[Pretrain Reformer for Masked Language Modeling](https://github.com/patrickvonplaten/notebooks/blob/master/Reformer_For_Masked_LM.ipynb)| How to train a Reformer model with bi-directional self-attention layers | [Patrick von Platen](https://github.com/patrickvonplaten) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1tzzh0i8PgDQGV3SMFUGxM7_gGae3K-uW?usp=sharing)|
-|[Expand and Fine Tune Sci-BERT](https://github.com/lordtt13/word-embeddings/blob/master/COVID-19%20Research%20Data/COVID-SciBERT.ipynb)| How to increase vocabulary of a pretrained SciBERT model from AllenAI on the CORD dataset and pipeline it. | [Tanmay Thakur](https://github.com/lordtt13) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1rqAR40goxbAfez1xvF3hBJphSCsvXmh8)|
-|[Fine Tune BlenderBotSmall for Summarization using the Trainer API](https://github.com/lordtt13/transformers-experiments/blob/master/Custom%20Tasks/fine-tune-blenderbot_small-for-summarization.ipynb)| How to fine tune BlenderBotSmall for summarization on a custom dataset, using the Trainer API. | [Tanmay Thakur](https://github.com/lordtt13) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/19Wmupuls7mykSGyRN_Qo6lPQhgp56ymq?usp=sharing)|
-|[Fine-tune Electra and interpret with Integrated Gradients](https://github.com/elsanns/xai-nlp-notebooks/blob/master/electra_fine_tune_interpret_captum_ig.ipynb) | How to fine-tune Electra for sentiment analysis and interpret predictions with Captum Integrated Gradients | [Eliza Szczechla](https://elsanns.github.io) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/elsanns/xai-nlp-notebooks/blob/master/electra_fine_tune_interpret_captum_ig.ipynb)|
-|[fine-tune a non-English GPT-2 Model with Trainer class](https://github.com/philschmid/fine-tune-GPT-2/blob/master/Fine_tune_a_non_English_GPT_2_Model_with_Huggingface.ipynb) | How to fine-tune a non-English GPT-2 Model with Trainer class | [Philipp Schmid](https://www.philschmid.de) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/philschmid/fine-tune-GPT-2/blob/master/Fine_tune_a_non_English_GPT_2_Model_with_Huggingface.ipynb)|
-|[Fine-tune a DistilBERT Model for Multi Label Classification task](https://github.com/DhavalTaunk08/Transformers_scripts/blob/master/Transformers_multilabel_distilbert.ipynb) | How to fine-tune a DistilBERT Model for Multi Label Classification task | [Dhaval Taunk](https://github.com/DhavalTaunk08) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/DhavalTaunk08/Transformers_scripts/blob/master/Transformers_multilabel_distilbert.ipynb)|
-|[Fine-tune ALBERT for sentence-pair classification](https://github.com/NadirEM/nlp-notebooks/blob/master/Fine_tune_ALBERT_sentence_pair_classification.ipynb) | How to fine-tune an ALBERT model or another BERT-based model for the sentence-pair classification task | [Nadir El Manouzi](https://github.com/NadirEM) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NadirEM/nlp-notebooks/blob/master/Fine_tune_ALBERT_sentence_pair_classification.ipynb)|
-|[Fine-tune Roberta for sentiment analysis](https://github.com/DhavalTaunk08/NLP_scripts/blob/master/sentiment_analysis_using_roberta.ipynb) | How to fine-tune an Roberta model for sentiment analysis | [Dhaval Taunk](https://github.com/DhavalTaunk08) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/DhavalTaunk08/NLP_scripts/blob/master/sentiment_analysis_using_roberta.ipynb)|
-|[Evaluating Question Generation Models](https://github.com/flexudy-pipe/qugeev) | How accurate are the answers to questions generated by your seq2seq transformer model? | [Pascal Zoleko](https://github.com/zolekode) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1bpsSqCQU-iw_5nNoRm_crPq6FRuJthq_?usp=sharing)|
-|[Classify text with DistilBERT and Tensorflow](https://github.com/peterbayerle/huggingface_notebook/blob/main/distilbert_tf.ipynb) | How to fine-tune DistilBERT for text classification in TensorFlow | [Peter Bayerle](https://github.com/peterbayerle) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/peterbayerle/huggingface_notebook/blob/main/distilbert_tf.ipynb)|
-|[Leverage BERT for Encoder-Decoder Summarization on CNN/Dailymail](https://github.com/patrickvonplaten/notebooks/blob/master/BERT2BERT_for_CNN_Dailymail.ipynb) | How to warm-start a *EncoderDecoderModel* with a *bert-base-uncased* checkpoint for summarization on CNN/Dailymail | [Patrick von Platen](https://github.com/patrickvonplaten) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/BERT2BERT_for_CNN_Dailymail.ipynb)|
-|[Leverage RoBERTa for Encoder-Decoder Summarization on BBC XSum](https://github.com/patrickvonplaten/notebooks/blob/master/RoBERTaShared_for_BBC_XSum.ipynb) | How to warm-start a shared *EncoderDecoderModel* with a *roberta-base* checkpoint for summarization on BBC/XSum | [Patrick von Platen](https://github.com/patrickvonplaten) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/RoBERTaShared_for_BBC_XSum.ipynb)|
-|[Fine-tune TAPAS on Sequential Question Answering (SQA)](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/TAPAS/Fine_tuning_TapasForQuestionAnswering_on_SQA.ipynb) | How to fine-tune *TapasForQuestionAnswering* with a *tapas-base* checkpoint on the Sequential Question Answering (SQA) dataset | [Niels Rogge](https://github.com/nielsrogge) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/TAPAS/Fine_tuning_TapasForQuestionAnswering_on_SQA.ipynb)|
-|[Evaluate TAPAS on Table Fact Checking (TabFact)](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/TAPAS/Evaluating_TAPAS_on_the_Tabfact_test_set.ipynb) | How to evaluate a fine-tuned *TapasForSequenceClassification* with a *tapas-base-finetuned-tabfact* checkpoint using a combination of the 🤗 datasets and 🤗 transformers libraries | [Niels Rogge](https://github.com/nielsrogge) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/TAPAS/Evaluating_TAPAS_on_the_Tabfact_test_set.ipynb)|
-|[Fine-tuning mBART for translation](https://colab.research.google.com/github/vasudevgupta7/huggingface-tutorials/blob/main/translation_training.ipynb) | How to fine-tune mBART using Seq2SeqTrainer for Hindi to English translation | [Vasudev Gupta](https://github.com/vasudevgupta7) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/vasudevgupta7/huggingface-tutorials/blob/main/translation_training.ipynb)|
-|[Fine-tune LayoutLM on FUNSD (a form understanding dataset)](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/LayoutLM/Fine_tuning_LayoutLMForTokenClassification_on_FUNSD.ipynb) | How to fine-tune *LayoutLMForTokenClassification* on the FUNSD dataset for information extraction from scanned documents | [Niels Rogge](https://github.com/nielsrogge) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/LayoutLM/Fine_tuning_LayoutLMForTokenClassification_on_FUNSD.ipynb)|
-|[Fine-Tune DistilGPT2 and Generate Text](https://colab.research.google.com/github/tripathiaakash/DistilGPT2-Tutorial/blob/main/distilgpt2_fine_tuning.ipynb) | How to fine-tune DistilGPT2 and generate text | [Aakash Tripathi](https://github.com/tripathiaakash) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/tripathiaakash/DistilGPT2-Tutorial/blob/main/distilgpt2_fine_tuning.ipynb)|
-|[Fine-Tune LED on up to 8K tokens](https://github.com/patrickvonplaten/notebooks/blob/master/Fine_tune_Longformer_Encoder_Decoder_(LED)_for_Summarization_on_pubmed.ipynb) | How to fine-tune LED on pubmed for long-range summarization | [Patrick von Platen](https://github.com/patrickvonplaten) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Fine_tune_Longformer_Encoder_Decoder_(LED)_for_Summarization_on_pubmed.ipynb)|
-|[Evaluate LED on Arxiv](https://github.com/patrickvonplaten/notebooks/blob/master/LED_on_Arxiv.ipynb) | How to effectively evaluate LED on long-range summarization | [Patrick von Platen](https://github.com/patrickvonplaten) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/LED_on_Arxiv.ipynb)|
-|[Fine-tune LayoutLM on RVL-CDIP (a document image classification dataset)](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/LayoutLM/Fine_tuning_LayoutLMForSequenceClassification_on_RVL_CDIP.ipynb) | How to fine-tune *LayoutLMForSequenceClassification* on the RVL-CDIP dataset for scanned document classification | [Niels Rogge](https://github.com/nielsrogge) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/LayoutLM/Fine_tuning_LayoutLMForSequenceClassification_on_RVL_CDIP.ipynb)|
-|[Wav2Vec2 CTC decoding with GPT2 adjustment](https://github.com/voidful/huggingface_notebook/blob/main/xlsr_gpt.ipynb) | How to decode CTC sequence with language model adjustment | [Eric Lam](https://github.com/voidful) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1e_z5jQHYbO2YKEaUgzb1ww1WwiAyydAj?usp=sharing)|
-|[Fine-tune BART for summarization in two languages with Trainer class](https://github.com/elsanns/xai-nlp-notebooks/blob/master/fine_tune_bart_summarization_two_langs.ipynb) | How to fine-tune BART for summarization in two languages with Trainer class | [Eliza Szczechla](https://github.com/elsanns) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/elsanns/xai-nlp-notebooks/blob/master/fine_tune_bart_summarization_two_langs.ipynb)|
-|[Evaluate Big Bird on Trivia QA](https://github.com/patrickvonplaten/notebooks/blob/master/Evaluating_Big_Bird_on_TriviaQA.ipynb) | How to evaluate BigBird on long document question answering on Trivia QA | [Patrick von Platen](https://github.com/patrickvonplaten) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Evaluating_Big_Bird_on_TriviaQA.ipynb)|
-| [Create video captions using Wav2Vec2](https://github.com/Muennighoff/ytclipcc/blob/main/wav2vec_youtube_captions.ipynb) | How to create YouTube captions from any video by transcribing the audio with Wav2Vec | [Niklas Muennighoff](https://github.com/Muennighoff) |[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Muennighoff/ytclipcc/blob/main/wav2vec_youtube_captions.ipynb) |
-| [Fine-tune the Vision Transformer on CIFAR-10 using PyTorch Lightning](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/VisionTransformer/Fine_tuning_the_Vision_Transformer_on_CIFAR_10_with_PyTorch_Lightning.ipynb) | How to fine-tune the Vision Transformer (ViT) on CIFAR-10 using HuggingFace Transformers, Datasets and PyTorch Lightning | [Niels Rogge](https://github.com/nielsrogge) |[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/VisionTransformer/Fine_tuning_the_Vision_Transformer_on_CIFAR_10_with_PyTorch_Lightning.ipynb) |
-| [Fine-tune the Vision Transformer on CIFAR-10 using the 🤗 Trainer](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/VisionTransformer/Fine_tuning_the_Vision_Transformer_on_CIFAR_10_with_the_%F0%9F%A4%97_Trainer.ipynb) | How to fine-tune the Vision Transformer (ViT) on CIFAR-10 using HuggingFace Transformers, Datasets and the 🤗 Trainer | [Niels Rogge](https://github.com/nielsrogge) |[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/VisionTransformer/Fine_tuning_the_Vision_Transformer_on_CIFAR_10_with_the_%F0%9F%A4%97_Trainer.ipynb) |
-| [Evaluate LUKE on Open Entity, an entity typing dataset](https://github.com/studio-ousia/luke/blob/master/notebooks/huggingface_open_entity.ipynb) | How to evaluate *LukeForEntityClassification* on the Open Entity dataset | [Ikuya Yamada](https://github.com/ikuyamada) |[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/studio-ousia/luke/blob/master/notebooks/huggingface_open_entity.ipynb) |
-| [Evaluate LUKE on TACRED, a relation extraction dataset](https://github.com/studio-ousia/luke/blob/master/notebooks/huggingface_tacred.ipynb) | How to evaluate *LukeForEntityPairClassification* on the TACRED dataset | [Ikuya Yamada](https://github.com/ikuyamada) |[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/studio-ousia/luke/blob/master/notebooks/huggingface_tacred.ipynb) |
-| [Evaluate LUKE on CoNLL-2003, an important NER benchmark](https://github.com/studio-ousia/luke/blob/master/notebooks/huggingface_conll_2003.ipynb) | How to evaluate *LukeForEntitySpanClassification* on the CoNLL-2003 dataset | [Ikuya Yamada](https://github.com/ikuyamada) |[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/studio-ousia/luke/blob/master/notebooks/huggingface_conll_2003.ipynb) |
-| [Evaluate BigBird-Pegasus on PubMed dataset](https://github.com/vasudevgupta7/bigbird/blob/main/notebooks/bigbird_pegasus_evaluation.ipynb) | How to evaluate *BigBirdPegasusForConditionalGeneration* on PubMed dataset | [Vasudev Gupta](https://github.com/vasudevgupta7) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/vasudevgupta7/bigbird/blob/main/notebooks/bigbird_pegasus_evaluation.ipynb) |
-| [Speech Emotion Classification with Wav2Vec2](https://github/m3hrdadfi/soxan/blob/main/notebooks/Emotion_recognition_in_Greek_speech_using_Wav2Vec2.ipynb) | How to leverage a pretrained Wav2Vec2 model for Emotion Classification on the MEGA dataset | [Mehrdad Farahani](https://github.com/m3hrdadfi) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/m3hrdadfi/soxan/blob/main/notebooks/Emotion_recognition_in_Greek_speech_using_Wav2Vec2.ipynb) |
-| [Detect objects in an image with DETR](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/DETR/DETR_minimal_example_(with_DetrFeatureExtractor).ipynb) | How to use a trained *DetrForObjectDetection* model to detect objects in an image and visualize attention | [Niels Rogge](https://github.com/NielsRogge) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/DETR/DETR_minimal_example_(with_DetrFeatureExtractor).ipynb) |
-| [Fine-tune DETR on a custom object detection dataset](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/DETR/Fine_tuning_DetrForObjectDetection_on_custom_dataset_(balloon).ipynb) | How to fine-tune *DetrForObjectDetection* on a custom object detection dataset | [Niels Rogge](https://github.com/NielsRogge) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/DETR/Fine_tuning_DetrForObjectDetection_on_custom_dataset_(balloon).ipynb) |

docs/source/conf.py

@@ -14,31 +14,21 @@
 #
 import os
 import sys
-
-sys.path.insert(0, os.path.abspath("../../src"))
+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"
+project = u'transformers'
+copyright = u'2020, The Hugging Face Team, Licenced under the Apache License, Version 2.0'
+author = u'huggingface'
 
 # The short X.Y version
-version = ""
+version = u''
 # The full version, including alpha/beta/rc tags
-release = "4.11.3"
-
-
-
-
-
-
-
-
-
+release = u'4.2.0'
 # Prefix link to point to master, comment this during version release and uncomment below line
-extlinks = {"prefix_link": ("https://github.com/huggingface/transformers/blob/master/%s", "")}
+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', '')}
 
@@ -52,28 +42,27 @@ extlinks = {"prefix_link": ("https://github.com/huggingface/transformers/blob/ma
 # 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",
+    'sphinx.ext.autodoc',
+    'sphinx.ext.extlinks',
+    'sphinx.ext.coverage',
+    'sphinx.ext.napoleon',
+    'recommonmark',
+    'sphinx.ext.viewcode',
+    'sphinx_markdown_tables',
+    'sphinx_copybutton'
 ]
 
 # Add any paths that contain templates here, relative to this directory.
-templates_path = ["_templates"]
+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', '.md']
 # source_suffix = '.rst'
 
 # The master toctree document.
-master_doc = "index"
+master_doc = 'index'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.
@@ -85,7 +74,7 @@ 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"]
+exclude_patterns = [u'_build', 'Thumbs.db', '.DS_Store']
 
 # The name of the Pygments (syntax highlighting) style to use.
 pygments_style = None
@@ -99,30 +88,20 @@ copybutton_prompt_is_regexp = True
 # The theme to use for HTML and HTML Help pages.  See the documentation for
 # a list of builtin themes.
 #
-html_theme = "sphinx_rtd_theme"
+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}">',
-]
+html_theme_options = {
+    'analytics_id': 'UA-83738774-2'
+}
 
 # 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"]
+html_static_path = ['_static']
 
 # Custom sidebar templates, must be a dictionary that maps document names
 # to template names.
@@ -134,17 +113,17 @@ html_static_path = ["_static"]
 #
 # 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
+# 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"
+html_favicon = 'favicon.ico'
 
 
 # -- Options for HTMLHelp output ---------------------------------------------
 
 # Output file base name for HTML help builder.
-htmlhelp_basename = "transformersdoc"
+htmlhelp_basename = 'transformersdoc'
 
 
 # -- Options for LaTeX output ------------------------------------------------
@@ -153,12 +132,15 @@ 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',
@@ -168,7 +150,8 @@ latex_elements = {
 # (source start file, target name, title,
 #  author, documentclass [howto, manual, or own class]).
 latex_documents = [
-    (master_doc, "transformers.tex", "transformers Documentation", "huggingface", "manual"),
+    (master_doc, 'transformers.tex', u'transformers Documentation',
+     u'huggingface', 'manual'),
 ]
 
 
@@ -176,7 +159,10 @@ latex_documents = [
 
 # 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)]
+man_pages = [
+    (master_doc, 'transformers', u'transformers Documentation',
+     [author], 1)
+]
 
 
 # -- Options for Texinfo output ----------------------------------------------
@@ -185,15 +171,9 @@ man_pages = [(master_doc, "transformers", "transformers Documentation", [author]
 # (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",
-    ),
+    (master_doc, 'transformers', u'transformers Documentation',
+     author, 'transformers', 'One line description of project.',
+     'Miscellaneous'),
 ]
 
 
@@ -212,16 +192,11 @@ epub_title = project
 # 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
+epub_exclude_files = ['search.html']
 
 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")
-
+    app.add_css_file('css/huggingface.css')
+    app.add_css_file('css/code-snippets.css')
+    app.add_js_file('js/custom.js')
 
 # -- Extension configuration -------------------------------------------------

docs/source/converting_tensorflow_models.rst

@@ -28,13 +28,17 @@ BERT
 You can convert any TensorFlow checkpoint for BERT (in particular `the pre-trained models released by Google
 <https://github.com/google-research/bert#pre-trained-models>`_\ ) in a PyTorch save file by using the
 :prefix_link:`convert_bert_original_tf_checkpoint_to_pytorch.py
-<src/transformers/models/bert/convert_bert_original_tf_checkpoint_to_pytorch.py>` script.
+<src/transformers/convert_bert_original_tf_checkpoint_to_pytorch.py>` script.
 
 This CLI takes as input a TensorFlow checkpoint (three files starting with ``bert_model.ckpt``\ ) and the associated
 configuration file (\ ``bert_config.json``\ ), and creates a PyTorch model for this configuration, loads the weights
 from the TensorFlow checkpoint in the PyTorch model and saves the resulting model in a standard PyTorch save file that
-can be imported using ``from_pretrained()`` (see example in :doc:`quicktour` , :prefix_link:`run_glue.py
-<examples/pytorch/text-classification/run_glue.py>` \ ).
+can be imported using ``torch.load()`` (see examples in `run_bert_extract_features.py
+<https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/examples/run_bert_extract_features.py>`_\ ,
+`run_bert_classifier.py
+<https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/examples/run_bert_classifier.py>`_ and
+`run_bert_squad.py <https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/examples/run_bert_squad.py>`_\
+).
 
 You only need to run this conversion script **once** to get a PyTorch model. You can then disregard the TensorFlow
 checkpoint (the three files starting with ``bert_model.ckpt``\ ) but be sure to keep the configuration file (\
@@ -47,12 +51,12 @@ Here is an example of the conversion process for a pre-trained ``BERT-Base Uncas
 
 .. code-block:: shell
 
-    export BERT_BASE_DIR=/path/to/bert/uncased_L-12_H-768_A-12
+   export BERT_BASE_DIR=/path/to/bert/uncased_L-12_H-768_A-12
 
-    transformers-cli convert --model_type bert \
-      --tf_checkpoint $BERT_BASE_DIR/bert_model.ckpt \
-      --config $BERT_BASE_DIR/bert_config.json \
-      --pytorch_dump_output $BERT_BASE_DIR/pytorch_model.bin
+   transformers-cli convert --model_type bert \
+     --tf_checkpoint $BERT_BASE_DIR/bert_model.ckpt \
+     --config $BERT_BASE_DIR/bert_config.json \
+     --pytorch_dump_output $BERT_BASE_DIR/pytorch_model.bin
 
 You can download Google's pre-trained models for the conversion `here
 <https://github.com/google-research/bert#pre-trained-models>`__.
@@ -62,7 +66,7 @@ ALBERT
 
 Convert TensorFlow model checkpoints of ALBERT to PyTorch using the
 :prefix_link:`convert_albert_original_tf_checkpoint_to_pytorch.py
-<src/transformers/models/albert/convert_albert_original_tf_checkpoint_to_pytorch.py>` script.
+<src/transformers/convert_bert_original_tf_checkpoint_to_pytorch.py>` script.
 
 The CLI takes as input a TensorFlow checkpoint (three files starting with ``model.ckpt-best``\ ) and the accompanying
 configuration file (\ ``albert_config.json``\ ), then creates and saves a PyTorch model. To run this conversion you
@@ -72,12 +76,12 @@ Here is an example of the conversion process for the pre-trained ``ALBERT Base``
 
 .. code-block:: shell
 
-    export ALBERT_BASE_DIR=/path/to/albert/albert_base
+   export ALBERT_BASE_DIR=/path/to/albert/albert_base
 
-    transformers-cli convert --model_type albert \
-      --tf_checkpoint $ALBERT_BASE_DIR/model.ckpt-best \
-      --config $ALBERT_BASE_DIR/albert_config.json \
-      --pytorch_dump_output $ALBERT_BASE_DIR/pytorch_model.bin
+   transformers-cli convert --model_type albert \
+     --tf_checkpoint $ALBERT_BASE_DIR/model.ckpt-best \
+     --config $ALBERT_BASE_DIR/albert_config.json \
+     --pytorch_dump_output $ALBERT_BASE_DIR/pytorch_model.bin
 
 You can download Google's pre-trained models for the conversion `here
 <https://github.com/google-research/albert#pre-trained-models>`__.
@@ -91,13 +95,13 @@ save as the same format than OpenAI pretrained model (see `here <https://github.
 
 .. code-block:: shell
 
-    export OPENAI_GPT_CHECKPOINT_FOLDER_PATH=/path/to/openai/pretrained/numpy/weights
+   export OPENAI_GPT_CHECKPOINT_FOLDER_PATH=/path/to/openai/pretrained/numpy/weights
 
-    transformers-cli convert --model_type gpt \
-      --tf_checkpoint $OPENAI_GPT_CHECKPOINT_FOLDER_PATH \
-      --pytorch_dump_output $PYTORCH_DUMP_OUTPUT \
-      [--config OPENAI_GPT_CONFIG] \
-      [--finetuning_task_name OPENAI_GPT_FINETUNED_TASK] \
+   transformers-cli convert --model_type gpt \
+     --tf_checkpoint $OPENAI_GPT_CHECKPOINT_FOLDER_PATH \
+     --pytorch_dump_output $PYTORCH_DUMP_OUTPUT \
+     [--config OPENAI_GPT_CONFIG] \
+     [--finetuning_task_name OPENAI_GPT_FINETUNED_TASK] \
 
 
 OpenAI GPT-2
@@ -108,13 +112,13 @@ Here is an example of the conversion process for a pre-trained OpenAI GPT-2 mode
 
 .. code-block:: shell
 
-    export OPENAI_GPT2_CHECKPOINT_PATH=/path/to/gpt2/pretrained/weights
+   export OPENAI_GPT2_CHECKPOINT_PATH=/path/to/gpt2/pretrained/weights
 
-    transformers-cli convert --model_type gpt2 \
-      --tf_checkpoint $OPENAI_GPT2_CHECKPOINT_PATH \
-      --pytorch_dump_output $PYTORCH_DUMP_OUTPUT \
-      [--config OPENAI_GPT2_CONFIG] \
-      [--finetuning_task_name OPENAI_GPT2_FINETUNED_TASK]
+   transformers-cli convert --model_type gpt2 \
+     --tf_checkpoint $OPENAI_GPT2_CHECKPOINT_PATH \
+     --pytorch_dump_output $PYTORCH_DUMP_OUTPUT \
+     [--config OPENAI_GPT2_CONFIG] \
+     [--finetuning_task_name OPENAI_GPT2_FINETUNED_TASK]
 
 Transformer-XL
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -124,13 +128,13 @@ Here is an example of the conversion process for a pre-trained Transformer-XL mo
 
 .. code-block:: shell
 
-    export TRANSFO_XL_CHECKPOINT_FOLDER_PATH=/path/to/transfo/xl/checkpoint
+   export TRANSFO_XL_CHECKPOINT_FOLDER_PATH=/path/to/transfo/xl/checkpoint
 
-    transformers-cli convert --model_type transfo_xl \
-      --tf_checkpoint $TRANSFO_XL_CHECKPOINT_FOLDER_PATH \
-      --pytorch_dump_output $PYTORCH_DUMP_OUTPUT \
-      [--config TRANSFO_XL_CONFIG] \
-      [--finetuning_task_name TRANSFO_XL_FINETUNED_TASK]
+   transformers-cli convert --model_type transfo_xl \
+     --tf_checkpoint $TRANSFO_XL_CHECKPOINT_FOLDER_PATH \
+     --pytorch_dump_output $PYTORCH_DUMP_OUTPUT \
+     [--config TRANSFO_XL_CONFIG] \
+     [--finetuning_task_name TRANSFO_XL_FINETUNED_TASK]
 
 
 XLNet
@@ -140,14 +144,14 @@ Here is an example of the conversion process for a pre-trained XLNet model:
 
 .. code-block:: shell
 
-    export TRANSFO_XL_CHECKPOINT_PATH=/path/to/xlnet/checkpoint
-    export TRANSFO_XL_CONFIG_PATH=/path/to/xlnet/config
+   export TRANSFO_XL_CHECKPOINT_PATH=/path/to/xlnet/checkpoint
+   export TRANSFO_XL_CONFIG_PATH=/path/to/xlnet/config
 
-    transformers-cli convert --model_type xlnet \
-      --tf_checkpoint $TRANSFO_XL_CHECKPOINT_PATH \
-      --config $TRANSFO_XL_CONFIG_PATH \
-      --pytorch_dump_output $PYTORCH_DUMP_OUTPUT \
-      [--finetuning_task_name XLNET_FINETUNED_TASK] \
+   transformers-cli convert --model_type xlnet \
+     --tf_checkpoint $TRANSFO_XL_CHECKPOINT_PATH \
+     --config $TRANSFO_XL_CONFIG_PATH \
+     --pytorch_dump_output $PYTORCH_DUMP_OUTPUT \
+     [--finetuning_task_name XLNET_FINETUNED_TASK] \
 
 
 XLM
@@ -157,25 +161,10 @@ Here is an example of the conversion process for a pre-trained XLM model:
 
 .. code-block:: shell
 
-    export XLM_CHECKPOINT_PATH=/path/to/xlm/checkpoint
+   export XLM_CHECKPOINT_PATH=/path/to/xlm/checkpoint
 
-    transformers-cli convert --model_type xlm \
-      --tf_checkpoint $XLM_CHECKPOINT_PATH \
-      --pytorch_dump_output $PYTORCH_DUMP_OUTPUT
-     [--config XML_CONFIG] \
-     [--finetuning_task_name XML_FINETUNED_TASK]
-
-
-T5
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Here is an example of the conversion process for a pre-trained T5 model:
-
-.. code-block:: shell
-
-    export T5=/path/to/t5/uncased_L-12_H-768_A-12
-
-    transformers-cli convert --model_type t5 \
-      --tf_checkpoint $T5/t5_model.ckpt \
-      --config $T5/t5_config.json \
-      --pytorch_dump_output $T5/pytorch_model.bin
+   transformers-cli convert --model_type xlm \
+     --tf_checkpoint $XLM_CHECKPOINT_PATH \
+     --pytorch_dump_output $PYTORCH_DUMP_OUTPUT
+    [--config XML_CONFIG] \
+    [--finetuning_task_name XML_FINETUNED_TASK]

docs/source/custom_datasets.rst

@@ -15,10 +15,10 @@ 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 of introduction can be found at the end of the
-    tutorial in the section ":ref:`datasetslib`".
+    The datasets used in this tutorial are available and can be more easily accessed using the `🤗 NLP library
+    <https://github.com/huggingface/nlp>`_. We do not use this library to access the datasets here since this tutorial
+    meant to illustrate how to work with your own data. A brief of introduction can be found at the end of the tutorial
+    in the section ":ref:`nlplib`".
 
 This tutorial will take you through several examples of using 🤗 Transformers models with your own datasets. The guide
 shows one of many valid workflows for using these models and is meant to be illustrative rather than definitive. We
@@ -41,7 +41,7 @@ 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")``.
+    can be alternatively downloaded with the 🤗 NLP library with ``load_dataset("imdb")``.
 
 In this example, we'll show how to download, tokenize, and train a model on the IMDb reviews dataset. This task takes
 the text of a review and requires the model to predict whether the sentiment of the review is positive or negative.
@@ -75,7 +75,7 @@ read this in.
     test_texts, test_labels = read_imdb_split('aclImdb/test')
 
 We now have a train and test dataset, but let's also also create a validation set which we can use for for evaluation
-and tuning without tainting our test set results. Sklearn has a convenient utility for creating such splits:
+and tuning without training our test set results. Sklearn has a convenient utility for creating such splits:
 
 .. code-block:: python
 
@@ -260,7 +260,7 @@ 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")``.
+    and can be alternatively downloaded with the 🤗 NLP library with ``load_dataset("wnut_17")``.
 
 Next we will look at token classification. Rather than classifying an entire sequence, this task classifies token by
 token. We'll demonstrate how to do this with `Named Entity Recognition
@@ -459,7 +459,7 @@ 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
+    <https://huggingface.co/datasets/squad_v2>`_), and can be alternatively downloaded with the 🤗 NLP library with
     ``load_dataset("squad_v2")``.
 
 Question answering comes in many forms. In this example, we'll look at the particular type of extractive QA that
@@ -558,14 +558,15 @@ we can use the built in :func:`~transformers.BatchEncoding.char_to_token` method
         end_positions = []
         for i in range(len(answers)):
             start_positions.append(encodings.char_to_token(i, answers[i]['answer_start']))
-            end_positions.append(encodings.char_to_token(i, answers[i]['answer_end'] - 1))
+            end_positions.append(encodings.char_to_token(i, answers[i]['answer_end']))
 
             # 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
 
+            # if end position is None, the 'char_to_token' function points to the space before the correct token - > add + 1
+            if end_positions[-1] is None:
+                end_positions[-1] = encodings.char_to_token(i, answers[i]['answer_end'] + 1)
         encodings.update({'start_positions': start_positions, 'end_positions': end_positions})
 
     add_token_positions(train_encodings, train_answers)
@@ -677,23 +678,22 @@ Additional Resources
   - :doc:`Preprocessing <preprocessing>`. Docs page on data preprocessing.
   - :doc:`Training <training>`. Docs page on training and fine-tuning.
 
-.. _datasetslib:
+.. _nlplib:
 
-Using the 🤗 Datasets & Metrics library
+Using the 🤗 NLP Datasets & Metrics library
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 This tutorial demonstrates how to read in datasets from various raw text formats and prepare them for training with 🤗
 Transformers so that you can do the same thing with your own custom datasets. However, we recommend users use the `🤗
-Datasets library <https://github.com/huggingface/datasets>`_ for working with the 150+ datasets included in the `hub
+NLP library <https://github.com/huggingface/nlp>`_ for working with the 150+ datasets included in the `hub
 <https://huggingface.co/datasets>`_, including the three datasets used in this tutorial. As a very brief overview, we
-will show how to use the Datasets library to download and prepare the IMDb dataset from the first example,
-:ref:`seq_imdb`.
+will show how to use the NLP library to download and prepare the IMDb dataset from the first example, :ref:`seq_imdb`.
 
 Start by downloading the dataset:
 
 .. code-block:: python
 
-    from datasets import load_dataset
+    from nlp 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.
@@ -725,5 +725,5 @@ dataset elements.
     >>> {key: val.shape for key, val in train[0].items()})
     {'labels': TensorShape([]), 'input_ids': TensorShape([512]), 'attention_mask': TensorShape([512])}
 
-We now have a fully-prepared dataset. Check out `the 🤗 Datasets docs
-<https://huggingface.co/docs/datasets/processing.html>`_ for a more thorough introduction.
+We now have a fully-prepared dataset. Check out `the 🤗 NLP docs <https://huggingface.co/nlp/processing.html>`_ for a
+more thorough introduction.

docs/source/debugging.rst

@@ -1,299 +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.
-
-
-
-Debugging
-=======================================================================================================================
-
-Underflow and Overflow Detection
------------------------------------------------------------------------------------------------------------------------
-
-.. note::
-
-   This feature is currently available for PyTorch-only.
-
-.. note::
-
-   For multi-GPU training it requires DDP (``torch.distributed.launch``).
-
-.. note::
-
-   This feature can be used with any ``nn.Module``-based model.
-
-If you start getting ``loss=NaN`` or the model inhibits some other abnormal behavior due to ``inf`` or ``nan`` in
-activations or weights one needs to discover where the first underflow or overflow happens and what led to it. Luckily
-you can accomplish that easily by activating a special module that will do the detection automatically.
-
-If you're using :class:`~transformers.Trainer`, you just need to add:
-
-.. code-block:: bash
-
-    --debug underflow_overflow
-
-to the normal command line arguments, or pass ``debug="underflow_overflow"`` when creating the
-:class:`~transformers.TrainingArguments` object.
-
-If you're using your own training loop or another Trainer you can accomplish the same with:
-
-.. code-block:: python
-
-    from .debug_utils import DebugUnderflowOverflow
-    debug_overflow = DebugUnderflowOverflow(model)
-
-:class:`~transformers.debug_utils.DebugUnderflowOverflow` inserts hooks into the model that immediately after each
-forward call will test input and output variables and also the corresponding module's weights. As soon as ``inf`` or
-``nan`` is detected in at least one element of the activations or weights, the program will assert and print a report
-like this (this was caught with ``google/mt5-small`` under fp16 mixed precision):
-
-.. code-block::
-
-    Detected inf/nan during batch_number=0
-    Last 21 forward frames:
-    abs min  abs max  metadata
-                      encoder.block.1.layer.1.DenseReluDense.dropout Dropout
-    0.00e+00 2.57e+02 input[0]
-    0.00e+00 2.85e+02 output
-    [...]
-                      encoder.block.2.layer.0 T5LayerSelfAttention
-    6.78e-04 3.15e+03 input[0]
-    2.65e-04 3.42e+03 output[0]
-                 None output[1]
-    2.25e-01 1.00e+04 output[2]
-                      encoder.block.2.layer.1.layer_norm T5LayerNorm
-    8.69e-02 4.18e-01 weight
-    2.65e-04 3.42e+03 input[0]
-    1.79e-06 4.65e+00 output
-                      encoder.block.2.layer.1.DenseReluDense.wi_0 Linear
-    2.17e-07 4.50e+00 weight
-    1.79e-06 4.65e+00 input[0]
-    2.68e-06 3.70e+01 output
-                      encoder.block.2.layer.1.DenseReluDense.wi_1 Linear
-    8.08e-07 2.66e+01 weight
-    1.79e-06 4.65e+00 input[0]
-    1.27e-04 2.37e+02 output
-                      encoder.block.2.layer.1.DenseReluDense.dropout Dropout
-    0.00e+00 8.76e+03 input[0]
-    0.00e+00 9.74e+03 output
-                      encoder.block.2.layer.1.DenseReluDense.wo Linear
-    1.01e-06 6.44e+00 weight
-    0.00e+00 9.74e+03 input[0]
-    3.18e-04 6.27e+04 output
-                      encoder.block.2.layer.1.DenseReluDense T5DenseGatedGeluDense
-    1.79e-06 4.65e+00 input[0]
-    3.18e-04 6.27e+04 output
-                      encoder.block.2.layer.1.dropout Dropout
-    3.18e-04 6.27e+04 input[0]
-    0.00e+00      inf output
-
-The example output has been trimmed in the middle for brevity.
-
-The second column shows the value of the absolute largest element, so if you have a closer look at the last few frames,
-the inputs and outputs were in the range of ``1e4``. So when this training was done under fp16 mixed precision the very
-last step overflowed (since under ``fp16`` the largest number before ``inf`` is ``64e3``). To avoid overflows under
-``fp16`` the activations must remain way below ``1e4``, because ``1e4 * 1e4 = 1e8`` so any matrix multiplication with
-large activations is going to lead to a numerical overflow condition.
-
-At the very start of the trace you can discover at which batch number the problem occurred (here ``Detected inf/nan
-during batch_number=0`` means the problem occurred on the first batch).
-
-Each reported frame starts by declaring the fully qualified entry for the corresponding module this frame is reporting
-for. If we look just at this frame:
-
-.. code-block::
-
-                      encoder.block.2.layer.1.layer_norm T5LayerNorm
-    8.69e-02 4.18e-01 weight
-    2.65e-04 3.42e+03 input[0]
-    1.79e-06 4.65e+00 output
-
-Here, ``encoder.block.2.layer.1.layer_norm`` indicates that it was a layer norm for the first layer, of the second
-block of the encoder. And the specific calls of the ``forward`` is ``T5LayerNorm``.
-
-Let's look at the last few frames of that report:
-
-.. code-block::
-
-        Detected inf/nan during batch_number=0
-        Last 21 forward frames:
-        abs min  abs max  metadata
-        [...]
-                          encoder.block.2.layer.1.DenseReluDense.wi_0 Linear
-        2.17e-07 4.50e+00 weight
-        1.79e-06 4.65e+00 input[0]
-        2.68e-06 3.70e+01 output
-                          encoder.block.2.layer.1.DenseReluDense.wi_1 Linear
-        8.08e-07 2.66e+01 weight
-        1.79e-06 4.65e+00 input[0]
-        1.27e-04 2.37e+02 output
-                          encoder.block.2.layer.1.DenseReluDense.wo Linear
-        1.01e-06 6.44e+00 weight
-        0.00e+00 9.74e+03 input[0]
-        3.18e-04 6.27e+04 output
-                          encoder.block.2.layer.1.DenseReluDense T5DenseGatedGeluDense
-        1.79e-06 4.65e+00 input[0]
-        3.18e-04 6.27e+04 output
-                          encoder.block.2.layer.1.dropout Dropout
-        3.18e-04 6.27e+04 input[0]
-        0.00e+00      inf output
-
-The last frame reports for ``Dropout.forward`` function with the first entry for the only input and the second for the
-only output. You can see that it was called from an attribute ``dropout`` inside ``DenseReluDense`` class. We can see
-that it happened during the first layer, of the 2nd block, during the very first batch. Finally, the absolute largest
-input elements was ``6.27e+04`` and same for the output was ``inf``.
-
-You can see here, that ``T5DenseGatedGeluDense.forward`` resulted in output activations, whose absolute max value was
-around 62.7K, which is very close to fp16's top limit of 64K. In the next frame we have ``Dropout`` which renormalizes
-the weights, after it zeroed some of the elements, which pushes the absolute max value to more than 64K, and we get an
-overlow (``inf``).
-
-As you can see it's the previous frames that we need to look into when the numbers start going into very large for fp16
-numbers.
-
-Let's match the report to the code from ``models/t5/modeling_t5.py``:
-
-.. code-block:: python
-
-    class T5DenseGatedGeluDense(nn.Module):
-        def __init__(self, config):
-            super().__init__()
-            self.wi_0 = nn.Linear(config.d_model, config.d_ff, bias=False)
-            self.wi_1 = nn.Linear(config.d_model, config.d_ff, bias=False)
-            self.wo = nn.Linear(config.d_ff, config.d_model, bias=False)
-            self.dropout = nn.Dropout(config.dropout_rate)
-            self.gelu_act = ACT2FN["gelu_new"]
-
-        def forward(self, hidden_states):
-            hidden_gelu = self.gelu_act(self.wi_0(hidden_states))
-            hidden_linear = self.wi_1(hidden_states)
-            hidden_states = hidden_gelu * hidden_linear
-            hidden_states = self.dropout(hidden_states)
-            hidden_states = self.wo(hidden_states)
-            return hidden_states
-
-Now it's easy to see the ``dropout`` call, and all the previous calls as well.
-
-Since the detection is happening in a forward hook, these reports are printed immediately after each ``forward``
-returns.
-
-Going back to the full report, to act on it and to fix the problem, we need to go a few frames up where the numbers
-started to go up and most likely switch to the ``fp32`` mode here, so that the numbers don't overflow when multiplied
-or summed up. Of course, there might be other solutions. For example, we could turn off ``amp`` temporarily if it's
-enabled, after moving the original ``forward`` into a helper wrapper, like so:
-
-.. code-block:: python
-
-    def _forward(self, hidden_states):
-        hidden_gelu = self.gelu_act(self.wi_0(hidden_states))
-        hidden_linear = self.wi_1(hidden_states)
-        hidden_states = hidden_gelu * hidden_linear
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.wo(hidden_states)
-        return hidden_states
-
-    import torch
-    def forward(self, hidden_states):
-        if torch.is_autocast_enabled():
-             with torch.cuda.amp.autocast(enabled=False):
-                 return self._forward(hidden_states)
-         else:
-             return self._forward(hidden_states)
-
-Since the automatic detector only reports on inputs and outputs of full frames, once you know where to look, you may
-want to analyse the intermediary stages of any specific ``forward`` function as well. In such a case you can use the
-``detect_overflow`` helper function to inject the detector where you want it, for example:
-
-.. code-block:: python
-
-    from debug_utils import detect_overflow
-
-    class T5LayerFF(nn.Module):
-        [...]
-        def forward(self, hidden_states):
-            forwarded_states = self.layer_norm(hidden_states)
-            detect_overflow(forwarded_states, "after layer_norm")
-            forwarded_states = self.DenseReluDense(forwarded_states)
-            detect_overflow(forwarded_states, "after DenseReluDense")
-            return hidden_states + self.dropout(forwarded_states)
-
-You can see that we added 2 of these and now we track if ``inf`` or ``nan`` for ``forwarded_states`` was detected
-somewhere in between.
-
-Actually, the detector already reports these because each of the calls in the example above is a `nn.Module``, but
-let's say if you had some local direct calculations this is how you'd do that.
-
-Additionally, if you're instantiating the debugger in your own code, you can adjust the number of frames printed from
-its default, e.g.:
-
-.. code-block:: python
-
-    from .debug_utils import DebugUnderflowOverflow
-    debug_overflow = DebugUnderflowOverflow(model, max_frames_to_save=100)
-
-Specific batch absolute mix and max value tracing
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The same debugging class can be used for per-batch tracing with the underflow/overflow detection feature turned off.
-
-Let's say you want to watch the absolute min and max values for all the ingredients of each ``forward`` call of a given
-batch, and only do that for batches 1 and 3. Then you instantiate this class as:
-
-.. code-block:: python
-
-    debug_overflow = DebugUnderflowOverflow(model, trace_batch_nums=[1,3])
-
-And now full batches 1 and 3 will be traced using the same format as the underflow/overflow detector does.
-
-Batches are 0-indexed.
-
-This is helpful if you know that the program starts misbehaving after a certain batch number, so you can fast-forward
-right to that area. Here is a sample truncated output for such configuration:
-
-.. code-block::
-
-                      *** Starting batch number=1 ***
-    abs min  abs max  metadata
-                      shared Embedding
-    1.01e-06 7.92e+02 weight
-    0.00e+00 2.47e+04 input[0]
-    5.36e-05 7.92e+02 output
-    [...]
-                      decoder.dropout Dropout
-    1.60e-07 2.27e+01 input[0]
-    0.00e+00 2.52e+01 output
-                      decoder T5Stack
-         not a tensor output
-                      lm_head Linear
-    1.01e-06 7.92e+02 weight
-    0.00e+00 1.11e+00 input[0]
-    6.06e-02 8.39e+01 output
-                       T5ForConditionalGeneration
-         not a tensor output
-
-                      *** Starting batch number=3 ***
-    abs min  abs max  metadata
-                      shared Embedding
-    1.01e-06 7.92e+02 weight
-    0.00e+00 2.78e+04 input[0]
-    5.36e-05 7.92e+02 output
-    [...]
-
-Here you will get a huge number of frames dumped - as many as there were forward calls in your model, so it may or may
-not what you want, but sometimes it can be easier to use for debugging purposes than a normal debugger. For example, if
-a problem starts happening at batch number 150. So you can dump traces for batches 149 and 150 and compare where
-numbers started to diverge.
-
-You can also specify the batch number after which to stop the training, with:
-
-.. code-block:: python
-
-    debug_overflow = DebugUnderflowOverflow(model, trace_batch_nums=[1,3], abort_after_batch_num=3)

docs/source/fast_tokenizers.rst

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

docs/source/glossary.rst

@@ -21,25 +21,22 @@ General terms
 - CLM: causal language modeling, a pretraining task where the model reads the texts in order and has to predict the
   next word. It's usually done by reading the whole sentence but using a mask inside the model to hide the future
   tokens at a certain timestep.
-- deep learning: machine learning algorithms which uses neural networks with several layers.
 - MLM: masked language modeling, a pretraining task where the model sees a corrupted version of the texts, usually done
   by masking some tokens randomly, and has to predict the original text.
 - multimodal: a task that combines texts with another kind of inputs (for instance images).
-- NLG: natural language generation, all tasks related to generating text (for instance talk with transformers,
-  translation).
+- NLG: natural language generation, all tasks related to generating text ( for instance talk with transformers,
+  translation)
 - NLP: natural language processing, a generic way to say "deal with texts".
 - NLU: natural language understanding, all tasks related to understanding what is in a text (for instance classifying
-  the whole text, individual words).
+  the whole text, individual words)
 - pretrained model: a model that has been pretrained on some data (for instance all of Wikipedia). Pretraining methods
   involve a self-supervised objective, which can be reading the text and trying to predict the next word (see CLM) or
   masking some words and trying to predict them (see MLM).
 - RNN: recurrent neural network, a type of model that uses a loop over a layer to process texts.
-- self-attention: each element of the input finds out which other elements of the input they should attend to.
 - seq2seq or sequence-to-sequence: models that generate a new sequence from an input, like translation models, or
   summarization models (such as :doc:`Bart </model_doc/bart>` or :doc:`T5 </model_doc/t5>`).
 - token: a part of a sentence, usually a word, but can also be a subword (non-common words are often split in subwords)
   or a punctuation symbol.
-- transformer: self-attention based deep learning model architecture.
 
 Model inputs
 -----------------------------------------------------------------------------------------------------------------------
@@ -55,12 +52,6 @@ Input IDs
 The input ids are often the only required parameters to be passed to the model as input. *They are token indices,
 numerical representations of tokens building the sequences that will be used as input by the model*.
 
-.. raw:: html
-
-   <iframe width="560" height="315" src="https://www.youtube.com/embed/VFp38yj8h3A" title="YouTube video player"
-   frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope;
-   picture-in-picture" allowfullscreen></iframe>
-
 Each tokenizer works differently but the underlying mechanism remains the same. Here's an example using the BERT
 tokenizer, which is a `WordPiece <https://arxiv.org/pdf/1609.08144.pdf>`__ tokenizer:
 
@@ -126,15 +117,8 @@ because this is the way a :class:`~transformers.BertModel` is going to expect it
 Attention mask
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-The attention mask is an optional argument used when batching sequences together.
-
-.. raw:: html
-
-   <iframe width="560" height="315" src="https://www.youtube.com/embed/M6adb1j2jPI" title="YouTube video player"
-   frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope;
-   picture-in-picture" allowfullscreen></iframe>
-
-This argument indicates to the model which tokens should be attended to, and which should not.
+The attention mask is an optional argument used when batching sequences together. This argument indicates to the model
+which tokens should be attended to, and which should not.
 
 For example, consider these two sequences:
 
@@ -188,21 +172,14 @@ in the dictionary returned by the tokenizer under the key "attention_mask":
 Token Type IDs
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Some models' purpose is to do classification on pairs of sentences or question answering.
-
-.. raw:: html
-
-   <iframe width="560" height="315" src="https://www.youtube.com/embed/0u3ioSwev3s" title="YouTube video player"
-   frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope;
-   picture-in-picture" allowfullscreen></iframe>
-
-These require two different sequences to be joined in a single "input_ids" entry, which usually is performed with the
-help of special tokens, such as the classifier (``[CLS]``) and separator (``[SEP]``) tokens. For example, the BERT
-model builds its two sequence input as such:
+Some models' purpose is to do sequence classification or question answering. These require two different sequences to
+be joined in a single "input_ids" entry, which usually is performed with the help of special tokens, such as the
+classifier (``[CLS]``) and separator (``[SEP]``) tokens. For example, the BERT model builds its two sequence input as
+such:
 
 .. code-block::
 
-    >>> # [CLS] SEQUENCE_A [SEP] SEQUENCE_B [SEP]
+   >>> # [CLS] SEQUENCE_A [SEP] SEQUENCE_B [SEP]
 
 We can use our tokenizer to automatically generate such a sentence by passing the two sequences to ``tokenizer`` as two
 arguments (and not a list, like before) like this:

docs/source/index.rst

@@ -1,25 +1,14 @@
 Transformers
 =======================================================================================================================
 
-State-of-the-art Natural Language Processing for Jax, Pytorch and TensorFlow
+State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0.
 
 🤗 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 Jax,
-PyTorch and TensorFlow.
+Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between
+TensorFlow 2.0 and PyTorch.
 
-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
------------------------------------------------------------------------------------------------------------------------
-
-.. raw:: html
-
-    <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>
+This is the documentation of our repository `transformers <https://github.com/huggingface/transformers>`_.
 
 Features
 -----------------------------------------------------------------------------------------------------------------------
@@ -33,7 +22,7 @@ State-of-the-art NLP for everyone:
 - Hands-on practitioners
 - AI/ML/NLP teachers and educators
 
-..
+.. 
     Copyright 2020 The HuggingFace Team. All rights reserved.
 
     Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
@@ -54,11 +43,11 @@ Lower compute costs, smaller carbon footprint:
 Choose the right framework for every part of a model's lifetime:
 
 - Train state-of-the-art models in 3 lines of code
-- Deep interoperability between Jax, Pytorch and TensorFlow models
-- Move a single model between Jax/PyTorch/TensorFlow frameworks at will
+- Deep interoperability between TensorFlow 2.0 and PyTorch models
+- Move a single model between TF2.0/PyTorch frameworks at will
 - Seamlessly pick the right framework for training, evaluation, production
 
-The support for Jax is still experimental (with a few models right now), expect to see it grow in the coming months!
+Experimental support for Flax with a few models right now, expected to grow in the coming months.
 
 `All the model checkpoints <https://huggingface.co/models>`__ are seamlessly integrated from the huggingface.co `model
 hub <https://huggingface.co>`__ where they are uploaded directly by `users <https://huggingface.co/users>`__ and
@@ -85,11 +74,8 @@ The documentation is organized in five parts:
     - **MODELS** for the classes and functions related to each model implemented in the library.
     - **INTERNAL HELPERS** for the classes and functions we use internally.
 
-The library currently contains Jax, PyTorch and Tensorflow implementations, pretrained model weights, usage scripts and
-conversion utilities for the following models.
-
-Supported models
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+The library currently contains PyTorch, Tensorflow and Flax implementations, pretrained model weights, usage scripts
+and conversion utilities for the following models:
 
 ..
     This list is updated automatically from the README with `make fix-copies`. Do not update manually!
@@ -105,229 +91,121 @@ Supported models
 3. :doc:`BARThez <model_doc/barthez>` (from École polytechnique) released with the paper `BARThez: a Skilled Pretrained
    French Sequence-to-Sequence Model <https://arxiv.org/abs/2010.12321>`__ by Moussa Kamal Eddine, Antoine J.-P.
    Tixier, Michalis Vazirgiannis.
-4. :doc:`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.
-5. :doc:`BERT <model_doc/bert>` (from Google) released with the paper `BERT: Pre-training of Deep Bidirectional
+4. :doc:`BERT <model_doc/bert>` (from Google) released with the paper `BERT: Pre-training of Deep Bidirectional
    Transformers for Language Understanding <https://arxiv.org/abs/1810.04805>`__ by Jacob Devlin, Ming-Wei Chang,
    Kenton Lee and Kristina Toutanova.
-6. :doc:`BERT For Sequence Generation <model_doc/bertgeneration>` (from Google) released with the paper `Leveraging
+5. :doc:`BERT For Sequence Generation <model_doc/bertgeneration>` (from Google) released with the paper `Leveraging
    Pre-trained Checkpoints for Sequence Generation Tasks <https://arxiv.org/abs/1907.12461>`__ by Sascha Rothe, Shashi
    Narayan, Aliaksei Severyn.
-7. :doc:`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.
-8. :doc:`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.
-9. :doc:`Blenderbot <model_doc/blenderbot>` (from Facebook) released with the paper `Recipes for building an
+6. :doc:`Blenderbot <model_doc/blenderbot>` (from Facebook) released with the paper `Recipes for building an
    open-domain chatbot <https://arxiv.org/abs/2004.13637>`__ by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary
    Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
-10. :doc:`BlenderbotSmall <model_doc/blenderbot_small>` (from Facebook) released with the paper `Recipes for building
-    an open-domain chatbot <https://arxiv.org/abs/2004.13637>`__ by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju,
-    Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
-11. :doc:`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.
-12. :doc:`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.
-13. :doc:`CamemBERT <model_doc/camembert>` (from Inria/Facebook/Sorbonne) released with the paper `CamemBERT: a Tasty
-    French Language Model <https://arxiv.org/abs/1911.03894>`__ by Louis Martin*, Benjamin Muller*, Pedro Javier Ortiz
-    Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
-14. :doc:`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.
-15. :doc:`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.
-16. :doc:`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.
-17. :doc:`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.
-18. :doc:`CTRL <model_doc/ctrl>` (from Salesforce) released with the paper `CTRL: A Conditional Transformer Language
-    Model for Controllable Generation <https://arxiv.org/abs/1909.05858>`__ by Nitish Shirish Keskar*, Bryan McCann*,
-    Lav R. Varshney, Caiming Xiong and Richard Socher.
-19. :doc:`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.
-20. :doc:`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,
+7. :doc:`BlenderbotSmall <model_doc/blenderbot_small>` (from Facebook) released with the paper `Recipes for building an
+   open-domain chatbot <https://arxiv.org/abs/2004.13637>`__ by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary
+   Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
+8. :doc:`CamemBERT <model_doc/camembert>` (from Inria/Facebook/Sorbonne) released with the paper `CamemBERT: a Tasty
+   French Language Model <https://arxiv.org/abs/1911.03894>`__ by Louis Martin*, Benjamin Muller*, Pedro Javier Ortiz
+   Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
+9. :doc:`CTRL <model_doc/ctrl>` (from Salesforce) released with the paper `CTRL: A Conditional Transformer Language
+   Model for Controllable Generation <https://arxiv.org/abs/1909.05858>`__ by Nitish Shirish Keskar*, Bryan McCann*,
+   Lav R. Varshney, Caiming Xiong and Richard Socher.
+10. :doc:`DeBERTa <model_doc/deberta>` (from Microsoft Research) released with the paper `DeBERTa: Decoding-enhanced
+    BERT with Disentangled Attention <https://arxiv.org/abs/2006.03654>`__ by Pengcheng He, Xiaodong Liu, Jianfeng Gao,
     Weizhu Chen.
-21. :doc:`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.
-22. :doc:`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.
-23. :doc:`DialoGPT <model_doc/dialogpt>` (from Microsoft Research) released with the paper `DialoGPT: Large-Scale
+11. :doc:`DialoGPT <model_doc/dialogpt>` (from Microsoft Research) released with the paper `DialoGPT: Large-Scale
     Generative Pre-training for Conversational Response Generation <https://arxiv.org/abs/1911.00536>`__ by Yizhe
     Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan.
-24. :doc:`DistilBERT <model_doc/distilbert>` (from HuggingFace), released together with the paper `DistilBERT, a
+12. :doc:`DistilBERT <model_doc/distilbert>` (from HuggingFace), released together with the paper `DistilBERT, a
     distilled version of BERT: smaller, faster, cheaper and lighter <https://arxiv.org/abs/1910.01108>`__ by Victor
     Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into `DistilGPT2
     <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__, RoBERTa into `DistilRoBERTa
     <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__, Multilingual BERT into
     `DistilmBERT <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__ and a German
     version of DistilBERT.
-25. :doc:`DPR <model_doc/dpr>` (from Facebook) released with the paper `Dense Passage Retrieval for Open-Domain
+13. :doc:`DPR <model_doc/dpr>` (from Facebook) released with the paper `Dense Passage Retrieval for Open-Domain
     Question Answering <https://arxiv.org/abs/2004.04906>`__ by Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick
     Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
-26. :doc:`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.
-27. :doc:`ELECTRA <model_doc/electra>` (from Google Research/Stanford University) released with the paper `ELECTRA:
+14. :doc:`ELECTRA <model_doc/electra>` (from Google Research/Stanford University) released with the paper `ELECTRA:
     Pre-training text encoders as discriminators rather than generators <https://arxiv.org/abs/2003.10555>`__ by Kevin
     Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning.
-28. :doc:`FlauBERT <model_doc/flaubert>` (from CNRS) released with the paper `FlauBERT: Unsupervised Language Model
+15. :doc:`FlauBERT <model_doc/flaubert>` (from CNRS) released with the paper `FlauBERT: Unsupervised Language Model
     Pre-training for French <https://arxiv.org/abs/1912.05372>`__ by Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne,
     Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, Didier Schwab.
-29. :doc:`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.
-30. :doc:`Funnel Transformer <model_doc/funnel>` (from CMU/Google Brain) released with the paper `Funnel-Transformer:
+16. :doc:`Funnel Transformer <model_doc/funnel>` (from CMU/Google Brain) released with the paper `Funnel-Transformer:
     Filtering out Sequential Redundancy for Efficient Language Processing <https://arxiv.org/abs/2006.03236>`__ by
     Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le.
-31. :doc:`GPT <model_doc/gpt>` (from OpenAI) released with the paper `Improving Language Understanding by Generative
+17. :doc:`GPT <model_doc/gpt>` (from OpenAI) released with the paper `Improving Language Understanding by Generative
     Pre-Training <https://blog.openai.com/language-unsupervised/>`__ by Alec Radford, Karthik Narasimhan, Tim Salimans
     and Ilya Sutskever.
-32. :doc:`GPT-2 <model_doc/gpt2>` (from OpenAI) released with the paper `Language Models are Unsupervised Multitask
+18. :doc:`GPT-2 <model_doc/gpt2>` (from OpenAI) released with the paper `Language Models are Unsupervised Multitask
     Learners <https://blog.openai.com/better-language-models/>`__ by Alec Radford*, Jeffrey Wu*, Rewon Child, David
     Luan, Dario Amodei** and Ilya Sutskever**.
-33. :doc:`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.
-34. :doc:`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.
-35. :doc:`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.
-36. :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.
-37. :doc:`LayoutLM <model_doc/layoutlm>` (from Microsoft Research Asia) released with the paper `LayoutLM: Pre-training
+19. :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.
-38. :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.
-39. :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.
-40. :doc:`LED <model_doc/led>` (from AllenAI) released with the paper `Longformer: The Long-Document Transformer
+20. :doc:`LED <model_doc/led>` (from AllenAI) released with the paper `Longformer: The Long-Document Transformer
     <https://arxiv.org/abs/2004.05150>`__ by Iz Beltagy, Matthew E. Peters, Arman Cohan.
-41. :doc:`Longformer <model_doc/longformer>` (from AllenAI) released with the paper `Longformer: The Long-Document
+21. :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.
-42. :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.
-43. :doc:`LXMERT <model_doc/lxmert>` (from UNC Chapel Hill) released with the paper `LXMERT: Learning Cross-Modality
+22. :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.
-44. :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.
-45. :doc:`MarianMT <model_doc/marian>` Machine translation models trained using `OPUS <http://opus.nlpl.eu/>`__ data by
+23. :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.
-46. :doc:`MBart <model_doc/mbart>` (from Facebook) released with the paper `Multilingual Denoising Pre-training for
+24. :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.
-47. :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.
-48. :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.
-49. :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.
-50. :doc:`MPNet <model_doc/mpnet>` (from Microsoft Research) released with the paper `MPNet: Masked and Permuted
+25. :doc:`MPNet <model_doc/mpnet>` (from Microsoft Research) released with the paper `MPNet: Masked and Permuted
     Pre-training for Language Understanding <https://arxiv.org/abs/2004.09297>`__ by Kaitao Song, Xu Tan, Tao Qin,
     Jianfeng Lu, Tie-Yan Liu.
-51. :doc:`MT5 <model_doc/mt5>` (from Google AI) released with the paper `mT5: A massively multilingual pre-trained
+26. :doc:`MT5 <model_doc/mt5>` (from Google AI) released with the paper `mT5: A massively multilingual pre-trained
     text-to-text transformer <https://arxiv.org/abs/2010.11934>`__ by Linting Xue, Noah Constant, Adam Roberts, Mihir
     Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel.
-52. :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,
+27. :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.
-53. :doc:`ProphetNet <model_doc/prophetnet>` (from Microsoft Research) released with the paper `ProphetNet: Predicting
+28. :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.
-54. :doc:`Reformer <model_doc/reformer>` (from Google Research) released with the paper `Reformer: The Efficient
+29. :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.
-55. :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.
-56. :doc:`RoBERTa <model_doc/roberta>` (from Facebook), released together with the paper a `Robustly Optimized BERT
+30. :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.
-57. :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.
-58. :doc:`SpeechEncoderDecoder <model_doc/speechencoderdecoder>`
-59. :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.
-60. :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.
-61. :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.
-62. :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.
-63. :doc:`T5 <model_doc/t5>` (from Google AI) released with the paper `Exploring the Limits of Transfer Learning with a
+    Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov. ultilingual BERT into `DistilmBERT
+    <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__ and a German version of
+    DistilBERT.
+31. :doc:`SqueezeBert <model_doc/squeezebert>` released with the paper `SqueezeBERT: What can computer vision teach NLP
+    about efficient neural networks? <https://arxiv.org/abs/2006.11316>`__ by Forrest N. Iandola, Albert E. Shaw, Ravi
+    Krishna, and Kurt W. Keutzer.
+32. :doc:`T5 <model_doc/t5>` (from Google AI) released with the paper `Exploring the Limits of Transfer Learning with a
     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.
-64. :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.
-65. :doc:`TAPAS <model_doc/tapas>` (from Google AI) released with the paper `TAPAS: Weakly Supervised Table Parsing via
+33. :doc:`TAPAS <model_doc/tapas>` (from Google AI) released with the paper `TAPAS: Weakly Supervised Table Parsing via
     Pre-training <https://arxiv.org/abs/2004.02349>`__ by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller,
     Francesco Piccinno and Julian Martin Eisenschlos.
-66. :doc:`Transformer-XL <model_doc/transformerxl>` (from Google/CMU) released with the paper `Transformer-XL:
+34. :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.
-67. :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.
-68. :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.
-69. :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.
-70. :doc:`XLM <model_doc/xlm>` (from Facebook) released together with the paper `Cross-lingual Language Model
+35. :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.
-71. :doc:`XLM-ProphetNet <model_doc/xlmprophetnet>` (from Microsoft Research) released with the paper `ProphetNet:
+36. :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.
-72. :doc:`XLM-RoBERTa <model_doc/xlmroberta>` (from Facebook AI), released together with the paper `Unsupervised
+37. :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.
-73. :doc:`XLNet <model_doc/xlnet>` (from Google/CMU) released with the paper `​XLNet: Generalized Autoregressive
+38. :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.
-74. :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.
 
 
-Supported frameworks
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. _bigtable:
 
 The table below represents the current support in the library for each of those models, whether they have a Python
-tokenizer (called "slow"). A "fast" tokenizer backed by the 🤗 Tokenizers library, whether they have support in Jax (via
-Flax), PyTorch, and/or TensorFlow.
+tokenizer (called "slow"). A "fast" tokenizer backed by the 🤗 Tokenizers library, whether they have support in PyTorch,
+TensorFlow and/or Flax.
 
 ..
     This table is updated automatically from the auto modules with `make fix-copies`. Do not update manually!
@@ -337,134 +215,76 @@ Flax), PyTorch, and/or TensorFlow.
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |            Model            | Tokenizer slow | Tokenizer fast | PyTorch support | TensorFlow support | Flax Support |
 +=============================+================+================+=================+====================+==============+
-|           ALBERT            |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+|           ALBERT            |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|            BART             |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|            BeiT             |       ❌       |       ❌       |       ✅        |         ❌         |      ✅      |
+|            BART             |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |            BERT             |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |       Bert Generation       |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|           BigBird           |       ✅       |       ✅       |       ✅        |         ❌         |      ✅      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|       BigBirdPegasus        |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |         Blenderbot          |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|       BlenderbotSmall       |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|          CamemBERT          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|           Canine            |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|            CLIP             |       ✅       |       ✅       |       ✅        |         ❌         |      ✅      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|          ConvBERT           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+|       BlenderbotSmall       |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |            CTRL             |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|           DeBERTa           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|         DeBERTa-v2          |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|            DeiT             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|            DETR             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|         DistilBERT          |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+|          CamemBERT          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |             DPR             |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|           ELECTRA           |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+|           DeBERTa           |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|       Encoder decoder       |       ❌       |       ❌       |       ✅        |         ❌         |      ✅      |
+|         DistilBERT          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
++-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|           ELECTRA           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
++-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|       Encoder decoder       |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 | FairSeq Machine-Translation |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |          FlauBERT           |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|            FNet             |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |     Funnel Transformer      |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|           GPT Neo           |       ❌       |       ❌       |       ✅        |         ❌         |      ✅      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|            GPT-J            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|           Hubert            |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|           I-BERT            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|          LayoutLM           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|         LayoutLMv2          |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |             LED             |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|         Longformer          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|            LUKE             |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |           LXMERT            |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|           M2M100            |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
+|          LayoutLM           |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|           Marian            |       ✅       |       ❌       |       ✅        |         ✅         |      ✅      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|            mBART            |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|        MegatronBert         |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|         MobileBERT          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+|         Longformer          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |            MPNet            |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|             mT5             |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+|           Marian            |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
++-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|         MobileBERT          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |         OpenAI GPT          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|        OpenAI GPT-2         |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+|        OpenAI GPT-2         |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|           Pegasus           |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+|           Pegasus           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |         ProphetNet          |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|             RAG             |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
+|             RAG             |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |          Reformer           |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|           RemBERT           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |          RetriBERT          |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |           RoBERTa           |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|          RoFormer           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|   Speech Encoder decoder    |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|         Speech2Text         |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|        Speech2Text2         |       ✅       |       ❌       |       ❌        |         ❌         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|          Splinter           |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |         SqueezeBERT         |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|             T5              |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+|             T5              |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |            TAPAS            |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |       Transformer-XL        |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|         VisualBert          |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|             ViT             |       ❌       |       ❌       |       ✅        |         ❌         |      ✅      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|          Wav2Vec2           |       ✅       |       ❌       |       ✅        |         ✅         |      ✅      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |             XLM             |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |         XLM-RoBERTa         |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
@@ -473,6 +293,10 @@ Flax), PyTorch, and/or TensorFlow.
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |            XLNet            |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|            mBART            |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
++-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|             mT5             |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
++-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 
 .. toctree::
     :maxdepth: 2
@@ -501,21 +325,12 @@ Flax), PyTorch, and/or TensorFlow.
 
     pretrained_models
     examples
-    troubleshooting
     custom_datasets
     notebooks
-    sagemaker
-    community
     converting_tensorflow_models
     migration
     contributing
-    add_new_model
-    add_new_pipeline
-    fast_tokenizers
-    performance
-    parallelism
     testing
-    debugging
     serialization
 
 .. toctree::
@@ -532,7 +347,6 @@ Flax), PyTorch, and/or TensorFlow.
 
     main_classes/callback
     main_classes/configuration
-    main_classes/data_collator
     main_classes/logging
     main_classes/model
     main_classes/optimizer_schedules
@@ -541,8 +355,6 @@ Flax), PyTorch, and/or TensorFlow.
     main_classes/processors
     main_classes/tokenizer
     main_classes/trainer
-    main_classes/deepspeed
-    main_classes/feature_extractor
 
 .. toctree::
     :maxdepth: 2
@@ -552,84 +364,49 @@ Flax), PyTorch, and/or TensorFlow.
     model_doc/auto
     model_doc/bart
     model_doc/barthez
-    model_doc/beit
     model_doc/bert
     model_doc/bertweet
     model_doc/bertgeneration
-    model_doc/bert_japanese
-    model_doc/bigbird
-    model_doc/bigbird_pegasus
     model_doc/blenderbot
     model_doc/blenderbot_small
-    model_doc/bort
-    model_doc/byt5
     model_doc/camembert
-    model_doc/canine
-    model_doc/clip
-    model_doc/convbert
-    model_doc/cpm
     model_doc/ctrl
     model_doc/deberta
-    model_doc/deberta_v2
-    model_doc/deit
-    model_doc/detr
     model_doc/dialogpt
     model_doc/distilbert
     model_doc/dpr
     model_doc/electra
     model_doc/encoderdecoder
     model_doc/flaubert
-    model_doc/fnet
     model_doc/fsmt
     model_doc/funnel
     model_doc/herbert
-    model_doc/ibert
     model_doc/layoutlm
-    model_doc/layoutlmv2
-    model_doc/layoutxlm
     model_doc/led
     model_doc/longformer
-    model_doc/luke
     model_doc/lxmert
     model_doc/marian
-    model_doc/m2m_100
     model_doc/mbart
-    model_doc/megatron_bert
-    model_doc/megatron_gpt2
     model_doc/mobilebert
     model_doc/mpnet
     model_doc/mt5
     model_doc/gpt
     model_doc/gpt2
-    model_doc/gptj
-    model_doc/gpt_neo
-    model_doc/hubert
     model_doc/pegasus
     model_doc/phobert
     model_doc/prophetnet
     model_doc/rag
     model_doc/reformer
-    model_doc/rembert
     model_doc/retribert
     model_doc/roberta
-    model_doc/roformer
-    model_doc/speechencoderdecoder
-    model_doc/speech_to_text
-    model_doc/speech_to_text_2
-    model_doc/splinter
     model_doc/squeezebert
     model_doc/t5
-    model_doc/t5v1.1
     model_doc/tapas
     model_doc/transformerxl
-    model_doc/vit
-    model_doc/visual_bert
-    model_doc/wav2vec2
     model_doc/xlm
     model_doc/xlmprophetnet
     model_doc/xlmroberta
     model_doc/xlnet
-    model_doc/xlsr_wav2vec2
 
 .. toctree::
     :maxdepth: 2
@@ -640,4 +417,3 @@ Flax), PyTorch, and/or TensorFlow.
     internal/tokenization_utils
     internal/trainer_utils
     internal/generation_utils
-    internal/file_utils

docs/source/installation.md

@@ -19,7 +19,7 @@ limitations under the License.
 🤗 Transformers is tested on Python 3.6+, and PyTorch 1.1.0+ or TensorFlow 2.0+.
 
 You should install 🤗 Transformers in a [virtual environment](https://docs.python.org/3/library/venv.html). If you're
-unfamiliar with Python virtual environments, check out the [user guide](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/). Create a virtual environment with the version of Python you're going
+unfamiliar with Python virtual environments, check out the [user guide](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/). Create a virtual environment with the version of Python you're going 
 to use and activate it.
 
 Now, if you want to use 🤗 Transformers, you can install it with pip. If you'd like to play with the examples, you
@@ -28,8 +28,8 @@ must install it from source.
 ## Installation with pip
 
 First you need to install one of, or both, TensorFlow 2.0 and PyTorch.
-Please refer to [TensorFlow installation page](https://www.tensorflow.org/install/pip#tensorflow-2.0-rc-is-available),
-[PyTorch installation page](https://pytorch.org/get-started/locally/#start-locally) and/or
+Please refer to [TensorFlow installation page](https://www.tensorflow.org/install/pip#tensorflow-2.0-rc-is-available), 
+[PyTorch installation page](https://pytorch.org/get-started/locally/#start-locally) and/or 
 [Flax installation page](https://github.com/google/flax#quick-install)
 regarding the specific install command for your platform.
 
@@ -73,27 +73,7 @@ It should download a pretrained model then print something like
 
 ## Installing from source
 
-Here is how to quickly install `transformers` from source:
-
-```bash
-pip install git+https://github.com/huggingface/transformers
-```
-
-Note that this will install not the latest released version, but the bleeding edge `master` version, which you may want to use in case a bug has been fixed since the last official release and a new release hasn't  been yet rolled out.
-
-While we strive to keep `master` operational at all times, if you notice some issues, they usually get fixed within a few hours or a day and and you're more than welcome to help us detect any problems by opening an [Issue](https://github.com/huggingface/transformers/issues) and this way, things will get fixed even sooner.
-
-Again, you can run:
-
-```bash
-python -c "from transformers import pipeline; print(pipeline('sentiment-analysis')('I hate you'))"
-```
-
-to check 🤗 Transformers is properly installed.
-
-## Editable install
-
-If you want to constantly use the bleeding edge `master` version of the source code, or if you want to contribute to the library and need to test the changes in the code you're making, you will need an editable install. This is done by cloning the repository and installing with the following commands:
+To install from source, clone the repository and install with the following commands:
 
 ``` bash
 git clone https://github.com/huggingface/transformers.git
@@ -101,22 +81,13 @@ cd transformers
 pip install -e .
 ```
 
-This command performs a magical link between the folder you cloned the repository to and your python library paths, and it'll look inside this folder in addition to the normal library-wide paths. So if normally your python packages get installed into:
-```
-~/anaconda3/envs/main/lib/python3.7/site-packages/
-```
-now this editable install will reside where you clone the folder to, e.g. `~/transformers/` and python will search it too.
+Again, you can run 
 
-Do note that you have to keep that `transformers` folder around and not delete it to continue using the  `transformers` library.
-
-Now, let's get to the real benefit of this installation approach. Say, you saw some new feature has been just committed into `master`. If you have already performed all the steps above, to update your transformers to include all the latest commits, all you need to do is to `cd` into that cloned repository folder and update the clone to the latest version:
-
-```
-cd ~/transformers/
-git pull
+```bash
+python -c "from transformers import pipeline; print(pipeline('sentiment-analysis')('I hate you'))"
 ```
 
-There is nothing else to do. Your python environment will find the bleeding edge version of `transformers` on the next run.
+to check 🤗 Transformers is properly installed.
 
 
 ## With conda
@@ -129,7 +100,7 @@ Since Transformers version v4.0.0, we now have a conda channel: `huggingface`.
 conda install -c huggingface transformers
 ```
 
-Follow the installation pages of TensorFlow, PyTorch or Flax to see how to install them with conda.
+Follow the installation pages of TensorFlow, PyTorch or Flax to see how to install them with conda. 
 
 ## Caching models
 
@@ -138,7 +109,7 @@ This library provides pretrained models that will be downloaded and cached local
 folder given by the shell environment variable ``TRANSFORMERS_CACHE``. The default value for it will be the Hugging
 Face cache home followed by ``/transformers/``. This is (by order of priority):
 
-  * shell environment variable ``HF_HOME``
+  * shell environment variable ``HF_HOME`` 
   * shell environment variable ``XDG_CACHE_HOME`` + ``/huggingface/``
   * default: ``~/.cache/huggingface/``
 
@@ -149,48 +120,17 @@ So if you don't have any specific environment variable set, the cache directory
 (``PYTORCH_TRANSFORMERS_CACHE`` or ``PYTORCH_PRETRAINED_BERT_CACHE``), those will be used if there is no shell
 environment variable for ``TRANSFORMERS_CACHE``.
 
-### Offline mode
+### Note on model downloads (Continuous Integration or large-scale deployments)
 
-It's possible to run 🤗 Transformers in a firewalled or a no-network environment.
-
-Setting environment variable `TRANSFORMERS_OFFLINE=1` will tell 🤗 Transformers to use local files only and will not try to look things up.
-
-Most likely you may want to couple this with `HF_DATASETS_OFFLINE=1` that performs the same for 🤗 Datasets if you're using the latter.
-
-Here is an example of how this can be used on a filesystem that is shared between a normally networked and a firewalled to the external world instances.
-
-On the instance with the normal network run your program which will download and cache models (and optionally datasets if you use 🤗 Datasets). For example:
-
-```
-python examples/pytorch/translation/run_translation.py --model_name_or_path t5-small --dataset_name wmt16 --dataset_config ro-en ...
-```
-
-and then with the same filesystem you can now run the same program on a firewalled instance:
-```
-HF_DATASETS_OFFLINE=1 TRANSFORMERS_OFFLINE=1 \
-python examples/pytorch/translation/run_translation.py --model_name_or_path t5-small --dataset_name wmt16 --dataset_config ro-en ...
-```
-and it should succeed without any hanging waiting to timeout.
-
-#### Fetching models and tokenizers to use offline
-
-When running a script the first time like mentioned above, the downloaded files will be cached for future reuse. 
-However, it is also possible to download files and point to their local path instead.
-
-Downloading files can be done through the Web Interface by clicking on the "Download" button, but it can also be handled
-programmatically using the `huggingface_hub` library that is a dependency to `transformers`:
-
-- Using `snapshot_download` to download an entire repository
-- Using `hf_hub_download` to download a specific file
-
-See the reference for these methods in the huggingface_hub
-[documentation](https://github.com/huggingface/huggingface_hub/tree/main/src/huggingface_hub).
+If you expect to be downloading large volumes of models (more than 1,000) from our hosted bucket (for instance through
+your CI setup, or a large-scale production deployment), please cache the model files on your end. It will be way
+faster, and cheaper. Feel free to contact us privately if you need any help.
 
 ## Do you want to run a Transformer model on a mobile device?
 
 You should check out our [swift-coreml-transformers](https://github.com/huggingface/swift-coreml-transformers) repo.
 
-It contains a set of tools to convert PyTorch or TensorFlow 2.0 trained Transformer models (currently contains `GPT-2`,
+It contains a set of tools to convert PyTorch or TensorFlow 2.0 trained Transformer models (currently contains `GPT-2`, 
 `DistilGPT-2`, `BERT`, and `DistilBERT`) to CoreML models that run on iOS devices.
 
 At some point in the future, you'll be able to seamlessly move from pretraining or fine-tuning models in PyTorch or

docs/source/internal/file_utils.rst

@@ -1,54 +0,0 @@
-.. 
-    Copyright 2021 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
-General Utilities
------------------------------------------------------------------------------------------------------------------------
-
-This page lists all of Transformers general utility functions that are found in the file ``file_utils.py``.
-
-Most of those are only useful if you are studying the general code in the library.
-
-
-Enums and namedtuples
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.file_utils.ExplicitEnum
-
-.. autoclass:: transformers.file_utils.PaddingStrategy
-
-.. autoclass:: transformers.file_utils.TensorType
-
-
-Special Decorators
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autofunction:: transformers.file_utils.add_start_docstrings
-
-.. autofunction:: transformers.file_utils.add_start_docstrings_to_model_forward
-
-.. autofunction:: transformers.file_utils.add_end_docstrings
-
-.. autofunction:: transformers.file_utils.add_code_sample_docstrings
-
-.. autofunction:: transformers.file_utils.replace_return_docstrings
-
-
-Special Properties
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.file_utils.cached_property
-
-
-Other Utilities
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.file_utils._LazyModule

docs/source/internal/generation_utils.rst

@@ -13,21 +13,19 @@
 Utilities for Generation
 -----------------------------------------------------------------------------------------------------------------------
 
-This page lists all the utility functions used by :meth:`~transformers.generation_utils.GenerationMixin.generate`,
-:meth:`~transformers.generation_utils.GenerationMixin.greedy_search`,
-:meth:`~transformers.generation_utils.GenerationMixin.sample`,
-:meth:`~transformers.generation_utils.GenerationMixin.beam_search`,
-:meth:`~transformers.generation_utils.GenerationMixin.beam_sample`, and
-:meth:`~transformers.generation_utils.GenerationMixin.group_beam_search`.
+This page lists all the utility functions used by :meth:`~transformers.PreTrainedModel.generate`,
+:meth:`~transformers.PreTrainedModel.greedy_search`, :meth:`~transformers.PreTrainedModel.sample`,
+:meth:`~transformers.PreTrainedModel.beam_search`, :meth:`~transformers.PreTrainedModel.beam_sample`, and
+:meth:`~transformers.PreTrainedModel.group_beam_search`.
 
 Most of those are only useful if you are studying the code of the generate methods in the library.
 
 Generate Outputs
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-The output of :meth:`~transformers.generation_utils.GenerationMixin.generate` is an instance of a subclass of
+The output of :meth:`~transformers.PreTrainedModel.generate` is an instance of a subclass of
 :class:`~transformers.file_utils.ModelOutput`. This output is a data structure containing all the information returned
-by :meth:`~transformers.generation_utils.GenerationMixin.generate`, but that can also be used as tuple or dictionary.
+by :meth:`~transformers.PreTrainedModel.generate`, but that can also be used as tuple or dictionary.
 
 Here's an example:
 
@@ -80,9 +78,6 @@ GreedySearchOutput
 .. autoclass:: transformers.generation_utils.GreedySearchEncoderDecoderOutput
     :members:
 
-.. autoclass:: transformers.generation_flax_utils.FlaxGreedySearchOutput
-    :members:
-
 
 SampleOutput
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -93,9 +88,6 @@ SampleOutput
 .. autoclass:: transformers.generation_utils.SampleEncoderDecoderOutput
     :members:
 
-.. autoclass:: transformers.generation_flax_utils.FlaxSampleOutput
-    :members:
-
 
 BeamSearchOutput
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -159,60 +151,6 @@ generation.
 .. autoclass:: transformers.HammingDiversityLogitsProcessor
     :members: __call__
 
-.. autoclass:: transformers.ForcedBOSTokenLogitsProcessor
-    :members: __call__
-
-.. autoclass:: transformers.ForcedEOSTokenLogitsProcessor
-    :members: __call__
-
-.. autoclass:: transformers.InfNanRemoveLogitsProcessor
-    :members: __call__
-
-.. autoclass:: transformers.FlaxLogitsProcessor
-    :members: __call__
-
-.. autoclass:: transformers.FlaxLogitsProcessorList
-    :members: __call__
-
-.. autoclass:: transformers.FlaxLogitsWarper
-    :members: __call__
-
-.. autoclass:: transformers.FlaxTemperatureLogitsWarper
-    :members: __call__
-
-.. autoclass:: transformers.FlaxTopPLogitsWarper
-    :members: __call__
-
-.. autoclass:: transformers.FlaxTopKLogitsWarper
-    :members: __call__
-
-.. autoclass:: transformers.FlaxForcedBOSTokenLogitsProcessor
-    :members: __call__
-
-.. autoclass:: transformers.FlaxForcedEOSTokenLogitsProcessor
-    :members: __call__
-
-.. autoclass:: transformers.FlaxMinLengthLogitsProcessor
-    :members: __call__
-
-
-StoppingCriteria
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-A :class:`~transformers.StoppingCriteria` can be used to change when to stop generation (other than EOS token).
-
-.. autoclass:: transformers.StoppingCriteria
-    :members: __call__
-
-.. autoclass:: transformers.StoppingCriteriaList
-    :members: __call__
-
-.. autoclass:: transformers.MaxLengthCriteria
-    :members: __call__
-
-.. autoclass:: transformers.MaxTimeCriteria
-    :members: __call__
-
 BeamSearch
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

docs/source/internal/modeling_utils.rst

@@ -63,6 +63,7 @@ TensorFlow custom layers
     :members: call
 
 .. autoclass:: transformers.modeling_tf_utils.TFSequenceSummary
+    :members: call
 
 
 TensorFlow loss functions

docs/source/internal/pipelines_utils.rst

@@ -47,4 +47,6 @@ Data format
 Utilities
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
+.. autofunction:: transformers.pipelines.get_framework
+
 .. autoclass:: transformers.pipelines.PipelineException

docs/source/internal/tokenization_utils.rst

@@ -38,6 +38,12 @@ SpecialTokensMixin
 Enums and namedtuples
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
+.. autoclass:: transformers.tokenization_utils_base.ExplicitEnum
+
+.. autoclass:: transformers.tokenization_utils_base.PaddingStrategy
+
+.. autoclass:: transformers.tokenization_utils_base.TensorType
+
 .. autoclass:: transformers.tokenization_utils_base.TruncationStrategy
 
 .. autoclass:: transformers.tokenization_utils_base.CharSpan

docs/source/internal/trainer_utils.rst

@@ -1,4 +1,4 @@
-..
+.. 
     Copyright 2020 The HuggingFace Team. All rights reserved.
 
     Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
@@ -22,7 +22,7 @@ Utilities
 
 .. autoclass:: transformers.EvalPrediction
 
-.. autoclass:: transformers.IntervalStrategy
+.. autoclass:: transformers.EvaluationStrategy
 
 .. autofunction:: transformers.set_seed
 
@@ -46,9 +46,3 @@ Distributed Evaluation
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.HfArgumentParser
-
-
-Debug Utilities
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.debug_utils.DebugUnderflowOverflow

docs/source/main_classes/callback.rst

@@ -74,32 +74,6 @@ TrainerCallback
 .. autoclass:: transformers.TrainerCallback
     :members:
 
-Here is an example of how to register a custom callback with the PyTorch :class:`~transformers.Trainer`:
-
-.. code-block:: python
-
-    class MyCallback(TrainerCallback):
-        "A callback that prints a message at the beginning of training"
-
-        def on_train_begin(self, args, state, control, **kwargs):
-            print("Starting training")
-
-    trainer = Trainer(
-        model,
-        args,
-        train_dataset=train_dataset,
-        eval_dataset=eval_dataset,
-        callbacks=[MyCallback]  # We can either pass the callback class this way or an instance of it (MyCallback())
-    )
-
-Another way to register a callback is to call ``trainer.add_callback()`` as follows:
-
-.. code-block:: python
-
-    trainer = Trainer(...)
-    trainer.add_callback(MyCallback)
-    # Alternatively, we can pass an instance of the callback class
-    trainer.add_callback(MyCallback())
 
 TrainerState
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

docs/source/main_classes/configuration.rst

@@ -17,15 +17,9 @@ The base class :class:`~transformers.PretrainedConfig` implements the common met
 either from a local file or directory, or from a pretrained model configuration provided by the library (downloaded
 from HuggingFace's AWS S3 repository).
 
-Each derived config class implements model specific attributes. Common attributes present in all config classes are:
-:obj:`hidden_size`, :obj:`num_attention_heads`, and :obj:`num_hidden_layers`. Text models further implement:
-:obj:`vocab_size`.
-
-
 
 PretrainedConfig
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.PretrainedConfig
-    :special-members: push_to_hub
     :members:

docs/source/main_classes/data_collator.rst

@@ -1,71 +0,0 @@
-.. 
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
-Data Collator
------------------------------------------------------------------------------------------------------------------------
-
-Data collators are objects that will form a batch by using a list of dataset elements as input. These elements are of
-the same type as the elements of :obj:`train_dataset` or :obj:`eval_dataset`.
-
-To be able to build batches, data collators may apply some processing (like padding). Some of them (like
-:class:`~transformers.DataCollatorForLanguageModeling`) also apply some random data augmentation (like random masking)
-on the formed batch.
-
-Examples of use can be found in the :doc:`example scripts <../examples>` or :doc:`example notebooks <../notebooks>`.
-
-
-Default data collator
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autofunction:: transformers.data.data_collator.default_data_collator
-
-
-DataCollatorWithPadding
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.data.data_collator.DataCollatorWithPadding
-    :members:
-
-
-DataCollatorForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.data.data_collator.DataCollatorForTokenClassification
-    :members:
-
-
-DataCollatorForSeq2Seq
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.data.data_collator.DataCollatorForSeq2Seq
-    :members:
-
-
-DataCollatorForLanguageModeling
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.data.data_collator.DataCollatorForLanguageModeling
-    :members: numpy_mask_tokens, tf_mask_tokens, torch_mask_tokens
-
-
-DataCollatorForWholeWordMask
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.data.data_collator.DataCollatorForWholeWordMask
-    :members: numpy_mask_tokens, tf_mask_tokens, torch_mask_tokens
-
-
-DataCollatorForPermutationLanguageModeling
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.data.data_collator.DataCollatorForPermutationLanguageModeling
-    :members: numpy_mask_tokens, tf_mask_tokens, torch_mask_tokens

docs/source/main_classes/feature_extractor.rst

@@ -1,48 +0,0 @@
-.. 
-    Copyright 2021 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
-
-Feature Extractor
------------------------------------------------------------------------------------------------------------------------
-
-A feature extractor is in charge of preparing input features for a multi-modal model. This includes feature extraction
-from sequences, *e.g.*, pre-processing audio files to Log-Mel Spectrogram features, feature extraction from images
-*e.g.* cropping image image files, but also padding, normalization, and conversion to Numpy, PyTorch, and TensorFlow
-tensors.
-
-
-FeatureExtractionMixin
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.feature_extraction_utils.FeatureExtractionMixin
-    :members: from_pretrained, save_pretrained
-
-
-SequenceFeatureExtractor
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.SequenceFeatureExtractor
-    :members: pad
-
-
-BatchFeature
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BatchFeature
-    :members:
-
-
-ImageFeatureExtractionMixin
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.image_utils.ImageFeatureExtractionMixin
-    :members:

docs/source/main_classes/logging.rst

@@ -65,10 +65,6 @@ Other functions
 
 .. autofunction:: transformers.logging.get_logger
 
-.. autofunction:: transformers.logging.enable_default_handler
-
-.. autofunction:: transformers.logging.disable_default_handler
-
 .. autofunction:: transformers.logging.enable_explicit_format
 
 .. autofunction:: transformers.logging.reset_format

docs/source/main_classes/model.rst

@@ -1,4 +1,4 @@
-..
+.. 
     Copyright 2020 The HuggingFace Team. All rights reserved.
 
     Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
@@ -26,50 +26,17 @@ are common among all the models to:
 
 The other methods that are common to each model are defined in :class:`~transformers.modeling_utils.ModuleUtilsMixin`
 (for the PyTorch models) and :class:`~transformers.modeling_tf_utils.TFModuleUtilsMixin` (for the TensorFlow models) or
-for text generation, :class:`~transformers.generation_utils.GenerationMixin` (for the PyTorch models),
-:class:`~transformers.generation_tf_utils.TFGenerationMixin` (for the TensorFlow models) and
-:class:`~transformers.generation_flax_utils.FlaxGenerationMixin` (for the Flax/JAX models).
+for text generation, :class:`~transformers.generation_utils.GenerationMixin` (for the PyTorch models) and
+:class:`~transformers.generation_tf_utils.TFGenerationMixin` (for the TensorFlow models)
 
 
 PreTrainedModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.PreTrainedModel
-    :special-members: push_to_hub
     :members:
 
 
-.. _from_pretrained-torch-dtype:
-
-Model Instantiation dtype
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Under Pytorch a model normally gets instantiated with ``torch.float32`` format. This can be an issue if one tries to
-load a model whose weights are in fp16, since it'd require twice as much memory. To overcome this limitation, you can
-either explicitly pass the desired ``dtype`` using ``torch_dtype`` argument:
-
-.. code-block:: python
-
-    model = T5ForConditionalGeneration.from_pretrained("t5", torch_dtype=torch.float16)
-
-or, if you want the model to always load in the most optimal memory pattern, you can use the special value ``"auto"``,
-and then ``dtype`` will be automatically derived from the model's weights:
-
-.. code-block:: python
-
-    model = T5ForConditionalGeneration.from_pretrained("t5", torch_dtype="auto")
-
-Models instantiated from scratch can also be told which ``dtype`` to use with:
-
-.. code-block:: python
-
-    config = T5Config.from_pretrained("t5")
-    model = AutoModel.from_config(config)
-
-Due to Pytorch design, this functionality is only available for floating dtypes.
-
-
-
 ModuleUtilsMixin
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -81,7 +48,6 @@ TFPreTrainedModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFPreTrainedModel
-    :special-members: push_to_hub
     :members:
 
 
@@ -96,7 +62,6 @@ FlaxPreTrainedModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.FlaxPreTrainedModel
-    :special-members: push_to_hub
     :members:
 
 
@@ -108,13 +73,3 @@ Generation
 
 .. autoclass:: transformers.generation_tf_utils.TFGenerationMixin
     :members:
-
-.. autoclass:: transformers.generation_flax_utils.FlaxGenerationMixin
-    :members:
-
-
-Pushing to the Hub
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.file_utils.PushToHubMixin
-    :members:

docs/source/main_classes/output.rst

@@ -13,8 +13,8 @@
 Model outputs
 -----------------------------------------------------------------------------------------------------------------------
 
-All models have outputs that are instances of subclasses of :class:`~transformers.file_utils.ModelOutput`. Those are
-data structures containing all the information returned by the model, but that can also be used as tuples or
+PyTorch models have outputs that are instances of subclasses of :class:`~transformers.file_utils.ModelOutput`. Those
+are data structures containing all the information returned by the model, but that can also be used as tuples or
 dictionaries.
 
 Let's see of this looks on an example:
@@ -60,7 +60,7 @@ ModelOutput
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.file_utils.ModelOutput
-    :members: to_tuple
+    :members:
 
 
 BaseModelOutput
@@ -299,93 +299,3 @@ TFSeq2SeqQuestionAnsweringModelOutput
 
 .. autoclass:: transformers.modeling_tf_outputs.TFSeq2SeqQuestionAnsweringModelOutput
     :members:
-
-
-FlaxBaseModelOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_flax_outputs.FlaxBaseModelOutput
-
-
-FlaxBaseModelOutputWithPast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPast
-
-
-FlaxBaseModelOutputWithPooling
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling
-
-
-FlaxBaseModelOutputWithPastAndCrossAttentions
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions
-
-
-FlaxSeq2SeqModelOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput
-
-
-FlaxCausalLMOutputWithCrossAttentions
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions
-
-
-FlaxMaskedLMOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_flax_outputs.FlaxMaskedLMOutput
-
-
-FlaxSeq2SeqLMOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput
-
-
-FlaxNextSentencePredictorOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_flax_outputs.FlaxNextSentencePredictorOutput
-
-
-FlaxSequenceClassifierOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput
-
-
-FlaxSeq2SeqSequenceClassifierOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_flax_outputs.FlaxSeq2SeqSequenceClassifierOutput
-
-
-FlaxMultipleChoiceModelOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput
-
-
-FlaxTokenClassifierOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_flax_outputs.FlaxTokenClassifierOutput
-
-
-FlaxQuestionAnsweringModelOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput
-
-
-FlaxSeq2SeqQuestionAnsweringModelOutput
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_flax_outputs.FlaxSeq2SeqQuestionAnsweringModelOutput

docs/source/main_classes/pipelines.rst

@@ -23,22 +23,18 @@ There are two categories of pipeline abstractions to be aware about:
 - The :func:`~transformers.pipeline` which is the most powerful object encapsulating all other pipelines.
 - The other task-specific pipelines:
 
-    - :class:`~transformers.AudioClassificationPipeline`
-    - :class:`~transformers.AutomaticSpeechRecognitionPipeline`
     - :class:`~transformers.ConversationalPipeline`
     - :class:`~transformers.FeatureExtractionPipeline`
     - :class:`~transformers.FillMaskPipeline`
-    - :class:`~transformers.ImageClassificationPipeline`
-    - :class:`~transformers.ObjectDetectionPipeline`
     - :class:`~transformers.QuestionAnsweringPipeline`
     - :class:`~transformers.SummarizationPipeline`
-    - :class:`~transformers.TableQuestionAnsweringPipeline`
     - :class:`~transformers.TextClassificationPipeline`
     - :class:`~transformers.TextGenerationPipeline`
-    - :class:`~transformers.Text2TextGenerationPipeline`
     - :class:`~transformers.TokenClassificationPipeline`
     - :class:`~transformers.TranslationPipeline`
     - :class:`~transformers.ZeroShotClassificationPipeline`
+    - :class:`~transformers.Text2TextGenerationPipeline`
+    - :class:`~transformers.TableQuestionAnsweringPipeline`
 
 The pipeline abstraction
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -46,67 +42,12 @@ 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`.
 
-Simple call on one item:
-
-.. code-block::
-
-    >>> pipe = pipeline("text-classification")
-    >>> pipe("This restaurant is awesome")
-    [{'label': 'POSITIVE', 'score': 0.9998743534088135}]
-
-To call a pipeline on many items, you can either call with a `list`.
-
-.. code-block::
-
-    >>> pipe = pipeline("text-classification")
-    >>> pipe(["This restaurant is awesome", "This restaurant is aweful"])
-    [{'label': 'POSITIVE', 'score': 0.9998743534088135},
-     {'label': 'NEGATIVE', 'score': 0.9996669292449951}]
-
-
-To iterate of full datasets it is recommended to use a :obj:`dataset` directly. This means you don't need to allocate
-the whole dataset at once, nor do you need to do batching yourself. This should work just as fast as custom loops on
-GPU. If it doesn't don't hesitate to create an issue.
-
-.. code-block::
-
-    pipe = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-960h", device=0)
-    dataset = datasets.load_dataset("superb", name="asr", split="test")
-
-    # KeyDataset (only `pt`) will simply return the item in the dict returned by the dataset item
-    # as we're not interested in the `target` part of the dataset.
-    for out in tqdm.tqdm(pipe(KeyDataset(dataset, "file"))):
-        print(out)
-        # {"text": "NUMBER TEN FRESH NELLY IS WAITING ON YOU GOOD NIGHT HUSBAND"}
-        # {"text": ....}
-        # ....
-
-
 .. autofunction:: transformers.pipeline
 
-Implementing a pipeline
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-:doc:`Implementing a new pipeline <../add_new_pipeline>`
 
 The task specific pipelines
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-
-AudioClassificationPipeline
-=======================================================================================================================
-
-.. autoclass:: transformers.AudioClassificationPipeline
-    :special-members: __call__
-    :members:
-
-AutomaticSpeechRecognitionPipeline
-=======================================================================================================================
-
-.. autoclass:: transformers.AutomaticSpeechRecognitionPipeline
-    :special-members: __call__
-    :members:
-
 ConversationalPipeline
 =======================================================================================================================
 
@@ -130,13 +71,6 @@ FillMaskPipeline
     :special-members: __call__
     :members:
 
-ImageClassificationPipeline
-=======================================================================================================================
-
-.. autoclass:: transformers.ImageClassificationPipeline
-    :special-members: __call__
-    :members:
-
 NerPipeline
 =======================================================================================================================
 
@@ -144,13 +78,6 @@ NerPipeline
 
 See :class:`~transformers.TokenClassificationPipeline` for all details.
 
-ObjectDetectionPipeline
-=======================================================================================================================
-
-.. autoclass:: transformers.ObjectDetectionPipeline
-    :special-members: __call__
-    :members:
-
 QuestionAnsweringPipeline
 =======================================================================================================================
 

docs/source/main_classes/processors.rst

@@ -68,8 +68,8 @@ Additionally, the following method can be used to load values from a data file a
 Example usage
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-An example using these processors is given in the :prefix_link:`run_glue.py
-<examples/legacy/text-classification/run_glue.py>` script.
+An example using these processors is given in the `run_glue.py
+<https://github.com/huggingface/pytorch-transformers/blob/master/examples/text-classification/run_glue.py>`__ script.
 
 
 XNLI
@@ -89,8 +89,8 @@ This library hosts the processor to load the XNLI data:
 
 Please note that since the gold labels are available on the test set, evaluation is performed on the test set.
 
-An example using these processors is given in the :prefix_link:`run_xnli.py
-<examples/legacy/text-classification/run_xnli.py>` script.
+An example using these processors is given in the `run_xnli.py
+<https://github.com/huggingface/pytorch-transformers/blob/master/examples/text-classification/run_xnli.py>`__ script.
 
 
 SQuAD
@@ -169,4 +169,4 @@ Using `tensorflow_datasets` is as easy as using a data file:
 
 
 Another example using these processors is given in the :prefix_link:`run_squad.py
-<examples/legacy/question-answering/run_squad.py>` script.
+<examples/question-answering/run_squad.py>` script.

docs/source/main_classes/tokenizer.rst

@@ -53,20 +53,15 @@ PreTrainedTokenizer
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.PreTrainedTokenizer
-    :special-members: __call__, batch_decode, decode, encode, push_to_hub
-    :members: 
+    :special-members: __call__
+    :members:
 
 
 PreTrainedTokenizerFast
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-The :class:`~transformers.PreTrainedTokenizerFast` depend on the `tokenizers
-<https://huggingface.co/docs/tokenizers>`__ library. The tokenizers obtained from the 🤗 tokenizers library can be
-loaded very simply into 🤗 transformers. Take a look at the :doc:`Using tokenizers from 🤗 tokenizers
-<../fast_tokenizers>` page to understand how this is done.
-
 .. autoclass:: transformers.PreTrainedTokenizerFast
-    :special-members: __call__, batch_decode, decode, encode, push_to_hub
+    :special-members: __call__
     :members:
 
 

docs/source/main_classes/trainer.rst

@@ -21,20 +21,17 @@ Before instantiating your :class:`~transformers.Trainer`/:class:`~transformers.T
 customization during training.
 
 The API supports distributed training on multiple GPUs/TPUs, mixed precision through `NVIDIA Apex
-<https://github.com/NVIDIA/apex>`__ and Native AMP for PyTorch and :obj:`tf.keras.mixed_precision` for TensorFlow.
+<https://github.com/NVIDIA/apex>`__ for PyTorch and :obj:`tf.keras.mixed_precision` for TensorFlow.
 
-Both :class:`~transformers.Trainer` and :class:`~transformers.TFTrainer` contain the basic training loop which supports
-the above features. To inject custom behavior you can subclass them and override the following methods:
+Both :class:`~transformers.Trainer` and :class:`~transformers.TFTrainer` contain the basic training loop supporting the
+previous features. To inject custom behavior you can subclass them and override the following methods:
 
 - **get_train_dataloader**/**get_train_tfdataset** -- Creates the training DataLoader (PyTorch) or TF Dataset.
 - **get_eval_dataloader**/**get_eval_tfdataset** -- Creates the evaluation DataLoader (PyTorch) or TF Dataset.
 - **get_test_dataloader**/**get_test_tfdataset** -- Creates the test DataLoader (PyTorch) or TF Dataset.
 - **log** -- Logs information on the various objects watching training.
-- **create_optimizer_and_scheduler** -- Sets up the optimizer and learning rate scheduler if they were not passed at
-  init. Note, that you can also subclass or override the ``create_optimizer`` and ``create_scheduler`` methods
-  separately.
-- **create_optimizer** -- Sets up the optimizer if it wasn't passed at init.
-- **create_scheduler** -- Sets up the learning rate scheduler if it wasn't passed at init.
+- **create_optimizer_and_scheduler** -- Setups the optimizer and learning rate scheduler if they were not passed at
+  init.
 - **compute_loss** - Computes the loss on a batch of training inputs.
 - **training_step** -- Performs a training step.
 - **prediction_step** -- Performs an evaluation/test step.
@@ -42,35 +39,17 @@ the above features. To inject custom behavior you can subclass them and override
 - **evaluate** -- Runs an evaluation loop and returns metrics.
 - **predict** -- Returns predictions (with metrics if labels are available) on a test set.
 
-.. warning::
-
-    The :class:`~transformers.Trainer` class is optimized for 🤗 Transformers models and can have surprising behaviors
-    when you use it on other models. When using it on your own model, make sure:
-
-    - your model always return tuples or subclasses of :class:`~transformers.file_utils.ModelOutput`.
-    - your model can compute the loss if a :obj:`labels` argument is provided and that loss is returned as the first
-      element of the tuple (if your model returns tuples)
-    - your model can accept multiple label arguments (use the :obj:`label_names` in your
-      :class:`~transformers.TrainingArguments` to indicate their name to the :class:`~transformers.Trainer`) but none
-      of them should be named :obj:`"label"`.
-
-Here is an example of how to customize :class:`~transformers.Trainer` using a custom loss function for multi-label
-classification:
+Here is an example of how to customize :class:`~transformers.Trainer` using a custom loss function:
 
 .. code-block:: python
 
-    from torch import nn
     from transformers import Trainer
-
-    class MultilabelTrainer(Trainer):
-        def compute_loss(self, model, inputs, return_outputs=False):
-            labels = inputs.get("labels")
+    class MyTrainer(Trainer):
+        def compute_loss(self, model, inputs):
+            labels = inputs.pop("labels")
             outputs = model(**inputs)
-            logits = outputs.get('logits')
-            loss_fct = nn.BCEWithLogitsLoss()
-            loss = loss_fct(logits.view(-1, self.model.config.num_labels),
-                            labels.float().view(-1, self.model.config.num_labels))
-            return (loss, outputs) if return_outputs else loss
+            logits = outputs[0]
+            return my_custom_loss(logits, labels)
 
 Another way to customize the training loop behavior for the PyTorch :class:`~transformers.Trainer` is to use
 :doc:`callbacks <callback>` that can inspect the training loop state (for progress reporting, logging on TensorBoard or
@@ -119,111 +98,6 @@ TFTrainingArguments
     :members:
 
 
-Checkpoints
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-By default, :class:`~transformers.Trainer` will save all checkpoints in the :obj:`output_dir` you set in the
-:class:`~transformers.TrainingArguments` you are using. Those will go in subfolder named :obj:`checkpoint-xxx` with xxx
-being the step at which the training was at.
-
-Resuming training from a checkpoint can be done when calling :meth:`~transformers.Trainer.train` with either:
-
-- :obj:`resume_from_checkpoint=True` which will resume training from the latest checkpoint
-- :obj:`resume_from_checkpoint=checkpoint_dir` which will resume training from the specific checkpoint in the directory
-  passed.
-
-In addition, you can easily save your checkpoints on the Model Hub when using :obj:`push_to_hub=True`. By default, all
-the models saved in intermediate checkpoints are saved in different commits, but not the optimizer state. You can adapt
-the :obj:`hub-strategy` value of your :class:`~transformers.TrainingArguments` to either:
-
-- :obj:`"checkpoint"`: the latest checkpoint is also pushed in a subfolder named last-checkpoint, allowing you to
-  resume training easily with :obj:`trainer.train(resume_from_checkpoint="output_dir/last-checkpoint")`.
-- :obj:`"all_checkpoints"`: all checkpoints are pushed like they appear in the output folder (so you will get one
-  checkpoint folder per folder in your final repository)
-
-
-Logging
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-By default :class:`~transformers.Trainer` will use ``logging.INFO`` for the main process and ``logging.WARNING`` for
-the replicas if any.
-
-These defaults can be overridden to use any of the 5 ``logging`` levels with :class:`~transformers.TrainingArguments`'s
-arguments:
-
-- ``log_level`` - for the main process
-- ``log_level_replica`` - for the replicas
-
-Further, if :class:`~transformers.TrainingArguments`'s ``log_on_each_node`` is set to ``False`` only the main node will
-use the log level settings for its main process, all other nodes will use the log level settings for replicas.
-
-Note that :class:`~transformers.Trainer` is going to set ``transformers``'s log level separately for each node in its
-:meth:`~transformers.Trainer.__init__`. So you may want to set this sooner (see the next example) if you tap into other
-``transformers`` functionality before creating the :class:`~transformers.Trainer` object.
-
-Here is an example of how this can be used in an application:
-
-.. code-block:: python
-
-    [...]
-    logger = logging.getLogger(__name__)
-
-    # Setup logging
-    logging.basicConfig(
-        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
-        datefmt="%m/%d/%Y %H:%M:%S",
-        handlers=[logging.StreamHandler(sys.stdout)],
-    )
-
-    # set the main code and the modules it uses to the same log-level according to the node
-    log_level = training_args.get_process_log_level()
-    logger.setLevel(log_level)
-    datasets.utils.logging.set_verbosity(log_level)
-    transformers.utils.logging.set_verbosity(log_level)
-
-    trainer = Trainer(...)
-
-And then if you only want to see warnings on the main node and all other nodes to not print any most likely duplicated
-warnings you could run it as:
-
-.. code-block:: bash
-
-    my_app.py ... --log_level warning --log_level_replica error
-
-In the multi-node environment if you also don't want the logs to repeat for each node's main process, you will want to
-change the above to:
-
-.. code-block:: bash
-
-    my_app.py ... --log_level warning --log_level_replica error --log_on_each_node 0
-
-and then only the main process of the first node will log at the "warning" level, and all other processes on the main
-node and all processes on other nodes will log at the "error" level.
-
-If you need your application to be as quiet as possible you could do:
-
-.. code-block:: bash
-
-    my_app.py ... --log_level error --log_level_replica error --log_on_each_node 0
-
-(add ``--log_on_each_node 0`` if on multi-node environment)
-
-
-
-Randomness
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-When resuming from a checkpoint generated by :class:`~transformers.Trainer` all efforts are made to restore the
-`python`, `numpy` and `pytorch` RNG states to the same states as they were at the moment of saving that checkpoint,
-which should make the "stop and resume" style of training as close as possible to non-stop training.
-
-However, due to various default non-deterministic pytorch settings this might not fully work. If you want full
-determinism please refer to `Controlling sources of randomness
-<https://pytorch.org/docs/stable/notes/randomness.html>`__. As explained in the document, that some of those settings
-that make things deterministic (.e.g., ``torch.backends.cudnn.deterministic``) may slow things down, therefore this
-can't be done by default, but you can enable those yourself if needed.
-
-
 Trainer Integrations
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -239,123 +113,7 @@ Toward Training Trillion Parameter Models, by Samyam Rajbhandari, Jeff Rasley, O
 
 This provided support is new and experimental as of this writing.
 
-.. _zero-install-notes:
-
-CUDA Extension Installation Notes
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-As of this writing, both FairScale and Deepspeed require compilation of CUDA C++ code, before they can be used.
-
-While all installation issues should be dealt with through the corresponding GitHub Issues of `FairScale
-<https://github.com/facebookresearch/fairscale/issues>`__ and `Deepspeed
-<https://github.com/microsoft/DeepSpeed/issues>`__, there are a few common issues that one may encounter while building
-any PyTorch extension that needs to build CUDA extensions.
-
-Therefore, if you encounter a CUDA-related build issue while doing one of the following or both:
-
-.. code-block:: bash
-
-    pip install fairscale
-    pip install deepspeed
-
-please, read the following notes first.
-
-In these notes we give examples for what to do when ``pytorch`` has been built with CUDA ``10.2``. If your situation is
-different remember to adjust the version number to the one you are after.
-
-Possible problem #1
-=======================================================================================================================
-
-While, Pytorch comes with its own CUDA toolkit, to build these two projects you must have an identical version of CUDA
-installed system-wide.
-
-For example, if you installed ``pytorch`` with ``cudatoolkit==10.2`` in the Python environment, you also need to have
-CUDA ``10.2`` installed system-wide.
-
-The exact location may vary from system to system, but ``/usr/local/cuda-10.2`` is the most common location on many
-Unix systems. When CUDA is correctly set up and added to the ``PATH`` environment variable, one can find the
-installation location by doing:
-
-.. code-block:: bash
-
-    which nvcc
-
-If you don't have CUDA installed system-wide, install it first. You will find the instructions by using your favorite
-search engine. For example, if you're on Ubuntu you may want to search for: `ubuntu cuda 10.2 install
-<https://www.google.com/search?q=ubuntu+cuda+10.2+install>`__.
-
-Possible problem #2
-=======================================================================================================================
-
-Another possible common problem is that you may have more than one CUDA toolkit installed system-wide. For example you
-may have:
-
-.. code-block:: bash
-
-    /usr/local/cuda-10.2
-    /usr/local/cuda-11.0
-
-Now, in this situation you need to make sure that your ``PATH`` and ``LD_LIBRARY_PATH`` environment variables contain
-the correct paths to the desired CUDA version. Typically, package installers will set these to contain whatever the
-last version was installed. If you encounter the problem, where the package build fails because it can't find the right
-CUDA version despite you having it installed system-wide, it means that you need to adjust the 2 aforementioned
-environment variables.
-
-First, you may look at their contents:
-
-.. code-block:: bash
-
-    echo $PATH
-    echo $LD_LIBRARY_PATH
-
-so you get an idea of what is inside.
-
-It's possible that ``LD_LIBRARY_PATH`` is empty.
-
-``PATH`` lists the locations of where executables can be found and ``LD_LIBRARY_PATH`` is for where shared libraries
-are to looked for. In both cases, earlier entries have priority over the later ones. ``:`` is used to separate multiple
-entries.
-
-Now, to tell the build program where to find the specific CUDA toolkit, insert the desired paths to be listed first by
-doing:
-
-.. code-block:: bash
-
-    export PATH=/usr/local/cuda-10.2/bin:$PATH
-    export LD_LIBRARY_PATH=/usr/local/cuda-10.2/lib64:$LD_LIBRARY_PATH
-
-Note that we aren't overwriting the existing values, but prepending instead.
-
-Of course, adjust the version number, the full path if need be. Check that the directories you assign actually do
-exist. ``lib64`` sub-directory is where the various CUDA ``.so`` objects, like ``libcudart.so`` reside, it's unlikely
-that your system will have it named differently, but if it is adjust it to reflect your reality.
-
-
-Possible problem #3
-=======================================================================================================================
-
-Some older CUDA versions may refuse to build with newer compilers. For example, you my have ``gcc-9`` but it wants
-``gcc-7``.
-
-There are various ways to go about it.
-
-If you can install the latest CUDA toolkit it typically should support the newer compiler.
-
-Alternatively, you could install the lower version of the compiler in addition to the one you already have, or you may
-already have it but it's not the default one, so the build system can't see it. If you have ``gcc-7`` installed but the
-build system complains it can't find it, the following might do the trick:
-
-.. code-block:: bash
-
-    sudo ln -s /usr/bin/gcc-7  /usr/local/cuda-10.2/bin/gcc
-    sudo ln -s /usr/bin/g++-7  /usr/local/cuda-10.2/bin/g++
-
-
-Here, we are making a symlink to ``gcc-7`` from ``/usr/local/cuda-10.2/bin/gcc`` and since
-``/usr/local/cuda-10.2/bin/`` should be in the ``PATH`` environment variable (see the previous problem's solution), it
-should find ``gcc-7`` (and ``g++7``) and then the build will succeed.
-
-As always make sure to edit the paths in the example to match your situation.
+You will need at least 2 GPUs to benefit from these features.
 
 FairScale
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -365,268 +123,411 @@ provides support for the following features from `the ZeRO paper <https://arxiv.
 
 1. Optimizer State Sharding
 2. Gradient Sharding
-3. Model Parameters Sharding (new and very experimental)
-4. CPU offload (new and very experimental)
 
-You will need at least two GPUs to use this feature.
+To deploy this feature:
 
+1. Install the library via pypi:
 
-**Installation**:
+   .. code-block:: bash
 
-Install the library via pypi:
+       pip install fairscale
 
-.. code-block:: bash
+   or find more details on `the FairScale's github page
+   <https://github.com/facebookresearch/fairscale/#installation>`__.
 
-    pip install fairscale
-
-or via ``transformers``' ``extras``:
-
-.. code-block:: bash
-
-    pip install transformers[fairscale]
-
-(will become available starting from ``transformers==4.6.0``)
-
-or find more details on `the FairScale's GitHub page <https://github.com/facebookresearch/fairscale/#installation>`__.
-
-If you're still struggling with the build, first make sure to read :ref:`zero-install-notes`.
-
-If it's still not resolved the build issue, here are a few more ideas.
-
-``fairscale`` seems to have an issue with the recently introduced by pip build isolation feature. If you have a problem
-with it, you may want to try one of:
-
-.. code-block:: bash
-
-    pip install fairscale --no-build-isolation .
-
-or:
-
-.. code-block:: bash
-
-    git clone https://github.com/facebookresearch/fairscale/
-    cd fairscale
-    rm -r dist build
-    python setup.py bdist_wheel
-    pip uninstall -y fairscale
-    pip install dist/fairscale-*.whl
-
-``fairscale`` also has issues with building against pytorch-nightly, so if you use it you may have to try one of:
-
-.. code-block:: bash
-
-    pip uninstall -y fairscale; pip install fairscale --pre \
-    -f https://download.pytorch.org/whl/nightly/cu110/torch_nightly.html \
-    --no-cache --no-build-isolation
-
-or:
-
-.. code-block:: bash
-
-    pip install -v --disable-pip-version-check . \
-    -f https://download.pytorch.org/whl/nightly/cu110/torch_nightly.html --pre
-
-Of course, adjust the urls to match the cuda version you use.
-
-If after trying everything suggested you still encounter build issues, please, proceed with the GitHub Issue of
-`FairScale <https://github.com/facebookresearch/fairscale/issues>`__.
-
-
-
-**Usage**:
-
-To use the first version of Sharded data-parallelism, add ``--sharded_ddp simple`` to the command line arguments, and
-make sure you have added the distributed launcher ``-m torch.distributed.launch
---nproc_per_node=NUMBER_OF_GPUS_YOU_HAVE`` if you haven't been using it already.
-
-For example here is how you could use it for ``run_translation.py`` with 2 GPUs:
-
-.. code-block:: bash
-
-    python -m torch.distributed.launch --nproc_per_node=2 examples/pytorch/translation/run_translation.py \
-    --model_name_or_path t5-small --per_device_train_batch_size 1   \
-    --output_dir output_dir --overwrite_output_dir \
-    --do_train --max_train_samples 500 --num_train_epochs 1 \
-    --dataset_name wmt16 --dataset_config "ro-en" \
-    --source_lang en --target_lang ro \
-    --fp16 --sharded_ddp simple
-
-Notes:
-
-- This feature requires distributed training (so multiple GPUs).
-- It is not implemented for TPUs.
-- It works with ``--fp16`` too, to make things even faster.
-- One of the main benefits of enabling ``--sharded_ddp simple`` is that it uses a lot less GPU memory, so you should be
-  able to use significantly larger batch sizes using the same hardware (e.g. 3x and even bigger) which should lead to
-  significantly shorter training time.
-
-3. To use the second version of Sharded data-parallelism, add ``--sharded_ddp zero_dp_2`` or ``--sharded_ddp
-   zero_dp_3`` to the command line arguments, and make sure you have added the distributed launcher ``-m
+2. Add ``--sharded_ddp`` to the command line arguments, and make sure you have added the distributed launcher ``-m
    torch.distributed.launch --nproc_per_node=NUMBER_OF_GPUS_YOU_HAVE`` if you haven't been using it already.
 
-For example here is how you could use it for ``run_translation.py`` with 2 GPUs:
+For example here is how you could use it for ``finetune_trainer.py`` with 2 GPUs:
 
 .. code-block:: bash
 
-    python -m torch.distributed.launch --nproc_per_node=2 examples/pytorch/translation/run_translation.py \
-    --model_name_or_path t5-small --per_device_train_batch_size 1   \
+    cd examples/seq2seq
+    python -m torch.distributed.launch --nproc_per_node=2 ./finetune_trainer.py \
+    --model_name_or_path sshleifer/distill-mbart-en-ro-12-4 --data_dir wmt_en_ro \
     --output_dir output_dir --overwrite_output_dir \
-    --do_train --max_train_samples 500 --num_train_epochs 1 \
-    --dataset_name wmt16 --dataset_config "ro-en" \
-    --source_lang en --target_lang ro \
-    --fp16 --sharded_ddp zero_dp_2
-
-:obj:`zero_dp_2` is an optimized version of the simple wrapper, while :obj:`zero_dp_3` fully shards model weights,
-gradients and optimizer states.
-
-Both are compatible with adding :obj:`cpu_offload` to enable ZeRO-offload (activate it like this: :obj:`--sharded_ddp
-"zero_dp_2 cpu_offload"`).
+    --do_train --n_train 500 --num_train_epochs 1 \
+    --per_device_train_batch_size 1  --freeze_embeds \
+    --src_lang en_XX --tgt_lang ro_RO --task translation \
+    --fp16 --sharded_ddp
 
 Notes:
 
 - This feature requires distributed training (so multiple GPUs).
 - It is not implemented for TPUs.
 - It works with ``--fp16`` too, to make things even faster.
-- The ``cpu_offload`` additional option requires ``--fp16``.
-- This is an area of active development, so make sure you have a source install of fairscale to use this feature as
-  some bugs you encounter may have been fixed there already.
-
-Known caveats:
-
-- This feature is incompatible with :obj:`--predict_with_generate` in the `run_translation.py` script.
-- Using :obj:`--sharded_ddp zero_dp_3` requires wrapping each layer of the model in the special container
-  :obj:`FullyShardedDataParallelism` of fairscale. It should be used with the option :obj:`auto_wrap` if you are not
-  doing this yourself: :obj:`--sharded_ddp "zero_dp_3 auto_wrap"`.
+- One of the main benefits of enabling ``--sharded_ddp`` is that it uses a lot less GPU memory, so you should be able
+  to use significantly larger batch sizes using the same hardware (e.g. 3x and even bigger) which should lead to
+  significantly shorter training time.
 
 
 DeepSpeed
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 
-Moved to :ref:`deepspeed-trainer-integration`.
+`DeepSpeed <https://github.com/microsoft/DeepSpeed>`__ implements everything described in the `ZeRO paper
+<https://arxiv.org/abs/1910.02054>`__, except ZeRO's stage 3. "Parameter Partitioning (Pos+g+p)". Currently it provides
+full support for:
 
+1. Optimizer State Partitioning (ZeRO stage 1)
+2. Add Gradient Partitioning (ZeRO stage 2)
 
-Installation
-=======================================================================================================================
+To deploy this feature:
 
-Moved to :ref:`deepspeed-installation`.
+1. Install the library via pypi:
 
+   .. code-block:: bash
 
-Deployment with multiple GPUs
-=======================================================================================================================
+       pip install deepspeed
 
-Moved to :ref:`deepspeed-multi-gpu`.
+   or find more details on `the DeepSpeed's github page <https://github.com/microsoft/deepspeed#installation>`__.
 
+2. Adjust the :class:`~transformers.Trainer` command line arguments as following:
 
-Deployment with one GPU
-=======================================================================================================================
+   1. replace ``python -m torch.distributed.launch`` with ``deepspeed``.
+   2. add a new argument ``--deepspeed ds_config.json``, where ``ds_config.json`` is the DeepSpeed configuration file
+      as documented `here <https://www.deepspeed.ai/docs/config-json/>`__. The file naming is up to you.
 
-Moved to :ref:`deepspeed-one-gpu`.
+   Therefore, if your original command line looked as following:
 
+   .. code-block:: bash
 
-Deployment in Notebooks
-=======================================================================================================================
+       python -m torch.distributed.launch --nproc_per_node=2 your_program.py <normal cl args>
 
-Moved to :ref:`deepspeed-notebook`.
+   Now it should be:
 
+   .. code-block:: bash
 
-Configuration
-=======================================================================================================================
+       deepspeed --num_gpus=2 your_program.py <normal cl args> --deepspeed ds_config.json
 
-Moved to :ref:`deepspeed-config`.
+   Unlike, ``torch.distributed.launch`` where you have to specify how many GPUs to use with ``--nproc_per_node``, with
+   the ``deepspeed`` launcher you don't have to use the corresponding ``--num_gpus`` if you want all of your GPUs used.
+   The full details on how to configure various nodes and GPUs can be found `here
+   <https://www.deepspeed.ai/getting-started/#resource-configuration-multi-node>`__.
 
+   Here is an example of running ``finetune_trainer.py`` under DeepSpeed deploying all available GPUs:
 
-Passing Configuration
-=======================================================================================================================
+   .. code-block:: bash
 
-Moved to :ref:`deepspeed-config-passing`.
+       cd examples/seq2seq
+       deepspeed ./finetune_trainer.py --deepspeed ds_config.json \
+       --model_name_or_path sshleifer/distill-mbart-en-ro-12-4 --data_dir wmt_en_ro \
+       --output_dir output_dir --overwrite_output_dir \
+       --do_train --n_train 500 --num_train_epochs 1 \
+       --per_device_train_batch_size 1  --freeze_embeds \
+       --src_lang en_XX --tgt_lang ro_RO --task translation
 
+   Note that in the DeepSpeed documentation you are likely to see ``--deepspeed --deepspeed_config ds_config.json`` -
+   i.e. two DeepSpeed-related arguments, but for the sake of simplicity, and since there are already so many arguments
+   to deal with, we combined the two into a single argument.
 
-Shared Configuration
-=======================================================================================================================
+Before you can deploy DeepSpeed, let's discuss its configuration.
 
-Moved to :ref:`deepspeed-config-shared`.
+**Configuration:**
 
-ZeRO
-=======================================================================================================================
+For the complete guide to the DeepSpeed configuration options that can be used in its configuration file please refer
+to the `following documentation <https://www.deepspeed.ai/docs/config-json/>`__.
 
-Moved to :ref:`deepspeed-zero`.
+While you always have to supply the DeepSpeed configuration file, you can configure the DeepSpeed integration in
+several ways:
 
-ZeRO-2 Config
-+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+1. Supply most of the configuration inside the file, and just use a few required command line arguments. This is the
+   recommended way as it puts most of the configuration params in one place.
+2. Supply just the ZeRO configuration params inside the file, and configure the rest using the normal
+   :class:`~transformers.Trainer` command line arguments.
+3. Any variation of the first two ways.
 
-Moved to :ref:`deepspeed-zero2-config`.
+To get an idea of what DeepSpeed configuration file looks like, here is one that activates ZeRO stage 2 features,
+enables FP16, uses AdamW optimizer and WarmupLR scheduler:
 
-ZeRO-3 Config
-+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+.. code-block:: json
 
-Moved to :ref:`deepspeed-zero3-config`.
+    {
+        "fp16": {
+            "enabled": true,
+            "loss_scale": 0,
+            "loss_scale_window": 1000,
+            "hysteresis": 2,
+            "min_loss_scale": 1
+        },
 
+       "zero_optimization": {
+           "stage": 2,
+           "allgather_partitions": true,
+           "allgather_bucket_size": 5e8,
+           "overlap_comm": true,
+           "reduce_scatter": true,
+           "reduce_bucket_size": 5e8,
+           "contiguous_gradients": true,
+           "cpu_offload": true
+       },
 
-NVMe Support
-=======================================================================================================================
+       "optimizer": {
+         "type": "AdamW",
+         "params": {
+           "lr": 3e-5,
+           "betas": [ 0.8, 0.999 ],
+           "eps": 1e-8,
+           "weight_decay": 3e-7
+         }
+       },
+       "zero_allow_untested_optimizer": true,
 
-Moved to :ref:`deepspeed-nvme`.
+       "scheduler": {
+         "type": "WarmupLR",
+         "params": {
+           "warmup_min_lr": 0,
+           "warmup_max_lr": 3e-5,
+           "warmup_num_steps": 500
+         }
+       }
+    }
 
-ZeRO-2 vs ZeRO-3 Performance
-+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+If you already have a command line that you have been using with :class:`transformers.Trainer` args, you can continue
+using those and the :class:`~transformers.Trainer` will automatically convert them into the corresponding DeepSpeed
+configuration at run time. For example, you could use the following configuration file:
 
-Moved to :ref:`deepspeed-zero2-zero3-performance`.
+.. code-block:: json
 
-ZeRO-2 Example
-+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+    {
+       "zero_optimization": {
+           "stage": 2,
+           "allgather_partitions": true,
+           "allgather_bucket_size": 5e8,
+           "overlap_comm": true,
+           "reduce_scatter": true,
+           "reduce_bucket_size": 5e8,
+           "contiguous_gradients": true,
+           "cpu_offload": true
+       }
+    }
 
-Moved to :ref:`deepspeed-zero2-example`.
+and the following command line arguments:
 
-ZeRO-3 Example
-+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+.. code-block:: bash
 
-Moved to :ref:`deepspeed-zero3-example`.
+    --learning_rate 3e-5 --warmup_steps 500 --adam_beta1 0.8 --adam_beta2 0.999 --adam_epsilon 1e-8 \
+    --weight_decay 3e-7 --lr_scheduler_type constant_with_warmup --fp16 --fp16_backend amp
 
-Optimizer and Scheduler
-=======================================================================================================================
+to achieve the same configuration as provided by the longer json file in the first example.
+
+When you execute the program, DeepSpeed will log the configuration it received from the :class:`~transformers.Trainer`
+to the console, so you can see exactly what the final configuration was passed to it.
+
+**Shared Configuration:**
+
+Some configuration information is required by both the :class:`~transformers.Trainer` and DeepSpeed to function
+correctly, therefore, to prevent conflicting definitions, which could lead to hard to detect errors, we chose to
+configure those via the :class:`~transformers.Trainer` command line arguments.
+
+Therefore, the following DeepSpeed configuration params shouldn't be used with the :class:`~transformers.Trainer`:
+
+* ``train_batch_size``
+* ``train_micro_batch_size_per_gpu``
+* ``gradient_accumulation_steps``
+
+as these will be automatically derived from the run time environment and the following 2 command line arguments:
+
+.. code-block:: bash
+
+    --per_device_train_batch_size 8 --gradient_accumulation_steps 2
+
+which are always required to be supplied.
+
+Of course, you will need to adjust the values in this example to your situation.
 
 
 
-Optimizer
-+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+**ZeRO:**
 
-Moved to :ref:`deepspeed-optimizer`.
+The ``zero_optimization`` section of the configuration file is the most important part (`docs
+<https://www.deepspeed.ai/docs/config-json/#zero-optimizations-for-fp16-training>`__), since that is where you define
+which ZeRO stages you want to enable and how to configure them.
+
+.. code-block:: json
+
+    {
+       "zero_optimization": {
+           "stage": 2,
+           "allgather_partitions": true,
+           "allgather_bucket_size": 5e8,
+           "overlap_comm": true,
+           "reduce_scatter": true,
+           "reduce_bucket_size": 5e8,
+           "contiguous_gradients": true,
+           "cpu_offload": true
+       }
+    }
+
+Notes:
+
+- enabling ``cpu_offload`` should reduce GPU RAM usage (it requires ``"stage": 2``)
+- ``"overlap_comm": true`` trades off increased GPU RAM usage to lower all-reduce latency. ``overlap_comm`` uses 4.5x
+  the ``allgather_bucket_size`` and ``reduce_bucket_size`` values. So if they are set to 5e8, this requires a 9GB
+  footprint (``5e8 x 2Bytes x 2 x 4.5``). Therefore, if you have a GPU with 8GB or less RAM, to avoid getting
+  OOM-errors you will need to reduce those parameters to about ``2e8``, which would require 3.6GB.
+
+This section has to be configured exclusively via DeepSpeed configuration - the :class:`~transformers.Trainer` provides
+no equivalent command line arguments.
 
 
-Scheduler
-+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
-Moved to :ref:`deepspeed-scheduler`.
-
-fp32 Precision
-=======================================================================================================================
-
-Moved to :ref:`deepspeed-fp32`.
-
-Automatic Mixed Precision
-=======================================================================================================================
-
-Moved to :ref:`deepspeed-amp`.
-
-Batch Size
-=======================================================================================================================
-
-Moved to :ref:`deepspeed-bs`.
-
-Gradient Accumulation
-=======================================================================================================================
-
-Moved to :ref:`deepspeed-grad-acc`.
+**Optimizer:**
 
 
-Gradient Clipping
-=======================================================================================================================
+DeepSpeed's main optimizers are Adam, OneBitAdam, and Lamb. These have been thoroughly tested with ZeRO and are thus
+recommended to be used. It, however, can import other optimizers from ``torch``. The full documentation is `here
+<https://www.deepspeed.ai/docs/config-json/#optimizer-parameters>`__.
 
-Moved to :ref:`deepspeed-grad-clip`.
+If you don't configure the ``optimizer`` entry in the configuration file, the :class:`~transformers.Trainer` will
+automatically set it to ``AdamW`` and will use the supplied values or the defaults for the following command line
+arguments: ``--learning_rate``, ``--adam_beta1``, ``--adam_beta2``, ``--adam_epsilon`` and ``--weight_decay``.
+
+Here is an example of the pre-configured ``optimizer`` entry for AdamW:
+
+.. code-block:: json
+
+    {
+       "zero_allow_untested_optimizer": true,
+       "optimizer": {
+           "type": "AdamW",
+           "params": {
+             "lr": 0.001,
+             "betas": [0.8, 0.999],
+             "eps": 1e-8,
+             "weight_decay": 3e-7
+           }
+         }
+    }
+
+Since AdamW isn't on the list of tested with DeepSpeed/ZeRO optimizers, we have to add
+``zero_allow_untested_optimizer`` flag.
+
+If you want to use one of the officially supported optimizers, configure them explicitly in the configuration file, and
+make sure to adjust the values. e.g. if use Adam you will want ``weight_decay`` around ``0.01``.
 
 
-Getting The Model Weights Out
-=======================================================================================================================
+**Scheduler:**
 
-Moved to :ref:`deepspeed-weight-extraction`.
+DeepSpeed supports LRRangeTest, OneCycle, WarmupLR and WarmupDecayLR LR schedulers. The full documentation is `here
+<https://www.deepspeed.ai/docs/config-json/#scheduler-parameters>`__.
+
+If you don't configure the ``scheduler`` entry in the configuration file, the :class:`~transformers.Trainer` will use
+the value of ``--lr_scheduler_type`` to configure it. Currently the :class:`~transformers.Trainer` supports only 2 LR
+schedulers that are also supported by DeepSpeed:
+
+* ``WarmupLR`` via ``--lr_scheduler_type constant_with_warmup``
+* ``WarmupDecayLR`` via ``--lr_scheduler_type linear``. This is also the default value for ``--lr_scheduler_type``,
+  therefore, if you don't configure the scheduler this is scheduler that will get configured by default.
+
+In either case, the values of ``--learning_rate`` and ``--warmup_steps`` will be used for the configuration.
+
+In other words, if you don't use the configuration file to set the ``scheduler`` entry, provide either:
+
+.. code-block:: bash
+
+    --lr_scheduler_type constant_with_warmup --learning_rate 3e-5 --warmup_steps 500
+
+or
+
+.. code-block:: bash
+
+    --lr_scheduler_type linear --learning_rate 3e-5 --warmup_steps 500
+
+with the desired values. If you don't pass these arguments, reasonable default values will be used instead.
+
+In the case of WarmupDecayLR ``total_num_steps`` gets set either via the ``--max_steps`` command line argument, or if
+it is not provided, derived automatically at run time based on the environment and the size of the dataset and other
+command line arguments.
+
+Here is an example of the pre-configured ``scheduler`` entry for WarmupLR (``constant_with_warmup`` in the
+:class:`~transformers.Trainer` API):
+
+.. code-block:: json
+
+    {
+       "scheduler": {
+             "type": "WarmupLR",
+             "params": {
+                 "warmup_min_lr": 0,
+                 "warmup_max_lr": 0.001,
+                 "warmup_num_steps": 1000
+             }
+         }
+    }
+
+**Automatic Mixed Precision:**
+
+You can work with FP16 in one of the following ways:
+
+1. Pytorch native amp, as documented `here <https://www.deepspeed.ai/docs/config-json/#fp16-training-options>`__.
+2. NVIDIA's apex, as documented `here
+   <https://www.deepspeed.ai/docs/config-json/#automatic-mixed-precision-amp-training-options>`__.
+
+If you want to use an equivalent of the pytorch native amp, you can either configure the ``fp16`` entry in the
+configuration file, or use the following command line arguments: ``--fp16 --fp16_backend amp``.
+
+Here is an example of the ``fp16`` configuration:
+
+.. code-block:: json
+
+    {
+        "fp16": {
+            "enabled": true,
+            "loss_scale": 0,
+            "loss_scale_window": 1000,
+            "hysteresis": 2,
+            "min_loss_scale": 1
+        },
+    }
+
+If you want to use NVIDIA's apex instead, you can can either configure the ``amp`` entry in the configuration file, or
+use the following command line arguments: ``--fp16 --fp16_backend apex --fp16_opt_level 01``.
+
+Here is an example of the ``amp`` configuration:
+
+.. code-block:: json
+
+    {
+        "amp": {
+            "enabled": true,
+            "opt_level": "O1"
+        }
+    }
+
+
+
+**Gradient Clipping:**
+
+If you don't configure the ``gradient_clipping`` entry in the configuration file, the :class:`~transformers.Trainer`
+will use the value of the ``--max_grad_norm`` command line argument to set it.
+
+Here is an example of the ``gradient_clipping`` configuration:
+
+.. code-block:: json
+
+    {
+        "gradient_clipping": 1.0,
+    }
+
+
+
+**Notes:**
+
+* DeepSpeed works with the PyTorch :class:`~transformers.Trainer` but not TF :class:`~transformers.TFTrainer`.
+* While DeepSpeed has a pip installable PyPI package, it is highly recommended that it gets installed from `source
+  <https://github.com/microsoft/deepspeed#installation>`__ to best match your hardware and also if you need to enable
+  certain features, like 1-bit Adam, which aren't available in the pypi distribution.
+* You don't have to use the :class:`~transformers.Trainer` to use DeepSpeed with HuggingFace ``transformers`` - you can
+  use any model with your own trainer, and you will have to adapt the latter according to `the DeepSpeed integration
+  instructions <https://www.deepspeed.ai/getting-started/#writing-deepspeed-models>`__.
+
+**Main DeepSpeed Resources:**
+
+- `github <https://github.com/microsoft/deepspeed>`__
+- `Usage docs <https://www.deepspeed.ai/getting-started/>`__
+- `API docs <https://deepspeed.readthedocs.io/en/latest/index.html>`__
+
+Finally, please, remember that, HuggingFace :class:`~transformers.Trainer` only integrates DeepSpeed, therefore if you
+have any problems or questions with regards to DeepSpeed usage, please, file an issue with `DeepSpeed github
+<https://github.com/microsoft/DeepSpeed/issues>`__.

docs/source/migration.md

@@ -23,7 +23,7 @@ expected changes:
 
 #### 1. AutoTokenizers and pipelines now use fast (rust) tokenizers by default.
 
-The python and rust tokenizers have roughly the same API, but the rust tokenizers have a more complete feature set.
+The python and rust tokenizers have roughly the same API, but the rust tokenizers have a more complete feature set. 
 
 This introduces two breaking changes:
 - The handling of overflowing tokens between the python and rust tokenizers is different.
@@ -85,7 +85,7 @@ This is a breaking change as importing intermediary layers using a model's modul
 
 ##### How to obtain the same behavior as v3.x in v4.x
 
-In order to obtain the same behavior as version `v3.x`, you should update the path used to access the layers.
+In order to obtain the same behavior as version `v3.x`, you should update the path used to access the layers. 
 
 In version `v3.x`:
 ```bash
@@ -169,8 +169,8 @@ Regarding the `TFTrainer` class:
 - The `TFTrainer` method `_setup_wandb` is deprecated in favor of `setup_wandb`.
 - The `TFTrainer` method `_run_model` is deprecated in favor of `run_model`.
 
-Regarding the `TrainingArguments` class:
-- The `TrainingArguments` argument `evaluate_during_training` is deprecated in favor of `evaluation_strategy`.
+Regarding the `TrainerArgument` class:
+- The `TrainerArgument` argument `evaluate_during_training` is deprecated in favor of `evaluation_strategy`.
 
 Regarding the Transfo-XL model:
 - The Transfo-XL configuration attribute `tie_weight` becomes `tie_words_embeddings`.

docs/source/model_doc/albert.rst

@@ -43,9 +43,7 @@ Tips:
   similar to a BERT-like architecture with the same number of hidden layers as it has to iterate through the same
   number of (repeating) layers.
 
-This model was contributed by `lysandre <https://huggingface.co/lysandre>`__. This model jax version was contributed by
-`kamalkraj <https://huggingface.co/kamalkraj>`__. The original code can be found `here
-<https://github.com/google-research/ALBERT>`__.
+The original code can be found `here <https://github.com/google-research/ALBERT>`__.
 
 AlbertConfig
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -175,52 +173,3 @@ TFAlbertForQuestionAnswering
 
 .. autoclass:: transformers.TFAlbertForQuestionAnswering
     :members: call
-
-
-FlaxAlbertModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxAlbertModel
-    :members: __call__
-
-
-FlaxAlbertForPreTraining
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxAlbertForPreTraining
-    :members: __call__
-
-
-FlaxAlbertForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxAlbertForMaskedLM
-    :members: __call__
-
-
-FlaxAlbertForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxAlbertForSequenceClassification
-    :members: __call__
-
-
-FlaxAlbertForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxAlbertForMultipleChoice
-    :members: __call__
-
-
-FlaxAlbertForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxAlbertForTokenClassification
-    :members: __call__
-
-
-FlaxAlbertForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxAlbertForQuestionAnswering
-    :members: __call__

docs/source/model_doc/auto.rst

@@ -44,13 +44,6 @@ AutoTokenizer
     :members:
 
 
-AutoFeatureExtractor
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AutoFeatureExtractor
-    :members:
-
-
 AutoModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -128,41 +121,6 @@ AutoModelForTableQuestionAnswering
     :members:
 
 
-AutoModelForImageClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AutoModelForImageClassification
-    :members:
-
-
-AutoModelForAudioClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AutoModelForAudioClassification
-    :members:
-
-
-AutoModelForCTC
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AutoModelForCTC
-    :members:
-
-
-AutoModelForSpeechSeq2Seq
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AutoModelForSpeechSeq2Seq
-    :members:
-
-
-AutoModelForObjectDetection
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AutoModelForObjectDetection
-    :members:
-
-
 TFAutoModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -231,73 +189,3 @@ FlaxAutoModel
 
 .. autoclass:: transformers.FlaxAutoModel
     :members:
-
-
-FlaxAutoModelForCausalLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxAutoModelForCausalLM
-    :members:
-
-
-FlaxAutoModelForPreTraining
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxAutoModelForPreTraining
-    :members:
-
-
-FlaxAutoModelForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxAutoModelForMaskedLM
-    :members:
-
-
-FlaxAutoModelForSeq2SeqLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxAutoModelForSeq2SeqLM
-    :members:
-
-
-FlaxAutoModelForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxAutoModelForSequenceClassification
-    :members:
-
-
-FlaxAutoModelForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxAutoModelForQuestionAnswering
-    :members:
-
-
-FlaxAutoModelForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxAutoModelForTokenClassification
-    :members:
-
-
-FlaxAutoModelForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxAutoModelForMultipleChoice
-    :members:
-
-
-FlaxAutoModelForNextSentencePrediction
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxAutoModelForNextSentencePrediction
-    :members:
-
-
-FlaxAutoModelForImageClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxAutoModelForImageClassification
-    :members:

docs/source/model_doc/bart.rst

@@ -35,15 +35,14 @@ According to the abstract,
   state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains
   of up to 6 ROUGE.
 
-This model was contributed by `sshleifer <https://huggingface.co/sshleifer>`__. The Authors' code can be found `here
-<https://github.com/pytorch/fairseq/tree/master/examples/bart>`__.
+The Authors' code can be found `here <https://github.com/pytorch/fairseq/tree/master/examples/bart>`__.
 
 
 Examples
 _______________________________________________________________________________________________________________________
 
 - Examples and scripts for fine-tuning BART and other models for sequence to sequence tasks can be found in
-  :prefix_link:`examples/pytorch/summarization/ <examples/pytorch/summarization/README.md>`.
+  :prefix_link:`examples/seq2seq/ <examples/seq2seq/README.md>`.
 - An example of how to train :class:`~transformers.BartForConditionalGeneration` with a Hugging Face :obj:`datasets`
   object can be found in this `forum discussion
   <https://discuss.huggingface.co/t/train-bart-for-conditional-generation-e-g-summarization/1904>`__.
@@ -61,7 +60,7 @@ Implementation Notes
 - Model predictions are intended to be identical to the original implementation when
   :obj:`force_bos_token_to_be_generated=True`. This only works, however, if the string you pass to
   :func:`fairseq.encode` starts with a space.
-- :meth:`~transformers.generation_utils.GenerationMixin.generate` should be used for conditional generation tasks like
+- :meth:`~transformers.BartForConditionalGeneration.generate` should be used for conditional generation tasks like
   summarization, see the example in that docstrings.
 - Models that load the `facebook/bart-large-cnn` weights will not have a :obj:`mask_token_id`, or be able to perform
   mask-filling tasks.
@@ -132,12 +131,6 @@ BartForQuestionAnswering
     :members: forward
 
 
-BartForCausalLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BartForCausalLM
-    :members: forward
-
 
 TFBartModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -151,32 +144,3 @@ TFBartForConditionalGeneration
 
 .. autoclass:: transformers.TFBartForConditionalGeneration
     :members: call
-
-
-FlaxBartModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxBartModel
-    :members: __call__, encode, decode
-
-
-FlaxBartForConditionalGeneration
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxBartForConditionalGeneration
-    :members: __call__, encode, decode
-
-
-FlaxBartForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxBartForSequenceClassification
-    :members: __call__, encode, decode
-
-
-FlaxBartForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxBartForQuestionAnswering
-    :members: __call__, encode, decode
-

docs/source/model_doc/barthez.rst

@@ -16,7 +16,7 @@ BARThez
 Overview
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-The BARThez model was proposed in `BARThez: a Skilled Pretrained French Sequence-to-Sequence Model
+The BARThez model was proposed in `BARThez: a Skilled Pretrained French Sequence-to-Sequence Model`
 <https://arxiv.org/abs/2010.12321>`__ by Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis on 23 Oct,
 2020.
 
@@ -35,15 +35,14 @@ summarization dataset, OrangeSum, that we release with this paper. We also conti
 pretrained multilingual BART on BARThez's corpus, and we show that the resulting model, which we call mBARTHez,
 provides a significant boost over vanilla BARThez, and is on par with or outperforms CamemBERT and FlauBERT.*
 
-This model was contributed by `moussakam <https://huggingface.co/moussakam>`__. The Authors' code can be found `here
-<https://github.com/moussaKam/BARThez>`__.
+The Authors' code can be found `here <https://github.com/moussaKam/BARThez>`__.
 
 
 Examples
 _______________________________________________________________________________________________________________________
 
 - BARThez can be fine-tuned on sequence-to-sequence tasks in a similar way as BART, check:
-  :prefix_link:`examples/pytorch/summarization/ <examples/pytorch/summarization/README.md>`.
+  :prefix_link:`examples/seq2seq/ <examples/seq2seq/README.md>`.
 
 
 BarthezTokenizer

docs/source/model_doc/beit.rst

@@ -1,119 +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.
-
-BEiT
------------------------------------------------------------------------------------------------------------------------
-
-Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The BEiT model was proposed in `BEiT: BERT Pre-Training of Image Transformers <https://arxiv.org/abs/2106.08254>`__ by
-Hangbo Bao, Li Dong and Furu Wei. Inspired by BERT, BEiT is the first paper that makes self-supervised pre-training of
-Vision Transformers (ViTs) outperform supervised pre-training. Rather than pre-training the model to predict the class
-of an image (as done in the `original ViT paper <https://arxiv.org/abs/2010.11929>`__), BEiT models are pre-trained to
-predict visual tokens from the codebook of OpenAI's `DALL-E model <https://arxiv.org/abs/2102.12092>`__ given masked
-patches.
-
-The abstract from the paper is the following:
-
-*We introduce a self-supervised vision representation model BEiT, which stands for Bidirectional Encoder representation
-from Image Transformers. Following BERT developed in the natural language processing area, we propose a masked image
-modeling task to pretrain vision Transformers. Specifically, each image has two views in our pre-training, i.e, image
-patches (such as 16x16 pixels), and visual tokens (i.e., discrete tokens). We first "tokenize" the original image into
-visual tokens. Then we randomly mask some image patches and fed them into the backbone Transformer. The pre-training
-objective is to recover the original visual tokens based on the corrupted image patches. After pre-training BEiT, we
-directly fine-tune the model parameters on downstream tasks by appending task layers upon the pretrained encoder.
-Experimental results on image classification and semantic segmentation show that our model achieves competitive results
-with previous pre-training methods. For example, base-size BEiT achieves 83.2% top-1 accuracy on ImageNet-1K,
-significantly outperforming from-scratch DeiT training (81.8%) with the same setup. Moreover, large-size BEiT obtains
-86.3% only using ImageNet-1K, even outperforming ViT-L with supervised pre-training on ImageNet-22K (85.2%).*
-
-Tips:
-
-- BEiT models are regular Vision Transformers, but pre-trained in a self-supervised way rather than supervised. They
-  outperform both the original model (ViT) as well as Data-efficient Image Transformers (DeiT) when fine-tuned on
-  ImageNet-1K and CIFAR-100.
-- As the BEiT models expect each image to be of the same size (resolution), one can use
-  :class:`~transformers.BeitFeatureExtractor` to resize (or rescale) and normalize images for the model.
-- Both the patch resolution and image resolution used during pre-training or fine-tuning are reflected in the name of
-  each checkpoint. For example, :obj:`microsoft/beit-base-patch16-224` refers to a base-sized architecture with patch
-  resolution of 16x16 and fine-tuning resolution of 224x224. All checkpoints can be found on the `hub
-  <https://huggingface.co/models?search=microsoft/beit>`__.
-- The available checkpoints are either (1) pre-trained on `ImageNet-22k <http://www.image-net.org/>`__ (a collection of
-  14 million images and 22k classes) only, (2) also fine-tuned on ImageNet-22k or (3) also fine-tuned on `ImageNet-1k
-  <http://www.image-net.org/challenges/LSVRC/2012/>`__ (also referred to as ILSVRC 2012, a collection of 1.3 million
-  images and 1,000 classes).
-- BEiT uses relative position embeddings, inspired by the T5 model. During pre-training, the authors shared the
-  relative position bias among the several self-attention layers. During fine-tuning, each layer's relative position
-  bias is initialized with the shared relative position bias obtained after pre-training. Note that, if one wants to
-  pre-train a model from scratch, one needs to either set the :obj:`use_relative_position_bias` or the
-  :obj:`use_relative_position_bias` attribute of :class:`~transformers.BeitConfig` to :obj:`True` in order to add
-  position embeddings.
-
-This model was contributed by `nielsr <https://huggingface.co/nielsr>`__. The JAX/FLAX version of this model was
-contributed by `kamalkraj <https://huggingface.co/kamalkraj>`__. The original code can be found `here
-<https://github.com/microsoft/unilm/tree/master/beit>`__.
-
-BeitConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BeitConfig
-    :members:
-
-
-BeitFeatureExtractor
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BeitFeatureExtractor
-    :members: __call__
-
-
-BeitModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BeitModel
-    :members: forward
-
-
-BeitForMaskedImageModeling
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BeitForMaskedImageModeling
-    :members: forward
-
-
-BeitForImageClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BeitForImageClassification
-    :members: forward
-
-
-FlaxBeitModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxBeitModel
-    :members: __call__
-
-
-FlaxBeitForMaskedImageModeling
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxBeitForMaskedImageModeling
-    :members: __call__
-
-
-FlaxBeitForImageClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxBeitForImageClassification
-    :members: __call__

docs/source/model_doc/bert.rst

@@ -42,8 +42,7 @@ Tips:
 - BERT was trained with the masked language modeling (MLM) and next sentence prediction (NSP) objectives. It is
   efficient at predicting masked tokens and at NLU in general, but is not optimal for text generation.
 
-This model was contributed by `thomwolf <https://huggingface.co/thomwolf>`__. The original code can be found `here
-<https://github.com/google-research/bert>`__.
+The original code can be found `here <https://github.com/google-research/bert>`__.
 
 BertConfig
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -76,9 +75,6 @@ Bert specific outputs
 .. autoclass:: transformers.models.bert.modeling_tf_bert.TFBertForPreTrainingOutput
     :members:
 
-.. autoclass:: transformers.models.bert.modeling_flax_bert.FlaxBertForPreTrainingOutput
-    :members:
-
 
 BertModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -94,7 +90,7 @@ BertForPreTraining
     :members: forward
 
 
-BertLMHeadModel
+BertModelLMHeadModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.BertLMHeadModel
@@ -213,50 +209,8 @@ FlaxBertModel
     :members: __call__
 
 
-FlaxBertForPreTraining
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxBertForPreTraining
-    :members: __call__
-
-
 FlaxBertForMaskedLM
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.FlaxBertForMaskedLM
     :members: __call__
-
-
-FlaxBertForNextSentencePrediction
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxBertForNextSentencePrediction
-    :members: __call__
-
-
-FlaxBertForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxBertForSequenceClassification
-    :members: __call__
-
-
-FlaxBertForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxBertForMultipleChoice
-    :members: __call__
-
-
-FlaxBertForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxBertForTokenClassification
-    :members: __call__
-
-
-FlaxBertForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxBertForQuestionAnswering
-    :members: __call__

docs/source/model_doc/bert_japanese.rst

@@ -1,80 +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.
-
-BertJapanese
------------------------------------------------------------------------------------------------------------------------
-
-Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The BERT models trained on Japanese text.
-
-There are models with two different tokenization methods:
-
-- Tokenize with MeCab and WordPiece. This requires some extra dependencies, `fugashi
-  <https://github.com/polm/fugashi>`__ which is a wrapper around `MeCab <https://taku910.github.io/mecab/>`__.
-- Tokenize into characters.
-
-To use `MecabTokenizer`, you should ``pip install transformers["ja"]`` (or ``pip install -e .["ja"]`` if you install
-from source) to install dependencies.
-
-See `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__.
-
-Example of using a model with MeCab and WordPiece tokenization:
-
-.. code-block::
-
-    >>> import torch
-    >>> from transformers import AutoModel, AutoTokenizer 
-
-    >>> bertjapanese = AutoModel.from_pretrained("cl-tohoku/bert-base-japanese")
-    >>> tokenizer = AutoTokenizer.from_pretrained("cl-tohoku/bert-base-japanese")
-
-    >>> ## Input Japanese Text
-    >>> line = "吾輩は猫である。"
-
-    >>> inputs = tokenizer(line, return_tensors="pt")
-
-    >>> print(tokenizer.decode(inputs['input_ids'][0]))
-    [CLS] 吾輩 は 猫 で ある 。 [SEP]
-
-    >>> outputs = bertjapanese(**inputs)
-
-Example of using a model with Character tokenization:
-
-.. code-block::
-
-    >>> bertjapanese = AutoModel.from_pretrained("cl-tohoku/bert-base-japanese-char")
-    >>> tokenizer = AutoTokenizer.from_pretrained("cl-tohoku/bert-base-japanese-char")
-
-    >>> ## Input Japanese Text
-    >>> line = "吾輩は猫である。"
-
-    >>> inputs = tokenizer(line, return_tensors="pt")
-
-    >>> print(tokenizer.decode(inputs['input_ids'][0]))
-    [CLS] 吾 輩 は 猫 で あ る 。 [SEP]
-
-    >>> outputs = bertjapanese(**inputs)
-
-Tips:
-
-- This implementation is the same as BERT, except for tokenization method. Refer to the :doc:`documentation of BERT
-  <bert>` for more usage examples.
-
-This model was contributed by `cl-tohoku <https://huggingface.co/cl-tohoku>`__.
-
-BertJapaneseTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BertJapaneseTokenizer
-    :members: 

docs/source/model_doc/bertgeneration.rst

@@ -38,22 +38,22 @@ Usage:
 
 .. code-block::
 
-    >>> # leverage checkpoints for Bert2Bert model...
-    >>> # use BERT's cls token as BOS token and sep token as EOS token
-    >>> encoder = BertGenerationEncoder.from_pretrained("bert-large-uncased", bos_token_id=101, eos_token_id=102)
-    >>> # add cross attention layers and use BERT's cls token as BOS token and sep token as EOS token
-    >>> decoder = BertGenerationDecoder.from_pretrained("bert-large-uncased", add_cross_attention=True, is_decoder=True, bos_token_id=101, eos_token_id=102)
-    >>> bert2bert = EncoderDecoderModel(encoder=encoder, decoder=decoder)
+  # leverage checkpoints for Bert2Bert model...
+  # use BERT's cls token as BOS token and sep token as EOS token
+  encoder = BertGenerationEncoder.from_pretrained("bert-large-uncased", bos_token_id=101, eos_token_id=102)
+  # add cross attention layers and use BERT's cls token as BOS token and sep token as EOS token
+  decoder = BertGenerationDecoder.from_pretrained("bert-large-uncased", add_cross_attention=True, is_decoder=True, bos_token_id=101, eos_token_id=102)
+  bert2bert = EncoderDecoderModel(encoder=encoder, decoder=decoder)
 
-    >>> # create tokenizer...
-    >>> tokenizer = BertTokenizer.from_pretrained("bert-large-uncased")
+  # create tokenizer...
+  tokenizer = BertTokenizer.from_pretrained("bert-large-uncased")
 
-    >>> input_ids = tokenizer('This is a long article to summarize', add_special_tokens=False, return_tensors="pt").input_ids
-    >>> labels = tokenizer('This is a short summary', return_tensors="pt").input_ids
+  input_ids = tokenizer('This is a long article to summarize', add_special_tokens=False, return_tensors="pt").input_ids
+  labels = tokenizer('This is a short summary', return_tensors="pt").input_ids
 
-    >>> # train...
-    >>> loss = bert2bert(input_ids=input_ids, decoder_input_ids=labels, labels=labels).loss
-    >>> loss.backward()
+  # train...
+  loss = bert2bert(input_ids=input_ids, decoder_input_ids=labels, labels=labels).loss
+  loss.backward()
 
 
 - Pretrained :class:`~transformers.EncoderDecoderModel` are also directly available in the model hub, e.g.,
@@ -61,15 +61,15 @@ Usage:
 
 .. code-block::
 
-    >>> # instantiate sentence fusion model
-    >>> sentence_fuser = EncoderDecoderModel.from_pretrained("google/roberta2roberta_L-24_discofuse")
-    >>> tokenizer = AutoTokenizer.from_pretrained("google/roberta2roberta_L-24_discofuse")
+  # instantiate sentence fusion model
+  sentence_fuser = EncoderDecoderModel.from_pretrained("google/roberta2roberta_L-24_discofuse")
+  tokenizer = AutoTokenizer.from_pretrained("google/roberta2roberta_L-24_discofuse")
 
-    >>> input_ids = tokenizer('This is the first sentence. This is the second sentence.', add_special_tokens=False, return_tensors="pt").input_ids
+  input_ids = tokenizer('This is the first sentence. This is the second sentence.', add_special_tokens=False, return_tensors="pt").input_ids
 
-    >>> outputs = sentence_fuser.generate(input_ids)
+  outputs = sentence_fuser.generate(input_ids)
 
-    >>> print(tokenizer.decode(outputs[0]))
+  print(tokenizer.decode(outputs[0]))
 
 
 Tips:
@@ -79,8 +79,7 @@ Tips:
 - For summarization, sentence splitting, sentence fusion and translation, no special tokens are required for the input.
   Therefore, no EOS token should be added to the end of the input.
 
-This model was contributed by `patrickvonplaten <https://huggingface.co/patrickvonplaten>`__. The original code can be
-found `here <https://tfhub.dev/s?module-type=text-generation&subtype=module,placeholder>`__.
+The original code can be found `here <https://tfhub.dev/s?module-type=text-generation&subtype=module,placeholder>`__.
 
 BertGenerationConfig
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

docs/source/model_doc/bertweet.rst

@@ -31,31 +31,31 @@ Example of use:
 
 .. code-block::
 
-    >>> import torch
-    >>> from transformers import AutoModel, AutoTokenizer 
+  import torch
+  from transformers import AutoModel, AutoTokenizer 
 
-    >>> bertweet = AutoModel.from_pretrained("vinai/bertweet-base")
+  bertweet = AutoModel.from_pretrained("vinai/bertweet-base")
 
-    >>> # For transformers v4.x+: 
-    >>> tokenizer = AutoTokenizer.from_pretrained("vinai/bertweet-base", use_fast=False)
+  # For transformers v4.x+: 
+  tokenizer = AutoTokenizer.from_pretrained("vinai/bertweet-base", use_fast=False)
 
-    >>> # For transformers v3.x: 
-    >>> # tokenizer = AutoTokenizer.from_pretrained("vinai/bertweet-base")
+  # For transformers v3.x: 
+  # tokenizer = AutoTokenizer.from_pretrained("vinai/bertweet-base")
 
-    >>> # INPUT TWEET IS ALREADY NORMALIZED!
-    >>> line = "SC has first two presumptive cases of coronavirus , DHEC confirms HTTPURL via @USER :cry:"
+  # INPUT TWEET IS ALREADY NORMALIZED!
+  line = "SC has first two presumptive cases of coronavirus , DHEC confirms HTTPURL via @USER :cry:"
 
-    >>> input_ids = torch.tensor([tokenizer.encode(line)])
+  input_ids = torch.tensor([tokenizer.encode(line)])
 
-    >>> with torch.no_grad():
-    ...     features = bertweet(input_ids)  # Models outputs are now tuples
+  with torch.no_grad():
+      features = bertweet(input_ids)  # Models outputs are now tuples
 
-    >>> # With TensorFlow 2.0+:
-    >>> # from transformers import TFAutoModel
-    >>> # bertweet = TFAutoModel.from_pretrained("vinai/bertweet-base")
+  ## With TensorFlow 2.0+:
+  # from transformers import TFAutoModel
+  # bertweet = TFAutoModel.from_pretrained("vinai/bertweet-base")
 
-This model was contributed by `dqnguyen <https://huggingface.co/dqnguyen>`__. The original code can be found `here
-<https://github.com/VinAIResearch/BERTweet>`__.
+
+The original code can be found `here <https://github.com/VinAIResearch/BERTweet>`__.
 
 BertweetTokenizer
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

docs/source/model_doc/bigbird.rst

@@ -1,185 +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.
-
-BigBird
------------------------------------------------------------------------------------------------------------------------
-
-Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The BigBird model was proposed in `Big Bird: Transformers for Longer Sequences <https://arxiv.org/abs/2007.14062>`__ by
-Zaheer, Manzil and Guruganesh, Guru and Dubey, Kumar Avinava and Ainslie, Joshua and Alberti, Chris and Ontanon,
-Santiago and Pham, Philip and Ravula, Anirudh and Wang, Qifan and Yang, Li and others. BigBird, is a sparse-attention
-based transformer which extends Transformer based models, such as BERT to much longer sequences. In addition to sparse
-attention, BigBird also applies global attention as well as random attention to the input sequence. Theoretically, it
-has been shown that applying sparse, global, and random attention approximates full attention, while being
-computationally much more efficient for longer sequences. As a consequence of the capability to handle longer context,
-BigBird has shown improved performance on various long document NLP tasks, such as question answering and
-summarization, compared to BERT or RoBERTa.
-
-The abstract from the paper is the following:
-
-*Transformers-based models, such as BERT, have been one of the most successful deep learning models for NLP.
-Unfortunately, one of their core limitations is the quadratic dependency (mainly in terms of memory) on the sequence
-length due to their full attention mechanism. To remedy this, we propose, BigBird, a sparse attention mechanism that
-reduces this quadratic dependency to linear. We show that BigBird is a universal approximator of sequence functions and
-is Turing complete, thereby preserving these properties of the quadratic, full attention model. Along the way, our
-theoretical analysis reveals some of the benefits of having O(1) global tokens (such as CLS), that attend to the entire
-sequence as part of the sparse attention mechanism. The proposed sparse attention can handle sequences of length up to
-8x of what was previously possible using similar hardware. As a consequence of the capability to handle longer context,
-BigBird drastically improves performance on various NLP tasks such as question answering and summarization. We also
-propose novel applications to genomics data.*
-
-Tips:
-
-- For an in-detail explanation on how BigBird's attention works, see `this blog post
-  <https://huggingface.co/blog/big-bird>`__.
-- BigBird comes with 2 implementations: **original_full** & **block_sparse**. For the sequence length < 1024, using
-  **original_full** is advised as there is no benefit in using **block_sparse** attention.
-- The code currently uses window size of 3 blocks and 2 global blocks.
-- Sequence length must be divisible by block size.
-- Current implementation supports only **ITC**.
-- Current implementation doesn't support **num_random_blocks = 0**
-
-This model was contributed by `vasudevgupta <https://huggingface.co/vasudevgupta>`__. The original code can be found
-`here <https://github.com/google-research/bigbird>`__.
-
-BigBirdConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BigBirdConfig
-    :members:
-
-
-BigBirdTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BigBirdTokenizer
-    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
-        create_token_type_ids_from_sequences, save_vocabulary
-
-BigBirdTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BigBirdTokenizerFast
-    :members:
-
-BigBird specific outputs
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.models.big_bird.modeling_big_bird.BigBirdForPreTrainingOutput
-    :members:
-
-
-BigBirdModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BigBirdModel
-    :members: forward
-
-
-BigBirdForPreTraining
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BigBirdForPreTraining
-    :members: forward
-
-
-BigBirdForCausalLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BigBirdForCausalLM
-    :members: forward
-
-
-BigBirdForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BigBirdForMaskedLM
-    :members: forward
-
-
-BigBirdForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BigBirdForSequenceClassification
-    :members: forward
-
-
-BigBirdForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BigBirdForMultipleChoice
-    :members: forward
-
-
-BigBirdForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BigBirdForTokenClassification
-    :members: forward
-
-
-BigBirdForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BigBirdForQuestionAnswering
-    :members: forward
-
-
-FlaxBigBirdModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxBigBirdModel
-    :members: __call__
-
-
-FlaxBigBirdForPreTraining
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxBigBirdForPreTraining
-    :members: __call__
-
-
-FlaxBigBirdForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxBigBirdForMaskedLM
-    :members: __call__
-
-
-FlaxBigBirdForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxBigBirdForSequenceClassification
-    :members: __call__
-
-
-FlaxBigBirdForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxBigBirdForMultipleChoice
-    :members: __call__
-
-
-FlaxBigBirdForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxBigBirdForTokenClassification
-    :members: __call__
-
-
-FlaxBigBirdForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxBigBirdForQuestionAnswering
-    :members: __call__

docs/source/model_doc/bigbird_pegasus.rst

@@ -1,98 +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.
-
-BigBirdPegasus
------------------------------------------------------------------------------------------------------------------------
-
-Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The BigBird model was proposed in `Big Bird: Transformers for Longer Sequences <https://arxiv.org/abs/2007.14062>`__ by
-Zaheer, Manzil and Guruganesh, Guru and Dubey, Kumar Avinava and Ainslie, Joshua and Alberti, Chris and Ontanon,
-Santiago and Pham, Philip and Ravula, Anirudh and Wang, Qifan and Yang, Li and others. BigBird, is a sparse-attention
-based transformer which extends Transformer based models, such as BERT to much longer sequences. In addition to sparse
-attention, BigBird also applies global attention as well as random attention to the input sequence. Theoretically, it
-has been shown that applying sparse, global, and random attention approximates full attention, while being
-computationally much more efficient for longer sequences. As a consequence of the capability to handle longer context,
-BigBird has shown improved performance on various long document NLP tasks, such as question answering and
-summarization, compared to BERT or RoBERTa.
-
-The abstract from the paper is the following:
-
-*Transformers-based models, such as BERT, have been one of the most successful deep learning models for NLP.
-Unfortunately, one of their core limitations is the quadratic dependency (mainly in terms of memory) on the sequence
-length due to their full attention mechanism. To remedy this, we propose, BigBird, a sparse attention mechanism that
-reduces this quadratic dependency to linear. We show that BigBird is a universal approximator of sequence functions and
-is Turing complete, thereby preserving these properties of the quadratic, full attention model. Along the way, our
-theoretical analysis reveals some of the benefits of having O(1) global tokens (such as CLS), that attend to the entire
-sequence as part of the sparse attention mechanism. The proposed sparse attention can handle sequences of length up to
-8x of what was previously possible using similar hardware. As a consequence of the capability to handle longer context,
-BigBird drastically improves performance on various NLP tasks such as question answering and summarization. We also
-propose novel applications to genomics data.*
-
-Tips:
-
-- For an in-detail explanation on how BigBird's attention works, see `this blog post
-  <https://huggingface.co/blog/big-bird>`__.
-- BigBird comes with 2 implementations: **original_full** & **block_sparse**. For the sequence length < 1024, using
-  **original_full** is advised as there is no benefit in using **block_sparse** attention.
-- The code currently uses window size of 3 blocks and 2 global blocks.
-- Sequence length must be divisible by block size.
-- Current implementation supports only **ITC**.
-- Current implementation doesn't support **num_random_blocks = 0**.
-- BigBirdPegasus uses the `PegasusTokenizer
-  <https://github.com/huggingface/transformers/blob/master/src/transformers/models/pegasus/tokenization_pegasus.py>`__.
-
-The original code can be found `here <https://github.com/google-research/bigbird>`__.
-
-BigBirdPegasusConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BigBirdPegasusConfig
-    :members:
-
-
-BigBirdPegasusModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BigBirdPegasusModel
-    :members: forward
-
-
-BigBirdPegasusForConditionalGeneration
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BigBirdPegasusForConditionalGeneration
-    :members: forward
-
-
-BigBirdPegasusForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BigBirdPegasusForSequenceClassification
-    :members: forward
-
-
-BigBirdPegasusForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BigBirdPegasusForQuestionAnswering
-    :members: forward
-
-
-BigBirdPegasusForCausalLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BigBirdPegasusForCausalLM
-    :members: forward
-
-

docs/source/model_doc/blenderbot.rst

@@ -36,8 +36,7 @@ and code publicly available. Human evaluations show our best models are superior
 dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing
 failure cases of our models.*
 
-This model was contributed by `sshleifer <https://huggingface.co/sshleifer>`__. The authors' code can be found `here
-<https://github.com/facebookresearch/ParlAI>`__ .
+The authors' code can be found `here <https://github.com/facebookresearch/ParlAI>`__ .
 
 
 Implementation Notes
@@ -99,13 +98,6 @@ See :obj:`transformers.BartForConditionalGeneration` for arguments to `forward`
     :members: forward
 
 
-BlenderbotForCausalLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BlenderbotForCausalLM
-    :members: forward
-
-
 TFBlenderbotModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

docs/source/model_doc/blenderbot_small.rst

@@ -39,8 +39,7 @@ and code publicly available. Human evaluations show our best models are superior
 dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing
 failure cases of our models.*
 
-This model was contributed by `patrickvonplaten <https://huggingface.co/patrickvonplaten>`__. The authors' code can be
-found `here <https://github.com/facebookresearch/ParlAI>`__ .
+The authors' code can be found `here <https://github.com/facebookresearch/ParlAI>`__ .
 
 BlenderbotSmallConfig
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -57,13 +56,6 @@ BlenderbotSmallTokenizer
         create_token_type_ids_from_sequences, save_vocabulary
 
 
-BlenderbotSmallTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BlenderbotSmallTokenizerFast
-    :members:
-
-
 BlenderbotSmallModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -78,13 +70,6 @@ BlenderbotSmallForConditionalGeneration
     :members: forward
 
 
-BlenderbotSmallForCausalLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BlenderbotSmallForCausalLM
-    :members: forward
-
-
 TFBlenderbotSmallModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

docs/source/model_doc/bort.rst

@@ -1,47 +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.
-
-BORT
------------------------------------------------------------------------------------------------------------------------
-
-Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The BORT model was proposed in `Optimal Subarchitecture Extraction for BERT <https://arxiv.org/abs/2010.10499>`__ by
-Adrian de Wynter and Daniel J. Perry. It is an optimal subset of architectural parameters for the BERT, which the
-authors refer to as "Bort".
-
-The abstract from the paper is the following:
-
-*We extract an optimal subset of architectural parameters for the BERT architecture from Devlin et al. (2018) by
-applying recent breakthroughs in algorithms for neural architecture search. This optimal subset, which we refer to as
-"Bort", is demonstrably smaller, having an effective (that is, not counting the embedding layer) size of 5.5% the
-original BERT-large architecture, and 16% of the net size. Bort is also able to be pretrained in 288 GPU hours, which
-is 1.2% of the time required to pretrain the highest-performing BERT parametric architectural variant, RoBERTa-large
-(Liu et al., 2019), and about 33% of that of the world-record, in GPU hours, required to train BERT-large on the same
-hardware. It is also 7.9x faster on a CPU, as well as being better performing than other compressed variants of the
-architecture, and some of the non-compressed variants: it obtains performance improvements of between 0.3% and 31%,
-absolute, with respect to BERT-large, on multiple public natural language understanding (NLU) benchmarks.*
-
-Tips:
-
-- BORT's model architecture is based on BERT, so one can refer to :doc:`BERT's documentation page <bert>` for the
-  model's API as well as usage examples.
-- BORT uses the RoBERTa tokenizer instead of the BERT tokenizer, so one can refer to :doc:`RoBERTa's documentation page
-  <roberta>` for the tokenizer's API as well as usage examples.
-- BORT requires a specific fine-tuning algorithm, called `Agora
-  <https://adewynter.github.io/notes/bort_algorithms_and_applications.html#fine-tuning-with-algebraic-topology>`__ ,
-  that is sadly not open-sourced yet. It would be very useful for the community, if someone tries to implement the
-  algorithm to make BORT fine-tuning work.
-
-This model was contributed by `stefan-it <https://huggingface.co/stefan-it>`__. The original code can be found `here
-<https://github.com/alexa/bort/>`__.

docs/source/model_doc/byt5.rst

@@ -1,86 +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.
-
-ByT5
------------------------------------------------------------------------------------------------------------------------
-
-Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The ByT5 model was presented in `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.
-
-The abstract from the paper is the following:
-
-*Most widely-used pre-trained language models operate on sequences of tokens corresponding to word or subword units.
-Encoding text as a sequence of tokens requires a tokenizer, which is typically created as an independent artifact from
-the model. Token-free models that instead operate directly on raw text (bytes or characters) have many benefits: they
-can process text in any language out of the box, they are more robust to noise, and they minimize technical debt by
-removing complex and error-prone text preprocessing pipelines. Since byte or character sequences are longer than token
-sequences, past work on token-free models has often introduced new model architectures designed to amortize the cost of
-operating directly on raw text. In this paper, we show that a standard Transformer architecture can be used with
-minimal modifications to process byte sequences. We carefully characterize the trade-offs in terms of parameter count,
-training FLOPs, and inference speed, and show that byte-level models are competitive with their token-level
-counterparts. We also demonstrate that byte-level models are significantly more robust to noise and perform better on
-tasks that are sensitive to spelling and pronunciation. As part of our contribution, we release a new set of
-pre-trained byte-level Transformer models based on the T5 architecture, as well as all code and data used in our
-experiments.*
-
-This model was contributed by `patrickvonplaten <https://huggingface.co/patrickvonplaten>`__. The original code can be
-found `here <https://github.com/google-research/byt5>`__.
-
-ByT5's architecture is based on the T5v1.1 model, so one can refer to :doc:`T5v1.1's documentation page <t5v1.1>`. They
-only differ in how inputs should be prepared for the model, see the code examples below.
-
-Since ByT5 was pre-trained unsupervisedly, there's no real advantage to using a task prefix during single-task
-fine-tuning. If you are doing multi-task fine-tuning, you should use a prefix.
-
-
-Example
-_______________________________________________________________________________________________________________________
-
-ByT5 works on raw UTF-8 bytes, so it can be used without a tokenizer:
-
-.. code-block::
-
-    from transformers import T5ForConditionalGeneration
-    import torch
-
-    model = T5ForConditionalGeneration.from_pretrained('google/byt5-small')
-
-    input_ids = torch.tensor([list("Life is like a box of chocolates.".encode("utf-8"))]) + 3  # add 3 for special tokens
-    labels = torch.tensor([list("La vie est comme une boîte de chocolat.".encode("utf-8"))]) + 3  # add 3 for special tokens
-
-    loss = model(input_ids, labels=labels).loss # forward pass
-
-
-For batched inference and training it is however recommended to make use of the tokenizer:
-
-.. code-block::
-
-    from transformers import T5ForConditionalGeneration, AutoTokenizer
-
-    model = T5ForConditionalGeneration.from_pretrained('google/byt5-small')
-    tokenizer = AutoTokenizer.from_pretrained('google/byt5-small')
-
-    model_inputs = tokenizer(["Life is like a box of chocolates.", "Today is Monday."], padding="longest", return_tensors="pt")
-    labels = tokenizer(["La vie est comme une boîte de chocolat.", "Aujourd'hui c'est lundi."], padding="longest", return_tensors="pt").input_ids
-
-    loss = model(**model_inputs, labels=labels).loss # forward pass
-
-ByT5Tokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.ByT5Tokenizer
-
-See :class:`~transformers.ByT5Tokenizer` for all details.

docs/source/model_doc/camembert.rst

@@ -37,8 +37,7 @@ Tips:
 - This implementation is the same as RoBERTa. Refer to the :doc:`documentation of RoBERTa <roberta>` for usage examples
   as well as the information relative to the inputs and outputs.
 
-This model was contributed by `camembert <https://huggingface.co/camembert>`__. The original code can be found `here
-<https://camembert-model.fr/>`__.
+The original code can be found `here <https://camembert-model.fr/>`__.
 
 CamembertConfig
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

docs/source/model_doc/canine.rst

@@ -1,155 +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.
-
-CANINE
------------------------------------------------------------------------------------------------------------------------
-
-Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The CANINE model was proposed in `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. It's
-among the first papers that trains a Transformer without using an explicit tokenization step (such as Byte Pair
-Encoding (BPE), WordPiece or SentencePiece). Instead, the model is trained directly at a Unicode character-level.
-Training at a character-level inevitably comes with a longer sequence length, which CANINE solves with an efficient
-downsampling strategy, before applying a deep Transformer encoder.
-
-The abstract from the paper is the following:
-
-*Pipelined NLP systems have largely been superseded by end-to-end neural modeling, yet nearly all commonly-used models
-still require an explicit tokenization step. While recent tokenization approaches based on data-derived subword
-lexicons are less brittle than manually engineered tokenizers, these techniques are not equally suited to all
-languages, and the use of any fixed vocabulary may limit a model's ability to adapt. In this paper, we present CANINE,
-a neural encoder that operates directly on character sequences, without explicit tokenization or vocabulary, and a
-pre-training strategy that operates either directly on characters or optionally uses subwords as a soft inductive bias.
-To use its finer-grained input effectively and efficiently, CANINE combines downsampling, which reduces the input
-sequence length, with a deep transformer stack, which encodes context. CANINE outperforms a comparable mBERT model by
-2.8 F1 on TyDi QA, a challenging multilingual benchmark, despite having 28% fewer model parameters.*
-
-Tips:
-
-- CANINE uses no less than 3 Transformer encoders internally: 2 "shallow" encoders (which only consist of a single
-  layer) and 1 "deep" encoder (which is a regular BERT encoder). First, a "shallow" encoder is used to contextualize
-  the character embeddings, using local attention. Next, after downsampling, a "deep" encoder is applied. Finally,
-  after upsampling, a "shallow" encoder is used to create the final character embeddings. Details regarding up- and
-  downsampling can be found in the paper.
-- CANINE uses a max sequence length of 2048 characters by default. One can use :class:`~transformers.CanineTokenizer`
-  to prepare text for the model.
-- Classification can be done by placing a linear layer on top of the final hidden state of the special [CLS] token
-  (which has a predefined Unicode code point). For token classification tasks however, the downsampled sequence of
-  tokens needs to be upsampled again to match the length of the original character sequence (which is 2048). The
-  details for this can be found in the paper.
--  Models:
-
-      - `google/canine-c <https://huggingface.co/google/canine-c>`__: Pre-trained with autoregressive character loss,
-        12-layer, 768-hidden, 12-heads, 121M parameters (size ~500 MB).
-      - `google/canine-s <https://huggingface.co/google/canine-s>`__: Pre-trained with subword loss, 12-layer,
-        768-hidden, 12-heads, 121M parameters (size ~500 MB).
-
-This model was contributed by `nielsr <https://huggingface.co/nielsr>`__. The original code can be found `here
-<https://github.com/google-research/language/tree/master/language/canine>`__.
-
-
-Example
-_______________________________________________________________________________________________________________________
-
-CANINE works on raw characters, so it can be used without a tokenizer:
-
-.. code-block::
-
-    from transformers import CanineModel
-    import torch
-
-    model = CanineModel.from_pretrained('google/canine-c') # model pre-trained with autoregressive character loss
-
-    text = "hello world"
-    # use Python's built-in ord() function to turn each character into its unicode code point id
-    input_ids = torch.tensor([[ord(char) for char in text]])
-
-    outputs = model(input_ids) # forward pass
-    pooled_output = outputs.pooler_output
-    sequence_output = outputs.last_hidden_state
-
-
-For batched inference and training, it is however recommended to make use of the tokenizer (to pad/truncate all
-sequences to the same length):
-
-.. code-block::
-
-    from transformers import CanineTokenizer, CanineModel
-
-    model = CanineModel.from_pretrained('google/canine-c')
-    tokenizer = CanineTokenizer.from_pretrained('google/canine-c')
-
-    inputs = ["Life is like a box of chocolates.", "You never know what you gonna get."]
-    encoding = tokenizer(inputs, padding="longest", truncation=True, return_tensors="pt")
-
-    outputs = model(**encoding) # forward pass
-    pooled_output = outputs.pooler_output
-    sequence_output = outputs.last_hidden_state
-
-
-CANINE specific outputs
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.models.canine.modeling_canine.CanineModelOutputWithPooling
-    :members:
-
-
-CanineConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.CanineConfig
-    :members:
-
-
-CanineTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.CanineTokenizer
-    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
-        create_token_type_ids_from_sequences
-
-
-CanineModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.CanineModel
-    :members: forward
-
-
-CanineForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.CanineForSequenceClassification
-    :members: forward
-
-
-CanineForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.CanineForMultipleChoice
-    :members: forward
-
-
-CanineForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.CanineForTokenClassification
-    :members: forward
-
-
-CanineForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.CanineForQuestionAnswering
-    :members: forward

docs/source/model_doc/clip.rst

@@ -1,174 +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.
-
-CLIP
------------------------------------------------------------------------------------------------------------------------
-
-Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The CLIP model was proposed in `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. CLIP
-(Contrastive Language-Image Pre-Training) is a neural network trained on a variety of (image, text) pairs. It can be
-instructed in natural language to predict the most relevant text snippet, given an image, without directly optimizing
-for the task, similarly to the zero-shot capabilities of GPT-2 and 3.
-
-The abstract from the paper is the following:
-
-*State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This
-restricted form of supervision limits their generality and usability since additional labeled data is needed to specify
-any other visual concept. Learning directly from raw text about images is a promising alternative which leverages a
-much broader source of supervision. We demonstrate that the simple pre-training task of predicting which caption goes
-with which image is an efficient and scalable way to learn SOTA image representations from scratch on a dataset of 400
-million (image, text) pairs collected from the internet. After pre-training, natural language is used to reference
-learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks. We study
-the performance of this approach by benchmarking on over 30 different existing computer vision datasets, spanning tasks
-such as OCR, action recognition in videos, geo-localization, and many types of fine-grained object classification. The
-model transfers non-trivially to most tasks and is often competitive with a fully supervised baseline without the need
-for any dataset specific training. For instance, we match the accuracy of the original ResNet-50 on ImageNet zero-shot
-without needing to use any of the 1.28 million training examples it was trained on. We release our code and pre-trained
-model weights at this https URL.*
-
-Usage
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-CLIP is a multi-modal vision and language model. It can be used for image-text similarity and for zero-shot image
-classification. CLIP uses a ViT like transformer to get visual features and a causal language model to get the text
-features. Both the text and visual features are then projected to a latent space with identical dimension. The dot
-product between the projected image and text features is then used as a similar score.
-
-To feed images to the Transformer encoder, each image is split into a sequence of fixed-size non-overlapping patches,
-which are then linearly embedded. A [CLS] token is added to serve as representation of an entire image. The authors
-also add absolute position embeddings, and feed the resulting sequence of vectors to a standard Transformer encoder.
-The :class:`~transformers.CLIPFeatureExtractor` can be used to resize (or rescale) and normalize images for the model.
-
-The :class:`~transformers.CLIPTokenizer` is used to encode the text. The :class:`~transformers.CLIPProcessor` wraps
-:class:`~transformers.CLIPFeatureExtractor` and :class:`~transformers.CLIPTokenizer` into a single instance to both
-encode the text and prepare the images. The following example shows how to get the image-text similarity scores using
-:class:`~transformers.CLIPProcessor` and :class:`~transformers.CLIPModel`.
-
-
-.. code-block::
-
-        >>> from PIL import Image
-        >>> import requests
-
-        >>> from transformers import CLIPProcessor, CLIPModel
-
-        >>> model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
-        >>> processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
-
-        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-        >>> image = Image.open(requests.get(url, stream=True).raw)
-
-        >>> inputs = processor(text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True)
-
-        >>> outputs = model(**inputs)
-        >>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score
-        >>> probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities
-
-
-This model was contributed by `valhalla <https://huggingface.co/valhalla>`__. The original code can be found `here
-<https://github.com/openai/CLIP>`__.
-
-CLIPConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.CLIPConfig
-    :members: from_text_vision_configs
-
-
-CLIPTextConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.CLIPTextConfig
-    :members:
-
-
-CLIPVisionConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.CLIPVisionConfig
-    :members:
-
-
-
-CLIPTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.CLIPTokenizer
-    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
-        create_token_type_ids_from_sequences, save_vocabulary
-
-CLIPTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.CLIPTokenizerFast
-    :members:
-
-
-CLIPFeatureExtractor
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.CLIPFeatureExtractor
-    :members:
-
-
-CLIPProcessor
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.CLIPProcessor
-    :members:
-
-
-
-CLIPModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.CLIPModel
-    :members: forward, get_text_features, get_image_features
-
-
-CLIPTextModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.CLIPTextModel
-    :members: forward
-
-
-CLIPVisionModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.CLIPVisionModel
-    :members: forward
-
-
-FlaxCLIPModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxCLIPModel
-    :members: __call__, get_text_features, get_image_features
-
-
-FlaxCLIPTextModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxCLIPTextModel
-    :members: __call__
-
-
-FlaxCLIPVisionModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxCLIPVisionModel
-    :members: __call__

docs/source/model_doc/convbert.rst

@@ -1,145 +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.
-
-ConvBERT
------------------------------------------------------------------------------------------------------------------------
-
-Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The ConvBERT model was proposed in `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.
-
-The abstract from the paper is the following:
-
-*Pre-trained language models like BERT and its variants have recently achieved impressive performance in various
-natural language understanding tasks. However, BERT heavily relies on the global self-attention block and thus suffers
-large memory footprint and computation cost. Although all its attention heads query on the whole input sequence for
-generating the attention map from a global perspective, we observe some heads only need to learn local dependencies,
-which means the existence of computation redundancy. We therefore propose a novel span-based dynamic convolution to
-replace these self-attention heads to directly model local dependencies. The novel convolution heads, together with the
-rest self-attention heads, form a new mixed attention block that is more efficient at both global and local context
-learning. We equip BERT with this mixed attention design and build a ConvBERT model. Experiments have shown that
-ConvBERT significantly outperforms BERT and its variants in various downstream tasks, with lower training cost and
-fewer model parameters. Remarkably, ConvBERTbase model achieves 86.4 GLUE score, 0.7 higher than ELECTRAbase, while
-using less than 1/4 training cost. Code and pre-trained models will be released.*
-
-ConvBERT training tips are similar to those of BERT.
-
-This model was contributed by `abhishek <https://huggingface.co/abhishek>`__. The original implementation can be found
-here: https://github.com/yitu-opensource/ConvBert
-
-ConvBertConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.ConvBertConfig
-    :members:
-
-
-ConvBertTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.ConvBertTokenizer
-    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
-        create_token_type_ids_from_sequences, save_vocabulary
-
-
-ConvBertTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.ConvBertTokenizerFast
-    :members:
-
-
-ConvBertModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.ConvBertModel
-    :members: forward
-
-
-ConvBertForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.ConvBertForMaskedLM
-    :members: forward
-
-
-ConvBertForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.ConvBertForSequenceClassification
-    :members: forward
-
-
-ConvBertForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.ConvBertForMultipleChoice
-    :members: forward
-
-
-ConvBertForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.ConvBertForTokenClassification
-    :members: forward
-
-
-ConvBertForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.ConvBertForQuestionAnswering
-    :members: forward
-
-
-TFConvBertModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFConvBertModel
-    :members: call
-
-
-TFConvBertForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFConvBertForMaskedLM
-    :members: call
-
-
-TFConvBertForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFConvBertForSequenceClassification
-    :members: call
-
-
-TFConvBertForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFConvBertForMultipleChoice
-    :members: call
-
-
-TFConvBertForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFConvBertForTokenClassification
-    :members: call
-
-
-TFConvBertForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFConvBertForQuestionAnswering
-    :members: call

docs/source/model_doc/cpm.rst

@@ -1,45 +0,0 @@
-..
-    Copyright 2020 The HuggingFace Team. All rights reserved.
-
-    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-    the License. You may obtain a copy of the License at
-
-        http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-    specific language governing permissions and limitations under the License.
-
-CPM
------------------------------------------------------------------------------------------------------------------------
-
-Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The CPM model was proposed in `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.
-
-The abstract from the paper is the following:
-
-*Pre-trained Language Models (PLMs) have proven to be beneficial for various downstream NLP tasks. Recently, GPT-3,
-with 175 billion parameters and 570GB training data, drew a lot of attention due to the capacity of few-shot (even
-zero-shot) learning. However, applying GPT-3 to address Chinese NLP tasks is still challenging, as the training corpus
-of GPT-3 is primarily English, and the parameters are not publicly available. In this technical report, we release the
-Chinese Pre-trained Language Model (CPM) with generative pre-training on large-scale Chinese training data. To the best
-of our knowledge, CPM, with 2.6 billion parameters and 100GB Chinese training data, is the largest Chinese pre-trained
-language model, which could facilitate several downstream Chinese NLP tasks, such as conversation, essay generation,
-cloze test, and language understanding. Extensive experiments demonstrate that CPM achieves strong performance on many
-NLP tasks in the settings of few-shot (even zero-shot) learning.*
-
-This model was contributed by `canwenxu <https://huggingface.co/canwenxu>`__. The original implementation can be found
-here: https://github.com/TsinghuaAI/CPM-Generate
-
-Note: We only have a tokenizer here, since the model architecture is the same as GPT-2.
-
-CpmTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.CpmTokenizer
-    :members:

docs/source/model_doc/ctrl.rst

@@ -46,8 +46,7 @@ Tips:
   `reusing the past in generative models <../quickstart.html#using-the-past>`__ for more information on the usage of
   this argument.
 
-This model was contributed by `keskarnitishr <https://huggingface.co/keskarnitishr>`__. The original code can be found
-`here <https://github.com/salesforce/ctrl>`__.
+The original code can be found `here <https://github.com/salesforce/ctrl>`__.
 
 
 CTRLConfig

docs/source/model_doc/deberta.rst

@@ -38,9 +38,7 @@ the training data performs consistently better on a wide range of NLP tasks, ach
 pre-trained models will be made publicly available at https://github.com/microsoft/DeBERTa.*
 
 
-This model was contributed by `DeBERTa <https://huggingface.co/DeBERTa>`__. This model TF 2.0 implementation was
-contributed by `kamalkraj <https://huggingface.co/kamalkraj>`__ . The original code can be found `here
-<https://github.com/microsoft/DeBERTa>`__.
+The original code can be found `here <https://github.com/microsoft/DeBERTa>`__.
 
 
 DebertaConfig
@@ -57,18 +55,12 @@ DebertaTokenizer
     :members: build_inputs_with_special_tokens, get_special_tokens_mask,
         create_token_type_ids_from_sequences, save_vocabulary
 
-DebertaTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.DebertaTokenizerFast
-    :members: build_inputs_with_special_tokens, create_token_type_ids_from_sequences
-
 
 DebertaModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.DebertaModel
-    :members: forward
+    :members:
 
 
 DebertaPreTrainedModel
@@ -78,71 +70,8 @@ DebertaPreTrainedModel
     :members:
 
 
-DebertaForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.DebertaForMaskedLM
-    :members: forward
-
-
 DebertaForSequenceClassification
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.DebertaForSequenceClassification
-    :members: forward
-
-
-DebertaForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.DebertaForTokenClassification
-    :members: forward
-
-
-DebertaForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.DebertaForQuestionAnswering
-    :members: forward
-
-
-TFDebertaModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFDebertaModel
-    :members: call
-
-
-TFDebertaPreTrainedModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFDebertaPreTrainedModel
-    :members: call
-
-
-TFDebertaForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFDebertaForMaskedLM
-    :members: call
-
-
-TFDebertaForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFDebertaForSequenceClassification
-    :members: call
-
-
-TFDebertaForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFDebertaForTokenClassification
-    :members: call
-
-
-TFDebertaForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFDebertaForQuestionAnswering
-    :members: call
+    :members:

docs/source/model_doc/deberta_v2.rst

@@ -1,162 +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.
-
-DeBERTa-v2
------------------------------------------------------------------------------------------------------------------------
-
-Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The DeBERTa model was proposed in `DeBERTa: Decoding-enhanced BERT with Disentangled Attention
-<https://arxiv.org/abs/2006.03654>`__ by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen It is based on Google's
-BERT model released in 2018 and Facebook's RoBERTa model released in 2019.
-
-It builds on RoBERTa with disentangled attention and enhanced mask decoder training with half of the data used in
-RoBERTa.
-
-The abstract from the paper is the following:
-
-*Recent progress in pre-trained neural language models has significantly improved the performance of many natural
-language processing (NLP) tasks. In this paper we propose a new model architecture DeBERTa (Decoding-enhanced BERT with
-disentangled attention) that improves the BERT and RoBERTa models using two novel techniques. The first is the
-disentangled attention mechanism, where each word is represented using two vectors that encode its content and
-position, respectively, and the attention weights among words are computed using disentangled matrices on their
-contents and relative positions. Second, an enhanced mask decoder is used to replace the output softmax layer to
-predict the masked tokens for model pretraining. We show that these two techniques significantly improve the efficiency
-of model pretraining and performance of downstream tasks. Compared to RoBERTa-Large, a DeBERTa model trained on half of
-the training data performs consistently better on a wide range of NLP tasks, achieving improvements on MNLI by +0.9%
-(90.2% vs. 91.1%), on SQuAD v2.0 by +2.3% (88.4% vs. 90.7%) and RACE by +3.6% (83.2% vs. 86.8%). The DeBERTa code and
-pre-trained models will be made publicly available at https://github.com/microsoft/DeBERTa.*
-
-
-The following information is visible directly on the [original implementation
-repository](https://github.com/microsoft/DeBERTa). DeBERTa v2 is the second version of the DeBERTa model. It includes
-the 1.5B model used for the SuperGLUE single-model submission and achieving 89.9, versus human baseline 89.8. You can
-find more details about this submission in the authors'
-[blog](https://www.microsoft.com/en-us/research/blog/microsoft-deberta-surpasses-human-performance-on-the-superglue-benchmark/)
-
-New in v2:
-
-- **Vocabulary** In v2 the tokenizer is changed to use a new vocabulary of size 128K built from the training data.
-  Instead of a GPT2-based tokenizer, the tokenizer is now
-  [sentencepiece-based](https://github.com/google/sentencepiece) tokenizer.
-- **nGiE(nGram Induced Input Encoding)** The DeBERTa-v2 model uses an additional convolution layer aside with the first
-  transformer layer to better learn the local dependency of input tokens.
-- **Sharing position projection matrix with content projection matrix in attention layer** Based on previous
-  experiments, this can save parameters without affecting the performance.
-- **Apply bucket to encode relative positions** The DeBERTa-v2 model uses log bucket to encode relative positions
-  similar to T5.
-- **900M model & 1.5B model** Two additional model sizes are available: 900M and 1.5B, which significantly improves the
-  performance of downstream tasks.
-
-This model was contributed by `DeBERTa <https://huggingface.co/DeBERTa>`__. This model TF 2.0 implementation was
-contributed by `kamalkraj <https://huggingface.co/kamalkraj>`__. The original code can be found `here
-<https://github.com/microsoft/DeBERTa>`__.
-
-
-DebertaV2Config
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.DebertaV2Config
-    :members:
-
-
-DebertaV2Tokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.DebertaV2Tokenizer
-    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
-        create_token_type_ids_from_sequences, save_vocabulary
-
-
-DebertaV2Model
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.DebertaV2Model
-    :members: forward
-
-
-DebertaV2PreTrainedModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.DebertaV2PreTrainedModel
-    :members: forward
-
-
-DebertaV2ForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.DebertaV2ForMaskedLM
-    :members: forward
-
-
-DebertaV2ForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.DebertaV2ForSequenceClassification
-    :members: forward
-
-
-DebertaV2ForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.DebertaV2ForTokenClassification
-    :members: forward
-
-
-DebertaV2ForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.DebertaV2ForQuestionAnswering
-    :members: forward
-
-
-TFDebertaV2Model
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFDebertaV2Model
-    :members: call
-
-
-TFDebertaV2PreTrainedModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFDebertaV2PreTrainedModel
-    :members: call
-
-
-TFDebertaV2ForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFDebertaV2ForMaskedLM
-    :members: call
-
-
-TFDebertaV2ForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFDebertaV2ForSequenceClassification
-    :members: call
-
-
-TFDebertaV2ForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFDebertaV2ForTokenClassification
-    :members: call
-
-
-TFDebertaV2ForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFDebertaV2ForQuestionAnswering
-    :members: call

docs/source/model_doc/deit.rst

@@ -1,111 +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.
-
-DeiT
------------------------------------------------------------------------------------------------------------------------
-
-.. note::
-
-    This is a recently introduced model so the API hasn't been tested extensively. There may be some bugs or slight
-    breaking changes to fix it in the future. If you see something strange, file a `Github Issue
-    <https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title>`__.
-
-
-Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The 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.
-
-The abstract from the paper is the following:
-
-*Recently, neural networks purely based on attention were shown to address image understanding tasks such as image
-classification. However, these visual transformers are pre-trained with hundreds of millions of images using an
-expensive infrastructure, thereby limiting their adoption. In this work, we produce a competitive convolution-free
-transformer by training on Imagenet only. We train them on a single computer in less than 3 days. Our reference vision
-transformer (86M parameters) achieves top-1 accuracy of 83.1% (single-crop evaluation) on ImageNet with no external
-data. More importantly, we introduce a teacher-student strategy specific to transformers. It relies on a distillation
-token ensuring that the student learns from the teacher through attention. We show the interest of this token-based
-distillation, especially when using a convnet as a teacher. This leads us to report results competitive with convnets
-for both Imagenet (where we obtain up to 85.2% accuracy) and when transferring to other tasks. We share our code and
-models.*
-
-Tips:
-
-- Compared to ViT, DeiT models use a so-called distillation token to effectively learn from a teacher (which, in the
-  DeiT paper, is a ResNet like-model). The distillation token is learned through backpropagation, by interacting with
-  the class ([CLS]) and patch tokens through the self-attention layers.
-- There are 2 ways to fine-tune distilled models, either (1) in a classic way, by only placing a prediction head on top
-  of the final hidden state of the class token and not using the distillation signal, or (2) by placing both a
-  prediction head on top of the class token and on top of the distillation token. In that case, the [CLS] prediction
-  head is trained using regular cross-entropy between the prediction of the head and the ground-truth label, while the
-  distillation prediction head is trained using hard distillation (cross-entropy between the prediction of the
-  distillation head and the label predicted by the teacher). At inference time, one takes the average prediction
-  between both heads as final prediction. (2) is also called "fine-tuning with distillation", because one relies on a
-  teacher that has already been fine-tuned on the downstream dataset. In terms of models, (1) corresponds to
-  :class:`~transformers.DeiTForImageClassification` and (2) corresponds to
-  :class:`~transformers.DeiTForImageClassificationWithTeacher`.
-- Note that the authors also did try soft distillation for (2) (in which case the distillation prediction head is
-  trained using KL divergence to match the softmax output of the teacher), but hard distillation gave the best results.
-- All released checkpoints were pre-trained and fine-tuned on ImageNet-1k only. No external data was used. This is in
-  contrast with the original ViT model, which used external data like the JFT-300M dataset/Imagenet-21k for
-  pre-training.
-- The authors of DeiT also released more efficiently trained ViT models, which you can directly plug into
-  :class:`~transformers.ViTModel` or :class:`~transformers.ViTForImageClassification`. Techniques like data
-  augmentation, optimization, and regularization were used in order to simulate training on a much larger dataset
-  (while only using ImageNet-1k for pre-training). There are 4 variants available (in 3 different sizes):
-  `facebook/deit-tiny-patch16-224`, `facebook/deit-small-patch16-224`, `facebook/deit-base-patch16-224` and
-  `facebook/deit-base-patch16-384`. Note that one should use :class:`~transformers.DeiTFeatureExtractor` in order to
-  prepare images for the model.
-
-This model was contributed by `nielsr <https://huggingface.co/nielsr>`__.
-
-
-DeiTConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.DeiTConfig
-    :members:
-
-
-DeiTFeatureExtractor
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.DeiTFeatureExtractor
-    :members: __call__
-
-
-DeiTModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.DeiTModel
-    :members: forward
-
-
-DeiTForImageClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.DeiTForImageClassification
-    :members: forward
-
-
-DeiTForImageClassificationWithTeacher
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.DeiTForImageClassificationWithTeacher
-    :members: forward

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

docs/source/model_doc/dialogpt.rst

@@ -48,6 +48,7 @@ modeling. We first concatenate all dialog turns within a dialogue session into a
 sequence length), ended by the end-of-text token.* For more information please confer to the original paper.
 
 
-DialoGPT's architecture is based on the GPT2 model, so one can refer to :doc:`GPT2's documentation page <gpt2>`.
+DialoGPT's architecture is based on the GPT2 model, so one can refer to GPT2's `docstring
+<https://huggingface.co/transformers/model_doc/gpt2.html>`_.
 
 The original code can be found `here <https://github.com/microsoft/DialoGPT>`_.

docs/source/model_doc/distilbert.rst

@@ -44,9 +44,8 @@ Tips:
 - DistilBERT doesn't have options to select the input positions (:obj:`position_ids` input). This could be added if
   necessary though, just let us know if you need this option.
 
-This model was contributed by `victorsanh <https://huggingface.co/victorsanh>`__. This model jax version was
-contributed by `kamalkraj <https://huggingface.co/kamalkraj>`__. The original code can be found :prefix_link:`here
-<examples/research_projects/distillation>`.
+The original code can be found `here
+<https://github.com/huggingface/transformers/tree/master/examples/distillation>`__.
 
 
 DistilBertConfig
@@ -153,45 +152,3 @@ TFDistilBertForQuestionAnswering
 
 .. autoclass:: transformers.TFDistilBertForQuestionAnswering
     :members: call
-
-
-FlaxDistilBertModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxDistilBertModel
-    :members: __call__
-
-
-FlaxDistilBertForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxDistilBertForMaskedLM
-    :members: __call__
-
-
-FlaxDistilBertForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxDistilBertForSequenceClassification
-    :members: __call__
-
-
-FlaxDistilBertForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxDistilBertForMultipleChoice
-    :members: __call__
-
-
-FlaxDistilBertForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxDistilBertForTokenClassification
-    :members: __call__
-
-
-FlaxDistilBertForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.FlaxDistilBertForQuestionAnswering
-    :members: __call__

docs/source/model_doc/dpr.rst

@@ -30,8 +30,7 @@ our dense retriever outperforms a strong Lucene-BM25 system largely by 9%-19% ab
 retrieval accuracy, and helps our end-to-end QA system establish new state-of-the-art on multiple open-domain QA
 benchmarks.*
 
-This model was contributed by `lhoestq <https://huggingface.co/lhoestq>`__. The original code can be found `here
-<https://github.com/facebookresearch/DPR>`__.
+The original code can be found `here <https://github.com/facebookresearch/DPR>`__.
 
 
 DPRConfig
@@ -41,13 +40,6 @@ DPRConfig
     :members:
 
 
-DPRPreTrainedModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.DPRPreTrainedModel
-    :members:
-
-
 DPRContextEncoderTokenizer
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~