Update README.md

* Bulk of the work

* Polish and tests

* Update QA Trainer

* Avoid breaking the predict method

* Deprecation warnings

* Store real eval dataloder

* Get eval dataset reference before wrap

.circleci/config.yml

@@ -3,7 +3,6 @@ orbs:
     gcp-gke: circleci/gcp-gke@1.0.4
     go: circleci/go@1.3.0
 
-
 # TPU REFERENCES
 references:
     checkout_ml_testing: &checkout_ml_testing
@@ -69,6 +68,8 @@ 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:
@@ -77,14 +78,45 @@ 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]
-            - run: pip install tapas torch-scatter -f https://pytorch-geometric.com/whl/torch-1.7.0+cpu.html
+            - run: pip install .[sklearn,tf-cpu,torch,testing,sentencepiece,speech,vision]
+            - run: pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.8.0+cpu.html
             - save_cache:
                 key: v0.4-{{ checksum "setup.py" }}
                 paths:
                     - '~/.cache/pip'
-            - run: RUN_PT_TF_CROSS_TESTS=1 python -m pytest -n 8 --dist=loadfile -rA -s --make-reports=tests_torch_and_tf ./tests/ -m is_pt_tf_cross_test --durations=0 | tee tests_output.txt
+            - run: python -m pytest -n 8 --dist=loadfile -rA -s --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,speech,vision]
+            - run: pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.8.0+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
             - store_artifacts:
                   path: ~/transformers/tests_output.txt
             - store_artifacts:
@@ -96,6 +128,7 @@ jobs:
             - image: circleci/python:3.7
         environment:
             OMP_NUM_THREADS: 1
+            TRANSFORMERS_IS_CI: yes
         resource_class: xlarge
         parallelism: 1
         steps:
@@ -104,14 +137,15 @@ 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]
-            - run: pip install tapas torch-scatter -f https://pytorch-geometric.com/whl/torch-1.7.0+cpu.html
+            - run: pip install .[sklearn,torch,testing,sentencepiece,speech,vision]
+            - run: pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.8.0+cpu.html
             - save_cache:
                   key: v0.4-torch-{{ checksum "setup.py" }}
                   paths:
                       - '~/.cache/pip'
-            - run: python -m pytest -n 8 --dist=loadfile -s --make-reports=tests_torch ./tests/ | tee tests_output.txt
+            - run: python -m pytest -n 4 --dist=loadfile -s --make-reports=tests_torch ./tests/ | tee tests_output.txt
             - store_artifacts:
                   path: ~/transformers/tests_output.txt
             - store_artifacts:
@@ -123,6 +157,7 @@ jobs:
             - image: circleci/python:3.7
         environment:
             OMP_NUM_THREADS: 1
+            TRANSFORMERS_IS_CI: yes
         resource_class: xlarge
         parallelism: 1
         steps:
@@ -149,6 +184,7 @@ jobs:
             - image: circleci/python:3.7
         environment:
             OMP_NUM_THREADS: 1
+            TRANSFORMERS_IS_CI: yes
         resource_class: xlarge
         parallelism: 1
         steps:
@@ -158,7 +194,7 @@ jobs:
                     - v0.4-flax-{{ checksum "setup.py" }}
                     - v0.4-{{ checksum "setup.py" }}
             - run: pip install --upgrade pip
-            - run: sudo pip install .[flax,sklearn,torch,testing,sentencepiece]
+            - run: sudo pip install .[flax,testing,sentencepiece]
             - save_cache:
                   key: v0.4-flax-{{ checksum "setup.py" }}
                   paths:
@@ -175,6 +211,8 @@ 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:
@@ -183,14 +221,15 @@ 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]
-            - run: pip install tapas torch-scatter -f https://pytorch-geometric.com/whl/torch-1.7.0+cpu.html
+            - run: pip install .[sklearn,torch,testing,sentencepiece,speech,vision]
+            - run: pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.8.0+cpu.html
             - save_cache:
                   key: v0.4-torch-{{ checksum "setup.py" }}
                   paths:
                       - '~/.cache/pip'
-            - run: RUN_PIPELINE_TESTS=1 python -m pytest -n 8 --dist=loadfile -rA -s --make-reports=tests_pipelines_torch -m is_pipeline_test ./tests/ | tee tests_output.txt
+            - run: 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:
@@ -202,6 +241,8 @@ 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:
@@ -216,7 +257,7 @@ jobs:
                   key: v0.4-tf-{{ checksum "setup.py" }}
                   paths:
                       - '~/.cache/pip'
-            - run: RUN_PIPELINE_TESTS=1 python -m pytest -n 8 --dist=loadfile -rA -s --make-reports=tests_pipelines_tf ./tests/ -m is_pipeline_test | tee tests_output.txt
+            - run: 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:
@@ -228,6 +269,7 @@ jobs:
             - image: circleci/python:3.7
         environment:
             RUN_CUSTOM_TOKENIZERS: yes
+            TRANSFORMERS_IS_CI: yes
         steps:
             - checkout
             - restore_cache:
@@ -235,7 +277,7 @@ jobs:
                       - v0.4-custom_tokenizers-{{ checksum "setup.py" }}
                       - v0.4-{{ checksum "setup.py" }}
             - run: pip install --upgrade pip
-            - run: pip install .[ja,testing,sentencepiece]
+            - run: pip install .[ja,testing,sentencepiece,jieba]
             - run: python -m unidic download
             - save_cache:
                   key: v0.4-custom_tokenizers-{{ checksum "setup.py" }}
@@ -253,6 +295,7 @@ jobs:
             - image: circleci/python:3.6
         environment:
             OMP_NUM_THREADS: 1
+            TRANSFORMERS_IS_CI: yes
         resource_class: xlarge
         parallelism: 1
         steps:
@@ -268,7 +311,7 @@ jobs:
                   key: v0.4-torch_examples-{{ checksum "setup.py" }}
                   paths:
                       - '~/.cache/pip'
-            - run: python -m pytest -n 8 --dist=loadfile -s --make-reports=examples_torch ./examples/ | tee examples_output.txt
+            - run: TRANSFORMERS_IS_CI=1 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:
@@ -278,6 +321,9 @@ jobs:
         working_directory: ~/transformers
         docker:
             - image: circleci/python:3.7
+        environment:
+            RUN_GIT_LFS_TESTS: yes
+            TRANSFORMERS_IS_CI: yes
         resource_class: xlarge
         parallelism: 1
         steps:
@@ -288,7 +334,7 @@ jobs:
                 git config --global user.name "ci"
             - run: pip install --upgrade pip
             - run: pip install .[testing]
-            - run: RUN_GIT_LFS_TESTS=1 python -m pytest -sv ./tests/test_hf_api.py -k "HfLargefilesTest"
+            - run: python -m pytest -sv ./tests/test_hf_api.py -k "HfLargefilesTest"
 
     build_doc:
         working_directory: ~/transformers
@@ -300,13 +346,14 @@ jobs:
                   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 ."[all, docs]"
+            - run: pip install ."[docs]"
             - save_cache:
                   key: v0.4-build_doc-{{ checksum "setup.py" }}
                   paths:
                       - '~/.cache/pip'
-            - run: cd docs && make html SPHINXOPTS="-W"
+            - run: cd docs && make html SPHINXOPTS="-W -j 4"
             - store_artifacts:
                 path: ./docs/_build
 
@@ -323,7 +370,7 @@ jobs:
                   keys:
                       - v0.4-deploy_doc-{{ checksum "setup.py" }}
                       - v0.4-{{ checksum "setup.py" }}
-            - run: pip install ."[all,docs]"
+            - run: pip install ."[docs]"
             - save_cache:
                   key: v0.4-deploy_doc-{{ checksum "setup.py" }}
                   paths:
@@ -351,12 +398,14 @@ 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
 
     check_repository_consistency:
         working_directory: ~/transformers
@@ -375,6 +424,7 @@ jobs:
             - image: circleci/python:3.6
         environment:
             OMP_NUM_THREADS: 1
+            TRANSFORMERS_IS_CI: yes
         resource_class: xlarge
         parallelism: 1
         steps:
@@ -413,6 +463,7 @@ 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
@@ -421,15 +472,15 @@ workflows:
             - run_tests_git_lfs
             - build_doc
             - deploy_doc: *workflow_filters
-    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
+#    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

.circleci/deploy.sh

@@ -3,6 +3,7 @@ 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
@@ -45,7 +46,7 @@ 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
@@ -53,5 +54,12 @@ deploy_doc "3ebb1b3" v3.2.0
 deploy_doc "0613f05" v3.3.1
 deploy_doc "eb0e0ce" v3.4.0
 deploy_doc "818878d" v3.5.1
-deploy_doc "c781171" v4.0.0
-deploy_doc "bfa4ccf" # v4.1.1 Latest stable release
+deploy_doc "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 Latest stable release

.gitattributes

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

.github/ISSUE_TEMPLATE/bug-report.md

@@ -25,32 +25,44 @@ 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.
 
- 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
+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
+
  -->
 
 ## Information

.github/PULL_REQUEST_TEMPLATE.md

@@ -30,33 +30,45 @@ 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 which may be interested in your PR.
+members/contributors who may be interested in your PR.
 
 <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @
 
  If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
  Please tag fewer than 3 people.
 
- albert, bert, XLM: @LysandreJik
- GPT2: @LysandreJik, @patrickvonplaten
- tokenizers: @mfuntowicz
- Trainer: @sgugger
- Benchmarks: @patrickvonplaten
- Model Cards: @julien-c
- examples/distillation: @VictorSanh
- nlp datasets: [different repo](https://github.com/huggingface/nlp)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)
- Text Generation: @patrickvonplaten, @TevenLeScao
- Blenderbot, Bart, Marian, Pegasus: @patrickvonplaten
- T5: @patrickvonplaten
- Rag: @patrickvonplaten, @lhoestq
- EncoderDecoder: @patrickvonplaten
- Longformer, Reformer: @patrickvonplaten
- TransfoXL, XLNet: @TevenLeScao, @patrickvonplaten
- examples/seq2seq: @patil-suraj
- examples/bert-loses-patience: @JetRunner
- tensorflow: @jplu
- examples/token-classification: @stefan-it
- documentation: @sgugger
- FSMT: @stas00
+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
+
  -->

.github/conda/meta.yaml

@@ -14,7 +14,7 @@ requirements:
   host:
     - python
     - pip
-    - numpy
+    - numpy >=1.17
     - dataclasses
     - packaging
     - filelock
@@ -23,10 +23,10 @@ requirements:
     - sacremoses
     - regex !=2019.12.17
     - protobuf
-    - tokenizers ==0.9.4
+    - tokenizers >=0.10.1,<0.11.0
   run:
     - python
-    - numpy
+    - numpy >=1.17
     - dataclasses
     - packaging
     - filelock
@@ -35,7 +35,7 @@ requirements:
     - sacremoses
     - regex !=2019.12.17
     - protobuf
-    - tokenizers ==0.9.4
+    - tokenizers >=0.10.1,<0.11.0
 
 test:
   imports:

.github/stale.yml

@@ -1,18 +0,0 @@
-# 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/model-templates.yml

@@ -1,12 +1,13 @@
 name: Model templates runner
 
 on:
-  push:
+  pull_request:
     paths:
       - "src/**"
       - "tests/**"
       - ".github/**"
       - "templates/**"
+    types: [assigned, opened, synchronize, reopened]
 
 jobs:
   run_tests_templates:

.github/workflows/release-conda.yml

@@ -37,7 +37,8 @@ jobs:
       - name: Build conda packages
         run: |
           conda info
-          conda build .github/conda
+          conda list
+          conda-build .github/conda
 
       - name: Upload to Anaconda
-        run: anaconda upload `conda build .github/conda --output` --force
+        run: anaconda upload `conda-build .github/conda --output` --force

.github/workflows/self-push.yml

@@ -5,148 +5,96 @@ on:
     branches:
       - master
       - ci_*
+      - ci-*
     paths:
       - "src/**"
       - "tests/**"
       - ".github/**"
       - "templates/**"
-  # pull_request:
   repository_dispatch:
 
+env:
+  HF_HOME: /mnt/cache
+  TRANSFORMERS_IS_CI: yes
+  OMP_NUM_THREADS: 8
+  MKL_NUM_THREADS: 8
 
 jobs:
   run_tests_torch_gpu:
-    runs-on: [self-hosted, gpu, single-gpu]
+    runs-on: [self-hosted, docker-gpu, single-gpu]
+    container:
+      image: pytorch/pytorch:1.8.0-cuda11.1-cudnn8-runtime
+      options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
     steps:
-      - uses: actions/checkout@v2
-      - name: Python version
+      - name: Launcher docker
+        uses: actions/checkout@v2
+
+      - name: NVIDIA-SMI
         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
+          nvidia-smi
 
       - name: Install dependencies
         run: |
-          source .env/bin/activate
+          apt -y update && apt install -y libsndfile1-dev
           pip install --upgrade pip
-          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
+          pip install .[sklearn,testing,onnxruntime,sentencepiece,speech]
 
       - 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: Create model files
-#        run: |
-#          source .env/bin/activate
-#          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/encoder-bert-tokenizer.json --path=templates/adding_a_new_model
-#          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/pt-encoder-bert-tokenizer.json --path=templates/adding_a_new_model
-#          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/standalone.json --path=templates/adding_a_new_model
-#          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/tf-encoder-bert-tokenizer.json --path=templates/adding_a_new_model
-
       - name: Run all non-slow tests on GPU
-        env:
-          OMP_NUM_THREADS: 1
-          CUDA_VISIBLE_DEVICES: 0
         run: |
-          source .env/bin/activate
-          python -m pytest -n 2 --dist=loadfile -s --make-reports=tests_torch_gpu tests
+          python -m pytest -n 2 --dist=loadfile --make-reports=tests_torch_gpu tests
 
       - name: Failure short reports
         if: ${{ always() }}
         run: cat reports/tests_torch_gpu_failures_short.txt
-        
+
       - name: Test suite reports artifacts
         if: ${{ always() }}
         uses: actions/upload-artifact@v2
         with:
           name: run_all_tests_torch_gpu_test_reports
           path: reports
-                  
 
   run_tests_tf_gpu:
-    runs-on: [self-hosted, gpu, single-gpu]
+    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/
     steps:
-      - uses: actions/checkout@v2
-      - name: Python version
-        run: |
-          which python
-          python --version
-          pip --version
-      - name: Current dir
-        run: pwd
-      - run: nvidia-smi
+      - name: Launcher docker
+        uses: actions/checkout@v2
 
-      - 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)
+      - name: NVIDIA-SMI
         run: |
-          python -m venv .env
-          source .env/bin/activate
-          which python
-          python --version
-          pip --version
+          nvidia-smi
 
       - name: Install dependencies
         run: |
-          source .env/bin/activate
           pip install --upgrade pip
-          pip install .[tf,sklearn,testing,onnxruntime,sentencepiece]
-          pip install git+https://github.com/huggingface/datasets
+          pip install .[sklearn,testing,onnxruntime,sentencepiece]
 
       - name: Are GPUs recognized by our DL frameworks
         run: |
-          source .env/bin/activate
           TF_CPP_MIN_LOG_LEVEL=3 python -c "import tensorflow as tf; print('TF GPUs available:', bool(tf.config.list_physical_devices('GPU')))"
           TF_CPP_MIN_LOG_LEVEL=3 python -c "import tensorflow as tf; print('Number of TF GPUs available:', len(tf.config.list_physical_devices('GPU')))"
 
-      - name: Create model files
-        run: |
-          source .env/bin/activate
-#          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/encoder-bert-tokenizer.json --path=templates/adding_a_new_model
-#          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/pt-encoder-bert-tokenizer.json --path=templates/adding_a_new_model
-#          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/standalone.json --path=templates/adding_a_new_model
-#          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/tf-encoder-bert-tokenizer.json --path=templates/adding_a_new_model
-
       - name: Run all non-slow tests on GPU
         env:
-          OMP_NUM_THREADS: 1
-          CUDA_VISIBLE_DEVICES: 0
+          TF_NUM_INTRAOP_THREADS: 8
+          TF_NUM_INTEROP_THREADS: 1
         run: |
-          source .env/bin/activate
-          python -m pytest -n 2 --dist=loadfile -s --make-reports=tests_tf_gpu tests
+          python -m pytest -n 2 --dist=loadfile --make-reports=tests_tf_gpu tests
 
       - name: Failure short reports
         if: ${{ always() }}
         run: cat reports/tests_tf_gpu_failures_short.txt
-        
+
       - name: Test suite reports artifacts
         if: ${{ always() }}
         uses: actions/upload-artifact@v2
@@ -154,58 +102,42 @@ jobs:
           name: run_all_tests_tf_gpu_test_reports
           path: reports
 
+
   run_tests_torch_multi_gpu:
-    runs-on: [self-hosted, gpu, multi-gpu]
+    runs-on: [self-hosted, docker-gpu, multi-gpu]
+    container:
+      image: pytorch/pytorch:1.8.0-cuda11.1-cudnn8-runtime
+      options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
     steps:
-      - uses: actions/checkout@v2
-      - name: Python version
+      - name: Launcher docker
+        uses: actions/checkout@v2
+
+      - name: NVIDIA-SMI
         run: |
-          which python
-          python --version
-          pip --version
+          nvidia-smi
 
-      - name: Current dir
-        run: pwd
-      - run: nvidia-smi
-
-      - name: Loading cache.
-        uses: actions/cache@v2
-        id: cache
-        with:
-          path: .env
-          key: v1.1-tests_torch_multi_gpu-${{ hashFiles('setup.py') }}
-
-      - name: Create new python env (on self-hosted runners we have to handle isolation ourselves)
-        run: |
-          python -m venv .env
-          source .env/bin/activate
-          which python
-          python --version
-          pip --version
       - name: Install dependencies
         run: |
-          source .env/bin/activate
+          apt -y update && apt install -y libsndfile1-dev
           pip install --upgrade pip
-          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
+          pip install .[sklearn,testing,onnxruntime,sentencepiece,speech]
 
       - 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 non-slow tests on GPU
         env:
-          OMP_NUM_THREADS: 1
+          MKL_SERVICE_FORCE_INTEL: 1
         run: |
-          source .env/bin/activate
-          python -m pytest -n 2 --dist=loadfile -s --make-reports=tests_torch_multi_gpu tests
+          python -m pytest -n 2 --dist=loadfile --make-reports=tests_torch_multi_gpu tests
 
       - name: Failure short reports
         if: ${{ always() }}
-        run: cat reports/tests_torch_multi_gpu_failures_short.txt          
+        run: cat reports/tests_torch_multi_gpu_failures_short.txt
 
       - name: Test suite reports artifacts
         if: ${{ always() }}
@@ -215,52 +147,34 @@ jobs:
           path: reports
 
   run_tests_tf_multi_gpu:
-    runs-on: [self-hosted, gpu, 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/
     steps:
-      - uses: actions/checkout@v2
-      - name: Python version
+      - name: Launcher docker
+        uses: actions/checkout@v2
+
+      - name: NVIDIA-SMI
         run: |
-          which python
-          python --version
-          pip --version
+          nvidia-smi
 
-      - 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
+          pip install .[sklearn,testing,onnxruntime,sentencepiece]
 
       - 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
+          TF_NUM_INTRAOP_THREADS: 8
+          TF_NUM_INTEROP_THREADS: 1
         run: |
-          source .env/bin/activate
-          python -m pytest -n 2 --dist=loadfile -s --make-reports=tests_tf_multi_gpu tests
+          python -m pytest -n 2 --dist=loadfile --make-reports=tests_tf_multi_gpu tests
 
       - name: Failure short reports
         if: ${{ always() }}
@@ -272,4 +186,110 @@ jobs:
         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
+
+      - name: NVIDIA-SMI
+        run: |
+          nvidia-smi
+
+      - name: Install dependencies
+        run: |
+          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 --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_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
+        run: |
+          nvidia-smi
+
+      - name: Install dependencies
+        run: |
+          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 --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_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
+    ]
+    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 }}
+
+        run: |
+          pip install slack_sdk
+          python utils/notification_service.py push

.github/workflows/self-scheduled.yml

@@ -1,82 +1,66 @@
-# 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
+
 jobs:
   run_all_tests_torch_gpu:
-    runs-on: [self-hosted, gpu, single-gpu]
+    runs-on: [self-hosted, docker-gpu, single-gpu]
+    container:
+      image: pytorch/pytorch:1.8.0-cuda11.1-cudnn8-runtime
+      options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
     steps:
-      - uses: actions/checkout@v2
+      - name: Launcher docker
+        uses: actions/checkout@v2
 
-      - name: Loading cache.
-        uses: actions/cache@v2
-        id: cache
-        with:
-          path: .env
-          key: v  1.1-slow_tests_torch_gpu-${{ hashFiles('setup.py') }}
-
-      - name: Python version
+      - name: NVIDIA-SMI
         run: |
-          which python
-          python --version
-          pip --version
-
-      - name: Current dir
-        run: pwd
-      - run: nvidia-smi
-
-      - name: Create new python env (on self-hosted runners we have to handle isolation ourselves)
-        if: steps.cache.outputs.cache-hit != 'true'
-        run: |
-          python -m venv .env
-          source .env/bin/activate
-          which python
-          python --version
-          pip --version
+          nvidia-smi
 
       - name: Install dependencies
         run: |
-          source .env/bin/activate
+          apt -y update && apt install -y libsndfile1-dev
           pip install --upgrade pip
-          pip install .[torch,sklearn,testing,onnxruntime,sentencepiece]
-          pip install git+https://github.com/huggingface/datasets
-          pip list
+          pip install .[sklearn,testing,onnxruntime,sentencepiece,speech]
 
       - 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: |
-          source .env/bin/activate
-          python -m pytest -n 1 --dist=loadfile -s --make-reports=tests_torch_gpu tests
+          python -m pytest -n 1 --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: 1
+          OMP_NUM_THREADS: 16
+          MKL_NUM_THREADS: 16
           RUN_SLOW: yes
+          HF_HOME: /mnt/cache
+          TRANSFORMERS_IS_CI: yes
         run: |
-          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
+          python -m pytest -n 1 --dist=loadfile --make-reports=examples_torch_gpu examples
 
       - name: Failure short reports
         if: ${{ always() }}
@@ -85,13 +69,9 @@ 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: |
-          source .env/bin/activate
-          python -m pytest -n 1 --dist=loadfile -s -m is_pipeline_test --make-reports=tests_torch_pipeline_gpu tests
+          python -m pytest -n 1 --dist=loadfile -m is_pipeline_test --make-reports=tests_torch_pipeline_gpu tests
 
       - name: Failure short reports
         if: ${{ always() }}
@@ -104,60 +84,36 @@ jobs:
           name: run_all_tests_torch_gpu_test_reports
           path: reports
 
-
   run_all_tests_tf_gpu:
-    runs-on: [self-hosted, gpu, single-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/
     steps:
-      - uses: actions/checkout@v2
+      - name: Launcher docker
+        uses: actions/checkout@v2
 
-      - name: Loading cache.
-        uses: actions/cache@v2
-        id: cache
-        with:
-          path: .env
-          key: v1.1-slow_tests_tf_gpu-${{ hashFiles('setup.py') }}
-
-      - name: Python version
+      - name: NVIDIA-SMI
         run: |
-          which python
-          python --version
-          pip --version
-
-      - name: Current dir
-        run: pwd
-      - run: nvidia-smi
-
-      - name: Create new python env (on self-hosted runners we have to handle isolation ourselves)
-        if: steps.cache.outputs.cache-hit != 'true'
-        run: |
-          python -m venv .env
-          source .env/bin/activate
-          which python
-          python --version
-          pip --version
+          nvidia-smi
 
       - name: Install dependencies
         run: |
-          source .env/bin/activate
           pip install --upgrade pip
-          pip install .[tf,sklearn,testing,onnxruntime,sentencepiece]
-          pip install git+https://github.com/huggingface/datasets
-          pip list
+          pip install .[sklearn,testing,onnx,sentencepiece]
 
       - name: Are GPUs recognized by our DL frameworks
         run: |
-          source .env/bin/activate
           TF_CPP_MIN_LOG_LEVEL=3 python -c "import tensorflow as tf; print('TF GPUs available:', bool(tf.config.list_physical_devices('GPU')))"
           TF_CPP_MIN_LOG_LEVEL=3 python -c "import tensorflow as tf; print('Number of TF GPUs available:', len(tf.config.list_physical_devices('GPU')))"
 
       - name: Run all tests on GPU
         env:
-          OMP_NUM_THREADS: 1
-          RUN_SLOW: yes
+          TF_NUM_INTEROP_THREADS: 1
+          TF_NUM_INTRAOP_THREADS: 16
         run: |
-          source .env/bin/activate
-          python -m pytest -n 1 --dist=loadfile -s --make-reports=tests_tf_gpu tests
-          
+          python -m pytest -n 1 --dist=loadfile --make-reports=tests_tf_gpu tests
+
       - name: Failure short reports
         if: ${{ always() }}
         run: cat reports/tests_tf_gpu_failures_short.txt
@@ -165,17 +121,15 @@ 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: |
-          source .env/bin/activate
-          python -m pytest -n 1 --dist=loadfile -s -m is_pipeline_test --make-reports=tests_tf_pipelines_gpu tests
+          python -m pytest -n 1 --dist=loadfile -m is_pipeline_test --make-reports=tests_tf_pipeline_gpu tests
 
       - name: Failure short reports
         if: ${{ always() }}
-        run: cat reports/tests_tf_pipelines_gpu_failures_short.txt
+        run: cat reports/tests_tf_pipeline_gpu_failures_short.txt
 
       - name: Test suite reports artifacts
         if: ${{ always() }}
@@ -183,86 +137,49 @@ jobs:
         with:
           name: run_all_tests_tf_gpu_test_reports
           path: reports
-          
+
   run_all_tests_torch_multi_gpu:
-    runs-on: [self-hosted, gpu, multi-gpu]
+    runs-on: [self-hosted, docker-gpu, multi-gpu]
+    container:
+      image: pytorch/pytorch:1.8.0-cuda11.1-cudnn8-runtime
+      options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
     steps:
-      - uses: actions/checkout@v2
+      - name: Launcher docker
+        uses: actions/checkout@v2
 
-      - name: Loading cache.
-        uses: actions/cache@v2
-        id: cache
-        with:
-          path: .env
-          key: v1.1-slow_tests_torch_multi_gpu-${{ hashFiles('setup.py') }}
-
-      - name: Python version
+      - name: NVIDIA-SMI
         run: |
-          which python
-          python --version
-          pip --version
-
-      - name: Current dir
-        run: pwd
-      - run: nvidia-smi
-
-      - name: Create new python env (on self-hosted runners we have to handle isolation ourselves)
-        if: steps.cache.outputs.cache-hit != 'true'
-        run: |
-          python -m venv .env
-          source .env/bin/activate
-          which python
-          python --version
-          pip --version
+          nvidia-smi
 
       - name: Install dependencies
         run: |
-          source .env/bin/activate
+          apt -y update && apt install -y libsndfile1-dev
           pip install --upgrade pip
-          pip install .[torch,sklearn,testing,onnxruntime,sentencepiece]
-          pip install git+https://github.com/huggingface/datasets
-          pip list
+          pip install .[sklearn,testing,onnxruntime,sentencepiece,speech]
 
       - 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 multi-GPU
+      - name: Run all tests on GPU
         env:
-          OMP_NUM_THREADS: 1
-          RUN_SLOW: yes
+          MKL_SERVICE_FORCE_INTEL: 1
         run: |
-          source .env/bin/activate
-          python -m pytest -n 1 --dist=loadfile -s --make-reports=tests_torch_multi_gpu tests
+          python -m pytest -n 1 --dist=loadfile --make-reports=tests_torch_multi_gpu tests
 
       - name: Failure short reports
         if: ${{ always() }}
         run: cat reports/tests_torch_multi_gpu_failures_short.txt
 
-      - name: Run examples tests on multi-GPU
-        env:
-          OMP_NUM_THREADS: 1
-          RUN_SLOW: yes
-        run: |
-          source .env/bin/activate
-          python -m pytest -n 1 --dist=loadfile -s --make-reports=tests_torch_examples_multi_gpu examples
-
-      - name: Failure short reports
-        if: ${{ always() }}
-        run: cat reports/tests_torch_examples_multi_gpu_failures_short.txt
-
-      - name: Run all pipeline tests on multi-GPU
+      - name: Run all pipeline tests on GPU
         if: ${{ always() }}
         env:
-          TF_FORCE_GPU_ALLOW_GROWTH: "true"
-          OMP_NUM_THREADS: 1
-          RUN_SLOW: yes
           RUN_PIPELINE_TESTS: yes
         run: |
-          source .env/bin/activate
-          python -m pytest -n 1 --dist=loadfile -s -m is_pipeline_test --make-reports=tests_torch_pipeline_multi_gpu tests
+          python -m pytest -n 1 --dist=loadfile -m is_pipeline_test --make-reports=tests_torch_pipeline_multi_gpu tests
 
       - name: Failure short reports
         if: ${{ always() }}
@@ -276,73 +193,48 @@ jobs:
           path: reports
 
   run_all_tests_tf_multi_gpu:
-    runs-on: [self-hosted, gpu, 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/
     steps:
-      - uses: actions/checkout@v2
+      - name: Launcher docker
+        uses: actions/checkout@v2
 
-      - name: Loading cache.
-        uses: actions/cache@v2
-        id: cache
-        with:
-          path: .env
-          key: v1.1-slow_tests_tf_multi_gpu-${{ hashFiles('setup.py') }}
-
-      - name: Python version
+      - name: NVIDIA-SMI
         run: |
-          which python
-          python --version
-          pip --version
-
-      - name: Current dir
-        run: pwd
-      - run: nvidia-smi
-
-      - name: Create new python env (on self-hosted runners we have to handle isolation ourselves)
-        if: steps.cache.outputs.cache-hit != 'true'
-        run: |
-          python -m venv .env
-          source .env/bin/activate
-          which python
-          python --version
-          pip --version
+          nvidia-smi
 
       - name: Install dependencies
         run: |
-          source .env/bin/activate
           pip install --upgrade pip
-          pip install .[tf,sklearn,testing,onnxruntime,sentencepiece]
-          pip install git+https://github.com/huggingface/datasets
-          pip list
+          pip install .[sklearn,testing,onnx,sentencepiece]
 
       - name: Are GPUs recognized by our DL frameworks
         run: |
-          source .env/bin/activate
           TF_CPP_MIN_LOG_LEVEL=3 python -c "import tensorflow as tf; print('TF GPUs available:', bool(tf.config.list_physical_devices('GPU')))"
           TF_CPP_MIN_LOG_LEVEL=3 python -c "import tensorflow as tf; print('Number of TF GPUs available:', len(tf.config.list_physical_devices('GPU')))"
 
-      - name: Run all tests on multi-GPU
+      - name: Run all tests on GPU
         env:
-          OMP_NUM_THREADS: 1
-          RUN_SLOW: yes
+          TF_NUM_INTEROP_THREADS: 1
+          TF_NUM_INTRAOP_THREADS: 16
         run: |
-          source .env/bin/activate
-          python -m pytest -n 1 --dist=loadfile -s --make-reports=tests_tf_multi_gpu tests
+          python -m pytest -n 1 --dist=loadfile --make-reports=tests_tf_multi_gpu tests
 
       - name: Failure short reports
         if: ${{ always() }}
         run: cat reports/tests_tf_multi_gpu_failures_short.txt
 
-      - name: Run all pipeline tests on multi-GPU
+      - 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: |
-          source .env/bin/activate
-          python -m pytest -n 1 --dist=loadfile -s -m is_pipeline_test --make-reports=tests_tf_pipeline_multi_gpu tests
-          
+          python -m pytest -n 1 --dist=loadfile -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
@@ -353,4 +245,110 @@ jobs:
         with:
           name: run_all_tests_tf_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: |
+          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 --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
+        run: |
+          nvidia-smi
+
+      - name: Install dependencies
+        run: |
+          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 --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 }}
+
+
+        run: |
+          pip install slack_sdk
+          python utils/notification_service.py scheduled

.github/workflows/stale.yml

@@ -0,0 +1,27 @@
+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

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

ISSUES.md

@@ -207,6 +207,8 @@ 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

@@ -19,33 +19,44 @@ modified_only_fixup:
 deps_table_update:
 	@python setup.py deps_table_update
 
+# autogenerating code
+
+autogenerate_code: deps_table_update
+	python utils/class_mapping_update.py
+
 # Check that source code meets quality standards
 
-extra_quality_checks: deps_table_update
+extra_quality_checks:
 	python utils/check_copies.py
 	python utils/check_table.py
 	python utils/check_dummies.py
 	python utils/check_repo.py
-	python utils/style_doc.py src/transformers docs/source --max_len 119
+	python utils/check_inits.py
 
 # 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
 
-style: deps_table_update
+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:
 	black $(check_dirs)
 	isort $(check_dirs)
-	python utils/style_doc.py src/transformers docs/source --max_len 119
+	${MAKE} autogenerate_code
+	${MAKE} extra_style_checks
 
 # Super fast fix and check target that only works on relevant modified files since the branch was made
 
-fixup: modified_only_fixup extra_quality_checks
+fixup: modified_only_fixup extra_style_checks autogenerate_code extra_quality_checks
 
 # Make marked copies of snippets of codes conform to the original
 
@@ -64,7 +75,27 @@ test:
 test-examples:
 	python -m pytest -n auto --dist=loadfile -s -v ./examples/
 
+# 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
+
+
 # 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

@@ -1,3 +1,12 @@
+# Patches
+
+This branch has the following patches:
+
+* gpt-neo model is loaded directly on GPU to save system memory
+* repetition_penalty has range and slope settings, so it doesn't penalize all tokens in the context window
+* no copy of the state dict is made while loading a pretrained model
+* local self attention uses padding so it doesn't OOM on long sequences
+
 <!---
 Copyright 2020 The HuggingFace Team. All rights reserved.
 
@@ -55,7 +64,7 @@ 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 Langugage Inference with RoBERTa](https://huggingface.co/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal)
+- [Natural Language 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)
@@ -167,7 +176,7 @@ When TensorFlow 2.0 and/or PyTorch has been installed, 🤗 Transformers can be
 pip install transformers
 ```
 
-If you'd like to play with the examples, you must [install the library from source](https://huggingface.co/transformers/installation.html#installing-from-source).
+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).
 
 ### With conda
 
@@ -179,7 +188,7 @@ 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.
 
 ## Models architectures
 
@@ -194,11 +203,17 @@ Current number of checkpoints: ![](https://img.shields.io/endpoint?url=https://h
 1. **[BARThez](https://huggingface.co/transformers/model_doc/barthez.html)** (from École polytechnique) released with the paper [BARThez: a Skilled Pretrained French Sequence-to-Sequence Model](https://arxiv.org/abs/2010.12321) by Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis.
 1. **[BERT](https://huggingface.co/transformers/model_doc/bert.html)** (from Google) released with the paper [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.
 1. **[BERT For Sequence Generation](https://huggingface.co/transformers/model_doc/bertgeneration.html)** (from Google) released with the paper [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
+1. **[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. **[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. **[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 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. **[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. **[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
@@ -209,27 +224,36 @@ Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
 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 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. **[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. **[LED](https://huggingface.co/transformers/model_doc/led.html)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
 1. **[Longformer](https://huggingface.co/transformers/model_doc/longformer.html)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
 1. **[LXMERT](https://huggingface.co/transformers/model_doc/lxmert.html)** (from UNC Chapel Hill) released with the paper [LXMERT: Learning Cross-Modality Encoder Representations from Transformers for Open-Domain Question Answering](https://arxiv.org/abs/1908.07490) by Hao Tan and Mohit Bansal.
+1. **[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 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. **[RoBERTa](https://huggingface.co/transformers/model_doc/roberta.html)** (from Facebook), released together with the paper a [Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692) by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
-ultilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers/tree/master/examples/distillation) and a German version of DistilBERT.
+1. **[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. **[SqueezeBert](https://huggingface.co/transformers/model_doc/squeezebert.html)** released with the paper [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer.
 1. **[T5](https://huggingface.co/transformers/model_doc/t5.html)** (from Google AI) released with the paper [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
 1. **[TAPAS](https://huggingface.co/transformers/model_doc/tapas.html)** (from Google AI) released with the paper [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://arxiv.org/abs/2004.02349) by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos.
 1. **[Transformer-XL](https://huggingface.co/transformers/model_doc/transformerxl.html)** (from Google/CMU) released with the paper [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
+1. **[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. **[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 PyTorch/TensorFlow/Flax or has an associated tokenizer backed by the 🤗 Tokenizers library, refer to [this table](https://huggingface.co/transformers/index.html#bigtable)

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.
 
@@ -258,3 +258,43 @@ 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

@@ -1,10 +1,14 @@
 // These two things need to be updated at each release for the version selector.
 // Last stable version
-const stableVersion = "v4.1.1"
+const stableVersion = "v4.5.1"
 // Dictionary doc folder to label. The last stable version should have an empty key.
 const versionMapping = {
     "master": "master",
-    "": "v4.1.1 (stable)",
+    "": "v4.5.0/v4.5.1 (stable)",
+    "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",
     "v4.0.1": "v4.0.0/v4.0.1",
     "v3.5.1": "v3.5.0/v3.5.1",
     "v3.4.0": "v3.4.0",
@@ -59,7 +63,7 @@ function addIcon() {
 function addCustomFooter() {
     const customFooter = document.createElement("div");
     const questionOrIssue = document.createElement("div");
-    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>";
+    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>";
     customFooter.appendChild(questionOrIssue);
     customFooter.classList.add("footer");
 
@@ -126,11 +130,11 @@ 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$|^search.html?/)) {
+    if (parts[parts.length - 1] != "" && ! parts[parts.length - 1].match(/\.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") {
+    else if (parts[versionIndex] == "main_classes" || parts[versionIndex] == "model_doc" || parts[versionIndex] == "internal") {
         versionIndex = versionIndex - 1;
     } 
     const version = parts[versionIndex];

docs/source/add_new_model.rst

@@ -0,0 +1,844 @@
+.. 
+    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
+
+   import torch.nn as 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/community.md

@@ -0,0 +1,54 @@
+# 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      |      |
+|:----------|:-------------|:-------------|------:|
+| [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) |

docs/source/conf.py

@@ -14,21 +14,24 @@
 #
 import os
 import sys
-sys.path.insert(0, os.path.abspath('../../src'))
+
+sys.path.insert(0, os.path.abspath("../../src"))
 
 
 # -- Project information -----------------------------------------------------
 
-project = u'transformers'
-copyright = u'2020, The Hugging Face Team, Licenced under the Apache License, Version 2.0'
-author = u'huggingface'
+project = "transformers"
+copyright = "2020, The Hugging Face Team, Licenced under the Apache License, Version 2.0"
+author = "huggingface"
 
 # The short X.Y version
-version = u''
+version = ""
 # The full version, including alpha/beta/rc tags
-release = u'4.2.0'
+release = "4.5.0.dev0"
+
+
 # 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', '')}
 
@@ -42,27 +45,28 @@ 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',
-    'sphinx_copybutton'
+    "sphinx.ext.autodoc",
+    "sphinx.ext.extlinks",
+    "sphinx.ext.coverage",
+    "sphinx.ext.napoleon",
+    "recommonmark",
+    "sphinx.ext.viewcode",
+    "sphinx_markdown_tables",
+    "sphinxext.opengraph",
+    "sphinx_copybutton",
 ]
 
 # Add any paths that contain templates here, relative to this directory.
-templates_path = ['_templates']
+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.
@@ -74,7 +78,7 @@ 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 = [u'_build', 'Thumbs.db', '.DS_Store']
+exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
 
 # The name of the Pygments (syntax highlighting) style to use.
 pygments_style = None
@@ -88,20 +92,30 @@ 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'
-}
+html_theme_options = {"analytics_id": "UA-83738774-2", "navigation_with_keys": True}
+
+#  Configuration for OpenGraph and Twitter Card Tags.
+# These are responsible for creating nice shareable social images https://ahrefs.com/blog/open-graph-meta-tags/
+# https://ogp.me/#type_website
+ogp_image = "https://huggingface.co/front/thumbnails/transformers.png"
+ogp_description = "State-of-the-art Natural Language Processing for PyTorch and TensorFlow 2.0. Transformers provides thousands of pretrained models to perform tasks on texts such as classification, information extraction, question answering, summarization, translation, text generation, etc in 100+ languages. Its aim is to make cutting-edge NLP easier to use for everyone"
+ogp_description_length = 160
+
+ogp_custom_meta_tags = [
+    f'<meta name="twitter:image" content="{ogp_image}">',
+    f'<meta name="twitter:description" content="{ogp_description}">',
+]
 
 # Add any paths that contain custom static files (such as style sheets) here,
 # relative to this directory. They are copied after the builtin static files,
 # so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ['_static']
+html_static_path = ["_static"]
 
 # Custom sidebar templates, must be a dictionary that maps document names
 # to template names.
@@ -113,17 +127,17 @@ 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 ------------------------------------------------
@@ -132,15 +146,12 @@ 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',
@@ -150,8 +161,7 @@ latex_elements = {
 # (source start file, target name, title,
 #  author, documentclass [howto, manual, or own class]).
 latex_documents = [
-    (master_doc, 'transformers.tex', u'transformers Documentation',
-     u'huggingface', 'manual'),
+    (master_doc, "transformers.tex", "transformers Documentation", "huggingface", "manual"),
 ]
 
 
@@ -159,10 +169,7 @@ latex_documents = [
 
 # One entry per manual page. List of tuples
 # (source start file, name, description, authors, manual section).
-man_pages = [
-    (master_doc, 'transformers', u'transformers Documentation',
-     [author], 1)
-]
+man_pages = [(master_doc, "transformers", "transformers Documentation", [author], 1)]
 
 
 # -- Options for Texinfo output ----------------------------------------------
@@ -171,9 +178,15 @@ man_pages = [
 # (source start file, target name, title, author,
 #  dir menu entry, description, category)
 texinfo_documents = [
-    (master_doc, 'transformers', u'transformers Documentation',
-     author, 'transformers', 'One line description of project.',
-     'Miscellaneous'),
+    (
+        master_doc,
+        "transformers",
+        "transformers Documentation",
+        author,
+        "transformers",
+        "One line description of project.",
+        "Miscellaneous",
+    ),
 ]
 
 
@@ -192,11 +205,13 @@ epub_title = project
 # epub_uid = ''
 
 # A list of files that should not be packed into the epub file.
-epub_exclude_files = ['search.html']
+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

@@ -28,17 +28,13 @@ BERT
 You can convert any TensorFlow checkpoint for BERT (in particular `the pre-trained models released by Google
 <https://github.com/google-research/bert#pre-trained-models>`_\ ) in a PyTorch save file by using the
 :prefix_link:`convert_bert_original_tf_checkpoint_to_pytorch.py
-<src/transformers/convert_bert_original_tf_checkpoint_to_pytorch.py>` script.
+<src/transformers/models/bert/convert_bert_original_tf_checkpoint_to_pytorch.py>` script.
 
 This CLI takes as input a TensorFlow checkpoint (three files starting with ``bert_model.ckpt``\ ) and the associated
 configuration file (\ ``bert_config.json``\ ), and creates a PyTorch model for this configuration, loads the weights
 from the TensorFlow checkpoint in the PyTorch model and saves the resulting model in a standard PyTorch save file that
-can be imported using ``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>`_\
-).
+can be imported using ``from_pretrained()`` (see example in :doc:`quicktour` , `run_glue.py
+<https://github.com/huggingface/transformers/blob/master/examples/text-classification/run_glue.py>`_\ ).
 
 You only need to run this conversion script **once** to get a PyTorch model. You can then disregard the TensorFlow
 checkpoint (the three files starting with ``bert_model.ckpt``\ ) but be sure to keep the configuration file (\
@@ -51,12 +47,12 @@ 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>`__.
@@ -66,7 +62,7 @@ ALBERT
 
 Convert TensorFlow model checkpoints of ALBERT to PyTorch using the
 :prefix_link:`convert_albert_original_tf_checkpoint_to_pytorch.py
-<src/transformers/convert_bert_original_tf_checkpoint_to_pytorch.py>` script.
+<src/transformers/models/albert/convert_albert_original_tf_checkpoint_to_pytorch.py>` script.
 
 The CLI takes as input a TensorFlow checkpoint (three files starting with ``model.ckpt-best``\ ) and the accompanying
 configuration file (\ ``albert_config.json``\ ), then creates and saves a PyTorch model. To run this conversion you
@@ -76,12 +72,12 @@ 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>`__.
@@ -95,13 +91,13 @@ 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
@@ -112,13 +108,13 @@ 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
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -128,13 +124,13 @@ 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
@@ -144,14 +140,14 @@ 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
@@ -161,10 +157,25 @@ 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]
+    transformers-cli convert --model_type xlm \
+      --tf_checkpoint $XLM_CHECKPOINT_PATH \
+      --pytorch_dump_output $PYTORCH_DUMP_OUTPUT
+     [--config XML_CONFIG] \
+     [--finetuning_task_name XML_FINETUNED_TASK]
+
+
+T5
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Here is an example of the conversion process for a pre-trained T5 model:
+
+.. code-block:: shell
+
+    export T5=/path/to/t5/uncased_L-12_H-768_A-12
+
+    transformers-cli convert --model_type t5 \
+      --tf_checkpoint $T5/t5_model.ckpt \
+      --config $T5/t5_config.json \
+      --pytorch_dump_output $T5/pytorch_model.bin

docs/source/custom_datasets.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 `🤗 NLP library
-    <https://github.com/huggingface/nlp>`_. We do not use this library to access the datasets here since this tutorial
-    meant to illustrate how to work with your own data. A brief of introduction can be found at the end of the tutorial
-    in the section ":ref:`nlplib`".
+    The datasets used in this tutorial are available and can be more easily accessed using the `🤗 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`".
 
 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 🤗 NLP library with ``load_dataset("imdb")``.
+    can be alternatively downloaded with the 🤗 Datasets library with ``load_dataset("imdb")``.
 
 In this example, we'll show how to download, tokenize, and train a model on the IMDb reviews dataset. This task takes
 the text of a review and requires the model to predict whether the sentiment of the review is positive or negative.
@@ -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 training our test set results. Sklearn has a convenient utility for creating such splits:
+and tuning without tainting 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 🤗 NLP library with ``load_dataset("wnut_17")``.
+    and can be alternatively downloaded with the 🤗 Datasets library with ``load_dataset("wnut_17")``.
 
 Next we will look at token classification. Rather than classifying an entire sequence, this task classifies token by
 token. We'll demonstrate how to do this with `Named Entity Recognition
@@ -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 🤗 NLP library with
+    <https://huggingface.co/datasets/squad_v2>`_), and can be alternatively downloaded with the 🤗 Datasets library with
     ``load_dataset("squad_v2")``.
 
 Question answering comes in many forms. In this example, we'll look at the particular type of extractive QA that
@@ -558,15 +558,14 @@ 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']))
+            end_positions.append(encodings.char_to_token(i, answers[i]['answer_end'] - 1))
 
             # if start position is None, the answer passage has been truncated
             if start_positions[-1] is None:
                 start_positions[-1] = tokenizer.model_max_length
-
-            # if end position is None, the 'char_to_token' function points to the space before the correct token - > add + 1
             if end_positions[-1] is None:
-                end_positions[-1] = encodings.char_to_token(i, answers[i]['answer_end'] + 1)
+                end_positions[-1] = tokenizer.model_max_length
+
         encodings.update({'start_positions': start_positions, 'end_positions': end_positions})
 
     add_token_positions(train_encodings, train_answers)
@@ -678,22 +677,23 @@ Additional Resources
   - :doc:`Preprocessing <preprocessing>`. Docs page on data preprocessing.
   - :doc:`Training <training>`. Docs page on training and fine-tuning.
 
-.. _nlplib:
+.. _datasetslib:
 
-Using the 🤗 NLP Datasets & Metrics library
+Using the 🤗 Datasets & Metrics library
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 This tutorial demonstrates how to read in datasets from various raw text formats and prepare them for training with 🤗
 Transformers so that you can do the same thing with your own custom datasets. However, we recommend users use the `🤗
-NLP library <https://github.com/huggingface/nlp>`_ for working with the 150+ datasets included in the `hub
+Datasets library <https://github.com/huggingface/datasets>`_ for working with the 150+ datasets included in the `hub
 <https://huggingface.co/datasets>`_, including the three datasets used in this tutorial. As a very brief overview, we
-will show how to use the NLP library to download and prepare the IMDb dataset from the first example, :ref:`seq_imdb`.
+will show how to use the Datasets library to download and prepare the IMDb dataset from the first example,
+:ref:`seq_imdb`.
 
 Start by downloading the dataset:
 
 .. code-block:: python
 
-    from nlp import load_dataset
+    from datasets import load_dataset
     train = load_dataset("imdb", split="train")
 
 Each dataset has multiple columns corresponding to different features. Let's see what our columns are.
@@ -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 🤗 NLP docs <https://huggingface.co/nlp/processing.html>`_ for a
-more thorough introduction.
+We now have a fully-prepared dataset. Check out `the 🤗 Datasets docs
+<https://huggingface.co/docs/datasets/processing.html>`_ for a more thorough introduction.

docs/source/fast_tokenizers.rst

@@ -0,0 +1,62 @@
+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

@@ -21,22 +21,25 @@ 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
 -----------------------------------------------------------------------------------------------------------------------
@@ -179,7 +182,7 @@ 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

@@ -22,7 +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
@@ -97,108 +97,155 @@ and conversion utilities for the following models:
 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.
-6. :doc:`Blenderbot <model_doc/blenderbot>` (from Facebook) released with the paper `Recipes for building an
+6. :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.
+7. :doc:`Blenderbot <model_doc/blenderbot>` (from Facebook) released with the paper `Recipes for building an
    open-domain chatbot <https://arxiv.org/abs/2004.13637>`__ by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary
    Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
-7. :doc:`BlenderbotSmall <model_doc/blenderbot_small>` (from Facebook) released with the paper `Recipes for building an
+8. :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,
+9. :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.
+10. :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.
+11. :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.
+12. :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.
+13. :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.
+14. :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.
+15. :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,
     Weizhu Chen.
-11. :doc:`DialoGPT <model_doc/dialogpt>` (from Microsoft Research) released with the paper `DialoGPT: Large-Scale
+16. :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.
+17. :doc:`DialoGPT <model_doc/dialogpt>` (from Microsoft Research) released with the paper `DialoGPT: Large-Scale
     Generative Pre-training for Conversational Response Generation <https://arxiv.org/abs/1911.00536>`__ by Yizhe
     Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan.
-12. :doc:`DistilBERT <model_doc/distilbert>` (from HuggingFace), released together with the paper `DistilBERT, a
+18. :doc:`DistilBERT <model_doc/distilbert>` (from HuggingFace), released together with the paper `DistilBERT, a
     distilled version of BERT: smaller, faster, cheaper and lighter <https://arxiv.org/abs/1910.01108>`__ by Victor
     Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into `DistilGPT2
     <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__, RoBERTa into `DistilRoBERTa
     <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__, Multilingual BERT into
     `DistilmBERT <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__ and a German
     version of DistilBERT.
-13. :doc:`DPR <model_doc/dpr>` (from Facebook) released with the paper `Dense Passage Retrieval for Open-Domain
+19. :doc:`DPR <model_doc/dpr>` (from Facebook) released with the paper `Dense Passage Retrieval for Open-Domain
     Question Answering <https://arxiv.org/abs/2004.04906>`__ by Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick
     Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
-14. :doc:`ELECTRA <model_doc/electra>` (from Google Research/Stanford University) released with the paper `ELECTRA:
+20. :doc:`ELECTRA <model_doc/electra>` (from Google Research/Stanford University) released with the paper `ELECTRA:
     Pre-training text encoders as discriminators rather than generators <https://arxiv.org/abs/2003.10555>`__ by Kevin
     Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning.
-15. :doc:`FlauBERT <model_doc/flaubert>` (from CNRS) released with the paper `FlauBERT: Unsupervised Language Model
+21. :doc:`FlauBERT <model_doc/flaubert>` (from CNRS) released with the paper `FlauBERT: Unsupervised Language Model
     Pre-training for French <https://arxiv.org/abs/1912.05372>`__ by Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne,
     Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, Didier Schwab.
-16. :doc:`Funnel Transformer <model_doc/funnel>` (from CMU/Google Brain) released with the paper `Funnel-Transformer:
+22. :doc:`Funnel Transformer <model_doc/funnel>` (from CMU/Google Brain) released with the paper `Funnel-Transformer:
     Filtering out Sequential Redundancy for Efficient Language Processing <https://arxiv.org/abs/2006.03236>`__ by
     Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le.
-17. :doc:`GPT <model_doc/gpt>` (from OpenAI) released with the paper `Improving Language Understanding by Generative
+23. :doc:`GPT <model_doc/gpt>` (from OpenAI) released with the paper `Improving Language Understanding by Generative
     Pre-Training <https://blog.openai.com/language-unsupervised/>`__ by Alec Radford, Karthik Narasimhan, Tim Salimans
     and Ilya Sutskever.
-18. :doc:`GPT-2 <model_doc/gpt2>` (from OpenAI) released with the paper `Language Models are Unsupervised Multitask
+24. :doc:`GPT-2 <model_doc/gpt2>` (from OpenAI) released with the paper `Language Models are Unsupervised Multitask
     Learners <https://blog.openai.com/better-language-models/>`__ by Alec Radford*, Jeffrey Wu*, Rewon Child, David
     Luan, Dario Amodei** and Ilya Sutskever**.
-19. :doc:`LayoutLM <model_doc/layoutlm>` (from Microsoft Research Asia) released with the paper `LayoutLM: Pre-training
+25. :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.
+26. :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
+27. :doc:`LayoutLM <model_doc/layoutlm>` (from Microsoft Research Asia) released with the paper `LayoutLM: Pre-training
     of Text and Layout for Document Image Understanding <https://arxiv.org/abs/1912.13318>`__ by Yiheng Xu, Minghao Li,
     Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou.
-20. :doc:`LED <model_doc/led>` (from AllenAI) released with the paper `Longformer: The Long-Document Transformer
+28. :doc:`LED <model_doc/led>` (from AllenAI) released with the paper `Longformer: The Long-Document Transformer
     <https://arxiv.org/abs/2004.05150>`__ by Iz Beltagy, Matthew E. Peters, Arman Cohan.
-21. :doc:`Longformer <model_doc/longformer>` (from AllenAI) released with the paper `Longformer: The Long-Document
+29. :doc:`Longformer <model_doc/longformer>` (from AllenAI) released with the paper `Longformer: The Long-Document
     Transformer <https://arxiv.org/abs/2004.05150>`__ by Iz Beltagy, Matthew E. Peters, Arman Cohan.
-22. :doc:`LXMERT <model_doc/lxmert>` (from UNC Chapel Hill) released with the paper `LXMERT: Learning Cross-Modality
+30. :doc:`LXMERT <model_doc/lxmert>` (from UNC Chapel Hill) released with the paper `LXMERT: Learning Cross-Modality
     Encoder Representations from Transformers for Open-Domain Question Answering <https://arxiv.org/abs/1908.07490>`__
     by Hao Tan and Mohit Bansal.
-23. :doc:`MarianMT <model_doc/marian>` Machine translation models trained using `OPUS <http://opus.nlpl.eu/>`__ data by
+31. :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 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.
+32. :doc:`MarianMT <model_doc/marian>` Machine translation models trained using `OPUS <http://opus.nlpl.eu/>`__ data by
     Jörg Tiedemann. The `Marian Framework <https://marian-nmt.github.io/>`__ is being developed by the Microsoft
     Translator Team.
-24. :doc:`MBart <model_doc/mbart>` (from Facebook) released with the paper `Multilingual Denoising Pre-training for
+33. :doc:`MBart <model_doc/mbart>` (from Facebook) released with the paper `Multilingual Denoising Pre-training for
     Neural Machine Translation <https://arxiv.org/abs/2001.08210>`__ by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li,
     Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
-25. :doc:`MPNet <model_doc/mpnet>` (from Microsoft Research) released with the paper `MPNet: Masked and Permuted
+34. :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.
+35. :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.
+36. :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.
+37. :doc:`MPNet <model_doc/mpnet>` (from Microsoft Research) released with the paper `MPNet: Masked and Permuted
     Pre-training for Language Understanding <https://arxiv.org/abs/2004.09297>`__ by Kaitao Song, Xu Tan, Tao Qin,
     Jianfeng Lu, Tie-Yan Liu.
-26. :doc:`MT5 <model_doc/mt5>` (from Google AI) released with the paper `mT5: A massively multilingual pre-trained
+38. :doc:`MT5 <model_doc/mt5>` (from Google AI) released with the paper `mT5: A massively multilingual pre-trained
     text-to-text transformer <https://arxiv.org/abs/2010.11934>`__ by Linting Xue, Noah Constant, Adam Roberts, Mihir
     Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel.
-27. :doc:`Pegasus <model_doc/pegasus>` (from Google) released with the paper `PEGASUS: Pre-training with Extracted
+39. :doc:`Pegasus <model_doc/pegasus>` (from Google) released with the paper `PEGASUS: Pre-training with Extracted
     Gap-sentences for Abstractive Summarization <https://arxiv.org/abs/1912.08777>`__> by Jingqing Zhang, Yao Zhao,
     Mohammad Saleh and Peter J. Liu.
-28. :doc:`ProphetNet <model_doc/prophetnet>` (from Microsoft Research) released with the paper `ProphetNet: Predicting
+40. :doc:`ProphetNet <model_doc/prophetnet>` (from Microsoft Research) released with the paper `ProphetNet: Predicting
     Future N-gram for Sequence-to-Sequence Pre-training <https://arxiv.org/abs/2001.04063>`__ by Yu Yan, Weizhen Qi,
     Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
-29. :doc:`Reformer <model_doc/reformer>` (from Google Research) released with the paper `Reformer: The Efficient
+41. :doc:`Reformer <model_doc/reformer>` (from Google Research) released with the paper `Reformer: The Efficient
     Transformer <https://arxiv.org/abs/2001.04451>`__ by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
-30. :doc:`RoBERTa <model_doc/roberta>` (from Facebook), released together with the paper a `Robustly Optimized BERT
+42. :doc:`RoBERTa <model_doc/roberta>` (from Facebook), released together with the paper a `Robustly Optimized BERT
     Pretraining Approach <https://arxiv.org/abs/1907.11692>`__ by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar
-    Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov. ultilingual BERT into `DistilmBERT
-    <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__ and a German version of
-    DistilBERT.
-31. :doc:`SqueezeBert <model_doc/squeezebert>` released with the paper `SqueezeBERT: What can computer vision teach NLP
+    Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
+43. :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.
+44. :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
+45. :doc:`T5 <model_doc/t5>` (from Google AI) released with the paper `Exploring the Limits of Transfer Learning with a
     Unified Text-to-Text Transformer <https://arxiv.org/abs/1910.10683>`__ by Colin Raffel and Noam Shazeer and Adam
     Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
-33. :doc:`TAPAS <model_doc/tapas>` (from Google AI) released with the paper `TAPAS: Weakly Supervised Table Parsing via
+46. :doc:`TAPAS <model_doc/tapas>` (from Google AI) released with the paper `TAPAS: Weakly Supervised Table Parsing via
     Pre-training <https://arxiv.org/abs/2004.02349>`__ by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller,
     Francesco Piccinno and Julian Martin Eisenschlos.
-34. :doc:`Transformer-XL <model_doc/transformerxl>` (from Google/CMU) released with the paper `Transformer-XL:
+47. :doc:`Transformer-XL <model_doc/transformerxl>` (from Google/CMU) released with the paper `Transformer-XL:
     Attentive Language Models Beyond a Fixed-Length Context <https://arxiv.org/abs/1901.02860>`__ by Zihang Dai*,
     Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
-35. :doc:`XLM <model_doc/xlm>` (from Facebook) released together with the paper `Cross-lingual Language Model
+48. :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.
+49. :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.
+50. :doc:`XLM <model_doc/xlm>` (from Facebook) released together with the paper `Cross-lingual Language Model
     Pretraining <https://arxiv.org/abs/1901.07291>`__ by Guillaume Lample and Alexis Conneau.
-36. :doc:`XLM-ProphetNet <model_doc/xlmprophetnet>` (from Microsoft Research) released with the paper `ProphetNet:
+51. :doc:`XLM-ProphetNet <model_doc/xlmprophetnet>` (from Microsoft Research) released with the paper `ProphetNet:
     Predicting Future N-gram for Sequence-to-Sequence Pre-training <https://arxiv.org/abs/2001.04063>`__ by Yu Yan,
     Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
-37. :doc:`XLM-RoBERTa <model_doc/xlmroberta>` (from Facebook AI), released together with the paper `Unsupervised
+52. :doc:`XLM-RoBERTa <model_doc/xlmroberta>` (from Facebook AI), released together with the paper `Unsupervised
     Cross-lingual Representation Learning at Scale <https://arxiv.org/abs/1911.02116>`__ by Alexis Conneau*, Kartikay
     Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke
     Zettlemoyer and Veselin Stoyanov.
-38. :doc:`XLNet <model_doc/xlnet>` (from Google/CMU) released with the paper `​XLNet: Generalized Autoregressive
+53. :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.
+54. :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.
 
 
 .. _bigtable:
@@ -223,6 +270,8 @@ TensorFlow and/or Flax.
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |       Bert Generation       |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|           BigBird           |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
++-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |         Blenderbot          |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |       BlenderbotSmall       |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
@@ -231,10 +280,16 @@ TensorFlow and/or Flax.
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |          CamemBERT          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|          ConvBERT           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
++-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |             DPR             |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |           DeBERTa           |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|         DeBERTa-v2          |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
++-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|            DeiT             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
++-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |         DistilBERT          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |           ELECTRA           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
@@ -247,18 +302,26 @@ TensorFlow and/or Flax.
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |     Funnel Transformer      |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|           GPT Neo           |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
++-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|           I-BERT            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
++-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |             LED             |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |           LXMERT            |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|          LayoutLM           |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
+|          LayoutLM           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |         Longformer          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|           M2M100            |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
++-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |            MPNet            |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |           Marian            |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|        MegatronBert         |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
++-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |         MobileBERT          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |         OpenAI GPT          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
@@ -269,7 +332,7 @@ TensorFlow and/or Flax.
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |         ProphetNet          |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|             RAG             |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
+|             RAG             |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |          Reformer           |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
@@ -277,6 +340,8 @@ TensorFlow and/or Flax.
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |           RoBERTa           |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|         Speech2Text         |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
++-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |         SqueezeBERT         |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |             T5              |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
@@ -285,6 +350,10 @@ TensorFlow and/or Flax.
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |       Transformer-XL        |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|             ViT             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
++-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|          Wav2Vec2           |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
++-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |             XLM             |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |         XLM-RoBERTa         |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
@@ -325,11 +394,16 @@ TensorFlow and/or Flax.
 
     pretrained_models
     examples
+    troubleshooting
     custom_datasets
     notebooks
+    sagemaker
+    community
     converting_tensorflow_models
     migration
     contributing
+    add_new_model
+    fast_tokenizers
     testing
     serialization
 
@@ -347,6 +421,7 @@ TensorFlow and/or Flax.
 
     main_classes/callback
     main_classes/configuration
+    main_classes/data_collator
     main_classes/logging
     main_classes/model
     main_classes/optimizer_schedules
@@ -355,6 +430,7 @@ TensorFlow and/or Flax.
     main_classes/processors
     main_classes/tokenizer
     main_classes/trainer
+    main_classes/feature_extractor
 
 .. toctree::
     :maxdepth: 2
@@ -367,11 +443,18 @@ TensorFlow and/or Flax.
     model_doc/bert
     model_doc/bertweet
     model_doc/bertgeneration
+    model_doc/bert_japanese
+    model_doc/bigbird
     model_doc/blenderbot
     model_doc/blenderbot_small
+    model_doc/bort
     model_doc/camembert
+    model_doc/convbert
+    model_doc/cpm
     model_doc/ctrl
     model_doc/deberta
+    model_doc/deberta_v2
+    model_doc/deit
     model_doc/dialogpt
     model_doc/distilbert
     model_doc/dpr
@@ -381,17 +464,22 @@ TensorFlow and/or Flax.
     model_doc/fsmt
     model_doc/funnel
     model_doc/herbert
+    model_doc/ibert
     model_doc/layoutlm
     model_doc/led
     model_doc/longformer
     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/gpt_neo
     model_doc/pegasus
     model_doc/phobert
     model_doc/prophetnet
@@ -399,14 +487,18 @@ TensorFlow and/or Flax.
     model_doc/reformer
     model_doc/retribert
     model_doc/roberta
+    model_doc/speech_to_text
     model_doc/squeezebert
     model_doc/t5
     model_doc/tapas
     model_doc/transformerxl
+    model_doc/vit
+    model_doc/wav2vec2
     model_doc/xlm
     model_doc/xlmprophetnet
     model_doc/xlmroberta
     model_doc/xlnet
+    model_doc/xlsr_wav2vec2
 
 .. toctree::
     :maxdepth: 2
@@ -417,3 +509,4 @@ TensorFlow and/or Flax.
     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.
 
@@ -73,7 +73,27 @@ It should download a pretrained model then print something like
 
 ## Installing from source
 
-To install from source, clone the repository and install with the following commands:
+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:
 
 ``` bash
 git clone https://github.com/huggingface/transformers.git
@@ -81,13 +101,22 @@ cd transformers
 pip install -e .
 ```
 
-Again, you can run 
+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.
 
-```bash
-python -c "from transformers import pipeline; print(pipeline('sentiment-analysis')('I hate you'))"
+Do note that you have to keep that `transformers` folder around and not delete it to continue using the  `transfomers` 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
 ```
 
-to check 🤗 Transformers is properly installed.
+There is nothing else to do. Your python environment will find the bleeding edge version of `transformers` on the next run.
 
 
 ## With conda
@@ -100,7 +129,7 @@ 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
 
@@ -109,7 +138,7 @@ 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/``
 
@@ -122,15 +151,40 @@ environment variable for ``TRANSFORMERS_CACHE``.
 
 ### Note on model downloads (Continuous Integration or large-scale deployments)
 
-If you expect to be downloading large volumes of models (more than 1,000) from our hosted bucket (for instance through
+If you expect to be downloading large volumes of models (more than 10,000) from huggingface.co (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.
+faster, and cheaper. Feel free to contact us privately, we'd love to help with this.
+
+### Offline mode
+
+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/seq2seq/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/seq2seq/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.
+
+
 
 ## 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

@@ -0,0 +1,54 @@
+.. 
+    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._BaseLazyModule

docs/source/internal/generation_utils.rst

@@ -151,6 +151,33 @@ generation.
 .. autoclass:: transformers.HammingDiversityLogitsProcessor
     :members: __call__
 
+.. autoclass:: transformers.ForcedBOSTokenLogitsProcessor
+    :members: __call__
+
+.. autoclass:: transformers.ForcedEOSTokenLogitsProcessor
+    :members: __call__
+
+.. autoclass:: transformers.InfNanRemoveLogitsProcessor
+    :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/pipelines_utils.rst

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

docs/source/internal/tokenization_utils.rst

@@ -38,12 +38,6 @@ 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

@@ -22,7 +22,7 @@ Utilities
 
 .. autoclass:: transformers.EvalPrediction
 
-.. autoclass:: transformers.EvaluationStrategy
+.. autoclass:: transformers.IntervalStrategy
 
 .. autofunction:: transformers.set_seed
 

docs/source/main_classes/callback.rst

@@ -74,6 +74,32 @@ 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/data_collator.rst

@@ -0,0 +1,71 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
+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)
+oin 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: mask_tokens
+
+
+DataCollatorForWholeWordMask
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.data.data_collator.DataCollatorForWholeWordMask
+    :members: mask_tokens
+
+
+DataCollatorForPermutationLanguageModeling
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.data.data_collator.DataCollatorForPermutationLanguageModeling
+    :members: mask_tokens

docs/source/main_classes/feature_extractor.rst

@@ -0,0 +1,48 @@
+.. 
+    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

@@ -65,6 +65,10 @@ 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/output.rst

@@ -60,7 +60,7 @@ ModelOutput
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.file_utils.ModelOutput
-    :members:
+    :members: to_tuple
 
 
 BaseModelOutput

docs/source/main_classes/tokenizer.rst

@@ -54,15 +54,24 @@ PreTrainedTokenizer
 
 .. autoclass:: transformers.PreTrainedTokenizer
     :special-members: __call__
-    :members:
+    :members: batch_decode, convert_ids_to_tokens, convert_tokens_to_ids, convert_tokens_to_string, decode, encode, 
+        get_added_vocab, get_special_tokens_mask, num_special_tokens_to_add, prepare_for_tokenization, tokenize,
+        vocab_size
 
 
 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__
-    :members:
+    :members: batch_decode, convert_ids_to_tokens, convert_tokens_to_ids, convert_tokens_to_string, decode, encode, 
+        get_added_vocab, get_special_tokens_mask, num_special_tokens_to_add,
+        set_truncation_and_padding,tokenize, vocab_size
 
 
 BatchEncoding

docs/source/model_doc/auto.rst

@@ -44,6 +44,13 @@ AutoTokenizer
     :members:
 
 
+AutoFeatureExtractor
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.AutoFeatureExtractor
+    :members:
+
+
 AutoModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -189,3 +196,52 @@ FlaxAutoModel
 
 .. autoclass:: transformers.FlaxAutoModel
     :members:
+
+
+FlaxAutoModelForPreTraining
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaxAutoModelForPreTraining
+    :members:
+
+
+FlaxAutoModelForMaskedLM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaxAutoModelForMaskedLM
+    :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:

docs/source/model_doc/bart.rst

@@ -130,6 +130,12 @@ BartForQuestionAnswering
 .. autoclass:: transformers.BartForQuestionAnswering
     :members: forward
 
+BartForCausalLM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.BartForCausalLM
+    :members: forward
+
 
 
 TFBartModel

docs/source/model_doc/bert.rst

@@ -90,7 +90,7 @@ BertForPreTraining
     :members: forward
 
 
-BertModelLMHeadModel
+BertLMHeadModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.BertLMHeadModel
@@ -209,8 +209,50 @@ 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

@@ -0,0 +1,78 @@
+.. 
+    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.
+
+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:

docs/source/model_doc/bertweet.rst

@@ -31,28 +31,28 @@ 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")
 
 
 The original code can be found `here <https://github.com/VinAIResearch/BERTweet>`__.

docs/source/model_doc/bigbird.rst

@@ -0,0 +1,130 @@
+.. 
+    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**
+
+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
+
+
+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

docs/source/model_doc/blenderbot.rst

@@ -98,6 +98,13 @@ See :obj:`transformers.BartForConditionalGeneration` for arguments to `forward`
     :members: forward
 
 
+BlenderbotForCausalLM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.BlenderbotForCausalLM
+    :members: forward
+
+
 TFBlenderbotModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

docs/source/model_doc/blenderbot_small.rst

@@ -70,6 +70,13 @@ BlenderbotSmallForConditionalGeneration
     :members: forward
 
 
+BlenderbotSmallForCausalLM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.BlenderbotSmallForCausalLM
+    :members: forward
+
+
 TFBlenderbotSmallModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

docs/source/model_doc/bort.rst

@@ -0,0 +1,46 @@
+.. 
+    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.
+
+The original code can be found `here <https://github.com/alexa/bort/>`__.

docs/source/model_doc/convbert.rst

@@ -0,0 +1,143 @@
+.. 
+    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. 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

@@ -0,0 +1,44 @@
+..
+    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.*
+
+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/deberta.rst

@@ -60,7 +60,7 @@ DebertaModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.DebertaModel
-    :members:
+    :members: forward
 
 
 DebertaPreTrainedModel
@@ -70,8 +70,29 @@ DebertaPreTrainedModel
     :members:
 
 
+DebertaForMaskedLM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.DebertaForMaskedLM
+    :members: forward
+
+
 DebertaForSequenceClassification
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.DebertaForSequenceClassification
-    :members:
+    :members: forward
+
+
+DebertaForTokenClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.DebertaForTokenClassification
+    :members: forward
+
+
+DebertaForQuestionAnswering
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.DebertaForQuestionAnswering
+    :members: forward

docs/source/model_doc/deberta_v2.rst

@@ -0,0 +1,118 @@
+.. 
+    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 postions** 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.
+
+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

docs/source/model_doc/deit.rst

@@ -0,0 +1,109 @@
+.. 
+    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.
+
+
+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/dialogpt.rst

@@ -48,7 +48,6 @@ 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 GPT2's `docstring
-<https://huggingface.co/transformers/model_doc/gpt2.html>`_.
+DialoGPT's architecture is based on the GPT2 model, so one can refer to :doc:`GPT2's documentation page <gpt2>`.
 
 The original code can be found `here <https://github.com/microsoft/DialoGPT>`_.

docs/source/model_doc/fsmt.rst

@@ -56,7 +56,7 @@ FSMTTokenizer
 
 .. autoclass:: transformers.FSMTTokenizer
     :members: build_inputs_with_special_tokens, get_special_tokens_mask,
-        create_token_type_ids_from_sequences, prepare_seq2seq_batch, save_vocabulary
+        create_token_type_ids_from_sequences, save_vocabulary
 
 
 FSMTModel

docs/source/model_doc/gpt.rst

@@ -50,7 +50,7 @@ The original code can be found `here <https://github.com/openai/finetune-transfo
 Note:
 
 If you want to reproduce the original tokenization process of the `OpenAI GPT` paper, you will need to install ``ftfy``
-and ``SpaCy``::
+and ``SpaCy``:
 
 .. code-block:: bash
 

docs/source/model_doc/gpt_neo.rst

@@ -0,0 +1,65 @@
+.. 
+    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.
+
+GPT Neo
+-----------------------------------------------------------------------------------------------------------------------
+
+Overview
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The GPTNeo model was released in the `EleutherAI/gpt-neo <https://github.com/EleutherAI/gpt-neo>`__ repository by Sid
+Black, Stella Biderman, Leo Gao, Phil Wang and Connor Leahy. It is a GPT2 like causal language model trained on the
+`Pile <https://pile.eleuther.ai/>`__ dataset.
+
+The architecture is similar to GPT2 except that GPT Neo uses local attention in every other layer with a window size of
+256 tokens.
+
+Generation
+_______________________________________________________________________________________________________________________
+
+The :obj:`generate()` method can be used to generate text using GPT Neo model.
+
+.. code-block::
+
+    >>> from transformers import GPTNeoForCausalLM, GPT2Tokenizer
+    >>> model = GPTNeoForCausalLM.from_pretrained("EleutherAI/gpt-neo-1.3B")
+    >>> tokenizer = GPT2Tokenizer.from_pretrained("EleutherAI/gpt-neo-1.3B")
+
+    >>> prompt = "In a shocking finding, scientists discovered a herd of unicorns living in a remote, " \
+    ...          "previously unexplored valley, in the Andes Mountains. Even more surprising to the " \
+    ...          "researchers was the fact that the unicorns spoke perfect English."
+
+    >>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids
+
+    >>> gen_tokens = model.generate(input_ids, do_sample=True, temperature=0.9, max_length=100,)
+    >>> gen_text = tokenizer.batch_decode(gen_tokens)[0]
+
+
+GPTNeoConfig
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.GPTNeoConfig
+    :members:
+
+
+GPTNeoModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.GPTNeoModel
+    :members: forward
+
+
+GPTNeoForCausalLM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.GPTNeoForCausalLM
+    :members: forward

docs/source/model_doc/herbert.rst

@@ -40,20 +40,20 @@ Examples of use:
 
 .. code-block::
 
-  from transformers import HerbertTokenizer, RobertaModel
+    >>> from transformers import HerbertTokenizer, RobertaModel
 
-  tokenizer = HerbertTokenizer.from_pretrained("allegro/herbert-klej-cased-tokenizer-v1")
-  model = RobertaModel.from_pretrained("allegro/herbert-klej-cased-v1")
+    >>> tokenizer = HerbertTokenizer.from_pretrained("allegro/herbert-klej-cased-tokenizer-v1")
+    >>> model = RobertaModel.from_pretrained("allegro/herbert-klej-cased-v1")
 
-  encoded_input = tokenizer.encode("Kto ma lepszą sztukę, ma lepszy rząd – to jasne.", return_tensors='pt')
-  outputs = model(encoded_input)
+    >>> encoded_input = tokenizer.encode("Kto ma lepszą sztukę, ma lepszy rząd – to jasne.", return_tensors='pt')
+    >>> outputs = model(encoded_input)
 
-  # HerBERT can also be loaded using AutoTokenizer and AutoModel:
-  import torch
-  from transformers import AutoModel, AutoTokenizer
+    >>> # HerBERT can also be loaded using AutoTokenizer and AutoModel:
+    >>> import torch
+    >>> from transformers import AutoModel, AutoTokenizer
 
-  tokenizer = AutoTokenizer.from_pretrained("allegro/herbert-klej-cased-tokenizer-v1")
-  model = AutoModel.from_pretrained("allegro/herbert-klej-cased-v1")
+    >>> tokenizer = AutoTokenizer.from_pretrained("allegro/herbert-klej-cased-tokenizer-v1")
+    >>> model = AutoModel.from_pretrained("allegro/herbert-klej-cased-v1")
 
 
 The original code can be found `here <https://github.com/allegro/HerBERT>`__.

docs/source/model_doc/ibert.rst

@@ -0,0 +1,88 @@
+.. 
+    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.
+
+I-BERT
+-----------------------------------------------------------------------------------------------------------------------
+
+Overview
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The I-BERT model was proposed in `I-BERT: Integer-only BERT Quantization <https://arxiv.org/abs/2101.01321>`__ by
+Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney and Kurt Keutzer. It's a quantized version of RoBERTa running
+inference up to four times faster.
+
+The abstract from the paper is the following:
+
+*Transformer based models, like BERT and RoBERTa, have achieved state-of-the-art results in many Natural Language
+Processing tasks. However, their memory footprint, inference latency, and power consumption are prohibitive for
+efficient inference at the edge, and even at the data center. While quantization can be a viable solution for this,
+previous work on quantizing Transformer based models use floating-point arithmetic during inference, which cannot
+efficiently utilize integer-only logical units such as the recent Turing Tensor Cores, or traditional integer-only ARM
+processors. In this work, we propose I-BERT, a novel quantization scheme for Transformer based models that quantizes
+the entire inference with integer-only arithmetic. Based on lightweight integer-only approximation methods for
+nonlinear operations, e.g., GELU, Softmax, and Layer Normalization, I-BERT performs an end-to-end integer-only BERT
+inference without any floating point calculation. We evaluate our approach on GLUE downstream tasks using
+RoBERTa-Base/Large. We show that for both cases, I-BERT achieves similar (and slightly higher) accuracy as compared to
+the full-precision baseline. Furthermore, our preliminary implementation of I-BERT shows a speedup of 2.4 - 4.0x for
+INT8 inference on a T4 GPU system as compared to FP32 inference. The framework has been developed in PyTorch and has
+been open-sourced.*
+
+
+The original code can be found `here <https://github.com/kssteven418/I-BERT>`__.
+
+IBertConfig
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.IBertConfig
+    :members:
+
+
+IBertModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.IBertModel
+    :members: forward
+
+
+IBertForMaskedLM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.IBertForMaskedLM
+    :members: forward
+
+
+IBertForSequenceClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.IBertForSequenceClassification
+    :members: forward
+
+
+IBertForMultipleChoice
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.IBertForMultipleChoice
+    :members: forward
+
+
+IBertForTokenClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.IBertForTokenClassification
+    :members: forward
+
+
+IBertForQuestionAnswering
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.IBertForQuestionAnswering
+    :members: forward

docs/source/model_doc/layoutlm.rst

@@ -56,24 +56,24 @@ Tips:
 
 .. code-block::
 
-   def normalize_bbox(bbox, width, height):
-        return [
-            int(1000 * (bbox[0] / width)),
-            int(1000 * (bbox[1] / height)),
-            int(1000 * (bbox[2] / width)),
-            int(1000 * (bbox[3] / height)),
-        ]
+    def normalize_bbox(bbox, width, height):
+         return [
+             int(1000 * (bbox[0] / width)),
+             int(1000 * (bbox[1] / height)),
+             int(1000 * (bbox[2] / width)),
+             int(1000 * (bbox[3] / height)),
+         ]
 
 Here, :obj:`width` and :obj:`height` correspond to the width and height of the original document in which the token
 occurs. Those can be obtained using the Python Image Library (PIL) library for example, as follows:
 
 .. code-block::
 
-   from PIL import Image
+    from PIL import Image
 
-   image = Image.open("name_of_your_document - can be a png file, pdf, etc.")
+    image = Image.open("name_of_your_document - can be a png file, pdf, etc.")
 
-   width, height = image.size
+    width, height = image.size
 
 - For a demo which shows how to fine-tune :class:`LayoutLMForTokenClassification` on the `FUNSD dataset
   <https://guillaumejaume.github.io/FUNSD/>`__ (a collection of annotated forms), see `this notebook
@@ -130,3 +130,31 @@ LayoutLMForTokenClassification
 
 .. autoclass:: transformers.LayoutLMForTokenClassification
     :members:
+
+
+TFLayoutLMModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFLayoutLMModel
+    :members:
+
+
+TFLayoutLMForMaskedLM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFLayoutLMForMaskedLM
+    :members:
+
+
+TFLayoutLMForSequenceClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFLayoutLMForSequenceClassification
+    :members:
+
+
+TFLayoutLMForTokenClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFLayoutLMForTokenClassification
+    :members:

docs/source/model_doc/led.rst

@@ -73,8 +73,7 @@ LEDTokenizerFast
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.LEDTokenizerFast
-    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
-        create_token_type_ids_from_sequences, save_vocabulary
+    :members:
 
 
 LED specific outputs

docs/source/model_doc/m2m_100.rst

@@ -0,0 +1,128 @@
+.. 
+    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.
+
+M2M100
+-----------------------------------------------------------------------------------------------------------------------
+
+Overview
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The M2M100 model was proposed in `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.
+
+The abstract from the paper is the following:
+
+*Existing work in translation demonstrated the potential of massively multilingual machine translation by training a
+single model able to translate between any pair of languages. However, much of this work is English-Centric by training
+only on data which was translated from or to English. While this is supported by large sources of training data, it
+does not reflect translation needs worldwide. In this work, we create a true Many-to-Many multilingual translation
+model that can translate directly between any pair of 100 languages. We build and open source a training dataset that
+covers thousands of language directions with supervised data, created through large-scale mining. Then, we explore how
+to effectively increase model capacity through a combination of dense scaling and language-specific sparse parameters
+to create high quality models. Our focus on non-English-Centric models brings gains of more than 10 BLEU when directly
+translating between non-English directions while performing competitively to the best single systems of WMT. We
+open-source our scripts so that others may reproduce the data, evaluation, and final M2M-100 model.*
+
+
+Training and Generation
+_______________________________________________________________________________________________________________________
+
+M2M100 is a multilingual encoder-decoder (seq-to-seq) model primarily intended for translation tasks. As the model is
+multilingual it expects the sequences in a certain format: A special language id token is used as prefix in both the
+source and target text. The source text format is :obj:`[lang_code] X [eos]`, where :obj:`lang_code` is source language
+id for source text and target language id for target text, with :obj:`X` being the source or target text.
+
+The :class:`~transformers.M2M100Tokenizer` depends on :obj:`sentencepiece` so be sure to install it before running the
+examples. To install :obj:`sentencepiece` run ``pip install sentencepiece``.
+
+- Supervised Training
+
+.. code-block::
+
+    from transformers import M2M100Config, M2M100ForConditionalGeneration, M2M100Tokenizer
+
+    model = M2M100ForConditionalGeneration.from_pretrained('facebook/m2m100_418M')
+    tokenizer = M2M100Tokenizer.from_pretrained('facebook/m2m100_418M', src_lang="en", tgt_lang="fr")
+
+    src_text = "Life is like a box of chocolates."
+    tgt_lang = "La vie est comme une boîte de chocolat."
+
+    model_inputs = tokenizer(src_text, return_tensors="pt")
+    with tokenizer.as_target_tokenizer():
+        labels = tokenizer(tgt_text, return_tensors="pt").input_ids
+
+    loss = model(**model_inputs, labels=labels) # forward pass
+
+
+- Generation
+
+    M2M100 uses the :obj:`eos_token_id` as the :obj:`decoder_start_token_id` for generation with the target language id
+    being forced as the first generated token. To force the target language id as the first generated token, pass the
+    `forced_bos_token_id` parameter to the `generate` method. The following example shows how to translate between
+    Hindi to French and Chinese to English using the `facebook/m2m100_418M` checkpoint.
+
+.. code-block::
+
+    >>> from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer
+
+    >>> hi_text = "जीवन एक चॉकलेट बॉक्स की तरह है।"
+    >>> chinese_text = "生活就像一盒巧克力。"
+
+    >>> model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_418M")
+    >>> tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_418M")
+
+    >>> # translate Hindi to French
+    >>> tokenizer.src_lang = "hi"
+    >>> encoded_hi = tokenizer(hi_text, return_tensors="pt")
+    >>> generated_tokens = model.generate(**encoded_hi, forced_bos_token_id=tokenizer.get_lang_id("fr"))
+    >>> tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
+    "La vie est comme une boîte de chocolat."
+
+    >>> # translate Chinese to English
+    >>> tokenizer.src_lang = "zh"
+    >>> encoded_zh = tokenizer(chinese_text, return_tensors="pt")
+    >>> generated_tokens = model.generate(**encoded_zh, forced_bos_token_id=tokenizer.get_lang_id("en"))
+    >>> tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
+    "Life is like a box of chocolate."
+
+
+M2M100Config
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.M2M100Config
+    :members:
+
+
+M2M100Tokenizer
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.M2M100Tokenizer
+    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
+        create_token_type_ids_from_sequences, save_vocabulary
+
+
+M2M100Model
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.M2M100Model
+    :members: forward
+
+
+M2M100ForConditionalGeneration
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.M2M100ForConditionalGeneration
+    :members: forward
+
+

docs/source/model_doc/marian.rst

@@ -76,27 +76,29 @@ require 3 character language codes:
 
 .. code-block:: python
 
-    from transformers import MarianMTModel, MarianTokenizer
-    src_text = [
-        '>>fra<< this is a sentence in english that we want to translate to french',
-        '>>por<< This should go to portuguese',
-        '>>esp<< And this to Spanish'
-    ]
+    >>> from transformers import MarianMTModel, MarianTokenizer
+    >>> src_text = [
+    ...     '>>fra<< this is a sentence in english that we want to translate to french',
+    ...     '>>por<< This should go to portuguese',
+    ...     '>>esp<< And this to Spanish'
+    >>> ]
 
-    model_name = 'Helsinki-NLP/opus-mt-en-roa'
-    tokenizer = MarianTokenizer.from_pretrained(model_name)
-    print(tokenizer.supported_language_codes)
-    model = MarianMTModel.from_pretrained(model_name)
-    translated = model.generate(**tokenizer.prepare_seq2seq_batch(src_text, return_tensors="pt"))
-    tgt_text = [tokenizer.decode(t, skip_special_tokens=True) for t in translated]
-    # ["c'est une phrase en anglais que nous voulons traduire en français",
-    # 'Isto deve ir para o português.',
-    # 'Y esto al español']
+    >>> model_name = 'Helsinki-NLP/opus-mt-en-roa'
+    >>> tokenizer = MarianTokenizer.from_pretrained(model_name)
+    >>> print(tokenizer.supported_language_codes)
+    ['>>zlm_Latn<<', '>>mfe<<', '>>hat<<', '>>pap<<', '>>ast<<', '>>cat<<', '>>ind<<', '>>glg<<', '>>wln<<', '>>spa<<', '>>fra<<', '>>ron<<', '>>por<<', '>>ita<<', '>>oci<<', '>>arg<<', '>>min<<']
+
+    >>> model = MarianMTModel.from_pretrained(model_name)
+    >>> translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
+    >>> [tokenizer.decode(t, skip_special_tokens=True) for t in translated]
+    ["c'est une phrase en anglais que nous voulons traduire en français",
+     'Isto deve ir para o português.',
+     'Y esto al español']
 
 
 
 
-Code to see available pretrained models:
+Here is the code to see all available pretrained models on the hub:
 
 .. code-block:: python
 
@@ -147,21 +149,22 @@ Example of translating english to many romance languages, using old-style 2 char
 
 .. code-block::python
 
-    from transformers import MarianMTModel, MarianTokenizer
-    src_text = [
-        '>>fr<< this is a sentence in english that we want to translate to french',
-        '>>pt<< This should go to portuguese',
-        '>>es<< And this to Spanish'
-    ]
+    >>> from transformers import MarianMTModel, MarianTokenizer
+    >>> src_text = [
+    ...     '>>fr<< this is a sentence in english that we want to translate to french',
+    ...     '>>pt<< This should go to portuguese',
+    ...     '>>es<< And this to Spanish'
+    >>> ]
 
-    model_name = 'Helsinki-NLP/opus-mt-en-ROMANCE'
-    tokenizer = MarianTokenizer.from_pretrained(model_name)
-    print(tokenizer.supported_language_codes)
+    >>> model_name = 'Helsinki-NLP/opus-mt-en-ROMANCE'
+    >>> tokenizer = MarianTokenizer.from_pretrained(model_name)
 
-    model = MarianMTModel.from_pretrained(model_name)
-    translated = model.generate(**tokenizer.prepare_seq2seq_batch(src_text, return_tensors="pt"))
-    tgt_text = [tokenizer.decode(t, skip_special_tokens=True) for t in translated]
-    # ["c'est une phrase en anglais que nous voulons traduire en français", 'Isto deve ir para o português.',  'Y esto al español']
+    >>> model = MarianMTModel.from_pretrained(model_name)
+    >>> translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
+    >>> tgt_text = [tokenizer.decode(t, skip_special_tokens=True) for t in translated]
+    ["c'est une phrase en anglais que nous voulons traduire en français", 
+     'Isto deve ir para o português.',
+     'Y esto al español']
 
 
 
@@ -176,7 +179,7 @@ MarianTokenizer
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.MarianTokenizer
-    :members: prepare_seq2seq_batch
+    :members: as_target_tokenizer
 
 
 MarianModel
@@ -193,6 +196,13 @@ MarianMTModel
     :members: forward
 
 
+MarianForCausalLM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.MarianForCausalLM
+    :members: forward
+
+
 TFMarianModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

docs/source/model_doc/mbart.rst

@@ -10,14 +10,14 @@
     an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
     specific language governing permissions and limitations under the License.
 
-MBart
+MBart and MBart-50
 -----------------------------------------------------------------------------------------------------------------------
 
 **DISCLAIMER:** If you see something strange, file a `Github Issue
 <https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title>`__ and assign
 @patrickvonplaten
 
-Overview
+Overview of MBart
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 The MBart model was presented in `Multilingual Denoising Pre-training for Neural Machine Translation
@@ -31,33 +31,34 @@ on the encoder, decoder, or reconstructing parts of the text.
 
 The Authors' code can be found `here <https://github.com/pytorch/fairseq/tree/master/examples/mbart>`__
 
-Examples
+Training of MBart
 _______________________________________________________________________________________________________________________
 
-- Examples and scripts for fine-tuning mBART and other models for sequence to sequence tasks can be found in
-  :prefix_link:`examples/seq2seq/ <examples/seq2seq/README.md>`.
-- Given the large embeddings table, mBART consumes a large amount of GPU RAM, especially for fine-tuning.
-  :class:`MarianMTModel` is usually a better choice for bilingual machine translation.
+MBart is a multilingual encoder-decoder (sequence-to-sequence) model primarily intended for translation task. As the
+model is multilingual it expects the sequences in a different format. A special language id token is added in both the
+source and target text. The source text format is :obj:`X [eos, src_lang_code]` where :obj:`X` is the source text. The
+target text format is :obj:`[tgt_lang_code] X [eos]`. :obj:`bos` is never used.
 
-Training
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-MBart is a multilingual encoder-decoder (seq-to-seq) model primarily intended for translation task. As the model is
-multilingual it expects the sequences in a different format. A special language id token is added in both the source
-and target text. The source text format is :obj:`X [eos, src_lang_code]` where :obj:`X` is the source text. The target
-text format is :obj:`[tgt_lang_code] X [eos]`. :obj:`bos` is never used.
-
-The :meth:`~transformers.MBartTokenizer.prepare_seq2seq_batch` handles this automatically and should be used to encode
-the sequences for sequence-to-sequence fine-tuning.
+The regular :meth:`~transformers.MBartTokenizer.__call__` will encode source text format, and it should be wrapped
+inside the context manager :meth:`~transformers.MBartTokenizer.as_target_tokenizer` to encode target text format.
 
 - Supervised training
 
 .. code-block::
 
-    example_english_phrase = "UN Chief Says There Is No Military Solution in Syria"
-    expected_translation_romanian = "Şeful ONU declară că nu există o soluţie militară în Siria"
-    batch = tokenizer.prepare_seq2seq_batch(example_english_phrase, src_lang="en_XX", tgt_lang="ro_RO", tgt_texts=expected_translation_romanian, return_tensors="pt")
-    model(input_ids=batch['input_ids'], labels=batch['labels']) # forward pass
+    >>> from transformers import MBartForConditionalGeneration, MBartTokenizer
+
+    >>> tokenizer = MBartTokenizer.from_pretrained("facebook/mbart-large-en-ro")
+    >>> example_english_phrase = "UN Chief Says There Is No Military Solution in Syria"
+    >>> expected_translation_romanian = "Şeful ONU declară că nu există o soluţie militară în Siria"
+
+    >>> inputs = tokenizer(example_english_phrase, return_tensors="pt", src_lang="en_XX", tgt_lang="ro_RO")
+    >>> with tokenizer.as_target_tokenizer():
+    ...     labels = tokenizer(expected_translation_romanian, return_tensors="pt")
+
+    >>> model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-en-ro")
+    >>> # forward pass
+    >>> model(**inputs, labels=batch['labels'])
 
 - Generation
 
@@ -66,14 +67,95 @@ the sequences for sequence-to-sequence fine-tuning.
 
 .. code-block::
 
-    from transformers import MBartForConditionalGeneration, MBartTokenizer
-    model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-en-ro")
-    tokenizer = MBartTokenizer.from_pretrained("facebook/mbart-large-en-ro")
-    article = "UN Chief Says There Is No Military Solution in Syria"
-    batch = tokenizer.prepare_seq2seq_batch(src_texts=[article], src_lang="en_XX", return_tensors="pt")
-    translated_tokens = model.generate(**batch, decoder_start_token_id=tokenizer.lang_code_to_id["ro_RO"])
-    translation = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
-    assert translation == "Şeful ONU declară că nu există o soluţie militară în Siria"
+    >>> from transformers import MBartForConditionalGeneration, MBartTokenizer
+
+    >>> tokenizer = MBartTokenizer.from_pretrained("facebook/mbart-large-en-ro", src_lang="en_XX")
+    >>> article = "UN Chief Says There Is No Military Solution in Syria"
+    >>> inputs = tokenizer(article, return_tensors="pt")
+    >>> translated_tokens = model.generate(**inputs, decoder_start_token_id=tokenizer.lang_code_to_id["ro_RO"])
+    >>> tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
+    "Şeful ONU declară că nu există o soluţie militară în Siria"
+
+
+Overview of MBart-50
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+MBart-50 was introduced in the `Multilingual Translation with Extensible Multilingual Pretraining and Finetuning
+<https://arxiv.org/abs/2008.00401>` paper by Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav
+Chaudhary, Jiatao Gu, Angela Fan. MBart-50 is created using the original `mbart-large-cc25` checkpoint by extendeding
+its embedding layers with randomly initialized vectors for an extra set of 25 language tokens and then pretrained on 50
+languages.
+
+According to the abstract
+
+*Multilingual translation models can be created through multilingual finetuning. Instead of finetuning on one
+direction, a pretrained model is finetuned on many directions at the same time. It demonstrates that pretrained models
+can be extended to incorporate additional languages without loss of performance. Multilingual finetuning improves on
+average 1 BLEU over the strongest baselines (being either multilingual from scratch or bilingual finetuning) while
+improving 9.3 BLEU on average over bilingual baselines from scratch.*
+
+
+Training of MBart-50
+_______________________________________________________________________________________________________________________
+
+The text format for MBart-50 is slightly different from mBART. For MBart-50 the language id token is used as a prefix
+for both source and target text i.e the text format is :obj:`[lang_code] X [eos]`, where :obj:`lang_code` is source
+language id for source text and target language id for target text, with :obj:`X` being the source or target text
+respectively.
+
+
+MBart-50 has its own tokenizer :class:`~transformers.MBart50Tokenizer`.
+
+-  Supervised training
+
+.. code-block::
+
+    from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
+
+    model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50")
+    tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50", src_lang="en_XX", tgt_lang="ro_RO")
+
+    src_text = " UN Chief Says There Is No Military Solution in Syria"
+    tgt_text =  "Şeful ONU declară că nu există o soluţie militară în Siria"
+
+    model_inputs = tokenizer(src_text, return_tensors="pt")
+    with tokenizer.as_target_tokenizer():
+        labels = tokenizer(tgt_text, return_tensors="pt").input_ids
+
+    model(**model_inputs, labels=labels) # forward pass
+
+
+- Generation
+
+    To generate using the mBART-50 multilingual translation models, :obj:`eos_token_id` is used as the
+    :obj:`decoder_start_token_id` and the target language id is forced as the first generated token. To force the
+    target language id as the first generated token, pass the `forced_bos_token_id` parameter to the `generate` method.
+    The following example shows how to translate between Hindi to French and Arabic to English using the
+    `facebook/mbart-50-large-many-to-many` checkpoint.
+
+.. code-block::
+
+    from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
+
+    article_hi = "संयुक्त राष्ट्र के प्रमुख का कहना है कि सीरिया में कोई सैन्य समाधान नहीं है"
+    article_ar = "الأمين العام للأمم المتحدة يقول إنه لا يوجد حل عسكري في سوريا."
+
+    model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt")
+    tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-many-mmt")
+
+    # translate Hindi to French
+    tokenizer.src_lang = "hi_IN"
+    encoded_hi = tokenizer(article_hi, return_tensors="pt")
+    generated_tokens = model.generate(**encoded_hi, forced_bos_token_id=tokenizer.lang_code_to_id["fr_XX"])
+    tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
+    # => "Le chef de l 'ONU affirme qu 'il n 'y a pas de solution militaire en Syria."
+
+    # translate Arabic to English
+    tokenizer.src_lang = "ar_AR"
+    encoded_ar = tokenizer(article_ar, return_tensors="pt")
+    generated_tokens = model.generate(**encoded_ar, forced_bos_token_id=tokenizer.lang_code_to_id["en_XX"])
+    tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
+    # => "The Secretary-General of the United Nations says there is no military solution in Syria."
 
 
 MBartConfig
@@ -87,7 +169,7 @@ MBartTokenizer
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.MBartTokenizer
-    :members: build_inputs_with_special_tokens, prepare_seq2seq_batch
+    :members: as_target_tokenizer, build_inputs_with_special_tokens
 
 
 MBartTokenizerFast
@@ -97,6 +179,20 @@ MBartTokenizerFast
     :members:
 
 
+MBart50Tokenizer
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.MBart50Tokenizer
+    :members:
+
+
+MBart50TokenizerFast
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.MBart50TokenizerFast
+    :members:
+
+
 MBartModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -124,6 +220,13 @@ MBartForSequenceClassification
 .. autoclass:: transformers.MBartForSequenceClassification
 
 
+MBartForCausalLM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.MBartForCausalLM
+    :members: forward
+
+
 TFMBartModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

docs/source/model_doc/megatron_bert.rst

@@ -0,0 +1,153 @@
+.. 
+    Copyright 2021 NVIDIA Corporation and The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
+MegatronBERT
+-----------------------------------------------------------------------------------------------------------------------
+
+Overview
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The MegatronBERT model was proposed in `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.
+
+The abstract from the paper is the following:
+
+*Recent work in language modeling demonstrates that training large transformer models advances the state of the art in
+Natural Language Processing applications. However, very large models can be quite difficult to train due to memory
+constraints. In this work, we present our techniques for training very large transformer models and implement a simple,
+efficient intra-layer model parallel approach that enables training transformer models with billions of parameters. Our
+approach does not require a new compiler or library changes, is orthogonal and complimentary to pipeline model
+parallelism, and can be fully implemented with the insertion of a few communication operations in native PyTorch. We
+illustrate this approach by converging transformer based models up to 8.3 billion parameters using 512 GPUs. We sustain
+15.1 PetaFLOPs across the entire application with 76% scaling efficiency when compared to a strong single GPU baseline
+that sustains 39 TeraFLOPs, which is 30% of peak FLOPs. To demonstrate that large language models can further advance
+the state of the art (SOTA), we train an 8.3 billion parameter transformer language model similar to GPT-2 and a 3.9
+billion parameter model similar to BERT. We show that careful attention to the placement of layer normalization in
+BERT-like models is critical to achieving increased performance as the model size grows. Using the GPT-2 model we
+achieve SOTA results on the WikiText103 (10.8 compared to SOTA perplexity of 15.8) and LAMBADA (66.5% compared to SOTA
+accuracy of 63.2%) datasets. Our BERT model achieves SOTA results on the RACE dataset (90.9% compared to SOTA accuracy
+of 89.4%).*
+
+Tips:
+
+We have provided pretrained `BERT-345M <https://ngc.nvidia.com/catalog/models/nvidia:megatron_bert_345m>`__ checkpoints
+for use to evaluate or finetuning downstream tasks.
+
+To access these checkpoints, first `sign up <https://ngc.nvidia.com/signup>`__ for and setup the NVIDIA GPU Cloud (NGC)
+Registry CLI. Further documentation for downloading models can be found in the `NGC documentation
+<https://docs.nvidia.com/dgx/ngc-registry-cli-user-guide/index.html#topic_6_4_1>`__.
+
+Alternatively, you can directly download the checkpoints using:
+
+BERT-345M-uncased::
+
+.. code-block:: bash
+
+    wget --content-disposition https://api.ngc.nvidia.com/v2/models/nvidia/megatron_bert_345m/versions/v0.1_uncased/zip
+    -O megatron_bert_345m_v0_1_uncased.zip
+
+BERT-345M-cased::
+
+.. code-block:: bash
+
+    wget --content-disposition https://api.ngc.nvidia.com/v2/models/nvidia/megatron_bert_345m/versions/v0.1_cased/zip -O
+    megatron_bert_345m_v0_1_cased.zip
+
+Once you have obtained the checkpoints from NVIDIA GPU Cloud (NGC), you have to convert them to a format that will
+easily be loaded by Hugging Face Transformers and our port of the BERT code.
+
+The following commands allow you to do the conversion. We assume that the folder ``models/megatron_bert`` contains
+``megatron_bert_345m_v0_1_{cased, uncased}.zip`` and that the commands are run from inside that folder::
+
+.. code-block:: bash
+
+    python3 $PATH_TO_TRANSFORMERS/models/megatron_bert/convert_megatron_bert_checkpoint.py megatron_bert_345m_v0_1_uncased.zip 
+
+.. code-block:: bash
+
+    python3 $PATH_TO_TRANSFORMERS/models/megatron_bert/convert_megatron_bert_checkpoint.py megatron_bert_345m_v0_1_cased.zip
+
+The original code can be found `here <https://github.com/NVIDIA/Megatron-LM>`__. That repository contains a multi-GPU
+and multi-node implementation of the Megatron Language models. In particular, it contains a hybrid model parallel
+approach using "tensor parallel" and "pipeline parallel" techniques.
+
+MegatronBertConfig
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.MegatronBertConfig
+    :members:
+
+
+MegatronBertModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.MegatronBertModel
+    :members: forward
+
+
+MegatronBertForMaskedLM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.MegatronBertForMaskedLM
+    :members: forward
+
+
+MegatronBertForCausalLM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.MegatronBertForCausalLM
+    :members: forward
+
+
+MegatronBertForNextSentencePrediction
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.MegatronBertForNextSentencePrediction
+    :members: forward
+
+
+MegatronBertForPreTraining
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.MegatronBertForPreTraining
+    :members: forward
+
+
+MegatronBertForSequenceClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.MegatronBertForSequenceClassification
+    :members: forward
+
+
+MegatronBertForMultipleChoice
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.MegatronBertForMultipleChoice
+    :members: forward
+
+
+MegatronBertForTokenClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.MegatronBertForTokenClassification
+    :members: forward
+
+
+MegatronBertForQuestionAnswering
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.MegatronBertForQuestionAnswering
+    :members: forward
+
+

docs/source/model_doc/megatron_gpt2.rst

@@ -0,0 +1,70 @@
+.. 
+    Copyright 2021 NVIDIA Corporation and The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
+MegatronGPT2
+-----------------------------------------------------------------------------------------------------------------------
+
+Overview
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The MegatronGPT2 model was proposed in `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.
+
+The abstract from the paper is the following:
+
+*Recent work in language modeling demonstrates that training large transformer models advances the state of the art in
+Natural Language Processing applications. However, very large models can be quite difficult to train due to memory
+constraints. In this work, we present our techniques for training very large transformer models and implement a simple,
+efficient intra-layer model parallel approach that enables training transformer models with billions of parameters. Our
+approach does not require a new compiler or library changes, is orthogonal and complimentary to pipeline model
+parallelism, and can be fully implemented with the insertion of a few communication operations in native PyTorch. We
+illustrate this approach by converging transformer based models up to 8.3 billion parameters using 512 GPUs. We sustain
+15.1 PetaFLOPs across the entire application with 76% scaling efficiency when compared to a strong single GPU baseline
+that sustains 39 TeraFLOPs, which is 30% of peak FLOPs. To demonstrate that large language models can further advance
+the state of the art (SOTA), we train an 8.3 billion parameter transformer language model similar to GPT-2 and a 3.9
+billion parameter model similar to BERT. We show that careful attention to the placement of layer normalization in
+BERT-like models is critical to achieving increased performance as the model size grows. Using the GPT-2 model we
+achieve SOTA results on the WikiText103 (10.8 compared to SOTA perplexity of 15.8) and LAMBADA (66.5% compared to SOTA
+accuracy of 63.2%) datasets. Our BERT model achieves SOTA results on the RACE dataset (90.9% compared to SOTA accuracy
+of 89.4%).*
+
+Tips:
+
+We have provided pretrained `GPT2-345M <https://ngc.nvidia.com/catalog/models/nvidia:megatron_lm_345m>`__ checkpoints
+for use to evaluate or finetuning downstream tasks.
+
+To access these checkpoints, first `sign up <https://ngc.nvidia.com/signup>`__ for and setup the NVIDIA GPU Cloud (NGC)
+Registry CLI. Further documentation for downloading models can be found in the `NGC documentation
+<https://docs.nvidia.com/dgx/ngc-registry-cli-user-guide/index.html#topic_6_4_1>`__.
+
+Alternatively, you can directly download the checkpoints using::
+
+.. code-block:: bash
+
+    wget --content-disposition https://api.ngc.nvidia.com/v2/models/nvidia/megatron_lm_345m/versions/v0.0/zip -O
+    megatron_gpt2_345m_v0_0.zip
+
+Once you have obtained the checkpoint from NVIDIA GPU Cloud (NGC), you have to convert it to a format that will easily
+be loaded by Hugging Face Transformers GPT2 implementation.
+
+The following command allows you to do the conversion. We assume that the folder ``models/megatron_gpt2`` contains
+``megatron_gpt2_345m_v0_0.zip`` and that the command is run from that folder::
+
+.. code-block:: bash
+
+    python3 $PATH_TO_TRANSFORMERS/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py megatron_gpt2_345m_v0_0.zip
+
+The original code can be found `here <https://github.com/NVIDIA/Megatron-LM>`__. That repository contains a multi-GPU
+and multi-node implementation of the Megatron Language models. In particular, it contains a hybrid model parallel
+approach using "tensor parallel" and "pipeline parallel" techniques.
+

docs/source/model_doc/pegasus.rst

@@ -51,8 +51,8 @@ All the `checkpoints <https://huggingface.co/models?search=pegasus>`__ are fine-
 Examples
 _______________________________________________________________________________________________________________________
 
-- :prefix_link:`Script <examples/seq2seq/finetune_pegasus_xsum.sh>` to fine-tune pegasus on the XSUM dataset. Data
-  download instructions at :prefix_link:`examples/seq2seq/ <examples/seq2seq/README.md>`.
+- :prefix_link:`Script <examples/research_projects/seq2seq-distillation/finetune_pegasus_xsum.sh>` to fine-tune pegasus
+  on the XSUM dataset. Data download instructions at :prefix_link:`examples/seq2seq/ <examples/seq2seq/README.md>`.
 - FP16 is not supported (help/ideas on this appreciated!).
 - The adafactor optimizer is recommended for pegasus fine-tuning.
 
@@ -78,20 +78,20 @@ Usage Example
 
 .. code-block:: python
 
-    from transformers import PegasusForConditionalGeneration, PegasusTokenizer
-    import torch
-    src_text = [
-        """ PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow."""
-    ]
+    >>> from transformers import PegasusForConditionalGeneration, PegasusTokenizer
+    >>> import torch
+    >>> src_text = [
+    ...     """ PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow."""
+    >>> ]
 
-    model_name = 'google/pegasus-xsum'
-    torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'
-    tokenizer = PegasusTokenizer.from_pretrained(model_name)
-    model = PegasusForConditionalGeneration.from_pretrained(model_name).to(torch_device)
-    batch = tokenizer.prepare_seq2seq_batch(src_text, truncation=True, padding='longest', return_tensors="pt").to(torch_device)
-    translated = model.generate(**batch)
-    tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True)
-    assert tgt_text[0] == "California's largest electricity provider has turned off power to hundreds of thousands of customers."
+    >>> model_name = 'google/pegasus-xsum'
+    >>> device = 'cuda' if torch.cuda.is_available() else 'cpu'
+    >>> tokenizer = PegasusTokenizer.from_pretrained(model_name)
+    >>> model = PegasusForConditionalGeneration.from_pretrained(model_name).to(device)
+    >>> batch = tokenizer(src_text, truncation=True, padding='longest', return_tensors="pt").to(torch_device)
+    >>> translated = model.generate(**batch)
+    >>> tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True)
+    >>> assert tgt_text[0] == "California's largest electricity provider has turned off power to hundreds of thousands of customers."
 
 
 
@@ -107,7 +107,7 @@ PegasusTokenizer
 warning: ``add_tokens`` does not work at the moment.
 
 .. autoclass:: transformers.PegasusTokenizer
-    :members: __call__, prepare_seq2seq_batch
+    :members:
 
 
 PegasusTokenizerFast
@@ -131,6 +131,13 @@ PegasusForConditionalGeneration
     :members: forward
 
 
+PegasusForCausalLM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.PegasusForCausalLM
+    :members: forward
+
+
 TFPegasusModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

docs/source/model_doc/phobert.rst

@@ -31,23 +31,23 @@ Example of use:
 
 .. code-block::
 
-  import torch
-  from transformers import AutoModel, AutoTokenizer
+    >>> import torch
+    >>> from transformers import AutoModel, AutoTokenizer
 
-  phobert = AutoModel.from_pretrained("vinai/phobert-base")
-  tokenizer = AutoTokenizer.from_pretrained("vinai/phobert-base")
+    >>> phobert = AutoModel.from_pretrained("vinai/phobert-base")
+    >>> tokenizer = AutoTokenizer.from_pretrained("vinai/phobert-base")
 
-  # INPUT TEXT MUST BE ALREADY WORD-SEGMENTED!
-  line = "Tôi là sinh_viên trường đại_học Công_nghệ ."
+    >>> # INPUT TEXT MUST BE ALREADY WORD-SEGMENTED!
+    >>> line = "Tôi là sinh_viên trường đại_học Công_nghệ ."
 
-  input_ids = torch.tensor([tokenizer.encode(line)])
+    >>> input_ids = torch.tensor([tokenizer.encode(line)])
 
-  with torch.no_grad():
-      features = phobert(input_ids)  # Models outputs are now tuples
+    >>> with torch.no_grad():
+    ...     features = phobert(input_ids)  # Models outputs are now tuples
 
-  ## With TensorFlow 2.0+:
-  # from transformers import TFAutoModel
-  # phobert = TFAutoModel.from_pretrained("vinai/phobert-base")
+    >>> # With TensorFlow 2.0+:
+    >>> # from transformers import TFAutoModel
+    >>> # phobert = TFAutoModel.from_pretrained("vinai/phobert-base")
 
 
 The original code can be found `here <https://github.com/VinAIResearch/PhoBERT>`__.

docs/source/model_doc/rag.rst

@@ -56,7 +56,7 @@ RagTokenizer
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.RagTokenizer
-    :members: prepare_seq2seq_batch
+    :members:
 
 
 Rag specific outputs
@@ -94,3 +94,24 @@ RagTokenForGeneration
 
 .. autoclass:: transformers.RagTokenForGeneration
     :members: forward, generate
+
+
+TFRagModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFRagModel
+    :members: call
+
+
+TFRagSequenceForGeneration
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFRagSequenceForGeneration
+    :members: call, generate
+
+
+TFRagTokenForGeneration
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFRagTokenForGeneration
+    :members: call, generate

docs/source/model_doc/reformer.rst

@@ -145,8 +145,8 @@ For training, the :class:`~transformers.ReformerModelWithLMHead` should be used
 
 .. code-block::
 
-  input_ids = tokenizer.encode('This is a sentence from the training data', return_tensors='pt')
-  loss = model(input_ids, labels=input_ids)[0]
+    input_ids = tokenizer.encode('This is a sentence from the training data', return_tensors='pt')
+    loss = model(input_ids, labels=input_ids)[0]
 
 
 ReformerConfig

docs/source/model_doc/speech_to_text.rst

@@ -0,0 +1,152 @@
+.. 
+    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.
+
+Speech2Text
+-----------------------------------------------------------------------------------------------------------------------
+
+Overview
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The Speech2Text model was proposed in `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. It's a
+transformer-based seq2seq (encoder-decoder) model designed for end-to-end Automatic Speech Recognition (ASR) and Speech
+Translation (ST). It uses a convolutional downsampler to reduce the length of speech inputs by 3/4th before they are
+fed into the encoder. The model is trained with standard autoregressive cross-entropy loss and generates the
+transcripts/translations autoregressively. Speech2Text has been fine-tuned on several datasets for ASR and ST:
+`LibriSpeech <http://www.openslr.org/12>`__, `CoVoST 2 <https://github.com/facebookresearch/covost>`__, `MuST-C
+<https://ict.fbk.eu/must-c/>`__.
+
+The original code can be found `here <https://github.com/pytorch/fairseq/tree/master/examples/speech_to_text>`__.
+
+
+Inference
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Speech2Text is a speech model that accepts a float tensor of log-mel filter-bank features extracted from the speech
+signal. It's a transformer-based seq2seq model, so the transcripts/translations are generated autoregressively. The
+:obj:`generate()` method can be used for inference.
+
+The :class:`~transformers.Speech2TextFeatureExtractor` class is responsible for extracting the log-mel filter-bank
+features. The :class:`~transformers.Speech2TextProcessor` wraps :class:`~transformers.Speech2TextFeatureExtractor` and
+:class:`~transformers.Speech2TextTokenizer` into a single instance to both extract the input features and decode the
+predicted token ids.
+
+The feature extractor depends on :obj:`torchaudio` and the tokenizer depends on :obj:`sentencepiece` so be sure to
+install those packages before running the examples. You could either install those as extra speech dependancies with
+``pip install transformers"[speech, sentencepiece]"`` or install the packages seperatly with ``pip install torchaudio
+sentencepiece``. Also ``torchaudio`` requires the development version of the `libsndfile
+<http://www.mega-nerd.com/libsndfile/>`__ package which can be installed via a system package manager. On Ubuntu it can
+be installed as follows: ``apt install libsndfile1-dev``
+
+
+- ASR and Speech Translation
+
+.. code-block::
+
+        >>> import torch
+        >>> from transformers import Speech2TextProcessor, Speech2TextForConditionalGeneration
+        >>> from datasets import load_dataset
+        >>> import soundfile as sf
+
+        >>> model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-librispeech-asr")
+        >>> processor = Speech2TextProcessor.from_pretrained("facebook/s2t-small-librispeech-asr")
+
+        >>> def map_to_array(batch):
+        ...     speech, _ = sf.read(batch["file"])
+        ...     batch["speech"] = speech
+        ...     return batch
+
+        >>> ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
+        >>> ds = ds.map(map_to_array)
+
+        >>> inputs = processor(ds["speech"][0], sampling_rate=16_000, return_tensors="pt")
+        >>> generated_ids = model.generate(input_ids=inputs["input_features"], attention_mask=inputs["attention_mask"])
+
+        >>> transcription = processor.batch_decode(generated_ids)
+
+
+- Multilingual speech translation
+
+    For multilingual speech translation models, :obj:`eos_token_id` is used as the :obj:`decoder_start_token_id` and
+    the target language id is forced as the first generated token. To force the target language id as the first
+    generated token, pass the :obj:`forced_bos_token_id` parameter to the :obj:`generate()` method. The following
+    example shows how to transate English speech to French text using the `facebook/s2t-medium-mustc-multilingual-st`
+    checkpoint.
+
+.. code-block::
+
+        >>> import torch
+        >>> from transformers import Speech2TextProcessor, Speech2TextForConditionalGeneration
+        >>> from datasets import load_dataset
+        >>> import soundfile as sf
+
+        >>> model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-medium-mustc-multilingual-st")
+        >>> processor = Speech2TextProcessor.from_pretrained("facebook/s2t-medium-mustc-multilingual-st")
+
+        >>> def map_to_array(batch):
+        ...     speech, _ = sf.read(batch["file"])
+        ...     batch["speech"] = speech
+        ...     return batch
+
+        >>> ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
+        >>> ds = ds.map(map_to_array)
+
+        >>> inputs = processor(ds["speech"][0], sampling_rate=16_000, return_tensors="pt")
+        >>> generated_ids = model.generate(input_ids=inputs["input_features"], attention_mask=inputs["attention_mask], forced_bos_token_id=processor.tokenizer.lang_code_to_id["fr"])
+
+        >>> translation = processor.batch_decode(generated_ids)
+
+
+See the `model hub <https://huggingface.co/models?filter=speech_to_text>`__ to look for Speech2Text checkpoints.
+
+
+Speech2TextConfig
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.Speech2TextConfig
+    :members:
+
+
+Speech2TextTokenizer
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.Speech2TextTokenizer
+    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
+        create_token_type_ids_from_sequences, save_vocabulary
+
+
+Speech2TextFeatureExtractor
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.Speech2TextFeatureExtractor
+    :members: __call__
+
+
+Speech2TextProcessor
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.Speech2TextProcessor
+    :members: __call__, from_pretrained, save_pretrained, batch_decode, decode, as_target_processor
+
+
+Speech2TextModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.Speech2TextModel
+    :members: forward
+
+
+Speech2TextForConditionalGeneration
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.Speech2TextForConditionalGeneration
+    :members: forward

docs/source/model_doc/t5.rst

@@ -73,10 +73,10 @@ token. T5 can be trained / fine-tuned both in a supervised and unsupervised fash
 
 .. code-block::
 
-  input_ids = tokenizer('The <extra_id_0> walks in <extra_id_1> park', return_tensors='pt').input_ids
-  labels = tokenizer('<extra_id_0> cute dog <extra_id_1> the <extra_id_2>', return_tensors='pt').input_ids
-  # the forward function automatically creates the correct decoder_input_ids
-  loss = model(input_ids=input_ids, labels=labels).loss
+    input_ids = tokenizer('The <extra_id_0> walks in <extra_id_1> park', return_tensors='pt').input_ids
+    labels = tokenizer('<extra_id_0> cute dog <extra_id_1> the <extra_id_2>', return_tensors='pt').input_ids
+    # the forward function automatically creates the correct decoder_input_ids
+    loss = model(input_ids=input_ids, labels=labels).loss
 
 - Supervised training
 
@@ -86,10 +86,10 @@ token. T5 can be trained / fine-tuned both in a supervised and unsupervised fash
 
 .. code-block::
 
-  input_ids = tokenizer('translate English to German: The house is wonderful.', return_tensors='pt').input_ids
-  labels = tokenizer('Das Haus ist wunderbar.', return_tensors='pt').input_ids
-  # the forward function automatically creates the correct decoder_input_ids
-  loss = model(input_ids=input_ids, labels=labels).loss
+    input_ids = tokenizer('translate English to German: The house is wonderful.', return_tensors='pt').input_ids
+    labels = tokenizer('Das Haus ist wunderbar.', return_tensors='pt').input_ids
+    # the forward function automatically creates the correct decoder_input_ids
+    loss = model(input_ids=input_ids, labels=labels).loss
 
 
 T5Config
@@ -104,7 +104,7 @@ T5Tokenizer
 
 .. autoclass:: transformers.T5Tokenizer
     :members: build_inputs_with_special_tokens, get_special_tokens_mask,
-        create_token_type_ids_from_sequences, prepare_seq2seq_batch, save_vocabulary
+        create_token_type_ids_from_sequences, save_vocabulary
 
 
 T5TokenizerFast

docs/source/model_doc/vit.rst

@@ -0,0 +1,102 @@
+.. 
+    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.
+
+Vision Transformer (ViT)
+-----------------------------------------------------------------------------------------------------------------------
+
+.. 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 Vision Transformer (ViT) model was proposed in `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. It's the first paper that successfully trains a Transformer encoder on ImageNet, attaining
+very good results compared to familiar convolutional architectures.
+
+
+The abstract from the paper is the following:
+
+*While the Transformer architecture has become the de-facto standard for natural language processing tasks, its
+applications to computer vision remain limited. In vision, attention is either applied in conjunction with
+convolutional networks, or used to replace certain components of convolutional networks while keeping their overall
+structure in place. We show that this reliance on CNNs is not necessary and a pure transformer applied directly to
+sequences of image patches can perform very well on image classification tasks. When pre-trained on large amounts of
+data and transferred to multiple mid-sized or small image recognition benchmarks (ImageNet, CIFAR-100, VTAB, etc.),
+Vision Transformer (ViT) attains excellent results compared to state-of-the-art convolutional networks while requiring
+substantially fewer computational resources to train.*
+
+Tips:
+
+- 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, which can be
+  used for classification. The authors also add absolute position embeddings, and feed the resulting sequence of
+  vectors to a standard Transformer encoder.
+- As the Vision Transformer expects each image to be of the same size (resolution), one can use
+  :class:`~transformers.ViTFeatureExtractor` 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:`google/vit-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=vit>`__.
+- The available checkpoints are either (1) pre-trained on `ImageNet-21k <http://www.image-net.org/>`__ (a collection of
+  14 million images and 21k classes) only, or (2) also fine-tuned on `ImageNet
+  <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).
+- The Vision Transformer was pre-trained using a resolution of 224x224. During fine-tuning, it is often beneficial to
+  use a higher resolution than pre-training `(Touvron et al., 2019) <https://arxiv.org/abs/1906.06423>`__, `(Kolesnikov
+  et al., 2020) <https://arxiv.org/abs/1912.11370>`__. In order to fine-tune at higher resolution, the authors perform
+  2D interpolation of the pre-trained position embeddings, according to their location in the original image.
+- The best results are obtained with supervised pre-training, which is not the case in NLP. The authors also performed
+  an experiment with a self-supervised pre-training objective, namely masked patched prediction (inspired by masked
+  language modeling). With this approach, the smaller ViT-B/16 model achieves 79.9% accuracy on ImageNet, a significant
+  improvement of 2% to training from scratch, but still 4% behind supervised pre-training.
+
+
+The original code (written in JAX) can be found `here <https://github.com/google-research/vision_transformer>`__.
+
+Note that we converted the weights from Ross Wightman's `timm library
+<https://github.com/rwightman/pytorch-image-models>`__, who already converted the weights from JAX to PyTorch. Credits
+go to him!
+
+
+ViTConfig
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.ViTConfig
+    :members:
+
+
+ViTFeatureExtractor
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.ViTFeatureExtractor
+    :members: __call__
+
+
+ViTModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.ViTModel
+    :members: forward
+
+
+ViTForImageClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.ViTForImageClassification
+    :members: forward

docs/source/model_doc/wav2vec2.rst

@@ -0,0 +1,79 @@
+.. 
+    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.
+
+Wav2Vec2
+-----------------------------------------------------------------------------------------------------------------------
+
+Overview
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The Wav2Vec2 model was proposed in `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.
+
+The abstract from the paper is the following:
+
+*We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on
+transcribed speech can outperform the best semi-supervised methods while being conceptually simpler. wav2vec 2.0 masks
+the speech input in the latent space and solves a contrastive task defined over a quantization of the latent
+representations which are jointly learned. Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER on the
+clean/other test sets. When lowering the amount of labeled data to one hour, wav2vec 2.0 outperforms the previous state
+of the art on the 100 hour subset while using 100 times less labeled data. Using just ten minutes of labeled data and
+pre-training on 53k hours of unlabeled data still achieves 4.8/8.2 WER. This demonstrates the feasibility of speech
+recognition with limited amounts of labeled data.*
+
+Tips:
+
+- Wav2Vec2 is a speech model that accepts a float array corresponding to the raw waveform of the speech signal.
+- Wav2Vec2 model was trained using connectionist temporal classification (CTC) so the model output has to be decoded
+  using :class:`~transformers.Wav2Vec2CTCTokenizer`.
+
+
+Wav2Vec2Config
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.Wav2Vec2Config
+    :members:
+
+
+Wav2Vec2CTCTokenizer
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.Wav2Vec2CTCTokenizer
+    :members: __call__, save_vocabulary
+
+
+Wav2Vec2FeatureExtractor
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.Wav2Vec2FeatureExtractor
+    :members: __call__
+
+
+Wav2Vec2Processor
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.Wav2Vec2Processor
+    :members: __call__, pad, from_pretrained, save_pretrained, batch_decode, decode, as_target_processor
+
+
+Wav2Vec2Model
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.Wav2Vec2Model
+    :members: forward
+
+
+Wav2Vec2ForCTC
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.Wav2Vec2ForCTC
+    :members: forward

docs/source/model_doc/xlsr_wav2vec2.rst

@@ -0,0 +1,45 @@
+.. 
+    Copyright 2021 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
+XLSR-Wav2Vec2
+-----------------------------------------------------------------------------------------------------------------------
+
+Overview
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The XLSR-Wav2Vec2 model was proposed in `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.
+
+The abstract from the paper is the following:
+
+*This paper presents XLSR which learns cross-lingual speech representations by pretraining a single model from the raw
+waveform of speech in multiple languages. We build on wav2vec 2.0 which is trained by solving a contrastive task over
+masked latent speech representations and jointly learns a quantization of the latents shared across languages. The
+resulting model is fine-tuned on labeled data and experiments show that cross-lingual pretraining significantly
+outperforms monolingual pretraining. On the CommonVoice benchmark, XLSR shows a relative phoneme error rate reduction
+of 72% compared to the best known results. On BABEL, our approach improves word error rate by 16% relative compared to
+a comparable system. Our approach enables a single multilingual speech recognition model which is competitive to strong
+individual models. Analysis shows that the latent discrete speech representations are shared across languages with
+increased sharing for related languages. We hope to catalyze research in low-resource speech understanding by releasing
+XLSR-53, a large model pretrained in 53 languages.*
+
+Tips:
+
+- XLSR-Wav2Vec2 is a speech model that accepts a float array corresponding to the raw waveform of the speech signal.
+- XLSR-Wav2Vec2 model was trained using connectionist temporal classification (CTC) so the model output has to be
+  decoded using :class:`~transformers.Wav2Vec2CTCTokenizer`.
+
+XLSR-Wav2Vec2's architecture is based on the Wav2Vec2 model, so one can refer to :doc:`Wav2Vec2's documentation page
+<wav2vec2>`.
+
+The original code can be found `here <https://github.com/pytorch/fairseq/tree/master/fairseq/models/wav2vec>`__.

docs/source/model_sharing.rst

@@ -60,7 +60,7 @@ Basic steps
 In order to upload a model, you'll need to first create a git repo. This repo will live on the model hub, allowing
 users to clone it and you (and your organization members) to push to it.
 
-You can create a model repo **directly from `the /new page on the website <https://huggingface.co/new>`__.**
+You can create a model repo directly from `the /new page on the website <https://huggingface.co/new>`__.
 
 Alternatively, you can use the ``transformers-cli``. The next steps describe that process:
 

docs/source/model_summary.rst

@@ -330,6 +330,36 @@ the same probabilities as the larger model. The actual objective is a combinatio
 The library provides a version of the model for masked language modeling, token classification, sentence classification
 and question answering.
 
+ConvBERT
+-----------------------------------------------------------------------------------------------------------------------
+
+.. raw:: html
+
+   <a href="https://huggingface.co/models?filter=convbert">
+       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-convbert-blueviolet">
+   </a>
+   <a href="model_doc/convbert.html">
+       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-convbert-blueviolet">
+   </a>
+
+`ConvBERT: Improving BERT with Span-based Dynamic Convolution <https://arxiv.org/abs/1910.01108>`_, Zihang Jiang,
+Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan.
+
+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.
+
+The library provides a version of the model for masked language modeling, token classification, sentence classification
+and question answering.
+
 XLM
 -----------------------------------------------------------------------------------------------------------------------
 

docs/source/perplexity.rst

@@ -18,8 +18,8 @@ that the metric applies specifically to classical language models (sometimes cal
 models) and is not well defined for masked language models like BERT (see :doc:`summary of the models
 <model_summary>`).
 
-Perplexity is defined as the exponentiated average log-likelihood of a sequence. If we have a tokenized sequence
-:math:`X = (x_0, x_1, \dots, x_t)`, then the perplexity of :math:`X` is,
+Perplexity is defined as the exponentiated average negative log-likelihood of a sequence. If we have a tokenized
+sequence :math:`X = (x_0, x_1, \dots, x_t)`, then the perplexity of :math:`X` is,
 
 .. math::
 

docs/source/pretrained_models.rst

@@ -139,6 +139,12 @@ For the full list, refer to `https://huggingface.co/models <https://huggingface.
 |                    | ``gpt2-xl``                                                | | 48-layer, 1600-hidden, 25-heads, 1558M parameters.                                                                                  |
 |                    |                                                            | | OpenAI's XL-sized GPT-2 English model                                                                                               |
 +--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+| GPTNeo             | ``EleutherAI/gpt-neo-1.3B``                                | | 24-layer, 2048-hidden, 16-heads, 1.3B parameters.                                                                                   |
+|                    |                                                            | | EleutherAI's GPT-3 like language model.                                                                                             |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``EleutherAI/gpt-neo-2.7B``                                | | 32-layer, 2560-hidden, 20-heads, 2.7B parameters.                                                                                   |
+|                    |                                                            | | EleutherAI's GPT-3 like language model.                                                                                             |
++--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 | Transformer-XL     | ``transfo-xl-wt103``                                       | | 18-layer, 1024-hidden, 16-heads, 257M parameters.                                                                                   |
 |                    |                                                            | | English model trained on wikitext-103                                                                                               |
 +--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
@@ -365,6 +371,12 @@ For the full list, refer to `https://huggingface.co/models <https://huggingface.
 |                    | ``reformer-crime-and-punishment``                          | | 6-layer, 256-hidden, 2-heads, 3M parameters                                                                                         |
 |                    |                                                            | | Trained on English text: Crime and Punishment novel by Fyodor Dostoyevsky.                                                          |
 +--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+| M2M100             | ``facebook/m2m100_418M``                                   | | 24-layer, 1024-hidden, 16-heads, 418M parameters                                                                                    |
+|                    |                                                            | | multilingual machine translation model for 100 languages                                                                            |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``facebook/m2m100_1.2B``                                   | | 48-layer, 1024-hidden, 16-heads, 1.2B parameters                                                                                    |
+|                    |                                                            | | multilingual machine translation model for 100 languages                                                                            |
++--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 | MarianMT           | ``Helsinki-NLP/opus-mt-{src}-{tgt}``                       | | 12-layer, 512-hidden, 8-heads, ~74M parameter Machine translation models. Parameter counts vary depending on vocab size.            |
 |                    |                                                            | | (see `model list <https://huggingface.co/Helsinki-NLP>`_)                                                                           |
 +--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
@@ -381,6 +393,15 @@ For the full list, refer to `https://huggingface.co/models <https://huggingface.
 |                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                    | ``facebook/mbart-large-en-ro``                             | | 24-layer, 1024-hidden, 16-heads, 610M parameters                                                                                    |
 |                    |                                                            | | mbart-large-cc25 model finetuned on WMT english romanian translation.                                                               |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``facebook/mbart-large-50``                                | | 24-layer, 1024-hidden, 16-heads,                                                                                                    |
+|                    |                                                            | | mBART model trained on 50 languages' monolingual corpus.                                                                            |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``facebook/mbart-large-50-one-to-many-mmt``                | | 24-layer, 1024-hidden, 16-heads,                                                                                                    |
+|                    |                                                            | | mbart-50-large model finetuned for one (English) to many multilingual machine translation covering 50 languages.                    |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``facebook/mbart-large-50-many-to-many-mmt``               | | 24-layer, 1024-hidden, 16-heads,                                                                                                    |
+|                    |                                                            | | mbart-50-large model finetuned for many to many multilingual machine translation covering 50 languages.                             |
 +--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 | Lxmert             | ``lxmert-base-uncased``                                    | | 9-language layers, 9-relationship layers, and 12-cross-modality layers                                                              |
 |                    |                                                            | | 768-hidden, 12-heads (for each layer) ~ 228M parameters                                                                             |
@@ -434,15 +455,30 @@ For the full list, refer to `https://huggingface.co/models <https://huggingface.
 |                    |                                                            |                                                                                                                                       |
 |                    |                                                            | (see `details <https://github.com/microsoft/unilm/tree/master/layoutlm>`__)                                                           |
 +--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| DeBERTa            | ``microsoft/deberta-base``                                 | | 12-layer, 768-hidden, 12-heads, ~125M parameters                                                                                    |
+| DeBERTa            | ``microsoft/deberta-base``                                 | | 12-layer, 768-hidden, 12-heads, ~140M parameters                                                                                    |
 |                    |                                                            | | DeBERTa using the BERT-base architecture                                                                                            |
 |                    |                                                            |                                                                                                                                       |
 |                    |                                                            | (see `details <https://github.com/microsoft/DeBERTa>`__)                                                                              |
 |                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``microsoft/deberta-large``                                | | 24-layer, 1024-hidden, 16-heads, ~390M parameters                                                                                   |
+|                    | ``microsoft/deberta-large``                                | | 24-layer, 1024-hidden, 16-heads, ~400M parameters                                                                                   |
 |                    |                                                            | | DeBERTa using the BERT-large architecture                                                                                           |
 |                    |                                                            |                                                                                                                                       |
 |                    |                                                            | (see `details <https://github.com/microsoft/DeBERTa>`__)                                                                              |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``microsoft/deberta-xlarge``                               | | 48-layer, 1024-hidden, 16-heads, ~750M parameters                                                                                   |
+|                    |                                                            | | DeBERTa XLarge with similar BERT architecture                                                                                       |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details <https://github.com/microsoft/DeBERTa>`__)                                                                              |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``microsoft/deberta-xlarge-v2``                            | | 24-layer, 1536-hidden, 24-heads, ~900M parameters                                                                                   |
+|                    |                                                            | | DeBERTa XLarge V2 with similar BERT architecture                                                                                    |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details <https://github.com/microsoft/DeBERTa>`__)                                                                              |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``microsoft/deberta-xxlarge-v2``                           | | 48-layer, 1536-hidden, 24-heads, ~1.5B parameters                                                                                   |
+|                    |                                                            | | DeBERTa XXLarge V2 with similar BERT architecture                                                                                   |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details <https://github.com/microsoft/DeBERTa>`__)                                                                              |
 +--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 | SqueezeBERT        | ``squeezebert/squeezebert-uncased``                        | | 12-layer, 768-hidden, 12-heads, 51M parameters, 4.3x faster than bert-base-uncased on a smartphone.                                 |
 |                    |                                                            | | SqueezeBERT architecture pretrained from scratch on masked language model (MLM) and sentence order prediction (SOP) tasks.          |

docs/source/sagemaker.md

@@ -0,0 +1,393 @@
+<!---
+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.
+-->
+
+# Run training on Amazon SageMaker
+
+Hugging Face and Amazon are introducing new [Hugging Face Deep Learning Containers (DLCs)](https://github.com/aws/deep-learning-containers/blob/master/available_images.md#huggingface-training-containers) to make it easier than ever to train Hugging Face Transformer models in [Amazon SageMaker](https://aws.amazon.com/sagemaker/).
+
+To learn how to access and use the new Hugging Face DLCs with the Amazon SageMaker Python SDK, check out the guides and resources below.
+
+---
+
+## Deep Learning Container (DLC) overview
+
+The Deep Learning Container are in every available where Amazon SageMaker is available. You can see the [AWS region table](https://aws.amazon.com/about-aws/global-infrastructure/regional-product-services/) for all AWS global infrastructure. To get an detailed overview of all included packages look [here in the release notes](https://docs.aws.amazon.com/deep-learning-containers/latest/devguide/deep-learning-containers-images.html).
+
+| 🤗 Transformers version | 🤗 Datasets version | PyTorch/TensorFlow version | type     | device | Python Version | Example `image_uri`                                                                                                               |
+| ----------------------- | ------------------- | -------------------------- | -------- | ------ | -------------- | --------------------------------------------------------------------------------------------------------------------------------- |
+| 4.4.2                   | 1.5.0               | PyTorch 1.6.0              | training | GPU    | 3.6            | `763104351884.dkr.ecr.us-west-2.amazonaws.com/huggingface-pytorch-training:1.6.0-transformers4.4.2-gpu-py36-cu110-ubuntu18.04`    |
+| 4.4.2                   | 1.5.0               | TensorFlow 2.4.1           | training | GPU    | 3.7            | `763104351884.dkr.ecr.us-west-2.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.4.2-gpu-py37-cu110-ubuntu18.04` |
+
+---
+
+## Getting Started: Train a 🤗 Transformers Model
+
+To train a 🤗 Transformers model by using the `HuggingFace` SageMaker Python SDK you need to:
+
+- [Prepare a training script](#prepare-a-transformers-fine-tuning-script)
+- [Create a `HuggingFace` Estimator](#create-an-huggingface-estimator)
+- [Run training by calling the `fit` method](#execute-training)
+- [Access you model](#access-trained-model)
+
+### Setup & Installation
+
+Before you can train a transformers models with Amazon SageMaker you need to sign up for an AWS account. If you do not have an AWS account yet learn more [here](https://docs.aws.amazon.com/sagemaker/latest/dg/gs-set-up.html).
+
+After you complete these tasks you can get started using either [SageMaker Studio](https://docs.aws.amazon.com/sagemaker/latest/dg/gs-studio-onboard.html), [SageMaker Notebook Instances](https://docs.aws.amazon.com/sagemaker/latest/dg/gs-console.html), or a local environment. To start training locally you need configure the right [IAM permission](https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html).
+
+Upgrade to the latest `sagemaker` version.
+
+```bash
+pip install sagemaker --upgrade
+```
+
+**SageMaker environment**
+
+_Note: The execution role is intended to be available only when running a notebook within SageMaker. If you run `get_execution_role` in a notebook not on SageMaker, expect a "region" error._
+
+```python
+import sagemaker
+sess = sagemaker.Session()
+role = sagemaker.get_execution_role()
+```
+
+**Local environment**
+
+```python
+import sagemaker
+import boto3
+
+iam_client = boto3.client('iam')
+role = iam_client.get_role(RoleName='role-name-of-your-iam-role-with-right-permissions')['Role']['Arn']
+sess = sagemaker.Session()
+```
+
+### Prepare a 🤗 Transformers fine-tuning script.
+
+The training script is very similar to a training script you might run outside of SageMaker, but you can access useful properties about the training environment through various environment variables, including the following:
+
+- `SM_MODEL_DIR`: A string that represents the path where the training job writes the model artifacts to. After training, artifacts in this directory are uploaded to S3 for model hosting. `SM_MODEL_DIR` is always set to `/opt/ml/model`.
+
+- `SM_NUM_GPUS`: An integer representing the number of GPUs available to the host.
+
+- `SM_CHANNEL_XXXX:` A string that represents the path to the directory that contains the input data for the specified channel. For example, if you specify two input channels in the HuggingFace estimator’s fit call, named `train` and `test`, the environment variables `SM_CHANNEL_TRAIN` and `SM_CHANNEL_TEST` are set.
+
+You can find a full list of the exposed environment variables [here](https://github.com/aws/sagemaker-training-toolkit/blob/master/ENVIRONMENT_VARIABLES.md).
+
+Later we define `hyperparameters` in the [HuggingFace Estimator](#create-an-huggingface-estimator), which are passed in as named arguments and and can be processed with the `ArgumentParser()`.
+
+```python
+import transformers
+import datasets
+import argparse
+import os
+
+if __name__ == "__main__":
+
+    parser = argparse.ArgumentParser()
+
+    # hyperparameters sent by the client are passed as command-line arguments to the script.
+    parser.add_argument("--epochs", type=int, default=3)
+    parser.add_argument("--per_device_train_batch_size", type=int, default=32)
+    parser.add_argument("--model_name_or_path", type=str)
+
+    # Data, model, and output directories
+    parser.add_argument("--model-dir", type=str, default=os.environ["SM_MODEL_DIR"])
+    parser.add_argument("--training_dir", type=str, default=os.environ["SM_CHANNEL_TRAIN"])
+    parser.add_argument("--test_dir", type=str, default=os.environ["SM_CHANNEL_TEST"])
+```
+
+_Note that SageMaker doesn’t support argparse actions. For example, if you want to use a boolean hyperparameter, specify `type` as `bool` in your script and provide an explicit `True` or `False` value._
+
+For a complete example of a 🤗 Transformers training script, see [train.py](https://github.com/huggingface/notebooks/blob/master/sagemaker/01_getting_started_pytorch/scripts/train.py)
+
+### Create an HuggingFace Estimator
+
+You run 🤗 Transformers training scripts on SageMaker by creating `HuggingFace` Estimators. The Estimator handles end-to-end Amazon SageMaker training. The training of your script is invoked when you call `fit` on a `HuggingFace` Estimator. In the Estimator you define, which fine-tuning script should be used as `entry_point`, which `instance_type` should be used, which `hyperparameters` are passed in, you can find all possible `HuggingFace` Parameter [here](https://sagemaker.readthedocs.io/en/stable/frameworks/huggingface/sagemaker.huggingface.html#huggingface-estimator). and an example of a fine-tuning script [here](https://github.com/huggingface/notebooks/blob/master/sagemaker/01_getting_started_pytorch/scripts/train.py).
+You can find all useable `instance_types` [here](https://aws.amazon.com/de/sagemaker/pricing/).
+
+The following code sample shows how you train a custom `HuggingFace` script `train.py`, passing in three hyperparameters (`epochs`, `per_device_train_batch_size`, and `model_name_or_path`).
+
+```python
+from sagemaker.huggingface import HuggingFace
+
+
+# hyperparameters, which are passed into the training job
+hyperparameters={'epochs': 1,
+                 'per_device_train_batch_size': 32,
+                 'model_name_or_path': 'distilbert-base-uncased'
+                 }
+
+# create the Estimator
+huggingface_estimator = HuggingFace(
+        entry_point='train.py',
+        source_dir='./scripts',
+        instance_type='ml.p3.2xlarge',
+        instance_count=1,
+        role=role,
+        transformers_version='4.4',
+        pytorch_version='1.6',
+        py_version='py36',
+        hyperparameters = hyperparameters
+)
+```
+
+To run the `TrainingJob` locally you can define `instance_type='local'` or `instance_type='local-gpu'` for gpu usage. _Note: this does not working within SageMaker Studio_
+
+### Execute Training
+
+You start your `TrainingJob` by calling `fit` on a `HuggingFace` Estimator. In the `fit` method you specify your input training data, like a string S3 URI `s3://my-bucket/my-training-data` or a `FileSystemInput` for [EFS or FSx Lustre](https://sagemaker.readthedocs.io/en/stable/overview.html?highlight=FileSystemInput#use-file-systems-as-training-inputs), see [here](https://sagemaker.readthedocs.io/en/stable/overview.html?highlight=FileSystemInput#use-file-systems-as-training-inputs).
+
+```python
+huggingface_estimator.fit(
+  {'train': 's3://sagemaker-us-east-1-558105141721/samples/datasets/imdb/train',
+   'test': 's3://sagemaker-us-east-1-558105141721/samples/datasets/imdb/test'}
+)
+
+```
+
+SageMaker takes care of starting and managing all the required ec2 instances for ands starts the training job by running.
+
+```bash
+/opt/conda/bin/python train.py --epochs 1 --model_name_or_path distilbert-base-uncased --per_device_train_batch_size 32
+```
+
+### Access trained model
+
+After training is done you can access your model either through the [AWS console](https://console.aws.amazon.com/console/home?nc2=h_ct&src=header-signin) or downloading it directly from S3.
+
+```python
+from sagemaker.s3 import S3Downloader
+
+S3Downloader.download(
+    s3_uri=huggingface_estimator.model_data, # s3 uri where the trained model is located
+    local_path='.', # local path where *.targ.gz is saved
+    sagemaker_session=sess # sagemaker session used for training the model
+)
+```
+
+---
+
+## Sample Notebooks
+
+You can find here a list of the official notebooks provided by Hugging Face.
+
+| Notebook                                                                                                                                                                                        | Description                                                                                                      |
+| ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------- |
+| [Getting Started Pytorch](https://github.com/huggingface/notebooks/blob/master/sagemaker/01_getting_started_pytorch/sagemaker-notebook.ipynb)                                                   | End-to-End binary Text-Classification example using `Trainer` and `imdb` dataset                                 |
+| [Getting Started Tensorflow](https://github.com/huggingface/notebooks/blob/master/sagemaker/02_getting_started_tensorflow/sagemaker-notebook.ipynb)                                             | End-to-End binary Text-Classification example using `Keras` and `imdb` dataset                                   |
+| [Distributed Training Data Parallelism](https://github.com/huggingface/notebooks/blob/master/sagemaker/03_distributed_training_data_parallelism/sagemaker-notebook.ipynb)                       | End-to-End distributed Question-Answering example using `Trainer` and 🤗 Transformers example script for `SQAuD` |
+| [Distributed Training Model Parallelism](https://github.com/huggingface/notebooks/blob/master/sagemaker/04_distributed_training_model_parallelism/sagemaker-notebook.ipynb)                     | End-to-End model parallelism example using `SageMakerTrainer` and `run_glue.py` script                           |
+| [Spot Instances and continues training](https://github.com/huggingface/notebooks/blob/master/sagemaker/05_spot_instances/sagemaker-notebook.ipynb)                                              | End-to-End to Text-Classification example using spot instances with continued training.                          |
+| [SageMaker Metrics](https://github.com/huggingface/notebooks/blob/master/sagemaker/06_sagemaker_metrics/sagemaker-notebook.ipynb)                                                               | End-to-End to Text-Classification example using SageMaker Metrics to extract and log metrics during training     |
+| [Distributed Training Data Parallelism Tensorflow](https://github.com/huggingface/notebooks/blob/master/sagemaker/07_tensorflow_distributed_training_data_parallelism/sagemaker-notebook.ipynb) | End-to-End distributed binary Text-Classification example using `Keras` and `TensorFlow`                    
+| [Distributed Seq2Seq Training with Data Parallelism and BART](https://github.com/huggingface/notebooks/blob/master/sagemaker/08_distributed_summarization_bart_t5/sagemaker-notebook.ipynb) | End-to-End distributed summarization example `BART-large` and 🤗 Transformers example script for `summarization`                        |
+
+
+---
+
+## Advanced Features
+
+In addition to the Deep Learning Container and the SageMaker SDK, we have implemented other additional features.
+
+### Distributed Training: Data-Parallel
+
+You can use [SageMaker Data Parallelism Library](https://aws.amazon.com/blogs/aws/managed-data-parallelism-in-amazon-sagemaker-simplifies-training-on-large-datasets/) out of the box for distributed training. We added the functionality of Data Parallelism directly into the [Trainer](https://huggingface.co/transformers/main_classes/trainer.html). If your `train.py` uses the [Trainer](https://huggingface.co/transformers/main_classes/trainer.html) API you only need to define the distribution parameter in the HuggingFace Estimator.
+
+- [Example Notebook PyTorch](https://github.com/huggingface/notebooks/blob/master/sagemaker/04_distributed_training_model_parallelism/sagemaker-notebook.ipynb)
+- [Example Notebook TensorFlow](https://github.com/huggingface/notebooks/blob/master/sagemaker/07_tensorflow_distributed_training_data_parallelism/sagemaker-notebook.ipynb)
+
+```python
+# configuration for running training on smdistributed Data Parallel
+distribution = {'smdistributed':{'dataparallel':{ 'enabled': True }}}
+
+# create the Estimator
+huggingface_estimator = HuggingFace(
+        entry_point='train.py',
+        source_dir='./scripts',
+        instance_type='ml.p3dn.24xlarge',
+        instance_count=2,
+        role=role,
+        transformers_version='4.4.2',
+        pytorch_version='1.6.0',
+        py_version='py36',
+        hyperparameters = hyperparameters
+        distribution = distribution
+)
+
+```
+
+### Distributed Training: Model-Parallel
+
+You can use [SageMaker Model Parallelism Library](https://aws.amazon.com/blogs/aws/amazon-sagemaker-simplifies-training-deep-learning-models-with-billions-of-parameters/) out of the box for distributed training. We added the functionality of Model Parallelism directly into the [Trainer](https://huggingface.co/transformers/main_classes/trainer.html). If your `train.py` uses the [Trainer](https://huggingface.co/transformers/main_classes/trainer.html) API you only need to define the distribution parameter in the HuggingFace Estimator.  
+For detailed information about the adjustments take a look [here](https://sagemaker.readthedocs.io/en/stable/api/training/smd_model_parallel_general.html?highlight=modelparallel#required-sagemaker-python-sdk-parameters).
+
+
+- [Example Notebook](https://github.com/huggingface/notebooks/blob/master/sagemaker/04_distributed_training_model_parallelism/sagemaker-notebook.ipynb)
+
+
+```python
+# configuration for running training on smdistributed Model Parallel
+mpi_options = {
+    "enabled" : True,
+    "processes_per_host" : 8
+}
+
+smp_options = {
+    "enabled":True,
+    "parameters": {
+        "microbatches": 4,
+        "placement_strategy": "spread",
+        "pipeline": "interleaved",
+        "optimize": "speed",
+        "partitions": 4,
+        "ddp": True,
+    }
+}
+
+distribution={
+    "smdistributed": {"modelparallel": smp_options},
+    "mpi": mpi_options
+}
+
+ # create the Estimator
+huggingface_estimator = HuggingFace(
+        entry_point='train.py',
+        source_dir='./scripts',
+        instance_type='ml.p3dn.24xlarge',
+        instance_count=2,
+        role=role,
+        transformers_version='4.4.2',
+        pytorch_version='1.6.0',
+        py_version='py36',
+        hyperparameters = hyperparameters,
+        distribution = distribution
+)
+```
+
+### Spot Instances
+
+With the creation of HuggingFace Framework extension for the SageMaker Python SDK we can also leverage the benefit of [fully-managed EC2 spot instances](https://docs.aws.amazon.com/sagemaker/latest/dg/model-managed-spot-training.html) and save up to 90% of our training cost.
+
+_Note: Unless your training job completes quickly, we recommend you use [checkpointing](https://docs.aws.amazon.com/sagemaker/latest/dg/model-checkpoints.html) with managed spot training, therefore you need to define the `checkpoint_s3_uri`._
+
+To use spot instances with the `HuggingFace` Estimator we have to set the `use_spot_instances` parameter to `True` and define your `max_wait` and `max_run` time. You can read more about the [managed spot training lifecycle here](https://docs.aws.amazon.com/sagemaker/latest/dg/model-managed-spot-training.html).
+
+- [Example Notebook](https://github.com/huggingface/notebooks/blob/master/sagemaker/05_spot_instances/sagemaker-notebook.ipynb)
+
+```python
+# hyperparameters, which are passed into the training job
+hyperparameters={'epochs': 1,
+                 'train_batch_size': 32,
+                 'model_name':'distilbert-base-uncased',
+                 'output_dir':'/opt/ml/checkpoints'
+                 }
+# create the Estimator
+
+huggingface_estimator = HuggingFace(
+        entry_point='train.py',
+        source_dir='./scripts',
+        instance_type='ml.p3.2xlarge',
+        instance_count=1,
+	    checkpoint_s3_uri=f's3://{sess.default_bucket()}/checkpoints'
+        use_spot_instances=True,
+        max_wait=3600, # This should be equal to or greater than max_run in seconds'
+        max_run=1000,
+        role=role,
+        transformers_version='4.4',
+        pytorch_version='1.6',
+        py_version='py36',
+        hyperparameters = hyperparameters
+)
+
+# Training seconds: 874
+# Billable seconds: 262
+# Managed Spot Training savings: 70.0%
+
+```
+
+### Git Repository
+
+When you create a `HuggingFace` Estimator, you can specify a [training script that is stored in a GitHub repository](https://sagemaker.readthedocs.io/en/stable/overview.html#use-scripts-stored-in-a-git-repository) as the entry point for the estimator, so that you don’t have to download the scripts locally. If Git support is enabled, the `entry_point` and `source_dir` should be relative paths in the Git repo if provided. 
+
+If you are using `git_config` to run the [🤗 Transformers examples scripts](https://github.com/huggingface/transformers/tree/master/examples) keep in mind that you need to configure the right `'branch'` for you `transformers_version`, e.g. if you use `transformers_version='4.4.2` you have to use `'branch':'v4.4.2'`. 
+
+As an example to use `git_config` with an [example script from the transformers repository](https://github.com/huggingface/transformers/tree/master/examples/text-classification).
+
+_Tip: define `output_dir` as `/opt/ml/model` in the hyperparameter for the script to save your model to S3 after training._
+
+- [Example Notebook](https://github.com/huggingface/notebooks/blob/master/sagemaker/02_getting_started_tensorflow/sagemaker-notebook.ipynb)
+
+```python
+# configure git settings
+git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.4.2'} # v4.4.2 is referring to the `transformers_version you use in the estimator.
+
+ # create the Estimator
+huggingface_estimator = HuggingFace(
+        entry_point='run_glue.py',
+        source_dir='./examples/text-classification',
+        git_config=git_config,
+        instance_type='ml.p3.2xlarge',
+        instance_count=1,
+        role=role,
+        transformers_version='4.4',
+        pytorch_version='1.6',
+        py_version='py36',
+        hyperparameters=hyperparameters
+)
+
+```
+
+### SageMaker Metrics
+
+[SageMaker Metrics](https://docs.aws.amazon.com/sagemaker/latest/dg/training-metrics.html#define-train-metrics) can automatically parse the logs for metrics and send those metrics to CloudWatch. If you want SageMaker to parse logs you have to specify the metrics that you want SageMaker to send to CloudWatch when you configure the training job. You specify the name of the metrics that you want to send and the regular expressions that SageMaker uses to parse the logs that your algorithm emits to find those metrics.
+
+- [Example Notebook](https://github.com/huggingface/notebooks/blob/master/sagemaker/06_sagemaker_metrics/sagemaker-notebook.ipynb)
+
+```python
+# define metrics definitions
+
+metric_definitions = [
+{"Name": "train_runtime", "Regex": "train_runtime.*=\D*(.*?)$"},
+{"Name": "eval_accuracy", "Regex": "eval_accuracy.*=\D*(.*?)$"},
+{"Name": "eval_loss", "Regex": "eval_loss.*=\D*(.*?)$"},
+]
+
+# create the Estimator
+
+huggingface_estimator = HuggingFace(
+        entry_point='train.py',
+        source_dir='./scripts',
+        instance_type='ml.p3.2xlarge',
+        instance_count=1,
+        role=role,
+        transformers_version='4.4',
+        pytorch_version='1.6',
+        py_version='py36',
+        metric_definitions=metric_definitions,
+        hyperparameters = hyperparameters)
+
+```
+
+## Additional Resources
+
+- [Announcement Blog Post](https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face)
+
+- [AWS and Hugging Face collaborate to simplify and accelerate adoption of natural language processing](https://aws.amazon.com/blogs/machine-learning/aws-and-hugging-face-collaborate-to-simplify-and-accelerate-adoption-of-natural-language-processing-models/)
+
+- [Amazon SageMaker documentation for Hugging Face](https://docs.aws.amazon.com/sagemaker/latest/dg/hugging-face.html)
+
+- [SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable/frameworks/huggingface/index.html)

docs/source/task_summary.rst

@@ -54,12 +54,11 @@ Sequence Classification
 
 Sequence classification is the task of classifying sequences according to a given number of classes. An example of
 sequence classification is the GLUE dataset, which is entirely based on that task. If you would like to fine-tune a
-model on a GLUE sequence classification task, you may leverage the `run_glue.py
-<https://github.com/huggingface/transformers/tree/master/examples/text-classification/run_glue.py>`__ and
-`run_pl_glue.py
-<https://github.com/huggingface/transformers/tree/master/examples/text-classification/run_pl_glue.py>`__ or
-`run_tf_glue.py
-<https://github.com/huggingface/transformers/tree/master/examples/text-classification/run_tf_glue.py>`__ scripts.
+model on a GLUE sequence classification task, you may leverage the :prefix_link:`run_glue.py
+<examples/text-classification/run_glue.py>`, :prefix_link:`run_tf_glue.py
+<examples/text-classification/run_tf_glue.py>`, :prefix_link:`run_tf_text_classification.py
+<examples/text-classification/run_tf_text_classification.py>` or :prefix_link:`run_xnli.py
+<examples/text-classification/run_xnli.py>` scripts.
 
 Here is an example of using pipelines to do sentiment analysis: identifying if a sequence is positive or negative. It
 leverages a fine-tuned model on sst2, which is a GLUE task.
@@ -168,9 +167,8 @@ Extractive Question Answering
 
 Extractive Question Answering is the task of extracting an answer from a text given a question. An example of a
 question answering dataset is the SQuAD dataset, which is entirely based on that task. If you would like to fine-tune a
-model on a SQuAD task, you may leverage the `run_squad.py
-<https://github.com/huggingface/transformers/tree/master/examples/question-answering/run_squad.py>`__ and
-`run_tf_squad.py
+model on a SQuAD task, you may leverage the `run_qa.py
+<https://github.com/huggingface/transformers/tree/master/examples/question-answering/run_qa.py>`__ and `run_tf_squad.py
 <https://github.com/huggingface/transformers/tree/master/examples/question-answering/run_tf_squad.py>`__ scripts.
 
 
@@ -242,7 +240,6 @@ Here is an example of question answering using a model and a tokenizer. The proc
     ...     inputs = tokenizer(question, text, add_special_tokens=True, return_tensors="pt")
     ...     input_ids = inputs["input_ids"].tolist()[0]
     ...
-    ...     text_tokens = tokenizer.convert_ids_to_tokens(input_ids)
     ...     outputs = model(**inputs)
     ...     answer_start_scores = outputs.start_logits
     ...     answer_end_scores = outputs.end_logits
@@ -286,7 +283,6 @@ Here is an example of question answering using a model and a tokenizer. The proc
     ...     inputs = tokenizer(question, text, add_special_tokens=True, return_tensors="tf")
     ...     input_ids = inputs["input_ids"].numpy()[0]
     ...
-    ...     text_tokens = tokenizer.convert_ids_to_tokens(input_ids)
     ...     outputs = model(inputs)
     ...     answer_start_scores = outputs.start_logits
     ...     answer_end_scores = outputs.end_logits
@@ -328,7 +324,9 @@ Masked language modeling is the task of masking tokens in a sequence with a mask
 fill that mask with an appropriate token. This allows the model to attend to both the right context (tokens on the
 right of the mask) and the left context (tokens on the left of the mask). Such a training creates a strong basis for
 downstream tasks requiring bi-directional context, such as SQuAD (question answering, see `Lewis, Lui, Goyal et al.
-<https://arxiv.org/abs/1910.13461>`__, part 4.2).
+<https://arxiv.org/abs/1910.13461>`__, part 4.2). If you would like to fine-tune a model on a masked language modeling
+task, you may leverage the `run_mlm.py
+<https://github.com/huggingface/transformers/tree/master/examples/language-modeling/run_mlm.py>`__ script.
 
 Here is an example of using pipelines to replace a mask from a sequence:
 
@@ -436,7 +434,8 @@ Causal Language Modeling
 
 Causal language modeling is the task of predicting the token following a sequence of tokens. In this situation, the
 model only attends to the left context (tokens on the left of the mask). Such a training is particularly interesting
-for generation tasks.
+for generation tasks. If you would like to fine-tune a model on a causal language modeling task, you may leverage the
+`run_clm.py <https://github.com/huggingface/transformers/tree/master/examples/language-modeling/run_clm.py>`__ script.
 
 Usually, the next token is predicted by sampling from the logits of the last hidden state the model produces from the
 input sequence.
@@ -455,7 +454,7 @@ of tokens.
     >>> tokenizer = AutoTokenizer.from_pretrained("gpt2")
     >>> model = AutoModelWithLMHead.from_pretrained("gpt2")
 
-    >>> sequence = f"Hugging Face is based in DUMBO, New York City, and "
+    >>> sequence = f"Hugging Face is based in DUMBO, New York City, and"
 
     >>> input_ids = tokenizer.encode(sequence, return_tensors="pt")
 
@@ -604,11 +603,7 @@ Named Entity Recognition (NER) is the task of classifying tokens according to a
 as a person, an organisation or a location. An example of a named entity recognition dataset is the CoNLL-2003 dataset,
 which is entirely based on that task. If you would like to fine-tune a model on an NER task, you may leverage the
 `run_ner.py <https://github.com/huggingface/transformers/tree/master/examples/token-classification/run_ner.py>`__
-(PyTorch), `run_pl_ner.py
-<https://github.com/huggingface/transformers/tree/master/examples/token-classification/run_pl_ner.py>`__ (leveraging
-pytorch-lightning) or the `run_tf_ner.py
-<https://github.com/huggingface/transformers/tree/master/examples/token-classification/run_tf_ner.py>`__ (TensorFlow)
-scripts.
+script.
 
 Here is an example of using pipelines to do named entity recognition, specifically, trying to identify tokens as
 belonging to one of 9 classes:
@@ -746,7 +741,9 @@ token. The following array should be the output:
 Summarization
 -----------------------------------------------------------------------------------------------------------------------
 
-Summarization is the task of summarizing a document or an article into a shorter text.
+Summarization is the task of summarizing a document or an article into a shorter text. If you would like to fine-tune a
+model on a summarization task, you may leverage the `run_summarization.py
+<https://github.com/huggingface/transformers/tree/master/examples/seq2seq/run_summarization.py>`__ script.
 
 An example of a summarization dataset is the CNN / Daily Mail dataset, which consists of long news articles and was
 created for the task of summarization. If you would like to fine-tune a model on a summarization task, various
@@ -824,7 +821,9 @@ CNN / Daily Mail), it yields very good results.
 Translation
 -----------------------------------------------------------------------------------------------------------------------
 
-Translation is the task of translating a text from one language to another.
+Translation is the task of translating a text from one language to another. If you would like to fine-tune a model on a
+translation task, you may leverage the `run_translation.py
+<https://github.com/huggingface/transformers/tree/master/examples/seq2seq/run_translation.py>`__ script.
 
 An example of a translation dataset is the WMT English to German dataset, which has sentences in English as the input
 data and the corresponding sentences in German as the target data. If you would like to fine-tune a model on a

docs/source/testing.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
@@ -70,19 +70,19 @@ Run all:
 
 .. code-block:: console
 
-   pytest
+    pytest
 
 or:
 
 .. code-block:: bash
 
-   make test
+    make test
 
 Note that the latter is defined as:
 
 .. code-block:: bash
 
-   python -m pytest -n auto --dist=loadfile -s -v ./tests/
+    python -m pytest -n auto --dist=loadfile -s -v ./tests/
 
 which tells pytest to:
 
@@ -100,13 +100,13 @@ All tests of the test suite:
 
 .. code-block:: bash
 
-   pytest --collect-only -q
+    pytest --collect-only -q
 
 All tests of a given test file:
 
 .. code-block:: bash
 
-   pytest tests/test_optimization.py --collect-only -q
+    pytest tests/test_optimization.py --collect-only -q
 
 
 
@@ -117,7 +117,7 @@ To run an individual test module:
 
 .. code-block:: bash
 
-   pytest tests/test_logging.py
+    pytest tests/test_logging.py
 
 
 Run specific tests
@@ -128,7 +128,7 @@ class containing those tests. For example, it could be:
 
 .. code-block:: bash
 
-   pytest tests/test_optimization.py::OptimizationTest::test_adam_w
+    pytest tests/test_optimization.py::OptimizationTest::test_adam_w
 
 Here:
 
@@ -140,7 +140,7 @@ If the file contains multiple classes, you can choose to run only tests of a giv
 
 .. code-block:: bash
 
-   pytest tests/test_optimization.py::OptimizationTest
+    pytest tests/test_optimization.py::OptimizationTest
 
 
 will run all the tests inside that class.
@@ -149,8 +149,7 @@ As mentioned earlier you can see what tests are contained inside the ``Optimizat
 
 .. code-block:: bash
 
-   pytest tests/test_optimization.py::OptimizationTest --collect-only -q
-
+    pytest tests/test_optimization.py::OptimizationTest --collect-only -q
 
 You can run tests by keyword expressions.
 
@@ -158,20 +157,36 @@ To run only tests whose name contains ``adam``:
 
 .. code-block:: bash
 
-   pytest -k adam tests/test_optimization.py
+    pytest -k adam tests/test_optimization.py
+
+Logical ``and`` and ``or`` can be used to indicate whether all keywords should match or either. ``not`` can be used to
+negate.
 
 To run all tests except those whose name contains ``adam``:
 
 .. code-block:: bash
 
-   pytest -k "not adam" tests/test_optimization.py
+    pytest -k "not adam" tests/test_optimization.py
 
 And you can combine the two patterns in one:
 
+.. code-block:: bash
+
+    pytest -k "ada and not adam" tests/test_optimization.py
+
+For example to run both ``test_adafactor`` and ``test_adam_w`` you can use:
 
 .. code-block:: bash
 
-   pytest -k "ada and not adam" tests/test_optimization.py
+    pytest -k "test_adam_w or test_adam_w" tests/test_optimization.py
+
+Note that we use ``or`` here, since we want either of the keywords to match to include both.
+
+If you want to include only tests that include both patterns, ``and`` is to be used:
+
+.. code-block:: bash
+
+    pytest -k "test and ada" tests/test_optimization.py
 
 
 
@@ -236,7 +251,7 @@ example, to run all except ``test_modeling_*.py`` tests:
 
 .. code-block:: bash
 
-   pytest `ls -1 tests/*py | grep -v test_modeling`
+    pytest `ls -1 tests/*py | grep -v test_modeling`
 
 
 Clearing state
@@ -277,13 +292,13 @@ Repeat tests
 
 .. code-block:: bash
 
-   pip install pytest-flakefinder
+    pip install pytest-flakefinder
 
 And then run every test multiple times (50 by default):
 
 .. code-block:: bash
 
-   pytest --flake-finder --flake-runs=5 tests/test_failing_test.py
+    pytest --flake-finder --flake-runs=5 tests/test_failing_test.py
 
 .. note::
    This plugin doesn't work with ``-n`` flag from ``pytest-xdist``.
@@ -307,19 +322,19 @@ As explained earlier this allows detection of coupled tests - where one test's s
 
 .. code-block:: bash
 
-   pytest tests
-   [...]
-   Using --random-order-bucket=module
-   Using --random-order-seed=573663
+    pytest tests
+    [...]
+    Using --random-order-bucket=module
+    Using --random-order-seed=573663
 
 So that if the given particular sequence fails, you can reproduce it by adding that exact seed, e.g.:
 
 .. code-block:: bash
 
-   pytest --random-order-seed=573663
-   [...]
-   Using --random-order-bucket=module
-   Using --random-order-seed=573663
+    pytest --random-order-seed=573663
+    [...]
+    Using --random-order-bucket=module
+    Using --random-order-seed=573663
 
 It will only reproduce the exact order if you use the exact same list of tests (or no list at all). Once you start to
 manually narrowing down the list you can no longer rely on the seed, but have to list them manually in the exact order
@@ -327,7 +342,7 @@ they failed and tell pytest to not randomize them instead using ``--random-order
 
 .. code-block:: bash
 
-   pytest --random-order-bucket=none tests/test_a.py tests/test_c.py tests/test_b.py
+    pytest --random-order-bucket=none tests/test_a.py tests/test_c.py tests/test_b.py
 
 To disable the shuffling for all tests:
 
@@ -354,7 +369,7 @@ progressbar, and show tests that fail and the assert instantly. It gets activate
 
 .. code-block:: bash
 
-   pip install pytest-sugar
+    pip install pytest-sugar
 
 To run tests without it, run:
 
@@ -373,7 +388,7 @@ For a single or a group of tests via ``pytest`` (after ``pip install pytest-pspe
 
 .. code-block:: bash
 
-   pytest --pspec tests/test_optimization.py 
+    pytest --pspec tests/test_optimization.py
 
 
 
@@ -475,8 +490,8 @@ Inside tests:
 
 .. code-block:: bash
 
-   from transformers.testing_utils import get_gpu_count
-   n_gpu = get_gpu_count() # works with torch and tf
+    from transformers.testing_utils import get_gpu_count
+    n_gpu = get_gpu_count() # works with torch and tf
 
 
 
@@ -499,8 +514,8 @@ You will need at least 2 GPUs to see these tests in action:
 
 .. code-block:: bash
 
-   CUDA_VISIBLE_DEVICES="0,1" RUN_SLOW=1 pytest -sv examples/seq2seq/test_finetune_trainer.py \
-   examples/seq2seq/test_seq2seq_examples_multi_gpu.py
+    CUDA_VISIBLE_DEVICES="0,1" RUN_SLOW=1 pytest -sv examples/seq2seq/test_finetune_trainer.py \
+    examples/seq2seq/test_seq2seq_examples_multi_gpu.py
 
 
 Output capture
@@ -513,13 +528,13 @@ To disable output capturing and to get the ``stdout`` and ``stderr`` normally, u
 
 .. code-block:: bash
 
-   pytest -s tests/test_logging.py
+    pytest -s tests/test_logging.py
 
 To send test results to JUnit format output:
 
 .. code-block:: bash
 
-   py.test tests --junitxml=result.xml
+    py.test tests --junitxml=result.xml
 
 
 Color control
@@ -529,7 +544,7 @@ To have no color (e.g., yellow on white background is not readable):
 
 .. code-block:: bash
 
-   pytest --color=no tests/test_logging.py
+    pytest --color=no tests/test_logging.py
 
 
 
@@ -540,7 +555,7 @@ Creating a URL for each test failure:
 
 .. code-block:: bash
 
-   pytest --pastebin=failed tests/test_logging.py
+    pytest --pastebin=failed tests/test_logging.py
 
 This will submit test run information to a remote Paste service and provide a URL for each failure. You may select
 tests as usual or add for example -x if you only want to send one particular failure.
@@ -549,7 +564,7 @@ Creating a URL for a whole test session log:
 
 .. code-block:: bash
 
-   pytest --pastebin=all tests/test_logging.py
+    pytest --pastebin=all tests/test_logging.py
 
 
 
@@ -591,13 +606,13 @@ and you could run just the ``negative`` and ``integer`` sets of params with:
 
 .. code-block:: bash
 
-   pytest -k "negative and integer" tests/test_mytest.py
+    pytest -k "negative and integer" tests/test_mytest.py
 
 or all but ``negative`` sub-tests, with:
 
 .. code-block:: bash
 
-   pytest -k "not negative" tests/test_mytest.py
+    pytest -k "not negative" tests/test_mytest.py
 
 Besides using the ``-k`` filter that was just mentioned, you can find out the exact name of each sub-test and run any
 or all of them using their exact names.
@@ -657,7 +672,7 @@ and it will list:
 
     test_this2.py::test_floor[integer-1-1.0]
     test_this2.py::test_floor[negative--1.5--2.0]
-    test_this2.py::test_floor[large fraction-1.6-1]       
+    test_this2.py::test_floor[large fraction-1.6-1]
 
 So now you can run just the specific test:
 
@@ -780,6 +795,23 @@ leave any data in there.
    otherwise.
 
 
+Temporary sys.path override
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+If you need to temporary override ``sys.path`` to import from another test for example, you can use the
+``ExtendSysPath`` context manager. Example:
+
+
+.. code-block:: python
+
+    import os
+    from transformers.testing_utils import ExtendSysPath
+    bindir = os.path.abspath(os.path.dirname(__file__))
+    with ExtendSysPath(f"{bindir}/.."):
+        from test_trainer import TrainerIntegrationCommon  # noqa
+
+
+
 Skipping tests
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

docs/source/troubleshooting.md

@@ -0,0 +1,30 @@
+<!---
+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.
+-->
+
+# Troubleshooting
+
+This document is to help find solutions for common problems.
+
+## Firewalled environments
+
+Some cloud and intranet setups have their GPU instances firewalled to the outside world, so if your script is trying to download model weights or datasets it will first hang and then timeout with an error message like:
+
+```
+ValueError: Connection error, and we cannot find the requested files in the cached path.
+Please try again or make sure your Internet connection is on.
+```
+
+One possible solution in this situation is to use the ["offline-mode"](https://huggingface.co/transformers/installation.html#offline-mode).

examples/README.md

@@ -1,6 +1,5 @@
 <!---
 Copyright 2020 The HuggingFace Team. All rights reserved.
-
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
 You may obtain a copy of the License at
@@ -16,8 +15,13 @@ limitations under the License.
 
 # Examples
 
-This folder contains actively maintained examples of use of 🤗 Transformers organized along NLP tasks. If you are looking for an example that used to
-be in this folder, it may have moved to our [research projects](https://github.com/huggingface/transformers/tree/master/examples/research_projects) subfolder (which contains frozen snapshots of research projects).
+This folder contains actively maintained examples of use of 🤗 Transformers organized along NLP tasks. If you are looking for an example that used to be in this folder, it may have moved to our [research projects](https://github.com/huggingface/transformers/tree/master/examples/research_projects) subfolder (which contains frozen snapshots of research projects) or to the [legacy](https://github.com/huggingface/transformers/tree/master/examples/legacy) subfolder.
+
+While we strive to present as many use cases as possible, the scripts in this folder are just 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. To help you with that, all the PyTorch versions of the examples fully expose the preprocessing of the data. This way, you can easily tweak them.
+
+This is similar if you want the scripts to report another metric than the one they currently use: look at the `compute_metrics` function inside the script. It takes the full arrays of predictions and labels and has to return a dictionary of string keys and float values. Just change it to add (or replace) your own metric to the ones already reported.
+
+Please discuss on the [forum](https://discuss.huggingface.co/) or in an [issue](https://github.com/huggingface/transformers/issues) a feature you would like to implement in an example before submitting a PR: we welcome bug fixes but since we want to keep the examples as simple as possible, it's unlikely we will merge a pull request adding more functionality at the cost of readability.
 
 ## Important note
 
@@ -34,10 +38,43 @@ Then cd in the example folder of your choice and run
 pip install -r requirements.txt
 ```
 
-Alternatively, you can run the version of the examples as they were for your current version of Transformers via (for instance with v3.5.1):
+To browse the examples corresponding to released versions of 🤗 Transformers, click on the line below and then on your desired version of the library:
+
+<details>
+  <summary>Examples for older versions of 🤗 Transformers</summary>
+
+  - [v4.3.3](https://github.com/huggingface/transformers/tree/v4.3.3/examples)
+  - [v4.2.2](https://github.com/huggingface/transformers/tree/v4.2.2/examples)
+  - [v4.1.1](https://github.com/huggingface/transformers/tree/v4.1.1/examples)
+  - [v4.0.1](https://github.com/huggingface/transformers/tree/v4.0.1/examples)
+  - [v3.5.1](https://github.com/huggingface/transformers/tree/v3.5.1/examples)
+  - [v3.4.0](https://github.com/huggingface/transformers/tree/v3.4.0/examples)
+  - [v3.3.1](https://github.com/huggingface/transformers/tree/v3.3.1/examples)
+  - [v3.2.0](https://github.com/huggingface/transformers/tree/v3.2.0/examples)
+  - [v3.1.0](https://github.com/huggingface/transformers/tree/v3.1.0/examples)
+  - [v3.0.2](https://github.com/huggingface/transformers/tree/v3.0.2/examples)
+  - [v2.11.0](https://github.com/huggingface/transformers/tree/v2.11.0/examples)
+  - [v2.10.0](https://github.com/huggingface/transformers/tree/v2.10.0/examples)
+  - [v2.9.1](https://github.com/huggingface/transformers/tree/v2.9.1/examples)
+  - [v2.8.0](https://github.com/huggingface/transformers/tree/v2.8.0/examples)
+  - [v2.7.0](https://github.com/huggingface/transformers/tree/v2.7.0/examples)
+  - [v2.6.0](https://github.com/huggingface/transformers/tree/v2.6.0/examples)
+  - [v2.5.1](https://github.com/huggingface/transformers/tree/v2.5.1/examples)
+  - [v2.4.0](https://github.com/huggingface/transformers/tree/v2.4.0/examples)
+  - [v2.3.0](https://github.com/huggingface/transformers/tree/v2.3.0/examples)
+  - [v2.2.0](https://github.com/huggingface/transformers/tree/v2.2.0/examples)
+  - [v2.1.1](https://github.com/huggingface/transformers/tree/v2.1.0/examples)
+  - [v2.0.0](https://github.com/huggingface/transformers/tree/v2.0.0/examples)
+  - [v1.2.0](https://github.com/huggingface/transformers/tree/v1.2.0/examples)
+  - [v1.1.0](https://github.com/huggingface/transformers/tree/v1.1.0/examples)
+  - [v1.0.0](https://github.com/huggingface/transformers/tree/v1.0.0/examples)
+</details>
+
+Alternatively, you can find switch your cloned 🤗 Transformers to a specific version (for instance with v3.5.1) with
 ```bash
 git checkout tags/v3.5.1
 ```
+and run the example command as usual afterward.
 
 ## The Big Table of Tasks
 
@@ -53,21 +90,47 @@ Coming soon!
 
 | Task | Example datasets | Trainer support | TFTrainer support | 🤗 Datasets | Colab
 |---|---|:---:|:---:|:---:|:---:|
-| [**`language-modeling`**](https://github.com/huggingface/transformers/tree/master/examples/language-modeling)       | Raw text        | ✅ | -  | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/01_how_to_train.ipynb)
-| [**`multiple-choice`**](https://github.com/huggingface/transformers/tree/master/examples/multiple-choice)           | SWAG, RACE, ARC | ✅ | ✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ViktorAlm/notebooks/blob/master/MPC_GPU_Demo_for_TF_and_PT.ipynb)
-| [**`question-answering`**](https://github.com/huggingface/transformers/tree/master/examples/question-answering)     | SQuAD           | ✅ | ✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://github.com/huggingface/notebooks/blob/master/examples/question_answering.ipynb)
-| [**`summarization`**](https://github.com/huggingface/transformers/tree/master/examples/seq2seq)                     | CNN/Daily Mail  | ✅  | - | - | -
-| [**`text-classification`**](https://github.com/huggingface/transformers/tree/master/examples/text-classification)   | GLUE, XNLI      | ✅ | ✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://github.com/huggingface/notebooks/blob/master/examples/text_classification.ipynb)
+| [**`language-modeling`**](https://github.com/huggingface/transformers/tree/master/examples/language-modeling)       | WikiText-2      | ✅ | -  | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/language_modeling.ipynb)
+| [**`multiple-choice`**](https://github.com/huggingface/transformers/tree/master/examples/multiple-choice)           | SWAG            | ✅ | ✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/multiple_choice.ipynb)
+| [**`question-answering`**](https://github.com/huggingface/transformers/tree/master/examples/question-answering)     | SQuAD           | ✅ | ✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb)
+| [**`summarization`**](https://github.com/huggingface/transformers/tree/master/examples/seq2seq)                     |  XSum           | ✅ | -  | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/summarization.ipynb)
+| [**`text-classification`**](https://github.com/huggingface/transformers/tree/master/examples/text-classification)   | GLUE            | ✅ | ✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/text_classification.ipynb)
 | [**`text-generation`**](https://github.com/huggingface/transformers/tree/master/examples/text-generation)           | -               | n/a | n/a | - | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/02_how_to_generate.ipynb)
-| [**`token-classification`**](https://github.com/huggingface/transformers/tree/master/examples/token-classification) | CoNLL NER       | ✅ | ✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://github.com/huggingface/notebooks/blob/master/examples/token_classification.ipynb)
-| [**`translation`**](https://github.com/huggingface/transformers/tree/master/examples/seq2seq)                       | WMT             | ✅  | - | - | -
+| [**`token-classification`**](https://github.com/huggingface/transformers/tree/master/examples/token-classification) | CoNLL NER       | ✅ | ✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/token_classification.ipynb)
+| [**`translation`**](https://github.com/huggingface/transformers/tree/master/examples/seq2seq)                       | WMT             | ✅  | - | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/translation.ipynb)
 
 
-<!--
-## One-click Deploy to Cloud (wip)
+## Running quick tests
+
+Most examples are equipped with a mechanism to truncate the number of dataset samples to the desired length. This is useful for debugging purposes, for example to quickly check that all stages of the programs can complete, before running the same setup on the full dataset which may take hours to complete.
+
+For example here is how to truncate all three splits to just 50 samples each:
+```
+examples/token-classification/run_ner.py \
+--max_train_samples 50 \
+--max_val_samples 50 \
+--max_test_samples 50 \
+[...]
+```
+
+Most example scripts should have the first two command line arguments and some have the third one. You can quickly check if a given example supports any of these by passing a `-h` option, e.g.:
+```
+examples/token-classification/run_ner.py -h
+```
+
+## Resuming training
+
+You can resume training from a previous checkpoint like this:
+
+1. Pass `--output_dir previous_output_dir` without `--overwrite_output_dir` to resume training from the latest checkpoint in `output_dir` (what you would use if the training was interrupted, for instance).
+2. Pass `--model_name_or_path path_to_a_specific_checkpoint` to resume training from that checkpoint folder.
+
+Should you want to turn an example into a notebook where you'd no longer have access to the command
+line, 🤗 Trainer supports resuming from a checkpoint via `trainer.train(resume_from_checkpoint)`.
+
+1. If `resume_from_checkpoint` is `True` it will look for the last checkpoint in the value of `output_dir` passed via `TrainingArguments`.
+2. If `resume_from_checkpoint` is a path to a specific checkpoint it will use that saved checkpoint folder to resume the training from.
 
-**Coming soon!**
--->
 
 ## Distributed training and mixed precision
 
@@ -78,7 +141,7 @@ use the following command:
 ```bash
 python -m torch.distributed.launch \
     --nproc_per_node number_of_gpu_you_have path_to_script.py \
-	--all_arguments_of_the_script 
+	--all_arguments_of_the_script
 ```
 
 As an example, here is how you would fine-tune the BERT large model (with whole word masking) on the text
@@ -122,7 +185,7 @@ regular training script with its arguments (this is similar to the `torch.distri
 ```bash
 python xla_spawn.py --num_cores num_tpu_you_have \
     path_to_script.py \
-	--all_arguments_of_the_script 
+	--all_arguments_of_the_script
 ```
 
 As an example, here is how you would fine-tune the BERT large model (with whole word masking) on the text
@@ -147,7 +210,7 @@ python xla_spawn.py --num_cores 8 \
 You can easily log and monitor your runs code. The following are currently supported:
 
 * [TensorBoard](https://www.tensorflow.org/tensorboard)
-* [Weights & Biases](https://docs.wandb.com/library/integrations/huggingface)
+* [Weights & Biases](https://docs.wandb.ai/integrations/huggingface)
 * [Comet ML](https://www.comet.ml/docs/python-sdk/huggingface/)
 
 ### Weights & Biases
@@ -171,9 +234,23 @@ import wandb
 wandb.login()
 ```
 
+To enable logging to W&B, include `"wandb"` in the `report_to` of your `TrainingArguments` or script. Or just pass along `--report_to all` if you have `wandb` installed.
+
 Whenever you use `Trainer` or `TFTrainer` classes, your losses, evaluation metrics, model topology and gradients (for `Trainer` only) will automatically be logged.
 
-When using 🤗 Transformers with PyTorch Lightning, runs can be tracked through `WandbLogger`. Refer to related [documentation & examples](https://docs.wandb.com/library/integrations/lightning).
+Advanced configuration is possible by setting environment variables:
+
+| Environment Variable | Value |
+|---|---|
+| WANDB_LOG_MODEL | Log the model as artifact (log the model as artifact at the end of training (`false` by default) |
+| WANDB_WATCH | one of `gradients` (default) to log histograms of gradients, `all` to log histograms of both gradients and parameters, or `false` for no histogram logging |
+| WANDB_PROJECT | Organize runs by project |
+
+Set run names with `run_name` argument present in scripts or as part of `TrainingArguments`.
+
+Additional configuration options are available through generic [wandb environment variables](https://docs.wandb.com/library/environment-variables).
+
+Refer to related [documentation & examples](https://docs.wandb.ai/integrations/huggingface).
 
 ### Comet.ml
 

examples/_tests_requirements.txt

@@ -2,7 +2,7 @@ tensorboard
 scikit-learn
 seqeval
 psutil
-sacrebleu
+sacrebleu >= 1.4.12
 rouge-score
 tensorflow_datasets
 matplotlib

examples/benchmarking/run_benchmark.py

@@ -1,3 +1,4 @@
+#!/usr/bin/env python
 # coding=utf-8
 # Copyright 2020 The HuggingFace Inc. team.
 # Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.

examples/benchmarking/run_benchmark_tf.py

@@ -1,3 +1,4 @@
+#!/usr/bin/env python
 # coding=utf-8
 # Copyright 2018 The HuggingFace Inc. team.
 # Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.

examples/language-modeling/README.md

@@ -22,12 +22,11 @@ ALBERT, BERT, DistilBERT, RoBERTa, XLNet... GPT and GPT-2 are trained or fine-tu
 loss. XLNet uses permutation language modeling (PLM), you can find more information about the differences between those
 objectives in our [model summary](https://huggingface.co/transformers/model_summary.html).
 
-These scripts leverage the 🤗 Datasets library and the Trainer API. You can easily customize them to your needs if you
-need extra processing on your datasets.
+There are two sets of scripts provided. The first set leverages the Trainer API. The second set with `no_trainer` in the suffix uses a custom training loop and leverages the 🤗 Accelerate library . Both sets use the 🤗 Datasets library. You can easily customize them to your needs if you need extra processing on your datasets.
 
 **Note:** The old script `run_language_modeling.py` is still available [here](https://github.com/huggingface/transformers/blob/master/examples/legacy/run_language_modeling.py).
 
-The following examples, will run on a datasets hosted on our [hub](https://huggingface.co/datasets) or with your own
+The following examples, will run on datasets hosted on our [hub](https://huggingface.co/datasets) or with your own
 text files for training and validation. We give examples of both below.
 
 ### GPT-2/GPT and causal language modeling
@@ -60,6 +59,15 @@ python run_clm.py \
     --output_dir /tmp/test-clm
 ```
 
+This uses the built in HuggingFace `Trainer` for training. If you want to use a custom training loop, you can utilize or adapt the `run_clm_no_trainer.py` script. Take a look at the script for a list of supported arguments. An example is shown below:
+
+```bash
+python run_clm_no_trainer.py \
+    --dataset_name wikitext \
+    --dataset_config_name wikitext-2-raw-v1 \
+    --model_name_or_path gpt2 \
+    --output_dir /tmp/test-clm
+```
 
 ### RoBERTa/BERT/DistilBERT and masked language modeling
 
@@ -95,88 +103,33 @@ python run_mlm.py \
 If your dataset is organized with one sample per line, you can use the `--line_by_line` flag (otherwise the script
 concatenates all texts and then splits them in blocks of the same length).
 
+This uses the built in HuggingFace `Trainer` for training. If you want to use a custom training loop, you can utilize or adapt the `run_mlm_no_trainer.py` script. Take a look at the script for a list of supported arguments. An example is shown below:
+
+```bash
+python run_mlm_no_trainer.py \
+    --dataset_name wikitext \
+    --dataset_config_name wikitext-2-raw-v1 \
+    --model_name_or_path roberta-base \
+    --output_dir /tmp/test-mlm
+```
+
 **Note:** On TPU, you should use the flag `--pad_to_max_length` in conjunction with the `--line_by_line` flag to make
 sure all your batches have the same length.
 
 ### Whole word masking
 
-The BERT authors released a new version of BERT using Whole Word Masking in May 2019. Instead of masking randomly
-selected tokens (which may be part of words), they mask randomly selected words (masking all the tokens corresponding
-to that word). This technique has been refined for Chinese in [this paper](https://arxiv.org/abs/1906.08101).
-
-To fine-tune a model using whole word masking, use the following script:
-```bash
-python run_mlm_wwm.py \
-    --model_name_or_path roberta-base \
-    --dataset_name wikitext \
-    --dataset_config_name wikitext-2-raw-v1 \
-    --do_train \
-    --do_eval \
-    --output_dir /tmp/test-mlm-wwm
-```
-
-For Chinese models, we need to generate a reference files (which requires the ltp library), because it's tokenized at
-the character level.
-
-**Q :** Why a reference file?
-
-**A :** Suppose we have a Chinese sentence like: `我喜欢你` The original Chinese-BERT will tokenize it as
-`['我','喜','欢','你']` (character level). But `喜欢` is a whole word. For whole word masking proxy, we need a result
-like `['我','喜','##欢','你']`, so we need a reference file to tell the model which position of the BERT original token
-should be added `##`.
-
-**Q :** Why LTP ?
-
-**A :** Cause the best known Chinese WWM BERT is [Chinese-BERT-wwm](https://github.com/ymcui/Chinese-BERT-wwm) by HIT.
-It works well on so many Chines Task like CLUE (Chinese GLUE). They use LTP, so if we want to fine-tune their model,
-we need LTP.
-
-Now LTP only only works well on `transformers==3.2.0`. So we don't add it to requirements.txt.
-You need to create a separate environment with this version of Transformers to run the `run_chinese_ref.py` script that
-will create the reference files. The script is in `examples/contrib`. Once in the proper environment, run the
-following:
-
-
-```bash
-export TRAIN_FILE=/path/to/dataset/wiki.train.raw
-export LTP_RESOURCE=/path/to/ltp/tokenizer
-export BERT_RESOURCE=/path/to/bert/tokenizer
-export SAVE_PATH=/path/to/data/ref.txt
-
-python examples/contrib/run_chinese_ref.py \
-    --file_name=path_to_train_or_eval_file \
-    --ltp=path_to_ltp_tokenizer \
-    --bert=path_to_bert_tokenizer \
-    --save_path=path_to_reference_file
-```
-
-Then you can run the script like this: 
-
-
-```bash
-python run_mlm_wwm.py \
-    --model_name_or_path roberta-base \
-    --train_file path_to_train_file \
-    --validation_file path_to_validation_file \
-    --train_ref_file path_to_train_chinese_ref_file \
-    --validation_ref_file path_to_validation_chinese_ref_file \
-    --do_train \
-    --do_eval \
-    --output_dir /tmp/test-mlm-wwm
-```
-
-**Note:** On TPU, you should the flag `--pad_to_max_length` to make sure all your batches have the same length.
+This part was moved to `examples/research_projects/mlm_wwm`.
 
 ### XLNet and permutation language modeling
 
-XLNet uses a different training objective, which is permutation language modeling. It is an autoregressive method 
-to learn bidirectional contexts by maximizing the expected likelihood over all permutations of the input 
+XLNet uses a different training objective, which is permutation language modeling. It is an autoregressive method
+to learn bidirectional contexts by maximizing the expected likelihood over all permutations of the input
 sequence factorization order.
 
-We use the `--plm_probability` flag to define the ratio of length of a span of masked tokens to surrounding 
+We use the `--plm_probability` flag to define the ratio of length of a span of masked tokens to surrounding
 context length for permutation language modeling.
 
-The `--max_span_length` flag may also be used to limit the length of a span of masked tokens used 
+The `--max_span_length` flag may also be used to limit the length of a span of masked tokens used
 for permutation language modeling.
 
 Here is how to fine-tune XLNet on wikitext-2:

examples/language-modeling/run_clm.py

@@ -1,3 +1,4 @@
+#!/usr/bin/env python
 # coding=utf-8
 # Copyright 2020 The HuggingFace Inc. team. All rights reserved.
 #
@@ -42,9 +43,14 @@ from transformers import (
     default_data_collator,
     set_seed,
 )
-from transformers.trainer_utils import is_main_process
+from transformers.testing_utils import CaptureLogger
+from transformers.trainer_utils import get_last_checkpoint, is_main_process
+from transformers.utils import check_min_version
 
 
+# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
+check_min_version("4.6.0.dev0")
+
 logger = logging.getLogger(__name__)
 
 
@@ -113,11 +119,26 @@ class DataTrainingArguments:
         default=None,
         metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
     )
+    max_train_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "For debugging purposes or quicker training, truncate the number of training examples to this "
+            "value if set."
+        },
+    )
+    max_val_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "For debugging purposes or quicker training, truncate the number of validation examples to this "
+            "value if set."
+        },
+    )
+
     block_size: Optional[int] = field(
         default=None,
         metadata={
-            "help": "Optional input sequence length after tokenization."
-            "The training dataset will be truncated in block of this size for training."
+            "help": "Optional input sequence length after tokenization. "
+            "The training dataset will be truncated in block of this size for training. "
             "Default to the model max input length for single sentence inputs (take into account special tokens)."
         },
     )
@@ -160,23 +181,28 @@ def main():
     else:
         model_args, data_args, training_args = parser.parse_args_into_dataclasses()
 
-    if (
-        os.path.exists(training_args.output_dir)
-        and os.listdir(training_args.output_dir)
-        and training_args.do_train
-        and not training_args.overwrite_output_dir
-    ):
-        raise ValueError(
-            f"Output directory ({training_args.output_dir}) already exists and is not empty."
-            "Use --overwrite_output_dir to overcome."
-        )
+    # Detecting last checkpoint.
+    last_checkpoint = None
+    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
+        last_checkpoint = get_last_checkpoint(training_args.output_dir)
+        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
+            raise ValueError(
+                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
+                "Use --overwrite_output_dir to overcome."
+            )
+        elif last_checkpoint is not None:
+            logger.info(
+                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
+                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
+            )
 
     # Setup logging
     logging.basicConfig(
         format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
         datefmt="%m/%d/%Y %H:%M:%S",
-        level=logging.INFO if is_main_process(training_args.local_rank) else logging.WARN,
+        handlers=[logging.StreamHandler(sys.stdout)],
     )
+    logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN)
 
     # Log on each process the small summary:
     logger.warning(
@@ -188,7 +214,7 @@ def main():
         transformers.utils.logging.set_verbosity_info()
         transformers.utils.logging.enable_default_handler()
         transformers.utils.logging.enable_explicit_format()
-    logger.info("Training/evaluation parameters %s", training_args)
+    logger.info(f"Training/evaluation parameters {training_args}")
 
     # Set seed before initializing model.
     set_seed(training_args.seed)
@@ -204,17 +230,19 @@ def main():
     # download the dataset.
     if data_args.dataset_name is not None:
         # Downloading and loading a dataset from the hub.
-        datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
+        datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir)
         if "validation" not in datasets.keys():
             datasets["validation"] = load_dataset(
                 data_args.dataset_name,
                 data_args.dataset_config_name,
                 split=f"train[:{data_args.validation_split_percentage}%]",
+                cache_dir=model_args.cache_dir,
             )
             datasets["train"] = load_dataset(
                 data_args.dataset_name,
                 data_args.dataset_config_name,
                 split=f"train[{data_args.validation_split_percentage}%:]",
+                cache_dir=model_args.cache_dir,
             )
     else:
         data_files = {}
@@ -222,10 +250,14 @@ def main():
             data_files["train"] = data_args.train_file
         if data_args.validation_file is not None:
             data_files["validation"] = data_args.validation_file
-        extension = data_args.train_file.split(".")[-1]
+        extension = (
+            data_args.train_file.split(".")[-1]
+            if data_args.train_file is not None
+            else data_args.validation_file.split(".")[-1]
+        )
         if extension == "txt":
             extension = "text"
-        datasets = load_dataset(extension, data_files=data_files)
+        datasets = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)
     # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
     # https://huggingface.co/docs/datasets/loading_datasets.html.
 
@@ -287,8 +319,18 @@ def main():
         column_names = datasets["validation"].column_names
     text_column_name = "text" if "text" in column_names else column_names[0]
 
+    # since this will be pickled to avoid _LazyModule error in Hasher force logger loading before tokenize_function
+    tok_logger = transformers.utils.logging.get_logger("transformers.tokenization_utils_base")
+
     def tokenize_function(examples):
-        return tokenizer(examples[text_column_name])
+        with CaptureLogger(tok_logger) as cl:
+            output = tokenizer(examples[text_column_name])
+        # clm input could be much much longer than block_size
+        if "Token indices sequence length is longer than the" in cl.out:
+            tok_logger.warning(
+                "^^^^^^^^^^^^^^^^ Please ignore the warning above - this long input will be chunked into smaller bits before being passed to the model."
+            )
+        return output
 
     tokenized_datasets = datasets.map(
         tokenize_function,
@@ -301,14 +343,14 @@ def main():
     if data_args.block_size is None:
         block_size = tokenizer.model_max_length
         if block_size > 1024:
-            logger.warn(
+            logger.warning(
                 f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). "
                 "Picking 1024 instead. You can change that default value by passing --block_size xxx."
             )
         block_size = 1024
     else:
         if data_args.block_size > tokenizer.model_max_length:
-            logger.warn(
+            logger.warning(
                 f"The block_size passed ({data_args.block_size}) is larger than the maximum length for the model"
                 f"({tokenizer.model_max_length}). Using block_size={tokenizer.model_max_length}."
             )
@@ -336,6 +378,7 @@ def main():
     #
     # To speed up this part, we use multiprocessing. See the documentation of the map method for more information:
     # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map
+
     lm_datasets = tokenized_datasets.map(
         group_texts,
         batched=True,
@@ -343,12 +386,26 @@ def main():
         load_from_cache_file=not data_args.overwrite_cache,
     )
 
+    if training_args.do_train:
+        if "train" not in tokenized_datasets:
+            raise ValueError("--do_train requires a train dataset")
+        train_dataset = lm_datasets["train"]
+        if data_args.max_train_samples is not None:
+            train_dataset = train_dataset.select(range(data_args.max_train_samples))
+
+    if training_args.do_eval:
+        if "validation" not in tokenized_datasets:
+            raise ValueError("--do_eval requires a validation dataset")
+        eval_dataset = lm_datasets["validation"]
+        if data_args.max_val_samples is not None:
+            eval_dataset = eval_dataset.select(range(data_args.max_val_samples))
+
     # Initialize our Trainer
     trainer = Trainer(
         model=model,
         args=training_args,
-        train_dataset=lm_datasets["train"] if training_args.do_train else None,
-        eval_dataset=lm_datasets["validation"] if training_args.do_eval else None,
+        train_dataset=train_dataset if training_args.do_train else None,
+        eval_dataset=eval_dataset if training_args.do_eval else None,
         tokenizer=tokenizer,
         # Data collator will default to DataCollatorWithPadding, so we change it.
         data_collator=default_data_collator,
@@ -356,44 +413,39 @@ def main():
 
     # Training
     if training_args.do_train:
-        model_path = (
-            model_args.model_name_or_path
-            if (model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path))
-            else None
-        )
-        train_result = trainer.train(model_path=model_path)
+        if last_checkpoint is not None:
+            checkpoint = last_checkpoint
+        elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path):
+            checkpoint = model_args.model_name_or_path
+        else:
+            checkpoint = None
+        train_result = trainer.train(resume_from_checkpoint=checkpoint)
         trainer.save_model()  # Saves the tokenizer too for easy upload
 
-        output_train_file = os.path.join(training_args.output_dir, "train_results.txt")
-        if trainer.is_world_process_zero():
-            with open(output_train_file, "w") as writer:
-                logger.info("***** Train results *****")
-                for key, value in sorted(train_result.metrics.items()):
-                    logger.info(f"  {key} = {value}")
-                    writer.write(f"{key} = {value}\n")
+        metrics = train_result.metrics
 
-            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
-            trainer.state.save_to_json(os.path.join(training_args.output_dir, "trainer_state.json"))
+        max_train_samples = (
+            data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)
+        )
+        metrics["train_samples"] = min(max_train_samples, len(train_dataset))
+
+        trainer.log_metrics("train", metrics)
+        trainer.save_metrics("train", metrics)
+        trainer.save_state()
 
     # Evaluation
-    results = {}
     if training_args.do_eval:
         logger.info("*** Evaluate ***")
 
-        eval_output = trainer.evaluate()
+        metrics = trainer.evaluate()
 
-        perplexity = math.exp(eval_output["eval_loss"])
-        results["perplexity"] = perplexity
+        max_val_samples = data_args.max_val_samples if data_args.max_val_samples is not None else len(eval_dataset)
+        metrics["eval_samples"] = min(max_val_samples, len(eval_dataset))
+        perplexity = math.exp(metrics["eval_loss"])
+        metrics["perplexity"] = perplexity
 
-        output_eval_file = os.path.join(training_args.output_dir, "eval_results_clm.txt")
-        if trainer.is_world_process_zero():
-            with open(output_eval_file, "w") as writer:
-                logger.info("***** Eval results *****")
-                for key, value in sorted(results.items()):
-                    logger.info(f"  {key} = {value}")
-                    writer.write(f"{key} = {value}\n")
-
-    return results
+        trainer.log_metrics("eval", metrics)
+        trainer.save_metrics("eval", metrics)
 
 
 def _mp_fn(index):

examples/language-modeling/run_clm_no_trainer.py

@@ -0,0 +1,456 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Fine-tuning the library models for causal language modeling (BERT, ALBERT, RoBERTa...)
+on a text file or a dataset without using HuggingFace Trainer.
+
+Here is the full list of checkpoints on the hub that can be fine-tuned by this script:
+https://huggingface.co/models?filter=causal-lm
+"""
+# You can also adapt this script on your own causal language modeling task. Pointers for this are left as comments.
+
+import argparse
+import logging
+import math
+import os
+import random
+
+import datasets
+import torch
+from datasets import load_dataset
+from torch.utils.data.dataloader import DataLoader
+from tqdm.auto import tqdm
+
+import transformers
+from accelerate import Accelerator
+from transformers import (
+    CONFIG_MAPPING,
+    MODEL_MAPPING,
+    AdamW,
+    AutoConfig,
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    SchedulerType,
+    default_data_collator,
+    get_scheduler,
+    set_seed,
+)
+
+
+logger = logging.getLogger(__name__)
+MODEL_CONFIG_CLASSES = list(MODEL_MAPPING.keys())
+MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Finetune a transformers model on a causal language modeling task")
+    parser.add_argument(
+        "--dataset_name",
+        type=str,
+        default=None,
+        help="The name of the dataset to use (via the datasets library).",
+    )
+    parser.add_argument(
+        "--dataset_config_name",
+        type=str,
+        default=None,
+        help="The configuration name of the dataset to use (via the datasets library).",
+    )
+    parser.add_argument(
+        "--train_file", type=str, default=None, help="A csv or a json file containing the training data."
+    )
+    parser.add_argument(
+        "--validation_file", type=str, default=None, help="A csv or a json file containing the validation data."
+    )
+    parser.add_argument(
+        "--validation_split_percentage",
+        default=5,
+        help="The percentage of the train set used as validation set in case there's no validation split",
+    )
+    parser.add_argument(
+        "--model_name_or_path",
+        type=str,
+        help="Path to pretrained model or model identifier from huggingface.co/models.",
+        required=True,
+    )
+    parser.add_argument(
+        "--config_name",
+        type=str,
+        default=None,
+        help="Pretrained config name or path if not the same as model_name",
+    )
+    parser.add_argument(
+        "--tokenizer_name",
+        type=str,
+        default=None,
+        help="Pretrained tokenizer name or path if not the same as model_name",
+    )
+    parser.add_argument(
+        "--use_slow_tokenizer",
+        action="store_true",
+        help="If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).",
+    )
+    parser.add_argument(
+        "--per_device_train_batch_size",
+        type=int,
+        default=8,
+        help="Batch size (per device) for the training dataloader.",
+    )
+    parser.add_argument(
+        "--per_device_eval_batch_size",
+        type=int,
+        default=8,
+        help="Batch size (per device) for the evaluation dataloader.",
+    )
+    parser.add_argument(
+        "--learning_rate",
+        type=float,
+        default=5e-5,
+        help="Initial learning rate (after the potential warmup period) to use.",
+    )
+    parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.")
+    parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.")
+    parser.add_argument(
+        "--max_train_steps",
+        type=int,
+        default=None,
+        help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
+    )
+    parser.add_argument(
+        "--gradient_accumulation_steps",
+        type=int,
+        default=1,
+        help="Number of updates steps to accumulate before performing a backward/update pass.",
+    )
+    parser.add_argument(
+        "--lr_scheduler_type",
+        type=SchedulerType,
+        default="linear",
+        help="The scheduler type to use.",
+        choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"],
+    )
+    parser.add_argument(
+        "--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler."
+    )
+    parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.")
+    parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
+    parser.add_argument(
+        "--model_type",
+        type=str,
+        default=None,
+        help="Model type to use if training from scratch.",
+        choices=MODEL_TYPES,
+    )
+    parser.add_argument(
+        "--block_size",
+        type=int,
+        default=None,
+        help="Optional input sequence length after tokenization. The training dataset will be truncated in block of this size for training. Default to the model max input length for single sentence inputs (take into account special tokens).",
+    )
+    parser.add_argument(
+        "--preprocessing_num_workers",
+        type=int,
+        default=None,
+        help="The number of processes to use for the preprocessing.",
+    )
+    parser.add_argument(
+        "--overwrite_cache", type=bool, default=False, help="Overwrite the cached training and evaluation sets"
+    )
+
+    args = parser.parse_args()
+
+    # Sanity checks
+    if args.dataset_name is None and args.train_file is None and args.validation_file is None:
+        raise ValueError("Need either a dataset name or a training/validation file.")
+    else:
+        if args.train_file is not None:
+            extension = args.train_file.split(".")[-1]
+            assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, json or txt file."
+        if args.validation_file is not None:
+            extension = args.validation_file.split(".")[-1]
+            assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, json or txt file."
+
+    if args.output_dir is not None:
+        os.makedirs(args.output_dir, exist_ok=True)
+
+    return args
+
+
+def main():
+    args = parse_args()
+
+    # Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
+    accelerator = Accelerator()
+    # Make one log on every process with the configuration for debugging.
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        level=logging.INFO,
+    )
+    logger.info(accelerator.state)
+
+    # Setup logging, we only want one process per machine to log things on the screen.
+    # accelerator.is_local_main_process is only True for one process per machine.
+    logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
+    if accelerator.is_local_main_process:
+        datasets.utils.logging.set_verbosity_warning()
+        transformers.utils.logging.set_verbosity_info()
+    else:
+        datasets.utils.logging.set_verbosity_error()
+        transformers.utils.logging.set_verbosity_error()
+
+    # If passed along, set the training seed now.
+    if args.seed is not None:
+        set_seed(args.seed)
+
+    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
+    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
+    # (the dataset will be downloaded automatically from the datasets Hub).
+    #
+    # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
+    # 'text' is found. You can easily tweak this behavior (see below).
+    #
+    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
+    # download the dataset.
+    if args.dataset_name is not None:
+        # Downloading and loading a dataset from the hub.
+        raw_datasets = load_dataset(args.dataset_name, args.dataset_config_name)
+        if "validation" not in raw_datasets.keys():
+            raw_datasets["validation"] = load_dataset(
+                args.dataset_name,
+                args.dataset_config_name,
+                split=f"train[:{args.validation_split_percentage}%]",
+            )
+            raw_datasets["train"] = load_dataset(
+                args.dataset_name,
+                args.dataset_config_name,
+                split=f"train[{args.validation_split_percentage}%:]",
+            )
+    else:
+        data_files = {}
+        if args.train_file is not None:
+            data_files["train"] = args.train_file
+        if args.validation_file is not None:
+            data_files["validation"] = args.validation_file
+        extension = args.train_file.split(".")[-1]
+        if extension == "txt":
+            extension = "text"
+        raw_datasets = load_dataset(extension, data_files=data_files)
+    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
+    # https://huggingface.co/docs/datasets/loading_datasets.html.
+
+    # Load pretrained model and tokenizer
+    #
+    # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
+    # download model & vocab.
+    if args.config_name:
+        config = AutoConfig.from_pretrained(args.config_name)
+    elif args.model_name_or_path:
+        config = AutoConfig.from_pretrained(args.model_name_or_path)
+    else:
+        config = CONFIG_MAPPING[args.model_type]()
+        logger.warning("You are instantiating a new config instance from scratch.")
+
+    if args.tokenizer_name:
+        tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=not args.use_slow_tokenizer)
+    elif args.model_name_or_path:
+        tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, use_fast=not args.use_slow_tokenizer)
+    else:
+        raise ValueError(
+            "You are instantiating a new tokenizer from scratch. This is not supported by this script."
+            "You can do it from another script, save it, and load it from here, using --tokenizer_name."
+        )
+
+    if args.model_name_or_path:
+        model = AutoModelForCausalLM.from_pretrained(
+            args.model_name_or_path,
+            from_tf=bool(".ckpt" in args.model_name_or_path),
+            config=config,
+        )
+    else:
+        logger.info("Training new model from scratch")
+        model = AutoModelForCausalLM.from_config(config)
+
+    model.resize_token_embeddings(len(tokenizer))
+
+    # Preprocessing the datasets.
+    # First we tokenize all the texts.
+    column_names = raw_datasets["train"].column_names
+    text_column_name = "text" if "text" in column_names else column_names[0]
+
+    def tokenize_function(examples):
+        return tokenizer(examples[text_column_name])
+
+    tokenized_datasets = raw_datasets.map(
+        tokenize_function,
+        batched=True,
+        num_proc=args.preprocessing_num_workers,
+        remove_columns=column_names,
+        load_from_cache_file=not args.overwrite_cache,
+    )
+
+    if args.block_size is None:
+        block_size = tokenizer.model_max_length
+        if block_size > 1024:
+            logger.warning(
+                f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). "
+                "Picking 1024 instead. You can change that default value by passing --block_size xxx."
+            )
+        block_size = 1024
+    else:
+        if args.block_size > tokenizer.model_max_length:
+            logger.warning(
+                f"The block_size passed ({args.block_size}) is larger than the maximum length for the model"
+                f"({tokenizer.model_max_length}). Using block_size={tokenizer.model_max_length}."
+            )
+        block_size = min(args.block_size, tokenizer.model_max_length)
+
+    # Main data processing function that will concatenate all texts from our dataset and generate chunks of block_size.
+    def group_texts(examples):
+        # Concatenate all texts.
+        concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
+        total_length = len(concatenated_examples[list(examples.keys())[0]])
+        # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
+        # customize this part to your needs.
+        total_length = (total_length // block_size) * block_size
+        # Split by chunks of max_len.
+        result = {
+            k: [t[i : i + block_size] for i in range(0, total_length, block_size)]
+            for k, t in concatenated_examples.items()
+        }
+        result["labels"] = result["input_ids"].copy()
+        return result
+
+    # Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a remainder
+    # for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value might be slower
+    # to preprocess.
+    #
+    # To speed up this part, we use multiprocessing. See the documentation of the map method for more information:
+    # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map
+
+    lm_datasets = tokenized_datasets.map(
+        group_texts,
+        batched=True,
+        num_proc=args.preprocessing_num_workers,
+        load_from_cache_file=not args.overwrite_cache,
+    )
+
+    train_dataset = lm_datasets["train"]
+    eval_dataset = lm_datasets["validation"]
+
+    # Log a few random samples from the training set:
+    for index in random.sample(range(len(train_dataset)), 3):
+        logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
+
+    # DataLoaders creation:
+    train_dataloader = DataLoader(
+        train_dataset, shuffle=True, collate_fn=default_data_collator, batch_size=args.per_device_train_batch_size
+    )
+    eval_dataloader = DataLoader(
+        eval_dataset, collate_fn=default_data_collator, batch_size=args.per_device_eval_batch_size
+    )
+
+    # Optimizer
+    # Split weights in two groups, one with weight decay and the other not.
+    no_decay = ["bias", "LayerNorm.weight"]
+    optimizer_grouped_parameters = [
+        {
+            "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
+            "weight_decay": args.weight_decay,
+        },
+        {
+            "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
+            "weight_decay": 0.0,
+        },
+    ]
+    optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate)
+
+    # Prepare everything with our `accelerator`.
+    model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(
+        model, optimizer, train_dataloader, eval_dataloader
+    )
+
+    # Note -> the training dataloader needs to be prepared before we grab his length below (cause its length will be
+    # shorter in multiprocess)
+
+    # Scheduler and math around the number of training steps.
+    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
+    if args.max_train_steps is None:
+        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
+    else:
+        args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
+
+    lr_scheduler = get_scheduler(
+        name=args.lr_scheduler_type,
+        optimizer=optimizer,
+        num_warmup_steps=args.num_warmup_steps,
+        num_training_steps=args.max_train_steps,
+    )
+
+    # Train!
+    total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
+
+    logger.info("***** Running training *****")
+    logger.info(f"  Num examples = {len(train_dataset)}")
+    logger.info(f"  Num Epochs = {args.num_train_epochs}")
+    logger.info(f"  Instantaneous batch size per device = {args.per_device_train_batch_size}")
+    logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
+    logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
+    logger.info(f"  Total optimization steps = {args.max_train_steps}")
+    # Only show the progress bar once on each machine.
+    progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process)
+    completed_steps = 0
+
+    for epoch in range(args.num_train_epochs):
+        model.train()
+        for step, batch in enumerate(train_dataloader):
+            outputs = model(**batch)
+            loss = outputs.loss
+            loss = loss / args.gradient_accumulation_steps
+            accelerator.backward(loss)
+            if step % args.gradient_accumulation_steps == 0 or step == len(train_dataloader) - 1:
+                optimizer.step()
+                lr_scheduler.step()
+                optimizer.zero_grad()
+                progress_bar.update(1)
+                completed_steps += 1
+
+            if completed_steps >= args.max_train_steps:
+                break
+
+        model.eval()
+        losses = []
+        for step, batch in enumerate(eval_dataloader):
+            with torch.no_grad():
+                outputs = model(**batch)
+
+            loss = outputs.loss
+            losses.append(accelerator.gather(loss.repeat(args.per_device_eval_batch_size)))
+
+        losses = torch.cat(losses)
+        losses = losses[: len(eval_dataset)]
+        perplexity = math.exp(torch.mean(losses))
+
+        logger.info(f"epoch {epoch}: perplexity: {perplexity}")
+
+    if args.output_dir is not None:
+        accelerator.wait_for_everyone()
+        unwrapped_model = accelerator.unwrap_model(model)
+        unwrapped_model.save_pretrained(args.output_dir, save_function=accelerator.save)
+
+
+if __name__ == "__main__":
+    main()

examples/language-modeling/run_mlm.py

@@ -1,3 +1,4 @@
+#!/usr/bin/env python
 # coding=utf-8
 # Copyright 2020 The HuggingFace Team All rights reserved.
 #
@@ -42,9 +43,13 @@ from transformers import (
     TrainingArguments,
     set_seed,
 )
-from transformers.trainer_utils import is_main_process
+from transformers.trainer_utils import get_last_checkpoint, is_main_process
+from transformers.utils import check_min_version
 
 
+# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
+check_min_version("4.6.0.dev0")
+
 logger = logging.getLogger(__name__)
 MODEL_CONFIG_CLASSES = list(MODEL_FOR_MASKED_LM_MAPPING.keys())
 MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@@ -145,6 +150,20 @@ class DataTrainingArguments:
             "If False, will pad the samples dynamically when batching to the maximum length in the batch."
         },
     )
+    max_train_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "For debugging purposes or quicker training, truncate the number of training examples to this "
+            "value if set."
+        },
+    )
+    max_val_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "For debugging purposes or quicker training, truncate the number of validation examples to this "
+            "value if set."
+        },
+    )
 
     def __post_init__(self):
         if self.dataset_name is None and self.train_file is None and self.validation_file is None:
@@ -171,23 +190,28 @@ def main():
     else:
         model_args, data_args, training_args = parser.parse_args_into_dataclasses()
 
-    if (
-        os.path.exists(training_args.output_dir)
-        and os.listdir(training_args.output_dir)
-        and training_args.do_train
-        and not training_args.overwrite_output_dir
-    ):
-        raise ValueError(
-            f"Output directory ({training_args.output_dir}) already exists and is not empty."
-            "Use --overwrite_output_dir to overcome."
-        )
+    # Detecting last checkpoint.
+    last_checkpoint = None
+    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
+        last_checkpoint = get_last_checkpoint(training_args.output_dir)
+        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
+            raise ValueError(
+                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
+                "Use --overwrite_output_dir to overcome."
+            )
+        elif last_checkpoint is not None:
+            logger.info(
+                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
+                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
+            )
 
     # Setup logging
     logging.basicConfig(
         format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
         datefmt="%m/%d/%Y %H:%M:%S",
-        level=logging.INFO if is_main_process(training_args.local_rank) else logging.WARN,
+        handlers=[logging.StreamHandler(sys.stdout)],
     )
+    logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN)
 
     # Log on each process the small summary:
     logger.warning(
@@ -199,7 +223,7 @@ def main():
         transformers.utils.logging.set_verbosity_info()
         transformers.utils.logging.enable_default_handler()
         transformers.utils.logging.enable_explicit_format()
-    logger.info("Training/evaluation parameters %s", training_args)
+    logger.info(f"Training/evaluation parameters {training_args}")
 
     # Set seed before initializing model.
     set_seed(training_args.seed)
@@ -215,17 +239,19 @@ def main():
     # download the dataset.
     if data_args.dataset_name is not None:
         # Downloading and loading a dataset from the hub.
-        datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
+        datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir)
         if "validation" not in datasets.keys():
             datasets["validation"] = load_dataset(
                 data_args.dataset_name,
                 data_args.dataset_config_name,
                 split=f"train[:{data_args.validation_split_percentage}%]",
+                cache_dir=model_args.cache_dir,
             )
             datasets["train"] = load_dataset(
                 data_args.dataset_name,
                 data_args.dataset_config_name,
                 split=f"train[{data_args.validation_split_percentage}%:]",
+                cache_dir=model_args.cache_dir,
             )
     else:
         data_files = {}
@@ -236,7 +262,7 @@ def main():
         extension = data_args.train_file.split(".")[-1]
         if extension == "txt":
             extension = "text"
-        datasets = load_dataset(extension, data_files=data_files)
+        datasets = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)
     # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
     # https://huggingface.co/docs/datasets/loading_datasets.html.
 
@@ -297,6 +323,22 @@ def main():
         column_names = datasets["validation"].column_names
     text_column_name = "text" if "text" in column_names else column_names[0]
 
+    if data_args.max_seq_length is None:
+        max_seq_length = tokenizer.model_max_length
+        if max_seq_length > 1024:
+            logger.warning(
+                f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). "
+                "Picking 1024 instead. You can change that default value by passing --max_seq_length xxx."
+            )
+            max_seq_length = 1024
+    else:
+        if data_args.max_seq_length > tokenizer.model_max_length:
+            logger.warning(
+                f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"
+                f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
+            )
+        max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)
+
     if data_args.line_by_line:
         # When using line_by_line, we just tokenize each nonempty line.
         padding = "max_length" if data_args.pad_to_max_length else False
@@ -308,7 +350,7 @@ def main():
                 examples["text"],
                 padding=padding,
                 truncation=True,
-                max_length=data_args.max_seq_length,
+                max_length=max_seq_length,
                 # We use this option because DataCollatorForLanguageModeling (see below) is more efficient when it
                 # receives the `special_tokens_mask`.
                 return_special_tokens_mask=True,
@@ -336,16 +378,6 @@ def main():
             load_from_cache_file=not data_args.overwrite_cache,
         )
 
-        if data_args.max_seq_length is None:
-            max_seq_length = tokenizer.model_max_length
-        else:
-            if data_args.max_seq_length > tokenizer.model_max_length:
-                logger.warn(
-                    f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"
-                    f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
-                )
-            max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)
-
         # Main data processing function that will concatenate all texts from our dataset and generate chunks of
         # max_seq_length.
         def group_texts(examples):
@@ -368,6 +400,7 @@ def main():
         #
         # To speed up this part, we use multiprocessing. See the documentation of the map method for more information:
         # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map
+
         tokenized_datasets = tokenized_datasets.map(
             group_texts,
             batched=True,
@@ -375,60 +408,73 @@ def main():
             load_from_cache_file=not data_args.overwrite_cache,
         )
 
+    if training_args.do_train:
+        if "train" not in tokenized_datasets:
+            raise ValueError("--do_train requires a train dataset")
+        train_dataset = tokenized_datasets["train"]
+        if data_args.max_train_samples is not None:
+            train_dataset = train_dataset.select(range(data_args.max_train_samples))
+
+    if training_args.do_eval:
+        if "validation" not in tokenized_datasets:
+            raise ValueError("--do_eval requires a validation dataset")
+        eval_dataset = tokenized_datasets["validation"]
+        if data_args.max_val_samples is not None:
+            eval_dataset = eval_dataset.select(range(data_args.max_val_samples))
+
     # Data collator
     # This one will take care of randomly masking the tokens.
-    data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=data_args.mlm_probability)
+    pad_to_multiple_of_8 = data_args.line_by_line and training_args.fp16 and not data_args.pad_to_max_length
+    data_collator = DataCollatorForLanguageModeling(
+        tokenizer=tokenizer,
+        mlm_probability=data_args.mlm_probability,
+        pad_to_multiple_of=8 if pad_to_multiple_of_8 else None,
+    )
 
     # Initialize our Trainer
     trainer = Trainer(
         model=model,
         args=training_args,
-        train_dataset=tokenized_datasets["train"] if training_args.do_train else None,
-        eval_dataset=tokenized_datasets["validation"] if training_args.do_eval else None,
+        train_dataset=train_dataset if training_args.do_train else None,
+        eval_dataset=eval_dataset if training_args.do_eval else None,
         tokenizer=tokenizer,
         data_collator=data_collator,
     )
 
     # Training
     if training_args.do_train:
-        model_path = (
-            model_args.model_name_or_path
-            if (model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path))
-            else None
-        )
-        train_result = trainer.train(model_path=model_path)
+        if last_checkpoint is not None:
+            checkpoint = last_checkpoint
+        elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path):
+            checkpoint = model_args.model_name_or_path
+        else:
+            checkpoint = None
+        train_result = trainer.train(resume_from_checkpoint=checkpoint)
         trainer.save_model()  # Saves the tokenizer too for easy upload
+        metrics = train_result.metrics
 
-        output_train_file = os.path.join(training_args.output_dir, "train_results.txt")
-        if trainer.is_world_process_zero():
-            with open(output_train_file, "w") as writer:
-                logger.info("***** Train results *****")
-                for key, value in sorted(train_result.metrics.items()):
-                    logger.info(f"  {key} = {value}")
-                    writer.write(f"{key} = {value}\n")
+        max_train_samples = (
+            data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)
+        )
+        metrics["train_samples"] = min(max_train_samples, len(train_dataset))
 
-            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
-            trainer.state.save_to_json(os.path.join(training_args.output_dir, "trainer_state.json"))
+        trainer.log_metrics("train", metrics)
+        trainer.save_metrics("train", metrics)
+        trainer.save_state()
 
     # Evaluation
-    results = {}
     if training_args.do_eval:
         logger.info("*** Evaluate ***")
 
-        eval_output = trainer.evaluate()
+        metrics = trainer.evaluate()
 
-        perplexity = math.exp(eval_output["eval_loss"])
-        results["perplexity"] = perplexity
+        max_val_samples = data_args.max_val_samples if data_args.max_val_samples is not None else len(eval_dataset)
+        metrics["eval_samples"] = min(max_val_samples, len(eval_dataset))
+        perplexity = math.exp(metrics["eval_loss"])
+        metrics["perplexity"] = perplexity
 
-        output_eval_file = os.path.join(training_args.output_dir, "eval_results_mlm.txt")
-        if trainer.is_world_process_zero():
-            with open(output_eval_file, "w") as writer:
-                logger.info("***** Eval results *****")
-                for key, value in sorted(results.items()):
-                    logger.info(f"  {key} = {value}")
-                    writer.write(f"{key} = {value}\n")
-
-    return results
+        trainer.log_metrics("eval", metrics)
+        trainer.save_metrics("eval", metrics)
 
 
 def _mp_fn(index):

examples/language-modeling/run_mlm_flax.py

@@ -1,3 +1,4 @@
+#!/usr/bin/env python
 # coding=utf-8
 # Copyright 2020 The HuggingFace Team All rights reserved.
 #
@@ -306,7 +307,7 @@ def create_learning_rate_scheduler(
                 progress = jnp.maximum(0.0, (step - warmup_steps) / float(steps_per_cycle))
                 ret *= jnp.maximum(0.0, 0.5 * (1.0 + jnp.cos(jnp.pi * (progress % 1.0))))
             else:
-                raise ValueError("Unknown factor %s." % name)
+                raise ValueError(f"Unknown factor {name}.")
         return jnp.asarray(ret, dtype=jnp.float32)
 
     return step_fn
@@ -331,9 +332,7 @@ def accuracy(logits, targets, weights=None):
       Tuple of scalar loss and batch normalizing factor.
     """
     if logits.ndim != targets.ndim + 1:
-        raise ValueError(
-            "Incorrect shapes. Got shape %s logits and %s targets" % (str(logits.shape), str(targets.shape))
-        )
+        raise ValueError(f"Incorrect shapes. Got shape {logits.shape} logits and {targets.shape} targets")
 
     loss = jnp.equal(jnp.argmax(logits, axis=-1), targets)
     loss *= weights
@@ -352,9 +351,7 @@ def cross_entropy(logits, targets, weights=None, label_smoothing=0.0):
       Tuple of scalar loss and batch normalizing factor.
     """
     if logits.ndim != targets.ndim + 1:
-        raise ValueError(
-            "Incorrect shapes. Got shape %s logits and %s targets" % (str(logits.shape), str(targets.shape))
-        )
+        raise ValueError(f"Incorrect shapes. Got shape {logits.shape} logits and {targets.shape} targets")
 
     vocab_size = logits.shape[-1]
     confidence = 1.0 - label_smoothing
@@ -462,7 +459,7 @@ if __name__ == "__main__":
     )
 
     # Set the verbosity to info of the Transformers logger (on main process only):
-    logger.info("Training/evaluation parameters %s", training_args)
+    logger.info(f"Training/evaluation parameters {training_args}")
 
     # Set seed before initializing model.
     set_seed(training_args.seed)
@@ -478,17 +475,19 @@ if __name__ == "__main__":
     # download the dataset.
     if data_args.dataset_name is not None:
         # Downloading and loading a dataset from the hub.
-        datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
+        datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir)
         if "validation" not in datasets.keys():
             datasets["validation"] = load_dataset(
                 data_args.dataset_name,
                 data_args.dataset_config_name,
                 split=f"train[:{data_args.validation_split_percentage}%]",
+                cache_dir=model_args.cache_dir,
             )
             datasets["train"] = load_dataset(
                 data_args.dataset_name,
                 data_args.dataset_config_name,
                 split=f"train[{data_args.validation_split_percentage}%:]",
+                cache_dir=model_args.cache_dir,
             )
     else:
         data_files = {}
@@ -499,7 +498,7 @@ if __name__ == "__main__":
         extension = data_args.train_file.split(".")[-1]
         if extension == "txt":
             extension = "text"
-        datasets = load_dataset(extension, data_files=data_files)
+        datasets = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)
     # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
     # https://huggingface.co/docs/datasets/loading_datasets.html.
 

examples/language-modeling/run_mlm_no_trainer.py

@@ -0,0 +1,500 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Fine-tuning the library models for masked language modeling (BERT, ALBERT, RoBERTa...)
+on a text file or a dataset without using HuggingFace Trainer.
+
+Here is the full list of checkpoints on the hub that can be fine-tuned by this script:
+https://huggingface.co/models?filter=masked-lm
+"""
+# You can also adapt this script on your own mlm task. Pointers for this are left as comments.
+
+import argparse
+import logging
+import math
+import os
+import random
+
+import datasets
+import torch
+from datasets import load_dataset
+from torch.utils.data.dataloader import DataLoader
+from tqdm.auto import tqdm
+
+import transformers
+from accelerate import Accelerator
+from transformers import (
+    CONFIG_MAPPING,
+    MODEL_MAPPING,
+    AdamW,
+    AutoConfig,
+    AutoModelForMaskedLM,
+    AutoTokenizer,
+    DataCollatorForLanguageModeling,
+    SchedulerType,
+    get_scheduler,
+    set_seed,
+)
+
+
+logger = logging.getLogger(__name__)
+MODEL_CONFIG_CLASSES = list(MODEL_MAPPING.keys())
+MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Finetune a transformers model on a Masked Language Modeling task")
+    parser.add_argument(
+        "--dataset_name",
+        type=str,
+        default=None,
+        help="The name of the dataset to use (via the datasets library).",
+    )
+    parser.add_argument(
+        "--dataset_config_name",
+        type=str,
+        default=None,
+        help="The configuration name of the dataset to use (via the datasets library).",
+    )
+    parser.add_argument(
+        "--train_file", type=str, default=None, help="A csv or a json file containing the training data."
+    )
+    parser.add_argument(
+        "--validation_file", type=str, default=None, help="A csv or a json file containing the validation data."
+    )
+    parser.add_argument(
+        "--validation_split_percentage",
+        default=5,
+        help="The percentage of the train set used as validation set in case there's no validation split",
+    )
+    parser.add_argument(
+        "--pad_to_max_length",
+        action="store_true",
+        help="If passed, pad all samples to `max_length`. Otherwise, dynamic padding is used.",
+    )
+    parser.add_argument(
+        "--model_name_or_path",
+        type=str,
+        help="Path to pretrained model or model identifier from huggingface.co/models.",
+        required=True,
+    )
+    parser.add_argument(
+        "--config_name",
+        type=str,
+        default=None,
+        help="Pretrained config name or path if not the same as model_name",
+    )
+    parser.add_argument(
+        "--tokenizer_name",
+        type=str,
+        default=None,
+        help="Pretrained tokenizer name or path if not the same as model_name",
+    )
+    parser.add_argument(
+        "--use_slow_tokenizer",
+        action="store_true",
+        help="If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).",
+    )
+    parser.add_argument(
+        "--per_device_train_batch_size",
+        type=int,
+        default=8,
+        help="Batch size (per device) for the training dataloader.",
+    )
+    parser.add_argument(
+        "--per_device_eval_batch_size",
+        type=int,
+        default=8,
+        help="Batch size (per device) for the evaluation dataloader.",
+    )
+    parser.add_argument(
+        "--learning_rate",
+        type=float,
+        default=5e-5,
+        help="Initial learning rate (after the potential warmup period) to use.",
+    )
+    parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.")
+    parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.")
+    parser.add_argument(
+        "--max_train_steps",
+        type=int,
+        default=None,
+        help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
+    )
+    parser.add_argument(
+        "--gradient_accumulation_steps",
+        type=int,
+        default=1,
+        help="Number of updates steps to accumulate before performing a backward/update pass.",
+    )
+    parser.add_argument(
+        "--lr_scheduler_type",
+        type=SchedulerType,
+        default="linear",
+        help="The scheduler type to use.",
+        choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"],
+    )
+    parser.add_argument(
+        "--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler."
+    )
+    parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.")
+    parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
+    parser.add_argument(
+        "--model_type",
+        type=str,
+        default=None,
+        help="Model type to use if training from scratch.",
+        choices=MODEL_TYPES,
+    )
+    parser.add_argument(
+        "--max_seq_length",
+        type=int,
+        default=None,
+        help="The maximum total input sequence length after tokenization. Sequences longer than this will be truncated.",
+    )
+    parser.add_argument(
+        "--line_by_line",
+        type=bool,
+        default=False,
+        help="Whether distinct lines of text in the dataset are to be handled as distinct sequences.",
+    )
+    parser.add_argument(
+        "--preprocessing_num_workers",
+        type=int,
+        default=None,
+        help="The number of processes to use for the preprocessing.",
+    )
+    parser.add_argument(
+        "--overwrite_cache", type=bool, default=False, help="Overwrite the cached training and evaluation sets"
+    )
+    parser.add_argument(
+        "--mlm_probability", type=float, default=0.15, help="Ratio of tokens to mask for masked language modeling loss"
+    )
+
+    args = parser.parse_args()
+
+    # Sanity checks
+    if args.dataset_name is None and args.train_file is None and args.validation_file is None:
+        raise ValueError("Need either a dataset name or a training/validation file.")
+    else:
+        if args.train_file is not None:
+            extension = args.train_file.split(".")[-1]
+            assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, json or txt file."
+        if args.validation_file is not None:
+            extension = args.validation_file.split(".")[-1]
+            assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, json or txt file."
+
+    if args.output_dir is not None:
+        os.makedirs(args.output_dir, exist_ok=True)
+
+    return args
+
+
+def main():
+    args = parse_args()
+
+    # Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
+    accelerator = Accelerator()
+    # Make one log on every process with the configuration for debugging.
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        level=logging.INFO,
+    )
+    logger.info(accelerator.state)
+
+    # Setup logging, we only want one process per machine to log things on the screen.
+    # accelerator.is_local_main_process is only True for one process per machine.
+    logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
+    if accelerator.is_local_main_process:
+        datasets.utils.logging.set_verbosity_warning()
+        transformers.utils.logging.set_verbosity_info()
+    else:
+        datasets.utils.logging.set_verbosity_error()
+        transformers.utils.logging.set_verbosity_error()
+
+    # If passed along, set the training seed now.
+    if args.seed is not None:
+        set_seed(args.seed)
+
+    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
+    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
+    # (the dataset will be downloaded automatically from the datasets Hub).
+    #
+    # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
+    # 'text' is found. You can easily tweak this behavior (see below).
+    #
+    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
+    # download the dataset.
+    if args.dataset_name is not None:
+        # Downloading and loading a dataset from the hub.
+        raw_datasets = load_dataset(args.dataset_name, args.dataset_config_name)
+        if "validation" not in raw_datasets.keys():
+            raw_datasets["validation"] = load_dataset(
+                args.dataset_name,
+                args.dataset_config_name,
+                split=f"train[:{args.validation_split_percentage}%]",
+            )
+            raw_datasets["train"] = load_dataset(
+                args.dataset_name,
+                args.dataset_config_name,
+                split=f"train[{args.validation_split_percentage}%:]",
+            )
+    else:
+        data_files = {}
+        if args.train_file is not None:
+            data_files["train"] = args.train_file
+        if args.validation_file is not None:
+            data_files["validation"] = args.validation_file
+        extension = args.train_file.split(".")[-1]
+        if extension == "txt":
+            extension = "text"
+        raw_datasets = load_dataset(extension, data_files=data_files)
+    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
+    # https://huggingface.co/docs/datasets/loading_datasets.html.
+
+    # Load pretrained model and tokenizer
+    #
+    # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
+    # download model & vocab.
+    if args.config_name:
+        config = AutoConfig.from_pretrained(args.config_name)
+    elif args.model_name_or_path:
+        config = AutoConfig.from_pretrained(args.model_name_or_path)
+    else:
+        config = CONFIG_MAPPING[args.model_type]()
+        logger.warning("You are instantiating a new config instance from scratch.")
+
+    if args.tokenizer_name:
+        tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=not args.use_slow_tokenizer)
+    elif args.model_name_or_path:
+        tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, use_fast=not args.use_slow_tokenizer)
+    else:
+        raise ValueError(
+            "You are instantiating a new tokenizer from scratch. This is not supported by this script."
+            "You can do it from another script, save it, and load it from here, using --tokenizer_name."
+        )
+
+    if args.model_name_or_path:
+        model = AutoModelForMaskedLM.from_pretrained(
+            args.model_name_or_path,
+            from_tf=bool(".ckpt" in args.model_name_or_path),
+            config=config,
+        )
+    else:
+        logger.info("Training new model from scratch")
+        model = AutoModelForMaskedLM.from_config(config)
+
+    model.resize_token_embeddings(len(tokenizer))
+
+    # Preprocessing the datasets.
+    # First we tokenize all the texts.
+    column_names = raw_datasets["train"].column_names
+    text_column_name = "text" if "text" in column_names else column_names[0]
+
+    if args.max_seq_length is None:
+        max_seq_length = tokenizer.model_max_length
+        if max_seq_length > 1024:
+            logger.warning(
+                f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). "
+                "Picking 1024 instead. You can change that default value by passing --max_seq_length xxx."
+            )
+            max_seq_length = 1024
+    else:
+        if args.max_seq_length > tokenizer.model_max_length:
+            logger.warning(
+                f"The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the"
+                f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
+            )
+        max_seq_length = min(args.max_seq_length, tokenizer.model_max_length)
+
+    if args.line_by_line:
+        # When using line_by_line, we just tokenize each nonempty line.
+        padding = "max_length" if args.pad_to_max_length else False
+
+        def tokenize_function(examples):
+            # Remove empty lines
+            examples["text"] = [line for line in examples["text"] if len(line) > 0 and not line.isspace()]
+            return tokenizer(
+                examples["text"],
+                padding=padding,
+                truncation=True,
+                max_length=max_seq_length,
+                # We use this option because DataCollatorForLanguageModeling (see below) is more efficient when it
+                # receives the `special_tokens_mask`.
+                return_special_tokens_mask=True,
+            )
+
+        tokenized_datasets = raw_datasets.map(
+            tokenize_function,
+            batched=True,
+            num_proc=args.preprocessing_num_workers,
+            remove_columns=[text_column_name],
+            load_from_cache_file=not args.overwrite_cache,
+        )
+    else:
+        # Otherwise, we tokenize every text, then concatenate them together before splitting them in smaller parts.
+        # We use `return_special_tokens_mask=True` because DataCollatorForLanguageModeling (see below) is more
+        # efficient when it receives the `special_tokens_mask`.
+        def tokenize_function(examples):
+            return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
+
+        tokenized_datasets = raw_datasets.map(
+            tokenize_function,
+            batched=True,
+            num_proc=args.preprocessing_num_workers,
+            remove_columns=column_names,
+            load_from_cache_file=not args.overwrite_cache,
+        )
+
+        # Main data processing function that will concatenate all texts from our dataset and generate chunks of
+        # max_seq_length.
+        def group_texts(examples):
+            # Concatenate all texts.
+            concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
+            total_length = len(concatenated_examples[list(examples.keys())[0]])
+            # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
+            # customize this part to your needs.
+            total_length = (total_length // max_seq_length) * max_seq_length
+            # Split by chunks of max_len.
+            result = {
+                k: [t[i : i + max_seq_length] for i in range(0, total_length, max_seq_length)]
+                for k, t in concatenated_examples.items()
+            }
+            return result
+
+        # Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a
+        # remainder for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value
+        # might be slower to preprocess.
+        #
+        # To speed up this part, we use multiprocessing. See the documentation of the map method for more information:
+        # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map
+
+        tokenized_datasets = tokenized_datasets.map(
+            group_texts,
+            batched=True,
+            num_proc=args.preprocessing_num_workers,
+            load_from_cache_file=not args.overwrite_cache,
+        )
+
+    train_dataset = tokenized_datasets["train"]
+    eval_dataset = tokenized_datasets["validation"]
+
+    # Log a few random samples from the training set:
+    for index in random.sample(range(len(train_dataset)), 3):
+        logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
+
+    # Data collator
+    # This one will take care of randomly masking the tokens.
+    data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=args.mlm_probability)
+
+    # DataLoaders creation:
+    train_dataloader = DataLoader(
+        train_dataset, shuffle=True, collate_fn=data_collator, batch_size=args.per_device_train_batch_size
+    )
+    eval_dataloader = DataLoader(eval_dataset, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size)
+
+    # Optimizer
+    # Split weights in two groups, one with weight decay and the other not.
+    no_decay = ["bias", "LayerNorm.weight"]
+    optimizer_grouped_parameters = [
+        {
+            "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
+            "weight_decay": args.weight_decay,
+        },
+        {
+            "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
+            "weight_decay": 0.0,
+        },
+    ]
+    optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate)
+
+    # Prepare everything with our `accelerator`.
+    model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(
+        model, optimizer, train_dataloader, eval_dataloader
+    )
+
+    # Note -> the training dataloader needs to be prepared before we grab his length below (cause its length will be
+    # shorter in multiprocess)
+
+    # Scheduler and math around the number of training steps.
+    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
+    if args.max_train_steps is None:
+        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
+    else:
+        args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
+
+    lr_scheduler = get_scheduler(
+        name=args.lr_scheduler_type,
+        optimizer=optimizer,
+        num_warmup_steps=args.num_warmup_steps,
+        num_training_steps=args.max_train_steps,
+    )
+
+    # Train!
+    total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
+
+    logger.info("***** Running training *****")
+    logger.info(f"  Num examples = {len(train_dataset)}")
+    logger.info(f"  Num Epochs = {args.num_train_epochs}")
+    logger.info(f"  Instantaneous batch size per device = {args.per_device_train_batch_size}")
+    logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
+    logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
+    logger.info(f"  Total optimization steps = {args.max_train_steps}")
+    # Only show the progress bar once on each machine.
+    progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process)
+    completed_steps = 0
+
+    for epoch in range(args.num_train_epochs):
+        model.train()
+        for step, batch in enumerate(train_dataloader):
+            outputs = model(**batch)
+            loss = outputs.loss
+            loss = loss / args.gradient_accumulation_steps
+            accelerator.backward(loss)
+            if step % args.gradient_accumulation_steps == 0 or step == len(train_dataloader) - 1:
+                optimizer.step()
+                lr_scheduler.step()
+                optimizer.zero_grad()
+                progress_bar.update(1)
+                completed_steps += 1
+
+            if completed_steps >= args.max_train_steps:
+                break
+
+        model.eval()
+        losses = []
+        for step, batch in enumerate(eval_dataloader):
+            with torch.no_grad():
+                outputs = model(**batch)
+
+            loss = outputs.loss
+            losses.append(accelerator.gather(loss.repeat(args.per_device_eval_batch_size)))
+
+        losses = torch.cat(losses)
+        losses = losses[: len(eval_dataset)]
+        perplexity = math.exp(torch.mean(losses))
+
+        logger.info(f"epoch {epoch}: perplexity: {perplexity}")
+
+    if args.output_dir is not None:
+        accelerator.wait_for_everyone()
+        unwrapped_model = accelerator.unwrap_model(model)
+        unwrapped_model.save_pretrained(args.output_dir, save_function=accelerator.save)
+
+
+if __name__ == "__main__":
+    main()

examples/language-modeling/run_plm.py

@@ -1,3 +1,4 @@
+#!/usr/bin/env python
 # coding=utf-8
 # Copyright 2020 The HuggingFace Team All rights reserved.
 #
@@ -38,9 +39,13 @@ from transformers import (
     XLNetLMHeadModel,
     set_seed,
 )
-from transformers.trainer_utils import is_main_process
+from transformers.trainer_utils import get_last_checkpoint, is_main_process
+from transformers.utils import check_min_version
 
 
+# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
+check_min_version("4.6.0.dev0")
+
 logger = logging.getLogger(__name__)
 
 
@@ -142,6 +147,20 @@ class DataTrainingArguments:
             "If False, will pad the samples dynamically when batching to the maximum length in the batch."
         },
     )
+    max_train_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "For debugging purposes or quicker training, truncate the number of training examples to this "
+            "value if set."
+        },
+    )
+    max_val_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "For debugging purposes or quicker training, truncate the number of validation examples to this "
+            "value if set."
+        },
+    )
 
     def __post_init__(self):
         if self.dataset_name is None and self.train_file is None and self.validation_file is None:
@@ -168,23 +187,28 @@ def main():
     else:
         model_args, data_args, training_args = parser.parse_args_into_dataclasses()
 
-    if (
-        os.path.exists(training_args.output_dir)
-        and os.listdir(training_args.output_dir)
-        and training_args.do_train
-        and not training_args.overwrite_output_dir
-    ):
-        raise ValueError(
-            f"Output directory ({training_args.output_dir}) already exists and is not empty."
-            "Use --overwrite_output_dir to overcome."
-        )
+    # Detecting last checkpoint.
+    last_checkpoint = None
+    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
+        last_checkpoint = get_last_checkpoint(training_args.output_dir)
+        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
+            raise ValueError(
+                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
+                "Use --overwrite_output_dir to overcome."
+            )
+        elif last_checkpoint is not None:
+            logger.info(
+                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
+                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
+            )
 
     # Setup logging
     logging.basicConfig(
         format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
         datefmt="%m/%d/%Y %H:%M:%S",
-        level=logging.INFO if is_main_process(training_args.local_rank) else logging.WARN,
+        handlers=[logging.StreamHandler(sys.stdout)],
     )
+    logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN)
 
     # Log on each process the small summary:
     logger.warning(
@@ -196,7 +220,7 @@ def main():
         transformers.utils.logging.set_verbosity_info()
         transformers.utils.logging.enable_default_handler()
         transformers.utils.logging.enable_explicit_format()
-    logger.info("Training/evaluation parameters %s", training_args)
+    logger.info(f"Training/evaluation parameters {training_args}")
 
     # Set seed before initializing model.
     set_seed(training_args.seed)
@@ -212,17 +236,19 @@ def main():
     # download the dataset.
     if data_args.dataset_name is not None:
         # Downloading and loading a dataset from the hub.
-        datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
+        datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir)
         if "validation" not in datasets.keys():
             datasets["validation"] = load_dataset(
                 data_args.dataset_name,
                 data_args.dataset_config_name,
                 split=f"train[:{data_args.validation_split_percentage}%]",
+                cache_dir=model_args.cache_dir,
             )
             datasets["train"] = load_dataset(
                 data_args.dataset_name,
                 data_args.dataset_config_name,
                 split=f"train[{data_args.validation_split_percentage}%:]",
+                cache_dir=model_args.cache_dir,
             )
     else:
         data_files = {}
@@ -233,7 +259,7 @@ def main():
         extension = data_args.train_file.split(".")[-1]
         if extension == "txt":
             extension = "text"
-        datasets = load_dataset(extension, data_files=data_files)
+        datasets = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)
     # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
     # https://huggingface.co/docs/datasets/loading_datasets.html.
 
@@ -294,6 +320,13 @@ def main():
         column_names = datasets["validation"].column_names
     text_column_name = "text" if "text" in column_names else column_names[0]
 
+    if data_args.max_seq_length > tokenizer.model_max_length:
+        logger.warning(
+            f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"
+            f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
+        )
+    max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)
+
     if data_args.line_by_line:
         # When using line_by_line, we just tokenize each nonempty line.
         padding = "max_length" if data_args.pad_to_max_length else False
@@ -301,7 +334,7 @@ def main():
         def tokenize_function(examples):
             # Remove empty lines
             examples["text"] = [line for line in examples["text"] if len(line) > 0 and not line.isspace()]
-            return tokenizer(examples["text"], padding=padding, truncation=True, max_length=data_args.max_seq_length)
+            return tokenizer(examples["text"], padding=padding, truncation=True, max_length=max_seq_length)
 
         tokenized_datasets = datasets.map(
             tokenize_function,
@@ -323,13 +356,6 @@ def main():
             load_from_cache_file=not data_args.overwrite_cache,
         )
 
-        if data_args.max_seq_length > tokenizer.model_max_length:
-            logger.warn(
-                f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"
-                f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
-            )
-        max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)
-
         # Main data processing function that will concatenate all texts from our dataset and generate chunks of
         # max_seq_length.
         def group_texts(examples):
@@ -352,6 +378,7 @@ def main():
         #
         # To speed up this part, we use multiprocessing. See the documentation of the map method for more information:
         # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map
+
         tokenized_datasets = tokenized_datasets.map(
             group_texts,
             batched=True,
@@ -359,6 +386,20 @@ def main():
             load_from_cache_file=not data_args.overwrite_cache,
         )
 
+    if training_args.do_train:
+        if "train" not in tokenized_datasets:
+            raise ValueError("--do_train requires a train dataset")
+        train_dataset = tokenized_datasets["train"]
+        if data_args.max_train_samples is not None:
+            train_dataset = train_dataset.select(range(data_args.max_train_samples))
+
+    if training_args.do_eval:
+        if "validation" not in tokenized_datasets:
+            raise ValueError("--do_eval requires a validation dataset")
+        eval_dataset = tokenized_datasets["validation"]
+        if data_args.max_val_samples is not None:
+            eval_dataset = eval_dataset.select(range(data_args.max_val_samples))
+
     # Data collator
     data_collator = DataCollatorForPermutationLanguageModeling(
         tokenizer=tokenizer,
@@ -370,52 +411,46 @@ def main():
     trainer = Trainer(
         model=model,
         args=training_args,
-        train_dataset=tokenized_datasets["train"] if training_args.do_train else None,
-        eval_dataset=tokenized_datasets["validation"] if training_args.do_eval else None,
+        train_dataset=train_dataset if training_args.do_train else None,
+        eval_dataset=eval_dataset if training_args.do_eval else None,
         tokenizer=tokenizer,
         data_collator=data_collator,
     )
 
     # Training
     if training_args.do_train:
-        model_path = (
-            model_args.model_name_or_path
-            if (model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path))
-            else None
-        )
-        train_result = trainer.train(model_path=model_path)
+        if last_checkpoint is not None:
+            checkpoint = last_checkpoint
+        elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path):
+            checkpoint = model_args.model_name_or_path
+        else:
+            checkpoint = None
+        train_result = trainer.train(resume_from_checkpoint=checkpoint)
         trainer.save_model()  # Saves the tokenizer too for easy upload
+        metrics = train_result.metrics
 
-        output_train_file = os.path.join(training_args.output_dir, "train_results.txt")
-        if trainer.is_world_process_zero():
-            with open(output_train_file, "w") as writer:
-                logger.info("***** Train results *****")
-                for key, value in sorted(train_result.metrics.items()):
-                    logger.info(f"  {key} = {value}")
-                    writer.write(f"{key} = {value}\n")
+        max_train_samples = (
+            data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)
+        )
+        metrics["train_samples"] = min(max_train_samples, len(train_dataset))
 
-            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
-            trainer.state.save_to_json(os.path.join(training_args.output_dir, "trainer_state.json"))
+        trainer.log_metrics("train", metrics)
+        trainer.save_metrics("train", metrics)
+        trainer.save_state()
 
     # Evaluation
-    results = {}
     if training_args.do_eval:
         logger.info("*** Evaluate ***")
 
-        eval_output = trainer.evaluate()
+        metrics = trainer.evaluate()
 
-        perplexity = math.exp(eval_output["eval_loss"])
-        results["perplexity"] = perplexity
+        max_val_samples = data_args.max_val_samples if data_args.max_val_samples is not None else len(eval_dataset)
+        metrics["eval_samples"] = min(max_val_samples, len(eval_dataset))
+        perplexity = math.exp(metrics["eval_loss"])
+        metrics["perplexity"] = perplexity
 
-        output_eval_file = os.path.join(training_args.output_dir, "eval_results_plm.txt")
-        if trainer.is_world_process_zero():
-            with open(output_eval_file, "w") as writer:
-                logger.info("***** Eval results *****")
-                for key, value in sorted(results.items()):
-                    logger.info(f"  {key} = {value}")
-                    writer.write(f"{key} = {value}\n")
-
-    return results
+        trainer.log_metrics("eval", metrics)
+        trainer.save_metrics("eval", metrics)
 
 
 def _mp_fn(index):

examples/legacy/multiple_choice/run_multiple_choice.py

@@ -28,6 +28,7 @@ from transformers import (
     AutoConfig,
     AutoModelForMultipleChoice,
     AutoTokenizer,
+    DataCollatorWithPadding,
     EvalPrediction,
     HfArgumentParser,
     Trainer,
@@ -188,6 +189,9 @@ def main():
         preds = np.argmax(p.predictions, axis=1)
         return {"acc": simple_accuracy(preds, p.label_ids)}
 
+    # Data collator
+    data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8) if training_args.fp16 else None
+
     # Initialize our Trainer
     trainer = Trainer(
         model=model,
@@ -195,6 +199,7 @@ def main():
         train_dataset=train_dataset,
         eval_dataset=eval_dataset,
         compute_metrics=compute_metrics,
+        data_collator=data_collator,
     )
 
     # Training

examples/legacy/question-answering/run_squad.py

@@ -436,7 +436,7 @@ def load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=Fal
                 raise ImportError("If not data_dir is specified, tensorflow_datasets needs to be installed.")
 
             if args.version_2_with_negative:
-                logger.warn("tensorflow_datasets does not handle version 2 of SQuAD.")
+                logger.warning("tensorflow_datasets does not handle version 2 of SQuAD.")
 
             tfds_examples = tfds.load("squad")
             examples = SquadV1Processor().get_examples_from_dataset(tfds_examples, evaluate=evaluate)

examples/legacy/question-answering/run_squad_trainer.py

@@ -23,7 +23,14 @@ from dataclasses import dataclass, field
 from typing import Optional
 
 import transformers
-from transformers import AutoConfig, AutoModelForQuestionAnswering, AutoTokenizer, HfArgumentParser, SquadDataset
+from transformers import (
+    AutoConfig,
+    AutoModelForQuestionAnswering,
+    AutoTokenizer,
+    DataCollatorWithPadding,
+    HfArgumentParser,
+    SquadDataset,
+)
 from transformers import SquadDataTrainingArguments as DataTrainingArguments
 from transformers import Trainer, TrainingArguments
 from transformers.trainer_utils import is_main_process
@@ -145,12 +152,16 @@ def main():
         else None
     )
 
+    # Data collator
+    data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8) if training_args.fp16 else None
+
     # Initialize our Trainer
     trainer = Trainer(
         model=model,
         args=training_args,
         train_dataset=train_dataset,
         eval_dataset=eval_dataset,
+        data_collator=data_collator,
     )
 
     # Training

examples/legacy/run_camembert.py

@@ -1,3 +1,4 @@
+#!/usr/bin/env python
 import torch
 
 from transformers import CamembertForMaskedLM, CamembertTokenizer