Fix CI after killing archive maps (#4724)

* 🐛 Fix model ids for BART and Flaubert

.circleci/config.yml

@@ -1,112 +1,110 @@
 version: 2
 jobs:
-    build_py3_torch_and_tf:
+    run_tests_torch_and_tf:
         working_directory: ~/transformers
         docker:
-            - image: circleci/python:3.5
+            - image: circleci/python:3.6
+        environment:
+            OMP_NUM_THREADS: 1
         resource_class: xlarge
         parallelism: 1
         steps:
             - checkout
-            - run: sudo pip install torch
-            - run: sudo pip install tensorflow
-            - run: sudo pip install --progress-bar off .
-            - run: sudo pip install pytest codecov pytest-cov
-            - run: sudo pip install tensorboardX scikit-learn
-            - run: python -m pytest -sv ./transformers/tests/ --cov
+            - run: sudo pip install .[sklearn,tf-cpu,torch,testing]
+            - run: sudo pip install codecov pytest-cov
+            - run: python -m pytest -n 8 --dist=loadfile -s -v ./tests/ --cov
             - run: codecov
-    build_py3_torch:
+
+    run_tests_torch:
         working_directory: ~/transformers
         docker:
-            - image: circleci/python:3.5
+            - image: circleci/python:3.7
+        environment:
+            OMP_NUM_THREADS: 1
         resource_class: xlarge
         parallelism: 1
         steps:
             - checkout
-            - run: sudo pip install torch
-            - run: sudo pip install --progress-bar off .
-            - run: sudo pip install pytest codecov pytest-cov
-            - run: sudo pip install tensorboardX scikit-learn
-            - run: python -m pytest -sv ./transformers/tests/ --cov
-            - run: python -m pytest -sv ./examples/
+            - run: sudo pip install .[sklearn,torch,testing]
+            - run: sudo pip install codecov pytest-cov
+            - run: python -m pytest -n 8 --dist=loadfile -s -v ./tests/ --cov
             - run: codecov
-    build_py3_tf:
+    run_tests_tf:
         working_directory: ~/transformers
         docker:
-            - image: circleci/python:3.5
+            - image: circleci/python:3.7
+        environment:
+            OMP_NUM_THREADS: 1
         resource_class: xlarge
         parallelism: 1
         steps:
             - checkout
-            - run: sudo pip install tensorflow
-            - run: sudo pip install --progress-bar off .
-            - run: sudo pip install pytest codecov pytest-cov
-            - run: sudo pip install tensorboardX scikit-learn
-            - run: python -m pytest -sv ./transformers/tests/ --cov
+            - run: sudo pip install .[sklearn,tf-cpu,testing]
+            - run: sudo pip install codecov pytest-cov
+            - run: python -m pytest -n 8 --dist=loadfile -s -v ./tests/ --cov
             - run: codecov
-    build_py2_torch:
+    run_tests_custom_tokenizers:
         working_directory: ~/transformers
-        resource_class: large
+        docker:
+            - image: circleci/python:3.6
+        environment:
+            RUN_CUSTOM_TOKENIZERS: yes
+        steps:
+            - checkout
+            - run: sudo pip install .[mecab,testing]
+            - run: python -m pytest -sv ./tests/test_tokenization_bert_japanese.py
+    run_examples_torch:
+        working_directory: ~/transformers
+        docker:
+            - image: circleci/python:3.6
+        environment:
+            OMP_NUM_THREADS: 1
+        resource_class: xlarge
         parallelism: 1
-        docker:
-            - image: circleci/python:2.7
         steps:
             - checkout
-            - run: sudo pip install torch
-            - run: sudo pip install --progress-bar off .
-            - run: sudo pip install pytest codecov pytest-cov
-            - run: python -m pytest -sv ./transformers/tests/ --cov
-            - run: codecov
-    build_py2_tf:
-        working_directory: ~/transformers
-        resource_class: large
-        parallelism: 1
-        docker:
-            - image: circleci/python:2.7
-        steps:
-            - checkout
-            - run: sudo pip install tensorflow
-            - run: sudo pip install --progress-bar off .
-            - run: sudo pip install pytest codecov pytest-cov
-            - run: python -m pytest -sv ./transformers/tests/ --cov
-            - run: codecov
-    build_py3_custom_tokenizers:
+            - run: sudo pip install .[sklearn,torch,testing]
+            - run: sudo pip install -r examples/requirements.txt
+            - run: python -m pytest -n 8 --dist=loadfile -s -v ./examples/
+    build_doc:
         working_directory: ~/transformers
         docker:
-            - image: circleci/python:3.5
+            - image: circleci/python:3.6
         steps:
             - checkout
-            - run: sudo pip install --progress-bar off .
-            - run: sudo pip install pytest
-            - run: sudo pip install mecab-python3
-            - run: RUN_CUSTOM_TOKENIZERS=1 python -m pytest -sv ./transformers/tests/tokenization_bert_japanese_test.py
-    build_py2_custom_tokenizers:
-        working_directory: ~/transformers
-        docker:
-            - image: circleci/python:2.7
-        steps:
-            - checkout
-            - run: sudo pip install --progress-bar off .
-            - run: sudo pip install pytest
-            - run: sudo apt-get -y install libmecab-dev mecab mecab-ipadic-utf8 swig
-            - run: sudo pip install mecab-python
-            - run: RUN_CUSTOM_TOKENIZERS=1 python -m pytest -sv ./transformers/tests/tokenization_bert_japanese_test.py
+            - run: sudo pip install .[tf,torch,docs]
+            - run: cd docs && make html
+            - store_artifacts:
+                path: ./docs/_build
     deploy_doc:
         working_directory: ~/transformers
         docker:
-            - image: circleci/python:3.5
+            - image: circleci/python:3.6
         steps:
             - add_ssh_keys:
-                  fingerprints:
-                      - "5b:7a:95:18:07:8c:aa:76:4c:60:35:88:ad:60:56:71"
+                fingerprints:
+                    - "5b:7a:95:18:07:8c:aa:76:4c:60:35:88:ad:60:56:71"
             - checkout
-            - run: sudo pip install --progress-bar off -r docs/requirements.txt
-            - run: sudo pip install --progress-bar off -r requirements.txt
+            - run: sudo pip install .[tf,torch,docs]
             - run: ./.circleci/deploy.sh
-    repository_consistency:
+    check_code_quality:
         working_directory: ~/transformers
         docker:
-            - image: circleci/python:3.5
+            - image: circleci/python:3.6
+        resource_class: medium
+        parallelism: 1
+        steps:
+            - checkout
+            # we need a version of isort with https://github.com/timothycrosley/isort/pull/1000
+            - run: sudo pip install git+git://github.com/timothycrosley/isort.git@e63ae06ec7d70b06df9e528357650281a3d3ec22#egg=isort
+            - run: sudo pip install .[tf,torch,quality]
+            - run: black --check --line-length 119 --target-version py35 examples templates tests src utils
+            - run: isort --check-only --recursive examples templates tests src utils
+            - run: flake8 examples templates tests src utils
+    check_repository_consistency:
+        working_directory: ~/transformers
+        docker:
+            - image: circleci/python:3.6
         resource_class: small
         parallelism: 1
         steps:
@@ -122,12 +120,12 @@ workflows:
     version: 2
     build_and_test:
         jobs:
-            - repository_consistency
-            - build_py3_custom_tokenizers
-            - build_py2_custom_tokenizers
-            - build_py3_torch_and_tf
-            - build_py3_torch
-            - build_py3_tf
-            - build_py2_torch
-            - build_py2_tf
+            - check_code_quality
+            - check_repository_consistency
+            - run_examples_torch
+            - run_tests_custom_tokenizers
+            - run_tests_torch_and_tf
+            - run_tests_torch
+            - run_tests_tf
+            - build_doc
             - deploy_doc: *workflow_filters

.circleci/deploy.sh

@@ -3,7 +3,7 @@ cd docs
 function deploy_doc(){
 	echo "Creating doc at commit $1 and pushing to folder $2"
 	git checkout $1
-	if [ ! -z "$2" ] 
+	if [ ! -z "$2" ]
 	then
 		if [ -d "$dir/$2" ]; then
 			echo "Directory" $2 "already exists"
@@ -17,10 +17,13 @@ function deploy_doc(){
 	fi
 }
 
-deploy_doc "master" 
+deploy_doc "master"
 deploy_doc "b33a385" v1.0.0
 deploy_doc "fe02e45" v1.1.0
 deploy_doc "89fd345" v1.2.0
 deploy_doc "fc9faa8" v2.0.0
 deploy_doc "3ddce1d" v2.1.1
 deploy_doc "3616209" v2.2.0
+deploy_doc "d0f8b9a" v2.3.0
+deploy_doc "6664ea9" v2.4.0
+deploy_doc "fb560dc" v2.5.0

.github/ISSUE_TEMPLATE/---new-benchmark.md

@@ -1,17 +1,17 @@
 ---
-name: "\U0001F5A5 New Benchmark"
-about: You benchmark a part of this library and would like to share your results
+name: "\U0001F5A5 New benchmark"
+about: Benchmark a part of this library and share your results
 title: "[Benchmark]"
 labels: ''
 assignees: ''
 
 ---
 
-# Benchmarking Transformers
+# 🖥 Benchmarking `transformers`
 
 ## Benchmark
 
-Which part of Transformers did you benchmark?
+Which part of `transformers` did you benchmark?
 
 ## Set-up
 

.github/ISSUE_TEMPLATE/--new-model-addition.md

@@ -1,24 +1,20 @@
 ---
-name: "\U0001F31FNew model addition"
+name: "\U0001F31F New model addition"
 about: Submit a proposal/request to implement a new Transformer-based model
 title: ''
-labels: ''
+labels: New model
 assignees: ''
 
 ---
 
-# 🌟New model addition
+# 🌟 New model addition
 
 ## Model description
 
 <!-- Important information -->
 
-## Open Source status
+## Open source status
 
 * [ ] the model implementation is available: (give details)
 * [ ] the model weights are available: (give details)
-* [ ] who are the authors: (mention them)
-
-## Additional context
-
-<!-- Add any other context about the problem here. -->
+* [ ] who are the authors: (mention them, if possible by @gh-username)

.github/ISSUE_TEMPLATE/bug-report.md

@@ -1,29 +1,29 @@
 ---
 name: "\U0001F41B Bug Report"
-about: Submit a bug report to help us improve PyTorch Transformers
+about: Submit a bug report to help us improve transformers
 title: ''
 labels: ''
 assignees: ''
 
 ---
 
-## 🐛 Bug
+# 🐛 Bug
 
-<!-- Important information -->
+## Information
 
-Model I am using (Bert, XLNet....):
+Model I am using (Bert, XLNet ...):
 
-Language I am using the model on (English, Chinese....):
+Language I am using the model on (English, Chinese ...):
 
-The problem arise when using:
-* [ ] the official example scripts: (give details)
-* [ ] my own modified scripts: (give details)
+The problem arises when using:
+* [ ] the official example scripts: (give details below)
+* [ ] my own modified scripts: (give details below)
 
 The tasks I am working on is:
 * [ ] an official GLUE/SQUaD task: (give the name)
-* [ ] my own task or dataset: (give details)
+* [ ] my own task or dataset: (give details below)
 
-## To Reproduce
+## To reproduce
 
 Steps to reproduce the behavior:
 
@@ -31,22 +31,22 @@ Steps to reproduce the behavior:
 2.
 3.
 
-<!-- If you have a code sample, error messages, stack traces, please provide it here as well. -->
+<!-- If you have code snippets, error messages, stack traces please provide them here as well.
+     Important! Use code tags to correctly format your code. See https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting
+     Do not use screenshots, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.-->
 
 ## Expected behavior
 
-<!-- A clear and concise description of what you expected to happen. -->
+<!-- A clear and concise description of what you would expect to happen. -->
 
-## Environment
-
-* OS:
-* Python version:
-* PyTorch version:
-* PyTorch Transformers version (or branch):
-* Using GPU ?
-* Distributed of parallel setup ?
-* Any other relevant information:
-
-## Additional context
-
-<!-- Add any other context about the problem here. -->
+## Environment info
+<!-- You can run the command `transformers-cli env` and copy-and-paste its output below.
+     Don't forget to fill out the missing fields in that output! -->
+     
+- `transformers` version:
+- Platform:
+- Python version:
+- PyTorch version (GPU?):
+- Tensorflow version (GPU?):
+- Using GPU in script?:
+- Using distributed or parallel set-up in script?:

.github/ISSUE_TEMPLATE/feature-request.md

@@ -1,20 +1,25 @@
 ---
-name: "\U0001F680 Feature Request"
-about: Submit a proposal/request for a new PyTorch Transformers feature
+name: "\U0001F680 Feature request"
+about: Submit a proposal/request for a new transformers feature
 title: ''
 labels: ''
 assignees: ''
 
 ---
 
-## 🚀 Feature
+# 🚀 Feature request
 
-<!-- A clear and concise description of the feature proposal. Please provide a link to the paper and code in case they exist. -->
+<!-- A clear and concise description of the feature proposal.
+     Please provide a link to the paper and code in case they exist. -->
 
 ## Motivation
 
-<!-- Please outline the motivation for the proposal. Is your feature request related to a problem? e.g., I'm always frustrated when [...]. If this is related to another GitHub issue, please link here too. -->
+<!-- Please outline the motivation for the proposal. Is your feature request
+     related to a problem? e.g., I'm always frustrated when [...]. If this is related
+     to another GitHub issue, please link here too. -->
 
-## Additional context
+## Your contribution
 
-<!-- Add any other context or screenshots about the feature request here. -->
+<!-- Is there any way that you could help, e.g. by submitting a PR?
+     Make sure to read the CONTRIBUTING.MD readme:
+     https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md -->

.github/ISSUE_TEMPLATE/migration.md

@@ -1,47 +1,58 @@
 ---
-name: "\U0001F4DA Migration from PyTorch-pretrained-Bert"
-about: Report a problem when migrating from PyTorch-pretrained-Bert to Transformers
+name: "\U0001F4DA Migration from pytorch-pretrained-bert or pytorch-transformers"
+about: Report a problem when migrating from pytorch-pretrained-bert or pytorch-transformers
+  to transformers
 title: ''
-labels: ''
+labels: Migration
 assignees: ''
 
 ---
 
-## 📚 Migration
+# 📚 Migration
+
+## Information
 
 <!-- Important information -->
 
-Model I am using (Bert, XLNet....):
+Model I am using (Bert, XLNet ...):
 
-Language I am using the model on (English, Chinese....):
+Language I am using the model on (English, Chinese ...):
 
-The problem arise when using:
-* [ ] the official example scripts: (give details)
-* [ ] my own modified scripts: (give details)
+The problem arises when using:
+* [ ] the official example scripts: (give details below)
+* [ ] my own modified scripts: (give details below)
 
 The tasks I am working on is:
 * [ ] an official GLUE/SQUaD task: (give the name)
-* [ ] my own task or dataset: (give details)
+* [ ] my own task or dataset: (give details below)
 
-Details of the issue:
+## Details
 
-<!-- A clear and concise description of the migration issue. If you have code snippets, please provide it here as well. -->
+<!-- A clear and concise description of the migration issue.
+    If you have code snippets, please provide it here as well.
+    Important! Use code tags to correctly format your code. See https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting
+    Do not use screenshots, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.
+    -->
 
-## Environment
+## Environment info
+<!-- You can run the command `python transformers-cli env` and copy-and-paste its output below.
+     Don't forget to fill out the missing fields in that output! -->
+ 
+- `transformers` version:
+- Platform:
+- Python version:
+- PyTorch version (GPU?):
+- Tensorflow version (GPU?):
+- Using GPU in script?:
+- Using distributed or parallel set-up in script?:
+
+<!-- IMPORTANT: which version of the former library do you use? -->
+* `pytorch-transformers` or `pytorch-pretrained-bert` version (or branch):
 
-* OS:
-* Python version:
-* PyTorch version:
-* PyTorch Transformers version (or branch):
-* Using GPU ?
-* Distributed of parallel setup ?
-* Any other relevant information:
 
 ## Checklist
 
 - [ ] I have read the migration guide in the readme.
+ ([pytorch-transformers](https://github.com/huggingface/transformers#migrating-from-pytorch-transformers-to-transformers);
+  [pytorch-pretrained-bert](https://github.com/huggingface/transformers#migrating-from-pytorch-pretrained-bert-to-transformers))
 - [ ] I checked if a related official extension example runs on my machine.
-
-## Additional context
-
-<!-- Add any other context about the problem here. -->

.github/ISSUE_TEMPLATE/question-help.md

@@ -1,12 +1,29 @@
 ---
-name: "❓Questions & Help"
-about: Start a general discussion related to PyTorch Transformers
+name: "❓ Questions & Help"
+about: Post your general questions on Stack Overflow tagged huggingface-transformers
 title: ''
 labels: ''
 assignees: ''
 
 ---
 
-## ❓ Questions & Help
+# ❓ Questions & Help
 
-<!-- A clear and concise description of the question. -->
+<!-- The GitHub issue tracker is primarly intended for bugs, feature requests,
+     new models and benchmarks, and migration questions. For all other questions,
+     we direct you to Stack Overflow (SO) where a whole community of PyTorch and
+     Tensorflow enthusiast can help you out. Make sure to tag your question with the
+     right deep learning framework as well as the huggingface-transformers tag: 
+     https://stackoverflow.com/questions/tagged/huggingface-transformers 
+     
+     If your question wasn't answered after a period of time on Stack Overflow, you
+     can always open a question on GitHub. You should then link to the SO question 
+     that you posted.
+     -->
+
+## Details
+<!-- Description of your issue -->
+
+<!-- You should first ask your question on SO, and only if
+     you didn't get an answer ask it here on GitHub. -->
+**A link to original question on Stack Overflow**:

.github/workflows/github-push.yml

@@ -0,0 +1,19 @@
+name: GitHub-hosted runner
+
+on: push
+
+jobs:
+  check_code_quality:
+    runs-on: ubuntu-18.04
+    steps:
+    - uses: actions/checkout@v2
+    - name: Set up Python
+      uses: actions/setup-python@v1
+      with:
+        python-version: 3.7
+    # - name: Install dependencies
+    #   run: |
+    #     pip install .[tf,torch,quality]
+
+
+

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

@@ -0,0 +1,32 @@
+name: Torch hub integration
+
+on: 
+  push:
+    branches:
+      - "*"
+
+jobs:
+  torch_hub_integration:
+    runs-on: ubuntu-latest
+    steps:
+    # no checkout necessary here.
+    - name: Extract branch name
+      run: echo "::set-env name=BRANCH::${GITHUB_REF#refs/heads/}"
+    - name: Check branch name
+      run: echo $BRANCH
+    - name: Set up Python
+      uses: actions/setup-python@v1
+      with:
+        python-version: 3.7
+    - name: Install dependencies
+      run: |
+        pip install torch
+        pip install numpy tokenizers filelock requests tqdm regex sentencepiece sacremoses packaging
+
+    - name: Torch hub list
+      run: |
+        python -c "import torch; print(torch.hub.list('huggingface/transformers:$BRANCH'))"
+
+    - name: Torch hub help
+      run: |
+        python -c "import torch; print(torch.hub.help('huggingface/transformers:$BRANCH', 'modelForSequenceClassification'))"

.github/workflows/self-push.yml

@@ -0,0 +1,54 @@
+name: Self-hosted runner (push)
+
+on: 
+  push:
+    branches:
+      - master
+    paths: 
+      - "src/**"
+      - "tests/**"
+      - ".github/**"
+  # pull_request:
+  repository_dispatch:
+
+
+jobs:
+  run_tests_torch_and_tf_gpu:
+    runs-on: self-hosted
+    steps:
+    - uses: actions/checkout@v2
+    - name: Python version
+      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)
+      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 torch
+        pip install .[sklearn,testing]
+
+    - name: Are GPUs recognized by our DL frameworks
+      run: |
+        source .env/bin/activate
+        python -c "import torch; print(torch.cuda.is_available())"
+
+    - name: Run all non-slow tests on GPU
+      env:
+        TF_FORCE_GPU_ALLOW_GROWTH: "true"
+        # TF_GPU_MEMORY_LIMIT: 4096
+        OMP_NUM_THREADS: 1
+        USE_CUDA: yes
+      run: |
+        source .env/bin/activate
+        python -m pytest -n 2 --dist=loadfile -s -v ./tests/

.github/workflows/self-scheduled.yml

@@ -0,0 +1,50 @@
+name: Self-hosted runner (scheduled)
+
+on:
+  push:
+    branches:
+      - ci_*
+  repository_dispatch:
+  schedule:
+    - cron: "0 0 * * *"
+
+jobs:
+  run_all_tests_torch_and_tf_gpu:
+    runs-on: self-hosted
+    steps:
+    - uses: actions/checkout@v2
+    - name: Python version
+      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)
+      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 .[sklearn,torch,testing]
+
+    - name: Are GPUs recognized by our DL frameworks
+      run: |
+        source .env/bin/activate
+        python -c "import torch; print(torch.cuda.is_available())"
+
+    - name: Run all tests on GPU
+      env:
+        TF_FORCE_GPU_ALLOW_GROWTH: "true"
+        OMP_NUM_THREADS: 1
+        RUN_SLOW: yes
+        USE_CUDA: yes
+      run: |
+        source .env/bin/activate
+        python -m pytest -n 1 --dist=loadfile -s -v ./tests/
+        

.gitignore

@@ -130,7 +130,10 @@ proc_data
 
 # examples
 runs
-examples/runs
+/runs_old
+/wandb
+/examples/runs
+/examples/**/*.args
 
 # data
 /data
@@ -139,3 +142,9 @@ serialization_dir
 # emacs
 *.*~
 debug.env
+
+# vim
+.*.swp
+
+#ctags
+tags

CONTRIBUTING.md

@@ -41,16 +41,19 @@ Did not find it? :( So we can act quickly on it, please follow these steps:
   less than 30s;
 * Provide the *full* traceback if an exception is raised.
 
-To get the OS and software versions, execute the following code and copy-paste
-the output:
+To get the OS and software versions automatically, you can run the following command:
 
+```bash
+transformers-cli env
 ```
-import platform; print("Platform", platform.platform())
-import sys; print("Python", sys.version)
-import torch; print("PyTorch", torch.__version__)
-import tensorflow; print("Tensorflow", tensorflow.__version__)
+
+or from the root of the repository the following command:
+
+```bash
+python src/transformers/commands/transformers_cli.py env
 ```
 
+
 ### Do you want to implement a new model?
 
 Awesome! Please provide the following information:
@@ -100,9 +103,10 @@ Follow these steps to start contributing:
 
 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.
+   under your GitHub user account.
+
 2. Clone your fork to your local disk, and add the base repository as a remote:
-   
+
    ```bash
    $ git clone git@github.com:<your Github handle>/transformers.git
    $ cd transformers
@@ -114,43 +118,77 @@ Follow these steps to start contributing:
    ```bash
    $ git checkout -b a-descriptive-name-for-my-changes
    ```
-   
+
    **do not** work on the `master` branch.
-   
+
 4. Set up a development environment by running the following command in a virtual environment:
 
    ```bash
-   $ pip install -r requirements-dev.txt
+   $ pip install -e ".[dev]"
    ```
 
-5. Develop the features on your branch. Add changed files using `git add` and
-   then `git commit` to record your changes locally:
-   
+   (If transformers was already installed in the virtual environment, remove
+   it with `pip uninstall transformers` before reinstalling it in editable
+   mode with the `-e` flag.)
+
+   Right now, we need an unreleased version of `isort` to avoid a
+   [bug](https://github.com/timothycrosley/isort/pull/1000):
+
+   ```bash
+   $ pip install -U git+git://github.com/timothycrosley/isort.git@e63ae06ec7d70b06df9e528357650281a3d3ec22#egg=isort
+   ```
+5. Develop the features on your branch.
+
+   As you work on the features, you should make sure that the test suite
+   passes:
+
+   ```bash
+   $ make test
+   ```
+
+   `transformers` relies on `black` and `isort` to format its source code
+   consistently. After you make changes, format them with:
+
+   ```bash
+   $ make style
+   ```
+
+   `transformers` also uses `flake8` to check for coding mistakes. Quality
+   control runs in CI, however you can also run the same checks with:
+
+   ```bash
+   $ make quality
+   ```
+
+   Once you're happy with your changes, add changed files using `git add` and
+   make a commit with `git commit` to record your changes locally:
+
    ```bash
    $ git add modified_file.py
    $ git commit
    ```
-   
+
    Please write [good commit
-   messages](https://chris.beams.io/posts/git-commit/). It
-   is a good idea to sync your copy of the code with the original repository
-   regularly. This way you can quickly account for changes:
-   
+   messages](https://chris.beams.io/posts/git-commit/).
+
+   It is a good idea to sync your copy of the code with the original
+   repository regularly. This way you can quickly account for changes:
+
    ```bash
    $ git fetch upstream
    $ git rebase upstream/master
    ```
-   
+
    Push the changes to your account using:
-   
+
    ```bash
    $ git push -u origin a-descriptive-name-for-my-changes
    ```
-   
+
 6. Once you are satisfied (**and the checklist below is happy too**), go to the
-   webpage of your fork on Github. Click on 'Pull request' to send your changes
+   webpage of your fork on GitHub. Click on 'Pull request' to send your changes
    to the project maintainers for review.
-   
+
 7. It's ok if maintainers ask you for changes. It happens to core contributors
    too! So everyone can see the changes in the Pull request, work in your local
    branch and push the changes to your fork. They will automatically appear in
@@ -166,9 +204,59 @@ Follow these steps to start contributing:
 3. To indicate a work in progress please prefix the title with `[WIP]`. These
    are useful to avoid duplicated work, and to differentiate it from PRs ready
    to be merged;
-4. Make sure pre-existing tests still pass;
-5. Add high-coverage tests. No quality test, no merge;
-6. All public methods must have informative doctrings;
+4. Make sure existing tests pass;
+5. Add high-coverage tests. No quality testing = no merge. 
+ - If you are adding a new model, make sure that you use `ModelTester.all_model_classes = (MyModel, MyModelWithLMHead,...)`, which triggers the common tests.
+ - If you are adding new `@slow` tests, make sure they pass using `RUN_SLOW=1 python -m pytest tests/test_my_new_model.py`. 
+ - If you are adding a new tokenizer, write tests, and make sure `RUN_SLOW=1 python -m pytest tests/test_tokenization_{your_model_name}.py` passes.
+CircleCI does not run them. 
+6. All public methods must have informative docstrings that work nicely with sphinx. See `modeling_ctrl.py` for an example.
+
+### Tests
+
+You can run 🤗 Transformers tests with `unittest` or `pytest`.
+
+We like `pytest` and `pytest-xdist` because it's faster. From the root of the
+repository, here's how to run tests with `pytest` for the library:
+
+```bash
+$ python -m pytest -n auto --dist=loadfile -s -v ./tests/
+```
+
+and for the examples:
+
+```bash
+$ pip install -r examples/requirements.txt  # only needed the first time
+$ python -m pytest -n auto --dist=loadfile -s -v ./examples/
+```
+
+In fact, that's how `make test` and `make test-examples` are implemented!
+
+You can specify a smaller set of tests in order to test only the feature
+you're working on.
+
+By default, slow tests are skipped. Set the `RUN_SLOW` environment variable to
+`yes` to run them. This will download many gigabytes of models — make sure you
+have enough disk space and a good Internet connection, or a lot of patience!
+
+```bash
+$ RUN_SLOW=yes python -m pytest -n auto --dist=loadfile -s -v ./tests/
+$ RUN_SLOW=yes python -m pytest -n auto --dist=loadfile -s -v ./examples/
+```
+
+Likewise, set the `RUN_CUSTOM_TOKENIZERS` environment variable to `yes` to run
+tests for custom tokenizers, which don't run by default either.
+
+🤗 Transformers uses `pytest` as a test runner only. It doesn't use any
+`pytest`-specific features in the test suite itself.
+
+This means `unittest` is fully supported. Here's how to run tests with
+`unittest`:
+
+```bash
+$ python -m unittest discover -s tests -t . -v
+$ python -m unittest discover -s examples -t examples -v
+```
 
 
 ### Style guide

Makefile

@@ -0,0 +1,24 @@
+.PHONY: quality style test test-examples
+
+# Check that source code meets quality standards
+
+quality:
+	black --check --line-length 119 --target-version py35 examples templates tests src utils
+	isort --check-only --recursive examples templates tests src utils
+	flake8 examples templates tests src utils
+
+# Format source code automatically
+
+style:
+	black --line-length 119 --target-version py35 examples templates tests src utils
+	isort --recursive examples templates tests src utils
+
+# Run tests for the library
+
+test:
+	python -m pytest -n auto --dist=loadfile -s -v ./tests/
+
+# Run tests for examples
+
+test-examples:
+	python -m pytest -n auto --dist=loadfile -s -v ./examples/

README.md

@@ -19,15 +19,14 @@
 </p>
 
 <h3 align="center">
-<p>State-of-the-art Natural Language Processing for TensorFlow 2.0 and PyTorch
+<p>State-of-the-art Natural Language Processing for PyTorch and TensorFlow 2.0
 </h3>
 
-🤗 Transformers (formerly known as `pytorch-transformers` and `pytorch-pretrained-bert`) provides state-of-the-art general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet, CTRL...) for Natural Language Understanding (NLU) and Natural Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between TensorFlow 2.0 and PyTorch.
+🤗 Transformers (formerly known as `pytorch-transformers` and `pytorch-pretrained-bert`) provides state-of-the-art general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet, T5, CTRL...) for Natural Language Understanding (NLU) and Natural Language Generation (NLG) with over thousands of pretrained models in 100+ languages and deep interoperability between PyTorch & TensorFlow 2.0.
+
+[![](https://sourcerer.io/fame/clmnt/huggingface/transformers/images/0)](https://sourcerer.io/fame/clmnt/huggingface/transformers/links/0)[![](https://sourcerer.io/fame/clmnt/huggingface/transformers/images/1)](https://sourcerer.io/fame/clmnt/huggingface/transformers/links/1)[![](https://sourcerer.io/fame/clmnt/huggingface/transformers/images/2)](https://sourcerer.io/fame/clmnt/huggingface/transformers/links/2)[![](https://sourcerer.io/fame/clmnt/huggingface/transformers/images/3)](https://sourcerer.io/fame/clmnt/huggingface/transformers/links/3)[![](https://sourcerer.io/fame/clmnt/huggingface/transformers/images/4)](https://sourcerer.io/fame/clmnt/huggingface/transformers/links/4)[![](https://sourcerer.io/fame/clmnt/huggingface/transformers/images/5)](https://sourcerer.io/fame/clmnt/huggingface/transformers/links/5)[![](https://sourcerer.io/fame/clmnt/huggingface/transformers/images/6)](https://sourcerer.io/fame/clmnt/huggingface/transformers/links/6)[![](https://sourcerer.io/fame/clmnt/huggingface/transformers/images/7)](https://sourcerer.io/fame/clmnt/huggingface/transformers/links/7)
 
 ### Features
-
-- As easy to use as pytorch-transformers
-- As powerful and concise as Keras
 - High performance on NLU and NLG tasks
 - Low barrier to entry for educators and practitioners
 
@@ -39,7 +38,7 @@ State-of-the-art NLP for everyone
 Lower compute costs, smaller carbon footprint
 - Researchers can share trained models instead of always retraining
 - Practitioners can reduce compute time and production costs
-- 10 architectures with over 30 pretrained models, some in more than 100 languages
+- Dozens of architectures with over 1,000 pretrained models, some in more than 100 languages
 
 Choose the right framework for every part of a model's lifetime
 - Train state-of-the-art models in 3 lines of code
@@ -55,14 +54,22 @@ Choose the right framework for every part of a model's lifetime
 | [Online demo](#online-demo) | Experimenting with this repo’s text generation capabilities |
 | [Quick tour: Usage](#quick-tour) | Tokenizers & models usage: Bert and GPT-2 |
 | [Quick tour: TF 2.0 and PyTorch ](#Quick-tour-TF-20-training-and-PyTorch-interoperability) | Train a TF 2.0 model in 10 lines of code, load it in PyTorch |
+| [Quick tour: pipelines](#quick-tour-of-pipelines) | Using Pipelines: Wrapper around tokenizer and models to use finetuned models |
 | [Quick tour: Fine-tuning/usage scripts](#quick-tour-of-the-fine-tuningusage-scripts) | Using provided scripts: GLUE, SQuAD and Text generation |
+| [Quick tour: Share your models ](#Quick-tour-of-model-sharing) | Upload and share your fine-tuned models with the community |
 | [Migrating from pytorch-transformers to transformers](#Migrating-from-pytorch-transformers-to-transformers) | Migrating your code from pytorch-transformers to transformers |
 | [Migrating from pytorch-pretrained-bert to pytorch-transformers](#Migrating-from-pytorch-pretrained-bert-to-transformers) | Migrating your code from pytorch-pretrained-bert to transformers |
-| [Documentation][(v2.2.0/v2.2.1/v2.2.2)](https://huggingface.co/transformers/v2.2.0) [(v2.1.1)](https://huggingface.co/transformers/v2.1.1) [(v2.0.0)](https://huggingface.co/transformers/v2.0.0) [(v1.2.0)](https://huggingface.co/transformers/v1.2.0) [(v1.1.0)](https://huggingface.co/transformers/v1.1.0) [(v1.0.0)](https://huggingface.co/transformers/v1.0.0) [(master)](https://huggingface.co/transformers) | Full API documentation and more |
+| [Documentation][(v2.5.0)](https://huggingface.co/transformers/v2.5.0)[(v2.4.0/v2.4.1)](https://huggingface.co/transformers/v2.4.0)[(v2.3.0)](https://huggingface.co/transformers/v2.3.0)[(v2.2.0/v2.2.1/v2.2.2)](https://huggingface.co/transformers/v2.2.0) [(v2.1.1)](https://huggingface.co/transformers/v2.1.1) [(v2.0.0)](https://huggingface.co/transformers/v2.0.0) [(v1.2.0)](https://huggingface.co/transformers/v1.2.0) [(v1.1.0)](https://huggingface.co/transformers/v1.1.0) [(v1.0.0)](https://huggingface.co/transformers/v1.0.0) [(master)](https://huggingface.co/transformers) | Full API documentation and more |
 
 ## Installation
 
-This repo is tested on Python 2.7 and 3.5+ (examples are tested only on python 3.5+), PyTorch 1.0.0+ and TensorFlow 2.0.0-rc1
+This repo is tested on Python 3.6+, PyTorch 1.0.0+ (PyTorch 1.3.1+ for examples) and TensorFlow 2.0.
+
+You should install 🤗 Transformers in a [virtual environment](https://docs.python.org/3/library/venv.html). If you're unfamiliar with Python virtual environments, check out the [user guide](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/).
+
+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 must install it from source.
 
 ### With pip
 
@@ -83,43 +90,48 @@ Please refer to [TensorFlow installation page](https://www.tensorflow.org/instal
 When TensorFlow 2.0 and/or PyTorch has been installed, you can install from source by cloning the repository and running:
 
 ```bash
-pip install [--editable] .
+git clone https://github.com/huggingface/transformers
+cd transformers
+pip install .
+```
+
+When you update the repository, you should upgrade the transformers installation and its dependencies as follows:
+
+```bash
+git pull
+pip install --upgrade .
 ```
 
 ### Run the examples
 
 Examples are included in the repository but are not shipped with the library.
-Therefore, in order to run the latest versions of the examples you also need to install from source. To do so, create a new virtual environment and follow these steps:
 
-```bash
-git clone https://github.com/huggingface/transformers
-cd transformers
-pip install [--editable] .
-```
+Therefore, in order to run the latest versions of the examples, you need to install from source, as described above.
+
+Look at the [README](https://github.com/huggingface/transformers/blob/master/examples/README.md) for how to run examples.
 
 ### Tests
 
-A series of tests are included for the library and the example scripts. Library tests can be found in the [tests folder](https://github.com/huggingface/transformers/tree/master/transformers/tests) and examples tests in the [examples folder](https://github.com/huggingface/transformers/tree/master/examples).
-
-These tests can be run using `unittest` or `pytest` (install pytest if needed with `pip install pytest`).
+A series of tests are included for the library and for some example scripts. Library tests can be found in the [tests folder](https://github.com/huggingface/transformers/tree/master/tests) and examples tests in the [examples folder](https://github.com/huggingface/transformers/tree/master/examples).
 
 Depending on which framework is installed (TensorFlow 2.0 and/or PyTorch), the irrelevant tests will be skipped. Ensure that both frameworks are installed if you want to execute all tests.
 
-You can run the tests from the root of the cloned repository with the commands:
+Here's the easiest way to run tests for the library:
 
 ```bash
-python -m unittest discover -s transformers/tests -p "*test.py" -t .
-python -m unittest discover -s examples -p "*test.py" -t examples
+pip install -e ".[testing]"
+make test
 ```
 
-or
+and for the examples:
 
 ```bash
-python -m pytest -sv ./transformers/tests/
-python -m pytest -sv ./examples/
+pip install -e ".[testing]"
+pip install -r examples/requirements.txt
+make test-examples
 ```
 
-By default, slow tests are skipped. Set the `RUN_SLOW` environment variable to `yes` to run them.
+For details, refer to the [contributing guide](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md#tests).
 
 ### Do you want to run a Transformer model on a mobile device?
 
@@ -131,20 +143,31 @@ At some point in the future, you'll be able to seamlessly move from pre-training
 
 ## Model architectures
 
-🤗 Transformers currently provides 10 NLU/NLG architectures:
+🤗 Transformers currently provides the following NLU/NLG architectures:
 
-1. **[BERT](https://github.com/google-research/bert)** (from Google) released with the paper [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.
-2. **[GPT](https://github.com/openai/finetune-transformer-lm)** (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.
-3. **[GPT-2](https://blog.openai.com/better-language-models/)** (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**.
-4. **[Transformer-XL](https://github.com/kimiyoung/transformer-xl)** (from Google/CMU) released with the paper [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
-5. **[XLNet](https://github.com/zihangdai/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.
-6. **[XLM](https://github.com/facebookresearch/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.
-7. **[RoBERTa](https://github.com/pytorch/fairseq/tree/master/examples/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.
-8. **[DistilBERT](https://github.com/huggingface/transformers/tree/master/examples/distillation)** (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.
-9. **[CTRL](https://github.com/salesforce/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. **[CamemBERT](https://camembert-model.fr)** (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. **[ALBERT](https://github.com/google-research/ALBERT)** (from Google Research and the Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.
-11. 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.
+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.
+2. **[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.
+3. **[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**.
+4. **[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.
+5. **[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.
+6. **[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.
+7. **[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.
+8. **[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.
+9. **[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.
+10. **[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.
+11. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.
+12. **[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.
+13. **[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.
+14. **[MMBT](https://github.com/facebookresearch/mmbt/)** (from Facebook), released together with the paper a [Supervised Multimodal Bitransformers for Classifying Images and Text](https://arxiv.org/pdf/1909.02950.pdf) by Douwe Kiela, Suvrat Bhooshan, Hamed Firooz, Davide Testuggine.
+15. **[FlauBERT](https://huggingface.co/transformers/model_doc/flaubert.html)** (from CNRS) released with the paper [FlauBERT: Unsupervised Language Model Pre-training for French](https://arxiv.org/abs/1912.05372) by Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, Didier Schwab.
+16. **[BART](https://huggingface.co/transformers/model_doc/bart.html)** (from Facebook) released with the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/pdf/1910.13461.pdf) by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer.
+17. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning.
+18. **[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.
+19. **[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.
+20. **[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.
+21. **[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.
+22. **[Other community models](https://huggingface.co/models)**, contributed by the [community](https://huggingface.co/users).
+23. 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.
 
 These implementations have been tested on several datasets (see the example scripts) and should match the performances of the original implementations (e.g. ~93 F1 on SQuAD for BERT Whole-Word-Masking, ~88 F1 on RocStories for OpenAI GPT, ~18.3 perplexity on WikiText 103 for Transformer-XL, ~0.916 Peason R coefficient on STS-B for XLNet). You can find more details on the performances in the Examples section of the [documentation](https://huggingface.co/transformers/examples.html).
 
@@ -166,7 +189,7 @@ import torch
 from transformers import *
 
 # Transformers has a unified API
-# for 8 transformer architectures and 30 pretrained weights.
+# for 10 transformer architectures and 30 pretrained weights.
 #          Model          | Tokenizer          | Pretrained weights shortcut
 MODELS = [(BertModel,       BertTokenizer,       'bert-base-uncased'),
           (OpenAIGPTModel,  OpenAIGPTTokenizer,  'openai-gpt'),
@@ -175,8 +198,10 @@ MODELS = [(BertModel,       BertTokenizer,       'bert-base-uncased'),
           (TransfoXLModel,  TransfoXLTokenizer,  'transfo-xl-wt103'),
           (XLNetModel,      XLNetTokenizer,      'xlnet-base-cased'),
           (XLMModel,        XLMTokenizer,        'xlm-mlm-enfr-1024'),
-          (DistilBertModel, DistilBertTokenizer, 'distilbert-base-uncased'),
-          (RobertaModel,    RobertaTokenizer,    'roberta-base')]
+          (DistilBertModel, DistilBertTokenizer, 'distilbert-base-cased'),
+          (RobertaModel,    RobertaTokenizer,    'roberta-base'),
+          (XLMRobertaModel, XLMRobertaTokenizer, 'xlm-roberta-base'),
+         ]
 
 # To use TensorFlow 2.0 versions of the models, simply prefix the class names with 'TF', e.g. `TFRobertaModel` is the TF 2.0 counterpart of the PyTorch model `RobertaModel`
 
@@ -244,7 +269,7 @@ valid_dataset = glue_convert_examples_to_features(data['validation'], tokenizer,
 train_dataset = train_dataset.shuffle(100).batch(32).repeat(2)
 valid_dataset = valid_dataset.batch(64)
 
-# Prepare training: Compile tf.keras model with optimizer, loss and learning rate schedule 
+# Prepare training: Compile tf.keras model with optimizer, loss and learning rate schedule
 optimizer = tf.keras.optimizers.Adam(learning_rate=3e-5, epsilon=1e-08, clipnorm=1.0)
 loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
 metric = tf.keras.metrics.SparseCategoricalAccuracy('accuracy')
@@ -274,15 +299,16 @@ print("sentence_2 is", "a paraphrase" if pred_2 else "not a paraphrase", "of sen
 
 ## Quick tour of the fine-tuning/usage scripts
 
-**Important**  
+**Important**
 Before running the fine-tuning scripts, please read the
 [instructions](#run-the-examples) on how to
 setup your environment to run the examples.
 
 The library comprises several example scripts with SOTA performances for NLU and NLG tasks:
 
-- `run_glue.py`: an example fine-tuning Bert, XLNet and XLM on nine different GLUE tasks (*sequence-level classification*)
-- `run_squad.py`: an example fine-tuning Bert, XLNet and XLM on the question answering dataset SQuAD 2.0 (*token-level classification*)
+- `run_glue.py`: an example fine-tuning sequence classification models on nine different GLUE tasks (*sequence-level classification*)
+- `run_squad.py`: an example fine-tuning question answering models on the question answering dataset SQuAD 2.0 (*token-level classification*)
+- `run_ner.py`: an example fine-tuning token classification models on named entity recognition (*token-level classification*)
 - `run_generation.py`: an example using GPT, GPT-2, CTRL, Transformer-XL and XLNet for conditional language generation
 - other model-specific examples (see the documentation).
 
@@ -292,7 +318,7 @@ Here are three quick usage examples for these scripts:
 
 The [General Language Understanding Evaluation (GLUE) benchmark](https://gluebenchmark.com/) is a collection of nine sentence- or sentence-pair language understanding tasks for evaluating and analyzing natural language understanding systems.
 
-Before running anyone of these GLUE tasks you should download the
+Before running any of these GLUE tasks you should download the
 [GLUE data](https://gluebenchmark.com/tasks) by running
 [this script](https://gist.github.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e)
 and unpack it to some directory `$GLUE_DIR`.
@@ -307,17 +333,15 @@ pip install -r ./examples/requirements.txt
 export GLUE_DIR=/path/to/glue
 export TASK_NAME=MRPC
 
-python ./examples/run_glue.py \
-    --model_type bert \
+python ./examples/text-classification/run_glue.py \
     --model_name_or_path bert-base-uncased \
     --task_name $TASK_NAME \
     --do_train \
     --do_eval \
-    --do_lower_case \
     --data_dir $GLUE_DIR/$TASK_NAME \
     --max_seq_length 128 \
-    --per_gpu_eval_batch_size=8   \
-    --per_gpu_train_batch_size=8   \
+    --per_device_eval_batch_size=8   \
+    --per_device_train_batch_size=8   \
     --learning_rate 2e-5 \
     --num_train_epochs 3.0 \
     --output_dir /tmp/$TASK_NAME/
@@ -335,8 +359,7 @@ Parallel training is a simple way to use several GPUs (but is slower and less fl
 ```shell
 export GLUE_DIR=/path/to/glue
 
-python ./examples/run_glue.py \
-    --model_type xlnet \
+python ./examples/text-classification/run_glue.py \
     --model_name_or_path xlnet-large-cased \
     --do_train  \
     --do_eval   \
@@ -344,8 +367,8 @@ python ./examples/run_glue.py \
     --data_dir=${GLUE_DIR}/STS-B  \
     --output_dir=./proc_data/sts-b-110   \
     --max_seq_length=128   \
-    --per_gpu_eval_batch_size=8   \
-    --per_gpu_train_batch_size=8   \
+    --per_device_eval_batch_size=8   \
+    --per_device_train_batch_size=8   \
     --gradient_accumulation_steps=1 \
     --max_steps=1200  \
     --model_name=xlnet-large-cased   \
@@ -361,17 +384,15 @@ On this machine we thus have a batch size of 32, please increase `gradient_accum
 This example code fine-tunes the Bert Whole Word Masking model on the Microsoft Research Paraphrase Corpus (MRPC) corpus using distributed training on 8 V100 GPUs to reach a F1 > 92.
 
 ```bash
-python -m torch.distributed.launch --nproc_per_node 8 ./examples/run_glue.py   \
-    --model_type bert \
+python -m torch.distributed.launch --nproc_per_node 8 ./examples/text-classification/run_glue.py   \
     --model_name_or_path bert-large-uncased-whole-word-masking \
     --task_name MRPC \
     --do_train   \
     --do_eval   \
-    --do_lower_case   \
     --data_dir $GLUE_DIR/MRPC/   \
     --max_seq_length 128   \
-    --per_gpu_eval_batch_size=8   \
-    --per_gpu_train_batch_size=8   \
+    --per_device_eval_batch_size=8   \
+    --per_device_train_batch_size=8   \
     --learning_rate 2e-5   \
     --num_train_epochs 3.0  \
     --output_dir /tmp/mrpc_output/ \
@@ -395,12 +416,11 @@ Training with these hyper-parameters gave us the following results:
 This example code fine-tunes BERT on the SQuAD dataset using distributed training on 8 V100 GPUs and Bert Whole Word Masking uncased model to reach a F1 > 93 on SQuAD:
 
 ```bash
-python -m torch.distributed.launch --nproc_per_node=8 ./examples/run_squad.py \
+python -m torch.distributed.launch --nproc_per_node=8 ./examples/question-answering/run_squad.py \
     --model_type bert \
     --model_name_or_path bert-large-uncased-whole-word-masking \
     --do_train \
     --do_eval \
-    --do_lower_case \
     --train_file $SQUAD_DIR/train-v1.1.json \
     --predict_file $SQUAD_DIR/dev-v1.1.json \
     --learning_rate 3e-5 \
@@ -408,8 +428,8 @@ python -m torch.distributed.launch --nproc_per_node=8 ./examples/run_squad.py \
     --max_seq_length 384 \
     --doc_stride 128 \
     --output_dir ../models/wwm_uncased_finetuned_squad/ \
-    --per_gpu_eval_batch_size=3   \
-    --per_gpu_train_batch_size=3   \
+    --per_device_eval_batch_size=3   \
+    --per_device_train_batch_size=3   \
 ```
 
 Training with these hyper-parameters gave us the following results:
@@ -429,15 +449,15 @@ The generation script includes the [tricks](https://github.com/rusiaaman/XLNet-g
 Here is how to run the script with the small version of OpenAI GPT-2 model:
 
 ```shell
-python ./examples/run_generation.py \
+python ./examples/text-generation/run_generation.py \
     --model_type=gpt2 \
     --length=20 \
     --model_name_or_path=gpt2 \
 ```
 
-and from the Salesforce CTRL model: 
+and from the Salesforce CTRL model:
 ```shell
-python ./examples/run_generation.py \
+python ./examples/text-generation/run_generation.py \
     --model_type=ctrl \
     --length=20 \
     --model_name_or_path=ctrl \
@@ -445,6 +465,95 @@ python ./examples/run_generation.py \
     --repetition_penalty=1.2 \
 ```
 
+## Quick tour of model sharing
+
+Starting with `v2.2.2`, you can now upload and share your fine-tuned models with the community, using the <abbr title="Command-line interface">CLI</abbr> that's built-in to the library.
+
+**First, create an account on [https://huggingface.co/join](https://huggingface.co/join)**. Optionally, join an existing organization or create a new one. Then:
+
+```shell
+transformers-cli login
+# log in using the same credentials as on huggingface.co
+```
+Upload your model:
+```shell
+transformers-cli upload ./path/to/pretrained_model/
+
+# ^^ Upload folder containing weights/tokenizer/config
+# saved via `.save_pretrained()`
+
+transformers-cli upload ./config.json [--filename folder/foobar.json]
+
+# ^^ Upload a single file
+# (you can optionally override its filename, which can be nested inside a folder)
+```
+
+If you want your model to be namespaced by your organization name rather than your username, add the following flag to any command:
+```shell
+--organization organization_name
+```
+
+Your model will then be accessible through its identifier, a concatenation of your username (or organization name) and the folder name above:
+```python
+"username/pretrained_model"
+# or if an org:
+"organization_name/pretrained_model"
+```
+
+**Please add a README.md model card** to the repo under `model_cards/` with: model description, training params (dataset, preprocessing, hardware used, hyperparameters), evaluation results, intended uses & limitations, etc.
+
+Your model now has a page on huggingface.co/models 🔥
+
+Anyone can load it from code:
+```python
+tokenizer = AutoTokenizer.from_pretrained("namespace/pretrained_model")
+model = AutoModel.from_pretrained("namespace/pretrained_model")
+```
+
+List all your files on S3:
+```shell
+transformers-cli s3 ls
+```
+
+You can also delete unneeded files:
+
+```shell
+transformers-cli s3 rm …
+```
+
+## Quick tour of pipelines
+
+New in version `v2.3`: `Pipeline` are high-level objects which automatically handle tokenization, running your data through a transformers model
+and outputting the result in a structured object.
+
+You can create `Pipeline` objects for the following down-stream tasks:
+
+ - `feature-extraction`: Generates a tensor representation for the input sequence
+ - `ner`: Generates named entity mapping for each word in the input sequence.
+ - `sentiment-analysis`: Gives the polarity (positive / negative) of the whole input sequence.
+ - `text-classification`: Initialize a `TextClassificationPipeline` directly, or see `sentiment-analysis` for an example.
+ - `question-answering`: Provided some context and a question refering to the context, it will extract the answer to the question in the context.
+ - `fill-mask`: Takes an input sequence containing a masked token (e.g. `<mask>`) and return list of most probable filled sequences, with their probabilities.
+ - `summarization`
+ - `translation_xx_to_yy`
+
+```python
+from transformers import pipeline
+
+# Allocate a pipeline for sentiment-analysis
+nlp = pipeline('sentiment-analysis')
+nlp('We are very happy to include pipeline into the transformers repository.')
+>>> {'label': 'POSITIVE', 'score': 0.99893874}
+
+# Allocate a pipeline for question-answering
+nlp = pipeline('question-answering')
+nlp({
+    'question': 'What is the name of the repository ?',
+    'context': 'Pipeline have been included in the huggingface/transformers repository'
+})
+>>> {'score': 0.28756016668193496, 'start': 35, 'end': 59, 'answer': 'huggingface/transformers'}
+```
+
 ## Migrating from pytorch-transformers to transformers
 
 Here is a quick summary of what you should take care of when migrating from `pytorch-transformers` to `transformers`.
@@ -576,7 +685,7 @@ for batch in train_data:
 ## Citation
 
 We now have a paper you can cite for the 🤗 Transformers library:
-```
+```bibtex
 @article{Wolf2019HuggingFacesTS,
   title={HuggingFace's Transformers: State-of-the-art Natural Language Processing},
   author={Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and R'emi Louf and Morgan Funtowicz and Jamie Brew},

deploy_multi_version_doc.sh

@@ -19,4 +19,5 @@ deploy_doc "fe02e45" v1.1.0
 deploy_doc "89fd345" v1.2.0
 deploy_doc "fc9faa8" v2.0.0
 deploy_doc "3ddce1d" v2.1.1
-deploy_doc "f2f3294" v2.2.0
+deploy_doc "f2f3294" v2.2.0
+deploy_doc "d0f8b9a" v2.3.0

docker/Dockerfile

@@ -1,7 +0,0 @@
-FROM pytorch/pytorch:latest
-
-RUN git clone https://github.com/NVIDIA/apex.git && cd apex && python setup.py install --cuda_ext --cpp_ext
-
-RUN pip install transformers
-
-WORKDIR /workspace

docker/transformers-cpu/Dockerfile

@@ -0,0 +1,26 @@
+FROM ubuntu:18.04
+LABEL maintainer="Hugging Face"
+LABEL repository="transformers"
+
+RUN apt update && \
+    apt install -y bash \
+                   build-essential \
+                   git \
+                   curl \
+                   ca-certificates \
+                   python3 \
+                   python3-pip && \
+    rm -rf /var/lib/apt/lists
+
+RUN python3 -m pip install --no-cache-dir --upgrade pip && \
+    python3 -m pip install --no-cache-dir \
+    jupyter \
+    tensorflow-cpu \
+    torch
+
+WORKDIR /workspace
+COPY . transformers/
+RUN cd transformers/ && \
+    python3 -m pip install --no-cache-dir .
+
+CMD ["/bin/bash"]

docker/transformers-gpu/Dockerfile

@@ -0,0 +1,26 @@
+FROM nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04
+LABEL maintainer="Hugging Face"
+LABEL repository="transformers"
+
+RUN apt update && \
+    apt install -y bash \
+                   build-essential \
+                   git \
+                   curl \
+                   ca-certificates \
+                   python3 \
+                   python3-pip && \
+    rm -rf /var/lib/apt/lists
+
+RUN python3 -m pip install --no-cache-dir --upgrade pip && \
+    python3 -m pip install --no-cache-dir \
+    jupyter \
+    tensorflow \
+    torch
+
+WORKDIR /workspace
+COPY . transformers/
+RUN cd transformers/ && \
+    python3 -m pip install --no-cache-dir .
+
+CMD ["/bin/bash"]

docker/transformers-pytorch-cpu/Dockerfile

@@ -0,0 +1,25 @@
+FROM ubuntu:18.04
+LABEL maintainer="Hugging Face"
+LABEL repository="transformers"
+
+RUN apt update && \
+    apt install -y bash \
+                   build-essential \
+                   git \
+                   curl \
+                   ca-certificates \
+                   python3 \
+                   python3-pip && \
+    rm -rf /var/lib/apt/lists
+
+RUN python3 -m pip install --no-cache-dir --upgrade pip && \
+    python3 -m pip install --no-cache-dir \
+    jupyter \
+    torch
+
+WORKDIR /workspace
+COPY . transformers/
+RUN cd transformers/ && \
+    python3 -m pip install --no-cache-dir .
+
+CMD ["/bin/bash"]

docker/transformers-pytorch-gpu/Dockerfile

@@ -0,0 +1,25 @@
+FROM nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04
+LABEL maintainer="Hugging Face"
+LABEL repository="transformers"
+
+RUN apt update && \
+    apt install -y bash \
+                   build-essential \
+                   git \
+                   curl \
+                   ca-certificates \
+                   python3 \
+                   python3-pip && \
+    rm -rf /var/lib/apt/lists
+
+RUN python3 -m pip install --no-cache-dir --upgrade pip && \
+    python3 -m pip install --no-cache-dir \
+    mkl \
+    torch
+
+WORKDIR /workspace
+COPY . transformers/
+RUN cd transformers/ && \
+    python3 -m pip install --no-cache-dir .
+
+CMD ["/bin/bash"]

docker/transformers-tensorflow-cpu/Dockerfile

@@ -0,0 +1,25 @@
+FROM ubuntu:18.04
+LABEL maintainer="Hugging Face"
+LABEL repository="transformers"
+
+RUN apt update && \
+    apt install -y bash \
+                   build-essential \
+                   git \
+                   curl \
+                   ca-certificates \
+                   python3 \
+                   python3-pip && \
+    rm -rf /var/lib/apt/lists
+
+RUN python3 -m pip install --no-cache-dir --upgrade pip && \
+    python3 -m pip install --no-cache-dir \
+    mkl \
+    tensorflow-cpu
+
+WORKDIR /workspace
+COPY . transformers/
+RUN cd transformers/ && \
+    python3 -m pip install --no-cache-dir .
+
+CMD ["/bin/bash"]

docker/transformers-tensorflow-gpu/Dockerfile

@@ -0,0 +1,25 @@
+FROM nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04
+LABEL maintainer="Hugging Face"
+LABEL repository="transformers"
+
+RUN apt update && \
+    apt install -y bash \
+                   build-essential \
+                   git \
+                   curl \
+                   ca-certificates \
+                   python3 \
+                   python3-pip && \
+    rm -rf /var/lib/apt/lists
+
+RUN python3 -m pip install --no-cache-dir --upgrade pip && \
+    python3 -m pip install --no-cache-dir \
+    mkl \
+    tensorflow
+
+WORKDIR /workspace
+COPY . transformers/
+RUN cd transformers/ && \
+    python3 -m pip install --no-cache-dir .
+
+CMD ["/bin/bash"]

docs/README.md

@@ -1,25 +1,25 @@
 # Generating the documentation
 
 To generate the documentation, you first have to build it. Several packages are necessary to build the doc,
-you can install them using:
+you can install them with the following command, at the root of the code repository:
 
 ```bash
-pip install -r requirements.txt
+pip install -e ".[docs]"
 ```
- 
+
 ## Packages installed
 
-Here's an overview of all the packages installed. If you ran the previous command installing all packages from 
+Here's an overview of all the packages installed. If you ran the previous command installing all packages from
 `requirements.txt`, you do not need to run the following commands.
 
-Building it requires the package `sphinx` that you can 
+Building it requires the package `sphinx` that you can
 install using:
 
 ```bash
 pip install -U sphinx
 ```
 
-You would also need the custom installed [theme](https://github.com/readthedocs/sphinx_rtd_theme) by 
+You would also need the custom installed [theme](https://github.com/readthedocs/sphinx_rtd_theme) by
 [Read The Docs](https://readthedocs.org/). You can install it using the following command:
 
 ```bash
@@ -34,7 +34,7 @@ pip install recommonmark
 
 ## Building the documentation
 
-Make sure that there is a symlink from the `example` file (in /examples) inside the source folder. Run the following 
+Make sure that there is a symlink from the `example` file (in /examples) inside the source folder. Run the following
 command to generate it:
 
 ```bash
@@ -47,6 +47,8 @@ Once you have setup `sphinx`, you can build the documentation by running the fol
 make html
 ```
 
+A folder called ``_build/html`` should have been created. You can now open the file ``_build/html/index.html`` in your browser. 
+
 ---
 **NOTE**
 
@@ -65,3 +67,131 @@ It should build the static app that will be available under `/docs/_build/html`
 
 Accepted files are reStructuredText (.rst) and Markdown (.md). Create a file with its extension and put it
 in the source directory. You can then link it to the toc-tree by putting the filename without the extension.
+
+## 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), 
+[Sourceforge complete documentation](https://docutils.sourceforge.io/docs/ref/rst/restructuredtext.html))
+
+### Adding a new section
+
+A section is a page held in the `Notes` toc-tree on the documentation. Adding a new section is done in two steps:
+
+- Add a new file under `./source`. This file can either be ReStructuredText (.rst) or Markdown (.md).
+- Link that file in `./source/index.rst` on the correct toc-tree.
+
+### Adding a new model
+
+When adding a new model:
+ 
+- Create a file `xxx.rst` under `./source/model_doc`. 
+- Link that file in `./source/index.rst` on the `model_doc` toc-tree.
+- Write a short overview of the model:
+    - Overview with paper & authors
+    - Paper abstract
+    - 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, 
+    - Tokenizer
+    - PyTorch base model
+    - PyTorch head models
+    - TensorFlow base model
+    - TensorFlow head models
+
+These classes should be added using the RST syntax. Usually as follows:
+```
+XXXConfig
+~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.XXXConfig
+    :members:
+```
+
+This will include every public method of the configuration. If for some reason you wish for a method not to be displayed
+in the documentation, you can do so by specifying which methods should be in the docs:
+
+```
+XXXTokenizer
+~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.XXXTokenizer
+    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
+        create_token_type_ids_from_sequences, save_vocabulary
+
+```
+
+### Writing source documentation
+
+Values that should be put in `code` should either be surrounded by double backticks: \`\`like so\`\` or be written as an object
+using the :obj: syntax: :obj:\`like so\`.
+
+When mentionning a class, it is recommended to use the :class: syntax as the mentioned class will be automatically
+linked by Sphinx: :class:\`transformers.XXXClass\`
+
+When mentioning a function, it is recommended to use the :func: syntax as the mentioned method will be automatically
+linked by Sphinx: :func:\`transformers.XXXClass.method\`
+
+Links should be done as so (note the double underscore at the end): \`text for the link <./local-link-or-global-link#loc>\`__
+
+#### Defining arguments in a method
+
+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.
+
+Here's an example showcasing everything so far:
+
+```
+    Args:
+        input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary.
+
+            Indices can be obtained using :class:`transformers.AlbertTokenizer`.
+            See :func:`transformers.PreTrainedTokenizer.encode` and
+            :func:`transformers.PreTrainedTokenizer.encode_plus` for details.
+
+            `What are input IDs? <../glossary.html#input-ids>`__
+```
+
+#### Writing a multi-line code block 
+
+Multi-line code blocks can be useful for displaying examples. They are done like so:
+
+```
+Example::
+
+    # first line of code
+    # second line
+    # etc
+```
+
+The `Example` string at the beginning can be replaced by anything as long as there are two semicolons following it.
+
+#### Writing a return block
+
+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.
+
+Here's an example for tuple return, comprising several objects:
+
+```
+    Returns:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
+        loss (`optional`, returned when ``masked_lm_labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
+            Total loss as the sum of the masked language modeling loss and the next sequence prediction (classification) loss.
+        prediction_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`)
+            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+```
+
+Here's an example for a single value return:
+
+```
+    Returns:
+        A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+```

docs/requirements.txt

@@ -1,32 +0,0 @@
-alabaster==0.7.12
-Babel==2.7.0
-certifi==2019.6.16
-chardet==3.0.4
-commonmark==0.9.0
-docutils==0.14
-future==0.17.1
-idna==2.8
-imagesize==1.1.0
-Jinja2==2.10.1
-MarkupSafe==1.1.1
-packaging==19.0
-Pygments==2.4.2
-pyparsing==2.4.0
-pytz==2019.1
-recommonmark==0.5.0
-requests==2.22.0
-six==1.12.0
-snowballstemmer==1.9.0
-Sphinx==2.1.2
-sphinx-rtd-theme==0.4.3
-sphinxcontrib-applehelp==1.0.1
-sphinxcontrib-devhelp==1.0.1
-sphinxcontrib-htmlhelp==1.0.2
-sphinxcontrib-jsmath==1.0.1
-sphinxcontrib-qthelp==1.0.2
-sphinxcontrib-serializinghtml==1.1.3
-urllib3==1.25.3
-sphinx-markdown-tables==0.0.9
-numpy==1.17.2
-tensorflow==2.0.0rc2
-torch==1.2.0

docs/source/_static/css/huggingface.css

@@ -1,3 +1,25 @@
+/* Our DOM objects */
+
+.framework-selector {
+    display: flex;
+    flex-direction: row;
+    justify-content: flex-end;
+}
+
+.framework-selector > button {
+    background-color: white;
+    color: #6670FF;
+    border: 1px solid #6670FF;
+    padding: 5px;
+}
+
+.framework-selector > button.selected{
+    background-color: #6670FF;
+    color: white;
+    border: 1px solid #6670FF;
+    padding: 5px;
+}
+
 /* The literal code blocks */
 .rst-content tt.literal, .rst-content tt.literal, .rst-content code.literal {
     color: #6670FF;
@@ -194,3 +216,41 @@ h2, .rst-content .toctree-wrapper p.caption, h3, h4, h5, h6, legend{
     src: url(./Calibre-Thin.otf);
     font-weight:400;
 }
+
+
+/**
+ * Nav Links to other parts of huggingface.co
+ */
+ div.menu {
+    position: absolute;
+    top: 0;
+    right: 0;
+    padding-top: 20px;
+    padding-right: 20px;
+    z-index: 1000;
+}
+div.menu a {
+    font-size: 14px;
+    letter-spacing: 0.3px;
+    text-transform: uppercase;
+    color: white;
+    -webkit-font-smoothing: antialiased;
+    background: linear-gradient(0deg, #6671ffb8, #9a66ffb8 50%);
+    padding: 10px 16px 6px 16px;
+    border-radius: 3px;
+    margin-left: 12px;
+    position: relative;
+}
+div.menu a:active {
+    top: 1px;
+}
+@media (min-width: 768px) and (max-width: 1750px) {
+    .wy-breadcrumbs {
+        margin-top: 32px;
+    }
+}
+@media (max-width: 768px) {
+    div.menu {
+        display: none;
+    }
+}

docs/source/_static/js/custom.js

@@ -58,6 +58,84 @@ function addGithubButton() {
     document.querySelector(".wy-side-nav-search .icon-home").insertAdjacentHTML('afterend', div);
 }
 
+function addHfMenu() {
+    const div = `
+    <div class="menu">
+        <a href="/welcome">🔥 Sign in</a>
+        <a href="/models">🚀 Models</a>
+    </div>
+    `;
+    document.body.insertAdjacentHTML('afterbegin', div);
+}
+
+function platformToggle() {
+    const codeBlocks = Array.from(document.getElementsByClassName("highlight"));
+    const pytorchIdentifier = "## PYTORCH CODE";
+    const tensorflowIdentifier = "## TENSORFLOW CODE";
+    const pytorchSpanIdentifier = `<span class="c1">${pytorchIdentifier}</span>`;
+    const tensorflowSpanIdentifier = `<span class="c1">${tensorflowIdentifier}</span>`;
+
+    const getFrameworkSpans = filteredCodeBlock => {
+        const spans = filteredCodeBlock.element.innerHTML;
+        const pytorchSpanPosition = spans.indexOf(pytorchSpanIdentifier);
+        const tensorflowSpanPosition = spans.indexOf(tensorflowSpanIdentifier);
+
+        let pytorchSpans;
+        let tensorflowSpans;
+
+        if(pytorchSpanPosition < tensorflowSpanPosition){
+            pytorchSpans = spans.slice(pytorchSpanPosition + pytorchSpanIdentifier.length + 1, tensorflowSpanPosition);
+            tensorflowSpans = spans.slice(tensorflowSpanPosition + tensorflowSpanIdentifier.length + 1, spans.length);
+        }else{
+            tensorflowSpans = spans.slice(tensorflowSpanPosition + tensorflowSpanIdentifier.length + 1, pytorchSpanPosition);
+            pytorchSpans = spans.slice(pytorchSpanPosition + pytorchSpanIdentifier.length + 1, spans.length);
+        }
+
+        return {
+            ...filteredCodeBlock,
+            pytorchSample: pytorchSpans ,
+            tensorflowSample: tensorflowSpans
+        }
+    };
+
+    const createFrameworkButtons = sample => {
+            const pytorchButton = document.createElement("button");
+            pytorchButton.innerText = "PyTorch";
+
+            const tensorflowButton = document.createElement("button");
+            tensorflowButton.innerText = "TensorFlow";
+
+            const selectorDiv = document.createElement("div");
+            selectorDiv.classList.add("framework-selector");
+            selectorDiv.appendChild(pytorchButton);
+            selectorDiv.appendChild(tensorflowButton);
+            sample.element.parentElement.prepend(selectorDiv);
+
+            // Init on PyTorch
+            sample.element.innerHTML = sample.pytorchSample;
+            pytorchButton.classList.add("selected");
+            tensorflowButton.classList.remove("selected");
+
+            pytorchButton.addEventListener("click", () => {
+                sample.element.innerHTML = sample.pytorchSample;
+                pytorchButton.classList.add("selected");
+                tensorflowButton.classList.remove("selected");
+            });
+            tensorflowButton.addEventListener("click", () => {
+               sample.element.innerHTML = sample.tensorflowSample;
+                tensorflowButton.classList.add("selected");
+                pytorchButton.classList.remove("selected");
+            });
+        };
+
+    codeBlocks
+        .map(element => {return {element: element.firstChild, innerText: element.innerText}})
+        .filter(codeBlock => codeBlock.innerText.includes(pytorchIdentifier) && codeBlock.innerText.includes(tensorflowIdentifier))
+        .map(getFrameworkSpans)
+        .forEach(createFrameworkButtons);
+}
+
+
 /*!
  * github-buttons v2.2.10
  * (c) 2019 なつき
@@ -74,6 +152,8 @@ function onLoad() {
     addCustomFooter();
     addGithubButton();
     parseGithubButtons();
+    addHfMenu();
+    platformToggle();
 }
 
 window.addEventListener("load", onLoad);

docs/source/bertology.rst

@@ -8,11 +8,11 @@ There is a growing field of study concerned with investigating the inner working
 * Are Sixteen Heads Really Better than One? by Paul Michel, Omer Levy, Graham Neubig: https://arxiv.org/abs/1905.10650
 * What Does BERT Look At? An Analysis of BERT's Attention by Kevin Clark, Urvashi Khandelwal, Omer Levy, Christopher D. Manning: https://arxiv.org/abs/1906.04341
 
-In order to help this new field develop, we have included a few additional features in the BERT/GPT/GPT-2 models to help people access the inner representations, mainly adapted  from the great work of Paul Michel (https://arxiv.org/abs/1905.10650):
+In order to help this new field develop, we have included a few additional features in the BERT/GPT/GPT-2 models to help people access the inner representations, mainly adapted from the great work of Paul Michel (https://arxiv.org/abs/1905.10650):
 
 
 * accessing all the hidden-states of BERT/GPT/GPT-2,
 * accessing all the attention weights for each head of BERT/GPT/GPT-2,
 * retrieving heads output values and gradients to be able to compute head importance score and prune head as explained in https://arxiv.org/abs/1905.10650.
 
-To help you understand and use these features, we have added a specific example script: `bertology.py <https://github.com/huggingface/transformers/blob/master/examples/run_bertology.py>`_ while extract information and prune a model pre-trained on GLUE.
+To help you understand and use these features, we have added a specific example script: `bertology.py <https://github.com/huggingface/transformers/blob/master/examples/bertology/run_bertology.py>`_ while extract information and prune a model pre-trained on GLUE.

docs/source/conf.py

@@ -14,19 +14,19 @@
 #
 import os
 import sys
-sys.path.insert(0, os.path.abspath('../..'))
+sys.path.insert(0, os.path.abspath('../../src'))
 
 
 # -- Project information -----------------------------------------------------
 
 project = u'transformers'
-copyright = u'2019, huggingface'
+copyright = u'2020, huggingface'
 author = u'huggingface'
 
 # The short X.Y version
 version = u''
 # The full version, including alpha/beta/rc tags
-release = u'2.2.2'
+release = u'2.11.0'
 
 
 # -- General configuration ---------------------------------------------------
@@ -105,6 +105,12 @@ 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 
+# icon file (.ico).
+html_favicon = 'favicon.ico'
+
 
 # -- Options for HTMLHelp output ---------------------------------------------
 

docs/source/converting_tensorflow_models.rst

@@ -3,10 +3,16 @@ Converting Tensorflow Checkpoints
 
 A command-line interface is provided to convert original Bert/GPT/GPT-2/Transformer-XL/XLNet/XLM checkpoints in models than be loaded using the ``from_pretrained`` methods of the library.
 
+.. note::
+    Since 2.3.0 the conversion script is now part of the transformers CLI (**transformers-cli**)
+    available in any transformers >= 2.3.0 installation.
+
+    The documentation below reflects the **transformers-cli convert** command format.
+
 BERT
 ^^^^
 
-You can convert any TensorFlow checkpoint for BERT (in particular `the pre-trained models released by Google <https://github.com/google-research/bert#pre-trained-models>`_\ ) in a PyTorch save file by using the `convert_tf_checkpoint_to_pytorch.py <https://github.com/huggingface/transformers/blob/master/transformers/convert_tf_checkpoint_to_pytorch.py>`_ script.
+You can convert any TensorFlow checkpoint for BERT (in particular `the pre-trained models released by Google <https://github.com/google-research/bert#pre-trained-models>`_\ ) in a PyTorch save file by using the `convert_bert_original_tf_checkpoint_to_pytorch.py <https://github.com/huggingface/transformers/blob/master/src/transformers/convert_bert_original_tf_checkpoint_to_pytorch.py>`_ script.
 
 This CLI takes as input a TensorFlow checkpoint (three files starting with ``bert_model.ckpt``\ ) and the associated configuration file (\ ``bert_config.json``\ ), and creates a PyTorch model for this configuration, loads the weights 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>`_\ ).
 
@@ -20,13 +26,33 @@ Here is an example of the conversion process for a pre-trained ``BERT-Base Uncas
 
    export BERT_BASE_DIR=/path/to/bert/uncased_L-12_H-768_A-12
 
-   transformers bert \
-     $BERT_BASE_DIR/bert_model.ckpt \
-     $BERT_BASE_DIR/bert_config.json \
-     $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>`__.
 
+ALBERT
+^^^^^^
+
+Convert TensorFlow model checkpoints of ALBERT to PyTorch using the `convert_albert_original_tf_checkpoint_to_pytorch.py <https://github.com/huggingface/transformers/blob/master/src/transformers/convert_bert_original_tf_checkpoint_to_pytorch.py>`_ script.
+
+The CLI takes as input a TensorFlow checkpoint (three files starting with ``model.ckpt-best``\ ) and the accompanying configuration file (\ ``albert_config.json``\ ), then creates and saves a PyTorch model. To run this conversion you will need to have TensorFlow and PyTorch installed.
+
+Here is an example of the conversion process for the pre-trained ``ALBERT Base`` model:
+
+.. code-block:: shell
+
+   export ALBERT_BASE_DIR=/path/to/albert/albert_base
+
+   transformers-cli convert --model_type albert \
+     --tf_checkpoint $ALBERT_BASE_DIR/model.ckpt-best \
+     --config $ALBERT_BASE_DIR/albert_config.json \
+     --pytorch_dump_output $ALBERT_BASE_DIR/pytorch_model.bin
+
+You can download Google's pre-trained models for the conversion `here <https://github.com/google-research/albert#pre-trained-models>`__.
+
 OpenAI GPT
 ^^^^^^^^^^
 
@@ -36,10 +62,12 @@ Here is an example of the conversion process for a pre-trained OpenAI GPT model,
 
    export OPENAI_GPT_CHECKPOINT_FOLDER_PATH=/path/to/openai/pretrained/numpy/weights
 
-   transformers gpt \
-     $OPENAI_GPT_CHECKPOINT_FOLDER_PATH \
-     $PYTORCH_DUMP_OUTPUT \
-     [OPENAI_GPT_CONFIG]
+   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
 ^^^^^^^^^^^^
@@ -50,10 +78,11 @@ Here is an example of the conversion process for a pre-trained OpenAI GPT-2 mode
 
    export OPENAI_GPT2_CHECKPOINT_PATH=/path/to/gpt2/pretrained/weights
 
-   transformers gpt2 \
-     $OPENAI_GPT2_CHECKPOINT_PATH \
-     $PYTORCH_DUMP_OUTPUT \
-     [OPENAI_GPT2_CONFIG]
+   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
 ^^^^^^^^^^^^^^
@@ -64,27 +93,28 @@ Here is an example of the conversion process for a pre-trained Transformer-XL mo
 
    export TRANSFO_XL_CHECKPOINT_FOLDER_PATH=/path/to/transfo/xl/checkpoint
 
-   transformers transfo_xl \
-     $TRANSFO_XL_CHECKPOINT_FOLDER_PATH \
-     $PYTORCH_DUMP_OUTPUT \
-     [TRANSFO_XL_CONFIG]
+   transformers-cli convert --model_type transfo_xl \
+     --tf_checkpoint $TRANSFO_XL_CHECKPOINT_FOLDER_PATH \
+     --pytorch_dump_output $PYTORCH_DUMP_OUTPUT \
+     [--config TRANSFO_XL_CONFIG] \
+     [--finetuning_task_name TRANSFO_XL_FINETUNED_TASK]
 
 
 XLNet
 ^^^^^
 
-Here is an example of the conversion process for a pre-trained XLNet model, fine-tuned on STS-B using the TensorFlow script:
+Here is an example of the conversion process for a pre-trained XLNet model:
 
 .. code-block:: shell
 
    export TRANSFO_XL_CHECKPOINT_PATH=/path/to/xlnet/checkpoint
    export TRANSFO_XL_CONFIG_PATH=/path/to/xlnet/config
 
-   transformers xlnet \
-     $TRANSFO_XL_CHECKPOINT_PATH \
-     $TRANSFO_XL_CONFIG_PATH \
-     $PYTORCH_DUMP_OUTPUT \
-     STS-B \
+   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
@@ -96,6 +126,8 @@ Here is an example of the conversion process for a pre-trained XLM model:
 
    export XLM_CHECKPOINT_PATH=/path/to/xlm/checkpoint
 
-   transformers xlm \
-     $XLM_CHECKPOINT_PATH \
-     $PYTORCH_DUMP_OUTPUT \
+   transformers-cli convert --model_type xlm \
+     --tf_checkpoint $XLM_CHECKPOINT_PATH \
+     --pytorch_dump_output $PYTORCH_DUMP_OUTPUT
+    [--config XML_CONFIG] \
+    [--finetuning_task_name XML_FINETUNED_TASK]

docs/source/glossary.rst

@@ -0,0 +1,156 @@
+Glossary
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Every model is different yet bears similarities with the others. Therefore most models use the same inputs, which are
+detailed here alongside usage examples.
+
+Input IDs
+--------------------------
+
+The input ids are often the only required parameters to be passed to the model as input. *They are token indices,
+numerical representations of tokens building the sequences that will be used as input by the model*.
+
+Each tokenizer works differently but the underlying mechanism remains the same. Here's an example using the BERT
+tokenizer, which is a `WordPiece <https://arxiv.org/pdf/1609.08144.pdf>`__ tokenizer:
+
+::
+
+    from transformers import BertTokenizer
+    tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
+
+    sequence = "A Titan RTX has 24GB of VRAM"
+
+The tokenizer takes care of splitting the sequence into tokens available in the tokenizer vocabulary.
+
+::
+
+    # Continuation of the previous script
+    tokenized_sequence = tokenizer.tokenize(sequence)
+    assert tokenized_sequence == ['A', 'Titan', 'R', '##T', '##X', 'has', '24', '##GB', 'of', 'V', '##RA', '##M']
+
+These tokens can then be converted into IDs which are understandable by the model. Several methods are available for
+this, the recommended being `encode` or `encode_plus`, which leverage the Rust implementation of
+`huggingface/tokenizers <https://github.com/huggingface/tokenizers>`__ for peak performance.
+
+::
+
+    # Continuation of the previous script
+    encoded_sequence = tokenizer.encode(sequence)
+    assert encoded_sequence == [101, 138, 18696, 155, 1942, 3190, 1144, 1572, 13745, 1104, 159, 9664, 2107, 102]
+
+The `encode` and `encode_plus` methods automatically add "special tokens" which are special IDs the model uses.
+
+Attention mask
+--------------------------
+
+The attention mask is an optional argument used when batching sequences together. This argument indicates to the
+model which tokens should be attended to, and which should not.
+
+For example, consider these two sequences:
+
+::
+
+    from transformers import BertTokenizer
+    tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
+
+    sequence_a = "This is a short sequence."
+    sequence_b = "This is a rather long sequence. It is at least longer than the sequence A."
+
+    encoded_sequence_a = tokenizer.encode(sequence_a)
+    assert len(encoded_sequence_a) == 8
+
+    encoded_sequence_b = tokenizer.encode(sequence_b)
+    assert len(encoded_sequence_b) == 19
+
+These two sequences have different lengths and therefore can't be put together in a same tensor as-is. The first
+sequence needs to be padded up to the length of the second one, or the second one needs to be truncated down to
+the length of the first one.
+
+In the first case, the list of IDs will be extended by the padding indices:
+
+::
+
+    # Continuation of the previous script
+    padded_sequence_a = tokenizer.encode(sequence_a, max_length=19, pad_to_max_length=True)
+
+    assert padded_sequence_a == [101, 1188, 1110, 170, 1603, 4954,  119, 102,    0,    0,    0,    0,    0,    0,    0,    0,   0,   0,   0]
+    assert encoded_sequence_b == [101, 1188, 1110, 170, 1897, 1263, 4954, 119, 1135, 1110, 1120, 1655, 2039, 1190, 1103, 4954, 138, 119, 102]
+
+These can then be converted into a tensor in PyTorch or TensorFlow. The attention mask is a binary tensor indicating
+the position of the padded indices so that the model does not attend to them. For the
+:class:`~transformers.BertTokenizer`, :obj:`1` indicate a value that should be attended to while :obj:`0` indicate
+a padded value.
+
+The method :func:`~transformers.PreTrainedTokenizer.encode_plus` may be used to obtain the attention mask directly:
+
+::
+
+    # Continuation of the previous script
+    sequence_a_dict = tokenizer.encode_plus(sequence_a, max_length=19, pad_to_max_length=True)
+
+    assert sequence_a_dict['input_ids'] == [101, 1188, 1110, 170, 1603, 4954, 119, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+    assert sequence_a_dict['attention_mask'] == [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+
+
+Token Type IDs
+--------------------------
+
+Some models' purpose is to do sequence classification or question answering. These require two different sequences to
+be encoded in the same input IDs. They are usually separated by special tokens, such as the classifier and separator
+tokens. For example, the BERT model builds its two sequence input as such:
+
+::
+
+    from transformers import BertTokenizer
+    tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
+
+    # [CLS] SEQ_A [SEP] SEQ_B [SEP]
+
+    sequence_a = "HuggingFace is based in NYC"
+    sequence_b = "Where is HuggingFace based?"
+
+    encoded_sequence = tokenizer.encode(sequence_a, sequence_b)
+    assert tokenizer.decode(encoded_sequence) == "[CLS] HuggingFace is based in NYC [SEP] Where is HuggingFace based? [SEP]"
+
+This is enough for some models to understand where one sequence ends and where another begins. However, other models
+such as BERT have an additional mechanism, which are the segment IDs. The Token Type IDs are a binary mask identifying
+the different sequences in the model.
+
+We can leverage :func:`~transformers.PreTrainedTokenizer.encode_plus` to output the Token Type IDs for us:
+
+::
+
+    # Continuation of the previous script
+    encoded_dict = tokenizer.encode_plus(sequence_a, sequence_b)
+
+    assert encoded_dict['input_ids'] == [101, 20164, 10932, 2271, 7954, 1110, 1359, 1107, 17520, 102, 2777, 1110, 20164, 10932, 2271, 7954, 1359, 136, 102]
+    assert encoded_dict['token_type_ids'] == [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]
+
+The first sequence, the "context" used for the question, has all its tokens represented by :obj:`0`, whereas the
+question has all its tokens represented by :obj:`1`. Some models, like :class:`~transformers.XLNetModel` use an
+additional token represented by a :obj:`2`.
+
+
+Position IDs
+--------------------------
+
+The position IDs are used by the model to identify which token is at which position. Contrary to RNNs that have the
+position of each token embedded within them, transformers are unaware of the position of each token. The position
+IDs are created for this purpose.
+
+They are an optional parameter. If no position IDs are passed to the model, they are automatically created as absolute
+positional embeddings.
+
+Absolute positional embeddings are selected in the range ``[0, config.max_position_embeddings - 1]``. Some models
+use other types of positional embeddings, such as sinusoidal position embeddings or relative position embeddings.
+
+
+Feed Forward Chunking
+--------------------------
+
+In transformers two feed forward layers usually follows the self attention layer in each residual attention block. The intermediate embedding size of the feed forward layers is often bigger than the hidden size of the model (*e.g.* for ``bert-base-uncased``). 
+
+For an input of size ``[batch_size, sequence_length]``, the memory required to store the intermediate feed forward embeddings ``[batch_size, sequence_length, config.intermediate_size]`` can account for a large fraction of the memory use. The authors of `Reformer: The Efficient Transformer <https://arxiv.org/abs/2001.04451>`_ noticed that since the computation is independent of the ``sequence_length`` dimension, it is mathematically equivalent to compute the output embeddings of both feed forward layers ``[batch_size, config.hidden_size]_0, ..., [batch_size, config.hidden_size]_n``  individually and concat them afterward to ``[batch_size, sequence_length, config.hidden_size]`` with ``n = sequence_length``, which trades increased computation time against reduced memory use, but yields a mathematically **equivalent** result.
+
+For models employing the function :func:`~.transformers.apply_chunking_to_forward`, the ``chunk_size`` defines the number of output embeddings that are computed in parallel and thus defines the trade-off between memory and time complexity. 
+If ``chunk_size`` is set to 0, no feed forward chunking is done.

docs/source/index.rst

@@ -50,6 +50,8 @@ The library currently contains PyTorch and Tensorflow implementations, pre-train
 9. `CTRL <https://github.com/pytorch/fairseq/tree/master/examples/ctrl>`_ (from Salesforce), released together with the paper `CTRL: A Conditional Transformer Language Model for Controllable Generation <https://www.github.com/salesforce/ctrl>`_ by Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and Richard Socher.
 10. `CamemBERT <https://huggingface.co/transformers/model_doc/camembert.html>`_ (from FAIR, Inria, Sorbonne Université) released together with the paper `CamemBERT: a Tasty French Language Model <https://arxiv.org/abs/1911.03894>`_ by Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suarez, Yoann Dupont, Laurent Romary, Eric Villemonte de la Clergerie, Djame Seddah, and Benoît Sagot.
 11. `ALBERT <https://github.com/google-research/ALBERT>`_ (from Google Research), released together with the paper a `ALBERT: A Lite BERT for Self-supervised Learning of Language Representations <https://arxiv.org/abs/1909.11942>`_ by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.
+12. `XLM-RoBERTa <https://github.com/pytorch/fairseq/tree/master/examples/xlmr>`_ (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.
+13. `FlauBERT <https://github.com/getalp/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.
 
 .. toctree::
     :maxdepth: 2
@@ -57,7 +59,10 @@ The library currently contains PyTorch and Tensorflow implementations, pre-train
 
     installation
     quickstart
+    glossary
     pretrained_models
+    usage
+    model_sharing
     examples
     notebooks
     serialization
@@ -75,6 +80,7 @@ The library currently contains PyTorch and Tensorflow implementations, pre-train
     main_classes/configuration
     main_classes/model
     main_classes/tokenizer
+    main_classes/pipelines
     main_classes/optimizer_schedules
     main_classes/processors
 
@@ -83,6 +89,7 @@ The library currently contains PyTorch and Tensorflow implementations, pre-train
     :caption: Package Reference
 
     model_doc/auto
+    model_doc/encoderdecoder
     model_doc/bert
     model_doc/gpt
     model_doc/transformerxl
@@ -94,3 +101,12 @@ The library currently contains PyTorch and Tensorflow implementations, pre-train
     model_doc/ctrl
     model_doc/camembert
     model_doc/albert
+    model_doc/xlmroberta
+    model_doc/flaubert
+    model_doc/bart
+    model_doc/t5
+    model_doc/electra
+    model_doc/dialogpt
+    model_doc/reformer
+    model_doc/marian
+    model_doc/longformer

docs/source/installation.md

@@ -1,6 +1,6 @@
 # Installation
 
-Transformers is tested on Python 2.7 and 3.5+ (examples are tested only on python 3.5+) and PyTorch 1.1.0
+Transformers is tested on Python 3.6+ and PyTorch 1.1.0
 
 ## With pip
 
@@ -17,34 +17,18 @@ To install from source, clone the repository and install with:
 ``` bash
 git clone https://github.com/huggingface/transformers.git
 cd transformers
-pip install [--editable] .
+pip install .
 ```
 
 ## Tests
 
-An extensive test suite is included to test the library behavior and several examples. Library tests can be found in the [tests folder](https://github.com/huggingface/transformers/tree/master/transformers/tests) and examples tests in the [examples folder](https://github.com/huggingface/transformers/tree/master/examples).
+An extensive test suite is included to test the library behavior and several examples. Library tests can be found in the [tests folder](https://github.com/huggingface/transformers/tree/master/tests) and examples tests in the [examples folder](https://github.com/huggingface/transformers/tree/master/examples).
 
-Tests can be run using `unittest` or `pytest` (install pytest if needed with `pip install pytest`).
-
-Run all the tests from the root of the cloned repository with the commands:
-
-```bash
-python -m unittest discover -s transformers/tests -p "*test.py" -t .
-python -m unittest discover -s examples -p "*test.py" -t examples
-```
-
-or
-
-``` bash
-python -m pytest -sv ./transformers/tests/
-python -m pytest -sv ./examples/
-```
-
-By default, slow tests are skipped. Set the `RUN_SLOW` environment variable to `yes` to run them.
+Refer to the [contributing guide](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md#tests) for details about running tests.
 
 ## OpenAI GPT original tokenization workflow
 
-If you want to reproduce the original tokenization process of the `OpenAI GPT` paper, you will need to install `ftfy` (use version 4.4.3 if you are using Python 2) and `SpaCy`:
+If you want to reproduce the original tokenization process of the `OpenAI GPT` paper, you will need to install `ftfy` and `SpaCy`:
 
 ``` bash
 pip install spacy ftfy==4.4.3

docs/source/main_classes/model.rst

@@ -14,6 +14,12 @@ The base class ``PreTrainedModel`` implements the common methods for loading/sav
 .. autoclass:: transformers.PreTrainedModel
     :members:
 
+``Helper Functions``
+~~~~~~~~~~~~~~~~~~~~~
+
+.. autofunction:: transformers.apply_chunking_to_forward
+
+
 ``TFPreTrainedModel``
 ~~~~~~~~~~~~~~~~~~~~~
 

docs/source/main_classes/optimizer_schedules.rst

@@ -20,14 +20,12 @@ The ``.optimization`` module provides:
     :members:
 
 .. autofunction:: transformers.create_optimizer
-    :members:
 
 Schedules
 ----------------------------------------------------
 
 Learning Rate Schedules
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 .. autofunction:: transformers.get_constant_schedule
 
 
@@ -39,7 +37,6 @@ Learning Rate Schedules
 
 
 .. autofunction:: transformers.get_cosine_schedule_with_warmup
-    :members:
 
 .. image:: /imgs/warmup_cosine_schedule.png
     :target: /imgs/warmup_cosine_schedule.png
@@ -63,7 +60,7 @@ Learning Rate Schedules
 ``Warmup``
 ~~~~~~~~~~~~~~~~
 
-.. autoclass:: transformers.Warmup
+.. autoclass:: transformers.WarmUp
     :members:
 
 Gradient Strategies

docs/source/main_classes/pipelines.rst

@@ -0,0 +1,74 @@
+Pipelines
+----------------------------------------------------
+
+The pipelines are a great and easy way to use models for inference. These pipelines are objects that abstract most
+of the complex code from the library, offering a simple API dedicated to several tasks, including Named Entity
+Recognition, Masked Language Modeling, Sentiment Analysis, Feature Extraction and Question Answering.
+
+There are two categories of pipeline abstractions to be aware about:
+
+- The :class:`~transformers.pipeline` which is the most powerful object encapsulating all other pipelines
+- The other task-specific pipelines, such as :class:`~transformers.NerPipeline`
+  or :class:`~transformers.QuestionAnsweringPipeline`
+
+The pipeline abstraction
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The `pipeline` abstraction is a wrapper around all the other available pipelines. It is instantiated as any
+other pipeline but requires an additional argument which is the `task`.
+
+.. autoclass:: transformers.pipeline
+    :members:
+
+
+The task specific pipelines
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Parent class: Pipeline
+=========================================
+
+.. autoclass:: transformers.Pipeline
+    :members: predict, transform, save_pretrained
+
+NerPipeline
+==========================================
+
+.. autoclass:: transformers.NerPipeline
+
+TokenClassificationPipeline
+==========================================
+
+This class is an alias of the :class:`~transformers.NerPipeline` defined above. Please refer to that pipeline for
+documentation and usage examples.
+
+FillMaskPipeline
+==========================================
+
+.. autoclass:: transformers.FillMaskPipeline
+
+FeatureExtractionPipeline
+==========================================
+
+.. autoclass:: transformers.FeatureExtractionPipeline
+
+TextClassificationPipeline
+==========================================
+
+.. autoclass:: transformers.TextClassificationPipeline
+
+QuestionAnsweringPipeline
+==========================================
+
+.. autoclass:: transformers.QuestionAnsweringPipeline
+
+
+SummarizationPipeline
+==========================================
+
+.. autoclass:: transformers.SummarizationPipeline
+
+
+TextGenerationPipeline
+==========================================
+
+.. autoclass:: transformers.TextGenerationPipeline

docs/source/main_classes/processors.rst

@@ -54,7 +54,7 @@ Additionally, the following method  can be used to load values from a data file
 Example usage
 ^^^^^^^^^^^^^^^^^^^^^^^^^
 
-An example using these processors is given in the `run_glue.py <https://github.com/huggingface/pytorch-transformers/blob/master/examples/run_glue.py>`__ script.
+An example using these processors is given in the `run_glue.py <https://github.com/huggingface/pytorch-transformers/blob/master/examples/text-classification/run_glue.py>`__ script.
 
 
 XNLI
@@ -63,7 +63,7 @@ XNLI
 `The Cross-Lingual NLI Corpus (XNLI) <https://www.nyu.edu/projects/bowman/xnli/>`__ is a benchmark that evaluates
 the quality of cross-lingual text representations. 
 XNLI is crowd-sourced dataset based on `MultiNLI <http://www.nyu.edu/projects/bowman/multinli/>`: pairs of text are labeled with textual entailment 
-annotations for 15 different languages (including both high-ressource language such as English and low-ressource languages such as Swahili).
+annotations for 15 different languages (including both high-resource language such as English and low-resource languages such as Swahili).
 
 It was released together with the paper
 `XNLI: Evaluating Cross-lingual Sentence Representations <https://arxiv.org/abs/1809.05053>`__
@@ -74,7 +74,7 @@ This library hosts the processor to load the XNLI data:
 Please note that since the gold labels are available on the test set, evaluation is performed on the test set.
 
 An example using these processors is given in the
-`run_xnli.py <https://github.com/huggingface/pytorch-transformers/blob/master/examples/run_xnli.py>`__ script.
+`run_xnli.py <https://github.com/huggingface/pytorch-transformers/blob/master/examples/text-classification/run_xnli.py>`__ script.
 
 
 SQuAD
@@ -150,4 +150,4 @@ Example::
 
 
 Another example using these processors is given in the
-`run_squad.py <https://github.com/huggingface/transformers/blob/master/examples/run_squad.py>`__ script.
+`run_squad.py <https://github.com/huggingface/transformers/blob/master/examples/question-answering/run_squad.py>`__ script.

docs/source/main_classes/tokenizer.rst

@@ -1,16 +1,38 @@
 Tokenizer
 ----------------------------------------------------
 
-The base class ``PreTrainedTokenizer`` implements the common methods for loading/saving a tokenizer either from a local file or directory, or from a pretrained tokenizer provided by the library (downloaded from HuggingFace's AWS S3 repository).
+A tokenizer is in charge of preparing the inputs for a model. The library comprise tokenizers for all the models. Most of the tokenizers are available in two flavors: a full python implementation and a "Fast" implementation based on the Rust library `tokenizers`. The "Fast" implementations allows (1) a significant speed-up in particular when doing batched tokenization and (2) additional methods to map between the original string (character and words) and the token space (e.g. getting the index of the token comprising a given character or the span of characters corresponding to a given token). Currently no "Fast" implementation is available for the SentencePiece-based tokenizers (for T5, ALBERT, CamemBERT, XLMRoBERTa and XLNet models).
 
-``PreTrainedTokenizer`` is the main entry point into tokenizers as it also implements the main methods for using all the tokenizers:
+The base classes ``PreTrainedTokenizer`` and ``PreTrainedTokenizerFast`` implements the common methods for encoding string inputs in model inputs (see below) and instantiating/saving python and "Fast" tokenizers either from a local file or directory or from a pretrained tokenizer provided by the library (downloaded from HuggingFace's AWS S3 repository).
 
-- tokenizing, converting tokens to ids and back and encoding/decoding,
+``PreTrainedTokenizer`` and ``PreTrainedTokenizerFast`` thus implements the main methods for using all the tokenizers:
+
+- tokenizing (spliting strings in sub-word token strings), converting tokens strings to ids and back, and encoding/decoding (i.e. tokenizing + convert to integers),
 - adding new tokens to the vocabulary in a way that is independant of the underlying structure (BPE, SentencePiece...),
-- managing special tokens (adding them, assigning them to roles, making sure they are not split during tokenization)
+- managing special tokens like mask, beginning-of-sentence, etc tokens (adding them, assigning them to attributes in the tokenizer for easy access and making sure they are not split during tokenization)
+
+``BatchEncoding`` holds the output of the tokenizer's encoding methods (``encode_plus`` and ``batch_encode_plus``) and is derived from a Python dictionary. When the tokenizer is a pure python tokenizer, this class behave just like a standard python dictionary and hold the various model inputs computed by these methodes (``input_ids``, ``attention_mask``...). When the tokenizer is a "Fast" tokenizer (i.e. backed by HuggingFace tokenizers library), this class provides in addition several advanced alignement methods which can be used to map between the original string (character and words) and the token space (e.g. getting the index of the token comprising a given character or the span of characters corresponding to a given token).
 
 ``PreTrainedTokenizer``
 ~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.PreTrainedTokenizer
     :members:
+
+``PreTrainedTokenizerFast``
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.PreTrainedTokenizerFast
+    :members:
+
+``BatchEncoding``
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.BatchEncoding
+    :members:
+
+``SpecialTokensMixin``
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.SpecialTokensMixin
+    :members:

docs/source/migration.md

@@ -1,5 +1,18 @@
-# Migrating from pytorch-pretrained-bert
+# Migrating from previous packages
 
+## Migrating from pytorch-transformers to transformers
+
+Here is a quick summary of what you should take care of when migrating from `pytorch-transformers` to `transformers`.
+
+### Positional order of some models' keywords inputs (`attention_mask`, `token_type_ids`...) changed
+
+To be able to use Torchscript (see #1010, #1204 and #1195) the specific order of some models **keywords inputs** (`attention_mask`, `token_type_ids`...) has been changed.
+
+If you used to call the models with keyword names for keyword arguments, e.g. `model(inputs_ids, attention_mask=attention_mask, token_type_ids=token_type_ids)`, this should not cause any change.
+
+If you used to call the models with positional inputs for keyword arguments, e.g. `model(inputs_ids, attention_mask, token_type_ids)`, you may have to double check the exact order of input arguments.
+
+## Migrating from pytorch-pretrained-bert
 
 Here is a quick summary of what you should take care of when migrating from `pytorch-pretrained-bert` to `transformers`
 
@@ -27,7 +40,7 @@ loss = outputs[0]
 # In transformers you can also have access to the logits:
 loss, logits = outputs[:2]
 
-# And even the attention weigths if you configure the model to output them (and other outputs too, see the docstrings and documentation)
+# And even the attention weights if you configure the model to output them (and other outputs too, see the docstrings and documentation)
 model = BertForSequenceClassification.from_pretrained('bert-base-uncased', output_attentions=True)
 outputs = model(input_ids, labels=labels)
 loss, logits, attentions = outputs

docs/source/model_doc/albert.rst

@@ -1,64 +1,110 @@
 ALBERT
 ----------------------------------------------------
 
-``AlbrtConfig``
+Overview
+~~~~~~~~~~~~~~~~~~~~~
+
+The ALBERT model was proposed in `ALBERT: A Lite BERT for Self-supervised Learning of Language Representations <https://arxiv.org/abs/1909.11942>`_
+by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut. It presents
+two parameter-reduction techniques to lower memory consumption and increase the training speed of BERT:
+
+- Splitting the embedding matrix into two smaller matrices
+- Using repeating layers split among groups
+
+The abstract from the paper is the following:
+
+*Increasing model size when pretraining natural language representations often results in improved performance on
+downstream tasks. However, at some point further model increases become harder due to GPU/TPU memory limitations,
+longer training times, and unexpected model degradation. To address these problems, we present two parameter-reduction
+techniques to lower memory consumption and increase the training speed of BERT. Comprehensive empirical evidence shows
+that our proposed methods lead to models that scale much better compared to the original BERT. We also use a
+self-supervised loss that focuses on modeling inter-sentence coherence, and show it consistently helps downstream
+tasks with multi-sentence inputs. As a result, our best model establishes new state-of-the-art results on the GLUE,
+RACE, and SQuAD benchmarks while having fewer parameters compared to BERT-large.*
+
+Tips:
+
+- ALBERT is a model with absolute position embeddings so it's usually advised to pad the inputs on
+  the right rather than the left.
+- ALBERT uses repeating layers which results in a small memory footprint, however the computational cost remains
+  similar to a BERT-like architecture with the same number of hidden layers as it has to iterate through the same
+  number of (repeating) layers.
+
+The original code can be found `here <https://github.com/google-research/ALBERT>`_.
+
+AlbertConfig
 ~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.AlbertConfig
     :members:
 
 
-``AlbertTokenizer``
+AlbertTokenizer
 ~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.AlbertTokenizer
-    :members:
+    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
+        create_token_type_ids_from_sequences, save_vocabulary
 
 
-``AlbertModel``
+AlbertModel
 ~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.AlbertModel
     :members:
 
 
-``AlbertForMaskedLM``
+AlbertForMaskedLM
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.AlbertForMaskedLM
     :members:
 
 
-``AlbertForSequenceClassification``
-~~~~~~~~~~~~~~~~~~~~~~~~~~
+AlbertForSequenceClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.AlbertForSequenceClassification
     :members:
 
 
-``AlbertForQuestionAnswering``
+AlbertForQuestionAnswering
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.AlbertForQuestionAnswering
     :members:
 
 
-``TFAlbertModel``
+TFAlbertModel
 ~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFAlbertModel
     :members:
 
 
-``TFAlbertForMaskedLM``
+TFAlbertForMaskedLM
 ~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFAlbertForMaskedLM
     :members:
 
 
-``TFAlbertForSequenceClassification``
+TFAlbertForSequenceClassification
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFAlbertForSequenceClassification
     :members:
+
+
+TFAlbertForMultipleChoice
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFAlbertForMultipleChoice
+    :members:
+
+
+TFAlbertForQuestionAnswering
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFAlbertForQuestionAnswering
+    :members:

docs/source/model_doc/auto.rst

@@ -3,7 +3,7 @@ AutoModels
 
 In many cases, the architecture you want to use can be guessed from the name or the path of the pretrained model you are supplying to the ``from_pretrained`` method.
 
-AutoClasses are here to do this job for you so that you automatically retreive the relevant model given the name/path to the pretrained weights/config/vocabulary:
+AutoClasses are here to do this job for you so that you automatically retrieve the relevant model given the name/path to the pretrained weights/config/vocabulary:
 
 Instantiating one of ``AutoModel``, ``AutoConfig`` and ``AutoTokenizer`` will directly create a class of the relevant architecture (ex: ``model = AutoModel.from_pretrained('bert-base-cased')`` will create a instance of ``BertModel``).
 
@@ -15,6 +15,13 @@ Instantiating one of ``AutoModel``, ``AutoConfig`` and ``AutoTokenizer`` will di
     :members:
 
 
+``AutoTokenizer``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.AutoTokenizer
+    :members:
+
+
 ``AutoModel``
 ~~~~~~~~~~~~~~~~~~~~~
 
@@ -22,8 +29,37 @@ Instantiating one of ``AutoModel``, ``AutoConfig`` and ``AutoTokenizer`` will di
     :members:
 
 
-``AutoTokenizer``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+``AutoModelForPreTraining``
+~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: transformers.AutoTokenizer
+.. autoclass:: transformers.AutoModelForPreTraining
     :members:
+
+
+``AutoModelWithLMHead``
+~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.AutoModelWithLMHead
+    :members:
+
+
+``AutoModelForSequenceClassification``
+~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.AutoModelForSequenceClassification
+    :members:
+
+
+``AutoModelForQuestionAnswering``
+~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.AutoModelForQuestionAnswering
+    :members:
+
+
+``AutoModelForTokenClassification``
+~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.AutoModelForTokenClassification
+    :members:
+

docs/source/model_doc/bart.rst

@@ -0,0 +1,56 @@
+Bart
+----------------------------------------------------
+**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
+@sshleifer
+
+Paper
+~~~~~
+The Bart model was `proposed <https://arxiv.org/abs/1910.13461>`_ by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer on 29 Oct, 2019.
+According to the abstract,
+
+- Bart uses a standard seq2seq/machine translation architecture with a bidirectional encoder (like BERT) and a left-to-right decoder (like GPT).
+- The pretraining task involves randomly shuffling the order of the original sentences and a novel in-filling scheme, where spans of text are replaced with a single mask token.
+- BART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. It matches the performance of RoBERTa with comparable training resources on GLUE and SQuAD, achieves new state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains of up to 6 ROUGE.
+
+The Authors' code can be found `here <https://github.com/pytorch/fairseq/tree/master/examples/bart>`_
+
+
+Implementation Notes
+~~~~~~~~~~~~~~~~~~~~
+- Bart doesn't use :obj:`token_type_ids` for sequence classification. Use BartTokenizer.encode to get the proper splitting.
+- The forward pass of ``BartModel`` will create decoder inputs (using the helper function ``transformers.modeling_bart._prepare_bart_decoder_inputs``)  if they are not passed. This is different than some other modeling APIs.
+- Model predictions are intended to be identical to the original implementation. This only works, however, if the string you pass to ``fairseq.encode`` starts with a space.
+- ``BartForConditionalGeneration.generate`` should be used for conditional generation tasks like summarization, see the example in that docstrings
+- Models that load the ``"facebook/bart-large-cnn"`` weights will not have a ``mask_token_id``, or be able to perform mask filling tasks.
+
+
+
+BartModel
+~~~~~~~~~~~~~
+
+.. autoclass:: transformers.BartModel
+    :members: forward
+
+.. autofunction:: transformers.modeling_bart._prepare_bart_decoder_inputs
+
+
+BartForConditionalGeneration
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.BartForConditionalGeneration
+    :members: generate, forward
+
+
+BartForSequenceClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.BartForSequenceClassification
+    :members: forward
+
+BartConfig
+~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.BartConfig
+    :members:
+

docs/source/model_doc/bert.rst

@@ -1,126 +1,170 @@
 BERT
 ----------------------------------------------------
 
-``BertConfig``
+Overview
+~~~~~~~~~~~~~~~~~~~~~
+
+The BERT model was proposed in `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. It's a bidirectional transformer
+pre-trained using a combination of masked language modeling objective and next sentence prediction
+on a large corpus comprising the Toronto Book Corpus and Wikipedia.
+
+The abstract from the paper is the following:
+
+*We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations
+from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional
+representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result,
+the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models
+for a wide range of tasks, such as question answering and language inference, without substantial task-specific
+architecture modifications.*
+
+*BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural
+language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI
+accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute
+improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).*
+
+Tips:
+
+- BERT is a model with absolute position embeddings so it's usually advised to pad the inputs on
+  the right rather than the left.
+- BERT was trained with a masked language modeling (MLM) objective. It is therefore efficient at predicting masked
+  tokens and at NLU in general, but is not optimal for text generation. Models trained with a causal language
+  modeling (CLM) objective are better in that regard.
+- Alongside MLM, BERT was trained using a next sentence prediction (NSP) objective using the [CLS] token as a sequence
+  approximate. The user may use this token (the first token in a sequence built with special tokens) to get a sequence
+  prediction rather than a token prediction. However, averaging over the sequence may yield better results than using
+  the [CLS] token.
+
+The original code can be found `here <https://github.com/google-research/bert>`_.
+
+BertConfig
 ~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.BertConfig
     :members:
 
 
-``BertTokenizer``
+BertTokenizer
 ~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.BertTokenizer
+    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
+        create_token_type_ids_from_sequences, save_vocabulary
+
+
+BertTokenizerFast
+~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.BertTokenizerFast
     :members:
 
 
-``BertModel``
+BertModel
 ~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.BertModel
     :members:
 
 
-``BertForPreTraining``
+BertForPreTraining
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.BertForPreTraining
     :members:
 
 
-``BertForMaskedLM``
+BertForMaskedLM
 ~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.BertForMaskedLM
     :members:
 
 
-``BertForNextSentencePrediction``
+BertForNextSentencePrediction
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.BertForNextSentencePrediction
     :members:
 
 
-``BertForSequenceClassification``
+BertForSequenceClassification
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.BertForSequenceClassification
     :members:
 
 
-``BertForMultipleChoice``
+BertForMultipleChoice
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.BertForMultipleChoice
     :members:
 
 
-``BertForTokenClassification``
+BertForTokenClassification
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.BertForTokenClassification
     :members:
 
 
-``BertForQuestionAnswering``
+BertForQuestionAnswering
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.BertForQuestionAnswering
     :members:
 
 
-``TFBertModel``
+TFBertModel
 ~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFBertModel
     :members:
 
 
-``TFBertForPreTraining``
+TFBertForPreTraining
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFBertForPreTraining
     :members:
 
 
-``TFBertForMaskedLM``
+TFBertForMaskedLM
 ~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFBertForMaskedLM
     :members:
 
 
-``TFBertForNextSentencePrediction``
+TFBertForNextSentencePrediction
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFBertForNextSentencePrediction
     :members:
 
 
-``TFBertForSequenceClassification``
+TFBertForSequenceClassification
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFBertForSequenceClassification
     :members:
 
 
-``TFBertForMultipleChoice``
+TFBertForMultipleChoice
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFBertForMultipleChoice
     :members:
 
 
-``TFBertForTokenClassification``
+TFBertForTokenClassification
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFBertForTokenClassification
     :members:
 
 
-``TFBertForQuestionAnswering``
+TFBertForQuestionAnswering
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFBertForQuestionAnswering

docs/source/model_doc/camembert.rst

@@ -1,50 +1,102 @@
 CamemBERT
 ----------------------------------------------------
 
-``CamembertConfig``
-~~~~~~~~~~~~~~~~~~~~~
+The CamemBERT model was proposed in `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. It is based on Facebook's RoBERTa model released in 2019. It is a model
+trained on 138GB of French text.
+
+The abstract from the paper is the following:
+
+*Pretrained language models are now ubiquitous in Natural Language Processing. Despite their success,
+most available models have either been trained on English data or on the concatenation of data in multiple
+languages. This makes practical use of such models --in all languages except English-- very limited. Aiming
+to address this issue for French, we release CamemBERT, a French version of the Bi-directional Encoders for
+Transformers (BERT). We measure the performance of CamemBERT compared to multilingual models in multiple
+downstream tasks, namely part-of-speech tagging, dependency parsing, named-entity recognition, and natural
+language inference. CamemBERT improves the state of the art for most of the tasks considered. We release the
+pretrained model for CamemBERT hoping to foster research and downstream applications for French NLP.*
+
+Tips:
+
+- This implementation is the same as RoBERTa. Refer to the `documentation of RoBERTa <./roberta.html>`__ for usage
+  examples as well as the information relative to the inputs and outputs.
+
+The original code can be found `here <https://camembert-model.fr/>`_.
+
+CamembertConfig
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.CamembertConfig
     :members:
 
 
-``CamembertTokenizer``
-~~~~~~~~~~~~~~~~~~~~~
+CamembertTokenizer
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.CamembertTokenizer
-    :members:
+    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
+        create_token_type_ids_from_sequences, save_vocabulary
 
 
-``CamembertModel``
-~~~~~~~~~~~~~~~~~~~~
+CamembertModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.CamembertModel
     :members:
 
 
-``CamembertForMaskedLM``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+CamembertForMaskedLM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.CamembertForMaskedLM
     :members:
 
 
-``CamembertForSequenceClassification``
-~~~~~~~~~~~~~~~~~~~~~~~~~~
+CamembertForSequenceClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.CamembertForSequenceClassification
     :members:
 
 
-``CamembertForMultipleChoice``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+CamembertForMultipleChoice
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.CamembertForMultipleChoice
     :members:
 
 
-``CamembertForTokenClassification``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+CamembertForTokenClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.CamembertForTokenClassification
     :members:
+
+
+TFCamembertModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFCamembertModel
+    :members:
+
+
+TFCamembertForMaskedLM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFCamembertForMaskedLM
+    :members:
+
+
+TFCamembertForSequenceClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFCamembertForSequenceClassification
+    :members:
+
+
+TFCamembertForTokenClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFCamembertForTokenClassification
+    :members:

docs/source/model_doc/ctrl.rst

@@ -1,47 +1,75 @@
 CTRL
 ----------------------------------------------------
 
-Note: if you fine-tune a CTRL model using the Salesforce code (https://github.com/salesforce/ctrl),
-you'll be able to convert from TF to our HuggingFace/Transformers format using the 
-``convert_tf_to_huggingface_pytorch.py`` script (see `issue #1654 <https://github.com/huggingface/transformers/issues/1654>`_).
+CTRL model was proposed in `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.
+It's a causal (unidirectional) transformer pre-trained using language modeling on a very large
+corpus of ~140 GB of text data with the first token reserved as a control code (such as Links, Books, Wikipedia etc.).
+
+The abstract from the paper is the following:
+
+*Large-scale language models show promising text generation capabilities, but users cannot easily control particular
+aspects of the generated text. We release CTRL, a 1.63 billion-parameter conditional transformer language model,
+trained to condition on control codes that govern style, content, and task-specific behavior. Control codes were
+derived from structure that naturally co-occurs with raw text, preserving the advantages of unsupervised learning
+while providing more explicit control over text generation. These codes also allow CTRL to predict which parts of
+the training data are most likely given a sequence. This provides a potential method for analyzing large amounts
+of data via model-based source attribution.*
+
+Tips:
+
+- CTRL makes use of control codes to generate text: it requires generations to be started by certain words, sentences
+  or links to generate coherent text. Refer to the `original implementation <https://github.com/salesforce/ctrl>`__
+  for more information.
+- CTRL is a model with absolute position embeddings so it's usually advised to pad the inputs on
+  the right rather than the left.
+- CTRL was trained with a causal language modeling (CLM) objective and is therefore powerful at predicting the next
+  token in a sequence. Leveraging this feature allows CTRL to generate syntactically coherent text as
+  it can be observed in the `run_generation.py` example script.
+- The PyTorch models can take the `past` as input, which is the previously computed key/value attention pairs. Using
+  this `past` value prevents the model from re-computing pre-computed values in the context of text generation.
+  See `reusing the past in generative models <../quickstart.html#using-the-past>`_ for more information on the usage
+  of this argument.
+
+The original code can be found `here <https://github.com/salesforce/ctrl>`_.
 
 
-``CTRLConfig``
+CTRLConfig
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.CTRLConfig
     :members:
 
 
-``CTRLTokenizer``
+CTRLTokenizer
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.CTRLTokenizer
-    :members:
+    :members: save_vocabulary
 
 
-``CTRLModel``
+CTRLModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.CTRLModel
     :members:
 
 
-``CTRLLMHeadModel``
+CTRLLMHeadModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.CTRLLMHeadModel
     :members:
 
 
-``TFCTRLModel``
+TFCTRLModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFCTRLModel
     :members:
 
 
-``TFCTRLLMHeadModel``
+TFCTRLLMHeadModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFCTRLLMHeadModel

docs/source/model_doc/dialogpt.rst

@@ -0,0 +1,39 @@
+DialoGPT
+----------------------------------------------------
+
+Overview
+~~~~~~~~~~~~~~~~~~~~~
+
+DialoGPT was proposed in
+`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.
+It's a GPT2 Model trained on 147M conversation-like exchanges extracted from Reddit.
+
+The abstract from the paper is the following:
+
+*We present a large, tunable neural conversational response generation model, DialoGPT (dialogue generative pre-trained transformer). 
+Trained on 147M conversation-like exchanges extracted from Reddit comment chains over a period spanning from 2005 through 2017, DialoGPT extends the Hugging Face PyTorch transformer to attain a performance close to human both in terms of automatic and human evaluation in single-turn dialogue settings.
+We show that conversational systems that leverage DialoGPT generate more relevant, contentful and context-consistent responses than strong baseline systems.
+The pre-trained model and training pipeline are publicly released to facilitate research into neural response generation and the development of more intelligent open-domain dialogue systems.*
+
+Tips:
+
+- DialoGPT is a model with absolute position embeddings so it's usually advised to pad the inputs on
+  the right rather than the left.
+- DialoGPT was trained with a causal language modeling (CLM) objective on conversational data and is therefore powerful at response generation in open-domain dialogue systems.
+- DialoGPT enables the user to create a chat bot in just 10 lines of code as shown on `DialoGPT's model card <https://huggingface.co/microsoft/DialoGPT-medium>`_.
+
+Training:
+
+In order to train or fine-tune DialoGPT, one can use causal language modeling training. 
+To cite the official paper: 
+*We follow the OpenAI GPT-2 to model a multiturn dialogue session 
+as a long text and frame the generation task as language modeling. We first
+concatenate all dialog turns within a dialogue session into a long text 
+x_1,..., x_N (N is the 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>`_.
+
+The original code can be found `here <https://github.com/microsoft/DialoGPT>`_.

docs/source/model_doc/distilbert.rst

@@ -1,69 +1,105 @@
 DistilBERT
 ----------------------------------------------------
 
-``DistilBertConfig``
+The DistilBERT model was proposed in the blog post
+`Smaller, faster, cheaper, lighter: Introducing DistilBERT, a distilled version of BERT <https://medium.com/huggingface/distilbert-8cf3380435b5>`__,
+and the paper `DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter <https://arxiv.org/abs/1910.01108>`__.
+DistilBERT is a small, fast, cheap and light Transformer model trained by distilling Bert base. It has 40% less
+parameters than `bert-base-uncased`, runs 60% faster while preserving over 95% of Bert's performances as measured on
+the GLUE language understanding benchmark.
+
+The abstract from the paper is the following:
+
+*As Transfer Learning from large-scale pre-trained models becomes more prevalent in Natural Language Processing (NLP),
+operating these large models in on-the-edge and/or under constrained computational training or inference budgets
+remains challenging. In this work, we propose a method to pre-train a smaller general-purpose language representation
+model, called DistilBERT, which can then be fine-tuned with good performances on a wide range of tasks like its larger
+counterparts. While most prior work investigated the use of distillation for building task-specific models, we
+leverage knowledge distillation during the pre-training phase and show that it is possible to reduce the size of a
+BERT model by 40%, while retaining 97% of its language understanding capabilities and being 60% faster. To leverage
+the inductive biases learned by larger models during pre-training, we introduce a triple loss combining language
+modeling, distillation and cosine-distance losses. Our smaller, faster and lighter model is cheaper to pre-train
+and we demonstrate its capabilities for on-device computations in a proof-of-concept experiment and a comparative
+on-device study.*
+
+Tips:
+
+- DistilBert doesn't have `token_type_ids`, you don't need to indicate which token belongs to which segment. Just separate your segments with the separation token `tokenizer.sep_token` (or `[SEP]`)
+- DistilBert doesn't have options to select the input positions (`position_ids` input). This could be added if necessary though, just let's us know if you need this option.
+
+The original code can be found `here <https://github.com/huggingface/transformers/tree/master/examples/distillation>`_.
+
+
+DistilBertConfig
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.DistilBertConfig
     :members:
 
 
-``DistilBertTokenizer``
+DistilBertTokenizer
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.DistilBertTokenizer
     :members:
 
 
-``DistilBertModel``
+DistilBertTokenizerFast
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.DistilBertTokenizerFast
+    :members:
+
+
+DistilBertModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.DistilBertModel
     :members:
 
 
-``DistilBertForMaskedLM``
+DistilBertForMaskedLM
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.DistilBertForMaskedLM
     :members:
 
 
-``DistilBertForSequenceClassification``
+DistilBertForSequenceClassification
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.DistilBertForSequenceClassification
     :members:
 
 
-``DistilBertForQuestionAnswering``
+DistilBertForQuestionAnswering
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.DistilBertForQuestionAnswering
     :members:
 
-``TFDistilBertModel``
+TFDistilBertModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFDistilBertModel
     :members:
 
 
-``TFDistilBertForMaskedLM``
+TFDistilBertForMaskedLM
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFDistilBertForMaskedLM
     :members:
 
 
-``TFDistilBertForSequenceClassification``
+TFDistilBertForSequenceClassification
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFDistilBertForSequenceClassification
     :members:
 
 
-``TFDistilBertForQuestionAnswering``
+TFDistilBertForQuestionAnswering
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFDistilBertForQuestionAnswering

docs/source/model_doc/electra.rst

@@ -0,0 +1,124 @@
+ELECTRA
+----------------------------------------------------
+
+The ELECTRA model was proposed in the paper.
+`ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators <https://openreview.net/pdf?id=r1xMH1BtvB>`__.
+ELECTRA is a new pre-training approach which trains two transformer models: the generator and the discriminator. The
+generator's role is to replace tokens in a sequence, and is therefore trained as a masked language model. The discriminator,
+which is the model we're interested in, tries to identify which tokens were replaced by the generator in the sequence.
+
+The abstract from the paper is the following:
+
+*Masked language modeling (MLM) pre-training methods such as BERT corrupt
+the input by replacing some tokens with [MASK] and then train a model to
+reconstruct the original tokens. While they produce good results when transferred
+to downstream NLP tasks, they generally require large amounts of compute to be
+effective. As an alternative, we propose a more sample-efficient pre-training task
+called replaced token detection. Instead of masking the input, our approach
+corrupts it by replacing some tokens with plausible alternatives sampled from a small
+generator network. Then, instead of training a model that predicts the original
+identities of the corrupted tokens, we train a discriminative model that predicts
+whether each token in the corrupted input was replaced by a generator sample
+or not. Thorough experiments demonstrate this new pre-training task is more
+efficient than MLM because the task is defined over all input tokens rather than
+just the small subset that was masked out. As a result, the contextual representations
+learned by our approach substantially outperform the ones learned by BERT
+given the same model size, data, and compute. The gains are particularly strong
+for small models; for example, we train a model on one GPU for 4 days that
+outperforms GPT (trained using 30x more compute) on the GLUE natural language
+understanding benchmark. Our approach also works well at scale, where it
+performs comparably to RoBERTa and XLNet while using less than 1/4 of their
+compute and outperforms them when using the same amount of compute.*
+
+Tips:
+
+- ELECTRA is the pre-training approach, therefore there is nearly no changes done to the underlying model: BERT. The
+  only change is the separation of the embedding size and the hidden size -> The embedding size is generally smaller,
+  while the hidden size is larger. An additional projection layer (linear) is used to project the embeddings from
+  their embedding size to the hidden size. In the case where the embedding size is the same as the hidden size, no
+  projection layer is used.
+- The ELECTRA checkpoints saved using `Google Research's implementation <https://github.com/google-research/electra>`__
+  contain both the generator and discriminator. The conversion script requires the user to name which model to export
+  into the correct architecture. Once converted to the HuggingFace format, these checkpoints may be loaded into all
+  available ELECTRA models, however. This means that the discriminator may be loaded in the `ElectraForMaskedLM` model,
+  and the generator may be loaded in the `ElectraForPreTraining` model (the classification head will be randomly
+  initialized as it doesn't exist in the generator).
+
+The original code can be found `here <https://github.com/google-research/electra>`_.
+
+
+ElectraConfig
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.ElectraConfig
+    :members:
+
+
+ElectraTokenizer
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.ElectraTokenizer
+    :members:
+
+
+ElectraTokenizerFast
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.ElectraTokenizerFast
+    :members:
+
+
+ElectraModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.ElectraModel
+    :members:
+
+
+ElectraForPreTraining
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.ElectraForPreTraining
+    :members:
+
+
+ElectraForMaskedLM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.ElectraForMaskedLM
+    :members:
+
+
+ElectraForTokenClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.ElectraForTokenClassification
+    :members:
+
+
+TFElectraModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFElectraModel
+    :members:
+
+
+TFElectraForPreTraining
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFElectraForPreTraining
+    :members:
+
+
+TFElectraForMaskedLM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFElectraForMaskedLM
+    :members:
+
+
+TFElectraForTokenClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFElectraForTokenClassification
+    :members:

docs/source/model_doc/encoderdecoder.rst

@@ -0,0 +1,23 @@
+Encoder Decoder Models
+-----------
+
+This class can wrap an encoder model, such as ``BertModel`` and a decoder modeling with a language modeling head, such as ``BertForMaskedLM`` into a encoder-decoder model.
+
+The ``EncoderDecoderModel`` class allows to instantiate a encoder decoder model using the ``from_encoder_decoder_pretrain`` class method taking a pretrained encoder and pretrained decoder model as an input. 
+The ``EncoderDecoderModel`` is saved using the standard ``save_pretrained()`` method and can also again be loaded using the standard ``from_pretrained()`` method. 
+
+An application of this architecture could be *summarization* using two pretrained Bert models as is shown in the paper: `Text Summarization with Pretrained Encoders <https://arxiv.org/abs/1910.13461>`_ by Yang Liu and Mirella Lapata. 
+
+
+``EncoderDecoderConfig``
+~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.EncoderDecoderConfig
+    :members:
+
+
+``EncoderDecoderModel``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.EncoderDecoderModel
+    :members:

docs/source/model_doc/flaubert.rst

@@ -0,0 +1,74 @@
+FlauBERT
+----------------------------------------------------
+
+The FlauBERT model was proposed in the paper
+`FlauBERT: Unsupervised Language Model Pre-training for French <https://arxiv.org/abs/1912.05372>`__ by Hang Le et al.
+It's a transformer pre-trained using a masked language modeling (MLM) objective (BERT-like).
+
+The abstract from the paper is the following:
+
+*Language models have become a key step to achieve state-of-the art results in many different Natural Language
+Processing (NLP) tasks. Leveraging the huge amount of unlabeled texts nowadays available, they provide an efficient
+way to pre-train continuous word representations that can be fine-tuned for a downstream task, along with their
+contextualization at the sentence level. This has been widely demonstrated for English using contextualized
+representations (Dai and Le, 2015; Peters et al., 2018; Howard and Ruder, 2018; Radford et al., 2018; Devlin et
+al., 2019; Yang et al., 2019b). In this paper, we introduce and share FlauBERT, a model learned on a very large
+and heterogeneous French corpus. Models of different sizes are trained using the new CNRS (French National Centre
+for Scientific Research) Jean Zay supercomputer. We apply our French language models to diverse NLP tasks (text
+classification, paraphrasing, natural language inference, parsing, word sense disambiguation) and show that most
+of the time they outperform other pre-training approaches. Different versions of FlauBERT as well as a unified
+evaluation protocol for the downstream tasks, called FLUE (French Language Understanding Evaluation), are shared
+to the research community for further reproducible experiments in French NLP.*
+
+The original code can be found `here <https://github.com/getalp/Flaubert>`_.
+
+
+FlaubertConfig
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaubertConfig
+    :members:
+
+
+FlaubertTokenizer
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaubertTokenizer
+    :members:
+
+
+FlaubertModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaubertModel
+    :members:
+
+
+FlaubertWithLMHeadModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaubertWithLMHeadModel
+    :members:
+
+
+FlaubertForSequenceClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaubertForSequenceClassification
+    :members:
+
+
+FlaubertForQuestionAnsweringSimple
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaubertForQuestionAnsweringSimple
+    :members:
+
+
+FlaubertForQuestionAnswering
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaubertForQuestionAnswering
+    :members:
+
+

docs/source/model_doc/gpt.rst

@@ -1,56 +1,101 @@
 OpenAI GPT
 ----------------------------------------------------
 
-``OpenAIGPTConfig``
+Overview
+~~~~~~~~~~~~~~~~~~~~~
+
+OpenAI GPT model was proposed in `Improving Language Understanding by Generative Pre-Training <https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf>`__
+by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever. It's a causal (unidirectional)
+transformer pre-trained using language modeling on a large corpus will long range dependencies, the Toronto Book Corpus.
+
+The abstract from the paper is the following:
+
+*Natural language understanding comprises a wide range of diverse tasks such
+as textual entailment, question answering, semantic similarity assessment, and
+document classification. Although large unlabeled text corpora are abundant,
+labeled data for learning these specific tasks is scarce, making it challenging for
+discriminatively trained models to perform adequately. We demonstrate that large
+gains on these tasks can be realized by generative pre-training of a language model
+on a diverse corpus of unlabeled text, followed by discriminative fine-tuning on each
+specific task. In contrast to previous approaches, we make use of task-aware input
+transformations during fine-tuning to achieve effective transfer while requiring
+minimal changes to the model architecture. We demonstrate the effectiveness of
+our approach on a wide range of benchmarks for natural language understanding.
+Our general task-agnostic model outperforms discriminatively trained models that
+use architectures specifically crafted for each task, significantly improving upon the
+state of the art in 9 out of the 12 tasks studied.*
+
+Tips:
+
+- GPT is a model with absolute position embeddings so it's usually advised to pad the inputs on
+  the right rather than the left.
+- GPT was trained with a causal language modeling (CLM) objective and is therefore powerful at predicting the next
+  token in a sequence. Leveraging this feature allows GPT-2 to generate syntactically coherent text as
+  it can be observed in the `run_generation.py` example script.
+
+`Write With Transformer <https://transformer.huggingface.co/doc/gpt>`__ is a webapp created and hosted by
+Hugging Face showcasing the generative capabilities of several models. GPT is one of them.
+
+The original code can be found `here <https://github.com/openai/finetune-transformer-lm>`_.
+
+
+OpenAIGPTConfig
 ~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.OpenAIGPTConfig
     :members:
 
 
-``OpenAIGPTTokenizer``
+OpenAIGPTTokenizer
 ~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.OpenAIGPTTokenizer
+    :members: save_vocabulary
+
+
+OpenAIGPTTokenizerFast
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.OpenAIGPTTokenizerFast
     :members:
 
 
-``OpenAIGPTModel``
+OpenAIGPTModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.OpenAIGPTModel
     :members:
 
 
-``OpenAIGPTLMHeadModel``
+OpenAIGPTLMHeadModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.OpenAIGPTLMHeadModel
     :members:
 
 
-``OpenAIGPTDoubleHeadsModel``
+OpenAIGPTDoubleHeadsModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.OpenAIGPTDoubleHeadsModel
     :members:
 
 
-``TFOpenAIGPTModel``
+TFOpenAIGPTModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFOpenAIGPTModel
     :members:
 
 
-``TFOpenAIGPTLMHeadModel``
+TFOpenAIGPTLMHeadModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFOpenAIGPTLMHeadModel
     :members:
 
 
-``TFOpenAIGPTDoubleHeadsModel``
+TFOpenAIGPTDoubleHeadsModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFOpenAIGPTDoubleHeadsModel

docs/source/model_doc/gpt2.rst

@@ -1,56 +1,99 @@
 OpenAI GPT2
 ----------------------------------------------------
 
-``GPT2Config``
+Overview
+~~~~~~~~~~~~~~~~~~~~~
+
+OpenAI GPT-2 model was proposed in
+`Language Models are Unsupervised Multitask Learners <https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf>`_
+by Alec Radford*, Jeffrey Wu*, Rewon Child, David Luan, Dario Amodei** and Ilya Sutskever**.
+It's a causal (unidirectional) transformer pre-trained using  language modeling on a very large
+corpus of ~40 GB of text data.
+
+The abstract from the paper is the following:
+
+*GPT-2 is a large transformer-based language model with 1.5 billion parameters, trained on a dataset[1]
+of 8 million web pages. GPT-2 is trained with a simple objective: predict the next word, given all of the previous
+words within some text. The diversity of the dataset causes this simple goal to contain naturally occurring
+demonstrations of many tasks across diverse domains. GPT-2 is a direct scale-up of GPT, with more than 10X
+the parameters and trained on more than 10X the amount of data.*
+
+Tips:
+
+- GPT-2 is a model with absolute position embeddings so it's usually advised to pad the inputs on
+  the right rather than the left.
+- GPT-2 was trained with a causal language modeling (CLM) objective and is therefore powerful at predicting the next
+  token in a sequence. Leveraging this feature allows GPT-2 to generate syntactically coherent text as
+  it can be observed in the `run_generation.py` example script.
+- The PyTorch models can take the `past` as input, which is the previously computed key/value attention pairs. Using
+  this `past` value prevents the model from re-computing pre-computed values in the context of text generation.
+  See `reusing the past in generative models <../quickstart.html#using-the-past>`_ for more information on the usage
+  of this argument.
+
+`Write With Transformer <https://transformer.huggingface.co/doc/gpt2-large>`__ is a webapp created and hosted by
+Hugging Face showcasing the generative capabilities of several models. GPT-2 is one of them and is available in five
+different sizes: small, medium, large, xl and a distilled version of the small checkpoint: distilgpt-2.
+
+The original code can be found `here <https://openai.com/blog/better-language-models/>`_.
+
+
+GPT2Config
 ~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.GPT2Config
     :members:
 
 
-``GPT2Tokenizer``
+GPT2Tokenizer
 ~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.GPT2Tokenizer
+    :members: save_vocabulary
+
+
+GPT2TokenizerFast
+~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.GPT2TokenizerFast
     :members:
 
 
-``GPT2Model``
+GPT2Model
 ~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.GPT2Model
     :members:
 
 
-``GPT2LMHeadModel``
+GPT2LMHeadModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.GPT2LMHeadModel
     :members:
 
 
-``GPT2DoubleHeadsModel``
+GPT2DoubleHeadsModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.GPT2DoubleHeadsModel
     :members:
 
 
-``TFGPT2Model``
+TFGPT2Model
 ~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFGPT2Model
     :members:
 
 
-``TFGPT2LMHeadModel``
+TFGPT2LMHeadModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFGPT2LMHeadModel
     :members:
 
 
-``TFGPT2DoubleHeadsModel``
+TFGPT2DoubleHeadsModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFGPT2DoubleHeadsModel

docs/source/model_doc/longformer.rst

@@ -0,0 +1,91 @@
+Longformer
+----------------------------------------------------
+**DISCLAIMER:** This model is still a work in progress, 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 Longformer model was presented in `Longformer: The Long-Document Transformer <https://arxiv.org/pdf/2004.05150.pdf>`_ by Iz Beltagy, Matthew E. Peters, Arman Cohan.
+Here the abstract: 
+
+*Transformer-based models are unable to process long sequences due to their self-attention operation, which scales quadratically with the sequence length. To address this limitation, we introduce the Longformer with an attention mechanism that scales linearly with sequence length, making it easy to process documents of thousands of tokens or longer. Longformer's attention mechanism is a drop-in replacement for the standard self-attention and combines a local windowed attention with a task motivated global attention. Following prior work on long-sequence transformers, we evaluate Longformer on character-level language modeling and achieve state-of-the-art results on text8 and enwik8. In contrast to most prior work, we also pretrain Longformer and finetune it on a variety of downstream tasks. Our pretrained Longformer consistently outperforms RoBERTa on long document tasks and sets new state-of-the-art results on WikiHop and TriviaQA.*
+
+The Authors' code can be found `here <https://github.com/allenai/longformer>`_ .
+
+Longformer Self Attention
+~~~~~~~~~~~~~~~~~~~~
+Longformer self attention employs self attention on both a "local" context and a "global" context.
+Most tokens only attend "locally" to each other meaning that each token attends to its :math:`\frac{1}{2} w` previous tokens and :math:`\frac{1}{2} w` succeding tokens with :math:`w` being the window length as defined in `config.attention_window`. Note that `config.attention_window` can be of type ``list`` to define a different :math:`w` for each layer. 
+A selecetd few tokens attend "globally" to all other tokens, as it is conventionally done for all tokens in *e.g.* `BertSelfAttention`.
+
+Note that "locally" and "globally" attending tokens are projected by different query, key and value matrices.
+Also note that every "locally" attending token not only attends to tokens within its window :math:`w`, but also to all "globally" attending tokens so that global attention is *symmetric*.
+
+The user can define which tokens attend "locally" and which tokens attend "globally" by setting the tensor `global_attention_mask` at run-time appropriately. `Longformer` employs the following logic for `global_attention_mask`: `0` - the token attends "locally", `1` - token attends "globally". For more information please also refer to :func:`~transformers.LongformerModel.forward` method.
+
+Using Longformer self attention, the memory and time complexity of the query-key matmul operation, which usually represents the memory and time bottleneck, can be reduced from :math:`\mathcal{O}(n_s \times n_s)` to :math:`\mathcal{O}(n_s \times w)`, with :math:`n_s` being the sequence length and :math:`w` being the average window size. It is assumed that the number of "globally" attending tokens is insignificant as compared to the number of "locally" attending tokens.
+
+For more information, please refer to the official `paper <https://arxiv.org/pdf/2004.05150.pdf>`_ .
+
+
+Training
+~~~~~~~~~~~~~~~~~~~~
+``LongformerForMaskedLM`` is trained the exact same way, ``RobertaForMaskedLM`` is trained and 
+should be used as follows:
+
+::
+
+  input_ids = tokenizer.encode('This is a sentence from [MASK] training data', return_tensors='pt')
+  mlm_labels = tokenizer.encode('This is a sentence from the training data', return_tensors='pt')
+
+  loss = model(input_ids, labels=input_ids, masked_lm_labels=mlm_labels)[0]
+
+
+LongformerConfig
+~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.LongformerConfig
+    :members:
+
+
+LongformerTokenizer
+~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.LongformerTokenizer
+    :members: 
+
+
+LongformerModel
+~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.LongformerModel
+    :members:
+
+
+LongformerForMaskedLM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.LongformerForMaskedLM
+    :members:
+
+
+LongformerForQuestionAnswering
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.LongformerForQuestionAnswering
+    :members:
+
+
+LongformerForMultipleChoice
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.LongformerForMultipleChoice
+    :members:
+
+
+LongformerForTokenClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.LongformerForTokenClassification
+    :members:
+

docs/source/model_doc/marian.rst

@@ -0,0 +1,105 @@
+MarianMT
+----------------------------------------------------
+**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
+@sshleifer. Translations should be similar, but not identical to, output in the test set linked to in each model card.
+
+Implementation Notes
+~~~~~~~~~~~~~~~~~~~~
+- each model is about 298 MB on disk, there are 1,000+ models.
+- The list of supported language pairs can be found `here <https://huggingface.co/Helsinki-NLP>`__.
+- The 1,000+ models were originally trained by `Jörg Tiedemann <https://researchportal.helsinki.fi/en/persons/j%C3%B6rg-tiedemann>`__ using the `Marian <https://marian-nmt.github.io/>`_ C++ library, which supports fast training and translation.
+- All models are transformer encoder-decoders with 6 layers in each component. Each model's performance is documented in a model card.
+- the 80 opus models that require BPE preprocessing are not supported.
+- The modeling code is the same as ``BartForConditionalGeneration`` with a few minor modifications:
+    - static (sinusoid) positional embeddings (``MarianConfig.static_position_embeddings=True``)
+    - a new final_logits_bias (``MarianConfig.add_bias_logits=True``)
+    - no layernorm_embedding (``MarianConfig.normalize_embedding=False``)
+    - the model starts generating with pad_token_id (which has 0 token_embedding) as the prefix. (Bart uses <s/>)
+- Code to bulk convert models can be found in ``convert_marian_to_pytorch.py``
+
+Naming
+~~~~~~
+- All  model names use the following format: ``Helsinki-NLP/opus-mt-{src}-{tgt}``
+- The language codes used to name models are inconsistent. Two digit codes can usually be found `here <https://developers.google.com/admin-sdk/directory/v1/languages>`_, three digit codes require googling "language code {code}".
+- Codes formatted like ``es_AR`` are usually ``code_{region}``. That one is spanish documents from Argentina.
+
+
+Multilingual Models
+~~~~~~~~~~~~~~~~~~~~
+
+All  model names use the following format: ``Helsinki-NLP/opus-mt-{src}-{tgt}``:
+    - if ``src`` is in all caps, the model supports multiple input languages, you can figure out which ones by looking at the model card, or the Group Members `mapping <https://gist.github.com/sshleifer/6d20e7761931b08e73c3219027b97b8a>`_ .
+    - if ``tgt`` is in all caps, the model can output multiple languages, and you should specify a language code by prepending the desired output language to the src_text
+    - You can see a tokenizer's supported language codes in ``tokenizer.supported_language_codes``
+
+Example of translating english to many romance languages, using language codes:
+
+.. code-block:: python
+
+    from transformers import MarianMTModel, MarianTokenizer
+    src_text = [
+        '>>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 = MarianMTModel.from_pretrained(model_name)
+    translated = model.generate(**tokenizer.prepare_translation_batch(src_text))
+    tgt_text = [tokenizer.decode(t, skip_special_tokens=True) for t in translated]
+    # ["c'est une phrase en anglais que nous voulons traduire en français",
+    # 'Isto deve ir para o português.',
+    # 'Y esto al español']
+
+Sometimes, models were trained on collections of languages that do not resolve to a group. In this case, _ is used as a separator for src or tgt, as in ``'Helsinki-NLP/opus-mt-en_el_es_fi-en_el_es_fi'``. These still require language codes.
+There are many supported regional language codes, like ``>>es_ES<<`` (Spain) and ``>>es_AR<<`` (Argentina), that do not seem to change translations. I have not found these to provide different results than just using ``>>es<<``.
+
+For Example:
+    - ``Helsinki-NLP/opus-mt-NORTH_EU-NORTH_EU``: translates from all NORTH_EU languages (see `mapping <https://gist.github.com/sshleifer/6d20e7761931b08e73c3219027b97b8a>`_) to all NORTH_EU languages. Use a special language code like ``>>de<<`` to specify output language.
+    - ``Helsinki-NLP/opus-mt-ROMANCE-en``: translates from many romance languages to english, no codes needed since there is only 1 tgt language.
+
+
+
+.. code-block:: python
+
+    GROUP_MEMBERS = {
+     'ZH': ['cmn', 'cn', 'yue', 'ze_zh', 'zh_cn', 'zh_CN', 'zh_HK', 'zh_tw', 'zh_TW', 'zh_yue', 'zhs', 'zht', 'zh'],
+     'ROMANCE': ['fr', 'fr_BE', 'fr_CA', 'fr_FR', 'wa', 'frp', 'oc', 'ca', 'rm', 'lld', 'fur', 'lij', 'lmo', 'es', 'es_AR', 'es_CL', 'es_CO', 'es_CR', 'es_DO', 'es_EC', 'es_ES', 'es_GT', 'es_HN', 'es_MX', 'es_NI', 'es_PA', 'es_PE', 'es_PR', 'es_SV', 'es_UY', 'es_VE', 'pt', 'pt_br', 'pt_BR', 'pt_PT', 'gl', 'lad', 'an', 'mwl', 'it', 'it_IT', 'co', 'nap', 'scn', 'vec', 'sc', 'ro', 'la'],
+     'NORTH_EU': ['de', 'nl', 'fy', 'af', 'da', 'fo', 'is', 'no', 'nb', 'nn', 'sv'],
+     'SCANDINAVIA': ['da', 'fo', 'is', 'no', 'nb', 'nn', 'sv'],
+     'SAMI': ['se', 'sma', 'smj', 'smn', 'sms'],
+     'NORWAY': ['nb_NO', 'nb', 'nn_NO', 'nn', 'nog', 'no_nb', 'no'],
+     'CELTIC': ['ga', 'cy', 'br', 'gd', 'kw', 'gv']
+    }
+
+Code to see available pretrained models:
+
+.. code-block:: python
+
+    from transformers.hf_api import HfApi
+    model_list = HfApi().model_list()
+    org = "Helsinki-NLP"
+    model_ids = [x.modelId for x in model_list if x.modelId.startswith(org)]
+    suffix = [x.split('/')[1] for x in model_ids]
+    multi_models = [f'{org}/{s}' for s in suffix if s != s.lower()]
+
+MarianMTModel
+~~~~~~~~~~~~~
+
+Pytorch version of marian-nmt's transformer.h (c++). Designed for the OPUS-NMT translation checkpoints.
+Model API is identical to BartForConditionalGeneration.
+Available models are listed at `Model List <https://huggingface.co/models?search=Helsinki-NLP>`__
+This class inherits all functionality from ``BartForConditionalGeneration``, see that page for method signatures.
+
+.. autoclass:: transformers.MarianMTModel
+    :members:
+
+
+MarianTokenizer
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.MarianTokenizer
+    :members: prepare_translation_batch

docs/source/model_doc/reformer.rst

@@ -0,0 +1,114 @@
+Reformer
+----------------------------------------------------
+**DISCLAIMER:** This model is still a work in progress, 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 Reformer model was presented in `Reformer: The Efficient Transformer <https://arxiv.org/abs/2001.04451.pdf>`_ by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
+Here the abstract: 
+
+*Large Transformer models routinely achieve state-of-the-art results on a number of tasks but training these models can be prohibitively costly, especially on long sequences. We introduce two techniques to improve the efficiency of Transformers. For one, we replace dot-product attention by one that uses locality-sensitive hashing, changing its complexity from O(L^2) to O(Llog(L)), where L is the length of the sequence. Furthermore, we use reversible residual layers instead of the standard residuals, which allows storing activations only once in the training process instead of N times, where N is the number of layers. The resulting model, the Reformer, performs on par with Transformer models while being much more memory-efficient and much faster on long sequences.*
+
+The Authors' code can be found `here <https://github.com/google/trax/tree/master/trax/models/reformer>`_ .
+
+Axial Positional Encodings
+~~~~~~~~~~~~~~~~~~~~
+Axial Positional Encodings were first implemented in Google's `trax library <https://github.com/google/trax/blob/4d99ad4965bab1deba227539758d59f0df0fef48/trax/layers/research/position_encodings.py#L29>`_ and developed by the authors of this model's paper. In models that are treating very long input sequences, the conventional position id encodings store an embedings vector of size :math:`d` being the ``config.hidden_size`` for every position :math:`i, \ldots, n_s`, with :math:`n_s` being ``config.max_embedding_size``. *E.g.*, having a sequence length of :math:`n_s = 2^{19} \approx 0.5M` and a ``config.hidden_size`` of :math:`d = 2^{10} \approx 1000` would result in a position encoding matrix:
+
+.. math::
+    X_{i,j}, \text{ with } i \in \left[1,\ldots, d\right] \text{ and } j \in \left[1,\ldots, n_s\right] 
+
+which alone has over 500M parameters to store. Axial positional encodings factorize :math:`X_{i,j}` into two matrices: 
+
+.. math::
+    X^{1}_{i,j}, \text{ with } i \in \left[1,\ldots, d^1\right] \text{ and } j \in \left[1,\ldots, n_s^1\right] 
+
+and 
+
+.. math::
+    X^{2}_{i,j}, \text{ with } i \in \left[1,\ldots, d^2\right] \text{ and } j \in \left[1,\ldots, n_s^2\right] 
+
+with:
+
+.. math::
+    d = d^1 + d^2 \text{ and } n_s = n_s^1 \times n_s^2 .
+
+Therefore the following holds:
+
+.. math::
+    X_{i,j} = \begin{cases}
+                X^{1}_{i, k}, & \text{if }\ i < d^1 \text{ with } k = j \mod n_s^1 \\
+                X^{2}_{i - d^1, l}, & \text{if } i \ge d^1 \text{ with } l = \lfloor\frac{j}{n_s^1}\rfloor
+              \end{cases}
+
+Intuitively, this means that a position embedding vector :math:`x_j \in \mathbb{R}^{d}` is now the composition of two factorized embedding vectors: :math:`x^1_{k, l} + x^2_{l, k}`, where as the ``config.max_embedding_size`` dimension :math:`j` is factorized into :math:`k \text{ and } l`.
+This design ensures that each position embedding vector :math:`x_j` is unique.
+
+Using the above example again, axial position encoding with :math:`d^1 = 2^5, d^2 = 2^5, n_s^1 = 2^9, n_s^2 = 2^{10}` can drastically reduced the number of parameters to :math:`2^{14} + 2^{15} \approx 49000` parameters.
+
+In practice, the parameter ``config.axial_pos_embds_dim`` is set to ``list``:math:`(d^1, d^2)` which sum has to be equal to ``config.hidden_size`` and ``config.axial_pos_shape`` is set to ``list``:math:`(n_s^1, n_s^2)` and which product has to be equal to ``config.max_embedding_size`` which during training has to be equal to the ``sequence length`` of the ``input_ids``.
+
+
+
+LSH Self Attention
+~~~~~~~~~~~~~~~~~~~~
+In Locality sensitive hashing (LSH) self attention the key and query projection weights are tied. Therefore, the key query embedding vectors are also tied.
+LSH self attention uses the locality sensitive 
+hashing mechanism proposed in `Practical and Optimal LSH for Angular Distance <https://arxiv.org/abs/1509.02897>`_ to assign each of the tied key query embedding vectors to one of ``config.num_buckets`` possible buckets. The premise is that the more "similar" key query embedding vectors (in terms of *cosine similarity*) are to each other, the more likely they are assigned to the same bucket. 
+The accuracy of the LSH mechanism can be improved by increasing ``config.num_hashes`` or directly the argument ``num_hashes`` of the forward function so that the output of the LSH self attention better approximates the output of the "normal" full self attention.
+The buckets are then sorted and chunked into query key embedding vector chunks each of length ``config.lsh_chunk_length``. For each chunk, the query embedding vectors attend to its key vectors (which are tied to themselves) and to the key embedding vectors of ``config.lsh_num_chunks_before`` previous neighboring chunks and ``config.lsh_num_chunks_after`` following neighboring chunks.
+For more information, see the `original Paper <https://arxiv.org/abs/2001.04451>`_ or this great `blog post <https://www.pragmatic.ml/reformer-deep-dive/>`_.
+
+Note that ``config.num_buckets`` can also be factorized into a ``list``:math:`(n_{\text{buckets}}^1, n_{\text{buckets}}^2)`. This way instead of assigning the query key embedding vectors to one of :math:`(1,\ldots, n_{\text{buckets}})` they are assigned to one of :math:`(1-1,\ldots, n_{\text{buckets}}^1-1, \ldots, 1-n_{\text{buckets}}^2, \ldots, n_{\text{buckets}}^1-n_{\text{buckets}}^2)`. This is crucial for very long sequences to save memory.
+
+When training a model from scratch, it is recommended to leave ``config.num_buckets=None``, so that depending on the sequence length a good value for ``num_buckets`` is calculated on the fly. This value will then automatically be saved in the config and should be reused for inference.
+
+Using LSH self attention, the memory and time complexity of the query-key matmul operation can be reduced from :math:`\mathcal{O}(n_s \times n_s)` to :math:`\mathcal{O}(n_s \times \log(n_s))`, which usually represents the memory and time bottleneck in a transformer model, with :math:`n_s` being the sequence length.
+
+
+Local Self Attention
+~~~~~~~~~~~~~~~~~~~~
+Local self attention is essentially a "normal" self attention layer with 
+key, query and value projections, but is chunked so that in each chunk of length ``config.local_chunk_length`` the query embedding vectors only attends to the key embedding vectors in its chunk and to the key embedding vectors of ``config.local_num_chunks_before`` previous neighboring chunks and ``config.local_num_chunks_after`` following neighboring chunks.
+
+Using Local self attention, the memory and time complexity of the query-key matmul operation can be reduced from :math:`\mathcal{O}(n_s \times n_s)` to :math:`\mathcal{O}(n_s \times \log(n_s))`, which usually represents the memory and time bottleneck in a transformer model, with :math:`n_s` being the sequence length.
+
+
+Training
+~~~~~~~~~~~~~~~~~~~~
+During training, we must ensure that the sequence length is set to a value that can be divided by the least common multiple of ``config.lsh_chunk_length`` and ``config.local_chunk_length`` and that the parameters of the Axial Positional Encodings are correctly set as described above. Reformer is very memory efficient so that the model can easily be trained on sequences as long as 64000 tokens.
+For training, the ``ReformerModelWithLMHead`` should be used as follows: 
+
+::
+
+  input_ids = tokenizer.encode('This is a sentence from the training data', return_tensors='pt')
+  loss = model(input_ids, labels=input_ids)[0]
+
+
+ReformerConfig
+~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.ReformerConfig
+    :members:
+
+
+ReformerTokenizer
+~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.ReformerTokenizer
+    :members: 
+
+
+ReformerModel
+~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.ReformerModel
+    :members:
+
+
+ReformerModelWithLMHead
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.ReformerModelWithLMHead
+    :members:

docs/source/model_doc/roberta.rst

@@ -1,57 +1,115 @@
 RoBERTa
 ----------------------------------------------------
 
-``RobertaConfig``
+The RoBERTa model was proposed in `RoBERTa: 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. It is based on Google's BERT model released in 2018.
+
+It builds on BERT and modifies key hyperparameters, removing the next-sentence pretraining
+objective and training with much larger mini-batches and learning rates.
+
+The abstract from the paper is the following:
+
+*Language model pretraining has led to significant performance gains but careful comparison between different
+approaches is challenging. Training is computationally expensive, often done on private datasets of different sizes,
+and, as we will show, hyperparameter choices have significant impact on the final results. We present a replication
+study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and
+training data size. We find that BERT was significantly undertrained, and can match or exceed the performance of
+every model published after it. Our best model achieves state-of-the-art results on GLUE, RACE and SQuAD. These
+results highlight the importance of previously overlooked design choices, and raise questions about the source
+of recently reported improvements. We release our models and code.*
+
+Tips:
+
+- This implementation is the same as :class:`~transformers.BertModel` with a tiny embeddings tweak as well as a
+  setup for Roberta pretrained models.
+- RoBERTa has the same architecture as BERT, but uses a byte-level BPE as a tokenizer (same as GPT-2) and uses a
+  different pre-training scheme.
+- RoBERTa doesn't have `token_type_ids`, you don't need to indicate which token belongs to which segment. Just separate your segments with the separation token `tokenizer.sep_token` (or `</s>`)
+- `Camembert <./camembert.html>`__ is a wrapper around RoBERTa. Refer to this page for usage examples.
+
+The original code can be found `here <https://github.com/pytorch/fairseq/tree/master/examples/roberta>`_.
+
+
+RobertaConfig
 ~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.RobertaConfig
     :members:
 
 
-``RobertaTokenizer``
+RobertaTokenizer
 ~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.RobertaTokenizer
-    :members:
+    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
+        create_token_type_ids_from_sequences, save_vocabulary
 
 
-``RobertaModel``
+RobertaTokenizerFast
+~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.RobertaTokenizerFast
+    :members: build_inputs_with_special_tokens
+
+
+RobertaModel
 ~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.RobertaModel
     :members:
 
 
-``RobertaForMaskedLM``
+RobertaForMaskedLM
 ~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.RobertaForMaskedLM
     :members:
 
 
-``RobertaForSequenceClassification``
+RobertaForSequenceClassification
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.RobertaForSequenceClassification
     :members:
 
 
-``TFRobertaModel``
+RobertaForMultipleChoice
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.RobertaForMultipleChoice
+    :members:
+
+
+RobertaForTokenClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.RobertaForTokenClassification
+    :members:
+
+TFRobertaModel
 ~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFRobertaModel
     :members:
 
 
-``TFRobertaForMaskedLM``
+TFRobertaForMaskedLM
 ~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFRobertaForMaskedLM
     :members:
 
 
-``TFRobertaForSequenceClassification``
+TFRobertaForSequenceClassification
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFRobertaForSequenceClassification
     :members:
+
+
+TFRobertaForTokenClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFRobertaForTokenClassification
+    :members:

docs/source/model_doc/t5.rst

@@ -0,0 +1,105 @@
+T5
+----------------------------------------------------
+**DISCLAIMER:** This model is still a work in progress, 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 T5 model was presented in `Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer <https://arxiv.org/pdf/1910.10683.pdf>`_ by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu in 
+Here the abstract: 
+
+*Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. 
+In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. 
+Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. 
+By combining the insights from our exploration with scale and our new "Colossal Clean Crawled Corpus", we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. 
+To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code.*
+
+The Authors' code can be found `here <https://github.com/google-research/text-to-text-transfer-transformer>`_ .
+
+Training
+~~~~~~~~~~~~~~~~~~~~
+T5 is an encoder-decoder model and converts all NLP problems into a text-to-text format. It is trained using teacher forcing.
+This means that for training we always need an input sequence and a target sequence. 
+The input sequence is fed to the model using ``input_ids``. The target sequence is shifted to the right, *i.e.* prepended by a start-sequence token and fed to the decoder using the `decoder_input_ids`. In teacher-forcing style, the target sequence is then appended by the EOS token and corresponds to the ``lm_labels``. The PAD token is hereby used as the start-sequence token.
+T5 can be trained / fine-tuned both in a supervised and unsupervised fashion.
+
+- Unsupervised denoising training
+
+  In this setup spans of the input sequence are masked by so-called sentinel tokens (*a.k.a* unique mask tokens) 
+  and the output sequence is formed as a concatenation of the same sentinel tokens and the *real* masked tokens. 
+  Each sentinel token represents a unique mask token for this sentence and should start with ``<extra_id_1>``, ``<extra_id_2>``, ... up to ``<extra_id_100>``. As a default 100 sentinel tokens are available in ``T5Tokenizer``.
+  *E.g.* the sentence "The cute dog walks in the park" with the masks put on "cute dog" and "the" should be processed as follows: 
+
+::
+
+  input_ids = tokenizer.encode('The <extra_id_1> walks in <extra_id_2> park', return_tensors='pt')
+  lm_labels = tokenizer.encode('<extra_id_1> cute dog <extra_id_2> the <extra_id_3> </s>', return_tensors='pt')
+  # the forward function automatically creates the correct decoder_input_ids
+  model(input_ids=input_ids, lm_labels=lm_labels)
+
+- Supervised training
+
+  In this setup the input sequence and output sequence are standard sequence to sequence input output mapping.
+  In translation, *e.g.* the input sequence "The house is wonderful." and output sequence "Das Haus ist wunderbar." should 
+  be processed as follows:
+  
+::
+
+  input_ids = tokenizer.encode('translate English to German: The house is wonderful. </s>', return_tensors='pt')
+  lm_labels = tokenizer.encode('Das Haus ist wunderbar. </s>', return_tensors='pt')
+  # the forward function automatically creates the correct decoder_input_ids
+  model(input_ids=input_ids, lm_labels=lm_labels)
+
+Tips
+~~~~~~~~~~~~~~~~~~~~
+- T5 is an encoder-decoder model pre-trained on a multi-task mixture of unsupervised 
+  and supervised tasks and for which each task is converted into a text-to-text format.
+  T5 works well on a variety of tasks out-of-the-box by prepending a different prefix to the input corresponding to each task, e.g.: for translation: *translate English to German: ..., summarize: ...*.
+  For more information about which prefix to use, it is easiest to look into Appendix D of the `paper <https://arxiv.org/pdf/1910.10683.pdf>`_ .
+- For sequence to sequence generation, it is recommended to use ``T5ForConditionalGeneration.generate()``. The method takes care of feeding the encoded input via cross-attention layers to the decoder and auto-regressively generates the decoder output.
+- T5 uses relative scalar embeddings. Encoder input padding can be done on the left and on the right.
+
+The original code can be found `here <https://github.com/google-research/text-to-text-transfer-transformer>`_.
+
+
+T5Config
+~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.T5Config
+    :members:
+
+
+T5Tokenizer
+~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.T5Tokenizer
+    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
+        create_token_type_ids_from_sequences, save_vocabulary
+
+
+T5Model
+~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.T5Model
+    :members:
+
+
+T5ForConditionalGeneration
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.T5ForConditionalGeneration
+    :members:
+
+
+TFT5Model
+~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFT5Model
+    :members:
+
+
+TFT5ForConditionalGeneration
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFT5ForConditionalGeneration
+    :members:

docs/source/model_doc/transformerxl.rst

@@ -1,43 +1,81 @@
 Transformer XL
 ----------------------------------------------------
 
+Overview
+~~~~~~~~~~~~~~~~~~~~~
 
-``TransfoXLConfig``
+The Transformer-XL model was proposed in
+`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.
+It's a causal (uni-directional) transformer with relative positioning (sinusoïdal) embeddings which can reuse
+previously computed hidden-states to attend to longer context (memory).
+This model also uses adaptive softmax inputs and outputs (tied).
+
+The abstract from the paper is the following:
+
+*Transformers have a potential of learning longer-term dependency, but are limited by a fixed-length context in the
+setting of language modeling. We propose a novel neural architecture Transformer-XL that enables learning dependency
+beyond a fixed length without disrupting temporal coherence. It consists of a segment-level recurrence mechanism and
+a novel positional encoding scheme. Our method not only enables capturing longer-term dependency, but also resolves
+the context fragmentation problem. As a result, Transformer-XL learns dependency that is 80% longer than RNNs and
+450% longer than vanilla Transformers, achieves better performance on both short and long sequences, and is up
+to 1,800+ times faster than vanilla Transformers during evaluation. Notably, we improve the state-of-the-art results
+of bpc/perplexity to 0.99 on enwiki8, 1.08 on text8, 18.3 on WikiText-103, 21.8 on One Billion Word, and 54.5 on
+Penn Treebank (without finetuning). When trained only on WikiText-103, Transformer-XL manages to generate reasonably
+coherent, novel text articles with thousands of tokens.*
+
+Tips:
+
+- Transformer-XL uses relative sinusoidal positional embeddings. Padding can be done on the left or on the right.
+  The original implementation trains on SQuAD with padding on the left, therefore the padding defaults are set to left.
+- Transformer-XL is one of the few models that has no sequence length limit.
+
+The original code can be found `here <https://github.com/kimiyoung/transformer-xl>`_.
+
+
+TransfoXLConfig
 ~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TransfoXLConfig
     :members:
 
 
-``TransfoXLTokenizer``
+TransfoXLTokenizer
 ~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TransfoXLTokenizer
+    :members: save_vocabulary
+
+
+TransfoXLTokenizerFast
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TransfoXLTokenizerFast
     :members:
 
 
-``TransfoXLModel``
+TransfoXLModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TransfoXLModel
     :members:
 
 
-``TransfoXLLMHeadModel``
+TransfoXLLMHeadModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TransfoXLLMHeadModel
     :members:
 
 
-``TFTransfoXLModel``
+TFTransfoXLModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFTransfoXLModel
     :members:
 
 
-``TFTransfoXLLMHeadModel``
+TFTransfoXLLMHeadModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFTransfoXLLMHeadModel

docs/source/model_doc/xlm.rst

@@ -1,68 +1,108 @@
 XLM
 ----------------------------------------------------
 
-``XLMConfig``
+Overview
+~~~~~~~~~~~~~~~~~~~~~
+
+The XLM model was proposed in `Cross-lingual Language Model Pretraining <https://arxiv.org/abs/1901.07291>`_
+by Guillaume Lample*, Alexis Conneau*. It's a transformer pre-trained using one of the following objectives:
+
+- a causal language modeling (CLM) objective (next token prediction),
+- a masked language modeling (MLM) objective (Bert-like), or
+- a Translation Language Modeling (TLM) object (extension of Bert's MLM to multiple language inputs)
+
+The abstract from the paper is the following:
+
+*Recent studies have demonstrated the efficiency of generative pretraining for English natural language understanding.
+In this work, we extend this approach to multiple languages and show the effectiveness of cross-lingual pretraining.
+We propose two methods to learn cross-lingual language models (XLMs): one unsupervised that only relies on monolingual
+data, and one supervised that leverages parallel data with a new cross-lingual language model objective. We obtain
+state-of-the-art results on cross-lingual classification, unsupervised and supervised machine translation. On XNLI,
+our approach pushes the state of the art by an absolute gain of 4.9% accuracy. On unsupervised machine translation,
+we obtain 34.3 BLEU on WMT'16 German-English, improving the previous state of the art by more than 9 BLEU. On
+supervised machine translation, we obtain a new state of the art of 38.5 BLEU on WMT'16 Romanian-English, outperforming
+the previous best approach by more than 4 BLEU. Our code and pretrained models will be made publicly available.*
+
+Tips:
+
+- XLM has many different checkpoints, which were trained using different objectives: CLM, MLM or TLM. Make sure to
+  select the correct objective for your task (e.g. MLM checkpoints are not suitable for generation).
+- XLM has multilingual checkpoints which leverage a specific `lang` parameter. Check out the
+  `multi-lingual <../multilingual.html>`__ page for more information.
+
+The original code can be found `here <https://github.com/facebookresearch/XLM/>`_.
+
+
+XLMConfig
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.XLMConfig
     :members:
 
-``XLMTokenizer``
+XLMTokenizer
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.XLMTokenizer
-    :members:
+    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
+        create_token_type_ids_from_sequences, save_vocabulary
 
-``XLMModel``
+XLMModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.XLMModel
     :members:
 
 
-``XLMWithLMHeadModel``
+XLMWithLMHeadModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.XLMWithLMHeadModel
     :members:
 
 
-``XLMForSequenceClassification``
+XLMForSequenceClassification
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.XLMForSequenceClassification
     :members:
 
 
-``XLMForQuestionAnswering``
+XLMForQuestionAnsweringSimple
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.XLMForQuestionAnsweringSimple
+    :members:
+
+
+XLMForQuestionAnswering
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.XLMForQuestionAnswering
     :members:
 
 
-``TFXLMModel``
+TFXLMModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFXLMModel
     :members:
 
 
-``TFXLMWithLMHeadModel``
+TFXLMWithLMHeadModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFXLMWithLMHeadModel
     :members:
 
 
-``TFXLMForSequenceClassification``
+TFXLMForSequenceClassification
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFXLMForSequenceClassification
     :members:
 
 
-``TFXLMForQuestionAnsweringSimple``
+TFXLMForQuestionAnsweringSimple
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFXLMForQuestionAnsweringSimple

docs/source/model_doc/xlmroberta.rst

@@ -0,0 +1,109 @@
+XLM-RoBERTa
+------------------------------------------
+
+The XLM-RoBERTa model was proposed in `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. It is based on Facebook's RoBERTa model released in 2019.
+It is a large multi-lingual language model, trained on 2.5TB of filtered CommonCrawl data.
+
+The abstract from the paper is the following:
+
+*This paper shows that pretraining multilingual language models at scale leads to significant performance gains for
+a wide range of cross-lingual transfer tasks. We train a Transformer-based masked language model on one hundred
+languages, using more than two terabytes of filtered CommonCrawl data. Our model, dubbed XLM-R, significantly
+outperforms multilingual BERT (mBERT) on a variety of cross-lingual benchmarks, including +13.8% average accuracy
+on XNLI, +12.3% average F1 score on MLQA, and +2.1% average F1 score on NER. XLM-R performs particularly well on
+low-resource languages, improving 11.8% in XNLI accuracy for Swahili and 9.2% for Urdu over the previous XLM model.
+We also present a detailed empirical evaluation of the key factors that are required to achieve these gains,
+including the trade-offs between (1) positive transfer and capacity dilution and (2) the performance of high and
+low resource languages at scale. Finally, we show, for the first time, the possibility of multilingual modeling
+without sacrificing per-language performance; XLM-Ris very competitive with strong monolingual models on the GLUE
+and XNLI benchmarks. We will make XLM-R code, data, and models publicly available.*
+
+Tips:
+
+- XLM-R is a multilingual model trained on 100 different languages. Unlike some XLM multilingual models, it does
+  not require `lang` tensors to understand which language is used, and should be able to determine the correct
+  language from the input ids.
+- This implementation is the same as RoBERTa. Refer to the `documentation of RoBERTa <./roberta.html>`__ for usage
+  examples as well as the information relative to the inputs and outputs.
+
+The original code can be found `here <https://github.com/pytorch/fairseq/tree/master/examples/xlmr>`_.
+
+
+XLMRobertaConfig
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.XLMRobertaConfig
+    :members:
+
+
+XLMRobertaTokenizer
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.XLMRobertaTokenizer
+    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
+        create_token_type_ids_from_sequences, save_vocabulary
+
+
+XLMRobertaModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.XLMRobertaModel
+    :members:
+
+
+XLMRobertaForMaskedLM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.XLMRobertaForMaskedLM
+    :members:
+
+
+XLMRobertaForSequenceClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.XLMRobertaForSequenceClassification
+    :members:
+
+
+XLMRobertaForMultipleChoice
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.XLMRobertaForMultipleChoice
+    :members:
+
+
+XLMRobertaForTokenClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.XLMRobertaForTokenClassification
+    :members:
+
+
+TFXLMRobertaModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFXLMRobertaModel
+    :members:
+
+
+TFXLMRobertaForMaskedLM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFXLMRobertaForMaskedLM
+    :members:
+
+
+TFXLMRobertaForSequenceClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFXLMRobertaForSequenceClassification
+    :members:
+
+
+TFXLMRobertaForTokenClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFXLMRobertaForTokenClassification
+    :members:

docs/source/model_doc/xlnet.rst

@@ -1,70 +1,126 @@
 XLNet
 ----------------------------------------------------
 
-``XLNetConfig``
+Overview
+~~~~~~~~~~~~~~~~~~~~~
+
+The XLNet model was proposed in `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.
+XLnet is an extension of the Transformer-XL model pre-trained using an autoregressive method
+to learn bidirectional contexts by maximizing the expected likelihood over all permutations
+of the input sequence factorization order.
+
+The abstract from the paper is the following:
+
+*With the capability of modeling bidirectional contexts, denoising autoencoding based pretraining like BERT achieves
+better performance than pretraining approaches based on autoregressive language modeling. However, relying on
+corrupting the input with masks, BERT neglects dependency between the masked positions and suffers from a
+pretrain-finetune discrepancy. In light of these pros and cons, we propose XLNet, a generalized autoregressive
+pretraining method that (1) enables learning bidirectional contexts by maximizing the expected likelihood over
+all permutations of the factorization order and (2) overcomes the limitations of BERT thanks to its autoregressive
+formulation. Furthermore, XLNet integrates ideas from Transformer-XL, the state-of-the-art autoregressive model,
+into pretraining. Empirically, under comparable experiment settings, XLNet outperforms BERT on 20 tasks, often by
+a large margin, including question answering, natural language inference, sentiment analysis, and document ranking.*
+
+Tips:
+
+- The specific attention pattern can be controlled at training and test time using the `perm_mask` input.
+- Due to the difficulty of training a fully auto-regressive model over various factorization order,
+  XLNet is pretrained using only a sub-set of the output tokens as target which are selected
+  with the `target_mapping` input.
+- To use XLNet for sequential decoding (i.e. not in fully bi-directional setting), use the `perm_mask` and
+  `target_mapping` inputs to control the attention span and outputs (see examples in `examples/text-generation/run_generation.py`)
+- XLNet is one of the few models that has no sequence length limit.
+
+The original code can be found `here <https://github.com/zihangdai/xlnet/>`_.
+
+
+XLNetConfig
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.XLNetConfig
     :members:
 
 
-``XLNetTokenizer``
+XLNetTokenizer
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.XLNetTokenizer
-    :members:
+    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
+        create_token_type_ids_from_sequences, save_vocabulary
 
 
-``XLNetModel``
+XLNetModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.XLNetModel
     :members:
 
 
-``XLNetLMHeadModel``
+XLNetLMHeadModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.XLNetLMHeadModel
     :members:
 
 
-``XLNetForSequenceClassification``
+XLNetForSequenceClassification
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.XLNetForSequenceClassification
     :members:
 
 
-``XLNetForQuestionAnswering``
+XLNetForTokenClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.XLNetForTokenClassification
+    :members:
+
+
+XLNetForMultipleChoice
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.XLNetForMultipleChoice
+    :members:
+
+
+XLNetForQuestionAnsweringSimple
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.XLNetForQuestionAnsweringSimple
+    :members:
+
+
+XLNetForQuestionAnswering
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.XLNetForQuestionAnswering
     :members:
 
 
-``TFXLNetModel``
+TFXLNetModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFXLNetModel
     :members:
 
 
-``TFXLNetLMHeadModel``
+TFXLNetLMHeadModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFXLNetLMHeadModel
     :members:
 
 
-``TFXLNetForSequenceClassification``
+TFXLNetForSequenceClassification
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFXLNetForSequenceClassification
     :members:
 
 
-``TFXLNetForQuestionAnsweringSimple``
+TFXLNetForQuestionAnsweringSimple
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFXLNetForQuestionAnsweringSimple

docs/source/model_sharing.md

@@ -0,0 +1,55 @@
+# Model upload and sharing
+
+Starting with `v2.2.2`, you can now upload and share your fine-tuned models with the community, using the <abbr title="Command-line interface">CLI</abbr> that's built-in to the library.
+
+**First, create an account on [https://huggingface.co/join](https://huggingface.co/join)**. Optionally, join an existing organization or create a new one. Then:
+
+```shell
+transformers-cli login
+# log in using the same credentials as on huggingface.co
+```
+Upload your model:
+```shell
+transformers-cli upload ./path/to/pretrained_model/
+
+# ^^ Upload folder containing weights/tokenizer/config
+# saved via `.save_pretrained()`
+
+transformers-cli upload ./config.json [--filename folder/foobar.json]
+
+# ^^ Upload a single file
+# (you can optionally override its filename, which can be nested inside a folder)
+```
+
+If you want your model to be namespaced by your organization name rather than your username, add the following flag to any command:
+```shell
+--organization organization_name
+```
+
+Your model will then be accessible through its identifier, a concatenation of your username (or organization name) and the folder name above:
+```python
+"username/pretrained_model"
+# or if an org:
+"organization_name/pretrained_model"
+```
+
+**Please add a README.md model card** to the repo under `model_cards/` with: model description, training params (dataset, preprocessing, hardware used, hyperparameters), evaluation results, intended uses & limitations, etc.
+
+Your model now has a page on huggingface.co/models 🔥
+
+Anyone can load it from code:
+```python
+tokenizer = AutoTokenizer.from_pretrained("namespace/pretrained_model")
+model = AutoModel.from_pretrained("namespace/pretrained_model")
+```
+
+List all your files on S3:
+```shell
+transformers-cli s3 ls
+```
+
+You can also delete unneeded files:
+
+```shell
+transformers-cli s3 rm …
+```

docs/source/multilingual.rst

@@ -47,6 +47,7 @@ The different languages this model/tokenizer handles, as well as the ids of thes
 
 .. code-block::
 
+    # Continuation of the previous script
     print(tokenizer.lang2id)  # {'en': 0, 'fr': 1}
 
 
@@ -54,6 +55,7 @@ These ids should be used when passing a language parameter during a model pass.
 
 .. code-block::
 
+    # Continuation of the previous script
     input_ids = torch.tensor([tokenizer.encode("Wikipedia was used to")]) # batch size of 1
 
 
@@ -62,6 +64,7 @@ filled with the appropriate language ids, of the same size as input_ids. For eng
 
 .. code-block::
 
+    # Continuation of the previous script
     language_id = tokenizer.lang2id['en']  # 0
     langs = torch.tensor([language_id] * input_ids.shape[1])  # torch.tensor([0, 0, 0, ..., 0])
 
@@ -73,10 +76,11 @@ You can then feed it all as input to your model:
 
 .. code-block::
 
+    # Continuation of the previous script
     outputs = model(input_ids, langs=langs)
 
 
-The example `run_generation.py <https://github.com/huggingface/transformers/blob/master/examples/run_generation.py>`__
+The example `run_generation.py <https://github.com/huggingface/transformers/blob/master/examples/text-generation/run_generation.py>`__
 can generate text using the CLM checkpoints from XLM, using the language embeddings.
 
 XLM without Language Embeddings
@@ -100,4 +104,16 @@ BERT has two checkpoints that can be used for multi-lingual tasks:
 - ``bert-base-multilingual-cased`` (Masked language modeling + Next sentence prediction, 104 languages)
 
 These checkpoints do not require language embeddings at inference time. They should identify the language
-used in the context and infer accordingly.
+used in the context and infer accordingly.
+
+XLM-RoBERTa
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+XLM-RoBERTa was trained on 2.5TB of newly created clean CommonCrawl data in 100 languages. It provides strong
+gains over previously released multi-lingual models like mBERT or XLM on downstream taks like classification,
+sequence labeling and question answering.
+
+Two XLM-RoBERTa checkpoints can be used for multi-lingual tasks:
+
+- ``xlm-roberta-base`` (Masked language modeling, 100 languages)
+- ``xlm-roberta-large`` (Masked language modeling, 100 languages)

docs/source/notebooks.md

@@ -0,0 +1 @@
+../../notebooks/README.md

docs/source/notebooks.rst

@@ -1,16 +0,0 @@
-Notebooks
-================================================
-
-We include `three Jupyter Notebooks <https://github.com/huggingface/transformers/tree/master/notebooks>`_ that can be used to check that the predictions of the PyTorch model are identical to the predictions of the original TensorFlow model.
-
-
-*
-  The first NoteBook (\ `Comparing-TF-and-PT-models.ipynb <https://github.com/huggingface/transformers/blob/master/notebooks/Comparing-TF-and-PT-models.ipynb>`_\ ) extracts the hidden states of a full sequence on each layers of the TensorFlow and the PyTorch models and computes the standard deviation between them. In the given example, we get a standard deviation of 1.5e-7 to 9e-7 on the various hidden state of the models.
-
-*
-  The second NoteBook (\ `Comparing-TF-and-PT-models-SQuAD.ipynb <https://github.com/huggingface/transformers/blob/master/notebooks/Comparing-TF-and-PT-models-SQuAD.ipynb>`_\ ) compares the loss computed by the TensorFlow and the PyTorch models for identical initialization of the fine-tuning layer of the ``BertForQuestionAnswering`` and computes the standard deviation between them. In the given example, we get a standard deviation of 2.5e-7 between the models.
-
-*
-  The third NoteBook (\ `Comparing-TF-and-PT-models-MLM-NSP.ipynb <https://github.com/huggingface/transformers/blob/master/notebooks/Comparing-TF-and-PT-models-MLM-NSP.ipynb>`_\ ) compares the predictions computed by the TensorFlow and the PyTorch models for masked token language modeling using the pre-trained masked language modeling model.
-
-Please follow the instructions given in the notebooks to run and modify them.

docs/source/pretrained_models.rst

@@ -3,6 +3,7 @@ Pretrained models
 
 Here is the full list of the currently provided pretrained models together with a short presentation of each model.
 
+For a list that includes community-uploaded models, refer to `https://huggingface.co/models <https://huggingface.co/models>`__.
 
 +-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 | Architecture      | Shortcut name                                              | Details of the model                                                                                                                  |
@@ -62,23 +63,35 @@ Here is the full list of the currently provided pretrained models together with
 |                   |                                                            | | Trained on uncased German text by DBMDZ                                                                                             |
 |                   |                                                            | (see `details on dbmdz repository <https://github.com/dbmdz/german-bert>`__).                                                         |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``bert-base-japanese``                                     | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
+|                   | ``cl-tohoku/bert-base-japanese``                           | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
 |                   |                                                            | | Trained on Japanese text. Text is tokenized with MeCab and WordPiece.                                                               |
 |                   |                                                            | | `MeCab <https://taku910.github.io/mecab/>`__ is required for tokenization.                                                          |
 |                   |                                                            | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__).                                               |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``bert-base-japanese-whole-word-masking``                  | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
+|                   | ``cl-tohoku/bert-base-japanese-whole-word-masking``        | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
 |                   |                                                            | | Trained on Japanese text using Whole-Word-Masking. Text is tokenized with MeCab and WordPiece.                                      |
 |                   |                                                            | | `MeCab <https://taku910.github.io/mecab/>`__ is required for tokenization.                                                          |
 |                   |                                                            | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__).                                               |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``bert-base-japanese-char``                                | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
+|                   | ``cl-tohoku/bert-base-japanese-char``                      | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
 |                   |                                                            | | Trained on Japanese text. Text is tokenized into characters.                                                                        |
 |                   |                                                            | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__).                                               |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``bert-base-japanese-char-whole-word-masking``             | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
+|                   | ``cl-tohoku/bert-base-japanese-char-whole-word-masking``   | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
 |                   |                                                            | | Trained on Japanese text using Whole-Word-Masking. Text is tokenized into characters.                                               |
 |                   |                                                            | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__).                                               |
+|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                   | ``TurkuNLP/bert-base-finnish-cased-v1``                    | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
+|                   |                                                            | | Trained on cased Finnish text.                                                                                                      |
+|                   |                                                            | (see `details on turkunlp.org <http://turkunlp.org/FinBERT/>`__).                                                                     |
+|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                   | ``TurkuNLP/bert-base-finnish-uncased-v1``                  | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
+|                   |                                                            | | Trained on uncased Finnish text.                                                                                                    |
+|                   |                                                            | (see `details on turkunlp.org <http://turkunlp.org/FinBERT/>`__).                                                                     |
+|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                   | ``wietsedv/bert-base-dutch-cased``                         | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
+|                   |                                                            | | Trained on cased Dutch text.                                                                                                        |
+|                   |                                                            | (see `details on wietsedv repository <https://github.com/wietsedv/bertje/>`__).                                                       |
 +-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 | GPT               | ``openai-gpt``                                             | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
 |                   |                                                            | | OpenAI GPT English model                                                                                                            |
@@ -146,6 +159,10 @@ Here is the full list of the currently provided pretrained models together with
 |                   |                                                            | | ``roberta-large`` fine-tuned on `MNLI <http://www.nyu.edu/projects/bowman/multinli/>`__.                                            |
 |                   |                                                            | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/roberta>`__)                                                   |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                   | ``distilroberta-base``                                     | | 6-layer, 768-hidden, 12-heads, 82M parameters                                                                                       |
+|                   |                                                            | | The DistilRoBERTa model distilled from the RoBERTa model `roberta-base` checkpoint.                                                 |
+|                   |                                                            | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__)                                     |
+|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``roberta-base-openai-detector``                           | | 12-layer, 768-hidden, 12-heads, 125M parameters                                                                                     |
 |                   |                                                            | | ``roberta-base`` fine-tuned by OpenAI on the outputs of the 1.5B-parameter GPT-2 model.                                             |
 |                   |                                                            | (see `details <https://github.com/openai/gpt-2-output-dataset/tree/master/detector>`__)                                               |
@@ -162,12 +179,16 @@ Here is the full list of the currently provided pretrained models together with
 |                   |                                                            | | The DistilBERT model distilled from the BERT model `bert-base-uncased` checkpoint, with an additional linear layer.                 |
 |                   |                                                            | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__)                                     |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``distilgpt2``                                             | | 6-layer, 768-hidden, 12-heads, 82M parameters                                                                                       |
-|                   |                                                            | | The DistilGPT2 model distilled from the GPT2 model `gpt2` checkpoint.                                                               |
+|                   | ``distilbert-base-cased``                                  | | 6-layer, 768-hidden, 12-heads, 65M parameters                                                                                       |
+|                   |                                                            | | The DistilBERT model distilled from the BERT model `bert-base-cased` checkpoint                                                     |
 |                   |                                                            | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__)                                     |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``distilroberta-base``                                     | | 6-layer, 768-hidden, 12-heads, 82M parameters                                                                                       |
-|                   |                                                            | | The DistilRoBERTa model distilled from the RoBERTa model `roberta-base` checkpoint.                                                 |
+|                   | ``distilbert-base-cased-distilled-squad``                  | | 6-layer, 768-hidden, 12-heads, 65M parameters                                                                                       |
+|                   |                                                            | | The DistilBERT model distilled from the BERT model `bert-base-cased` checkpoint, with an additional question answering layer.       |
+|                   |                                                            | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__)                                     |
+|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                   | ``distilgpt2``                                             | | 6-layer, 768-hidden, 12-heads, 82M parameters                                                                                       |
+|                   |                                                            | | The DistilGPT2 model distilled from the GPT2 model `gpt2` checkpoint.                                                               |
 |                   |                                                            | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__)                                     |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``distilbert-base-german-cased``                           | | 6-layer, 768-hidden, 12-heads, 66M parameters                                                                                       |
@@ -217,6 +238,76 @@ Here is the full list of the currently provided pretrained models together with
 |                   |                                                            | | ALBERT xxlarge model with no dropout, additional training data and longer training                                                  |
 |                   |                                                            | (see `details <https://github.com/google-research/ALBERT>`__)                                                                         |
 +-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-
-
-.. <https://huggingface.co/transformers/examples.html>`__
+| T5                | ``t5-small``                                               | | ~60M parameters with 6-layers, 512-hidden-state, 2048 feed-forward hidden-state, 8-heads,                                           |
+|                   |                                                            | | Trained on English text: the Colossal Clean Crawled Corpus (C4)                                                                     |
+|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                   | ``t5-base``                                                | | ~220M parameters with 12-layers, 768-hidden-state, 3072 feed-forward hidden-state, 12-heads,                                        |
+|                   |                                                            | | Trained on English text: the Colossal Clean Crawled Corpus (C4)                                                                     |
+|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                   | ``t5-large``                                               | | ~770M parameters with 24-layers, 1024-hidden-state, 4096 feed-forward hidden-state, 16-heads,                                       |
+|                   |                                                            | | Trained on English text: the Colossal Clean Crawled Corpus (C4)                                                                     |
+|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                   | ``t5-3B``                                                  | | ~2.8B parameters with 24-layers, 1024-hidden-state, 16384 feed-forward hidden-state, 32-heads,                                      |
+|                   |                                                            | | Trained on English text: the Colossal Clean Crawled Corpus (C4)                                                                     |
+|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                   | ``t5-11B``                                                 | | ~11B parameters with 24-layers, 1024-hidden-state, 65536 feed-forward hidden-state, 128-heads,                                      |
+|                   |                                                            | | Trained on English text: the Colossal Clean Crawled Corpus (C4)                                                                     |
++-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+| XLM-RoBERTa       | ``xlm-roberta-base``                                       | | ~125M parameters with 12-layers, 768-hidden-state, 3072 feed-forward hidden-state, 8-heads,                                         |
+|                   |                                                            | | Trained on on 2.5 TB of newly created clean CommonCrawl data in 100 languages                                                       |
+|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                   | ``xlm-roberta-large``                                      | | ~355M parameters with 24-layers, 1027-hidden-state, 4096 feed-forward hidden-state, 16-heads,                                       |
+|                   |                                                            | | Trained on 2.5 TB of newly created clean CommonCrawl data in 100 languages                                                          |
++-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+| FlauBERT          | ``flaubert/flaubert_small_cased``                          | | 6-layer, 512-hidden, 8-heads, 54M parameters                                                                                        |
+|                   |                                                            | | FlauBERT small architecture                                                                                                         |
+|                   |                                                            | (see `details <https://github.com/getalp/Flaubert>`__)                                                                                |
+|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                   | ``flaubert/flaubert_base_uncased``                         | | 12-layer, 768-hidden, 12-heads, 137M parameters                                                                                     |
+|                   |                                                            | | FlauBERT base architecture with uncased vocabulary                                                                                  |
+|                   |                                                            | (see `details <https://github.com/getalp/Flaubert>`__)                                                                                |
+|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                   | ``flaubert/flaubert_base_cased``                           | | 12-layer, 768-hidden, 12-heads, 138M parameters                                                                                     |
+|                   |                                                            | | FlauBERT base architecture with cased vocabulary                                                                                    |
+|                   |                                                            | (see `details <https://github.com/getalp/Flaubert>`__)                                                                                |
+|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                   | ``flaubert/flaubert_large_cased``                          | | 24-layer, 1024-hidden, 16-heads, 373M parameters                                                                                    |
+|                   |                                                            | | FlauBERT large architecture                                                                                                         |
+|                   |                                                            | (see `details <https://github.com/getalp/Flaubert>`__)                                                                                |
++-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+| Bart              | ``facebook/bart-large``                                    | | 24-layer, 1024-hidden, 16-heads, 406M parameters                                                                                    |
+|                   |                                                            | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/bart>`_)                                                       |
+|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                   | ``facebook/bart-large-mnli``                               | | Adds a 2 layer classification head with 1 million parameters                                                                        |
+|                   |                                                            | | bart-large base architecture with a classification head, finetuned on MNLI                                                          |
+|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                   | ``facebook/bart-large-cnn``                                | | 12-layer, 1024-hidden, 16-heads, 406M parameters       (same as base)                                                               |
+|                   |                                                            | | bart-large base architecture finetuned on cnn summarization task                                                                    |
+|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                   | ``facebook/mbart-large-en-ro``                             | | 12-layer, 1024-hidden, 16-heads, 880M parameters                                                                                    |
+|                   |                                                            | | bart-large architecture pretrained on cc25 multilingual data , finetuned on WMT english romanian translation.                       |
++-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+| DialoGPT          | ``DialoGPT-small``                                         | | 12-layer, 768-hidden, 12-heads, 124M parameters                                                                                     |
+|                   |                                                            | | Trained on English text: 147M conversation-like exchanges extracted from Reddit.                                                    |
+|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                   | ``DialoGPT-medium``                                        | | 24-layer, 1024-hidden, 16-heads, 355M parameters                                                                                    |
+|                   |                                                            | | Trained on English text: 147M conversation-like exchanges extracted from Reddit.                                                    |
+|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                   | ``DialoGPT-large``                                         | | 36-layer, 1280-hidden, 20-heads, 774M parameters                                                                                    |
+|                   |                                                            | | Trained on English text: 147M conversation-like exchanges extracted from Reddit.                                                    |
++-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+| Reformer          | ``reformer-enwik8``                                        | | 12-layer, 1024-hidden, 8-heads, 149M parameters                                                                                     |
+|                   |                                                            | | Trained on English Wikipedia data - enwik8.                                                                                         |
+|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                   | ``reformer-crime-and-punishment``                          | | 6-layer, 256-hidden, 2-heads, 3M parameters                                                                                         |
+|                   |                                                            | | Trained on English text: Crime and Punishment novel by Fyodor Dostoyevsky.                                                          |
++-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+| 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>`_)                                                                           |
++-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+| Longformer        | ``allenai/longformer-base-4096``                           | | 12-layer, 768-hidden, 12-heads, ~149M parameters                                                                                    |
+|                   |                                                            | | Starting from RoBERTa-base checkpoint, trained on documents of max length 4,096                                                     |
+|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                   | ``allenai/longformer-large-4096``                          | | 24-layer, 1024-hidden, 16-heads, ~435M parameters                                                                                   |
+|                   |                                                            | | Starting from RoBERTa-large checkpoint, trained on documents of max length 4,096                                                    |
++-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+

docs/source/quickstart.md

@@ -8,7 +8,7 @@ The library was designed with two strong goals in mind:
 
 - be as easy and fast to use as possible:
 
-  - we strongly limited the number of user-facing abstractions to learn, in fact there are almost no abstractions, just three standard classes required to use each model: configuration, models and tokenizer,
+  - we strongly limited the number of user-facing abstractions to learn, in fact, there are almost no abstractions, just three standard classes required to use each model: configuration, models and tokenizer,
   - all of these classes can be initialized in a simple and unified way from pretrained instances by using a common `from_pretrained()` instantiation method which will take care of downloading (if needed), caching and loading the related class from a pretrained instance supplied in the library or your own saved instance.
   - as a consequence, this library is NOT a modular toolbox of building blocks for neural nets. If you want to extend/build-upon the library, just use regular Python/PyTorch modules and inherit from the base classes of the library to reuse functionalities like model loading/saving.
 
@@ -31,27 +31,27 @@ A few other goals:
 
 ## Main concepts
 
-The library is build around three type of classes for each models:
+The library is build around three types of classes for each model:
 
-- **model classes** which are PyTorch models (`torch.nn.Modules`) of the 8 models architectures currently provided in the library, e.g. `BertModel`
-- **configuration classes** which store all the parameters required to build a model, e.g. `BertConfig`. You don't always need to instantiate these your-self, in particular if you are using a pretrained model without any modification, creating the model will automatically take care of instantiating the configuration (which is part of the model)
-- **tokenizer classes** which store the vocabulary for each model and provide methods for encoding/decoding strings in list of token embeddings indices to be fed to a model, e.g. `BertTokenizer`
+- **model classes**  e.g., `BertModel` which are 20+ PyTorch models (`torch.nn.Modules`) that work with the pretrained weights provided in the library. In TF2, these are `tf.keras.Model`.
+- **configuration classes** which store all the parameters required to build a model, e.g., `BertConfig`. You don't always need to instantiate these your-self. In particular, if you are using a pretrained model without any modification, creating the model will automatically take care of instantiating the configuration (which is part of the model)
+- **tokenizer classes** which store the vocabulary for each model and provide methods for encoding/decoding strings in a list of token embeddings indices to be fed to a model, e.g., `BertTokenizer`
 
 All these classes can be instantiated from pretrained instances and saved locally using two methods:
 
 - `from_pretrained()` let you instantiate a model/configuration/tokenizer from a pretrained version either provided by the library itself (currently 27 models are provided as listed [here](https://huggingface.co/transformers/pretrained_models.html)) or stored locally (or on a server) by the user,
 - `save_pretrained()` let you save a model/configuration/tokenizer locally so that it can be reloaded using `from_pretrained()`.
 
-We'll finish this quickstart tour by going through a few simple quick-start examples to see how we can instantiate and use these classes. The rest of the documentation is organized in two parts:
+We'll finish this quickstart tour by going through a few simple quick-start examples to see how we can instantiate and use these classes. The rest of the documentation is organized into two parts:
 
 - the **MAIN CLASSES** section details the common functionalities/method/attributes of the three main type of classes (configuration, model, tokenizer) plus some optimization related classes provided as utilities for training,
-- the **PACKAGE REFERENCE** section details all the variants of each class for each model architectures and in particular the input/output that you should expect when calling each of them.
+- the **PACKAGE REFERENCE** section details all the variants of each class for each model architectures and, in particular, the input/output that you should expect when calling each of them.
 
 ## Quick tour: Usage
 
 Here are two examples showcasing a few `Bert` and `GPT2` classes and pre-trained models.
 
-See full API reference for examples for each model class.
+See the full API reference for examples of each model class.
 
 ### BERT example
 
@@ -191,7 +191,7 @@ Examples for each model class of each model architecture (Bert, GPT, GPT-2, Tran
 
 #### Using the past
 
-GPT-2 as well as some other models (GPT, XLNet, Transfo-XL, CTRL) make use of a `past` or `mems` attribute which can be used to prevent re-computing the key/value pairs when using sequential decoding. It is useful when generating sequences as a big part of the attention mechanism benefits from previous computations.
+GPT-2, as well as some other models (GPT, XLNet, Transfo-XL, CTRL), make use of a `past` or `mems` attribute which can be used to prevent re-computing the key/value pairs when using sequential decoding. It is useful when generating sequences as a big part of the attention mechanism benefits from previous computations.
 
 Here is a fully-working example using the `past` with `GPT2LMHeadModel` and argmax decoding (which should only be used as an example, as argmax decoding introduces a lot of repetition):
 
@@ -209,7 +209,7 @@ past = None
 for i in range(100):
     print(i)
     output, past = model(context, past=past)
-    token = torch.argmax(output[0, :])
+    token = torch.argmax(output[..., -1, :])
 
     generated += [token.tolist()]
     context = token.unsqueeze(0)
@@ -219,4 +219,4 @@ sequence = tokenizer.decode(generated)
 print(sequence)
 ```
 
-The model only requires a single token as input as all the previous tokens' key/value pairs are contained in the `past`.
+The model only requires a single token as input as all the previous tokens' key/value pairs are contained in the `past`.

docs/source/serialization.rst

@@ -58,14 +58,14 @@ where
 
 ``Uncased`` means that the text has been lowercased before WordPiece tokenization, e.g., ``John Smith`` becomes ``john smith``. The Uncased model also strips out any accent markers. ``Cased`` means that the true case and accent markers are preserved. Typically, the Uncased model is better unless you know that case information is important for your task (e.g., Named Entity Recognition or Part-of-Speech tagging). For information about the Multilingual and Chinese model, see the `Multilingual README <https://github.com/google-research/bert/blob/master/multilingual.md>`__ or the original TensorFlow repository.
 
-When using an ``uncased model``\ , make sure to pass ``--do_lower_case`` to the example training scripts (or pass ``do_lower_case=True`` to FullTokenizer if you're using your own script and loading the tokenizer your-self.).
+When using an ``uncased model``\ , make sure your tokenizer has ``do_lower_case=True`` (either in its configuration, or passed as an additional parameter).
 
 Examples:
 
 .. code-block:: python
 
    # BERT
-   tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True, do_basic_tokenize=True)
+   tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_basic_tokenize=True)
    model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
 
    # OpenAI GPT
@@ -140,13 +140,13 @@ Here is the recommended way of saving the model, configuration and vocabulary to
 
    torch.save(model_to_save.state_dict(), output_model_file)
    model_to_save.config.to_json_file(output_config_file)
-   tokenizer.save_vocabulary(output_dir)
+   tokenizer.save_pretrained(output_dir)
 
    # Step 2: Re-load the saved model and vocabulary
 
    # Example for a Bert model
    model = BertForQuestionAnswering.from_pretrained(output_dir)
-   tokenizer = BertTokenizer.from_pretrained(output_dir, do_lower_case=args.do_lower_case)  # Add specific options if needed
+   tokenizer = BertTokenizer.from_pretrained(output_dir)  # Add specific options if needed
    # Example for a GPT model
    model = OpenAIGPTDoubleHeadsModel.from_pretrained(output_dir)
    tokenizer = OpenAIGPTTokenizer.from_pretrained(output_dir)

docs/source/usage.rst

@@ -0,0 +1,829 @@
+Usage
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+This page shows the most frequent use-cases when using the library. The models available allow for many different
+configurations and a great versatility in use-cases. The most simple ones are presented here, showcasing usage
+for tasks such as question answering, sequence classification, named entity recognition and others.
+
+These examples leverage auto-models, which are classes that will instantiate a model according to a given checkpoint,
+automatically selecting the correct model architecture. Please check the :class:`~transformers.AutoModel` documentation
+for more information.
+Feel free to modify the code to be more specific and adapt it to your specific use-case.
+
+In order for a model to perform well on a task, it must be loaded from a checkpoint corresponding to that task. These
+checkpoints are usually pre-trained on a large corpus of data and fine-tuned on a specific task. This means the
+following:
+
+- Not all models were fine-tuned on all tasks. If you want to fine-tune a model on a specific task, you can leverage
+  one of the `run_$TASK.py` script in the
+  `examples <https://github.com/huggingface/transformers/tree/master/examples>`_ directory.
+- Fine-tuned models were fine-tuned on a specific dataset. This dataset may or may not overlap with your use-case
+  and domain. As mentioned previously, you may leverage the
+  `examples <https://github.com/huggingface/transformers/tree/master/examples>`_ scripts to fine-tune your model, or you
+  may create your own training script.
+
+In order to do an inference on a task, several mechanisms are made available by the library:
+
+- Pipelines: very easy-to-use abstractions, which require as little as two lines of code.
+- Using a model directly with a tokenizer (PyTorch/TensorFlow): the full inference using the model. Less abstraction,
+  but much more powerful.
+
+Both approaches are showcased here.
+
+.. note::
+
+    All tasks presented here leverage pre-trained checkpoints that were fine-tuned on specific tasks. Loading a
+    checkpoint that was not fine-tuned on a specific task would load only the base transformer layers and not the
+    additional head that is used for the task, initializing the weights of that head randomly.
+
+    This would produce random output.
+
+Sequence Classification
+--------------------------
+
+Sequence classification is the task of classifying sequences according to a given number of classes. An example
+of sequence classification is the GLUE dataset, which is entirely based on that task. If you would like to fine-tune
+a model on a GLUE sequence classification task, you may leverage the
+`run_glue.py <https://github.com/huggingface/transformers/tree/master/examples/text-classification/run_glue.py>`_ or
+`run_tf_glue.py <https://github.com/huggingface/transformers/tree/master/examples/text-classification/run_tf_glue.py>`_ scripts.
+
+Here is an example using the pipelines do to sentiment analysis: identifying if a sequence is positive or negative.
+It leverages a fine-tuned model on sst2, which is a GLUE task.
+
+::
+
+    from transformers import pipeline
+
+    nlp = pipeline("sentiment-analysis")
+
+    print(nlp("I hate you"))
+    print(nlp("I love you"))
+
+This returns a label ("POSITIVE" or "NEGATIVE") alongside a score, as follows:
+
+::
+
+    [{'label': 'NEGATIVE', 'score': 0.9991129}]
+    [{'label': 'POSITIVE', 'score': 0.99986565}]
+
+
+Here is an example of doing a sequence classification using a model to determine if two sequences are paraphrases
+of each other. The process is the following:
+
+- Instantiate a tokenizer and a model from the checkpoint name. The model is identified as a BERT model and loads it
+  with the weights stored in the checkpoint.
+- Build a sequence from the two sentences, with the correct model-specific separators token type ids
+  and attention masks (:func:`~transformers.PreTrainedTokenizer.encode` and
+  :func:`~transformers.PreTrainedTokenizer.encode_plus` take care of this)
+- Pass this sequence through the model so that it is classified in one of the two available classes: 0
+  (not a paraphrase) and 1 (is a paraphrase)
+- Compute the softmax of the result to get probabilities over the classes
+- Print the results
+
+::
+
+    ## PYTORCH CODE
+    from transformers import AutoTokenizer, AutoModelForSequenceClassification
+    import torch
+
+    tokenizer = AutoTokenizer.from_pretrained("bert-base-cased-finetuned-mrpc")
+    model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased-finetuned-mrpc")
+
+    classes = ["not paraphrase", "is paraphrase"]
+
+    sequence_0 = "The company HuggingFace is based in New York City"
+    sequence_1 = "Apples are especially bad for your health"
+    sequence_2 = "HuggingFace's headquarters are situated in Manhattan"
+
+    paraphrase = tokenizer.encode_plus(sequence_0, sequence_2, return_tensors="pt")
+    not_paraphrase = tokenizer.encode_plus(sequence_0, sequence_1, return_tensors="pt")
+
+    paraphrase_classification_logits = model(**paraphrase)[0]
+    not_paraphrase_classification_logits = model(**not_paraphrase)[0]
+
+    paraphrase_results = torch.softmax(paraphrase_classification_logits, dim=1).tolist()[0]
+    not_paraphrase_results = torch.softmax(not_paraphrase_classification_logits, dim=1).tolist()[0]
+
+    print("Should be paraphrase")
+    for i in range(len(classes)):
+        print(f"{classes[i]}: {round(paraphrase_results[i] * 100)}%")
+
+    print("\nShould not be paraphrase")
+    for i in range(len(classes)):
+        print(f"{classes[i]}: {round(not_paraphrase_results[i] * 100)}%")
+    ## TENSORFLOW CODE
+    from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
+    import tensorflow as tf
+
+    tokenizer = AutoTokenizer.from_pretrained("bert-base-cased-finetuned-mrpc")
+    model = TFAutoModelForSequenceClassification.from_pretrained("bert-base-cased-finetuned-mrpc")
+
+    classes = ["not paraphrase", "is paraphrase"]
+
+    sequence_0 = "The company HuggingFace is based in New York City"
+    sequence_1 = "Apples are especially bad for your health"
+    sequence_2 = "HuggingFace's headquarters are situated in Manhattan"
+
+    paraphrase = tokenizer.encode_plus(sequence_0, sequence_2, return_tensors="tf")
+    not_paraphrase = tokenizer.encode_plus(sequence_0, sequence_1, return_tensors="tf")
+
+    paraphrase_classification_logits = model(paraphrase)[0]
+    not_paraphrase_classification_logits = model(not_paraphrase)[0]
+
+    paraphrase_results = tf.nn.softmax(paraphrase_classification_logits, axis=1).numpy()[0]
+    not_paraphrase_results = tf.nn.softmax(not_paraphrase_classification_logits, axis=1).numpy()[0]
+
+    print("Should be paraphrase")
+    for i in range(len(classes)):
+        print(f"{classes[i]}: {round(paraphrase_results[i] * 100)}%")
+
+    print("\nShould not be paraphrase")
+    for i in range(len(classes)):
+        print(f"{classes[i]}: {round(not_paraphrase_results[i] * 100)}%")
+
+This outputs the following results:
+
+::
+
+    Should be paraphrase
+    not paraphrase: 10%
+    is paraphrase: 90%
+
+    Should not be paraphrase
+    not paraphrase: 94%
+    is paraphrase: 6%
+
+Extractive Question Answering
+----------------------------------------------------
+
+Extractive Question Answering is the task of extracting an answer from a text given a question. An example of a
+question answering dataset is the SQuAD dataset, which is entirely based on that task. If you would like to fine-tune
+a model on a SQuAD task, you may leverage the `run_squad.py`.
+
+Here is an example using the pipelines do to question answering: extracting an answer from a text given a question.
+It leverages a fine-tuned model on SQuAD.
+
+::
+
+    from transformers import pipeline
+
+    nlp = pipeline("question-answering")
+
+    context = r"""
+    Extractive Question Answering is the task of extracting an answer from a text given a question. An example of a
+    question answering dataset is the SQuAD dataset, which is entirely based on that task. If you would like to fine-tune
+    a model on a SQuAD task, you may leverage the `run_squad.py`.
+    """
+
+    print(nlp(question="What is extractive question answering?", context=context))
+    print(nlp(question="What is a good example of a question answering dataset?", context=context))
+
+This returns an answer extracted from the text, a confidence score, alongside "start" and "end" values which
+are the positions of the extracted answer in the text.
+
+::
+
+    {'score': 0.622232091629833, 'start': 34, 'end': 96, 'answer': 'the task of extracting an answer from a text given a question.'}
+    {'score': 0.5115299158662765, 'start': 147, 'end': 161, 'answer': 'SQuAD dataset,'}
+
+
+Here is an example of question answering using a model and a tokenizer. The process is the following:
+
+- Instantiate a tokenizer and a model from the checkpoint name. The model is identified as a BERT model and loads it
+  with the weights stored in the checkpoint.
+- Define a text and a few questions.
+- Iterate over the questions and build a sequence from the text and the current question, with the correct
+  model-specific separators token type ids and attention masks
+- Pass this sequence through the model. This outputs a range of scores across the entire sequence tokens (question and
+  text), for both the start and end positions.
+- Compute the softmax of the result to get probabilities over the tokens
+- Fetch the tokens from the identified start and stop values, convert those tokens to a string.
+- Print the results
+
+::
+
+    ## PYTORCH CODE
+    from transformers import AutoTokenizer, AutoModelForQuestionAnswering
+    import torch
+
+    tokenizer = AutoTokenizer.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad")
+    model = AutoModelForQuestionAnswering.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad")
+
+    text = r"""
+    🤗 Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides general-purpose
+    architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet…) for Natural Language Understanding (NLU) and Natural
+    Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between
+    TensorFlow 2.0 and PyTorch.
+    """
+
+    questions = [
+        "How many pretrained models are available in Transformers?",
+        "What does Transformers provide?",
+        "Transformers provides interoperability between which frameworks?",
+    ]
+
+    for question in questions:
+        inputs = tokenizer.encode_plus(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)
+        answer_start_scores, answer_end_scores = model(**inputs)
+
+        answer_start = torch.argmax(
+            answer_start_scores
+        )  # Get the most likely beginning of answer with the argmax of the score
+        answer_end = torch.argmax(answer_end_scores) + 1  # Get the most likely end of answer with the argmax of the score
+
+        answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(input_ids[answer_start:answer_end]))
+
+        print(f"Question: {question}")
+        print(f"Answer: {answer}\n")
+    ## TENSORFLOW CODE
+    from transformers import AutoTokenizer, TFAutoModelForQuestionAnswering
+    import tensorflow as tf
+
+    tokenizer = AutoTokenizer.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad")
+    model = TFAutoModelForQuestionAnswering.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad")
+
+    text = r"""
+    🤗 Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides general-purpose
+    architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet…) for Natural Language Understanding (NLU) and Natural
+    Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between
+    TensorFlow 2.0 and PyTorch.
+    """
+
+    questions = [
+        "How many pretrained models are available in Transformers?",
+        "What does Transformers provide?",
+        "Transformers provides interoperability between which frameworks?",
+    ]
+
+    for question in questions:
+        inputs = tokenizer.encode_plus(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)
+        answer_start_scores, answer_end_scores = model(inputs)
+
+        answer_start = tf.argmax(
+            answer_start_scores, axis=1
+        ).numpy()[0]  # Get the most likely beginning of answer with the argmax of the score
+        answer_end = (
+            tf.argmax(answer_end_scores, axis=1) + 1
+        ).numpy()[0]  # Get the most likely end of answer with the argmax of the score
+        answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(input_ids[answer_start:answer_end]))
+
+        print(f"Question: {question}")
+        print(f"Answer: {answer}\n")
+
+This outputs the questions followed by the predicted answers:
+
+::
+
+    Question: How many pretrained models are available in Transformers?
+    Answer: over 32 +
+
+    Question: What does Transformers provide?
+    Answer: general - purpose architectures
+
+    Question: Transformers provides interoperability between which frameworks?
+    Answer: tensorflow 2 . 0 and pytorch
+
+
+
+Language Modeling
+----------------------------------------------------
+
+Language modeling is the task of fitting a model to a corpus, which can be domain specific. All popular transformer
+based models are trained using a variant of language modeling, e.g. BERT with masked language modeling, GPT-2 with
+causal language modeling.
+
+Language modeling can be useful outside of pre-training as well, for example to shift the model distribution to be
+domain-specific: using a language model trained over a very large corpus, and then fine-tuning it to a news dataset
+or on scientific papers e.g. `LysandreJik/arxiv-nlp <https://huggingface.co/lysandre/arxiv-nlp>`__.
+
+Masked Language Modeling
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Masked language modeling is the task of masking tokens in a sequence with a masking token, and prompting the model to
+fill that mask with an appropriate token. This allows the model to attend to both the right context (tokens on the
+right of the mask) and the left context (tokens on the left of the mask). Such a training creates a strong basis
+for downstream tasks requiring bi-directional context such as SQuAD (question answering,
+see `Lewis, Lui, Goyal et al. <https://arxiv.org/abs/1910.13461>`__, part 4.2).
+
+Here is an example of using pipelines to replace a mask from a sequence:
+
+::
+
+    from transformers import pipeline
+
+    nlp = pipeline("fill-mask")
+    print(nlp(f"HuggingFace is creating a {nlp.tokenizer.mask_token} that the community uses to solve NLP tasks."))
+
+This outputs the sequences with the mask filled, the confidence score as well as the token id in the tokenizer
+vocabulary:
+
+::
+
+    [
+        {'sequence': '<s> HuggingFace is creating a tool that the community uses to solve NLP tasks.</s>', 'score': 0.15627853572368622, 'token': 3944},
+        {'sequence': '<s> HuggingFace is creating a framework that the community uses to solve NLP tasks.</s>', 'score': 0.11690319329500198, 'token': 7208},
+        {'sequence': '<s> HuggingFace is creating a library that the community uses to solve NLP tasks.</s>', 'score': 0.058063216507434845, 'token': 5560},
+        {'sequence': '<s> HuggingFace is creating a database that the community uses to solve NLP tasks.</s>', 'score': 0.04211743175983429, 'token': 8503},
+        {'sequence': '<s> HuggingFace is creating a prototype that the community uses to solve NLP tasks.</s>', 'score': 0.024718601256608963, 'token': 17715}
+    ]
+
+Here is an example doing masked language modeling using a model and a tokenizer. The process is the following:
+
+- Instantiate a tokenizer and a model from the checkpoint name. The model is identified as a DistilBERT model and
+  loads it with the weights stored in the checkpoint.
+- Define a sequence with a masked token, placing the :obj:`tokenizer.mask_token` instead of a word.
+- Encode that sequence into IDs and find the position of the masked token in that list of IDs.
+- Retrieve the predictions at the index of the mask token: this tensor has the same size as the vocabulary, and the
+  values are the scores attributed to each token. The model gives higher score to tokens he deems probable in that
+  context.
+- Retrieve the top 5 tokens using the PyTorch :obj:`topk` or TensorFlow :obj:`top_k` methods.
+- Replace the mask token by the tokens and print the results
+
+::
+
+    ## PYTORCH CODE
+    from transformers import AutoModelWithLMHead, AutoTokenizer
+    import torch
+
+    tokenizer = AutoTokenizer.from_pretrained("distilbert-base-cased")
+    model = AutoModelWithLMHead.from_pretrained("distilbert-base-cased")
+
+    sequence = f"Distilled models are smaller than the models they mimic. Using them instead of the large versions would help {tokenizer.mask_token} our carbon footprint."
+
+    input = tokenizer.encode(sequence, return_tensors="pt")
+    mask_token_index = torch.where(input == tokenizer.mask_token_id)[1]
+
+    token_logits = model(input)[0]
+    mask_token_logits = token_logits[0, mask_token_index, :]
+
+    top_5_tokens = torch.topk(mask_token_logits, 5, dim=1).indices[0].tolist()
+
+    for token in top_5_tokens:
+        print(sequence.replace(tokenizer.mask_token, tokenizer.decode([token])))
+    ## TENSORFLOW CODE
+    from transformers import TFAutoModelWithLMHead, AutoTokenizer
+    import tensorflow as tf
+
+    tokenizer = AutoTokenizer.from_pretrained("distilbert-base-cased")
+    model = TFAutoModelWithLMHead.from_pretrained("distilbert-base-cased")
+
+    sequence = f"Distilled models are smaller than the models they mimic. Using them instead of the large versions would help {tokenizer.mask_token} our carbon footprint."
+
+    input = tokenizer.encode(sequence, return_tensors="tf")
+    mask_token_index = tf.where(input == tokenizer.mask_token_id)[0, 1]
+
+    token_logits = model(input)[0]
+    mask_token_logits = token_logits[0, mask_token_index, :]
+
+    top_5_tokens = tf.math.top_k(mask_token_logits, 5).indices.numpy()
+
+    for token in top_5_tokens:
+        print(sequence.replace(tokenizer.mask_token, tokenizer.decode([token])))
+
+This prints five sequences, with the top 5 tokens predicted by the model:
+
+::
+
+    Distilled models are smaller than the models they mimic. Using them instead of the large versions would help reduce our carbon footprint.
+    Distilled models are smaller than the models they mimic. Using them instead of the large versions would help increase our carbon footprint.
+    Distilled models are smaller than the models they mimic. Using them instead of the large versions would help decrease our carbon footprint.
+    Distilled models are smaller than the models they mimic. Using them instead of the large versions would help offset our carbon footprint.
+    Distilled models are smaller than the models they mimic. Using them instead of the large versions would help improve our carbon footprint.
+
+
+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.
+
+Usually, the next token is predicted by sampling from the logits of the last hidden state the model produces from the input sequence.
+
+Here is an example using the tokenizer and model and leveraging the :func:`~transformers.PreTrainedModel.top_k_top_p_filtering` method to sample the next token following an input sequence of tokens.
+
+::
+
+    ## PYTORCH CODE
+    from transformers import AutoModelWithLMHead, AutoTokenizer, top_k_top_p_filtering
+    import torch
+    from torch.nn import functional as F
+
+
+    tokenizer = AutoTokenizer.from_pretrained("gpt2")
+    model = AutoModelWithLMHead.from_pretrained("gpt2")
+
+    sequence = f"Hugging Face is based in DUMBO, New York City, and "
+
+    input_ids = tokenizer.encode(sequence, return_tensors="pt")
+
+    # get logits of last hidden state
+    next_token_logits = model(input_ids)[0][:, -1, :]
+
+    # filter
+    filtered_next_token_logits = top_k_top_p_filtering(next_token_logits, top_k=50, top_p=1.0)
+
+    # sample
+    probs = F.softmax(filtered_next_token_logits, dim=-1)
+    next_token = torch.multinomial(probs, num_samples=1)
+
+    generated = torch.cat([input_ids, next_token], dim=-1)
+
+    resulting_string = tokenizer.decode(generated.tolist()[0])
+    print(resulting_string)
+    ## TENSORFLOW CODE
+    from transformers import TFAutoModelWithLMHead, AutoTokenizer, tf_top_k_top_p_filtering
+    import tensorflow as tf
+
+    tokenizer = AutoTokenizer.from_pretrained("gpt2")
+    model = TFAutoModelWithLMHead.from_pretrained("gpt2")
+
+    sequence = f"Hugging Face is based in DUMBO, New York City, and "
+
+    input_ids = tokenizer.encode(sequence, return_tensors="tf")
+
+    # get logits of last hidden state
+    next_token_logits = model(input_ids)[0][:, -1, :]
+
+    # filter
+    filtered_next_token_logits = tf_top_k_top_p_filtering(next_token_logits, top_k=50, top_p=1.0)
+
+    # sample
+    next_token = tf.random.categorical(filtered_next_token_logits, dtype=tf.int32, num_samples=1)
+
+    generated = tf.concat([input_ids, next_token], axis=1)
+
+    resulting_string = tokenizer.decode(generated.numpy().tolist()[0])
+    print(resulting_string)
+
+
+This outputs a (hopefully) coherent next token following the original sequence, which is in our case is the word *has*:
+
+::
+
+    Hugging Face is based in DUMBO, New York City, and has
+
+In the next section, we show how this functionality is leveraged in :func:`~transformers.PreTrainedModel.generate` to generate multiple tokens up to a user-defined length.
+
+Text Generation
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+In text generation (*a.k.a* *open-ended text generation*) the goal is to create a coherent portion of text that is a continuation from the given context. As an example, is it shown how *GPT-2* can be used in pipelines to generate text. As a default all models apply *Top-K* sampling when used in pipelines as configured in their respective configurations (see `gpt-2 config <https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-config.json>`_ for example).
+
+::
+
+    from transformers import pipeline
+
+    text_generator = pipeline("text-generation")
+    print(text_generator("As far as I am concerned, I will", max_length=50))
+
+
+Here the model generates a random text with a total maximal length of *50* tokens from context *"As far as I am concerned, I will"*.
+The default arguments of ``PreTrainedModel.generate()`` can directly be overriden in the pipeline as is shown above for the argument ``max_length``.
+
+Here is an example for text generation using XLNet and its tokenzier. 
+
+::
+
+    ## PYTORCH CODE
+    from transformers import AutoModelWithLMHead, AutoTokenizer
+
+    model = AutoModelWithLMHead.from_pretrained("xlnet-base-cased")
+    tokenizer = AutoTokenizer.from_pretrained("xlnet-base-cased")
+
+    # Padding text helps XLNet with short prompts - proposed by Aman Rusia in https://github.com/rusiaaman/XLNet-gen#methodology
+    PADDING_TEXT = """In 1991, the remains of Russian Tsar Nicholas II and his family
+    (except for Alexei and Maria) are discovered.
+    The voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the
+    remainder of the story. 1883 Western Siberia,
+    a young Grigori Rasputin is asked by his father and a group of men to perform magic.
+    Rasputin has a vision and denounces one of the men as a horse thief. Although his
+    father initially slaps him for making such an accusation, Rasputin watches as the
+    man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
+    the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,
+    with people, even a bishop, begging for his blessing. <eod> </s> <eos>""" 
+
+    prompt = "Today the weather is really nice and I am planning on "
+    inputs = tokenizer.encode(PADDING_TEXT + prompt, add_special_tokens=False, return_tensors="pt")
+    
+    prompt_length = len(tokenizer.decode(inputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=True))
+    outputs = model.generate(inputs, max_length=250, do_sample=True, top_p=0.95, top_k=60)
+    generated = prompt + tokenizer.decode(outputs[0])[prompt_length:]
+
+    print(generated)
+    ## TENSORFLOW CODE
+    from transformers import TFAutoModelWithLMHead, AutoTokenizer
+
+    model = TFAutoModelWithLMHead.from_pretrained("xlnet-base-cased")
+    tokenizer = AutoTokenizer.from_pretrained("xlnet-base-cased")
+
+    # Padding text helps XLNet with short prompts - proposed by Aman Rusia in https://github.com/rusiaaman/XLNet-gen#methodology
+    PADDING_TEXT = """In 1991, the remains of Russian Tsar Nicholas II and his family
+    (except for Alexei and Maria) are discovered.
+    The voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the
+    remainder of the story. 1883 Western Siberia,
+    a young Grigori Rasputin is asked by his father and a group of men to perform magic.
+    Rasputin has a vision and denounces one of the men as a horse thief. Although his
+    father initially slaps him for making such an accusation, Rasputin watches as the
+    man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
+    the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,
+    with people, even a bishop, begging for his blessing. <eod> </s> <eos>""" 
+
+    prompt = "Today the weather is really nice and I am planning on "
+    inputs = tokenizer.encode(PADDING_TEXT + prompt, add_special_tokens=False, return_tensors="tf")
+
+    prompt_length = len(tokenizer.decode(inputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=True))
+    outputs = model.generate(inputs, max_length=250, do_sample=True, top_p=0.95, top_k=60)
+    generated = prompt + tokenizer.decode(outputs[0])[prompt_length:]
+
+    print(generated)
+
+Text generation is currently possible with *GPT-2*, *OpenAi-GPT*, *CTRL*, *XLNet*, *Transfo-XL* and *Reformer* in PyTorch and for most models in Tensorflow as well. As can be seen in the example above *XLNet* and *Transfo-xl* often need to be padded to work well.
+GPT-2 is usually a good choice for *open-ended text generation* because it was trained on millions on webpages with a causal language modeling objective.
+
+For more information on how to apply different decoding strategies for text generation, please also refer to our generation blog post `here <https://huggingface.co/blog/how-to-generate>`_.
+
+
+Named Entity Recognition
+----------------------------------------------------
+
+Named Entity Recognition (NER) is the task of classifying tokens according to a class, for example identifying a
+token as a person, an organisation or a location.
+An example of a named entity recognition dataset is the CoNLL-2003 dataset, which is entirely based on that task.
+If you would like to fine-tune a model on an NER task, you may leverage the `ner/run_ner.py` (PyTorch),
+`ner/run_pl_ner.py` (leveraging pytorch-lightning) or the `ner/run_tf_ner.py` (TensorFlow) scripts.
+
+Here is an example using the pipelines do to named entity recognition, trying to identify tokens as belonging to one
+of 9 classes:
+
+- O, Outside of a named entity
+- B-MIS, Beginning of a miscellaneous entity right after another miscellaneous entity
+- I-MIS, Miscellaneous entity
+- B-PER, Beginning of a person's name right after another person's name
+- I-PER, Person's name
+- B-ORG, Beginning of an organisation right after another organisation
+- I-ORG, Organisation
+- B-LOC, Beginning of a location right after another location
+- I-LOC, Location
+
+It leverages a fine-tuned model on CoNLL-2003, fine-tuned by `@stefan-it <https://github.com/stefan-it>`__ from
+`dbmdz <https://github.com/dbmdz>`__.
+
+::
+
+    from transformers import pipeline
+
+    nlp = pipeline("ner")
+
+    sequence = "Hugging Face Inc. is a company based in New York City. Its headquarters are in DUMBO, therefore very" \
+               "close to the Manhattan Bridge which is visible from the window."
+
+    print(nlp(sequence))
+
+This outputs a list of all words that have been identified as an entity from the 9 classes defined above. Here is the
+expected results:
+
+::
+
+    [
+        {'word': 'Hu', 'score': 0.9995632767677307, 'entity': 'I-ORG'},
+        {'word': '##gging', 'score': 0.9915938973426819, 'entity': 'I-ORG'},
+        {'word': 'Face', 'score': 0.9982671737670898, 'entity': 'I-ORG'},
+        {'word': 'Inc', 'score': 0.9994403719902039, 'entity': 'I-ORG'},
+        {'word': 'New', 'score': 0.9994346499443054, 'entity': 'I-LOC'},
+        {'word': 'York', 'score': 0.9993270635604858, 'entity': 'I-LOC'},
+        {'word': 'City', 'score': 0.9993864893913269, 'entity': 'I-LOC'},
+        {'word': 'D', 'score': 0.9825621843338013, 'entity': 'I-LOC'},
+        {'word': '##UM', 'score': 0.936983048915863, 'entity': 'I-LOC'},
+        {'word': '##BO', 'score': 0.8987102508544922, 'entity': 'I-LOC'},
+        {'word': 'Manhattan', 'score': 0.9758241176605225, 'entity': 'I-LOC'},
+        {'word': 'Bridge', 'score': 0.990249514579773, 'entity': 'I-LOC'}
+    ]
+
+Note how the words "Hugging Face" have been identified as an organisation, and "New York City", "DUMBO" and
+"Manhattan Bridge" have been identified as locations.
+
+Here is an example doing named entity recognition using a model and a tokenizer. The process is the following:
+
+- Instantiate a tokenizer and a model from the checkpoint name. The model is identified as a BERT model and
+  loads it with the weights stored in the checkpoint.
+- Define the label list with which the model was trained on.
+- Define a sequence with known entities, such as "Hugging Face" as an organisation and "New York City" as a location.
+- Split words into tokens so that they can be mapped to the predictions. We use a small hack by firstly completely
+  encoding and decoding the sequence, so that we're left with a string that contains the special tokens.
+- Encode that sequence into IDs (special tokens are added automatically).
+- Retrieve the predictions by passing the input to the model and getting the first output. This results in a
+  distribution over the 9 possible classes for each token. We take the argmax to retrieve the most likely class
+  for each token.
+- Zip together each token with its prediction and print it.
+
+::
+
+    ## PYTORCH CODE
+    from transformers import AutoModelForTokenClassification, AutoTokenizer
+    import torch
+
+    model = AutoModelForTokenClassification.from_pretrained("dbmdz/bert-large-cased-finetuned-conll03-english")
+    tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
+
+    label_list = [
+        "O",       # Outside of a named entity
+        "B-MISC",  # Beginning of a miscellaneous entity right after another miscellaneous entity
+        "I-MISC",  # Miscellaneous entity
+        "B-PER",   # Beginning of a person's name right after another person's name
+        "I-PER",   # Person's name
+        "B-ORG",   # Beginning of an organisation right after another organisation
+        "I-ORG",   # Organisation
+        "B-LOC",   # Beginning of a location right after another location
+        "I-LOC"    # Location
+    ]
+
+    sequence = "Hugging Face Inc. is a company based in New York City. Its headquarters are in DUMBO, therefore very" \
+               "close to the Manhattan Bridge."
+
+    # Bit of a hack to get the tokens with the special tokens
+    tokens = tokenizer.tokenize(tokenizer.decode(tokenizer.encode(sequence)))
+    inputs = tokenizer.encode(sequence, return_tensors="pt")
+
+    outputs = model(inputs)[0]
+    predictions = torch.argmax(outputs, dim=2)
+
+    print([(token, label_list[prediction]) for token, prediction in zip(tokens, predictions[0].tolist())])
+    ## TENSORFLOW CODE
+    from transformers import TFAutoModelForTokenClassification, AutoTokenizer
+    import tensorflow as tf
+
+    model = TFAutoModelForTokenClassification.from_pretrained("dbmdz/bert-large-cased-finetuned-conll03-english")
+    tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
+
+    label_list = [
+        "O",       # Outside of a named entity
+        "B-MISC",  # Beginning of a miscellaneous entity right after another miscellaneous entity
+        "I-MISC",  # Miscellaneous entity
+        "B-PER",   # Beginning of a person's name right after another person's name
+        "I-PER",   # Person's name
+        "B-ORG",   # Beginning of an organisation right after another organisation
+        "I-ORG",   # Organisation
+        "B-LOC",   # Beginning of a location right after another location
+        "I-LOC"    # Location
+    ]
+
+    sequence = "Hugging Face Inc. is a company based in New York City. Its headquarters are in DUMBO, therefore very" \
+               "close to the Manhattan Bridge."
+
+    # Bit of a hack to get the tokens with the special tokens
+    tokens = tokenizer.tokenize(tokenizer.decode(tokenizer.encode(sequence)))
+    inputs = tokenizer.encode(sequence, return_tensors="tf")
+
+    outputs = model(inputs)[0]
+    predictions = tf.argmax(outputs, axis=2)
+
+    print([(token, label_list[prediction]) for token, prediction in zip(tokens, predictions[0].numpy())])
+
+This outputs a list of each token mapped to their prediction. Differently from the pipeline, here every token has
+a prediction as we didn't remove the "0" class which means that no particular entity was found on that token. The
+following array should be the output:
+
+::
+
+    [('[CLS]', 'O'), ('Hu', 'I-ORG'), ('##gging', 'I-ORG'), ('Face', 'I-ORG'), ('Inc', 'I-ORG'), ('.', 'O'), ('is', 'O'), ('a', 'O'), ('company', 'O'), ('based', 'O'), ('in', 'O'), ('New', 'I-LOC'), ('York', 'I-LOC'), ('City', 'I-LOC'), ('.', 'O'), ('Its', 'O'), ('headquarters', 'O'), ('are', 'O'), ('in', 'O'), ('D', 'I-LOC'), ('##UM', 'I-LOC'), ('##BO', 'I-LOC'), (',', 'O'), ('therefore', 'O'), ('very', 'O'), ('##c', 'O'), ('##lose', 'O'), ('to', 'O'), ('the', 'O'), ('Manhattan', 'I-LOC'), ('Bridge', 'I-LOC'), ('.', 'O'), ('[SEP]', 'O')]   
+Summarization
+----------------------------------------------------
+
+Summarization is the task of summarizing a text / an article into a shorter text.
+
+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, you may leverage the ``examples/summarization/bart/run_train.sh`` (leveraging pytorch-lightning) script.
+
+Here is an example using the pipelines do to summarization. 
+It leverages a Bart model that was fine-tuned on the CNN / Daily Mail data set.
+
+::
+
+    from transformers import pipeline
+
+    summarizer = pipeline("summarization")
+
+    ARTICLE = """ New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County, New York. 
+    A year later, she got married again in Westchester County, but to a different man and without divorcing her first husband. 
+    Only 18 days after that marriage, she got hitched yet again. Then, Barrientos declared "I do" five more times, sometimes only within two weeks of each other. 
+    In 2010, she married once more, this time in the Bronx. In an application for a marriage license, she stated it was her "first and only" marriage. 
+    Barrientos, now 39, is facing two criminal counts of "offering a false instrument for filing in the first degree," referring to her false statements on the 
+    2010 marriage license application, according to court documents. 
+    Prosecutors said the marriages were part of an immigration scam. 
+    On Friday, she pleaded not guilty at State Supreme Court in the Bronx, according to her attorney, Christopher Wright, who declined to comment further. 
+    After leaving court, Barrientos was arrested and charged with theft of service and criminal trespass for allegedly sneaking into the New York subway through an emergency exit, said Detective 
+    Annette Markowski, a police spokeswoman. In total, Barrientos has been married 10 times, with nine of her marriages occurring between 1999 and 2002. 
+    All occurred either in Westchester County, Long Island, New Jersey or the Bronx. She is believed to still be married to four men, and at one time, she was married to eight men at once, prosecutors say. 
+    Prosecutors said the immigration scam involved some of her husbands, who filed for permanent residence status shortly after the marriages. 
+    Any divorces happened only after such filings were approved. It was unclear whether any of the men will be prosecuted. 
+    The case was referred to the Bronx District Attorney\'s Office by Immigration and Customs Enforcement and the Department of Homeland Security\'s 
+    Investigation Division. Seven of the men are from so-called "red-flagged" countries, including Egypt, Turkey, Georgia, Pakistan and Mali. 
+    Her eighth husband, Rashid Rajput, was deported in 2006 to his native Pakistan after an investigation by the Joint Terrorism Task Force. 
+    If convicted, Barrientos faces up to four years in prison.  Her next court appearance is scheduled for May 18.
+    """
+    
+    print(summarizer(ARTICLE, max_length=130, min_length=30))
+
+Because the summarization pipeline depends on the ``PretrainedModel.generate()`` method, we can override the default arguments 
+of ``PretrainedModel.generate()`` directly in the pipeline as is shown for ``max_length`` and ``min_length`` above.
+This outputs the following summary:
+
+::
+
+  Liana Barrientos has been married 10 times, sometimes within two weeks of each other. Prosecutors say the marriages were part of an immigration scam. She pleaded not guilty at State Supreme Court in the Bronx on Friday.
+  
+Here is an example doing summarization using a model and a tokenizer. The process is the following:
+
+- Instantiate a tokenizer and a model from the checkpoint name. Summarization is usually done using an encoder-decoder model, such as ``Bart`` or ``T5``.
+- Define the article that should be summarizaed.
+- Leverage the ``PretrainedModel.generate()`` method.
+- Add the T5 specific prefix "summarize: ".
+
+Here Google`s T5 model is used that was only pre-trained on a multi-task mixed data set (including CNN / Daily Mail), but nevertheless yields very good results.
+::
+
+    ## PYTORCH CODE
+    from transformers import AutoModelWithLMHead, AutoTokenizer
+
+    model = AutoModelWithLMHead.from_pretrained("t5-base")
+    tokenizer = AutoTokenizer.from_pretrained("t5-base")
+
+    # T5 uses a max_length of 512 so we cut the article to 512 tokens.
+    inputs = tokenizer.encode("summarize: " + ARTICLE, return_tensors="pt", max_length=512)
+    outputs = model.generate(inputs, max_length=150, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True)
+    print(outputs)
+    
+    ## TENSORFLOW CODE
+    from transformers import TFAutoModelWithLMHead, AutoTokenizer
+
+    model = TFAutoModelWithLMHead.from_pretrained("t5-base")
+    tokenizer = AutoTokenizer.from_pretrained("t5-base")
+
+    # T5 uses a max_length of 512 so we cut the article to 512 tokens.
+    inputs = tokenizer.encode("summarize: " + ARTICLE, return_tensors="tf", max_length=512)
+    outputs = model.generate(inputs, max_length=150, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True)
+    print(outputs)  
+Translation
+----------------------------------------------------
+
+Translation is the task of translating a text from one language to another.
+
+An example of a translation dataset is the WMT English to German dataset, which has English sentences as the input data 
+and German sentences as the target data.
+
+Here is an example using the pipelines do to translation. 
+It leverages a T5 model that was only pre-trained on a multi-task mixture dataset (including WMT), but yields impressive 
+translation results nevertheless.
+
+::
+
+    from transformers import pipeline
+
+    translator = pipeline("translation_en_to_de")
+    print(translator("Hugging Face is a technology company based in New York and Paris", max_length=40))
+
+Because the translation pipeline depends on the ``PretrainedModel.generate()`` method, we can override the default arguments 
+of ``PretrainedModel.generate()`` directly in the pipeline as is shown for ``max_length`` above.
+This outputs the following translation into German:
+
+::
+
+  Hugging Face ist ein Technologieunternehmen mit Sitz in New York und Paris.
+  
+Here is an example doing translation using a model and a tokenizer. The process is the following:
+
+- Instantiate a tokenizer and a model from the checkpoint name. Summarization is usually done using an encoder-decoder model, such as ``Bart`` or ``T5``.
+- Define the article that should be summarizaed.
+- Leverage the ``PretrainedModel.generate()`` method.
+- Add the T5 specific prefix "translate English to German: "
+
+::
+
+    ## PYTORCH CODE
+    from transformers import AutoModelWithLMHead, AutoTokenizer
+
+    model = AutoModelWithLMHead.from_pretrained("t5-base")
+    tokenizer = AutoTokenizer.from_pretrained("t5-base")
+
+    inputs = tokenizer.encode("translate English to German: Hugging Face is a technology company based in New York and Paris", return_tensors="pt")
+    outputs = model.generate(inputs, max_length=40, num_beams=4, early_stopping=True)
+
+    print(outputs)
+    
+    ## TENSORFLOW CODE
+    from transformers import TFAutoModelWithLMHead, AutoTokenizer
+
+    model = TFAutoModelWithLMHead.from_pretrained("t5-base")
+    tokenizer = AutoTokenizer.from_pretrained("t5-base")
+
+    inputs = tokenizer.encode("translate English to German: Hugging Face is a technology company based in New York and Paris", return_tensors="tf")
+    outputs = model.generate(inputs, max_length=40, num_beams=4, early_stopping=True)
+
+    print(outputs)

examples/README.md

@@ -1,685 +1,80 @@
-# Examples
+## Examples
 
-In this section a few examples are put together. All of these examples work for several models, making use of the very
-similar API between the different models.
+Version 2.9 of `transformers` introduces a new [`Trainer`](https://github.com/huggingface/transformers/blob/master/src/transformers/trainer.py) class for PyTorch, and its equivalent [`TFTrainer`](https://github.com/huggingface/transformers/blob/master/src/transformers/trainer_tf.py) for TF 2.
+Running the examples requires PyTorch 1.3.1+ or TensorFlow 2.0+.
 
-**Important**  
-To run the latest versions of the examples, you have to install from source and install some specific requirements for the examples.
+Here is the list of all our examples:
+- **grouped by task** (all official examples work for multiple models)
+- with information on whether they are **built on top of `Trainer`/`TFTrainer`** (if not, they still work, they might just lack some features),
+- whether they also include examples for **`pytorch-lightning`**, which is a great fully-featured, general-purpose training library for PyTorch,
+- links to **Colab notebooks** to walk through the scripts and run them easily,
+- links to **Cloud deployments** to be able to deploy large-scale trainings in the Cloud with little to no setup.
+
+This is still a work-in-progress – in particular documentation is still sparse – so please **contribute improvements/pull requests.**
+
+
+# The Big Table of Tasks
+
+| Task | Example datasets | Trainer support | TFTrainer support | pytorch-lightning | 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)
+| [**`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://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/trainer/01_text_classification.ipynb)
+| [**`token-classification`**](https://github.com/huggingface/transformers/tree/master/examples/token-classification) | CoNLL NER       | ✅ | ✅ | ✅ | -
+| [**`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           | -  | ✅ | -  | -
+| [**`text-generation`**](https://github.com/huggingface/transformers/tree/master/examples/text-generation)     | -           | -  | - | -  | [![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)
+| [**`distillation`**](https://github.com/huggingface/transformers/tree/master/examples/distillation)       | All               | -  | -  | -  | -
+| [**`summarization`**](https://github.com/huggingface/transformers/tree/master/examples/summarization)     | CNN/Daily Mail    | -  | -  | -  | -
+| [**`translation`**](https://github.com/huggingface/transformers/tree/master/examples/translation)         | WMT               | -  | -  | -  | -
+| [**`bertology`**](https://github.com/huggingface/transformers/tree/master/examples/bertology)             | -                 | -  | -  | -  | -
+| [**`adversarial`**](https://github.com/huggingface/transformers/tree/master/examples/adversarial)         | HANS              | -  | -  | -  | -
+
+
+<br>
+
+## Important note
+
+**Important**
+To make sure you can successfully run the latest versions of the example scripts, you have to install the library from source and install some example-specific requirements.
 Execute the following steps in a new virtual environment:
 
 ```bash
 git clone https://github.com/huggingface/transformers
 cd transformers
-pip install [--editable] .
+pip install .
 pip install -r ./examples/requirements.txt
 ```
 
-| Section                    | Description                                                                                                                                                |
-|----------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| [TensorFlow 2.0 models on GLUE](#TensorFlow-2.0-Bert-models-on-GLUE) | Examples running BERT TensorFlow 2.0 model on the GLUE tasks. 
-| [Language Model fine-tuning](#language-model-fine-tuning) | Fine-tuning the library models for language modeling on a text dataset. Causal language modeling for GPT/GPT-2, masked language modeling for BERT/RoBERTa. |
-| [Language Generation](#language-generation) | Conditional text generation using the auto-regressive models of the library: GPT, GPT-2, Transformer-XL and XLNet.                                         |
-| [GLUE](#glue) | Examples running BERT/XLM/XLNet/RoBERTa on the 9 GLUE tasks. Examples feature distributed training as well as half-precision.                              |
-| [SQuAD](#squad) | Using BERT/RoBERTa/XLNet/XLM for question answering, examples with distributed training.                                                                                  |
-| [Multiple Choice](#multiple-choice) | Examples running BERT/XLNet/RoBERTa on the SWAG/RACE/ARC tasks. 
-| [Named Entity Recognition](#named-entity-recognition) | Using BERT for Named Entity Recognition (NER) on the CoNLL 2003 dataset, examples with distributed training.                                                                                  |
-| [XNLI](#xnli) | Examples running BERT/XLM on the XNLI benchmark. |
+## One-click Deploy to Cloud (wip)
 
-## TensorFlow 2.0 Bert models on GLUE
+#### Azure
 
-Based on the script [`run_tf_glue.py`](https://github.com/huggingface/transformers/blob/master/examples/run_tf_glue.py).
+[![Deploy to Azure](https://aka.ms/deploytoazurebutton)](https://portal.azure.com/#create/Microsoft.Template/uri/https%3A%2F%2Fraw.githubusercontent.com%2FAzure%2Fazure-quickstart-templates%2Fmaster%2F101-storage-account-create%2Fazuredeploy.json)
 
-Fine-tuning the library TensorFlow 2.0 Bert model for sequence classification on the  MRPC task of the GLUE benchmark: [General Language Understanding Evaluation](https://gluebenchmark.com/).
+## Running on TPUs
 
-This script has an option for mixed precision (Automatic Mixed Precision / AMP) to run models on Tensor Cores (NVIDIA Volta/Turing GPUs) and future hardware and an option for XLA, which uses the XLA compiler to reduce model runtime.
-Options are toggled using `USE_XLA` or `USE_AMP` variables in the script.
-These options and the below benchmark are provided by @tlkh.
+When using Tensorflow, TPUs are supported out of the box as a `tf.distribute.Strategy`.
 
-Quick benchmarks from the script (no other modifications):
+When using PyTorch, we support TPUs thanks to `pytorch/xla`. For more context and information on how to setup your TPU environment refer to Google's documentation and to the
+very detailed [pytorch/xla README](https://github.com/pytorch/xla/blob/master/README.md).
 
-| GPU    | Mode | Time (2nd epoch) | Val Acc (3 runs) |
-| --------- | -------- | ----------------------- | ----------------------|
-| Titan V | FP32 | 41s | 0.8438/0.8281/0.8333 |
-| Titan V | AMP | 26s | 0.8281/0.8568/0.8411 |
-| V100    | FP32 | 35s | 0.8646/0.8359/0.8464 |
-| V100    | AMP | 22s | 0.8646/0.8385/0.8411 |
-| 1080 Ti | FP32 | 55s | - | 
+In this repo, we provide a very simple launcher script named [xla_spawn.py](https://github.com/huggingface/transformers/tree/master/examples/xla_spawn.py) that lets you run our example scripts on multiple TPU cores without any boilerplate.
+Just pass a `--num_cores` flag to this script, then your regular training script with its arguments (this is similar to the `torch.distributed.launch` helper for torch.distributed).
 
-Mixed precision (AMP) reduces the training time considerably for the same hardware and hyper-parameters (same batch size was used).
-
-## Language model fine-tuning
-
-Based on the script [`run_lm_finetuning.py`](https://github.com/huggingface/transformers/blob/master/examples/run_lm_finetuning.py).
-
-Fine-tuning the library models for language modeling on a text dataset for GPT, GPT-2, BERT and RoBERTa (DistilBERT 
-to be added soon). GPT and GPT-2 are fine-tuned using a causal language modeling (CLM) loss while BERT and RoBERTa 
-are fine-tuned using a masked language modeling (MLM) loss.
-
-Before running the following example, you should get a file that contains text on which the language model will be
-fine-tuned. A good example of such text is the [WikiText-2 dataset](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/).
-
-We will refer to two different files: `$TRAIN_FILE`, which contains text for training, and `$TEST_FILE`, which contains
-text that will be used for evaluation.
-
-### GPT-2/GPT and causal language modeling
-
-The following example fine-tunes GPT-2 on WikiText-2. We're using the raw WikiText-2 (no tokens were replaced before
-the tokenization). The loss here is that of causal language modeling.
+For example for `run_glue`:
 
 ```bash
-export TRAIN_FILE=/path/to/dataset/wiki.train.raw
-export TEST_FILE=/path/to/dataset/wiki.test.raw
-
-python run_lm_finetuning.py \
-    --output_dir=output \
-    --model_type=gpt2 \
-    --model_name_or_path=gpt2 \
-    --do_train \
-    --train_data_file=$TRAIN_FILE \
-    --do_eval \
-    --eval_data_file=$TEST_FILE
+python examples/xla_spawn.py --num_cores 8 \
+	examples/text-classification/run_glue.py
+	--model_name_or_path bert-base-cased \
+	--task_name mnli \
+	--data_dir ./data/glue_data/MNLI \
+	--output_dir ./models/tpu \
+	--overwrite_output_dir \
+	--do_train \
+	--do_eval \
+	--num_train_epochs 1 \
+	--save_steps 20000
 ```
 
-This takes about half an hour to train on a single K80 GPU and about one minute for the evaluation to run. It reaches
-a score of ~20 perplexity once fine-tuned on the dataset.
-
-### RoBERTa/BERT and masked language modeling
-
-The following example fine-tunes RoBERTa on WikiText-2. Here too, we're using the raw WikiText-2. The loss is different
-as BERT/RoBERTa have a bidirectional mechanism; we're therefore using the same loss that was used during their
-pre-training: masked language modeling. 
-
-In accordance to the RoBERTa paper, we use dynamic masking rather than static masking. The model may, therefore, converge
-slightly slower (over-fitting takes more epochs).
-
-We use the `--mlm` flag so that the script may change its loss function.
-
-```bash
-export TRAIN_FILE=/path/to/dataset/wiki.train.raw
-export TEST_FILE=/path/to/dataset/wiki.test.raw
-
-python run_lm_finetuning.py \
-    --output_dir=output \
-    --model_type=roberta \
-    --model_name_or_path=roberta-base \
-    --do_train \
-    --train_data_file=$TRAIN_FILE \
-    --do_eval \
-    --eval_data_file=$TEST_FILE \
-    --mlm
-```
-
-## Language generation
-
-Based on the script [`run_generation.py`](https://github.com/huggingface/transformers/blob/master/examples/run_generation.py).
-
-Conditional text generation using the auto-regressive models of the library: GPT, GPT-2, Transformer-XL, XLNet, CTRL.
-A similar script is used for our official demo [Write With Transfomer](https://transformer.huggingface.co), where you
-can try out the different models available in the library.
-
-Example usage:
-
-```bash
-python run_generation.py \
-    --model_type=gpt2 \
-    --model_name_or_path=gpt2
-```
-
-## GLUE
-
-Based on the script [`run_glue.py`](https://github.com/huggingface/transformers/blob/master/examples/run_glue.py).
-
-Fine-tuning the library models for sequence classification on the GLUE benchmark: [General Language Understanding 
-Evaluation](https://gluebenchmark.com/). This script can fine-tune the following models: BERT, XLM, XLNet and RoBERTa. 
-
-GLUE is made up of a total of 9 different tasks. We get the following results on the dev set of the benchmark with an
-uncased  BERT base model (the checkpoint `bert-base-uncased`). All experiments ran on 8 V100 GPUs with a total train
-batch size of 24. Some of these tasks have a small dataset and training can lead to high variance in the results
-between different runs. We report the median on 5 runs (with different seeds) for each of the metrics.
-
-| Task  | Metric                       | Result      |
-|-------|------------------------------|-------------|
-| CoLA  | Matthew's corr               | 48.87       |
-| SST-2 | Accuracy                     | 91.74       |
-| MRPC  | F1/Accuracy                  | 90.70/86.27 |
-| STS-B | Person/Spearman corr.        | 91.39/91.04 |
-| QQP   | Accuracy/F1                  | 90.79/87.66 |
-| MNLI  | Matched acc./Mismatched acc. | 83.70/84.83 |
-| QNLI  | Accuracy                     | 89.31       |
-| RTE   | Accuracy                     | 71.43       |
-| WNLI  | Accuracy                     | 43.66       |
-
-Some of these results are significantly different from the ones reported on the test set
-of GLUE benchmark on the website. For QQP and WNLI, please refer to [FAQ #12](https://gluebenchmark.com/faq) on the webite.
-
-Before running anyone of these GLUE tasks you should download the
-[GLUE data](https://gluebenchmark.com/tasks) by running
-[this script](https://gist.github.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e)
-and unpack it to some directory `$GLUE_DIR`.
-
-```bash
-export GLUE_DIR=/path/to/glue
-export TASK_NAME=MRPC
-
-python run_glue.py \
-  --model_type bert \
-  --model_name_or_path bert-base-cased \
-  --task_name $TASK_NAME \
-  --do_train \
-  --do_eval \
-  --do_lower_case \
-  --data_dir $GLUE_DIR/$TASK_NAME \
-  --max_seq_length 128 \
-  --per_gpu_train_batch_size 32 \
-  --learning_rate 2e-5 \
-  --num_train_epochs 3.0 \
-  --output_dir /tmp/$TASK_NAME/
-```
-
-where task name can be one of CoLA, SST-2, MRPC, STS-B, QQP, MNLI, QNLI, RTE, WNLI.
-
-The dev set results will be present within the text file `eval_results.txt` in the specified output_dir. 
-In case of MNLI, since there are two separate dev sets (matched and mismatched), there will be a separate 
-output folder called `/tmp/MNLI-MM/` in addition to `/tmp/MNLI/`.
-
-The code has not been tested with half-precision training with apex on any GLUE task apart from MRPC, MNLI, 
-CoLA, SST-2. The following section provides details on how to run half-precision training with MRPC. With that being 
-said, there shouldn’t be any issues in running half-precision training with the remaining GLUE tasks as well, 
-since the data processor for each task inherits from the base class DataProcessor.
-
-### MRPC
-
-#### Fine-tuning example
-
-The following examples fine-tune BERT on the Microsoft Research Paraphrase Corpus (MRPC) corpus and runs in less 
-than 10 minutes on a single K-80 and in 27 seconds (!) on single tesla V100 16GB with apex installed.
-
-Before running anyone of these GLUE tasks you should download the
-[GLUE data](https://gluebenchmark.com/tasks) by running
-[this script](https://gist.github.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e)
-and unpack it to some directory `$GLUE_DIR`.
-
-```bash
-export GLUE_DIR=/path/to/glue
-
-python run_glue.py \
-  --model_type bert \
-  --model_name_or_path bert-base-cased \
-  --task_name MRPC \
-  --do_train \
-  --do_eval \
-  --do_lower_case \
-  --data_dir $GLUE_DIR/MRPC/ \
-  --max_seq_length 128 \
-  --per_gpu_train_batch_size 32 \
-  --learning_rate 2e-5 \
-  --num_train_epochs 3.0 \
-  --output_dir /tmp/mrpc_output/
-```
-
-Our test ran on a few seeds with [the original implementation hyper-
-parameters](https://github.com/google-research/bert#sentence-and-sentence-pair-classification-tasks) gave evaluation 
-results between 84% and 88%.
-
-#### Using Apex and mixed-precision
-
-Using Apex and 16 bit precision, the fine-tuning on MRPC only takes 27 seconds. First install 
-[apex](https://github.com/NVIDIA/apex), then run the following example:
-
-```bash
-export GLUE_DIR=/path/to/glue
-
-python run_glue.py \
-  --model_type bert \
-  --model_name_or_path bert-base-cased \
-  --task_name MRPC \
-  --do_train \
-  --do_eval \
-  --do_lower_case \
-  --data_dir $GLUE_DIR/MRPC/ \
-  --max_seq_length 128 \
-  --per_gpu_train_batch_size 32 \
-  --learning_rate 2e-5 \
-  --num_train_epochs 3.0 \
-  --output_dir /tmp/mrpc_output/ \
-  --fp16
-```
-
-#### Distributed training
-
-Here is an example using distributed training on 8 V100 GPUs. The model used is the BERT whole-word-masking and it
-reaches F1 > 92 on MRPC.
-
-```bash
-export GLUE_DIR=/path/to/glue
-
-python -m torch.distributed.launch \
-    --nproc_per_node 8 run_glue.py \
-    --model_type bert \
-    --model_name_or_path bert-base-cased \
-    --task_name MRPC \
-    --do_train \
-    --do_eval \
-    --do_lower_case \
-    --data_dir $GLUE_DIR/MRPC/ \
-    --max_seq_length 128 \
-    --per_gpu_train_batch_size 8 \
-    --learning_rate 2e-5 \
-    --num_train_epochs 3.0 \
-    --output_dir /tmp/mrpc_output/
-```
-
-Training with these hyper-parameters gave us the following results:
-
-```bash
-acc = 0.8823529411764706
-acc_and_f1 = 0.901702786377709
-eval_loss = 0.3418912578906332
-f1 = 0.9210526315789473
-global_step = 174
-loss = 0.07231863956341798
-```
-
-### MNLI
-
-The following example uses the BERT-large, uncased, whole-word-masking model and fine-tunes it on the MNLI task.
-
-```bash
-export GLUE_DIR=/path/to/glue
-
-python -m torch.distributed.launch \
-    --nproc_per_node 8 run_glue.py \
-    --model_type bert \
-    --model_name_or_path bert-base-cased \
-    --task_name mnli \
-    --do_train \
-    --do_eval \
-    --do_lower_case \
-    --data_dir $GLUE_DIR/MNLI/ \
-    --max_seq_length 128 \
-    --per_gpu_train_batch_size 8 \
-    --learning_rate 2e-5 \
-    --num_train_epochs 3.0 \
-    --output_dir output_dir \
-```
-
-The results  are the following:
-
-```bash
-***** Eval results *****
-  acc = 0.8679706601466992
-  eval_loss = 0.4911287787382479
-  global_step = 18408
-  loss = 0.04755385363816904
-
-***** Eval results *****
-  acc = 0.8747965825874695
-  eval_loss = 0.45516540421714036
-  global_step = 18408
-  loss = 0.04755385363816904
-```
-
-## Multiple Choice
-
-Based on the script [`run_multiple_choice.py`]().
-
-#### Fine-tuning on SWAG
-Download [swag](https://github.com/rowanz/swagaf/tree/master/data) data
-
-```bash
-#training on 4 tesla V100(16GB) GPUS
-export SWAG_DIR=/path/to/swag_data_dir
-python ./examples/run_multiple_choice.py \
---model_type roberta \
---task_name swag \
---model_name_or_path roberta-base \
---do_train \
---do_eval \
---do_lower_case \
---data_dir $SWAG_DIR \
---learning_rate 5e-5 \
---num_train_epochs 3 \
---max_seq_length 80 \
---output_dir models_bert/swag_base \
---per_gpu_eval_batch_size=16 \
---per_gpu_train_batch_size=16 \
---gradient_accumulation_steps 2 \
---overwrite_output
-```
-Training with the defined hyper-parameters yields the following results:
-```
-***** Eval results *****
-eval_acc = 0.8338998300509847
-eval_loss = 0.44457291918821606
-```
-
-## SQuAD
-
-Based on the script [`run_squad.py`](https://github.com/huggingface/transformers/blob/master/examples/run_squad.py).
-
-#### Fine-tuning on SQuAD
-
-This example code fine-tunes BERT on the SQuAD dataset. It runs in 24 min (with BERT-base) or 68 min (with BERT-large) 
-on a single tesla V100 16GB. The data for SQuAD can be downloaded with the following links and should be saved in a 
-$SQUAD_DIR directory.
-
-* [train-v1.1.json](https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json)
-* [dev-v1.1.json](https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json)
-* [evaluate-v1.1.py](https://github.com/allenai/bi-att-flow/blob/master/squad/evaluate-v1.1.py)
-
-```bash
-export SQUAD_DIR=/path/to/SQUAD
-
-python run_squad.py \
-  --model_type bert \
-  --model_name_or_path bert-base-cased \
-  --do_train \
-  --do_eval \
-  --do_lower_case \
-  --train_file $SQUAD_DIR/train-v1.1.json \
-  --predict_file $SQUAD_DIR/dev-v1.1.json \
-  --per_gpu_train_batch_size 12 \
-  --learning_rate 3e-5 \
-  --num_train_epochs 2.0 \
-  --max_seq_length 384 \
-  --doc_stride 128 \
-  --output_dir /tmp/debug_squad/
-```
-
-Training with the previously defined hyper-parameters yields the following results:
-
-```bash
-f1 = 88.52
-exact_match = 81.22
-```
-
-#### Distributed training
-
-
-Here is an example using distributed training on 8 V100 GPUs and Bert Whole Word Masking uncased model to reach a F1 > 93 on SQuAD:
-
-```bash
-python -m torch.distributed.launch --nproc_per_node=8 run_squad.py \
-    --model_type bert \
-    --model_name_or_path bert-base-cased \
-    --do_train \
-    --do_eval \
-    --do_lower_case \
-    --train_file $SQUAD_DIR/train-v1.1.json \
-    --predict_file $SQUAD_DIR/dev-v1.1.json \
-    --learning_rate 3e-5 \
-    --num_train_epochs 2 \
-    --max_seq_length 384 \
-    --doc_stride 128 \
-    --output_dir ../models/wwm_uncased_finetuned_squad/ \
-    --per_gpu_train_batch_size 24 \
-    --gradient_accumulation_steps 12
-```
-
-Training with the previously defined hyper-parameters yields the following results:
-
-```bash
-f1 = 93.15
-exact_match = 86.91
-```
-
-This fine-tuned model is available as a checkpoint under the reference
-`bert-large-uncased-whole-word-masking-finetuned-squad`.
-
-#### Fine-tuning XLNet on SQuAD
-
-This example code fine-tunes XLNet on the SQuAD dataset. See above to download the data for SQuAD .
-
-```bash
-export SQUAD_DIR=/path/to/SQUAD
-
-python /data/home/hlu/transformers/examples/run_squad.py \
-    --model_type xlnet \
-    --model_name_or_path xlnet-large-cased \
-    --do_train \
-    --do_eval \
-    --do_lower_case \
-    --train_file /data/home/hlu/notebooks/NLP/examples/question_answering/train-v1.1.json \
-    --predict_file /data/home/hlu/notebooks/NLP/examples/question_answering/dev-v1.1.json \
-    --learning_rate 3e-5 \
-    --num_train_epochs 2 \
-    --max_seq_length 384 \
-    --doc_stride 128 \
-    --output_dir ./wwm_cased_finetuned_squad/ \
-    --per_gpu_eval_batch_size=4  \
-    --per_gpu_train_batch_size=4   \
-    --save_steps 5000
-```
-
-Training with the previously defined hyper-parameters yields the following results:
-
-```python
-{
-"exact": 85.45884578997162,
-"f1": 92.5974600601065,
-"total": 10570,
-"HasAns_exact": 85.45884578997162,
-"HasAns_f1": 92.59746006010651,
-"HasAns_total": 10570
-}
-```
-
-## Named Entity Recognition
-
-Based on the scripts [`run_ner.py`](https://github.com/huggingface/transformers/blob/master/examples/run_ner.py) for Pytorch and
-[`run_tf_ner.py`(https://github.com/huggingface/transformers/blob/master/examples/run_tf_ner.py)] for Tensorflow 2.
-This example fine-tune Bert Multilingual on GermEval 2014 (German NER).
-Details and results for the fine-tuning provided by @stefan-it.
-
-### Data (Download and pre-processing steps)
-
-Data can be obtained from the [GermEval 2014](https://sites.google.com/site/germeval2014ner/data) shared task page.
-
-Here are the commands for downloading and pre-processing train, dev and test datasets. The original data format has four (tab-separated) columns, in a pre-processing step only the two relevant columns (token and outer span NER annotation) are extracted:
-
-```bash
-curl -L 'https://sites.google.com/site/germeval2014ner/data/NER-de-train.tsv?attredirects=0&d=1' \
-| grep -v "^#" | cut -f 2,3 | tr '\t' ' ' > train.txt.tmp
-curl -L 'https://sites.google.com/site/germeval2014ner/data/NER-de-dev.tsv?attredirects=0&d=1' \
-| grep -v "^#" | cut -f 2,3 | tr '\t' ' ' > dev.txt.tmp
-curl -L 'https://sites.google.com/site/germeval2014ner/data/NER-de-test.tsv?attredirects=0&d=1' \
-| grep -v "^#" | cut -f 2,3 | tr '\t' ' ' > test.txt.tmp
-```
-
-The GermEval 2014 dataset contains some strange "control character" tokens like `'\x96', '\u200e', '\x95', '\xad' or '\x80'`. One problem with these tokens is, that `BertTokenizer` returns an empty token for them, resulting in misaligned `InputExample`s. I wrote a script that a) filters these tokens and b) splits longer sentences into smaller ones (once the max. subtoken length is reached).
-
-```bash
-wget "https://raw.githubusercontent.com/stefan-it/fine-tuned-berts-seq/master/scripts/preprocess.py"
-```
-Let's define some variables that we need for further pre-processing steps and training the model:
-
-```bash
-export MAX_LENGTH=128
-export BERT_MODEL=bert-base-multilingual-cased
-```
-
-Run the pre-processing script on training, dev and test datasets:
-
-```bash
-python3 preprocess.py train.txt.tmp $BERT_MODEL $MAX_LENGTH > train.txt
-python3 preprocess.py dev.txt.tmp $BERT_MODEL $MAX_LENGTH > dev.txt
-python3 preprocess.py test.txt.tmp $BERT_MODEL $MAX_LENGTH > test.txt
-```
-
-The GermEval 2014 dataset has much more labels than CoNLL-2002/2003 datasets, so an own set of labels must be used:
-
-```bash
-cat train.txt dev.txt test.txt | cut -d " " -f 2 | grep -v "^$"| sort | uniq > labels.txt
-```
-
-### Prepare the run
-
-Additional environment variables must be set:
-
-```bash
-export OUTPUT_DIR=germeval-model
-export BATCH_SIZE=32
-export NUM_EPOCHS=3
-export SAVE_STEPS=750
-export SEED=1
-```
-
-### Run the Pytorch version
-
-To start training, just run:
-
-```bash
-python3 run_ner.py --data_dir ./ \
---model_type bert \
---labels ./labels.txt \
---model_name_or_path $BERT_MODEL \
---output_dir $OUTPUT_DIR \
---max_seq_length  $MAX_LENGTH \
---num_train_epochs $NUM_EPOCHS \
---per_gpu_train_batch_size $BATCH_SIZE \
---save_steps $SAVE_STEPS \
---seed $SEED \
---do_train \
---do_eval \
---do_predict
-```
-
-If your GPU supports half-precision training, just add the `--fp16` flag. After training, the model will be both evaluated on development and test datasets.
-
-#### Evaluation
-
-Evaluation on development dataset outputs the following for our example:
-
-```bash
-10/04/2019 00:42:06 - INFO - __main__ -   ***** Eval results  *****
-10/04/2019 00:42:06 - INFO - __main__ -     f1 = 0.8623348017621146
-10/04/2019 00:42:06 - INFO - __main__ -     loss = 0.07183869666975543
-10/04/2019 00:42:06 - INFO - __main__ -     precision = 0.8467916366258111
-10/04/2019 00:42:06 - INFO - __main__ -     recall = 0.8784592370979806
-```
-
-On the test dataset the following results could be achieved:
-
-```bash
-10/04/2019 00:42:42 - INFO - __main__ -   ***** Eval results  *****
-10/04/2019 00:42:42 - INFO - __main__ -     f1 = 0.8614389652384803
-10/04/2019 00:42:42 - INFO - __main__ -     loss = 0.07064602487454782
-10/04/2019 00:42:42 - INFO - __main__ -     precision = 0.8604651162790697
-10/04/2019 00:42:42 - INFO - __main__ -     recall = 0.8624150210424085
-```
-
-#### Comparing BERT (large, cased), RoBERTa (large, cased) and DistilBERT (base, uncased)
-
-Here is a small comparison between BERT (large, cased), RoBERTa (large, cased) and DistilBERT (base, uncased) with the same hyperparameters as specified in the [example documentation](https://huggingface.co/transformers/examples.html#named-entity-recognition) (one run):
-
-| Model | F-Score Dev | F-Score Test
-| --------------------------------- | ------- | --------
-| `bert-large-cased`            | 95.59 | 91.70
-| `roberta-large`                  | 95.96 | 91.87
-| `distilbert-base-uncased` | 94.34 | 90.32
-
-### Run the Tensorflow 2 version
-
-To start training, just run:
-
-```bash
-python3 run_tf_ner.py --data_dir ./ \
---model_type bert \
---labels ./labels.txt \
---model_name_or_path $BERT_MODEL \
---output_dir $OUTPUT_DIR \
---max_seq_length  $MAX_LENGTH \
---num_train_epochs $NUM_EPOCHS \
---per_device_train_batch_size $BATCH_SIZE \
---save_steps $SAVE_STEPS \
---seed $SEED \
---do_train \
---do_eval \
---do_predict
-```
-
-Such as the Pytorch version, if your GPU supports half-precision training, just add the `--fp16` flag. After training, the model will be both evaluated on development and test datasets.
-
-#### Evaluation
-
-Evaluation on development dataset outputs the following for our example:
-```bash
-           precision    recall  f1-score   support
-
- LOCderiv     0.7619    0.6154    0.6809        52
-  PERpart     0.8724    0.8997    0.8858      4057
-  OTHpart     0.9360    0.9466    0.9413       711
-  ORGpart     0.7015    0.6989    0.7002       269
-  LOCpart     0.7668    0.8488    0.8057       496
-      LOC     0.8745    0.9191    0.8963       235
- ORGderiv     0.7723    0.8571    0.8125        91
- OTHderiv     0.4800    0.6667    0.5581        18
-      OTH     0.5789    0.6875    0.6286        16
- PERderiv     0.5385    0.3889    0.4516        18
-      PER     0.5000    0.5000    0.5000         2
-      ORG     0.0000    0.0000    0.0000         3
-
-micro avg     0.8574    0.8862    0.8715      5968
-macro avg     0.8575    0.8862    0.8713      5968
-```
-
-On the test dataset the following results could be achieved:
-```bash
-           precision    recall  f1-score   support
-
-  PERpart     0.8847    0.8944    0.8896      9397
-  OTHpart     0.9376    0.9353    0.9365      1639
-  ORGpart     0.7307    0.7044    0.7173       697
-      LOC     0.9133    0.9394    0.9262       561
-  LOCpart     0.8058    0.8157    0.8107      1150
-      ORG     0.0000    0.0000    0.0000         8
- OTHderiv     0.5882    0.4762    0.5263        42
- PERderiv     0.6571    0.5227    0.5823        44
-      OTH     0.4906    0.6667    0.5652        39
- ORGderiv     0.7016    0.7791    0.7383       172
- LOCderiv     0.8256    0.6514    0.7282       109
-      PER     0.0000    0.0000    0.0000        11
-
-micro avg     0.8722    0.8774    0.8748     13869
-macro avg     0.8712    0.8774    0.8740     13869
-```
-
-## XNLI
-
-Based on the script [`run_xnli.py`](https://github.com/huggingface/transformers/blob/master/examples/run_xnli.py).
-
-[XNLI](https://www.nyu.edu/projects/bowman/xnli/) is crowd-sourced dataset based on [MultiNLI](http://www.nyu.edu/projects/bowman/multinli/). It is an evaluation benchmark for cross-lingual text representations. Pairs of text are labeled with textual entailment annotations for 15 different languages (including both high-ressource language such as English and low-ressource languages such as Swahili).
-
-#### Fine-tuning on XNLI
-
-This example code fine-tunes mBERT (multi-lingual BERT) on the XNLI dataset. It runs in 106 mins
-on a single tesla V100 16GB. The data for XNLI can be downloaded with the following links and should be both saved (and un-zipped) in a 
-`$XNLI_DIR` directory.
-
-* [XNLI 1.0](https://www.nyu.edu/projects/bowman/xnli/XNLI-1.0.zip)
-* [XNLI-MT 1.0](https://www.nyu.edu/projects/bowman/xnli/XNLI-MT-1.0.zip)
-
-```bash
-export XNLI_DIR=/path/to/XNLI
-
-python run_xnli.py \
-  --model_type bert \
-  --model_name_or_path bert-base-multilingual-cased \
-  --language de \
-  --train_language en \
-  --do_train \
-  --do_eval \
-  --data_dir $XNLI_DIR \
-  --per_gpu_train_batch_size 32 \
-  --learning_rate 5e-5 \
-  --num_train_epochs 2.0 \
-  --max_seq_length 128 \
-  --output_dir /tmp/debug_xnli/ \
-  --save_steps -1
-```
-
-Training with the previously defined hyper-parameters yields the following results on the **test** set:
-
-```bash
-acc = 0.7093812375249501
-```
+Feedback and more use cases and benchmarks involving TPUs are welcome, please share with the community.

examples/adversarial/README.md

@@ -0,0 +1,38 @@
+## Adversarial evaluation of model performances
+
+Here is an example on evaluating a model using adversarial evaluation of natural language inference with the Heuristic Analysis for NLI Systems (HANS) dataset [McCoy et al., 2019](https://arxiv.org/abs/1902.01007). The example was gracefully provided by [Nafise Sadat Moosavi](https://github.com/ns-moosavi).
+
+The HANS dataset can be downloaded from [this location](https://github.com/tommccoy1/hans).
+
+This is an example of using test_hans.py:
+
+```bash
+export HANS_DIR=path-to-hans
+export MODEL_TYPE=type-of-the-model-e.g.-bert-roberta-xlnet-etc
+export MODEL_PATH=path-to-the-model-directory-that-is-trained-on-NLI-e.g.-by-using-run_glue.py
+
+python examples/hans/test_hans.py \
+        --task_name hans \
+        --model_type $MODEL_TYPE \
+        --do_eval \
+        --data_dir $HANS_DIR \
+        --model_name_or_path $MODEL_PATH \
+        --max_seq_length 128 \
+        --output_dir $MODEL_PATH \
+```
+
+This will create the hans_predictions.txt file in MODEL_PATH, which can then be evaluated using hans/evaluate_heur_output.py from the HANS dataset.
+
+The results of the BERT-base model that is trained on MNLI using batch size 8 and the random seed 42 on the HANS dataset is as follows:
+
+```bash
+Heuristic entailed results:
+lexical_overlap: 0.9702
+subsequence: 0.9942
+constituent: 0.9962
+
+Heuristic non-entailed results:
+lexical_overlap: 0.199
+subsequence: 0.0396
+constituent: 0.118
+```

examples/adversarial/hans_processors.py

@@ -0,0 +1,221 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION.  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.
+""" GLUE processors and helpers """
+
+import logging
+import os
+
+from transformers.file_utils import is_tf_available
+from utils_hans import DataProcessor, InputExample, InputFeatures
+
+
+if is_tf_available():
+    import tensorflow as tf
+
+logger = logging.getLogger(__name__)
+
+
+def hans_convert_examples_to_features(
+    examples,
+    tokenizer,
+    max_length=512,
+    task=None,
+    label_list=None,
+    output_mode=None,
+    pad_on_left=False,
+    pad_token=0,
+    pad_token_segment_id=0,
+    mask_padding_with_zero=True,
+):
+    """
+    Loads a data file into a list of ``InputFeatures``
+
+    Args:
+        examples: List of ``InputExamples`` or ``tf.data.Dataset`` containing the examples.
+        tokenizer: Instance of a tokenizer that will tokenize the examples
+        max_length: Maximum example length
+        task: HANS
+        label_list: List of labels. Can be obtained from the processor using the ``processor.get_labels()`` method
+        output_mode: String indicating the output mode. Either ``regression`` or ``classification``
+        pad_on_left: If set to ``True``, the examples will be padded on the left rather than on the right (default)
+        pad_token: Padding token
+        pad_token_segment_id: The segment ID for the padding token (It is usually 0, but can vary such as for XLNet where it is 4)
+        mask_padding_with_zero: If set to ``True``, the attention mask will be filled by ``1`` for actual values
+            and by ``0`` for padded values. If set to ``False``, inverts it (``1`` for padded values, ``0`` for
+            actual values)
+
+    Returns:
+        If the ``examples`` input is a ``tf.data.Dataset``, will return a ``tf.data.Dataset``
+        containing the task-specific features. If the input is a list of ``InputExamples``, will return
+        a list of task-specific ``InputFeatures`` which can be fed to the model.
+
+    """
+    is_tf_dataset = False
+    if is_tf_available() and isinstance(examples, tf.data.Dataset):
+        is_tf_dataset = True
+
+    if task is not None:
+        processor = glue_processors[task]()
+        if label_list is None:
+            label_list = processor.get_labels()
+            logger.info("Using label list %s for task %s" % (label_list, task))
+        if output_mode is None:
+            output_mode = glue_output_modes[task]
+            logger.info("Using output mode %s for task %s" % (output_mode, task))
+
+    label_map = {label: i for i, label in enumerate(label_list)}
+
+    features = []
+    for (ex_index, example) in enumerate(examples):
+        if ex_index % 10000 == 0:
+            logger.info("Writing example %d" % (ex_index))
+        if is_tf_dataset:
+            example = processor.get_example_from_tensor_dict(example)
+            example = processor.tfds_map(example)
+
+        inputs = tokenizer.encode_plus(example.text_a, example.text_b, add_special_tokens=True, max_length=max_length,)
+        input_ids, token_type_ids = inputs["input_ids"], inputs["token_type_ids"]
+
+        # The mask has 1 for real tokens and 0 for padding tokens. Only real
+        # tokens are attended to.
+        attention_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)
+
+        # Zero-pad up to the sequence length.
+        padding_length = max_length - len(input_ids)
+        if pad_on_left:
+            input_ids = ([pad_token] * padding_length) + input_ids
+            attention_mask = ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
+            token_type_ids = ([pad_token_segment_id] * padding_length) + token_type_ids
+        else:
+            input_ids = input_ids + ([pad_token] * padding_length)
+            attention_mask = attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)
+            token_type_ids = token_type_ids + ([pad_token_segment_id] * padding_length)
+
+        assert len(input_ids) == max_length, "Error with input length {} vs {}".format(len(input_ids), max_length)
+        assert len(attention_mask) == max_length, "Error with input length {} vs {}".format(
+            len(attention_mask), max_length
+        )
+        assert len(token_type_ids) == max_length, "Error with input length {} vs {}".format(
+            len(token_type_ids), max_length
+        )
+
+        if output_mode == "classification":
+            label = label_map[example.label] if example.label in label_map else 0
+        elif output_mode == "regression":
+            label = float(example.label)
+        else:
+            raise KeyError(output_mode)
+        pairID = str(example.pairID)
+
+        if ex_index < 10:
+            logger.info("*** Example ***")
+            logger.info("text_a: %s" % (example.text_a))
+            logger.info("text_b: %s" % (example.text_b))
+            logger.info("guid: %s" % (example.guid))
+            logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
+            logger.info("attention_mask: %s" % " ".join([str(x) for x in attention_mask]))
+            logger.info("token_type_ids: %s" % " ".join([str(x) for x in token_type_ids]))
+            logger.info("label: %s (id = %d)" % (example.label, label))
+
+        features.append(
+            InputFeatures(
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+                token_type_ids=token_type_ids,
+                label=label,
+                pairID=pairID,
+            )
+        )
+
+    if is_tf_available() and is_tf_dataset:
+
+        def gen():
+            for ex in features:
+                yield (
+                    {
+                        "input_ids": ex.input_ids,
+                        "attention_mask": ex.attention_mask,
+                        "token_type_ids": ex.token_type_ids,
+                    },
+                    ex.label,
+                )
+
+        return tf.data.Dataset.from_generator(
+            gen,
+            ({"input_ids": tf.int32, "attention_mask": tf.int32, "token_type_ids": tf.int32}, tf.int64),
+            (
+                {
+                    "input_ids": tf.TensorShape([None]),
+                    "attention_mask": tf.TensorShape([None]),
+                    "token_type_ids": tf.TensorShape([None]),
+                },
+                tf.TensorShape([]),
+            ),
+        )
+
+    return features
+
+
+class HansProcessor(DataProcessor):
+    """Processor for the HANS data set."""
+
+    def get_example_from_tensor_dict(self, tensor_dict):
+        """See base class."""
+        return InputExample(
+            tensor_dict["idx"].numpy(),
+            tensor_dict["premise"].numpy().decode("utf-8"),
+            tensor_dict["hypothesis"].numpy().decode("utf-8"),
+            str(tensor_dict["label"].numpy()),
+        )
+
+    def get_train_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(self._read_tsv(os.path.join(data_dir, "heuristics_train_set.txt")), "train")
+
+    def get_dev_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(self._read_tsv(os.path.join(data_dir, "heuristics_evaluation_set.txt")), "dev")
+
+    def get_labels(self):
+        """See base class."""
+        return ["contradiction", "entailment", "neutral"]
+
+    def _create_examples(self, lines, set_type):
+        """Creates examples for the training and dev sets."""
+        examples = []
+        for (i, line) in enumerate(lines):
+            if i == 0:
+                continue
+            guid = "%s-%s" % (set_type, line[0])
+            text_a = line[5]
+            text_b = line[6]
+            pairID = line[7][2:] if line[7].startswith("ex") else line[7]
+            label = line[-1]
+            examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label, pairID=pairID))
+        return examples
+
+
+glue_tasks_num_labels = {
+    "hans": 3,
+}
+
+glue_processors = {
+    "hans": HansProcessor,
+}
+
+glue_output_modes = {
+    "hans": "classification",
+}

examples/adversarial/test_hans.py

@@ -22,57 +22,57 @@ import glob
 import logging
 import os
 import random
-import json
 
 import numpy as np
 import torch
-from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
-                              TensorDataset)
+from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
 from torch.utils.data.distributed import DistributedSampler
+from tqdm import tqdm, trange
+
+from hans_processors import glue_output_modes as output_modes
+from hans_processors import glue_processors as processors
+from hans_processors import hans_convert_examples_to_features as convert_examples_to_features
+from transformers import (
+    WEIGHTS_NAME,
+    AdamW,
+    AlbertConfig,
+    AlbertForSequenceClassification,
+    AlbertTokenizer,
+    BertConfig,
+    BertForSequenceClassification,
+    BertTokenizer,
+    DistilBertConfig,
+    DistilBertForSequenceClassification,
+    DistilBertTokenizer,
+    RobertaConfig,
+    RobertaForSequenceClassification,
+    RobertaTokenizer,
+    XLMConfig,
+    XLMForSequenceClassification,
+    XLMTokenizer,
+    XLNetConfig,
+    XLNetForSequenceClassification,
+    XLNetTokenizer,
+    get_linear_schedule_with_warmup,
+)
+
 
 try:
     from torch.utils.tensorboard import SummaryWriter
-except:
+except ImportError:
     from tensorboardX import SummaryWriter
 
-from tqdm import tqdm, trange
-
-from transformers import (WEIGHTS_NAME, BertConfig,
-                                  BertForSequenceClassification, BertTokenizer,
-                                  RobertaConfig,
-                                  RobertaForSequenceClassification,
-                                  RobertaTokenizer,
-                                  XLMConfig, XLMForSequenceClassification,
-                                  XLMTokenizer, XLNetConfig,
-                                  XLNetForSequenceClassification,
-                                  XLNetTokenizer,
-                                  DistilBertConfig,
-                                  DistilBertForSequenceClassification,
-                                  DistilBertTokenizer,
-                                  AlbertConfig,
-                                  AlbertForSequenceClassification, 
-                                  AlbertTokenizer,
-                                )
-
-from transformers import AdamW, get_linear_schedule_with_warmup
-
-from transformers import glue_compute_metrics as compute_metrics
-from transformers import glue_output_modes as output_modes
-from transformers import glue_processors as processors
-from transformers import glue_convert_examples_to_features as convert_examples_to_features
 
 logger = logging.getLogger(__name__)
 
-ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) for conf in (BertConfig, XLNetConfig, XLMConfig, 
-                                                                                RobertaConfig, DistilBertConfig)), ())
 
 MODEL_CLASSES = {
-    'bert': (BertConfig, BertForSequenceClassification, BertTokenizer),
-    'xlnet': (XLNetConfig, XLNetForSequenceClassification, XLNetTokenizer),
-    'xlm': (XLMConfig, XLMForSequenceClassification, XLMTokenizer),
-    'roberta': (RobertaConfig, RobertaForSequenceClassification, RobertaTokenizer),
-    'distilbert': (DistilBertConfig, DistilBertForSequenceClassification, DistilBertTokenizer),
-    'albert': (AlbertConfig, AlbertForSequenceClassification, AlbertTokenizer)
+    "bert": (BertConfig, BertForSequenceClassification, BertTokenizer),
+    "xlnet": (XLNetConfig, XLNetForSequenceClassification, XLNetTokenizer),
+    "xlm": (XLMConfig, XLMForSequenceClassification, XLMTokenizer),
+    "roberta": (RobertaConfig, RobertaForSequenceClassification, RobertaTokenizer),
+    "distilbert": (DistilBertConfig, DistilBertForSequenceClassification, DistilBertTokenizer),
+    "albert": (AlbertConfig, AlbertForSequenceClassification, AlbertTokenizer),
 }
 
 
@@ -100,14 +100,19 @@ def train(args, train_dataset, model, tokenizer):
         t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
 
     # Prepare optimizer and schedule (linear warmup and decay)
-    no_decay = ['bias', 'LayerNorm.weight']
+    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}
-        ]
+        {
+            "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, eps=args.adam_epsilon)
-    scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total)
+    scheduler = get_linear_schedule_with_warmup(
+        optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total
+    )
     if args.fp16:
         try:
             from apex import amp
@@ -121,17 +126,21 @@ def train(args, train_dataset, model, tokenizer):
 
     # Distributed training (should be after apex fp16 initialization)
     if args.local_rank != -1:
-        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank],
-                                                          output_device=args.local_rank,
-                                                          find_unused_parameters=True)
+        model = torch.nn.parallel.DistributedDataParallel(
+            model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True
+        )
 
     # Train!
     logger.info("***** Running training *****")
     logger.info("  Num examples = %d", len(train_dataset))
     logger.info("  Num Epochs = %d", args.num_train_epochs)
     logger.info("  Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size)
-    logger.info("  Total train batch size (w. parallel, distributed & accumulation) = %d",
-                   args.train_batch_size * args.gradient_accumulation_steps * (torch.distributed.get_world_size() if args.local_rank != -1 else 1))
+    logger.info(
+        "  Total train batch size (w. parallel, distributed & accumulation) = %d",
+        args.train_batch_size
+        * args.gradient_accumulation_steps
+        * (torch.distributed.get_world_size() if args.local_rank != -1 else 1),
+    )
     logger.info("  Gradient Accumulation steps = %d", args.gradient_accumulation_steps)
     logger.info("  Total optimization steps = %d", t_total)
 
@@ -145,16 +154,16 @@ def train(args, train_dataset, model, tokenizer):
         for step, batch in enumerate(epoch_iterator):
             model.train()
             batch = tuple(t.to(args.device) for t in batch)
-            inputs = {'input_ids':      batch[0],
-                      'attention_mask': batch[1],
-                      'labels':         batch[3]}
-            if args.model_type != 'distilbert':
-                inputs['token_type_ids'] = batch[2] if args.model_type in ['bert', 'xlnet'] else None  # XLM, DistilBERT and RoBERTa don't use segment_ids
+            inputs = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
+            if args.model_type != "distilbert":
+                inputs["token_type_ids"] = (
+                    batch[2] if args.model_type in ["bert", "xlnet"] else None
+                )  # XLM, DistilBERT and RoBERTa don't use segment_ids
             outputs = model(**inputs)
             loss = outputs[0]  # model outputs are always tuple in transformers (see doc)
 
             if args.n_gpu > 1:
-                loss = loss.mean() # mean() to average on multi-gpu parallel training
+                loss = loss.mean()  # mean() to average on multi-gpu parallel training
             if args.gradient_accumulation_steps > 1:
                 loss = loss / args.gradient_accumulation_steps
 
@@ -178,30 +187,34 @@ def train(args, train_dataset, model, tokenizer):
 
                 if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
                     logs = {}
-                    if args.local_rank == -1 and args.evaluate_during_training:  # Only evaluate when single GPU otherwise metrics may not average well
+                    if (
+                        args.local_rank == -1 and args.evaluate_during_training
+                    ):  # Only evaluate when single GPU otherwise metrics may not average well
                         results = evaluate(args, model, tokenizer)
                         for key, value in results.items():
-                            eval_key = 'eval_{}'.format(key)
+                            eval_key = "eval_{}".format(key)
                             logs[eval_key] = value
 
                     loss_scalar = (tr_loss - logging_loss) / args.logging_steps
                     learning_rate_scalar = scheduler.get_lr()[0]
-                    logs['learning_rate'] = learning_rate_scalar
-                    logs['loss'] = loss_scalar
+                    logs["learning_rate"] = learning_rate_scalar
+                    logs["loss"] = loss_scalar
                     logging_loss = tr_loss
 
                     for key, value in logs.items():
                         tb_writer.add_scalar(key, value, global_step)
-                    print(json.dumps({**logs, **{'step': global_step}}))
+                    # print(json.dumps({**logs, **{'step': global_step}}))
 
                 if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
                     # Save model checkpoint
-                    output_dir = os.path.join(args.output_dir, 'checkpoint-{}'.format(global_step))
+                    output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(global_step))
                     if not os.path.exists(output_dir):
                         os.makedirs(output_dir)
-                    model_to_save = model.module if hasattr(model, 'module') else model  # Take care of distributed/parallel training
+                    model_to_save = (
+                        model.module if hasattr(model, "module") else model
+                    )  # Take care of distributed/parallel training
                     model_to_save.save_pretrained(output_dir)
-                    torch.save(args, os.path.join(output_dir, 'training_args.bin'))
+                    torch.save(args, os.path.join(output_dir, "training_args.bin"))
                     logger.info("Saving model checkpoint to %s", output_dir)
 
             if args.max_steps > 0 and global_step > args.max_steps:
@@ -220,11 +233,11 @@ def train(args, train_dataset, model, tokenizer):
 def evaluate(args, model, tokenizer, prefix=""):
     # Loop to handle MNLI double evaluation (matched, mis-matched)
     eval_task_names = ("mnli", "mnli-mm") if args.task_name == "mnli" else (args.task_name,)
-    eval_outputs_dirs = (args.output_dir, args.output_dir + '-MM') if args.task_name == "mnli" else (args.output_dir,)
+    eval_outputs_dirs = (args.output_dir, args.output_dir + "-MM") if args.task_name == "mnli" else (args.output_dir,)
 
     results = {}
     for eval_task, eval_output_dir in zip(eval_task_names, eval_outputs_dirs):
-        eval_dataset = load_and_cache_examples(args, eval_task, tokenizer, evaluate=True)
+        eval_dataset, label_list = load_and_cache_examples(args, eval_task, tokenizer, evaluate=True)
 
         if not os.path.exists(eval_output_dir) and args.local_rank in [-1, 0]:
             os.makedirs(eval_output_dir)
@@ -235,7 +248,7 @@ def evaluate(args, model, tokenizer, prefix=""):
         eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
 
         # multi-gpu eval
-        if args.n_gpu > 1:
+        if args.n_gpu > 1 and not isinstance(model, torch.nn.DataParallel):
             model = torch.nn.DataParallel(model)
 
         # Eval!
@@ -251,11 +264,11 @@ def evaluate(args, model, tokenizer, prefix=""):
             batch = tuple(t.to(args.device) for t in batch)
 
             with torch.no_grad():
-                inputs = {'input_ids':      batch[0],
-                          'attention_mask': batch[1],
-                          'labels':         batch[3]}
-                if args.model_type != 'distilbert':
-                    inputs['token_type_ids'] = batch[2] if args.model_type in ['bert', 'xlnet'] else None  # XLM, DistilBERT and RoBERTa don't use segment_ids
+                inputs = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
+                if args.model_type != "distilbert":
+                    inputs["token_type_ids"] = (
+                        batch[2] if args.model_type in ["bert", "xlnet"] else None
+                    )  # XLM, DistilBERT and RoBERTa don't use segment_ids
                 outputs = model(**inputs)
                 tmp_eval_loss, logits = outputs[:2]
 
@@ -263,25 +276,24 @@ def evaluate(args, model, tokenizer, prefix=""):
             nb_eval_steps += 1
             if preds is None:
                 preds = logits.detach().cpu().numpy()
-                out_label_ids = inputs['labels'].detach().cpu().numpy()
+                out_label_ids = inputs["labels"].detach().cpu().numpy()
+                pair_ids = batch[4].detach().cpu().numpy()
             else:
                 preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)
-                out_label_ids = np.append(out_label_ids, inputs['labels'].detach().cpu().numpy(), axis=0)
+                out_label_ids = np.append(out_label_ids, inputs["labels"].detach().cpu().numpy(), axis=0)
+                pair_ids = np.append(pair_ids, batch[4].detach().cpu().numpy(), axis=0)
 
         eval_loss = eval_loss / nb_eval_steps
         if args.output_mode == "classification":
             preds = np.argmax(preds, axis=1)
         elif args.output_mode == "regression":
             preds = np.squeeze(preds)
-        result = compute_metrics(eval_task, preds, out_label_ids)
-        results.update(result)
 
-        output_eval_file = os.path.join(eval_output_dir, prefix, "eval_results.txt")
+        output_eval_file = os.path.join(eval_output_dir, "hans_predictions.txt")
         with open(output_eval_file, "w") as writer:
-            logger.info("***** Eval results {} *****".format(prefix))
-            for key in sorted(result.keys()):
-                logger.info("  %s = %s", key, str(result[key]))
-                writer.write("%s = %s\n" % (key, str(result[key])))
+            writer.write("pairID,gld_label\n")
+            for pid, pred in zip(pair_ids, preds):
+                writer.write("ex" + str(pid) + "," + label_list[int(pred)] + "\n")
 
     return results
 
@@ -293,29 +305,38 @@ def load_and_cache_examples(args, task, tokenizer, evaluate=False):
     processor = processors[task]()
     output_mode = output_modes[task]
     # Load data features from cache or dataset file
-    cached_features_file = os.path.join(args.data_dir, 'cached_{}_{}_{}_{}'.format(
-        'dev' if evaluate else 'train',
-        list(filter(None, args.model_name_or_path.split('/'))).pop(),
-        str(args.max_seq_length),
-        str(task)))
+    cached_features_file = os.path.join(
+        args.data_dir,
+        "cached_{}_{}_{}_{}".format(
+            "dev" if evaluate else "train",
+            list(filter(None, args.model_name_or_path.split("/"))).pop(),
+            str(args.max_seq_length),
+            str(task),
+        ),
+    )
+
+    label_list = processor.get_labels()
+
     if os.path.exists(cached_features_file) and not args.overwrite_cache:
         logger.info("Loading features from cached file %s", cached_features_file)
         features = torch.load(cached_features_file)
     else:
         logger.info("Creating features from dataset file at %s", args.data_dir)
-        label_list = processor.get_labels()
-        if task in ['mnli', 'mnli-mm'] and args.model_type in ['roberta']:
+        if task in ["mnli", "mnli-mm"] and args.model_type in ["roberta"]:
             # HACK(label indices are swapped in RoBERTa pretrained model)
-            label_list[1], label_list[2] = label_list[2], label_list[1] 
-        examples = processor.get_dev_examples(args.data_dir) if evaluate else processor.get_train_examples(args.data_dir)
-        features = convert_examples_to_features(examples,
-                                                tokenizer,
-                                                label_list=label_list,
-                                                max_length=args.max_seq_length,
-                                                output_mode=output_mode,
-                                                pad_on_left=bool(args.model_type in ['xlnet']),                 # pad on the left for xlnet
-                                                pad_token=tokenizer.convert_tokens_to_ids([tokenizer.pad_token])[0],
-                                                pad_token_segment_id=4 if args.model_type in ['xlnet'] else 0,
+            label_list[1], label_list[2] = label_list[2], label_list[1]
+        examples = (
+            processor.get_dev_examples(args.data_dir) if evaluate else processor.get_train_examples(args.data_dir)
+        )
+        features = convert_examples_to_features(
+            examples,
+            tokenizer,
+            label_list=label_list,
+            max_length=args.max_seq_length,
+            output_mode=output_mode,
+            pad_on_left=bool(args.model_type in ["xlnet"]),  # pad on the left for xlnet
+            pad_token=tokenizer.pad_token_id,
+            pad_token_segment_id=tokenizer.pad_token_type_id,
         )
         if args.local_rank in [-1, 0]:
             logger.info("Saving features into cached file %s", cached_features_file)
@@ -332,99 +353,159 @@ def load_and_cache_examples(args, task, tokenizer, evaluate=False):
         all_labels = torch.tensor([f.label for f in features], dtype=torch.long)
     elif output_mode == "regression":
         all_labels = torch.tensor([f.label for f in features], dtype=torch.float)
- 
-    dataset = TensorDataset(all_input_ids, all_attention_mask, all_token_type_ids, all_labels)
-    return dataset
+    all_pair_ids = torch.tensor([int(f.pairID) for f in features], dtype=torch.long)
+
+    dataset = TensorDataset(all_input_ids, all_attention_mask, all_token_type_ids, all_labels, all_pair_ids)
+    return dataset, label_list
 
 
 def main():
     parser = argparse.ArgumentParser()
 
-    ## Required parameters
-    parser.add_argument("--data_dir", default=None, type=str, required=True,
-                        help="The input data dir. Should contain the .tsv files (or other data files) for the task.")
-    parser.add_argument("--model_type", default=None, type=str, required=True,
-                        help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()))
-    parser.add_argument("--model_name_or_path", default=None, type=str, required=True,
-                        help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS))
-    parser.add_argument("--task_name", default=None, type=str, required=True,
-                        help="The name of the task to train selected in the list: " + ", ".join(processors.keys()))
-    parser.add_argument("--output_dir", default=None, type=str, required=True,
-                        help="The output directory where the model predictions and checkpoints will be written.")
+    # Required parameters
+    parser.add_argument(
+        "--data_dir",
+        default=None,
+        type=str,
+        required=True,
+        help="The input data dir. Should contain the .tsv files (or other data files) for the task.",
+    )
+    parser.add_argument(
+        "--model_type",
+        default=None,
+        type=str,
+        required=True,
+        help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()),
+    )
+    parser.add_argument(
+        "--model_name_or_path",
+        default=None,
+        type=str,
+        required=True,
+        help="Path to pretrained model or model identifier from huggingface.co/models",
+    )
+    parser.add_argument(
+        "--task_name",
+        default=None,
+        type=str,
+        required=True,
+        help="The name of the task to train selected in the list: " + ", ".join(processors.keys()),
+    )
+    parser.add_argument(
+        "--output_dir",
+        default=None,
+        type=str,
+        required=True,
+        help="The output directory where the model predictions and checkpoints will be written.",
+    )
 
-    ## Other parameters
-    parser.add_argument("--config_name", default="", type=str,
-                        help="Pretrained config name or path if not the same as model_name")
-    parser.add_argument("--tokenizer_name", default="", type=str,
-                        help="Pretrained tokenizer name or path if not the same as model_name")
-    parser.add_argument("--cache_dir", default="", type=str,
-                        help="Where do you want to store the pre-trained models downloaded from s3")
-    parser.add_argument("--max_seq_length", default=128, type=int,
-                        help="The maximum total input sequence length after tokenization. Sequences longer "
-                             "than this will be truncated, sequences shorter will be padded.")
-    parser.add_argument("--do_train", action='store_true',
-                        help="Whether to run training.")
-    parser.add_argument("--do_eval", action='store_true',
-                        help="Whether to run eval on the dev set.")
-    parser.add_argument("--evaluate_during_training", action='store_true',
-                        help="Rul evaluation during training at each logging step.")
-    parser.add_argument("--do_lower_case", action='store_true',
-                        help="Set this flag if you are using an uncased model.")
+    # Other parameters
+    parser.add_argument(
+        "--config_name", default="", type=str, help="Pretrained config name or path if not the same as model_name"
+    )
+    parser.add_argument(
+        "--tokenizer_name",
+        default="",
+        type=str,
+        help="Pretrained tokenizer name or path if not the same as model_name",
+    )
+    parser.add_argument(
+        "--cache_dir",
+        default="",
+        type=str,
+        help="Where do you want to store the pre-trained models downloaded from s3",
+    )
+    parser.add_argument(
+        "--max_seq_length",
+        default=128,
+        type=int,
+        help="The maximum total input sequence length after tokenization. Sequences longer "
+        "than this will be truncated, sequences shorter will be padded.",
+    )
+    parser.add_argument("--do_train", action="store_true", help="Whether to run training.")
+    parser.add_argument("--do_eval", action="store_true", help="Whether to run eval on the dev set.")
+    parser.add_argument(
+        "--evaluate_during_training", action="store_true", help="Rul evaluation during training at each logging step."
+    )
+    parser.add_argument(
+        "--do_lower_case", action="store_true", help="Set this flag if you are using an uncased model."
+    )
 
-    parser.add_argument("--per_gpu_train_batch_size", default=8, type=int,
-                        help="Batch size per GPU/CPU for training.")
-    parser.add_argument("--per_gpu_eval_batch_size", default=8, type=int,
-                        help="Batch size per GPU/CPU for evaluation.")
-    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("--learning_rate", default=5e-5, type=float,
-                        help="The initial learning rate for Adam.")
-    parser.add_argument("--weight_decay", default=0.0, type=float,
-                        help="Weight decay if we apply some.")
-    parser.add_argument("--adam_epsilon", default=1e-8, type=float,
-                        help="Epsilon for Adam optimizer.")
-    parser.add_argument("--max_grad_norm", default=1.0, type=float,
-                        help="Max gradient norm.")
-    parser.add_argument("--num_train_epochs", default=3.0, type=float,
-                        help="Total number of training epochs to perform.")
-    parser.add_argument("--max_steps", default=-1, type=int,
-                        help="If > 0: set total number of training steps to perform. Override num_train_epochs.")
-    parser.add_argument("--warmup_steps", default=0, type=int,
-                        help="Linear warmup over warmup_steps.")
+    parser.add_argument("--per_gpu_train_batch_size", default=8, type=int, help="Batch size per GPU/CPU for training.")
+    parser.add_argument(
+        "--per_gpu_eval_batch_size", default=8, type=int, help="Batch size per GPU/CPU for evaluation."
+    )
+    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("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.")
+    parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.")
+    parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.")
+    parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
+    parser.add_argument(
+        "--num_train_epochs", default=3.0, type=float, help="Total number of training epochs to perform."
+    )
+    parser.add_argument(
+        "--max_steps",
+        default=-1,
+        type=int,
+        help="If > 0: set total number of training steps to perform. Override num_train_epochs.",
+    )
+    parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.")
 
-    parser.add_argument('--logging_steps', type=int, default=50,
-                        help="Log every X updates steps.")
-    parser.add_argument('--save_steps', type=int, default=50,
-                        help="Save checkpoint every X updates steps.")
-    parser.add_argument("--eval_all_checkpoints", action='store_true',
-                        help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number")
-    parser.add_argument("--no_cuda", action='store_true',
-                        help="Avoid using CUDA when available")
-    parser.add_argument('--overwrite_output_dir', action='store_true',
-                        help="Overwrite the content of the output directory")
-    parser.add_argument('--overwrite_cache', action='store_true',
-                        help="Overwrite the cached training and evaluation sets")
-    parser.add_argument('--seed', type=int, default=42,
-                        help="random seed for initialization")
+    parser.add_argument("--logging_steps", type=int, default=50, help="Log every X updates steps.")
+    parser.add_argument("--save_steps", type=int, default=50, help="Save checkpoint every X updates steps.")
+    parser.add_argument(
+        "--eval_all_checkpoints",
+        action="store_true",
+        help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number",
+    )
+    parser.add_argument("--no_cuda", action="store_true", help="Avoid using CUDA when available")
+    parser.add_argument(
+        "--overwrite_output_dir", action="store_true", help="Overwrite the content of the output directory"
+    )
+    parser.add_argument(
+        "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets"
+    )
+    parser.add_argument("--seed", type=int, default=42, help="random seed for initialization")
 
-    parser.add_argument('--fp16', action='store_true',
-                        help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit")
-    parser.add_argument('--fp16_opt_level', type=str, default='O1',
-                        help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
-                             "See details at https://nvidia.github.io/apex/amp.html")
-    parser.add_argument("--local_rank", type=int, default=-1,
-                        help="For distributed training: local_rank")
-    parser.add_argument('--server_ip', type=str, default='', help="For distant debugging.")
-    parser.add_argument('--server_port', type=str, default='', help="For distant debugging.")
+    parser.add_argument(
+        "--fp16",
+        action="store_true",
+        help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit",
+    )
+    parser.add_argument(
+        "--fp16_opt_level",
+        type=str,
+        default="O1",
+        help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
+        "See details at https://nvidia.github.io/apex/amp.html",
+    )
+    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
+    parser.add_argument("--server_ip", type=str, default="", help="For distant debugging.")
+    parser.add_argument("--server_port", type=str, default="", help="For distant debugging.")
     args = parser.parse_args()
 
-    if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train and not args.overwrite_output_dir:
-        raise ValueError("Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format(args.output_dir))
+    if (
+        os.path.exists(args.output_dir)
+        and os.listdir(args.output_dir)
+        and args.do_train
+        and not args.overwrite_output_dir
+    ):
+        raise ValueError(
+            "Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format(
+                args.output_dir
+            )
+        )
 
     # Setup distant debugging if needed
     if args.server_ip and args.server_port:
         # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
         import ptvsd
+
         print("Waiting for debugger attach")
         ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
         ptvsd.wait_for_attach()
@@ -432,20 +513,28 @@ def main():
     # Setup CUDA, GPU & distributed training
     if args.local_rank == -1 or args.no_cuda:
         device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
-        args.n_gpu = torch.cuda.device_count()
+        args.n_gpu = 0 if args.no_cuda else torch.cuda.device_count()
     else:  # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
         torch.cuda.set_device(args.local_rank)
         device = torch.device("cuda", args.local_rank)
-        torch.distributed.init_process_group(backend='nccl')
+        torch.distributed.init_process_group(backend="nccl")
         args.n_gpu = 1
     args.device = device
 
     # Setup logging
-    logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s',
-                        datefmt = '%m/%d/%Y %H:%M:%S',
-                        level = logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
-    logger.warning("Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
-                    args.local_rank, device, args.n_gpu, bool(args.local_rank != -1), args.fp16)
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN,
+    )
+    logger.warning(
+        "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
+        args.local_rank,
+        device,
+        args.n_gpu,
+        bool(args.local_rank != -1),
+        args.fp16,
+    )
 
     # Set seed
     set_seed(args)
@@ -465,17 +554,23 @@ def main():
 
     args.model_type = args.model_type.lower()
     config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
-    config = config_class.from_pretrained(args.config_name if args.config_name else args.model_name_or_path,
-                                          num_labels=num_labels,
-                                          finetuning_task=args.task_name,
-                                          cache_dir=args.cache_dir if args.cache_dir else None)
-    tokenizer = tokenizer_class.from_pretrained(args.tokenizer_name if args.tokenizer_name else args.model_name_or_path,
-                                                do_lower_case=args.do_lower_case,
-                                                cache_dir=args.cache_dir if args.cache_dir else None)
-    model = model_class.from_pretrained(args.model_name_or_path,
-                                        from_tf=bool('.ckpt' in args.model_name_or_path),
-                                        config=config,
-                                        cache_dir=args.cache_dir if args.cache_dir else None)
+    config = config_class.from_pretrained(
+        args.config_name if args.config_name else args.model_name_or_path,
+        num_labels=num_labels,
+        finetuning_task=args.task_name,
+        cache_dir=args.cache_dir if args.cache_dir else None,
+    )
+    tokenizer = tokenizer_class.from_pretrained(
+        args.tokenizer_name if args.tokenizer_name else args.model_name_or_path,
+        do_lower_case=args.do_lower_case,
+        cache_dir=args.cache_dir if args.cache_dir else None,
+    )
+    model = model_class.from_pretrained(
+        args.model_name_or_path,
+        from_tf=bool(".ckpt" in args.model_name_or_path),
+        config=config,
+        cache_dir=args.cache_dir if args.cache_dir else None,
+    )
 
     if args.local_rank == 0:
         torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab
@@ -484,14 +579,12 @@ def main():
 
     logger.info("Training/evaluation parameters %s", args)
 
-
     # Training
     if args.do_train:
-        train_dataset = load_and_cache_examples(args, args.task_name, tokenizer, evaluate=False)
+        train_dataset, _ = load_and_cache_examples(args, args.task_name, tokenizer, evaluate=False)
         global_step, tr_loss = train(args, train_dataset, model, tokenizer)
         logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)
 
-
     # Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
     if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
         # Create output directory if needed
@@ -501,36 +594,39 @@ def main():
         logger.info("Saving model checkpoint to %s", args.output_dir)
         # Save a trained model, configuration and tokenizer using `save_pretrained()`.
         # They can then be reloaded using `from_pretrained()`
-        model_to_save = model.module if hasattr(model, 'module') else model  # Take care of distributed/parallel training
+        model_to_save = (
+            model.module if hasattr(model, "module") else model
+        )  # Take care of distributed/parallel training
         model_to_save.save_pretrained(args.output_dir)
         tokenizer.save_pretrained(args.output_dir)
 
         # Good practice: save your training arguments together with the trained model
-        torch.save(args, os.path.join(args.output_dir, 'training_args.bin'))
+        torch.save(args, os.path.join(args.output_dir, "training_args.bin"))
 
         # Load a trained model and vocabulary that you have fine-tuned
         model = model_class.from_pretrained(args.output_dir)
         tokenizer = tokenizer_class.from_pretrained(args.output_dir)
         model.to(args.device)
 
-
     # Evaluation
     results = {}
     if args.do_eval and args.local_rank in [-1, 0]:
         tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
         checkpoints = [args.output_dir]
         if args.eval_all_checkpoints:
-            checkpoints = list(os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME, recursive=True)))
+            checkpoints = list(
+                os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + "/**/" + WEIGHTS_NAME, recursive=True))
+            )
             logging.getLogger("transformers.modeling_utils").setLevel(logging.WARN)  # Reduce logging
         logger.info("Evaluate the following checkpoints: %s", checkpoints)
         for checkpoint in checkpoints:
-            global_step = checkpoint.split('-')[-1] if len(checkpoints) > 1 else ""
-            prefix = checkpoint.split('/')[-1] if checkpoint.find('checkpoint') != -1 else ""
-            
+            global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else ""
+            prefix = checkpoint.split("/")[-1] if checkpoint.find("checkpoint") != -1 else ""
+
             model = model_class.from_pretrained(checkpoint)
             model.to(args.device)
             result = evaluate(args, model, tokenizer, prefix=prefix)
-            result = dict((k + '_{}'.format(global_step), v) for k, v in result.items())
+            result = dict((k + "_{}".format(global_step), v) for k, v in result.items())
             results.update(result)
 
     return results

examples/adversarial/utils_hans.py

@@ -14,11 +14,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import csv
-import sys
 import copy
+import csv
 import json
 
+
 class InputExample(object):
     """
     A single training/test example for simple sequence classification.
@@ -32,11 +32,13 @@ class InputExample(object):
         label: (Optional) string. The label of the example. This should be
         specified for train and dev examples, but not for test examples.
     """
-    def __init__(self, guid, text_a, text_b=None, label=None):
+
+    def __init__(self, guid, text_a, text_b=None, label=None, pairID=None):
         self.guid = guid
         self.text_a = text_a
         self.text_b = text_b
         self.label = label
+        self.pairID = pairID
 
     def __repr__(self):
         return str(self.to_json_string())
@@ -64,11 +66,12 @@ class InputFeatures(object):
         label: Label corresponding to the input
     """
 
-    def __init__(self, input_ids, attention_mask, token_type_ids, label):
+    def __init__(self, input_ids, attention_mask, token_type_ids, label, pairID=None):
         self.input_ids = input_ids
         self.attention_mask = attention_mask
         self.token_type_ids = token_type_ids
         self.label = label
+        self.pairID = pairID
 
     def __repr__(self):
         return str(self.to_json_string())
@@ -107,13 +110,6 @@ class DataProcessor(object):
         """Gets the list of labels for this data set."""
         raise NotImplementedError()
 
-    def tfds_map(self, example):
-        """Some tensorflow_datasets datasets are not formatted the same way the GLUE datasets are. 
-        This method converts examples to the correct format."""
-        if len(self.get_labels()) > 1:
-            example.label = self.get_labels()[int(example.label)]
-        return example
-
     @classmethod
     def _read_tsv(cls, input_file, quotechar=None):
         """Reads a tab separated value file."""
@@ -121,7 +117,5 @@ class DataProcessor(object):
             reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
             lines = []
             for line in reader:
-                if sys.version_info[0] == 2:
-                    line = list(unicode(cell, 'utf-8') for cell in line)
                 lines.append(line)
             return lines

examples/benchmarking/plot_csv_file.py

@@ -0,0 +1,113 @@
+import csv
+from collections import defaultdict
+from dataclasses import dataclass, field
+from typing import Optional
+
+import numpy as np
+
+import matplotlib.pyplot as plt
+from transformers import HfArgumentParser
+
+
+@dataclass
+class PlotArguments:
+    """
+    Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
+    """
+
+    csv_file: str = field(metadata={"help": "The csv file to plot."},)
+    plot_along_batch: bool = field(
+        default=False,
+        metadata={"help": "Whether to plot along batch size or sequence lengh. Defaults to sequence length."},
+    )
+    is_time: bool = field(
+        default=False,
+        metadata={"help": "Whether the csv file has time results or memory results. Defaults to memory results."},
+    )
+    is_train: bool = field(
+        default=False,
+        metadata={
+            "help": "Whether the csv file has training results or inference results. Defaults to inference results."
+        },
+    )
+    figure_png_file: Optional[str] = field(
+        default=None, metadata={"help": "Filename under which the plot will be saved. If unused no plot is saved."},
+    )
+
+
+class Plot:
+    def __init__(self, args):
+        self.args = args
+        self.result_dict = defaultdict(lambda: dict(bsz=[], seq_len=[], result={}))
+
+        with open(self.args.csv_file, newline="") as csv_file:
+            reader = csv.DictReader(csv_file)
+            for row in reader:
+                model_name = row["model"]
+                self.result_dict[model_name]["bsz"].append(int(row["batch_size"]))
+                self.result_dict[model_name]["seq_len"].append(int(row["sequence_length"]))
+                self.result_dict[model_name]["result"][(int(row["batch_size"]), int(row["sequence_length"]))] = row[
+                    "result"
+                ]
+
+    def plot(self):
+        fig, ax = plt.subplots()
+        title_str = "Time usage" if self.args.is_time else "Memory usage"
+        title_str = title_str + " for training" if self.args.is_train else title_str + " for inference"
+
+        for model_name in self.result_dict.keys():
+            batch_sizes = sorted(list(set(self.result_dict[model_name]["bsz"])))
+            sequence_lengths = sorted(list(set(self.result_dict[model_name]["seq_len"])))
+            results = self.result_dict[model_name]["result"]
+
+            (x_axis_array, inner_loop_array) = (
+                (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
+            )
+
+            plt.xlim(min(x_axis_array), max(x_axis_array))
+
+            for inner_loop_value in inner_loop_array:
+                if self.args.plot_along_batch:
+                    y_axis_array = np.asarray([results[(x, inner_loop_value)] for x in x_axis_array], dtype=np.int)
+                else:
+                    y_axis_array = np.asarray([results[(inner_loop_value, x)] for x in x_axis_array], dtype=np.float32)
+
+                ax.set_xscale("log", basex=2)
+                ax.set_yscale("log", basey=10)
+
+                (x_axis_label, inner_loop_label) = (
+                    ("batch_size", "sequence_length in #tokens")
+                    if self.args.plot_along_batch
+                    else ("sequence_length in #tokens", "batch_size")
+                )
+
+                x_axis_array = np.asarray(x_axis_array, np.int)
+                plt.scatter(x_axis_array, y_axis_array, label=f"{model_name} - {inner_loop_label}: {inner_loop_value}")
+                plt.plot(x_axis_array, y_axis_array, "--")
+
+            title_str += f" {model_name} vs."
+
+        title_str = title_str[:-4]
+        y_axis_label = "Time in s" if self.args.is_time else "Memory in MB"
+
+        # plot
+        plt.title(title_str)
+        plt.xlabel(x_axis_label)
+        plt.ylabel(y_axis_label)
+        plt.legend()
+
+        if self.args.figure_png_file is not None:
+            plt.savefig(self.args.figure_png_file)
+        else:
+            plt.show()
+
+
+def main():
+    parser = HfArgumentParser(PlotArguments)
+    plot_args = parser.parse_args_into_dataclasses()[0]
+    plot = Plot(args=plot_args)
+    plot.plot()
+
+
+if __name__ == "__main__":
+    main()

examples/benchmarking/run_benchmark.py

@@ -0,0 +1,29 @@
+# coding=utf-8
+# Copyright 2018 The HuggingFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION.  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.
+""" Benchmarking the library on inference and training """
+
+from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments
+
+
+def main():
+    parser = HfArgumentParser(PyTorchBenchmarkArguments)
+    benchmark_args = parser.parse_args_into_dataclasses()[0]
+    benchmark = PyTorchBenchmark(args=benchmark_args)
+    benchmark.run()
+
+
+if __name__ == "__main__":
+    main()

examples/benchmarks.py

@@ -1,477 +0,0 @@
-# coding=utf-8
-# Copyright 2018 The HuggingFace Inc. team.
-# Copyright (c) 2018, NVIDIA CORPORATION.  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.
-""" Benchmarking the library on inference and training """
-
-# If checking the tensors placement
-# tf.debugging.set_log_device_placement(True)
-
-from typing import List
-import timeit
-from transformers import is_tf_available, is_torch_available
-from time import time
-import argparse
-import csv
-
-if is_tf_available():
-    import tensorflow as tf
-    from transformers import TFAutoModel
-
-if is_torch_available():
-    import torch
-    from transformers import AutoModel
-
-from transformers import AutoConfig, AutoTokenizer
-
-input_text = """Bent over their instruments, three hundred Fertilizers were plunged, as 
-the Director of Hatcheries and Conditioning entered the room, in the 
-
-
-
-scarcely breathing silence, the absent-minded, soliloquizing hum or 
-whistle, of absorbed concentration. A troop of newly arrived students, 
-very young, pink and callow, followed nervously, rather abjectly, at the 
-Director's heels. Each of them carried a notebook, in which, whenever 
-the great man spoke, he desperately scribbled. Straight from the 
-horse's mouth. It was a rare privilege. The D. H. C. for Central London 
-always made a point of personally conducting his new students round 
-the various departments. 
-
-"Just to give you a general idea," he would explain to them. For of 
-course some sort of general idea they must have, if they were to do 
-their work intelligently-though as little of one, if they were to be good 
-and happy members of society, as possible. For particulars, as every 
-one knows, make for virtue and happiness; generalities are intellectu- 
-ally necessary evils. Not philosophers but fret-sawyers and stamp col- 
-lectors compose the backbone of society. 
-
-"To-morrow," he would add, smiling at them with a slightly menacing 
-geniality, "you'll be settling down to serious work. You won't have time 
-for generalities. Meanwhile ..." 
-
-Meanwhile, it was a privilege. Straight from the horse's mouth into the 
-notebook. The boys scribbled like mad. 
-
-Tall and rather thin but upright, the Director advanced into the room. 
-He had a long chin and big rather prominent teeth, just covered, when 
-he was not talking, by his full, floridly curved lips. Old, young? Thirty? 
-Fifty? Fifty-five? It was hard to say. And anyhow the question didn't 
-arise; in this year of stability, A. F. 632, it didn't occur to you to ask it. 
-
-"I shall begin at the beginning," said the D.H.C. and the more zealous 
-students recorded his intention in their notebooks: Begin at the begin- 
-ning. "These," he waved his hand, "are the incubators." And opening 
-an insulated door he showed them racks upon racks of numbered test- 
-tubes. "The week's supply of ova. Kept," he explained, "at blood heat; 
-whereas the male gametes," and here he opened another door, "they 
-have to be kept at thirty-five instead of thirty-seven. Full blood heat 
-sterilizes." Rams wrapped in theremogene beget no lambs. 
-
-Still leaning against the incubators he gave them, while the pencils 
-scurried illegibly across the pages, a brief description of the modern 
-
-
-
-fertilizing process; spoke first, of course, of its surgical introduc- 
-tion-"the operation undergone voluntarily for the good of Society, not 
-to mention the fact that it carries a bonus amounting to six months' 
-salary"; continued with some account of the technique for preserving 
-the excised ovary alive and actively developing; passed on to a consid- 
-eration of optimum temperature, salinity, viscosity; referred to the liq- 
-uor in which the detached and ripened eggs were kept; and, leading 
-his charges to the work tables, actually showed them how this liquor 
-was drawn off from the test-tubes; how it was let out drop by drop 
-onto the specially warmed slides of the microscopes; how the eggs 
-which it contained were inspected for abnormalities, counted and 
-transferred to a porous receptacle; how (and he now took them to 
-watch the operation) this receptacle was immersed in a warm bouillon 
-containing free-swimming spermatozoa-at a minimum concentration 
-of one hundred thousand per cubic centimetre, he insisted; and how, 
-after ten minutes, the container was lifted out of the liquor and its 
-contents re-examined; how, if any of the eggs remained unfertilized, it 
-was again immersed, and, if necessary, yet again; how the fertilized 
-ova went back to the incubators; where the Alphas and Betas re- 
-mained until definitely bottled; while the Gammas, Deltas and Epsilons 
-were brought out again, after only thirty-six hours, to undergo Bo- 
-kanovsky's Process. 
-
-"Bokanovsky's Process," repeated the Director, and the students un- 
-derlined the words in their little notebooks. 
-
-One egg, one embryo, one adult-normality. But a bokanovskified egg 
-will bud, will proliferate, will divide. From eight to ninety-six buds, and 
-every bud will grow into a perfectly formed embryo, and every embryo 
-into a full-sized adult. Making ninety-six human beings grow where 
-only one grew before. Progress. 
-
-"Essentially," the D.H.C. concluded, "bokanovskification consists of a 
-series of arrests of development. We check the normal growth and, 
-paradoxically enough, the egg responds by budding." 
-
-Responds by budding. The pencils were busy. 
-
-He pointed. On a very slowly moving band a rack-full of test-tubes was 
-entering a large metal box, another, rack-full was emerging. Machinery 
-faintly purred. It took eight minutes for the tubes to go through, he 
-
-
-
-told them. Eight minutes of hard X-rays being about as much as an 
-egg can stand. A few died; of the rest, the least susceptible divided 
-into two; most put out four buds; some eight; all were returned to the 
-incubators, where the buds began to develop; then, after two days, 
-were suddenly chilled, chilled and checked. Two, four, eight, the buds 
-in their turn budded; and having budded were dosed almost to death 
-with alcohol; consequently burgeoned again and having budded-bud 
-out of bud out of bud-were thereafter-further arrest being generally 
-fatal-left to develop in peace. By which time the original egg was in a 
-fair way to becoming anything from eight to ninety-six embryos- a 
-prodigious improvement, you will agree, on nature. Identical twins-but 
-not in piddling twos and threes as in the old viviparous days, when an 
-egg would sometimes accidentally divide; actually by dozens, by 
-scores at a time. 
-
-"Scores," the Director repeated and flung out his arms, as though he 
-were distributing largesse. "Scores." 
-
-But one of the students was fool enough to ask where the advantage 
-lay. 
-
-"My good boy!" The Director wheeled sharply round on him. "Can't you 
-see? Can't you see?" He raised a hand; his expression was solemn. 
-"Bokanovsky's Process is one of the major instruments of social stabil- 
-ity!" 
-
-Major instruments of social stability. 
-
-Standard men and women; in uniform batches. The whole of a small 
-factory staffed with the products of a single bokanovskified egg. 
-
-"Ninety-six identical twins working ninety-six identical machines!" The 
-voice was almost tremulous with enthusiasm. "You really know where 
-you are. For the first time in history." He quoted the planetary motto. 
-"Community, Identity, Stability." Grand words. "If we could bo- 
-kanovskify indefinitely the whole problem would be solved." 
-
-Solved by standard Gammas, unvarying Deltas, uniform Epsilons. Mil- 
-lions of identical twins. The principle of mass production at last applied 
-to biology. 
-
-
-
-"But, alas," the Director shook his head, "we can't bokanovskify indefi- 
-nitely." 
-
-Ninety-six seemed to be the limit; seventy-two a good average. From 
-the same ovary and with gametes of the same male to manufacture as 
-many batches of identical twins as possible-that was the best (sadly a 
-second best) that they could do. And even that was difficult. 
-
-"For in nature it takes thirty years for two hundred eggs to reach ma- 
-turity. But our business is to stabilize the population at this moment, 
-here and now. Dribbling out twins over a quarter of a century-what 
-would be the use of that?" 
-
-Obviously, no use at all. But Podsnap's Technique had immensely ac- 
-celerated the process of ripening. They could make sure of at least a 
-hundred and fifty mature eggs within two years. Fertilize and bo- 
-kanovskify-in other words, multiply by seventy-two-and you get an 
-average of nearly eleven thousand brothers and sisters in a hundred 
-and fifty batches of identical twins, all within two years of the same 
-age. 
-
-"And in exceptional cases we can make one ovary yield us over fifteen 
-thousand adult individuals." 
-
-Beckoning to a fair-haired, ruddy young man who happened to be 
-passing at the moment. "Mr. Foster," he called. The ruddy young man 
-approached. "Can you tell us the record for a single ovary, Mr. Foster?" 
-
-"Sixteen thousand and twelve in this Centre," Mr. Foster replied with- 
-out hesitation. He spoke very quickly, had a vivacious blue eye, and 
-took an evident pleasure in quoting figures. "Sixteen thousand and 
-twelve; in one hundred and eighty-nine batches of identicals. But of 
-course they've done much better," he rattled on, "in some of the tropi- 
-cal Centres. Singapore has often produced over sixteen thousand five 
-hundred; and Mombasa has actually touched the seventeen thousand 
-mark. But then they have unfair advantages. You should see the way a 
-negro ovary responds to pituitary! It's quite astonishing, when you're 
-used to working with European material. Still," he added, with a laugh 
-(but the light of combat was in his eyes and the lift of his chin was 
-challenging), "still, we mean to beat them if we can. I'm working on a 
-wonderful Delta-Minus ovary at this moment. Only just eighteen 
-
-
-
-months old. Over twelve thousand seven hundred children already, ei- 
-ther decanted or in embryo. And still going strong. We'll beat them 
-yet." 
-
-"That's the spirit I like!" cried the Director, and clapped Mr. Foster on 
-the shoulder. "Come along with us, and give these boys the benefit of 
-your expert knowledge." 
-
-Mr. Foster smiled modestly. "With pleasure." They went. 
-In the Bottling Room all was harmonious bustle and ordered activity. 
-Flaps of fresh sow's peritoneum ready cut to the proper size came 
-shooting up in little lifts from the Organ Store in the sub-basement. 
-Whizz and then, click! the lift-hatches hew open; the bottle-liner had 
-only to reach out a hand, take the flap, insert, smooth-down, and be- 
-fore the lined bottle had had time to travel out of reach along the end- 
-less band, whizz, click! another flap of peritoneum had shot up from 
-the depths, ready to be slipped into yet another bottle, the next of that 
-slow interminable procession on the band. 
-
-Next to the Liners stood the Matriculators. The procession advanced; 
-one by one the eggs were transferred from their test-tubes to the 
-larger containers; deftly the peritoneal lining was slit, the morula 
-dropped into place, the saline solution poured in ... and already the 
-bottle had passed, and it was the turn of the labellers. Heredity, date 
-of fertilization, membership of Bokanovsky Group-details were trans- 
-ferred from test-tube to bottle. No longer anonymous, but named, 
-identified, the procession marched slowly on; on through an opening in 
-the wall, slowly on into the Social Predestination Room. 
-"Eighty-eight cubic metres of card-index," said Mr. Foster with relish, 
-as they entered."""
-
-
-def create_setup_and_compute(model_names: List[str],
-                             gpu: bool = True,
-                             tensorflow: bool = False,
-                             average_over: int = 3,
-                             torchscript: bool = False,
-                             xla: bool = False,
-                             amp: bool = False,
-                             fp16: bool = False,
-                             save_to_csv: bool = False,
-                             csv_filename: str = f"results_{round(time())}.csv"):
-    if xla:
-        tf.config.optimizer.set_jit(True)
-    if amp:
-        tf.config.optimizer.set_experimental_options({"auto_mixed_precision": True})
-
-    if tensorflow:
-        dictionary = {model_name: {} for model_name in model_names}
-        results = _compute_tensorflow(model_names, dictionary, average_over, amp)
-    else:
-        device = 'cuda' if (gpu and torch.cuda.is_available()) else 'cpu'
-        dictionary = {model_name: {} for model_name in model_names}
-        results = _compute_pytorch(model_names, dictionary, average_over, device, torchscript, fp16)
-
-    print("=========== RESULTS ===========")
-    for model_name in model_names:
-        print("\t" + f"======= MODEL CHECKPOINT: {model_name} =======")
-        for batch_size in results[model_name]["bs"]:
-            print("\t\t" + f"===== BATCH SIZE: {batch_size} =====")
-            for slice_size in results[model_name]["ss"]:
-                result = results[model_name]['results'][batch_size][slice_size]
-                if isinstance(result, str):
-                    print(f"\t\t{model_name}/{batch_size}/{slice_size}: "
-                          f"{result}")
-                else:
-                    print(f"\t\t{model_name}/{batch_size}/{slice_size}: "
-                          f"{(round(1000 * result) / 1000)}"
-                          f"s")
-
-    if save_to_csv:
-        with open(csv_filename, mode='w') as csv_file:
-            fieldnames = ['model',
-                          '1x8', '1x64', '1x128', '1x256', '1x512', '1x1024',
-                          '2x8', '2x64', '2x128', '2x256', '2x512', '2x1024',
-                          '4x8', '4x64', '4x128', '4x256', '4x512', '4x1024',
-                          '8x8', '8x64', '8x128', '8x256', '8x512', '8x1024',
-                          ]
-
-            writer = csv.DictWriter(csv_file, fieldnames=fieldnames)
-            writer.writeheader()
-
-            for model_name in model_names:
-                model_results = {
-                    f'{bs}x{ss}': results[model_name]['results'][bs][ss]
-                    for bs in results[model_name]["results"]
-                    for ss in results[model_name]['results'][bs]
-                }
-                writer.writerow({'model': model_name, **model_results})
-
-
-def _compute_pytorch(model_names, dictionary, average_over, device, torchscript, fp16):
-    for c, model_name in enumerate(model_names):
-        print(f"{c + 1} / {len(model_names)}")
-        config = AutoConfig.from_pretrained(model_name, torchscript=torchscript)
-        model = AutoModel.from_pretrained(model_name, config=config)
-        tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-        tokenized_sequence = tokenizer.encode(input_text, add_special_tokens=False)
-
-        max_input_size = tokenizer.max_model_input_sizes[model_name]
-        batch_sizes = [1, 2, 4, 8]
-        slice_sizes = [8, 64, 128, 256, 512, 1024]
-
-        dictionary[model_name] = {"bs": batch_sizes, "ss": slice_sizes, "results": {}}
-        dictionary[model_name]["results"] = {i: {} for i in batch_sizes}
-
-        for batch_size in batch_sizes:
-            if fp16:
-                model.half()
-            model.to(device)
-            model.eval()
-            for slice_size in slice_sizes:
-                if max_input_size is not None and slice_size > max_input_size:
-                    dictionary[model_name]["results"][batch_size][slice_size] = "N/A"
-                else:
-                    sequence = torch.tensor(tokenized_sequence[:slice_size], device=device).repeat(batch_size, 1)
-                    try:
-                        if torchscript:
-                            print("Tracing model with sequence size", sequence.shape)
-                            inference = torch.jit.trace(model, sequence)
-                            inference(sequence)
-                        else:
-                            inference = model
-                            inference(sequence)
-
-                        print("Going through model with sequence of shape", sequence.shape)
-                        runtimes = timeit.repeat(lambda: inference(sequence), repeat=average_over, number=3)
-                        average_time = sum(runtimes)/float(len(runtimes)) / 3.0
-                        dictionary[model_name]["results"][batch_size][slice_size] = average_time
-                    except RuntimeError as e:
-                        print("Doesn't fit on GPU.", e)
-                        torch.cuda.empty_cache()
-                        dictionary[model_name]["results"][batch_size][slice_size] = "N/A"
-    return dictionary
-
-
-def _compute_tensorflow(model_names, dictionary, average_over, amp):
-    for c, model_name in enumerate(model_names):
-        print(f"{c + 1} / {len(model_names)}")
-        config = AutoConfig.from_pretrained(model_name)
-        model = TFAutoModel.from_pretrained(model_name, config=config)
-        tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-        tokenized_sequence = tokenizer.encode(input_text, add_special_tokens=False)
-
-        max_input_size = tokenizer.max_model_input_sizes[model_name]
-        batch_sizes = [1, 2, 4, 8]
-        slice_sizes = [8, 64, 128, 256, 512, 1024]
-
-        dictionary[model_name] = {"bs": batch_sizes, "ss": slice_sizes, "results": {}}
-        dictionary[model_name]["results"] = {i: {} for i in batch_sizes}
-
-        print("Using model", model)
-
-        @tf.function
-        def inference(inputs):
-            return model(inputs)
-
-        for batch_size in batch_sizes:
-            for slice_size in slice_sizes:
-                if max_input_size is not None and slice_size > max_input_size:
-                    dictionary[model_name]["results"][batch_size][slice_size] = "N/A"
-                else:
-                    sequence = tf.stack([tf.squeeze(tf.constant(tokenized_sequence[:slice_size])[None, :])] * batch_size)
-
-                    try:
-                        print("Going through model with sequence of shape", sequence.shape)
-                        # To make sure that the model is traced + that the tensors are on the appropriate device
-                        inference(sequence)
-
-                        runtimes = timeit.repeat(lambda: inference(sequence), repeat=average_over, number=3)
-                        average_time = sum(runtimes)/float(len(runtimes)) / 3.0
-                        dictionary[model_name]["results"][batch_size][slice_size] = average_time
-                    except tf.errors.ResourceExhaustedError as e:
-                        print("Doesn't fit on GPU.", e)
-                        torch.cuda.empty_cache()
-                        dictionary[model_name]["results"][batch_size][slice_size] = "N/A"
-    return dictionary
-
-
-def main():
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument("--models", required=False, type=str, default='all', help="Model checkpoints to be provided "
-                                                                                  "to the AutoModel classes. Leave "
-                                                                                  "blank to benchmark the base version "
-                                                                                  "of all available model "
-                                                                                  "architectures.")
-    parser.add_argument("--torch", required=False, action="store_true", help="Benchmark the Pytorch version of the "
-                                                                             "models")
-    parser.add_argument("--torch_cuda", required=False, action="store_true", help="Pytorch only: run on available "
-                                                                                  "cuda devices")
-    parser.add_argument("--torchscript", required=False, action="store_true", help="Pytorch only: trace the models "
-                                                                                   "using torchscript")
-    parser.add_argument("--tensorflow", required=False, action="store_true", help="Benchmark the TensorFlow version "
-                                                                                  "of the models. Will run on GPU if "
-                                                                                  "the correct dependencies are "
-                                                                                  "installed")
-    parser.add_argument("--xla", required=False, action="store_true", help="TensorFlow only: use XLA acceleration.")
-    parser.add_argument("--amp", required=False, action="store_true", help="TensorFlow only: use automatic mixed precision acceleration.")
-    parser.add_argument("--fp16", required=False, action="store_true", help="PyTorch only: use FP16 to accelerate inference.")
-    parser.add_argument("--keras_predict", required=False, action="store_true", help="Whether to use model.predict "
-                                                                                     "instead of model() to do a "
-                                                                                     "forward pass.")
-    parser.add_argument("--save_to_csv", required=False, action="store_true", help="Save to a CSV file.")
-    parser.add_argument("--csv_filename", required=False, default=None, help="CSV filename used if saving results to csv.")
-    parser.add_argument("--average_over", required=False, default=30, type=int, help="Times an experiment will be run.")
-
-    args = parser.parse_args()
-    if args.models == 'all':
-        args.models = [
-            "gpt2",
-            "bert-base-cased",
-            "xlnet-base-cased",
-            "xlm-mlm-en-2048",
-            "transfo-xl-wt103",
-            "openai-gpt",
-            "distilbert-base-uncased",
-            "distilgpt2",
-            "roberta-base",
-            "ctrl"
-        ]
-    else:
-        args.models = args.models.split()
-
-    print("Running with arguments", args)
-
-    if args.torch:
-        if is_torch_available():
-            create_setup_and_compute(
-                model_names=args.models,
-                tensorflow=False,
-                gpu=args.torch_cuda,
-                torchscript=args.torchscript,
-                fp16=args.fp16,
-                save_to_csv=args.save_to_csv,
-                csv_filename=args.csv_filename,
-                average_over=args.average_over
-            )
-        else:
-            raise ImportError("Trying to run a PyTorch benchmark but PyTorch was not found in the environment.")
-
-    if args.tensorflow:
-        if is_tf_available():
-            create_setup_and_compute(
-                model_names=args.models,
-                tensorflow=True,
-                xla=args.xla,
-                amp=args.amp,
-                save_to_csv=args.save_to_csv,
-                csv_filename=args.csv_filename,
-                average_over=args.average_over
-            )
-        else:
-            raise ImportError("Trying to run a TensorFlow benchmark but TensorFlow was not found in the environment.")
-
-if __name__ == '__main__':
-    main()
-

examples/bertology/run_bertology.py

@@ -19,29 +19,29 @@
     Some parts of this script are adapted from the code of Michel et al. (http://arxiv.org/abs/1905.10650)
     which is available at https://github.com/pmichel31415/are-16-heads-really-better-than-1
 """
-import os
 import argparse
 import logging
-from datetime import timedelta, datetime
-from tqdm import tqdm
+import os
+from datetime import datetime
 
 import numpy as np
-
 import torch
-from torch.utils.data import DataLoader, SequentialSampler, TensorDataset, Subset
+from torch.utils.data import DataLoader, SequentialSampler, Subset
 from torch.utils.data.distributed import DistributedSampler
-from torch.nn import CrossEntropyLoss, MSELoss
+from tqdm import tqdm
 
-from transformers import (WEIGHTS_NAME,
-                                  BertConfig, BertForSequenceClassification, BertTokenizer,
-                                  XLMConfig, XLMForSequenceClassification, XLMTokenizer,
-                                  XLNetConfig, XLNetForSequenceClassification, XLNetTokenizer)
+from transformers import (
+    AutoConfig,
+    AutoModelForSequenceClassification,
+    AutoTokenizer,
+    DefaultDataCollator,
+    GlueDataset,
+    glue_compute_metrics,
+    glue_output_modes,
+    glue_processors,
+    set_seed,
+)
 
-from run_glue import set_seed, load_and_cache_examples, ALL_MODELS, MODEL_CLASSES
-
-from transformers import glue_compute_metrics as compute_metrics
-from transformers import glue_output_modes as output_modes
-from transformers import glue_processors as processors
 
 logger = logging.getLogger(__name__)
 
@@ -63,35 +63,46 @@ def print_2d_tensor(tensor):
             logger.info(f"layer {row + 1}:\t" + "\t".join(f"{x:d}" for x in tensor[row].cpu().data))
 
 
-def compute_heads_importance(args, model, eval_dataloader, compute_entropy=True, compute_importance=True, head_mask=None):
+def compute_heads_importance(
+    args, model, eval_dataloader, compute_entropy=True, compute_importance=True, head_mask=None, actually_pruned=False
+):
     """ This method shows how to compute:
         - head attention entropy
         - head importance scores according to http://arxiv.org/abs/1905.10650
     """
     # Prepare our tensors
-    n_layers, n_heads = model.bert.config.num_hidden_layers, model.bert.config.num_attention_heads
+    n_layers, n_heads = model.config.num_hidden_layers, model.config.num_attention_heads
     head_importance = torch.zeros(n_layers, n_heads).to(args.device)
     attn_entropy = torch.zeros(n_layers, n_heads).to(args.device)
 
     if head_mask is None:
         head_mask = torch.ones(n_layers, n_heads).to(args.device)
+
     head_mask.requires_grad_(requires_grad=True)
+    # If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
+    if actually_pruned:
+        head_mask = None
+
     preds = None
     labels = None
     tot_tokens = 0.0
 
-    for step, batch in enumerate(tqdm(eval_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])):
-        batch = tuple(t.to(args.device) for t in batch)
-        input_ids, input_mask, segment_ids, label_ids = batch
+    for step, inputs in enumerate(tqdm(eval_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])):
+        for k, v in inputs.items():
+            inputs[k] = v.to(args.device)
 
         # Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
-        outputs = model(input_ids, token_type_ids=segment_ids, attention_mask=input_mask, labels=label_ids, head_mask=head_mask)
-        loss, logits, all_attentions = outputs[0], outputs[1], outputs[-1]  # Loss and logits are the first, attention the last
+        outputs = model(**inputs, head_mask=head_mask)
+        loss, logits, all_attentions = (
+            outputs[0],
+            outputs[1],
+            outputs[-1],
+        )  # Loss and logits are the first, attention the last
         loss.backward()  # Backpropagate to populate the gradients in the head mask
 
         if compute_entropy:
             for layer, attn in enumerate(all_attentions):
-                masked_entropy = entropy(attn.detach()) * input_mask.float().unsqueeze(1)
+                masked_entropy = entropy(attn.detach()) * inputs["attention_mask"].float().unsqueeze(1)
                 attn_entropy[layer] += masked_entropy.sum(-1).sum(0).detach()
 
         if compute_importance:
@@ -100,12 +111,12 @@ def compute_heads_importance(args, model, eval_dataloader, compute_entropy=True,
         # Also store our logits/labels if we want to compute metrics afterwards
         if preds is None:
             preds = logits.detach().cpu().numpy()
-            labels = label_ids.detach().cpu().numpy()
+            labels = inputs["labels"].detach().cpu().numpy()
         else:
             preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)
-            labels = np.append(labels, label_ids.detach().cpu().numpy(), axis=0)
+            labels = np.append(labels, inputs["labels"].detach().cpu().numpy(), axis=0)
 
-        tot_tokens += input_mask.float().detach().sum().data
+        tot_tokens += inputs["attention_mask"].float().detach().sum().data
 
     # Normalize
     attn_entropy /= tot_tokens
@@ -113,15 +124,15 @@ def compute_heads_importance(args, model, eval_dataloader, compute_entropy=True,
     # Layerwise importance normalization
     if not args.dont_normalize_importance_by_layer:
         exponent = 2
-        norm_by_layer = torch.pow(torch.pow(head_importance, exponent).sum(-1), 1/exponent)
+        norm_by_layer = torch.pow(torch.pow(head_importance, exponent).sum(-1), 1 / exponent)
         head_importance /= norm_by_layer.unsqueeze(-1) + 1e-20
 
     if not args.dont_normalize_global_importance:
         head_importance = (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())
 
     # Print/save matrices
-    np.save(os.path.join(args.output_dir, 'attn_entropy.npy'), attn_entropy.detach().cpu().numpy())
-    np.save(os.path.join(args.output_dir, 'head_importance.npy'), head_importance.detach().cpu().numpy())
+    np.save(os.path.join(args.output_dir, "attn_entropy.npy"), attn_entropy.detach().cpu().numpy())
+    np.save(os.path.join(args.output_dir, "head_importance.npy"), head_importance.detach().cpu().numpy())
 
     logger.info("Attention entropies")
     print_2d_tensor(attn_entropy)
@@ -129,7 +140,9 @@ def compute_heads_importance(args, model, eval_dataloader, compute_entropy=True,
     print_2d_tensor(head_importance)
     logger.info("Head ranked by importance scores")
     head_ranks = torch.zeros(head_importance.numel(), dtype=torch.long, device=args.device)
-    head_ranks[head_importance.view(-1).sort(descending=True)[1]] = torch.arange(head_importance.numel(), device=args.device)
+    head_ranks[head_importance.view(-1).sort(descending=True)[1]] = torch.arange(
+        head_importance.numel(), device=args.device
+    )
     head_ranks = head_ranks.view_as(head_importance)
     print_2d_tensor(head_ranks)
 
@@ -142,7 +155,7 @@ def mask_heads(args, model, eval_dataloader):
     """
     _, head_importance, preds, labels = compute_heads_importance(args, model, eval_dataloader, compute_entropy=False)
     preds = np.argmax(preds, axis=1) if args.output_mode == "classification" else np.squeeze(preds)
-    original_score = compute_metrics(args.task_name, preds, labels)[args.metric_name]
+    original_score = glue_compute_metrics(args.task_name, preds, labels)[args.metric_name]
     logger.info("Pruning: original score: %f, threshold: %f", original_score, original_score * args.masking_threshold)
 
     new_head_mask = torch.ones_like(head_importance)
@@ -150,9 +163,9 @@ def mask_heads(args, model, eval_dataloader):
 
     current_score = original_score
     while current_score >= original_score * args.masking_threshold:
-        head_mask = new_head_mask.clone() # save current head mask
+        head_mask = new_head_mask.clone()  # save current head mask
         # heads from least important to most - keep only not-masked heads
-        head_importance[head_mask == 0.0] = float('Inf')
+        head_importance[head_mask == 0.0] = float("Inf")
         current_heads_to_mask = head_importance.view(-1).sort()[1]
 
         if len(current_heads_to_mask) <= num_to_mask:
@@ -164,17 +177,25 @@ def mask_heads(args, model, eval_dataloader):
         new_head_mask = new_head_mask.view(-1)
         new_head_mask[current_heads_to_mask] = 0.0
         new_head_mask = new_head_mask.view_as(head_mask)
+        new_head_mask = new_head_mask.clone().detach()
         print_2d_tensor(new_head_mask)
 
         # Compute metric and head importance again
-        _, head_importance, preds, labels = compute_heads_importance(args, model, eval_dataloader, compute_entropy=False, head_mask=new_head_mask)
+        _, head_importance, preds, labels = compute_heads_importance(
+            args, model, eval_dataloader, compute_entropy=False, head_mask=new_head_mask
+        )
         preds = np.argmax(preds, axis=1) if args.output_mode == "classification" else np.squeeze(preds)
-        current_score = compute_metrics(args.task_name, preds, labels)[args.metric_name]
-        logger.info("Masking: current score: %f, remaning heads %d (%.1f percents)", current_score, new_head_mask.sum(), new_head_mask.sum()/new_head_mask.numel() * 100)
+        current_score = glue_compute_metrics(args.task_name, preds, labels)[args.metric_name]
+        logger.info(
+            "Masking: current score: %f, remaining heads %d (%.1f percents)",
+            current_score,
+            new_head_mask.sum(),
+            new_head_mask.sum() / new_head_mask.numel() * 100,
+        )
 
     logger.info("Final head mask")
     print_2d_tensor(head_mask)
-    np.save(os.path.join(args.output_dir, 'head_mask.npy'), head_mask.detach().cpu().numpy())
+    np.save(os.path.join(args.output_dir, "head_mask.npy"), head_mask.detach().cpu().numpy())
 
     return head_mask
 
@@ -186,86 +207,150 @@ def prune_heads(args, model, eval_dataloader, head_mask):
     # Try pruning and test time speedup
     # Pruning is like masking but we actually remove the masked weights
     before_time = datetime.now()
-    _, _, preds, labels = compute_heads_importance(args, model, eval_dataloader,
-                                                   compute_entropy=False, compute_importance=False, head_mask=head_mask)
+    _, _, preds, labels = compute_heads_importance(
+        args, model, eval_dataloader, compute_entropy=False, compute_importance=False, head_mask=head_mask
+    )
     preds = np.argmax(preds, axis=1) if args.output_mode == "classification" else np.squeeze(preds)
-    score_masking = compute_metrics(args.task_name, preds, labels)[args.metric_name]
+    score_masking = glue_compute_metrics(args.task_name, preds, labels)[args.metric_name]
     original_time = datetime.now() - before_time
 
     original_num_params = sum(p.numel() for p in model.parameters())
-    heads_to_prune = dict((layer, (1 - head_mask[layer].long()).nonzero().tolist()) for layer in range(len(head_mask)))
+    heads_to_prune = dict(
+        (layer, (1 - head_mask[layer].long()).nonzero().squeeze().tolist()) for layer in range(len(head_mask))
+    )
+
     assert sum(len(h) for h in heads_to_prune.values()) == (1 - head_mask.long()).sum().item()
     model.prune_heads(heads_to_prune)
     pruned_num_params = sum(p.numel() for p in model.parameters())
 
     before_time = datetime.now()
-    _, _, preds, labels = compute_heads_importance(args, model, eval_dataloader,
-                                                    compute_entropy=False, compute_importance=False, head_mask=None)
+    _, _, preds, labels = compute_heads_importance(
+        args,
+        model,
+        eval_dataloader,
+        compute_entropy=False,
+        compute_importance=False,
+        head_mask=None,
+        actually_pruned=True,
+    )
     preds = np.argmax(preds, axis=1) if args.output_mode == "classification" else np.squeeze(preds)
-    score_pruning = compute_metrics(args.task_name, preds, labels)[args.metric_name]
+    score_pruning = glue_compute_metrics(args.task_name, preds, labels)[args.metric_name]
     new_time = datetime.now() - before_time
 
-    logger.info("Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)", original_num_params, pruned_num_params, pruned_num_params/original_num_params * 100)
+    logger.info(
+        "Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)",
+        original_num_params,
+        pruned_num_params,
+        pruned_num_params / original_num_params * 100,
+    )
     logger.info("Pruning: score with masking: %f score with pruning: %f", score_masking, score_pruning)
-    logger.info("Pruning: speed ratio (new timing / original timing): %f percents", original_time/new_time * 100)
+    logger.info("Pruning: speed ratio (new timing / original timing): %f percents", original_time / new_time * 100)
 
 
 def main():
     parser = argparse.ArgumentParser()
-    ## Required parameters
-    parser.add_argument("--data_dir", default=None, type=str, required=True,
-                        help="The input data dir. Should contain the .tsv files (or other data files) for the task.")
-    parser.add_argument("--model_name_or_path", default=None, type=str, required=True,
-                        help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(
-                            ALL_MODELS))
-    parser.add_argument("--task_name", default=None, type=str, required=True,
-                        help="The name of the task to train selected in the list: " + ", ".join(processors.keys()))
-    parser.add_argument("--output_dir", default=None, type=str, required=True,
-                        help="The output directory where the model predictions and checkpoints will be written.")
+    # Required parameters
+    parser.add_argument(
+        "--data_dir",
+        default=None,
+        type=str,
+        required=True,
+        help="The input data dir. Should contain the .tsv files (or other data files) for the task.",
+    )
+    parser.add_argument(
+        "--model_name_or_path",
+        default=None,
+        type=str,
+        required=True,
+        help="Path to pretrained model or model identifier from huggingface.co/models",
+    )
+    parser.add_argument(
+        "--task_name",
+        default=None,
+        type=str,
+        required=True,
+        help="The name of the task to train selected in the list: " + ", ".join(glue_processors.keys()),
+    )
+    parser.add_argument(
+        "--output_dir",
+        default=None,
+        type=str,
+        required=True,
+        help="The output directory where the model predictions and checkpoints will be written.",
+    )
 
-    ## Other parameters
-    parser.add_argument("--config_name", default="", type=str,
-                        help="Pretrained config name or path if not the same as model_name_or_path")
-    parser.add_argument("--tokenizer_name", default="", type=str,
-                        help="Pretrained tokenizer name or path if not the same as model_name_or_path")
-    parser.add_argument("--cache_dir", default="", type=str,
-                        help="Where do you want to store the pre-trained models downloaded from s3")
-    parser.add_argument("--data_subset", type=int, default=-1,
-                        help="If > 0: limit the data to a subset of data_subset instances.")
-    parser.add_argument("--overwrite_output_dir", action='store_true',
-                        help="Whether to overwrite data in output directory")
-    parser.add_argument('--overwrite_cache', action='store_true',
-                        help="Overwrite the cached training and evaluation sets")
+    # Other parameters
+    parser.add_argument(
+        "--config_name",
+        default="",
+        type=str,
+        help="Pretrained config name or path if not the same as model_name_or_path",
+    )
+    parser.add_argument(
+        "--tokenizer_name",
+        default="",
+        type=str,
+        help="Pretrained tokenizer name or path if not the same as model_name_or_path",
+    )
+    parser.add_argument(
+        "--cache_dir",
+        default=None,
+        type=str,
+        help="Where do you want to store the pre-trained models downloaded from s3",
+    )
+    parser.add_argument(
+        "--data_subset", type=int, default=-1, help="If > 0: limit the data to a subset of data_subset instances."
+    )
+    parser.add_argument(
+        "--overwrite_output_dir", action="store_true", help="Whether to overwrite data in output directory"
+    )
+    parser.add_argument(
+        "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets"
+    )
 
-    parser.add_argument("--dont_normalize_importance_by_layer", action='store_true',
-                        help="Don't normalize importance score by layers")
-    parser.add_argument("--dont_normalize_global_importance", action='store_true',
-                        help="Don't normalize all importance scores between 0 and 1")
+    parser.add_argument(
+        "--dont_normalize_importance_by_layer", action="store_true", help="Don't normalize importance score by layers"
+    )
+    parser.add_argument(
+        "--dont_normalize_global_importance",
+        action="store_true",
+        help="Don't normalize all importance scores between 0 and 1",
+    )
 
-    parser.add_argument("--try_masking", action='store_true',
-                        help="Whether to try to mask head until a threshold of accuracy.")
-    parser.add_argument("--masking_threshold", default=0.9, type=float,
-                        help="masking threshold in term of metrics (stop masking when metric < threshold * original metric value).")
-    parser.add_argument("--masking_amount", default=0.1, type=float,
-                        help="Amount to heads to masking at each masking step.")
-    parser.add_argument("--metric_name", default="acc", type=str,
-                        help="Metric to use for head masking.")
+    parser.add_argument(
+        "--try_masking", action="store_true", help="Whether to try to mask head until a threshold of accuracy."
+    )
+    parser.add_argument(
+        "--masking_threshold",
+        default=0.9,
+        type=float,
+        help="masking threshold in term of metrics (stop masking when metric < threshold * original metric value).",
+    )
+    parser.add_argument(
+        "--masking_amount", default=0.1, type=float, help="Amount to heads to masking at each masking step."
+    )
+    parser.add_argument("--metric_name", default="acc", type=str, help="Metric to use for head masking.")
 
-    parser.add_argument("--max_seq_length", default=128, type=int,
-                        help="The maximum total input sequence length after WordPiece tokenization. \n"
-                             "Sequences longer than this will be truncated, sequences shorter padded.")
+    parser.add_argument(
+        "--max_seq_length",
+        default=128,
+        type=int,
+        help="The maximum total input sequence length after WordPiece tokenization. \n"
+        "Sequences longer than this will be truncated, sequences shorter padded.",
+    )
     parser.add_argument("--batch_size", default=1, type=int, help="Batch size.")
 
     parser.add_argument("--seed", type=int, default=42)
     parser.add_argument("--local_rank", type=int, default=-1, help="local_rank for distributed training on gpus")
-    parser.add_argument("--no_cuda", action='store_true', help="Whether not to use CUDA when available")
-    parser.add_argument('--server_ip', type=str, default='', help="Can be used for distant debugging.")
-    parser.add_argument('--server_port', type=str, default='', help="Can be used for distant debugging.")
+    parser.add_argument("--no_cuda", action="store_true", help="Whether not to use CUDA when available")
+    parser.add_argument("--server_ip", type=str, default="", help="Can be used for distant debugging.")
+    parser.add_argument("--server_port", type=str, default="", help="Can be used for distant debugging.")
     args = parser.parse_args()
 
     if args.server_ip and args.server_port:
         # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
         import ptvsd
+
         print("Waiting for debugger attach")
         ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
         ptvsd.wait_for_attach()
@@ -273,79 +358,78 @@ def main():
     # Setup devices and distributed training
     if args.local_rank == -1 or args.no_cuda:
         args.device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
-        args.n_gpu = torch.cuda.device_count()
+        args.n_gpu = 0 if args.no_cuda else torch.cuda.device_count()
     else:
         torch.cuda.set_device(args.local_rank)
         args.device = torch.device("cuda", args.local_rank)
         args.n_gpu = 1
-        torch.distributed.init_process_group(backend='nccl')  # Initializes the distributed backend
+        torch.distributed.init_process_group(backend="nccl")  # Initializes the distributed backend
 
     # Setup logging
-    logging.basicConfig(level = logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
+    logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
     logger.info("device: {} n_gpu: {}, distributed: {}".format(args.device, args.n_gpu, bool(args.local_rank != -1)))
 
     # Set seeds
-    set_seed(args)
+    set_seed(args.seed)
 
     # Prepare GLUE task
     args.task_name = args.task_name.lower()
-    if args.task_name not in processors:
+    if args.task_name not in glue_processors:
         raise ValueError("Task not found: %s" % (args.task_name))
-    processor = processors[args.task_name]()
-    args.output_mode = output_modes[args.task_name]
+    processor = glue_processors[args.task_name]()
+    args.output_mode = glue_output_modes[args.task_name]
     label_list = processor.get_labels()
     num_labels = len(label_list)
 
     # Load pretrained model and tokenizer
-    if args.local_rank not in [-1, 0]:
-        torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab
+    #
+    # Distributed training:
+    # The .from_pretrained methods guarantee that only one local process can concurrently
+    # download model & vocab.
 
-    args.model_type = ""
-    for key in MODEL_CLASSES:
-        if key in args.model_name_or_path.lower():
-            args.model_type = key  # take the first match in model types
-            break
-    config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
-    config = config_class.from_pretrained(args.config_name if args.config_name else args.model_name_or_path,
-                                          num_labels=num_labels,
-                                          finetuning_task=args.task_name,
-                                          output_attentions=True,
-                                          cache_dir=args.cache_dir if args.cache_dir else None)
-    tokenizer = tokenizer_class.from_pretrained(args.tokenizer_name if args.tokenizer_name else args.model_name_or_path,
-                                                cache_dir=args.cache_dir if args.cache_dir else None)
-    model = model_class.from_pretrained(args.model_name_or_path,
-                                        from_tf=bool('.ckpt' in args.model_name_or_path),
-                                        config=config,
-                                        cache_dir=args.cache_dir if args.cache_dir else None)
-
-    if args.local_rank == 0:
-        torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab
+    config = AutoConfig.from_pretrained(
+        args.config_name if args.config_name else args.model_name_or_path,
+        num_labels=num_labels,
+        finetuning_task=args.task_name,
+        output_attentions=True,
+        cache_dir=args.cache_dir,
+    )
+    tokenizer = AutoTokenizer.from_pretrained(
+        args.tokenizer_name if args.tokenizer_name else args.model_name_or_path, cache_dir=args.cache_dir,
+    )
+    model = AutoModelForSequenceClassification.from_pretrained(
+        args.model_name_or_path,
+        from_tf=bool(".ckpt" in args.model_name_or_path),
+        config=config,
+        cache_dir=args.cache_dir,
+    )
 
     # Distributed and parallel training
     model.to(args.device)
     if args.local_rank != -1:
-        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank],
-                                                          output_device=args.local_rank,
-                                                          find_unused_parameters=True)
+        model = torch.nn.parallel.DistributedDataParallel(
+            model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True
+        )
     elif args.n_gpu > 1:
         model = torch.nn.DataParallel(model)
 
     # Print/save training arguments
-    torch.save(args, os.path.join(args.output_dir, 'run_args.bin'))
+    os.makedirs(args.output_dir, exist_ok=True)
+    torch.save(args, os.path.join(args.output_dir, "run_args.bin"))
     logger.info("Training/evaluation parameters %s", args)
 
     # Prepare dataset for the GLUE task
-    eval_data = load_and_cache_examples(args, args.task_name, tokenizer, evaluate=True)
+    eval_dataset = GlueDataset(args, tokenizer=tokenizer, mode="dev")
     if args.data_subset > 0:
-        eval_data = Subset(eval_data, list(range(min(args.data_subset, len(eval_data)))))
-    eval_sampler = SequentialSampler(eval_data) if args.local_rank == -1 else DistributedSampler(eval_data)
-    eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.batch_size)
-
+        eval_dataset = Subset(eval_dataset, list(range(min(args.data_subset, len(eval_dataset)))))
+    eval_sampler = SequentialSampler(eval_dataset) if args.local_rank == -1 else DistributedSampler(eval_dataset)
+    eval_dataloader = DataLoader(
+        eval_dataset, sampler=eval_sampler, batch_size=args.batch_size, collate_fn=DefaultDataCollator().collate_batch
+    )
 
     # Compute head entropy and importance score
     compute_heads_importance(args, model, eval_dataloader)
 
-
     # Try head masking (set heads to zero until the score goes under a threshole)
     # and head pruning (remove masked heads and see the effect on the network)
     if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
@@ -353,5 +437,5 @@ def main():
         prune_heads(args, model, eval_dataloader, head_mask)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     main()

examples/contrib/mm-imdb/README.md

@@ -0,0 +1,23 @@
+## MM-IMDb
+
+Based on the script [`run_mmimdb.py`](https://github.com/huggingface/transformers/blob/master/examples/contrib/mm-imdb/run_mmimdb.py).
+
+[MM-IMDb](http://lisi1.unal.edu.co/mmimdb/) is a Multimodal dataset with around 26,000 movies including images, plots and other metadata.
+
+### Training on MM-IMDb
+
+```
+python run_mmimdb.py \
+    --data_dir /path/to/mmimdb/dataset/ \
+    --model_type bert \
+    --model_name_or_path bert-base-uncased \
+    --output_dir /path/to/save/dir/ \
+    --do_train \
+    --do_eval \
+    --max_seq_len 512 \
+    --gradient_accumulation_steps 20 \
+    --num_image_embeds 3 \
+    --num_train_epochs 100 \
+    --patience 5
+```
+

examples/contrib/mm-imdb/run_mmimdb.py

@@ -0,0 +1,570 @@
+# coding=utf-8
+# Copyright (c) Facebook, Inc. and its affiliates.
+# Copyright (c) HuggingFace Inc. team.
+#
+# 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.
+""" Finetuning the library models for multimodal multiclass prediction on MM-IMDB dataset."""
+
+
+import argparse
+import glob
+import json
+import logging
+import os
+import random
+
+import numpy as np
+import torch
+import torch.nn as nn
+from sklearn.metrics import f1_score
+from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
+from torch.utils.data.distributed import DistributedSampler
+from tqdm import tqdm, trange
+
+from transformers import (
+    WEIGHTS_NAME,
+    AdamW,
+    AutoConfig,
+    AutoModel,
+    AutoTokenizer,
+    MMBTConfig,
+    MMBTForClassification,
+    get_linear_schedule_with_warmup,
+)
+from utils_mmimdb import ImageEncoder, JsonlDataset, collate_fn, get_image_transforms, get_mmimdb_labels
+
+
+try:
+    from torch.utils.tensorboard import SummaryWriter
+except ImportError:
+    from tensorboardX import SummaryWriter
+
+
+logger = logging.getLogger(__name__)
+
+
+def set_seed(args):
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    if args.n_gpu > 0:
+        torch.cuda.manual_seed_all(args.seed)
+
+
+def train(args, train_dataset, model, tokenizer, criterion):
+    """ Train the model """
+    if args.local_rank in [-1, 0]:
+        tb_writer = SummaryWriter()
+
+    args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
+    train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset)
+    train_dataloader = DataLoader(
+        train_dataset,
+        sampler=train_sampler,
+        batch_size=args.train_batch_size,
+        collate_fn=collate_fn,
+        num_workers=args.num_workers,
+    )
+
+    if args.max_steps > 0:
+        t_total = args.max_steps
+        args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1
+    else:
+        t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
+
+    # Prepare optimizer and schedule (linear warmup and decay)
+    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, eps=args.adam_epsilon)
+    scheduler = get_linear_schedule_with_warmup(
+        optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total
+    )
+    if args.fp16:
+        try:
+            from apex import amp
+        except ImportError:
+            raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
+        model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)
+
+    # multi-gpu training (should be after apex fp16 initialization)
+    if args.n_gpu > 1:
+        model = torch.nn.DataParallel(model)
+
+    # Distributed training (should be after apex fp16 initialization)
+    if args.local_rank != -1:
+        model = torch.nn.parallel.DistributedDataParallel(
+            model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True
+        )
+
+    # Train!
+    logger.info("***** Running training *****")
+    logger.info("  Num examples = %d", len(train_dataset))
+    logger.info("  Num Epochs = %d", args.num_train_epochs)
+    logger.info("  Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size)
+    logger.info(
+        "  Total train batch size (w. parallel, distributed & accumulation) = %d",
+        args.train_batch_size
+        * args.gradient_accumulation_steps
+        * (torch.distributed.get_world_size() if args.local_rank != -1 else 1),
+    )
+    logger.info("  Gradient Accumulation steps = %d", args.gradient_accumulation_steps)
+    logger.info("  Total optimization steps = %d", t_total)
+
+    global_step = 0
+    tr_loss, logging_loss = 0.0, 0.0
+    best_f1, n_no_improve = 0, 0
+    model.zero_grad()
+    train_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0])
+    set_seed(args)  # Added here for reproductibility
+    for _ in train_iterator:
+        epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])
+        for step, batch in enumerate(epoch_iterator):
+            model.train()
+            batch = tuple(t.to(args.device) for t in batch)
+            labels = batch[5]
+            inputs = {
+                "input_ids": batch[0],
+                "input_modal": batch[2],
+                "attention_mask": batch[1],
+                "modal_start_tokens": batch[3],
+                "modal_end_tokens": batch[4],
+            }
+            outputs = model(**inputs)
+            logits = outputs[0]  # model outputs are always tuple in transformers (see doc)
+            loss = criterion(logits, labels)
+
+            if args.n_gpu > 1:
+                loss = loss.mean()  # mean() to average on multi-gpu parallel training
+            if args.gradient_accumulation_steps > 1:
+                loss = loss / args.gradient_accumulation_steps
+
+            if args.fp16:
+                with amp.scale_loss(loss, optimizer) as scaled_loss:
+                    scaled_loss.backward()
+            else:
+                loss.backward()
+
+            tr_loss += loss.item()
+            if (step + 1) % args.gradient_accumulation_steps == 0:
+                if args.fp16:
+                    torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
+                else:
+                    torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
+
+                optimizer.step()
+                scheduler.step()  # Update learning rate schedule
+                model.zero_grad()
+                global_step += 1
+
+                if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
+                    logs = {}
+                    if (
+                        args.local_rank == -1 and args.evaluate_during_training
+                    ):  # Only evaluate when single GPU otherwise metrics may not average well
+                        results = evaluate(args, model, tokenizer, criterion)
+                        for key, value in results.items():
+                            eval_key = "eval_{}".format(key)
+                            logs[eval_key] = value
+
+                    loss_scalar = (tr_loss - logging_loss) / args.logging_steps
+                    learning_rate_scalar = scheduler.get_lr()[0]
+                    logs["learning_rate"] = learning_rate_scalar
+                    logs["loss"] = loss_scalar
+                    logging_loss = tr_loss
+
+                    for key, value in logs.items():
+                        tb_writer.add_scalar(key, value, global_step)
+                    print(json.dumps({**logs, **{"step": global_step}}))
+
+                if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
+                    # Save model checkpoint
+                    output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(global_step))
+                    if not os.path.exists(output_dir):
+                        os.makedirs(output_dir)
+                    model_to_save = (
+                        model.module if hasattr(model, "module") else model
+                    )  # Take care of distributed/parallel training
+                    torch.save(model_to_save.state_dict(), os.path.join(output_dir, WEIGHTS_NAME))
+                    torch.save(args, os.path.join(output_dir, "training_args.bin"))
+                    logger.info("Saving model checkpoint to %s", output_dir)
+
+            if args.max_steps > 0 and global_step > args.max_steps:
+                epoch_iterator.close()
+                break
+        if args.max_steps > 0 and global_step > args.max_steps:
+            train_iterator.close()
+            break
+
+        if args.local_rank == -1:
+            results = evaluate(args, model, tokenizer, criterion)
+            if results["micro_f1"] > best_f1:
+                best_f1 = results["micro_f1"]
+                n_no_improve = 0
+            else:
+                n_no_improve += 1
+
+            if n_no_improve > args.patience:
+                train_iterator.close()
+                break
+
+    if args.local_rank in [-1, 0]:
+        tb_writer.close()
+
+    return global_step, tr_loss / global_step
+
+
+def evaluate(args, model, tokenizer, criterion, prefix=""):
+    # Loop to handle MNLI double evaluation (matched, mis-matched)
+    eval_output_dir = args.output_dir
+    eval_dataset = load_examples(args, tokenizer, evaluate=True)
+
+    if not os.path.exists(eval_output_dir) and args.local_rank in [-1, 0]:
+        os.makedirs(eval_output_dir)
+
+    args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
+    # Note that DistributedSampler samples randomly
+    eval_sampler = SequentialSampler(eval_dataset)
+    eval_dataloader = DataLoader(
+        eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size, collate_fn=collate_fn
+    )
+
+    # multi-gpu eval
+    if args.n_gpu > 1 and not isinstance(model, torch.nn.DataParallel):
+        model = torch.nn.DataParallel(model)
+
+    # Eval!
+    logger.info("***** Running evaluation {} *****".format(prefix))
+    logger.info("  Num examples = %d", len(eval_dataset))
+    logger.info("  Batch size = %d", args.eval_batch_size)
+    eval_loss = 0.0
+    nb_eval_steps = 0
+    preds = None
+    out_label_ids = None
+    for batch in tqdm(eval_dataloader, desc="Evaluating"):
+        model.eval()
+        batch = tuple(t.to(args.device) for t in batch)
+
+        with torch.no_grad():
+            batch = tuple(t.to(args.device) for t in batch)
+            labels = batch[5]
+            inputs = {
+                "input_ids": batch[0],
+                "input_modal": batch[2],
+                "attention_mask": batch[1],
+                "modal_start_tokens": batch[3],
+                "modal_end_tokens": batch[4],
+            }
+            outputs = model(**inputs)
+            logits = outputs[0]  # model outputs are always tuple in transformers (see doc)
+            tmp_eval_loss = criterion(logits, labels)
+            eval_loss += tmp_eval_loss.mean().item()
+        nb_eval_steps += 1
+        if preds is None:
+            preds = torch.sigmoid(logits).detach().cpu().numpy() > 0.5
+            out_label_ids = labels.detach().cpu().numpy()
+        else:
+            preds = np.append(preds, torch.sigmoid(logits).detach().cpu().numpy() > 0.5, axis=0)
+            out_label_ids = np.append(out_label_ids, labels.detach().cpu().numpy(), axis=0)
+
+    eval_loss = eval_loss / nb_eval_steps
+    result = {
+        "loss": eval_loss,
+        "macro_f1": f1_score(out_label_ids, preds, average="macro"),
+        "micro_f1": f1_score(out_label_ids, preds, average="micro"),
+    }
+
+    output_eval_file = os.path.join(eval_output_dir, prefix, "eval_results.txt")
+    with open(output_eval_file, "w") as writer:
+        logger.info("***** Eval results {} *****".format(prefix))
+        for key in sorted(result.keys()):
+            logger.info("  %s = %s", key, str(result[key]))
+            writer.write("%s = %s\n" % (key, str(result[key])))
+
+    return result
+
+
+def load_examples(args, tokenizer, evaluate=False):
+    path = os.path.join(args.data_dir, "dev.jsonl" if evaluate else "train.jsonl")
+    transforms = get_image_transforms()
+    labels = get_mmimdb_labels()
+    dataset = JsonlDataset(path, tokenizer, transforms, labels, args.max_seq_length - args.num_image_embeds - 2)
+    return dataset
+
+
+def main():
+    parser = argparse.ArgumentParser()
+
+    # Required parameters
+    parser.add_argument(
+        "--data_dir",
+        default=None,
+        type=str,
+        required=True,
+        help="The input data dir. Should contain the .jsonl files for MMIMDB.",
+    )
+    parser.add_argument(
+        "--model_name_or_path",
+        default=None,
+        type=str,
+        required=True,
+        help="Path to pretrained model or model identifier from huggingface.co/models",
+    )
+    parser.add_argument(
+        "--output_dir",
+        default=None,
+        type=str,
+        required=True,
+        help="The output directory where the model predictions and checkpoints will be written.",
+    )
+
+    # Other parameters
+    parser.add_argument(
+        "--config_name", default="", type=str, help="Pretrained config name or path if not the same as model_name"
+    )
+    parser.add_argument(
+        "--tokenizer_name",
+        default="",
+        type=str,
+        help="Pretrained tokenizer name or path if not the same as model_name",
+    )
+    parser.add_argument(
+        "--cache_dir",
+        default=None,
+        type=str,
+        help="Where do you want to store the pre-trained models downloaded from s3",
+    )
+    parser.add_argument(
+        "--max_seq_length",
+        default=128,
+        type=int,
+        help="The maximum total input sequence length after tokenization. Sequences longer "
+        "than this will be truncated, sequences shorter will be padded.",
+    )
+    parser.add_argument(
+        "--num_image_embeds", default=1, type=int, help="Number of Image Embeddings from the Image Encoder"
+    )
+    parser.add_argument("--do_train", action="store_true", help="Whether to run training.")
+    parser.add_argument("--do_eval", action="store_true", help="Whether to run eval on the dev set.")
+    parser.add_argument(
+        "--evaluate_during_training", action="store_true", help="Rul evaluation during training at each logging step."
+    )
+    parser.add_argument(
+        "--do_lower_case", action="store_true", help="Set this flag if you are using an uncased model."
+    )
+
+    parser.add_argument("--per_gpu_train_batch_size", default=8, type=int, help="Batch size per GPU/CPU for training.")
+    parser.add_argument(
+        "--per_gpu_eval_batch_size", default=8, type=int, help="Batch size per GPU/CPU for evaluation."
+    )
+    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("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.")
+    parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight deay if we apply some.")
+    parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.")
+    parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
+    parser.add_argument(
+        "--num_train_epochs", default=3.0, type=float, help="Total number of training epochs to perform."
+    )
+    parser.add_argument("--patience", default=5, type=int, help="Patience for Early Stopping.")
+    parser.add_argument(
+        "--max_steps",
+        default=-1,
+        type=int,
+        help="If > 0: set total number of training steps to perform. Override num_train_epochs.",
+    )
+    parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.")
+
+    parser.add_argument("--logging_steps", type=int, default=50, help="Log every X updates steps.")
+    parser.add_argument("--save_steps", type=int, default=50, help="Save checkpoint every X updates steps.")
+    parser.add_argument(
+        "--eval_all_checkpoints",
+        action="store_true",
+        help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number",
+    )
+    parser.add_argument("--no_cuda", action="store_true", help="Avoid using CUDA when available")
+    parser.add_argument("--num_workers", type=int, default=8, help="number of worker threads for dataloading")
+    parser.add_argument(
+        "--overwrite_output_dir", action="store_true", help="Overwrite the content of the output directory"
+    )
+    parser.add_argument(
+        "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets"
+    )
+    parser.add_argument("--seed", type=int, default=42, help="random seed for initialization")
+
+    parser.add_argument(
+        "--fp16",
+        action="store_true",
+        help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit",
+    )
+    parser.add_argument(
+        "--fp16_opt_level",
+        type=str,
+        default="O1",
+        help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
+        "See details at https://nvidia.github.io/apex/amp.html",
+    )
+    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
+    parser.add_argument("--server_ip", type=str, default="", help="For distant debugging.")
+    parser.add_argument("--server_port", type=str, default="", help="For distant debugging.")
+    args = parser.parse_args()
+
+    if (
+        os.path.exists(args.output_dir)
+        and os.listdir(args.output_dir)
+        and args.do_train
+        and not args.overwrite_output_dir
+    ):
+        raise ValueError(
+            "Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format(
+                args.output_dir
+            )
+        )
+
+    # Setup distant debugging if needed
+    if args.server_ip and args.server_port:
+        # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
+        import ptvsd
+
+        print("Waiting for debugger attach")
+        ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
+        ptvsd.wait_for_attach()
+
+    # Setup CUDA, GPU & distributed training
+    if args.local_rank == -1 or args.no_cuda:
+        device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
+        args.n_gpu = 0 if args.no_cuda else torch.cuda.device_count()
+    else:  # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
+        torch.cuda.set_device(args.local_rank)
+        device = torch.device("cuda", args.local_rank)
+        torch.distributed.init_process_group(backend="nccl")
+        args.n_gpu = 1
+
+    args.device = device
+
+    # Setup logging
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN,
+    )
+    logger.warning(
+        "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
+        args.local_rank,
+        device,
+        args.n_gpu,
+        bool(args.local_rank != -1),
+        args.fp16,
+    )
+
+    # Set seed
+    set_seed(args)
+
+    # Load pretrained model and tokenizer
+    if args.local_rank not in [-1, 0]:
+        torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab
+
+    # Setup model
+    labels = get_mmimdb_labels()
+    num_labels = len(labels)
+    transformer_config = AutoConfig.from_pretrained(args.config_name if args.config_name else args.model_name_or_path)
+    tokenizer = AutoTokenizer.from_pretrained(
+        args.tokenizer_name if args.tokenizer_name else args.model_name_or_path,
+        do_lower_case=args.do_lower_case,
+        cache_dir=args.cache_dir,
+    )
+    transformer = AutoModel.from_pretrained(
+        args.model_name_or_path, config=transformer_config, cache_dir=args.cache_dir
+    )
+    img_encoder = ImageEncoder(args)
+    config = MMBTConfig(transformer_config, num_labels=num_labels)
+    model = MMBTForClassification(config, transformer, img_encoder)
+
+    if args.local_rank == 0:
+        torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab
+
+    model.to(args.device)
+
+    logger.info("Training/evaluation parameters %s", args)
+
+    # Training
+    if args.do_train:
+        train_dataset = load_examples(args, tokenizer, evaluate=False)
+        label_frequences = train_dataset.get_label_frequencies()
+        label_frequences = [label_frequences[l] for l in labels]
+        label_weights = (
+            torch.tensor(label_frequences, device=args.device, dtype=torch.float) / len(train_dataset)
+        ) ** -1
+        criterion = nn.BCEWithLogitsLoss(pos_weight=label_weights)
+        global_step, tr_loss = train(args, train_dataset, model, tokenizer, criterion)
+        logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)
+
+    # Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
+    if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
+        # Create output directory if needed
+        if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
+            os.makedirs(args.output_dir)
+
+        logger.info("Saving model checkpoint to %s", args.output_dir)
+        # Save a trained model, configuration and tokenizer using `save_pretrained()`.
+        # They can then be reloaded using `from_pretrained()`
+        model_to_save = (
+            model.module if hasattr(model, "module") else model
+        )  # Take care of distributed/parallel training
+        torch.save(model_to_save.state_dict(), os.path.join(args.output_dir, WEIGHTS_NAME))
+        tokenizer.save_pretrained(args.output_dir)
+
+        # Good practice: save your training arguments together with the trained model
+        torch.save(args, os.path.join(args.output_dir, "training_args.bin"))
+
+        # Load a trained model and vocabulary that you have fine-tuned
+        model = MMBTForClassification(config, transformer, img_encoder)
+        model.load_state_dict(torch.load(os.path.join(args.output_dir, WEIGHTS_NAME)))
+        tokenizer = AutoTokenizer.from_pretrained(args.output_dir)
+        model.to(args.device)
+
+    # Evaluation
+    results = {}
+    if args.do_eval and args.local_rank in [-1, 0]:
+        checkpoints = [args.output_dir]
+        if args.eval_all_checkpoints:
+            checkpoints = list(
+                os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + "/**/" + WEIGHTS_NAME, recursive=True))
+            )
+            logging.getLogger("transformers.modeling_utils").setLevel(logging.WARN)  # Reduce logging
+        logger.info("Evaluate the following checkpoints: %s", checkpoints)
+        for checkpoint in checkpoints:
+            global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else ""
+            prefix = checkpoint.split("/")[-1] if checkpoint.find("checkpoint") != -1 else ""
+            model = MMBTForClassification(config, transformer, img_encoder)
+            model.load_state_dict(torch.load(checkpoint))
+            model.to(args.device)
+            result = evaluate(args, model, tokenizer, criterion, prefix=prefix)
+            result = dict((k + "_{}".format(global_step), v) for k, v in result.items())
+            results.update(result)
+
+    return results
+
+
+if __name__ == "__main__":
+    main()

examples/contrib/mm-imdb/utils_mmimdb.py

@@ -0,0 +1,143 @@
+# coding=utf-8
+# Copyright (c) Facebook, Inc. and its affiliates.
+# Copyright (c) HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import json
+import os
+from collections import Counter
+
+import torch
+import torch.nn as nn
+import torchvision
+import torchvision.transforms as transforms
+from PIL import Image
+from torch.utils.data import Dataset
+
+
+POOLING_BREAKDOWN = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}
+
+
+class ImageEncoder(nn.Module):
+    def __init__(self, args):
+        super().__init__()
+        model = torchvision.models.resnet152(pretrained=True)
+        modules = list(model.children())[:-2]
+        self.model = nn.Sequential(*modules)
+        self.pool = nn.AdaptiveAvgPool2d(POOLING_BREAKDOWN[args.num_image_embeds])
+
+    def forward(self, x):
+        # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
+        out = self.pool(self.model(x))
+        out = torch.flatten(out, start_dim=2)
+        out = out.transpose(1, 2).contiguous()
+        return out  # BxNx2048
+
+
+class JsonlDataset(Dataset):
+    def __init__(self, data_path, tokenizer, transforms, labels, max_seq_length):
+        self.data = [json.loads(l) for l in open(data_path)]
+        self.data_dir = os.path.dirname(data_path)
+        self.tokenizer = tokenizer
+        self.labels = labels
+        self.n_classes = len(labels)
+        self.max_seq_length = max_seq_length
+
+        self.transforms = transforms
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, index):
+        sentence = torch.LongTensor(self.tokenizer.encode(self.data[index]["text"], add_special_tokens=True))
+        start_token, sentence, end_token = sentence[0], sentence[1:-1], sentence[-1]
+        sentence = sentence[: self.max_seq_length]
+
+        label = torch.zeros(self.n_classes)
+        label[[self.labels.index(tgt) for tgt in self.data[index]["label"]]] = 1
+
+        image = Image.open(os.path.join(self.data_dir, self.data[index]["img"])).convert("RGB")
+        image = self.transforms(image)
+
+        return {
+            "image_start_token": start_token,
+            "image_end_token": end_token,
+            "sentence": sentence,
+            "image": image,
+            "label": label,
+        }
+
+    def get_label_frequencies(self):
+        label_freqs = Counter()
+        for row in self.data:
+            label_freqs.update(row["label"])
+        return label_freqs
+
+
+def collate_fn(batch):
+    lens = [len(row["sentence"]) for row in batch]
+    bsz, max_seq_len = len(batch), max(lens)
+
+    mask_tensor = torch.zeros(bsz, max_seq_len, dtype=torch.long)
+    text_tensor = torch.zeros(bsz, max_seq_len, dtype=torch.long)
+
+    for i_batch, (input_row, length) in enumerate(zip(batch, lens)):
+        text_tensor[i_batch, :length] = input_row["sentence"]
+        mask_tensor[i_batch, :length] = 1
+
+    img_tensor = torch.stack([row["image"] for row in batch])
+    tgt_tensor = torch.stack([row["label"] for row in batch])
+    img_start_token = torch.stack([row["image_start_token"] for row in batch])
+    img_end_token = torch.stack([row["image_end_token"] for row in batch])
+
+    return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor
+
+
+def get_mmimdb_labels():
+    return [
+        "Crime",
+        "Drama",
+        "Thriller",
+        "Action",
+        "Comedy",
+        "Romance",
+        "Documentary",
+        "Short",
+        "Mystery",
+        "History",
+        "Family",
+        "Adventure",
+        "Fantasy",
+        "Sci-Fi",
+        "Western",
+        "Horror",
+        "Sport",
+        "War",
+        "Music",
+        "Musical",
+        "Animation",
+        "Biography",
+        "Film-Noir",
+    ]
+
+
+def get_image_transforms():
+    return transforms.Compose(
+        [
+            transforms.Resize(256),
+            transforms.CenterCrop(224),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.46777044, 0.44531429, 0.40661017], std=[0.12221994, 0.12145835, 0.14380469],),
+        ]
+    )

examples/contrib/run_camembert.py

@@ -1,47 +1,42 @@
-from pathlib import Path
-import tarfile
-import urllib.request
-
 import torch
 
-from transformers.tokenization_camembert import CamembertTokenizer
 from transformers.modeling_camembert import CamembertForMaskedLM
+from transformers.tokenization_camembert import CamembertTokenizer
 
 
 def fill_mask(masked_input, model, tokenizer, topk=5):
     # Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
-    assert masked_input.count('<mask>') == 1
+    assert masked_input.count("<mask>") == 1
     input_ids = torch.tensor(tokenizer.encode(masked_input, add_special_tokens=True)).unsqueeze(0)  # Batch size 1
     logits = model(input_ids)[0]  # The last hidden-state is the first element of the output tuple
     masked_index = (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
     logits = logits[0, masked_index, :]
     prob = logits.softmax(dim=0)
     values, indices = prob.topk(k=topk, dim=0)
-    topk_predicted_token_bpe = ' '.join([tokenizer.convert_ids_to_tokens(indices[i].item())
-                                         for i in range(len(indices))])
+    topk_predicted_token_bpe = " ".join(
+        [tokenizer.convert_ids_to_tokens(indices[i].item()) for i in range(len(indices))]
+    )
     masked_token = tokenizer.mask_token
     topk_filled_outputs = []
-    for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(' ')):
-        predicted_token = predicted_token_bpe.replace('\u2581', ' ')
+    for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(" ")):
+        predicted_token = predicted_token_bpe.replace("\u2581", " ")
         if " {0}".format(masked_token) in masked_input:
-            topk_filled_outputs.append((
-                masked_input.replace(
-                    ' {0}'.format(masked_token), predicted_token
-                ),
-                values[index].item(),
-                predicted_token,
-            ))
+            topk_filled_outputs.append(
+                (
+                    masked_input.replace(" {0}".format(masked_token), predicted_token),
+                    values[index].item(),
+                    predicted_token,
+                )
+            )
         else:
-            topk_filled_outputs.append((
-                masked_input.replace(masked_token, predicted_token),
-                values[index].item(),
-                predicted_token,
-            ))
+            topk_filled_outputs.append(
+                (masked_input.replace(masked_token, predicted_token), values[index].item(), predicted_token,)
+            )
     return topk_filled_outputs
 
 
-tokenizer = CamembertTokenizer.from_pretrained('camembert-base')
-model = CamembertForMaskedLM.from_pretrained('camembert-base')
+tokenizer = CamembertTokenizer.from_pretrained("camembert-base")
+model = CamembertForMaskedLM.from_pretrained("camembert-base")
 model.eval()
 
 masked_input = "Le camembert est <mask> :)"

examples/contrib/run_openai_gpt.py

@@ -22,48 +22,54 @@
           --model_name openai-gpt \
           --do_train \
           --do_eval \
-          --train_dataset $ROC_STORIES_DIR/cloze_test_val__spring2016\ -\ cloze_test_ALL_val.csv \
-          --eval_dataset $ROC_STORIES_DIR/cloze_test_test__spring2016\ -\ cloze_test_ALL_test.csv \
+          --train_dataset "$ROC_STORIES_DIR/cloze_test_val__spring2016 - cloze_test_ALL_val.csv" \
+          --eval_dataset "$ROC_STORIES_DIR/cloze_test_test__spring2016 - cloze_test_ALL_test.csv" \
           --output_dir ../log \
           --train_batch_size 16 \
 """
 import argparse
-import os
 import csv
-import random
 import logging
-from tqdm import tqdm, trange
+import os
+import random
 
 import numpy as np
 import torch
-from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
-                              TensorDataset)
+from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
+from tqdm import tqdm, trange
 
-from transformers import (OpenAIGPTDoubleHeadsModel, OpenAIGPTTokenizer,
-                                     AdamW, cached_path, WEIGHTS_NAME, CONFIG_NAME,
-                                     get_linear_schedule_with_warmup)
+from transformers import (
+    CONFIG_NAME,
+    WEIGHTS_NAME,
+    AdamW,
+    OpenAIGPTDoubleHeadsModel,
+    OpenAIGPTTokenizer,
+    get_linear_schedule_with_warmup,
+)
 
-ROCSTORIES_URL = "https://s3.amazonaws.com/datasets.huggingface.co/ROCStories.tar.gz"
 
-logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s',
-                    datefmt = '%m/%d/%Y %H:%M:%S',
-                    level = logging.INFO)
+logging.basicConfig(
+    format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
+)
 logger = logging.getLogger(__name__)
 
+
 def accuracy(out, labels):
     outputs = np.argmax(out, axis=1)
     return np.sum(outputs == labels)
 
+
 def load_rocstories_dataset(dataset_path):
     """ Output a list of tuples(story, 1st continuation, 2nd continuation, label) """
-    with open(dataset_path, encoding='utf_8') as f:
+    with open(dataset_path, encoding="utf_8") as f:
         f = csv.reader(f)
         output = []
-        next(f) # skip the first line
+        next(f)  # skip the first line
         for line in tqdm(f):
-            output.append((' '.join(line[1:5]), line[5], line[6], int(line[-1])-1))
+            output.append((" ".join(line[1:5]), line[5], line[6], int(line[-1]) - 1))
     return output
 
+
 def pre_process_datasets(encoded_datasets, input_len, cap_length, start_token, delimiter_token, clf_token):
     """ Pre-process datasets containing lists of tuples(story, 1st continuation, 2nd continuation, label)
 
@@ -75,61 +81,73 @@ def pre_process_datasets(encoded_datasets, input_len, cap_length, start_token, d
         n_batch = len(dataset)
         input_ids = np.zeros((n_batch, 2, input_len), dtype=np.int64)
         mc_token_ids = np.zeros((n_batch, 2), dtype=np.int64)
-        lm_labels = np.full((n_batch, 2, input_len), fill_value=-1, dtype=np.int64)
+        lm_labels = np.full((n_batch, 2, input_len), fill_value=-100, dtype=np.int64)
         mc_labels = np.zeros((n_batch,), dtype=np.int64)
         for i, (story, cont1, cont2, mc_label), in enumerate(dataset):
             with_cont1 = [start_token] + story[:cap_length] + [delimiter_token] + cont1[:cap_length] + [clf_token]
             with_cont2 = [start_token] + story[:cap_length] + [delimiter_token] + cont2[:cap_length] + [clf_token]
-            input_ids[i, 0, :len(with_cont1)] = with_cont1
-            input_ids[i, 1, :len(with_cont2)] = with_cont2
+            input_ids[i, 0, : len(with_cont1)] = with_cont1
+            input_ids[i, 1, : len(with_cont2)] = with_cont2
             mc_token_ids[i, 0] = len(with_cont1) - 1
             mc_token_ids[i, 1] = len(with_cont2) - 1
-            lm_labels[i, 0, :len(with_cont1)] = with_cont1
-            lm_labels[i, 1, :len(with_cont2)] = with_cont2
+            lm_labels[i, 0, : len(with_cont1)] = with_cont1
+            lm_labels[i, 1, : len(with_cont2)] = with_cont2
             mc_labels[i] = mc_label
         all_inputs = (input_ids, mc_token_ids, lm_labels, mc_labels)
         tensor_datasets.append(tuple(torch.tensor(t) for t in all_inputs))
     return tensor_datasets
 
+
 def main():
     parser = argparse.ArgumentParser()
-    parser.add_argument('--model_name', type=str, default='openai-gpt',
-                        help='pretrained model name')
-    parser.add_argument("--do_train", action='store_true', help="Whether to run training.")
-    parser.add_argument("--do_eval", action='store_true', help="Whether to run eval on the dev set.")
-    parser.add_argument("--output_dir", default=None, type=str, required=True,
-                        help="The output directory where the model predictions and checkpoints will be written.")
-    parser.add_argument('--train_dataset', type=str, default='')
-    parser.add_argument('--eval_dataset', type=str, default='')
-    parser.add_argument('--seed', type=int, default=42)
-    parser.add_argument('--num_train_epochs', type=int, default=3)
-    parser.add_argument('--train_batch_size', type=int, default=8)
-    parser.add_argument('--eval_batch_size', type=int, default=16)
-    parser.add_argument("--adam_epsilon", default=1e-8, type=float,
-                        help="Epsilon for Adam optimizer.")
-    parser.add_argument('--max_grad_norm', type=int, default=1)
-    parser.add_argument("--max_steps", default=-1, type=int,
-                        help="If > 0: set total number of training \
-                        steps to perform. Override 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('--learning_rate', type=float, default=6.25e-5)
-    parser.add_argument("--warmup_steps", default=0, type=int,
-                        help="Linear warmup over warmup_steps.")
-    parser.add_argument('--lr_schedule', type=str, default='warmup_linear')
-    parser.add_argument('--weight_decay', type=float, default=0.01)
-    parser.add_argument('--lm_coef', type=float, default=0.9)
-    parser.add_argument('--n_valid', type=int, default=374)
+    parser.add_argument("--model_name", type=str, default="openai-gpt", help="pretrained model name")
+    parser.add_argument("--do_train", action="store_true", help="Whether to run training.")
+    parser.add_argument("--do_eval", action="store_true", help="Whether to run eval on the dev set.")
+    parser.add_argument(
+        "--output_dir",
+        default=None,
+        type=str,
+        required=True,
+        help="The output directory where the model predictions and checkpoints will be written.",
+    )
+    parser.add_argument("--train_dataset", type=str, default="")
+    parser.add_argument("--eval_dataset", type=str, default="")
+    parser.add_argument("--seed", type=int, default=42)
+    parser.add_argument("--num_train_epochs", type=int, default=3)
+    parser.add_argument("--train_batch_size", type=int, default=8)
+    parser.add_argument("--eval_batch_size", type=int, default=16)
+    parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.")
+    parser.add_argument("--max_grad_norm", type=int, default=1)
+    parser.add_argument(
+        "--max_steps",
+        default=-1,
+        type=int,
+        help="If > 0: set total number of training \
+                        steps to perform. Override 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("--learning_rate", type=float, default=6.25e-5)
+    parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.")
+    parser.add_argument("--lr_schedule", type=str, default="warmup_linear")
+    parser.add_argument("--weight_decay", type=float, default=0.01)
+    parser.add_argument("--lm_coef", type=float, default=0.9)
+    parser.add_argument("--n_valid", type=int, default=374)
 
-    parser.add_argument('--server_ip', type=str, default='', help="Can be used for distant debugging.")
-    parser.add_argument('--server_port', type=str, default='', help="Can be used for distant debugging.")
+    parser.add_argument("--server_ip", type=str, default="", help="Can be used for distant debugging.")
+    parser.add_argument("--server_port", type=str, default="", help="Can be used for distant debugging.")
     args = parser.parse_args()
     print(args)
 
     if args.server_ip and args.server_port:
         # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
         import ptvsd
+
         print("Waiting for debugger attach")
         ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
         ptvsd.wait_for_attach()
@@ -152,7 +170,7 @@ def main():
     # Load tokenizer and model
     # This loading functions also add new tokens and embeddings called `special tokens`
     # These new embeddings will be fine-tuned on the RocStories dataset
-    special_tokens = ['_start_', '_delimiter_', '_classify_']
+    special_tokens = ["_start_", "_delimiter_", "_classify_"]
     tokenizer = OpenAIGPTTokenizer.from_pretrained(args.model_name)
     tokenizer.add_tokens(special_tokens)
     special_tokens_ids = tokenizer.convert_tokens_to_ids(special_tokens)
@@ -161,8 +179,6 @@ def main():
     model.to(device)
 
     # Load and encode the datasets
-    if not args.train_dataset and not args.eval_dataset:
-        roc_stories = cached_path(ROCSTORIES_URL)
     def tokenize_and_encode(obj):
         """ Tokenize and encode a nested object """
         if isinstance(obj, str):
@@ -170,6 +186,7 @@ def main():
         elif isinstance(obj, int):
             return obj
         return list(tokenize_and_encode(o) for o in obj)
+
     logger.info("Encoding dataset...")
     train_dataset = load_rocstories_dataset(args.train_dataset)
     eval_dataset = load_rocstories_dataset(args.eval_dataset)
@@ -178,8 +195,11 @@ def main():
 
     # Compute the max input length for the Transformer
     max_length = model.config.n_positions // 2 - 2
-    input_length = max(len(story[:max_length]) + max(len(cont1[:max_length]), len(cont2[:max_length])) + 3  \
-                           for dataset in encoded_datasets for story, cont1, cont2, _ in dataset)
+    input_length = max(
+        len(story[:max_length]) + max(len(cont1[:max_length]), len(cont2[:max_length])) + 3
+        for dataset in encoded_datasets
+        for story, cont1, cont2, _ in dataset
+    )
     input_length = min(input_length, model.config.n_positions)  # Max size of input for the pre-trained model
 
     # Prepare inputs tensors and dataloaders
@@ -198,20 +218,23 @@ def main():
     if args.do_train:
         if args.max_steps > 0:
             t_total = args.max_steps
-            args.num_train_epochs = args.max_steps //\
-                (len(train_dataloader) // args.gradient_accumulation_steps) + 1
+            args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1
         else:
-            t_total = len(train_dataloader)\
-                // args.gradient_accumulation_steps * args.num_train_epochs
+            t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
 
         param_optimizer = list(model.named_parameters())
-        no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
+        no_decay = ["bias", "LayerNorm.bias", "LayerNorm.weight"]
         optimizer_grouped_parameters = [
-            {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': args.weight_decay},
-            {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
-            ]
+            {
+                "params": [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
+                "weight_decay": args.weight_decay,
+            },
+            {"params": [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], "weight_decay": 0.0},
+        ]
         optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
-        scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total)
+        scheduler = get_linear_schedule_with_warmup(
+            optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total
+        )
 
     if args.do_train:
         nb_tr_steps, tr_loss, exp_average_loss = 0, 0, None
@@ -226,18 +249,20 @@ def main():
                 losses = model(input_ids, mc_token_ids=mc_token_ids, lm_labels=lm_labels, mc_labels=mc_labels)
                 loss = args.lm_coef * losses[0] + losses[1]
                 loss.backward()
-                scheduler.step()
                 optimizer.step()
+                scheduler.step()
                 optimizer.zero_grad()
                 tr_loss += loss.item()
-                exp_average_loss = loss.item() if exp_average_loss is None else 0.7*exp_average_loss+0.3*loss.item()
+                exp_average_loss = (
+                    loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
+                )
                 nb_tr_steps += 1
                 tqdm_bar.desc = "Training loss: {:.2e} lr: {:.2e}".format(exp_average_loss, scheduler.get_lr()[0])
 
     # Save a trained model
     if args.do_train:
         # Save a trained model, configuration and tokenizer
-        model_to_save = model.module if hasattr(model, 'module') else model  # Only save the model itself
+        model_to_save = model.module if hasattr(model, "module") else model  # Only save the model itself
 
         # If we save using the predefined names, we can load using `from_pretrained`
         output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME)
@@ -260,10 +285,12 @@ def main():
             batch = tuple(t.to(device) for t in batch)
             input_ids, mc_token_ids, lm_labels, mc_labels = batch
             with torch.no_grad():
-               _, mc_loss, _, mc_logits = model(input_ids, mc_token_ids=mc_token_ids, lm_labels=lm_labels, mc_labels=mc_labels)
+                _, mc_loss, _, mc_logits = model(
+                    input_ids, mc_token_ids=mc_token_ids, lm_labels=lm_labels, mc_labels=mc_labels
+                )
 
             mc_logits = mc_logits.detach().cpu().numpy()
-            mc_labels = mc_labels.to('cpu').numpy()
+            mc_labels = mc_labels.to("cpu").numpy()
             tmp_eval_accuracy = accuracy(mc_logits, mc_labels)
 
             eval_loss += mc_loss.mean().item()
@@ -274,10 +301,8 @@ def main():
 
         eval_loss = eval_loss / nb_eval_steps
         eval_accuracy = eval_accuracy / nb_eval_examples
-        train_loss = tr_loss/nb_tr_steps if args.do_train else None
-        result = {'eval_loss': eval_loss,
-                  'eval_accuracy': eval_accuracy,
-                  'train_loss': train_loss}
+        train_loss = tr_loss / nb_tr_steps if args.do_train else None
+        result = {"eval_loss": eval_loss, "eval_accuracy": eval_accuracy, "train_loss": train_loss}
 
         output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
         with open(output_eval_file, "w") as writer:
@@ -286,5 +311,6 @@ def main():
                 logger.info("  %s = %s", key, str(result[key]))
                 writer.write("%s = %s\n" % (key, str(result[key])))
 
-if __name__ == '__main__':
+
+if __name__ == "__main__":
     main()

examples/contrib/run_swag.py

@@ -16,54 +16,38 @@
 """BERT finetuning runner.
    Finetuning the library models for multiple choice on SWAG (Bert).
 """
-from __future__ import absolute_import, division, print_function
+
 
 import argparse
-import logging
 import csv
+import glob
+import logging
 import os
 import random
-import sys
-import glob
 
 import numpy as np
 import torch
-from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
-                              TensorDataset)
+from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
 from torch.utils.data.distributed import DistributedSampler
+from tqdm import tqdm, trange
+
+from transformers import WEIGHTS_NAME, AdamW, AutoConfig, AutoTokenizer, get_linear_schedule_with_warmup
+from transformers.modeling_auto import AutoModelForMultipleChoice
+
 
 try:
     from torch.utils.tensorboard import SummaryWriter
-except:
+except ImportError:
     from tensorboardX import SummaryWriter
 
-from tqdm import tqdm, trange
-
-from transformers import (WEIGHTS_NAME, BertConfig,
-                                  BertForMultipleChoice, BertTokenizer)
-
-from transformers import AdamW, get_linear_schedule_with_warmup
 
 logger = logging.getLogger(__name__)
 
-ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) \
-                  for conf in [BertConfig]), ())
-
-MODEL_CLASSES = {
-    'bert': (BertConfig, BertForMultipleChoice, BertTokenizer),
-}
 
 class SwagExample(object):
     """A single training/test example for the SWAG dataset."""
-    def __init__(self,
-                 swag_id,
-                 context_sentence,
-                 start_ending,
-                 ending_0,
-                 ending_1,
-                 ending_2,
-                 ending_3,
-                 label = None):
+
+    def __init__(self, swag_id, context_sentence, start_ending, ending_0, ending_1, ending_2, ending_3, label=None):
         self.swag_id = swag_id
         self.context_sentence = context_sentence
         self.start_ending = start_ending
@@ -79,7 +63,7 @@ class SwagExample(object):
         return self.__repr__()
 
     def __repr__(self):
-        l = [
+        attributes = [
             "swag_id: {}".format(self.swag_id),
             "context_sentence: {}".format(self.context_sentence),
             "start_ending: {}".format(self.start_ending),
@@ -90,61 +74,48 @@ class SwagExample(object):
         ]
 
         if self.label is not None:
-            l.append("label: {}".format(self.label))
+            attributes.append("label: {}".format(self.label))
+
+        return ", ".join(attributes)
 
-        return ", ".join(l)
 
 class InputFeatures(object):
-    def __init__(self,
-                 example_id,
-                 choices_features,
-                 label
-
-    ):
+    def __init__(self, example_id, choices_features, label):
         self.example_id = example_id
         self.choices_features = [
-            {
-                'input_ids': input_ids,
-                'input_mask': input_mask,
-                'segment_ids': segment_ids
-            }
+            {"input_ids": input_ids, "input_mask": input_mask, "segment_ids": segment_ids}
             for _, input_ids, input_mask, segment_ids in choices_features
         ]
         self.label = label
 
-def read_swag_examples(input_file, is_training=True):
-    with open(input_file, 'r', encoding='utf-8') as f:
-        reader = csv.reader(f)
-        lines = []
-        for line in reader:
-            if sys.version_info[0] == 2:
-                line = list(unicode(cell, 'utf-8') for cell in line)
-            lines.append(line)
 
-    if is_training and lines[0][-1] != 'label':
-        raise ValueError(
-            "For training, the input file must contain a label column."
-        )
+def read_swag_examples(input_file, is_training=True):
+    with open(input_file, "r", encoding="utf-8") as f:
+        lines = list(csv.reader(f))
+
+    if is_training and lines[0][-1] != "label":
+        raise ValueError("For training, the input file must contain a label column.")
 
     examples = [
         SwagExample(
-            swag_id = line[2],
-            context_sentence = line[4],
-            start_ending = line[5], # in the swag dataset, the
-                                         # common beginning of each
-                                         # choice is stored in "sent2".
-            ending_0 = line[7],
-            ending_1 = line[8],
-            ending_2 = line[9],
-            ending_3 = line[10],
-            label = int(line[11]) if is_training else None
-        ) for line in lines[1:] # we skip the line with the column names
+            swag_id=line[2],
+            context_sentence=line[4],
+            start_ending=line[5],  # in the swag dataset, the
+            # common beginning of each
+            # choice is stored in "sent2".
+            ending_0=line[7],
+            ending_1=line[8],
+            ending_2=line[9],
+            ending_3=line[10],
+            label=int(line[11]) if is_training else None,
+        )
+        for line in lines[1:]  # we skip the line with the column names
     ]
 
     return examples
 
-def convert_examples_to_features(examples, tokenizer, max_seq_length,
-                                 is_training):
+
+def convert_examples_to_features(examples, tokenizer, max_seq_length, is_training):
     """Loads a data file into a list of `InputBatch`s."""
 
     # Swag is a multiple choice task. To perform this task using Bert,
@@ -204,23 +175,18 @@ def convert_examples_to_features(examples, tokenizer, max_seq_length,
             logger.info("swag_id: {}".format(example.swag_id))
             for choice_idx, (tokens, input_ids, input_mask, segment_ids) in enumerate(choices_features):
                 logger.info("choice: {}".format(choice_idx))
-                logger.info("tokens: {}".format(' '.join(tokens)))
-                logger.info("input_ids: {}".format(' '.join(map(str, input_ids))))
-                logger.info("input_mask: {}".format(' '.join(map(str, input_mask))))
-                logger.info("segment_ids: {}".format(' '.join(map(str, segment_ids))))
+                logger.info("tokens: {}".format(" ".join(tokens)))
+                logger.info("input_ids: {}".format(" ".join(map(str, input_ids))))
+                logger.info("input_mask: {}".format(" ".join(map(str, input_mask))))
+                logger.info("segment_ids: {}".format(" ".join(map(str, segment_ids))))
             if is_training:
                 logger.info("label: {}".format(label))
 
-        features.append(
-            InputFeatures(
-                example_id = example.swag_id,
-                choices_features = choices_features,
-                label = label
-            )
-        )
+        features.append(InputFeatures(example_id=example.swag_id, choices_features=choices_features, label=label))
 
     return features
 
+
 def _truncate_seq_pair(tokens_a, tokens_b, max_length):
     """Truncates a sequence pair in place to the maximum length."""
 
@@ -237,18 +203,14 @@ def _truncate_seq_pair(tokens_a, tokens_b, max_length):
         else:
             tokens_b.pop()
 
+
 def accuracy(out, labels):
     outputs = np.argmax(out, axis=1)
     return np.sum(outputs == labels)
 
+
 def select_field(features, field):
-    return [
-        [
-            choice[field]
-            for choice in feature.choices_features
-        ]
-        for feature in features
-    ]
+    return [[choice[field] for choice in feature.choices_features] for feature in features]
 
 
 def set_seed(args):
@@ -258,24 +220,28 @@ def set_seed(args):
     if args.n_gpu > 0:
         torch.cuda.manual_seed_all(args.seed)
 
+
 def load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=False):
     if args.local_rank not in [-1, 0]:
         torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache
 
     # Load data features from cache or dataset file
     input_file = args.predict_file if evaluate else args.train_file
-    cached_features_file = os.path.join(os.path.dirname(input_file), 'cached_{}_{}_{}'.format(
-        'dev' if evaluate else 'train',
-        list(filter(None, args.model_name_or_path.split('/'))).pop(),
-        str(args.max_seq_length)))
+    cached_features_file = os.path.join(
+        os.path.dirname(input_file),
+        "cached_{}_{}_{}".format(
+            "dev" if evaluate else "train",
+            list(filter(None, args.model_name_or_path.split("/"))).pop(),
+            str(args.max_seq_length),
+        ),
+    )
     if os.path.exists(cached_features_file) and not args.overwrite_cache and not output_examples:
         logger.info("Loading features from cached file %s", cached_features_file)
         features = torch.load(cached_features_file)
     else:
         logger.info("Creating features from dataset file at %s", input_file)
         examples = read_swag_examples(input_file)
-        features = convert_examples_to_features(
-            examples, tokenizer, args.max_seq_length, not evaluate)
+        features = convert_examples_to_features(examples, tokenizer, args.max_seq_length, not evaluate)
 
         if args.local_rank in [-1, 0]:
             logger.info("Saving features into cached file %s", cached_features_file)
@@ -285,21 +251,21 @@ def load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=Fal
         torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache
 
     # Convert to Tensors and build dataset
-    all_input_ids = torch.tensor(select_field(features, 'input_ids'), dtype=torch.long)
-    all_input_mask = torch.tensor(select_field(features, 'input_mask'), dtype=torch.long)
-    all_segment_ids = torch.tensor(select_field(features, 'segment_ids'), dtype=torch.long)
+    all_input_ids = torch.tensor(select_field(features, "input_ids"), dtype=torch.long)
+    all_input_mask = torch.tensor(select_field(features, "input_mask"), dtype=torch.long)
+    all_segment_ids = torch.tensor(select_field(features, "segment_ids"), dtype=torch.long)
     all_label = torch.tensor([f.label for f in features], dtype=torch.long)
 
     if evaluate:
-        dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids,
-                                all_label)
+        dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label)
     else:
-        dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids,
-                                all_label)
+        dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label)
 
     if output_examples:
         return dataset, examples, features
     return dataset
+
+
 def train(args, train_dataset, model, tokenizer):
     """ Train the model """
     if args.local_rank in [-1, 0]:
@@ -316,13 +282,18 @@ def train(args, train_dataset, model, tokenizer):
         t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
 
     # Prepare optimizer and schedule (linear warmup and decay)
-    no_decay = ['bias', 'LayerNorm.weight']
+    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}
-        ]
+        {
+            "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, eps=args.adam_epsilon)
-    scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total)
+    scheduler = get_linear_schedule_with_warmup(
+        optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total
+    )
     if args.fp16:
         try:
             from apex import amp
@@ -336,17 +307,21 @@ def train(args, train_dataset, model, tokenizer):
 
     # Distributed training (should be after apex fp16 initialization)
     if args.local_rank != -1:
-        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank],
-                                                          output_device=args.local_rank,
-                                                          find_unused_parameters=True)
+        model = torch.nn.parallel.DistributedDataParallel(
+            model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True
+        )
 
     # Train!
     logger.info("***** Running training *****")
     logger.info("  Num examples = %d", len(train_dataset))
     logger.info("  Num Epochs = %d", args.num_train_epochs)
     logger.info("  Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size)
-    logger.info("  Total train batch size (w. parallel, distributed & accumulation) = %d",
-                   args.train_batch_size * args.gradient_accumulation_steps * (torch.distributed.get_world_size() if args.local_rank != -1 else 1))
+    logger.info(
+        "  Total train batch size (w. parallel, distributed & accumulation) = %d",
+        args.train_batch_size
+        * args.gradient_accumulation_steps
+        * (torch.distributed.get_world_size() if args.local_rank != -1 else 1),
+    )
     logger.info("  Gradient Accumulation steps = %d", args.gradient_accumulation_steps)
     logger.info("  Total optimization steps = %d", t_total)
 
@@ -354,17 +329,19 @@ def train(args, train_dataset, model, tokenizer):
     tr_loss, logging_loss = 0.0, 0.0
     model.zero_grad()
     train_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0])
-    set_seed(args)  # Added here for reproductibility (even between python 2 and 3)
+    set_seed(args)  # Added here for reproductibility
     for _ in train_iterator:
         epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])
         for step, batch in enumerate(epoch_iterator):
             model.train()
             batch = tuple(t.to(args.device) for t in batch)
-            inputs = {'input_ids':       batch[0],
-                      'attention_mask':  batch[1],
-                      #'token_type_ids':  None if args.model_type == 'xlm' else batch[2],
-                      'token_type_ids': batch[2],
-                      'labels':         batch[3]}
+            inputs = {
+                "input_ids": batch[0],
+                "attention_mask": batch[1],
+                # 'token_type_ids':  None if args.model_type == 'xlm' else batch[2],
+                "token_type_ids": batch[2],
+                "labels": batch[3],
+            }
             # if args.model_type in ['xlnet', 'xlm']:
             #     inputs.update({'cls_index': batch[5],
             #                    'p_mask':       batch[6]})
@@ -372,7 +349,7 @@ def train(args, train_dataset, model, tokenizer):
             loss = outputs[0]  # model outputs are always tuple in transformers (see doc)
 
             if args.n_gpu > 1:
-                loss = loss.mean() # mean() to average on multi-gpu parallel (not distributed) training
+                loss = loss.mean()  # mean() to average on multi-gpu parallel (not distributed) training
             if args.gradient_accumulation_steps > 1:
                 loss = loss / args.gradient_accumulation_steps
 
@@ -393,23 +370,27 @@ def train(args, train_dataset, model, tokenizer):
 
                 if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
                     # Log metrics
-                    if args.local_rank == -1 and args.evaluate_during_training:  # Only evaluate when single GPU otherwise metrics may not average well
+                    if (
+                        args.local_rank == -1 and args.evaluate_during_training
+                    ):  # Only evaluate when single GPU otherwise metrics may not average well
                         results = evaluate(args, model, tokenizer)
                         for key, value in results.items():
-                            tb_writer.add_scalar('eval_{}'.format(key), value, global_step)
-                    tb_writer.add_scalar('lr', scheduler.get_lr()[0], global_step)
-                    tb_writer.add_scalar('loss', (tr_loss - logging_loss)/args.logging_steps, global_step)
+                            tb_writer.add_scalar("eval_{}".format(key), value, global_step)
+                    tb_writer.add_scalar("lr", scheduler.get_lr()[0], global_step)
+                    tb_writer.add_scalar("loss", (tr_loss - logging_loss) / args.logging_steps, global_step)
                     logging_loss = tr_loss
 
                 if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
                     # Save model checkpoint
-                    output_dir = os.path.join(args.output_dir, 'checkpoint-{}'.format(global_step))
+                    output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(global_step))
                     if not os.path.exists(output_dir):
                         os.makedirs(output_dir)
-                    model_to_save = model.module if hasattr(model, 'module') else model  # Take care of distributed/parallel training
+                    model_to_save = (
+                        model.module if hasattr(model, "module") else model
+                    )  # Take care of distributed/parallel training
                     model_to_save.save_pretrained(output_dir)
                     tokenizer.save_vocabulary(output_dir)
-                    torch.save(args, os.path.join(output_dir, 'training_args.bin'))
+                    torch.save(args, os.path.join(output_dir, "training_args.bin"))
                     logger.info("Saving model checkpoint to %s", output_dir)
 
             if args.max_steps > 0 and global_step > args.max_steps:
@@ -424,6 +405,7 @@ def train(args, train_dataset, model, tokenizer):
 
     return global_step, tr_loss / global_step
 
+
 def evaluate(args, model, tokenizer, prefix=""):
     dataset, examples, features = load_and_cache_examples(args, tokenizer, evaluate=True, output_examples=True)
 
@@ -440,7 +422,6 @@ def evaluate(args, model, tokenizer, prefix=""):
     logger.info("  Num examples = %d", len(dataset))
     logger.info("  Batch size = %d", args.eval_batch_size)
 
-
     eval_loss, eval_accuracy = 0, 0
     nb_eval_steps, nb_eval_examples = 0, 0
 
@@ -448,11 +429,13 @@ def evaluate(args, model, tokenizer, prefix=""):
         model.eval()
         batch = tuple(t.to(args.device) for t in batch)
         with torch.no_grad():
-            inputs = {'input_ids':      batch[0],
-                      'attention_mask': batch[1],
-                      # 'token_type_ids': None if args.model_type == 'xlm' else batch[2]  # XLM don't use segment_ids
-                      'token_type_ids': batch[2],
-                      'labels':         batch[3]}
+            inputs = {
+                "input_ids": batch[0],
+                "attention_mask": batch[1],
+                # 'token_type_ids': None if args.model_type == 'xlm' else batch[2]  # XLM don't use segment_ids
+                "token_type_ids": batch[2],
+                "labels": batch[3],
+            }
 
             # if args.model_type in ['xlnet', 'xlm']:
             #     inputs.update({'cls_index': batch[4],
@@ -462,17 +445,16 @@ def evaluate(args, model, tokenizer, prefix=""):
             eval_loss += tmp_eval_loss.mean().item()
 
         logits = logits.detach().cpu().numpy()
-        label_ids = inputs['labels'].to('cpu').numpy()
+        label_ids = inputs["labels"].to("cpu").numpy()
         tmp_eval_accuracy = accuracy(logits, label_ids)
         eval_accuracy += tmp_eval_accuracy
 
         nb_eval_steps += 1
-        nb_eval_examples += inputs['input_ids'].size(0)
+        nb_eval_examples += inputs["input_ids"].size(0)
 
     eval_loss = eval_loss / nb_eval_steps
     eval_accuracy = eval_accuracy / nb_eval_examples
-    result = {'eval_loss': eval_loss,
-              'eval_accuracy': eval_accuracy}
+    result = {"eval_loss": eval_loss, "eval_accuracy": eval_accuracy}
 
     output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
     with open(output_eval_file, "w") as writer:
@@ -483,92 +465,134 @@ def evaluate(args, model, tokenizer, prefix=""):
 
     return result
 
+
 def main():
     parser = argparse.ArgumentParser()
 
-    ## Required parameters
-    parser.add_argument("--train_file", default=None, type=str, required=True,
-                        help="SWAG csv for training. E.g., train.csv")
-    parser.add_argument("--predict_file", default=None, type=str, required=True,
-                        help="SWAG csv for predictions. E.g., val.csv or test.csv")
-    parser.add_argument("--model_type", default=None, type=str, required=True,
-                        help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()))
-    parser.add_argument("--model_name_or_path", default=None, type=str, required=True,
-                        help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS))
-    parser.add_argument("--output_dir", default=None, type=str, required=True,
-                        help="The output directory where the model checkpoints and predictions will be written.")
+    # Required parameters
+    parser.add_argument(
+        "--train_file", default=None, type=str, required=True, help="SWAG csv for training. E.g., train.csv"
+    )
+    parser.add_argument(
+        "--predict_file",
+        default=None,
+        type=str,
+        required=True,
+        help="SWAG csv for predictions. E.g., val.csv or test.csv",
+    )
+    parser.add_argument(
+        "--model_name_or_path",
+        default=None,
+        type=str,
+        required=True,
+        help="Path to pretrained model or model identifier from huggingface.co/models",
+    )
+    parser.add_argument(
+        "--output_dir",
+        default=None,
+        type=str,
+        required=True,
+        help="The output directory where the model checkpoints and predictions will be written.",
+    )
 
-    ## Other parameters
-    parser.add_argument("--config_name", default="", type=str,
-                        help="Pretrained config name or path if not the same as model_name")
-    parser.add_argument("--tokenizer_name", default="", type=str,
-                        help="Pretrained tokenizer name or path if not the same as model_name")
-    parser.add_argument("--max_seq_length", default=384, type=int,
-                        help="The maximum total input sequence length after tokenization. Sequences "
-                             "longer than this will be truncated, and sequences shorter than this will be padded.")
-    parser.add_argument("--do_train", action='store_true',
-                        help="Whether to run training.")
-    parser.add_argument("--do_eval", action='store_true',
-                        help="Whether to run eval on the dev set.")
-    parser.add_argument("--evaluate_during_training", action='store_true',
-                        help="Rul evaluation during training at each logging step.")
-    parser.add_argument("--do_lower_case", action='store_true',
-                        help="Set this flag if you are using an uncased model.")
+    # Other parameters
+    parser.add_argument(
+        "--config_name", default="", type=str, help="Pretrained config name or path if not the same as model_name"
+    )
+    parser.add_argument(
+        "--tokenizer_name",
+        default="",
+        type=str,
+        help="Pretrained tokenizer name or path if not the same as model_name",
+    )
+    parser.add_argument(
+        "--max_seq_length",
+        default=384,
+        type=int,
+        help="The maximum total input sequence length after tokenization. Sequences "
+        "longer than this will be truncated, and sequences shorter than this will be padded.",
+    )
+    parser.add_argument("--do_train", action="store_true", help="Whether to run training.")
+    parser.add_argument("--do_eval", action="store_true", help="Whether to run eval on the dev set.")
+    parser.add_argument(
+        "--evaluate_during_training", action="store_true", help="Rul evaluation during training at each logging step."
+    )
 
-    parser.add_argument("--per_gpu_train_batch_size", default=8, type=int,
-                        help="Batch size per GPU/CPU for training.")
-    parser.add_argument("--per_gpu_eval_batch_size", default=8, type=int,
-                        help="Batch size per GPU/CPU for evaluation.")
-    parser.add_argument("--learning_rate", default=5e-5, type=float,
-                        help="The initial learning rate for Adam.")
-    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("--weight_decay", default=0.0, type=float,
-                        help="Weight deay if we apply some.")
-    parser.add_argument("--adam_epsilon", default=1e-8, type=float,
-                        help="Epsilon for Adam optimizer.")
-    parser.add_argument("--max_grad_norm", default=1.0, type=float,
-                        help="Max gradient norm.")
-    parser.add_argument("--num_train_epochs", default=3.0, type=float,
-                        help="Total number of training epochs to perform.")
-    parser.add_argument("--max_steps", default=-1, type=int,
-                        help="If > 0: set total number of training steps to perform. Override num_train_epochs.")
-    parser.add_argument("--warmup_steps", default=0, type=int,
-                        help="Linear warmup over warmup_steps.")
+    parser.add_argument("--per_gpu_train_batch_size", default=8, type=int, help="Batch size per GPU/CPU for training.")
+    parser.add_argument(
+        "--per_gpu_eval_batch_size", default=8, type=int, help="Batch size per GPU/CPU for evaluation."
+    )
+    parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.")
+    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("--weight_decay", default=0.0, type=float, help="Weight deay if we apply some.")
+    parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.")
+    parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
+    parser.add_argument(
+        "--num_train_epochs", default=3.0, type=float, help="Total number of training epochs to perform."
+    )
+    parser.add_argument(
+        "--max_steps",
+        default=-1,
+        type=int,
+        help="If > 0: set total number of training steps to perform. Override num_train_epochs.",
+    )
+    parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.")
 
-    parser.add_argument('--logging_steps', type=int, default=50,
-                        help="Log every X updates steps.")
-    parser.add_argument('--save_steps', type=int, default=50,
-                        help="Save checkpoint every X updates steps.")
-    parser.add_argument("--eval_all_checkpoints", action='store_true',
-                        help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number")
-    parser.add_argument("--no_cuda", action='store_true',
-                        help="Whether not to use CUDA when available")
-    parser.add_argument('--overwrite_output_dir', action='store_true',
-                        help="Overwrite the content of the output directory")
-    parser.add_argument('--overwrite_cache', action='store_true',
-                        help="Overwrite the cached training and evaluation sets")
-    parser.add_argument('--seed', type=int, default=42,
-                        help="random seed for initialization")
+    parser.add_argument("--logging_steps", type=int, default=50, help="Log every X updates steps.")
+    parser.add_argument("--save_steps", type=int, default=50, help="Save checkpoint every X updates steps.")
+    parser.add_argument(
+        "--eval_all_checkpoints",
+        action="store_true",
+        help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number",
+    )
+    parser.add_argument("--no_cuda", action="store_true", help="Whether not to use CUDA when available")
+    parser.add_argument(
+        "--overwrite_output_dir", action="store_true", help="Overwrite the content of the output directory"
+    )
+    parser.add_argument(
+        "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets"
+    )
+    parser.add_argument("--seed", type=int, default=42, help="random seed for initialization")
 
-    parser.add_argument("--local_rank", type=int, default=-1,
-                        help="local_rank for distributed training on gpus")
-    parser.add_argument('--fp16', action='store_true',
-                        help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit")
-    parser.add_argument('--fp16_opt_level', type=str, default='O1',
-                        help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
-                             "See details at https://nvidia.github.io/apex/amp.html")
-    parser.add_argument('--server_ip', type=str, default='', help="Can be used for distant debugging.")
-    parser.add_argument('--server_port', type=str, default='', help="Can be used for distant debugging.")
+    parser.add_argument("--local_rank", type=int, default=-1, help="local_rank for distributed training on gpus")
+    parser.add_argument(
+        "--fp16",
+        action="store_true",
+        help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit",
+    )
+    parser.add_argument(
+        "--fp16_opt_level",
+        type=str,
+        default="O1",
+        help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
+        "See details at https://nvidia.github.io/apex/amp.html",
+    )
+    parser.add_argument("--server_ip", type=str, default="", help="Can be used for distant debugging.")
+    parser.add_argument("--server_port", type=str, default="", help="Can be used for distant debugging.")
     args = parser.parse_args()
 
-    if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train and not args.overwrite_output_dir:
-        raise ValueError("Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format(args.output_dir))
+    if (
+        os.path.exists(args.output_dir)
+        and os.listdir(args.output_dir)
+        and args.do_train
+        and not args.overwrite_output_dir
+    ):
+        raise ValueError(
+            "Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format(
+                args.output_dir
+            )
+        )
 
     # Setup distant debugging if needed
     if args.server_ip and args.server_port:
         # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
         import ptvsd
+
         print("Waiting for debugger attach")
         ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
         ptvsd.wait_for_attach()
@@ -576,20 +600,28 @@ def main():
     # Setup CUDA, GPU & distributed training
     if args.local_rank == -1 or args.no_cuda:
         device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
-        args.n_gpu = torch.cuda.device_count()
+        args.n_gpu = 0 if args.no_cuda else torch.cuda.device_count()
     else:  # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
         torch.cuda.set_device(args.local_rank)
         device = torch.device("cuda", args.local_rank)
-        torch.distributed.init_process_group(backend='nccl')
+        torch.distributed.init_process_group(backend="nccl")
         args.n_gpu = 1
     args.device = device
 
     # Setup logging
-    logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s',
-                        datefmt = '%m/%d/%Y %H:%M:%S',
-                        level = logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
-    logger.warning("Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
-                    args.local_rank, device, args.n_gpu, bool(args.local_rank != -1), args.fp16)
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN,
+    )
+    logger.warning(
+        "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
+        args.local_rank,
+        device,
+        args.n_gpu,
+        bool(args.local_rank != -1),
+        args.fp16,
+    )
 
     # Set seed
     set_seed(args)
@@ -598,11 +630,11 @@ def main():
     if args.local_rank not in [-1, 0]:
         torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab
 
-    args.model_type = args.model_type.lower()
-    config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
-    config = config_class.from_pretrained(args.config_name if args.config_name else args.model_name_or_path)
-    tokenizer = tokenizer_class.from_pretrained(args.tokenizer_name if args.tokenizer_name else args.model_name_or_path, do_lower_case=args.do_lower_case)
-    model = model_class.from_pretrained(args.model_name_or_path, from_tf=bool('.ckpt' in args.model_name_or_path), config=config)
+    config = AutoConfig.from_pretrained(args.config_name if args.config_name else args.model_name_or_path)
+    tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name if args.tokenizer_name else args.model_name_or_path,)
+    model = AutoModelForMultipleChoice.from_pretrained(
+        args.model_name_or_path, from_tf=bool(".ckpt" in args.model_name_or_path), config=config
+    )
 
     if args.local_rank == 0:
         torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab
@@ -617,7 +649,6 @@ def main():
         global_step, tr_loss = train(args, train_dataset, model, tokenizer)
         logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)
 
-
     # Save the trained model and the tokenizer
     if args.local_rank == -1 or torch.distributed.get_rank() == 0:
         # Create output directory if needed
@@ -627,19 +658,20 @@ def main():
         logger.info("Saving model checkpoint to %s", args.output_dir)
         # Save a trained model, configuration and tokenizer using `save_pretrained()`.
         # They can then be reloaded using `from_pretrained()`
-        model_to_save = model.module if hasattr(model, 'module') else model  # Take care of distributed/parallel training
+        model_to_save = (
+            model.module if hasattr(model, "module") else model
+        )  # Take care of distributed/parallel training
         model_to_save.save_pretrained(args.output_dir)
         tokenizer.save_pretrained(args.output_dir)
 
         # Good practice: save your training arguments together with the trained model
-        torch.save(args, os.path.join(args.output_dir, 'training_args.bin'))
+        torch.save(args, os.path.join(args.output_dir, "training_args.bin"))
 
         # Load a trained model and vocabulary that you have fine-tuned
-        model = model_class.from_pretrained(args.output_dir)
-        tokenizer = tokenizer_class.from_pretrained(args.output_dir)
+        model = AutoModelForMultipleChoice.from_pretrained(args.output_dir)
+        tokenizer = AutoTokenizer.from_pretrained(args.output_dir)
         model.to(args.device)
 
-
     # Evaluation - we can ask to evaluate all the checkpoints (sub-directories) in a directory
     results = {}
     if args.do_eval and args.local_rank in [-1, 0]:
@@ -650,22 +682,24 @@ def main():
             checkpoints = [args.model_name_or_path]
 
         if args.eval_all_checkpoints:
-            checkpoints = list(os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME, recursive=True)))
+            checkpoints = list(
+                os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + "/**/" + WEIGHTS_NAME, recursive=True))
+            )
             logging.getLogger("transformers.modeling_utils").setLevel(logging.WARN)  # Reduce model loading logs
 
         logger.info("Evaluate the following checkpoints: %s", checkpoints)
 
         for checkpoint in checkpoints:
             # Reload the model
-            global_step = checkpoint.split('-')[-1] if len(checkpoints) > 1 else ""
-            model = model_class.from_pretrained(checkpoint)
-            tokenizer = tokenizer_class.from_pretrained(checkpoint)
+            global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else ""
+            model = AutoModelForMultipleChoice.from_pretrained(checkpoint)
+            tokenizer = AutoTokenizer.from_pretrained(checkpoint)
             model.to(args.device)
 
             # Evaluate
             result = evaluate(args, model, tokenizer, prefix=global_step)
 
-            result = dict((k + ('_{}'.format(global_step) if global_step else ''), v) for k, v in result.items())
+            result = dict((k + ("_{}".format(global_step) if global_step else ""), v) for k, v in result.items())
             results.update(result)
 
     logger.info("Results: {}".format(results))

examples/contrib/run_transfo_xl.py

@@ -19,55 +19,48 @@
 
     This script with default values evaluates a pretrained Transformer-XL on WikiText 103
 """
-from __future__ import absolute_import, division, print_function, unicode_literals
+
 
 import argparse
 import logging
-import time
 import math
+import time
 
 import torch
 
-from transformers import TransfoXLLMHeadModel, TransfoXLCorpus, TransfoXLTokenizer
+from transformers import TransfoXLCorpus, TransfoXLLMHeadModel
 
-logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s',
-                    datefmt = '%m/%d/%Y %H:%M:%S',
-                    level = logging.INFO)
+
+logging.basicConfig(
+    format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
+)
 logger = logging.getLogger(__name__)
 
+
 def main():
-    parser = argparse.ArgumentParser(description='PyTorch Transformer Language Model')
-    parser.add_argument('--model_name', type=str, default='transfo-xl-wt103',
-                        help='pretrained model name')
-    parser.add_argument('--split', type=str, default='test',
-                        choices=['all', 'valid', 'test'],
-                        help='which split to evaluate')
-    parser.add_argument('--batch_size', type=int, default=10,
-                        help='batch size')
-    parser.add_argument('--tgt_len', type=int, default=128,
-                        help='number of tokens to predict')
-    parser.add_argument('--ext_len', type=int, default=0,
-                        help='length of the extended context')
-    parser.add_argument('--mem_len', type=int, default=1600,
-                        help='length of the retained previous heads')
-    parser.add_argument('--clamp_len', type=int, default=1000,
-                        help='max positional embedding index')
-    parser.add_argument('--no_cuda', action='store_true',
-                        help='Do not use CUDA even though CUA is available')
-    parser.add_argument('--work_dir', type=str, required=True,
-                        help='path to the work_dir')
-    parser.add_argument('--no_log', action='store_true',
-                        help='do not log the eval result')
-    parser.add_argument('--same_length', action='store_true',
-                        help='set same length attention with masking')
-    parser.add_argument('--server_ip', type=str, default='', help="Can be used for distant debugging.")
-    parser.add_argument('--server_port', type=str, default='', help="Can be used for distant debugging.")
+    parser = argparse.ArgumentParser(description="PyTorch Transformer Language Model")
+    parser.add_argument("--model_name", type=str, default="transfo-xl-wt103", help="pretrained model name")
+    parser.add_argument(
+        "--split", type=str, default="test", choices=["all", "valid", "test"], help="which split to evaluate"
+    )
+    parser.add_argument("--batch_size", type=int, default=10, help="batch size")
+    parser.add_argument("--tgt_len", type=int, default=128, help="number of tokens to predict")
+    parser.add_argument("--ext_len", type=int, default=0, help="length of the extended context")
+    parser.add_argument("--mem_len", type=int, default=1600, help="length of the retained previous heads")
+    parser.add_argument("--clamp_len", type=int, default=1000, help="max positional embedding index")
+    parser.add_argument("--no_cuda", action="store_true", help="Do not use CUDA even though CUA is available")
+    parser.add_argument("--work_dir", type=str, required=True, help="path to the work_dir")
+    parser.add_argument("--no_log", action="store_true", help="do not log the eval result")
+    parser.add_argument("--same_length", action="store_true", help="set same length attention with masking")
+    parser.add_argument("--server_ip", type=str, default="", help="Can be used for distant debugging.")
+    parser.add_argument("--server_port", type=str, default="", help="Can be used for distant debugging.")
     args = parser.parse_args()
-    assert args.ext_len >= 0, 'extended context length must be non-negative'
+    assert args.ext_len >= 0, "extended context length must be non-negative"
 
     if args.server_ip and args.server_port:
         # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
         import ptvsd
+
         print("Waiting for debugger attach")
         ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
         ptvsd.wait_for_attach()
@@ -80,21 +73,20 @@ def main():
     # The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
     # and tokenizing the dataset
     # The pre-processed corpus is a convertion (using the conversion script )
-    tokenizer = TransfoXLTokenizer.from_pretrained(args.model_name)
     corpus = TransfoXLCorpus.from_pretrained(args.model_name)
-    ntokens = len(corpus.vocab)
 
-    va_iter = corpus.get_iterator('valid', args.batch_size, args.tgt_len,
-        device=device, ext_len=args.ext_len)
-    te_iter = corpus.get_iterator('test', args.batch_size, args.tgt_len,
-        device=device, ext_len=args.ext_len)
+    va_iter = corpus.get_iterator("valid", args.batch_size, args.tgt_len, device=device, ext_len=args.ext_len)
+    te_iter = corpus.get_iterator("test", args.batch_size, args.tgt_len, device=device, ext_len=args.ext_len)
 
     # Load a pre-trained model
     model = TransfoXLLMHeadModel.from_pretrained(args.model_name)
-    model = model.to(device)
+    model.to(device)
 
-    logger.info('Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}'.format(
-        args.batch_size, args.tgt_len, args.ext_len, args.mem_len, args.clamp_len))
+    logger.info(
+        "Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}".format(
+            args.batch_size, args.tgt_len, args.ext_len, args.mem_len, args.clamp_len
+        )
+    )
 
     model.reset_length(args.tgt_len, args.ext_len, args.mem_len)
     if args.clamp_len > 0:
@@ -108,7 +100,7 @@ def main():
     def evaluate(eval_iter):
         # Turn on evaluation mode which disables dropout.
         model.eval()
-        total_len, total_loss = 0, 0.
+        total_len, total_loss = 0, 0.0
         start_time = time.time()
         with torch.no_grad():
             mems = None
@@ -119,35 +111,34 @@ def main():
                 total_loss += seq_len * loss.item()
                 total_len += seq_len
             total_time = time.time() - start_time
-        logger.info('Time : {:.2f}s, {:.2f}ms/segment'.format(
-                total_time, 1000 * total_time / (idx+1)))
+        logger.info("Time : {:.2f}s, {:.2f}ms/segment".format(total_time, 1000 * total_time / (idx + 1)))
         return total_loss / total_len
 
     # Run on test data.
-    if args.split == 'all':
+    if args.split == "all":
         test_loss = evaluate(te_iter)
         valid_loss = evaluate(va_iter)
-    elif args.split == 'valid':
+    elif args.split == "valid":
         valid_loss = evaluate(va_iter)
         test_loss = None
-    elif args.split == 'test':
+    elif args.split == "test":
         test_loss = evaluate(te_iter)
         valid_loss = None
 
     def format_log(loss, split):
-        log_str = '| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '.format(
-            split, loss, math.exp(loss))
+        log_str = "| {0} loss {1:5.2f} | {0} ppl {2:9.3f} ".format(split, loss, math.exp(loss))
         return log_str
 
-    log_str = ''
+    log_str = ""
     if valid_loss is not None:
-        log_str += format_log(valid_loss, 'valid')
+        log_str += format_log(valid_loss, "valid")
     if test_loss is not None:
-        log_str += format_log(test_loss, 'test')
+        log_str += format_log(test_loss, "test")
 
-    logger.info('=' * 100)
+    logger.info("=" * 100)
     logger.info(log_str)
-    logger.info('=' * 100)
+    logger.info("=" * 100)
 
-if __name__ == '__main__':
+
+if __name__ == "__main__":
     main()

examples/distillation/README.md

@@ -2,15 +2,17 @@
 
 This folder contains the original code used to train Distil* as well as examples showcasing how to use DistilBERT, DistilRoBERTa and DistilGPT2.
 
-**December 6th, 2019 - Update** We release **DistilmBERT**: 92% of `bert-base-multilingual-cased` on XNLI. The model supports 104 different languages listed [here](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages).
+**January 20, 2020 - Bug fixing** We have recently discovered and fixed [a bug](https://github.com/huggingface/transformers/commit/48cbf267c988b56c71a2380f748a3e6092ccaed3) in the evaluation of our `run_*.py` scripts that caused the reported metrics to be over-estimated on average. We have updated all the metrics with the latest runs.
 
-**November 19th, 2019 - Update** We release German **DistilBERT**: 98.8% of `bert-base-german-dbmdz-cased` on NER tasks.
+**December 6, 2019 - Update** We release **DistilmBERT**: 92% of `bert-base-multilingual-cased` on XNLI. The model supports 104 different languages listed [here](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages).
 
-**October 23rd, 2019 - Update** We release **DistilRoBERTa**: 95% of `RoBERTa-base`'s performance on GLUE, twice as fast as RoBERTa while being 35% smaller.
+**November 19, 2019 - Update** We release German **DistilBERT**: 98.8% of `bert-base-german-dbmdz-cased` on NER tasks.
 
-**October 3rd, 2019 - Update** We release our [NeurIPS workshop paper](https://arxiv.org/abs/1910.01108) explaining our approach on **DistilBERT**. It includes updated results and further experiments. We applied the same method to GPT2 and release the weights of **DistilGPT2**. DistilGPT2 is two times faster and 33% smaller than GPT2. **The paper superseeds our [previous blogpost](https://medium.com/huggingface/distilbert-8cf3380435b5) with a different distillation loss and better performances. Please use the paper as a reference when comparing/reporting results on DistilBERT.**
+**October 23, 2019 - Update** We release **DistilRoBERTa**: 95% of `RoBERTa-base`'s performance on GLUE, twice as fast as RoBERTa while being 35% smaller.
 
-**September 19th, 2019 - Update:** We fixed bugs in the code and released an upadted version of the weights trained with a modification of the distillation loss. DistilBERT now reaches 97% of `BERT-base`'s performance on GLUE, and 86.9 F1 score on SQuAD v1.1 dev set (compared to 88.5 for `BERT-base`). We will publish a formal write-up of our approach in the near future!
+**October 3, 2019 - Update** We release our [NeurIPS workshop paper](https://arxiv.org/abs/1910.01108) explaining our approach on **DistilBERT**. It includes updated results and further experiments. We applied the same method to GPT2 and release the weights of **DistilGPT2**. DistilGPT2 is two times faster and 33% smaller than GPT2. **The paper supersedes our [previous blogpost](https://medium.com/huggingface/distilbert-8cf3380435b5) with a different distillation loss and better performances. Please use the paper as a reference when comparing/reporting results on DistilBERT.**
+
+**September 19, 2019 - Update:** We fixed bugs in the code and released an upadted version of the weights trained with a modification of the distillation loss. DistilBERT now reaches 99% of `BERT-base`'s performance on GLUE, and 86.9 F1 score on SQuAD v1.1 dev set (compared to 88.5 for `BERT-base`). We will publish a formal write-up of our approach in the near future!
 
 
 ## What is Distil*
@@ -18,7 +20,7 @@ This folder contains the original code used to train Distil* as well as examples
 Distil* is a class of compressed models that started with DistilBERT. DistilBERT stands for Distillated-BERT. DistilBERT is a small, fast, cheap and light Transformer model based on Bert architecture. It has 40% less parameters than `bert-base-uncased`, runs 60% faster while preserving 97% of BERT's performances as measured on the GLUE language understanding benchmark. DistilBERT is trained using knowledge distillation, a technique to compress a large model called the teacher into a smaller model called the student. By distillating Bert, we obtain a smaller Transformer model that bears a lot of similarities with the original BERT model while being lighter, smaller and faster to run. DistilBERT is thus an interesting option to put large-scaled trained Transformer model into production.
 
 We have applied the same method to other Transformer architectures and released the weights:
-- GPT2: on the [WikiText-103](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/) benchmark, GPT2 reaches a perplexity on the test set of 15.0 compared to 18.5 for **DistilGPT2** (after fine-tuning on the train set).
+- GPT2: on the [WikiText-103](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/) benchmark, GPT2 reaches a perplexity on the test set of 16.3 compared to 21.1 for **DistilGPT2** (after fine-tuning on the train set).
 - RoBERTa: **DistilRoBERTa** reaches 95% of `RoBERTa-base`'s performance on GLUE while being twice faster and 35% smaller.
 - German BERT: **German DistilBERT** reaches 99% of `bert-base-german-dbmdz-cased`'s performance on German NER (CoNLL-2003).
 - Multilingual BERT: **DistilmBERT** reaches 92% of Multilingual BERT's performance on XNLI while being twice faster and 25% smaller. The model supports 104 languages listed [here](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages).
@@ -29,13 +31,15 @@ Here are the results on the dev sets of GLUE:
 
 | Model                     | Macro-score                    | CoLA | MNLI | MRPC | QNLI | QQP  | RTE  | SST-2| STS-B| WNLI              |
 | :---:                     |    :---:                       | :---:| :---:| :---:| :---:| :---:| :---:| :---:| :---:| :---:             |
-| BERT-base                 |  **77.6**                      | 48.9 | 84.3 | 88.6 | 89.3 | 89.5 | 71.3 | 91.7 | 91.2 | 43.7              |
-| DistilBERT                |  **76.8**                      | 49.1 | 81.8 | 90.2 | 90.2 | 89.2 | 62.9 | 92.7 | 90.7 | 44.4              |
+| BERT-base-uncased         |  **79.5**                      | 56.3 | 84.7 | 88.6 | 91.8 | 89.6 | 69.3 | 92.7 | 89.0 | 53.5              |
+| DistilBERT-base-uncased   |  **77.0**                      | 51.3 | 82.1 | 87.5 | 89.2 | 88.5 | 59.9 | 91.3 | 86.9 | 56.3              |
+| BERT-base-cased           |  **78.2**                      | 58.2 | 83.9 | 87.8 | 91.0 | 89.2 | 66.1 | 91.7 | 89.2 | 46.5              |
+| DistilBERT-base-cased     |  **75.9**                      | 47.2 | 81.5 | 85.6 | 88.2 | 87.8 | 60.6 | 90.4 | 85.5 | 56.3              |
 | ---                       |    ---                         |  --- |  --- |  --- |  --- |  --- |  --- |  --- |  --- |  ---              |
 | RoBERTa-base (reported)   |  **83.2**/**86.4**<sup>2</sup> | 63.6 | 87.6 | 90.2 | 92.8 | 91.9 | 78.7 | 94.8 | 91.2 | 57.7<sup>3</sup>  |
-| DistilRoBERTa<sup>1</sup> |  **79.0**/**82.3**<sup>2</sup> | 59.4 | 83.9 | 86.6 | 90.8 | 89.4 | 67.9 | 92.5 | 88.3 | 52.1              |
+| DistilRoBERTa<sup>1</sup> |  **79.0**/**82.3**<sup>2</sup> | 59.3 | 84.0 | 86.6 | 90.8 | 89.4 | 67.9 | 92.5 | 88.3 | 52.1              |
 
-<sup>1</sup> We did not use the MNLI checkpoint for fine-tuning but directy perform transfer learning on the pre-trained DistilRoBERTa.
+<sup>1</sup> We did not use the MNLI checkpoint for fine-tuning but directly perform transfer learning on the pre-trained DistilRoBERTa.
 
 <sup>2</sup> Macro-score computed without WNLI.
 
@@ -61,7 +65,9 @@ This part of the library has only be tested with Python3.6+. There are few speci
 Transformers includes five pre-trained Distil* models, currently only provided for English and German (we are investigating the possibility to train and release a multilingual version of DistilBERT):
 
 - `distilbert-base-uncased`: DistilBERT English language model pretrained on the same data used to pretrain Bert (concatenation of the Toronto Book Corpus and full English Wikipedia) using distillation with the supervision of the `bert-base-uncased` version of Bert. The model has 6 layers, 768 dimension and 12 heads, totalizing 66M parameters.
-- `distilbert-base-uncased-distilled-squad`: A finetuned version of `distilbert-base-uncased` finetuned using (a second step of) knwoledge distillation on SQuAD 1.0. This model reaches a F1 score of 86.9 on the dev set (for comparison, Bert `bert-base-uncased` version reaches a 88.5 F1 score).
+- `distilbert-base-uncased-distilled-squad`: A finetuned version of `distilbert-base-uncased` finetuned using (a second step of) knowledge distillation on SQuAD 1.0. This model reaches a F1 score of 86.9 on the dev set (for comparison, Bert `bert-base-uncased` version reaches a 88.5 F1 score).
+- `distilbert-base-cased`: DistilBERT English language model pretrained on the same data used to pretrain Bert (concatenation of the Toronto Book Corpus and full English Wikipedia) using distillation with the supervision of the `bert-base-cased` version of Bert. The model has 6 layers, 768 dimension and 12 heads, totalizing 65M parameters.
+- `distilbert-base-cased-distilled-squad`: A finetuned version of `distilbert-base-cased` finetuned using (a second step of) knowledge distillation on SQuAD 1.0. This model reaches a F1 score of 87.1 on the dev set (for comparison, Bert `bert-base-cased` version reaches a 88.7 F1 score).
 - `distilbert-base-german-cased`: DistilBERT German language model pretrained on 1/2 of the data used to pretrain Bert using distillation with the supervision of the `bert-base-german-dbmdz-cased` version of German DBMDZ Bert. For NER tasks the model reaches a F1 score of 83.49 on the CoNLL-2003 test set (for comparison, `bert-base-german-dbmdz-cased` reaches a 84.52 F1 score), and a F1 score of 85.23 on the GermEval 2014 test set (`bert-base-german-dbmdz-cased` reaches a 86.89 F1 score).
 - `distilgpt2`: DistilGPT2 English language model pretrained with the supervision of `gpt2` (the smallest version of GPT2) on [OpenWebTextCorpus](https://skylion007.github.io/OpenWebTextCorpus/), a reproduction of OpenAI's WebText dataset. The model has 6 layers, 768 dimension and 12 heads, totalizing 82M parameters (compared to 124M parameters for GPT2). On average, DistilGPT2 is two times faster than GPT2.
 - `distilroberta-base`: DistilRoBERTa English language model pretrained with the supervision of `roberta-base` solely on [OpenWebTextCorpus](https://skylion007.github.io/OpenWebTextCorpus/), a reproduction of OpenAI's WebText dataset (it is ~4 times less training data than the teacher RoBERTa). The model has 6 layers, 768 dimension and 12 heads, totalizing 82M parameters (compared to 125M parameters for RoBERTa-base). On average DistilRoBERTa is twice as fast as Roberta-base.
@@ -70,8 +76,8 @@ Transformers includes five pre-trained Distil* models, currently only provided f
 Using DistilBERT is very similar to using BERT. DistilBERT share the same tokenizer as BERT's `bert-base-uncased` even though we provide a link to this tokenizer under the `DistilBertTokenizer` name to have a consistent naming between the library models.
 
 ```python
-tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
-model = DistilBertModel.from_pretrained('distilbert-base-uncased')
+tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-cased')
+model = DistilBertModel.from_pretrained('distilbert-base-cased')
 
 input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)
 outputs = model(input_ids)
@@ -79,6 +85,7 @@ last_hidden_states = outputs[0]  # The last hidden-state is the first element of
 ```
 
 Similarly, using the other Distil* models simply consists in calling the base classes with a different pretrained checkpoint:
+- DistilBERT uncased: `model = DistilBertModel.from_pretrained('distilbert-base-uncased')`
 - DistilGPT2: `model = GPT2Model.from_pretrained('distilgpt2')`
 - DistilRoBERTa: `model = RobertaModel.from_pretrained('distilroberta-base')`
 - DistilmBERT: `model = DistilBertModel.from_pretrained('distilbert-base-multilingual-cased')`
@@ -104,7 +111,7 @@ python scripts/binarized_data.py \
     --dump_file data/binarized_text
 ```
 
-Our implementation of masked language modeling loss follows [XLM](https://github.com/facebookresearch/XLM)'s one and smoothes the probability of masking with a factor that put more emphasis on rare words. Thus we count the occurences of each tokens in the data:
+Our implementation of masked language modeling loss follows [XLM](https://github.com/facebookresearch/XLM)'s one and smoothes the probability of masking with a factor that put more emphasis on rare words. Thus we count the occurrences of each tokens in the data:
 
 ```bash
 python scripts/token_counts.py \
@@ -172,7 +179,7 @@ Happy distillation!
 
 ## Citation
 
-If you find the ressource useful, you should cite the following paper:
+If you find the resource useful, you should cite the following paper:
 
 ```
 @inproceedings{sanh2019distilbert,

examples/distillation/distiller.py

@@ -15,39 +15,36 @@
 """ The distiller to distil the student.
     Adapted in part from Facebook, Inc XLM model (https://github.com/facebookresearch/XLM)
 """
-import os
 import math
-import psutil
+import os
 import time
-from tqdm import trange, tqdm
-import numpy as np
 
+import psutil
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 from torch.optim import AdamW
+from torch.utils.data import BatchSampler, DataLoader, RandomSampler
 from torch.utils.data.distributed import DistributedSampler
-from torch.utils.data import RandomSampler, BatchSampler, DataLoader
+from tqdm import tqdm
+
+from grouped_batch_sampler import GroupedBatchSampler, create_lengths_groups
+from lm_seqs_dataset import LmSeqsDataset
+from transformers import get_linear_schedule_with_warmup
+from utils import logger
+
 
 try:
     from torch.utils.tensorboard import SummaryWriter
-except:
+except ImportError:
     from tensorboardX import SummaryWriter
 
-from transformers import get_linear_schedule_with_warmup
-
-from utils import logger
-from lm_seqs_dataset import LmSeqsDataset
-from grouped_batch_sampler import GroupedBatchSampler, create_lengths_groups
 
 class Distiller:
-    def __init__(self,
-                 params: dict,
-                 dataset: LmSeqsDataset,
-                 token_probs: torch.tensor,
-                 student: nn.Module,
-                 teacher: nn.Module):
-        logger.info('Initializing Distiller')
+    def __init__(
+        self, params: dict, dataset: LmSeqsDataset, token_probs: torch.tensor, student: nn.Module, teacher: nn.Module
+    ):
+        logger.info("Initializing Distiller")
         self.params = params
         self.dump_path = params.dump_path
         self.multi_gpu = params.multi_gpu
@@ -70,12 +67,10 @@ class Distiller:
         else:
             sampler = BatchSampler(sampler=sampler, batch_size=params.batch_size, drop_last=False)
 
-        self.dataloader = DataLoader(dataset=dataset,
-                                     batch_sampler=sampler,
-                                     collate_fn=dataset.batch_sequences)
+        self.dataloader = DataLoader(dataset=dataset, batch_sampler=sampler, collate_fn=dataset.batch_sequences)
 
         self.temperature = params.temperature
-        assert self.temperature > 0.
+        assert self.temperature > 0.0
 
         self.alpha_ce = params.alpha_ce
         self.alpha_mlm = params.alpha_mlm
@@ -85,18 +80,18 @@ class Distiller:
 
         self.mlm = params.mlm
         if self.mlm:
-            logger.info(f'Using MLM loss for LM step.')
+            logger.info("Using MLM loss for LM step.")
             self.mlm_mask_prop = params.mlm_mask_prop
             assert 0.0 <= self.mlm_mask_prop <= 1.0
             assert params.word_mask + params.word_keep + params.word_rand == 1.0
             self.pred_probs = torch.FloatTensor([params.word_mask, params.word_keep, params.word_rand])
-            self.pred_probs = self.pred_probs.to(f'cuda:{params.local_rank}') if params.n_gpu > 0 else self.pred_probs
-            self.token_probs = token_probs.to(f'cuda:{params.local_rank}') if params.n_gpu > 0 else token_probs
+            self.pred_probs = self.pred_probs.to(f"cuda:{params.local_rank}") if params.n_gpu > 0 else self.pred_probs
+            self.token_probs = token_probs.to(f"cuda:{params.local_rank}") if params.n_gpu > 0 else token_probs
             if self.fp16:
                 self.pred_probs = self.pred_probs.half()
                 self.token_probs = self.token_probs.half()
         else:
-            logger.info(f'Using CLM loss for LM step.')
+            logger.info("Using CLM loss for LM step.")
 
         self.epoch = 0
         self.n_iter = 0
@@ -107,38 +102,54 @@ class Distiller:
         self.last_loss_ce = 0
         self.last_loss_mlm = 0
         self.last_loss_clm = 0
-        if self.alpha_mse > 0.: self.last_loss_mse = 0
-        if self.alpha_cos > 0.: self.last_loss_cos = 0
+        if self.alpha_mse > 0.0:
+            self.last_loss_mse = 0
+        if self.alpha_cos > 0.0:
+            self.last_loss_cos = 0
         self.last_log = 0
 
-        self.ce_loss_fct = nn.KLDivLoss(reduction='batchmean')
-        self.lm_loss_fct = nn.CrossEntropyLoss(ignore_index=-1)
-        if self.alpha_mse > 0.:
-            self.mse_loss_fct = nn.MSELoss(reduction='sum')
-        if self.alpha_cos > 0.:
-            self.cosine_loss_fct = nn.CosineEmbeddingLoss(reduction='mean')
+        self.ce_loss_fct = nn.KLDivLoss(reduction="batchmean")
+        self.lm_loss_fct = nn.CrossEntropyLoss(ignore_index=-100)
+        if self.alpha_mse > 0.0:
+            self.mse_loss_fct = nn.MSELoss(reduction="sum")
+        if self.alpha_cos > 0.0:
+            self.cosine_loss_fct = nn.CosineEmbeddingLoss(reduction="mean")
 
-        logger.info('--- Initializing model optimizer')
+        logger.info("--- Initializing model optimizer")
         assert params.gradient_accumulation_steps >= 1
         self.num_steps_epoch = len(self.dataloader)
-        num_train_optimization_steps = int(self.num_steps_epoch / params.gradient_accumulation_steps * params.n_epoch) + 1
+        num_train_optimization_steps = (
+            int(self.num_steps_epoch / params.gradient_accumulation_steps * params.n_epoch) + 1
+        )
 
-        no_decay = ['bias', 'LayerNorm.weight']
+        no_decay = ["bias", "LayerNorm.weight"]
         optimizer_grouped_parameters = [
-            {'params': [p for n, p in student.named_parameters() if not any(nd in n for nd in no_decay) and p.requires_grad], 'weight_decay': params.weight_decay},
-            {'params': [p for n, p in student.named_parameters() if any(nd in n for nd in no_decay) and p.requires_grad], 'weight_decay': 0.0}
+            {
+                "params": [
+                    p for n, p in student.named_parameters() if not any(nd in n for nd in no_decay) and p.requires_grad
+                ],
+                "weight_decay": params.weight_decay,
+            },
+            {
+                "params": [
+                    p for n, p in student.named_parameters() if any(nd in n for nd in no_decay) and p.requires_grad
+                ],
+                "weight_decay": 0.0,
+            },
         ]
-        logger.info("------ Number of trainable parameters (student): %i" % sum([p.numel() for p in self.student.parameters() if p.requires_grad]))
+        logger.info(
+            "------ Number of trainable parameters (student): %i"
+            % sum([p.numel() for p in self.student.parameters() if p.requires_grad])
+        )
         logger.info("------ Number of parameters (student): %i" % sum([p.numel() for p in self.student.parameters()]))
-        self.optimizer = AdamW(optimizer_grouped_parameters,
-                               lr=params.learning_rate,
-                               eps=params.adam_epsilon,
-                               betas=(0.9, 0.98))
+        self.optimizer = AdamW(
+            optimizer_grouped_parameters, lr=params.learning_rate, eps=params.adam_epsilon, betas=(0.9, 0.98)
+        )
 
         warmup_steps = math.ceil(num_train_optimization_steps * params.warmup_prop)
-        self.scheduler = get_linear_schedule_with_warmup(self.optimizer,
-                                                num_warmup_steps=warmup_steps,
-                                                num_training_steps=num_train_optimization_steps)
+        self.scheduler = get_linear_schedule_with_warmup(
+            self.optimizer, num_warmup_steps=warmup_steps, num_training_steps=num_train_optimization_steps
+        )
 
         if self.fp16:
             try:
@@ -146,33 +157,36 @@ class Distiller:
             except ImportError:
                 raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
             logger.info(f"Using fp16 training: {self.params.fp16_opt_level} level")
-            self.student, self.optimizer = amp.initialize(self.student,
-                                                          self.optimizer,
-                                                          opt_level=self.params.fp16_opt_level)
+            self.student, self.optimizer = amp.initialize(
+                self.student, self.optimizer, opt_level=self.params.fp16_opt_level
+            )
             self.teacher = self.teacher.half()
 
         if self.multi_gpu:
             if self.fp16:
                 from apex.parallel import DistributedDataParallel
+
                 logger.info("Using apex.parallel.DistributedDataParallel for distributed training.")
                 self.student = DistributedDataParallel(self.student)
             else:
                 from torch.nn.parallel import DistributedDataParallel
+
                 logger.info("Using nn.parallel.DistributedDataParallel for distributed training.")
-                self.student = DistributedDataParallel(self.student,
-                                                       device_ids=[params.local_rank],
-                                                       output_device=params.local_rank,
-                                                       find_unused_parameters=True)
+                self.student = DistributedDataParallel(
+                    self.student,
+                    device_ids=[params.local_rank],
+                    output_device=params.local_rank,
+                    find_unused_parameters=True,
+                )
 
         self.is_master = params.is_master
         if self.is_master:
-            logger.info('--- Initializing Tensorboard')
-            self.tensorboard = SummaryWriter(log_dir=os.path.join(self.dump_path, 'log', 'train'))
-            self.tensorboard.add_text(tag='config/training', text_string=str(self.params), global_step=0)
-            self.tensorboard.add_text(tag='config/student', text_string=str(self.student_config), global_step=0)
+            logger.info("--- Initializing Tensorboard")
+            self.tensorboard = SummaryWriter(log_dir=os.path.join(self.dump_path, "log", "train"))
+            self.tensorboard.add_text(tag="config/training", text_string=str(self.params), global_step=0)
+            self.tensorboard.add_text(tag="config/student", text_string=str(self.student_config), global_step=0)
 
-    def prepare_batch_mlm(self,
-                          batch):
+    def prepare_batch_mlm(self, batch):
         """
         Prepare the batch: from the token_ids and the lenghts, compute the attention mask and the masked label for MLM.
 
@@ -186,13 +200,13 @@ class Distiller:
         -------
             token_ids: `torch.tensor(bs, seq_length)` - The token ids after the modifications for MLM.
             attn_mask: `torch.tensor(bs, seq_length)` - The attention mask for the self-attention.
-            mlm_labels: `torch.tensor(bs, seq_length)` - The masked languge modeling labels. There is a -1 where there is nothing to predict.
+            mlm_labels: `torch.tensor(bs, seq_length)` - The masked languge modeling labels. There is a -100 where there is nothing to predict.
         """
         token_ids, lengths = batch
         token_ids, lengths = self.round_batch(x=token_ids, lengths=lengths)
         assert token_ids.size(0) == lengths.size(0)
 
-        attn_mask = (torch.arange(token_ids.size(1), dtype=torch.long, device=lengths.device) < lengths[:, None])
+        attn_mask = torch.arange(token_ids.size(1), dtype=torch.long, device=lengths.device) < lengths[:, None]
 
         bs, max_seq_len = token_ids.size()
         mlm_labels = token_ids.new(token_ids.size()).copy_(token_ids)
@@ -200,11 +214,13 @@ class Distiller:
         x_prob = self.token_probs[token_ids.flatten()]
         n_tgt = math.ceil(self.mlm_mask_prop * lengths.sum().item())
         tgt_ids = torch.multinomial(x_prob / x_prob.sum(), n_tgt, replacement=False)
-        pred_mask = torch.zeros(bs * max_seq_len, dtype=torch.bool, device=token_ids.device) # previously `dtype=torch.uint8`, cf pytorch 1.2.0 compatibility
+        pred_mask = torch.zeros(
+            bs * max_seq_len, dtype=torch.bool, device=token_ids.device
+        )  # previously `dtype=torch.uint8`, cf pytorch 1.2.0 compatibility
         pred_mask[tgt_ids] = 1
         pred_mask = pred_mask.view(bs, max_seq_len)
 
-        pred_mask[token_ids == self.params.special_tok_ids['pad_token']] = 0
+        pred_mask[token_ids == self.params.special_tok_ids["pad_token"]] = 0
 
         # mask a number of words == 0 [8] (faster with fp16)
         if self.fp16:
@@ -213,26 +229,29 @@ class Distiller:
                 pred_mask = pred_mask.view(-1)
                 n2 = max(n1 % 8, 8 * (n1 // 8))
                 if n2 != n1:
-                    pred_mask[torch.nonzero(pred_mask).view(-1)[:n1-n2]] = 0
+                    pred_mask[torch.nonzero(pred_mask).view(-1)[: n1 - n2]] = 0
                 pred_mask = pred_mask.view(bs, max_seq_len)
                 assert pred_mask.sum().item() % 8 == 0, pred_mask.sum().item()
 
         _token_ids_real = token_ids[pred_mask]
         _token_ids_rand = _token_ids_real.clone().random_(self.vocab_size)
-        _token_ids_mask = _token_ids_real.clone().fill_(self.params.special_tok_ids['mask_token'])
+        _token_ids_mask = _token_ids_real.clone().fill_(self.params.special_tok_ids["mask_token"])
         probs = torch.multinomial(self.pred_probs, len(_token_ids_real), replacement=True)
-        _token_ids = _token_ids_mask * (probs == 0).long() + _token_ids_real * (probs == 1).long() + _token_ids_rand * (probs == 2).long()
+        _token_ids = (
+            _token_ids_mask * (probs == 0).long()
+            + _token_ids_real * (probs == 1).long()
+            + _token_ids_rand * (probs == 2).long()
+        )
         token_ids = token_ids.masked_scatter(pred_mask, _token_ids)
 
-        mlm_labels[~pred_mask] = -1 # previously `mlm_labels[1-pred_mask] = -1`, cf pytorch 1.2.0 compatibility
+        mlm_labels[~pred_mask] = -100  # previously `mlm_labels[1-pred_mask] = -1`, cf pytorch 1.2.0 compatibility
 
         # sanity checks
         assert 0 <= token_ids.min() <= token_ids.max() < self.vocab_size
 
         return token_ids, attn_mask, mlm_labels
 
-    def prepare_batch_clm(self,
-                          batch):
+    def prepare_batch_clm(self, batch):
         """
         Prepare the batch: from the token_ids and the lenghts, compute the attention mask and the labels for CLM.
 
@@ -246,24 +265,22 @@ class Distiller:
         -------
             token_ids: `torch.tensor(bs, seq_length)` - The token ids after the modifications for MLM.
             attn_mask: `torch.tensor(bs, seq_length)` - The attention mask for the self-attention.
-            clm_labels: `torch.tensor(bs, seq_length)` - The causal languge modeling labels. There is a -1 where there is nothing to predict.
+            clm_labels: `torch.tensor(bs, seq_length)` - The causal languge modeling labels. There is a -100 where there is nothing to predict.
         """
         token_ids, lengths = batch
         token_ids, lengths = self.round_batch(x=token_ids, lengths=lengths)
         assert token_ids.size(0) == lengths.size(0)
 
-        attn_mask = (torch.arange(token_ids.size(1), dtype=torch.long, device=lengths.device) < lengths[:, None])
+        attn_mask = torch.arange(token_ids.size(1), dtype=torch.long, device=lengths.device) < lengths[:, None]
         clm_labels = token_ids.new(token_ids.size()).copy_(token_ids)
-        clm_labels[~attn_mask] = -1 # previously `clm_labels[1-attn_mask] = -1`, cf pytorch 1.2.0 compatibility
+        clm_labels[~attn_mask] = -100  # previously `clm_labels[1-attn_mask] = -1`, cf pytorch 1.2.0 compatibility
 
         # sanity checks
         assert 0 <= token_ids.min() <= token_ids.max() < self.vocab_size
 
         return token_ids, attn_mask, clm_labels
 
-    def round_batch(self,
-                    x: torch.tensor,
-                    lengths: torch.tensor):
+    def round_batch(self, x: torch.tensor, lengths: torch.tensor):
         """
         For float16 only.
         Sub-sample sentences in a batch, and add padding, so that each dimension is a multiple of 8.
@@ -299,9 +316,9 @@ class Distiller:
             pad = 8 - (ml1 % 8)
             ml2 = ml1 + pad
             if self.mlm:
-                pad_id = self.params.special_tok_ids['pad_token']
+                pad_id = self.params.special_tok_ids["pad_token"]
             else:
-                pad_id = self.params.special_tok_ids['unk_token']
+                pad_id = self.params.special_tok_ids["unk_token"]
             padding_tensor = torch.zeros(bs2, pad, dtype=torch.long, device=x.device).fill_(pad_id)
             x = torch.cat([x, padding_tensor], 1)
             assert x.size() == (bs2, ml2)
@@ -314,20 +331,22 @@ class Distiller:
         """
         The real training loop.
         """
-        if self.is_master: logger.info('Starting training')
+        if self.is_master:
+            logger.info("Starting training")
         self.last_log = time.time()
         self.student.train()
         self.teacher.eval()
 
         for _ in range(self.params.n_epoch):
-            if self.is_master: logger.info(f'--- Starting epoch {self.epoch}/{self.params.n_epoch-1}')
+            if self.is_master:
+                logger.info(f"--- Starting epoch {self.epoch}/{self.params.n_epoch-1}")
             if self.multi_gpu:
                 torch.distributed.barrier()
 
             iter_bar = tqdm(self.dataloader, desc="-Iter", disable=self.params.local_rank not in [-1, 0])
             for batch in iter_bar:
                 if self.params.n_gpu > 0:
-                    batch = tuple(t.to(f'cuda:{self.params.local_rank}') for t in batch)
+                    batch = tuple(t.to(f"cuda:{self.params.local_rank}") for t in batch)
 
                 if self.mlm:
                     token_ids, attn_mask, lm_labels = self.prepare_batch_mlm(batch=batch)
@@ -336,22 +355,21 @@ class Distiller:
                 self.step(input_ids=token_ids, attention_mask=attn_mask, lm_labels=lm_labels)
 
                 iter_bar.update()
-                iter_bar.set_postfix({'Last_loss': f'{self.last_loss:.2f}',
-                                      'Avg_cum_loss': f'{self.total_loss_epoch/self.n_iter:.2f}'})
+                iter_bar.set_postfix(
+                    {"Last_loss": f"{self.last_loss:.2f}", "Avg_cum_loss": f"{self.total_loss_epoch/self.n_iter:.2f}"}
+                )
             iter_bar.close()
 
-            if self.is_master: logger.info(f'--- Ending epoch {self.epoch}/{self.params.n_epoch-1}')
+            if self.is_master:
+                logger.info(f"--- Ending epoch {self.epoch}/{self.params.n_epoch-1}")
             self.end_epoch()
 
         if self.is_master:
-            logger.info(f'Save very last checkpoint as `pytorch_model.bin`.')
-            self.save_checkpoint(checkpoint_name=f'pytorch_model.bin')
-            logger.info('Training is finished')
+            logger.info("Save very last checkpoint as `pytorch_model.bin`.")
+            self.save_checkpoint(checkpoint_name="pytorch_model.bin")
+            logger.info("Training is finished")
 
-    def step(self,
-             input_ids: torch.tensor,
-             attention_mask: torch.tensor,
-             lm_labels: torch.tensor):
+    def step(self, input_ids: torch.tensor, attention_mask: torch.tensor, lm_labels: torch.tensor):
         """
         One optimization step: forward of student AND teacher, backward on the loss (for gradient accumulation),
         and possibly a parameter update (depending on the gradient accumulation).
@@ -363,78 +381,91 @@ class Distiller:
         lm_labels: `torch.tensor(bs, seq_length)` - The language modeling labels (mlm labels for MLM and clm labels for CLM).
         """
         if self.mlm:
-            s_logits, s_hidden_states = self.student(input_ids=input_ids, attention_mask=attention_mask)     # (bs, seq_length, voc_size)
+            s_logits, s_hidden_states = self.student(
+                input_ids=input_ids, attention_mask=attention_mask
+            )  # (bs, seq_length, voc_size)
             with torch.no_grad():
-                t_logits, t_hidden_states = self.teacher(input_ids=input_ids, attention_mask=attention_mask) # (bs, seq_length, voc_size)
+                t_logits, t_hidden_states = self.teacher(
+                    input_ids=input_ids, attention_mask=attention_mask
+                )  # (bs, seq_length, voc_size)
         else:
-            s_logits, _, s_hidden_states = self.student(input_ids=input_ids, attention_mask=None)            # (bs, seq_length, voc_size)
+            s_logits, _, s_hidden_states = self.student(
+                input_ids=input_ids, attention_mask=None
+            )  # (bs, seq_length, voc_size)
             with torch.no_grad():
-                t_logits, _, t_hidden_states = self.teacher(input_ids=input_ids, attention_mask=None)           # (bs, seq_length, voc_size)
+                t_logits, _, t_hidden_states = self.teacher(
+                    input_ids=input_ids, attention_mask=None
+                )  # (bs, seq_length, voc_size)
         assert s_logits.size() == t_logits.size()
 
-        #https://github.com/peterliht/knowledge-distillation-pytorch/blob/master/model/net.py#L100
-        #https://github.com/peterliht/knowledge-distillation-pytorch/issues/2
+        # https://github.com/peterliht/knowledge-distillation-pytorch/blob/master/model/net.py#L100
+        # https://github.com/peterliht/knowledge-distillation-pytorch/issues/2
         if self.params.restrict_ce_to_mask:
-            mask = (lm_labels>-1).unsqueeze(-1).expand_as(s_logits)    # (bs, seq_lenth, voc_size)
+            mask = (lm_labels > -1).unsqueeze(-1).expand_as(s_logits)  # (bs, seq_lenth, voc_size)
         else:
-            mask = attention_mask.unsqueeze(-1).expand_as(s_logits)    # (bs, seq_lenth, voc_size)
-        s_logits_slct = torch.masked_select(s_logits, mask)            # (bs * seq_length * voc_size) modulo the 1s in mask
-        s_logits_slct = s_logits_slct.view(-1, s_logits.size(-1))      # (bs * seq_length, voc_size) modulo the 1s in mask
-        t_logits_slct = torch.masked_select(t_logits, mask)            # (bs * seq_length * voc_size) modulo the 1s in mask
-        t_logits_slct = t_logits_slct.view(-1, s_logits.size(-1))      # (bs * seq_length, voc_size) modulo the 1s in mask
+            mask = attention_mask.unsqueeze(-1).expand_as(s_logits)  # (bs, seq_lenth, voc_size)
+        s_logits_slct = torch.masked_select(s_logits, mask)  # (bs * seq_length * voc_size) modulo the 1s in mask
+        s_logits_slct = s_logits_slct.view(-1, s_logits.size(-1))  # (bs * seq_length, voc_size) modulo the 1s in mask
+        t_logits_slct = torch.masked_select(t_logits, mask)  # (bs * seq_length * voc_size) modulo the 1s in mask
+        t_logits_slct = t_logits_slct.view(-1, s_logits.size(-1))  # (bs * seq_length, voc_size) modulo the 1s in mask
         assert t_logits_slct.size() == s_logits_slct.size()
 
-        loss_ce = self.ce_loss_fct(F.log_softmax(s_logits_slct/self.temperature, dim=-1),
-                                   F.softmax(t_logits_slct/self.temperature, dim=-1)) * (self.temperature)**2
-        loss = self.alpha_ce*loss_ce
+        loss_ce = (
+            self.ce_loss_fct(
+                F.log_softmax(s_logits_slct / self.temperature, dim=-1),
+                F.softmax(t_logits_slct / self.temperature, dim=-1),
+            )
+            * (self.temperature) ** 2
+        )
+        loss = self.alpha_ce * loss_ce
 
-        if self.alpha_mlm > 0.:
+        if self.alpha_mlm > 0.0:
             loss_mlm = self.lm_loss_fct(s_logits.view(-1, s_logits.size(-1)), lm_labels.view(-1))
             loss += self.alpha_mlm * loss_mlm
-        if self.alpha_clm > 0.:
+        if self.alpha_clm > 0.0:
             shift_logits = s_logits[..., :-1, :].contiguous()
             shift_labels = lm_labels[..., 1:].contiguous()
-            loss_clm = self.lm_loss_fct(shift_logits.view(-1, shift_logits.size(-1)),
-                                        shift_labels.view(-1))
+            loss_clm = self.lm_loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
             loss += self.alpha_clm * loss_clm
 
-        if self.alpha_mse > 0.:
-            loss_mse = self.mse_loss_fct(s_logits_slct, t_logits_slct)/s_logits_slct.size(0) # Reproducing batchmean reduction
+        if self.alpha_mse > 0.0:
+            loss_mse = self.mse_loss_fct(s_logits_slct, t_logits_slct) / s_logits_slct.size(
+                0
+            )  # Reproducing batchmean reduction
             loss += self.alpha_mse * loss_mse
-        if self.alpha_cos > 0.:
-            s_hidden_states = s_hidden_states[-1]                              # (bs, seq_length, dim)
-            t_hidden_states = t_hidden_states[-1]                              # (bs, seq_length, dim)
-            mask = attention_mask.unsqueeze(-1).expand_as(s_hidden_states)     # (bs, seq_length, dim)
+        if self.alpha_cos > 0.0:
+            s_hidden_states = s_hidden_states[-1]  # (bs, seq_length, dim)
+            t_hidden_states = t_hidden_states[-1]  # (bs, seq_length, dim)
+            mask = attention_mask.unsqueeze(-1).expand_as(s_hidden_states)  # (bs, seq_length, dim)
             assert s_hidden_states.size() == t_hidden_states.size()
             dim = s_hidden_states.size(-1)
-            
-            s_hidden_states_slct = torch.masked_select(s_hidden_states, mask)        # (bs * seq_length * dim)
-            s_hidden_states_slct = s_hidden_states_slct.view(-1, dim)                # (bs * seq_length, dim)
-            t_hidden_states_slct = torch.masked_select(t_hidden_states, mask)        # (bs * seq_length * dim)
-            t_hidden_states_slct = t_hidden_states_slct.view(-1, dim)                # (bs * seq_length, dim)
-        
-            target = s_hidden_states_slct.new(s_hidden_states_slct.size(0)).fill_(1) # (bs * seq_length,)
+
+            s_hidden_states_slct = torch.masked_select(s_hidden_states, mask)  # (bs * seq_length * dim)
+            s_hidden_states_slct = s_hidden_states_slct.view(-1, dim)  # (bs * seq_length, dim)
+            t_hidden_states_slct = torch.masked_select(t_hidden_states, mask)  # (bs * seq_length * dim)
+            t_hidden_states_slct = t_hidden_states_slct.view(-1, dim)  # (bs * seq_length, dim)
+
+            target = s_hidden_states_slct.new(s_hidden_states_slct.size(0)).fill_(1)  # (bs * seq_length,)
             loss_cos = self.cosine_loss_fct(s_hidden_states_slct, t_hidden_states_slct, target)
             loss += self.alpha_cos * loss_cos
 
         self.total_loss_epoch += loss.item()
         self.last_loss = loss.item()
         self.last_loss_ce = loss_ce.item()
-        if self.alpha_mlm > 0.:
+        if self.alpha_mlm > 0.0:
             self.last_loss_mlm = loss_mlm.item()
-        if self.alpha_clm > 0.:
+        if self.alpha_clm > 0.0:
             self.last_loss_clm = loss_clm.item()
-        if self.alpha_mse > 0.:
+        if self.alpha_mse > 0.0:
             self.last_loss_mse = loss_mse.item()
-        if self.alpha_cos > 0.:
+        if self.alpha_cos > 0.0:
             self.last_loss_cos = loss_cos.item()
 
         self.optimize(loss)
 
         self.n_sequences_epoch += input_ids.size(0)
 
-    def optimize(self,
-                 loss):
+    def optimize(self, loss):
         """
         Normalization on the loss (gradient accumulation or distributed training), followed by
         backward pass on the loss, possibly followed by a parameter update (depending on the gradient accumulation).
@@ -442,7 +473,7 @@ class Distiller:
         """
         # Check for NaN
         if (loss != loss).data.any():
-            logger.error('NaN detected')
+            logger.error("NaN detected")
             exit()
 
         if self.multi_gpu:
@@ -452,6 +483,7 @@ class Distiller:
 
         if self.fp16:
             from apex import amp
+
             with amp.scale_loss(loss, self.optimizer) as scaled_loss:
                 scaled_loss.backward()
         else:
@@ -488,53 +520,84 @@ class Distiller:
             return
 
         for param_name, param in self.student.named_parameters():
-            self.tensorboard.add_scalar(tag='parameter_mean/' + param_name, scalar_value=param.data.mean(), global_step=self.n_total_iter)
-            self.tensorboard.add_scalar(tag='parameter_std/' + param_name, scalar_value=param.data.std(), global_step=self.n_total_iter)
+            self.tensorboard.add_scalar(
+                tag="parameter_mean/" + param_name, scalar_value=param.data.mean(), global_step=self.n_total_iter
+            )
+            self.tensorboard.add_scalar(
+                tag="parameter_std/" + param_name, scalar_value=param.data.std(), global_step=self.n_total_iter
+            )
             if param.grad is None:
                 continue
-            self.tensorboard.add_scalar(tag="grad_mean/" + param_name, scalar_value=param.grad.data.mean(),global_step=self.n_total_iter)
-            self.tensorboard.add_scalar(tag="grad_std/" + param_name, scalar_value=param.grad.data.std(), global_step=self.n_total_iter)
+            self.tensorboard.add_scalar(
+                tag="grad_mean/" + param_name, scalar_value=param.grad.data.mean(), global_step=self.n_total_iter
+            )
+            self.tensorboard.add_scalar(
+                tag="grad_std/" + param_name, scalar_value=param.grad.data.std(), global_step=self.n_total_iter
+            )
 
-        self.tensorboard.add_scalar(tag="losses/cum_avg_loss_epoch", scalar_value=self.total_loss_epoch/self.n_iter, global_step=self.n_total_iter)
+        self.tensorboard.add_scalar(
+            tag="losses/cum_avg_loss_epoch",
+            scalar_value=self.total_loss_epoch / self.n_iter,
+            global_step=self.n_total_iter,
+        )
         self.tensorboard.add_scalar(tag="losses/loss", scalar_value=self.last_loss, global_step=self.n_total_iter)
-        self.tensorboard.add_scalar(tag="losses/loss_ce", scalar_value=self.last_loss_ce, global_step=self.n_total_iter)
-        if self.alpha_mlm > 0.:
-            self.tensorboard.add_scalar(tag="losses/loss_mlm", scalar_value=self.last_loss_mlm, global_step=self.n_total_iter)
-        if self.alpha_clm > 0.:
-            self.tensorboard.add_scalar(tag="losses/loss_clm", scalar_value=self.last_loss_clm, global_step=self.n_total_iter)
-        if self.alpha_mse > 0.:
-            self.tensorboard.add_scalar(tag="losses/loss_mse", scalar_value=self.last_loss_mse, global_step=self.n_total_iter)
-        if self.alpha_cos > 0.:
-            self.tensorboard.add_scalar(tag="losses/loss_cos", scalar_value=self.last_loss_cos, global_step=self.n_total_iter)
-        self.tensorboard.add_scalar(tag="learning_rate/lr", scalar_value=self.scheduler.get_lr()[0], global_step=self.n_total_iter)
-        
-        self.tensorboard.add_scalar(tag="global/memory_usage", scalar_value=psutil.virtual_memory()._asdict()['used']/1_000_000, global_step=self.n_total_iter)
-        self.tensorboard.add_scalar(tag="global/speed", scalar_value=time.time()-self.last_log, global_step=self.n_total_iter)
+        self.tensorboard.add_scalar(
+            tag="losses/loss_ce", scalar_value=self.last_loss_ce, global_step=self.n_total_iter
+        )
+        if self.alpha_mlm > 0.0:
+            self.tensorboard.add_scalar(
+                tag="losses/loss_mlm", scalar_value=self.last_loss_mlm, global_step=self.n_total_iter
+            )
+        if self.alpha_clm > 0.0:
+            self.tensorboard.add_scalar(
+                tag="losses/loss_clm", scalar_value=self.last_loss_clm, global_step=self.n_total_iter
+            )
+        if self.alpha_mse > 0.0:
+            self.tensorboard.add_scalar(
+                tag="losses/loss_mse", scalar_value=self.last_loss_mse, global_step=self.n_total_iter
+            )
+        if self.alpha_cos > 0.0:
+            self.tensorboard.add_scalar(
+                tag="losses/loss_cos", scalar_value=self.last_loss_cos, global_step=self.n_total_iter
+            )
+        self.tensorboard.add_scalar(
+            tag="learning_rate/lr", scalar_value=self.scheduler.get_lr()[0], global_step=self.n_total_iter
+        )
+
+        self.tensorboard.add_scalar(
+            tag="global/memory_usage",
+            scalar_value=psutil.virtual_memory()._asdict()["used"] / 1_000_000,
+            global_step=self.n_total_iter,
+        )
+        self.tensorboard.add_scalar(
+            tag="global/speed", scalar_value=time.time() - self.last_log, global_step=self.n_total_iter
+        )
 
     def end_epoch(self):
         """
         Finally arrived at the end of epoch (full pass on dataset).
         Do some tensorboard logging and checkpoint saving.
         """
-        logger.info(f'{self.n_sequences_epoch} sequences have been trained during this epoch.')
+        logger.info(f"{self.n_sequences_epoch} sequences have been trained during this epoch.")
 
         if self.is_master:
-            self.save_checkpoint(checkpoint_name=f'model_epoch_{self.epoch}.pth')
-            self.tensorboard.add_scalar(tag='epoch/loss', scalar_value=self.total_loss_epoch/self.n_iter, global_step=self.epoch)
+            self.save_checkpoint(checkpoint_name=f"model_epoch_{self.epoch}.pth")
+            self.tensorboard.add_scalar(
+                tag="epoch/loss", scalar_value=self.total_loss_epoch / self.n_iter, global_step=self.epoch
+            )
 
         self.epoch += 1
         self.n_sequences_epoch = 0
         self.n_iter = 0
         self.total_loss_epoch = 0
 
-    def save_checkpoint(self,
-                        checkpoint_name: str = 'checkpoint.pth'):
+    def save_checkpoint(self, checkpoint_name: str = "checkpoint.pth"):
         """
         Save the current state. Only by the master process.
         """
         if not self.is_master:
             return
-        mdl_to_save = self.student.module if hasattr(self.student, 'module') else self.student
+        mdl_to_save = self.student.module if hasattr(self.student, "module") else self.student
         mdl_to_save.config.save_pretrained(self.dump_path)
         state_dict = mdl_to_save.state_dict()
         torch.save(state_dict, os.path.join(self.dump_path, checkpoint_name))

examples/distillation/grouped_batch_sampler.py

@@ -17,18 +17,20 @@
 import bisect
 import copy
 from collections import defaultdict
-import numpy as np
 
+import numpy as np
 from torch.utils.data.sampler import BatchSampler, Sampler
 
 from utils import logger
 
+
 def _quantize(x, bins):
     bins = copy.deepcopy(bins)
     bins = sorted(bins)
     quantized = list(map(lambda y: bisect.bisect_right(bins, y), x))
     return quantized
 
+
 def create_lengths_groups(lengths, k=0):
     bins = np.arange(start=3, stop=k, step=4).tolist() if k > 0 else [10]
     groups = _quantize(lengths, bins)
@@ -39,6 +41,7 @@ def create_lengths_groups(lengths, k=0):
     logger.info("Count of instances per bin: {}".format(counts))
     return groups
 
+
 class GroupedBatchSampler(BatchSampler):
     """
     Wraps another sampler to yield a mini-batch of indices.
@@ -53,11 +56,11 @@ class GroupedBatchSampler(BatchSampler):
             0, i.e. they must be in the range [0, num_groups).
         batch_size (int): Size of mini-batch.
     """
+
     def __init__(self, sampler, group_ids, batch_size):
         if not isinstance(sampler, Sampler):
             raise ValueError(
-                "sampler should be an instance of "
-                "torch.utils.data.Sampler, but got sampler={}".format(sampler)
+                "sampler should be an instance of " "torch.utils.data.Sampler, but got sampler={}".format(sampler)
             )
         self.sampler = sampler
         self.group_ids = group_ids
@@ -73,7 +76,7 @@ class GroupedBatchSampler(BatchSampler):
             buffer_per_group[group_id].append(idx)
             samples_per_group[group_id].append(idx)
             if len(buffer_per_group[group_id]) == self.batch_size:
-                yield buffer_per_group[group_id] #TODO
+                yield buffer_per_group[group_id]  # TODO
                 num_batches += 1
                 del buffer_per_group[group_id]
             assert len(buffer_per_group[group_id]) < self.batch_size
@@ -90,8 +93,8 @@ class GroupedBatchSampler(BatchSampler):
             for group_id, idxs in sorted(buffer_per_group.items(), key=lambda x: x[0]):
                 batch_idx.extend(idxs)
                 if len(batch_idx) >= self.batch_size:
-                    yield batch_idx[:self.batch_size]
-                    batch_idx = batch_idx[self.batch_size:]
+                    yield batch_idx[: self.batch_size]
+                    batch_idx = batch_idx[self.batch_size :]
                     num_remaining -= 1
             if len(batch_idx) > 0:
                 yield batch_idx

examples/distillation/lm_seqs_dataset.py

@@ -15,12 +15,13 @@
 """ Dataset to distilled models
     adapted in part from Facebook, Inc XLM model (https://github.com/facebookresearch/XLM)
 """
+import numpy as np
 import torch
 from torch.utils.data import Dataset
 
-import numpy as np
 from utils import logger
 
+
 class LmSeqsDataset(Dataset):
     """Custom Dataset wrapping language modeling sequences.
 
@@ -32,9 +33,7 @@ class LmSeqsDataset(Dataset):
         data: `List[np.array[int]]
     """
 
-    def __init__(self,
-                 params,
-                 data):
+    def __init__(self, params, data):
         self.params = params
 
         self.token_ids = np.array(data)
@@ -43,6 +42,7 @@ class LmSeqsDataset(Dataset):
         self.check()
         self.remove_long_sequences()
         self.remove_empty_sequences()
+        self.remove_unknown_sequences()
         self.check()
         self.print_statistics()
 
@@ -57,7 +57,7 @@ class LmSeqsDataset(Dataset):
         Some sanity checks
         """
         assert len(self.token_ids) == len(self.lengths)
-        assert all(self.lengths[i] == len(self.token_ids[i]) for i in range(len(self.lengths))) 
+        assert all(self.lengths[i] == len(self.token_ids[i]) for i in range(len(self.lengths)))
 
     def remove_long_sequences(self):
         """
@@ -65,17 +65,17 @@ class LmSeqsDataset(Dataset):
         """
         max_len = self.params.max_model_input_size
         indices = self.lengths > max_len
-        logger.info(f'Splitting {sum(indices)} too long sequences.')
+        logger.info(f"Splitting {sum(indices)} too long sequences.")
 
         def divide_chunks(l, n):
-            return [l[i:i + n] for i in range(0, len(l), n)]
+            return [l[i : i + n] for i in range(0, len(l), n)]
 
         new_tok_ids = []
         new_lengths = []
         if self.params.mlm:
-            cls_id, sep_id = self.params.special_tok_ids['cls_token'], self.params.special_tok_ids['sep_token']
+            cls_id, sep_id = self.params.special_tok_ids["cls_token"], self.params.special_tok_ids["sep_token"]
         else:
-            cls_id, sep_id = self.params.special_tok_ids['bos_token'], self.params.special_tok_ids['eos_token']
+            cls_id, sep_id = self.params.special_tok_ids["bos_token"], self.params.special_tok_ids["eos_token"]
 
         for seq_, len_ in zip(self.token_ids, self.lengths):
             assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
@@ -84,7 +84,7 @@ class LmSeqsDataset(Dataset):
                 new_lengths.append(len_)
             else:
                 sub_seqs = []
-                for sub_s in divide_chunks(seq_, max_len-2):
+                for sub_s in divide_chunks(seq_, max_len - 2):
                     if sub_s[0] != cls_id:
                         sub_s = np.insert(sub_s, 0, cls_id)
                     if sub_s[-1] != sep_id:
@@ -108,7 +108,23 @@ class LmSeqsDataset(Dataset):
         self.token_ids = self.token_ids[indices]
         self.lengths = self.lengths[indices]
         new_size = len(self)
-        logger.info(f'Remove {init_size - new_size} too short (<=11 tokens) sequences.')
+        logger.info(f"Remove {init_size - new_size} too short (<=11 tokens) sequences.")
+
+    def remove_unknown_sequences(self):
+        """
+        Remove sequences with a (too) high level of unknown tokens.
+        """
+        if "unk_token" not in self.params.special_tok_ids:
+            return
+        else:
+            unk_token_id = self.params.special_tok_ids["unk_token"]
+        init_size = len(self)
+        unk_occs = np.array([np.count_nonzero(a == unk_token_id) for a in self.token_ids])
+        indices = (unk_occs / self.lengths) < 0.5
+        self.token_ids = self.token_ids[indices]
+        self.lengths = self.lengths[indices]
+        new_size = len(self)
+        logger.info(f"Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).")
 
     def print_statistics(self):
         """
@@ -116,7 +132,7 @@ class LmSeqsDataset(Dataset):
         """
         if not self.params.is_master:
             return
-        logger.info(f'{len(self)} sequences')
+        logger.info(f"{len(self)} sequences")
         # data_len = sum(self.lengths)
         # nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
         # logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')
@@ -125,8 +141,7 @@ class LmSeqsDataset(Dataset):
         # nb_unkown = sum([(t==unk_idx).sum() for t in self.token_ids])
         # logger.info(f'{nb_unkown} unknown tokens (covering {100*nb_unkown/data_len:.2f}% of the data)')
 
-    def batch_sequences(self,
-                        batch):
+    def batch_sequences(self, batch):
         """
         Do the padding and transform into torch.tensor.
         """
@@ -139,13 +154,13 @@ class LmSeqsDataset(Dataset):
 
         # Pad token ids
         if self.params.mlm:
-            pad_idx = self.params.special_tok_ids['pad_token']
+            pad_idx = self.params.special_tok_ids["pad_token"]
         else:
-            pad_idx = self.params.special_tok_ids['unk_token']
-        tk_ = [list(t.astype(int)) + [pad_idx]*(max_seq_len_-len(t)) for t in token_ids]
+            pad_idx = self.params.special_tok_ids["unk_token"]
+        tk_ = [list(t.astype(int)) + [pad_idx] * (max_seq_len_ - len(t)) for t in token_ids]
         assert len(tk_) == len(token_ids)
         assert all(len(t) == max_seq_len_ for t in tk_)
 
-        tk_t = torch.tensor(tk_)      # (bs, max_seq_len_)
+        tk_t = torch.tensor(tk_)  # (bs, max_seq_len_)
         lg_t = torch.tensor(lengths)  # (bs)
         return tk_t, lg_t

examples/distillation/requirements.txt

@@ -1,6 +1,7 @@
+transformers
+
 gitpython==3.0.2
 tensorboard>=1.14.0
 tensorboardX==1.8
-psutil==5.6.3
+psutil==5.6.6
 scipy==1.3.1
-transformers

examples/distillation/scripts/binarized_data.py

@@ -16,75 +16,79 @@
 Preprocessing script before distillation.
 """
 import argparse
+import logging
 import pickle
 import random
 import time
-import numpy as np
-from transformers import BertTokenizer, RobertaTokenizer, GPT2Tokenizer
-import logging
 
-logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s',
-                    datefmt = '%m/%d/%Y %H:%M:%S',
-                    level = logging.INFO)
+import numpy as np
+
+from transformers import BertTokenizer, GPT2Tokenizer, RobertaTokenizer
+
+
+logging.basicConfig(
+    format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
+)
 logger = logging.getLogger(__name__)
 
+
 def main():
-    parser = argparse.ArgumentParser(description="Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).")
-    parser.add_argument('--file_path', type=str, default='data/dump.txt',
-                        help='The path to the data.')
-    parser.add_argument('--tokenizer_type', type=str, default='bert', choices=['bert', 'roberta', 'gpt2'])
-    parser.add_argument('--tokenizer_name', type=str, default='bert-base-uncased',
-                        help="The tokenizer to use.")
-    parser.add_argument('--dump_file', type=str, default='data/dump',
-                        help='The dump file prefix.')
+    parser = argparse.ArgumentParser(
+        description="Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids)."
+    )
+    parser.add_argument("--file_path", type=str, default="data/dump.txt", help="The path to the data.")
+    parser.add_argument("--tokenizer_type", type=str, default="bert", choices=["bert", "roberta", "gpt2"])
+    parser.add_argument("--tokenizer_name", type=str, default="bert-base-uncased", help="The tokenizer to use.")
+    parser.add_argument("--dump_file", type=str, default="data/dump", help="The dump file prefix.")
     args = parser.parse_args()
 
-
-    logger.info(f'Loading Tokenizer ({args.tokenizer_name})')
-    if args.tokenizer_type == 'bert':
+    logger.info(f"Loading Tokenizer ({args.tokenizer_name})")
+    if args.tokenizer_type == "bert":
         tokenizer = BertTokenizer.from_pretrained(args.tokenizer_name)
-        bos = tokenizer.special_tokens_map['cls_token'] # `[CLS]`
-        sep = tokenizer.special_tokens_map['sep_token'] # `[SEP]`
-    elif args.tokenizer_type == 'roberta':
+        bos = tokenizer.special_tokens_map["cls_token"]  # `[CLS]`
+        sep = tokenizer.special_tokens_map["sep_token"]  # `[SEP]`
+    elif args.tokenizer_type == "roberta":
         tokenizer = RobertaTokenizer.from_pretrained(args.tokenizer_name)
-        bos = tokenizer.special_tokens_map['cls_token'] # `<s>`
-        sep = tokenizer.special_tokens_map['sep_token'] # `</s>`
-    elif args.tokenizer_type == 'gpt2':
+        bos = tokenizer.special_tokens_map["cls_token"]  # `<s>`
+        sep = tokenizer.special_tokens_map["sep_token"]  # `</s>`
+    elif args.tokenizer_type == "gpt2":
         tokenizer = GPT2Tokenizer.from_pretrained(args.tokenizer_name)
-        bos = tokenizer.special_tokens_map['bos_token'] # `<|endoftext|>`
-        sep = tokenizer.special_tokens_map['eos_token'] # `<|endoftext|>`    
+        bos = tokenizer.special_tokens_map["bos_token"]  # `<|endoftext|>`
+        sep = tokenizer.special_tokens_map["eos_token"]  # `<|endoftext|>`
 
-    logger.info(f'Loading text from {args.file_path}')
-    with open(args.file_path, 'r', encoding='utf8') as fp:
+    logger.info(f"Loading text from {args.file_path}")
+    with open(args.file_path, "r", encoding="utf8") as fp:
         data = fp.readlines()
 
-
-    logger.info(f'Start encoding')
-    logger.info(f'{len(data)} examples to process.')
+    logger.info("Start encoding")
+    logger.info(f"{len(data)} examples to process.")
 
     rslt = []
     iter = 0
     interval = 10000
     start = time.time()
     for text in data:
-        text = f'{bos} {text.strip()} {sep}'
+        text = f"{bos} {text.strip()} {sep}"
         token_ids = tokenizer.encode(text, add_special_tokens=False)
         rslt.append(token_ids)
 
         iter += 1
         if iter % interval == 0:
             end = time.time()
-            logger.info(f'{iter} examples processed. - {(end-start)/interval:.2f}s/expl')
+            logger.info(f"{iter} examples processed. - {(end-start):.2f}s/{interval}expl")
             start = time.time()
-    logger.info('Finished binarization')
-    logger.info(f'{len(data)} examples processed.')
+    logger.info("Finished binarization")
+    logger.info(f"{len(data)} examples processed.")
 
-
-    dp_file = f'{args.dump_file}.{args.tokenizer_name}.pickle'
-    rslt_ = [np.uint16(d) for d in rslt]
+    dp_file = f"{args.dump_file}.{args.tokenizer_name}.pickle"
+    vocab_size = tokenizer.vocab_size
+    if vocab_size < (1 << 16):
+        rslt_ = [np.uint16(d) for d in rslt]
+    else:
+        rslt_ = [np.int32(d) for d in rslt]
     random.shuffle(rslt_)
-    logger.info(f'Dump to {dp_file}')
-    with open(dp_file, 'wb') as handle:
+    logger.info(f"Dump to {dp_file}")
+    with open(dp_file, "wb") as handle:
         pickle.dump(rslt_, handle, protocol=pickle.HIGHEST_PROTOCOL)
 
 

examples/distillation/scripts/extract.py

@@ -16,74 +16,87 @@
 Preprocessing script before training the distilled model.
 Specific to RoBERTa -> DistilRoBERTa and GPT2 -> DistilGPT2.
 """
-from transformers import BertForMaskedLM, RobertaForMaskedLM, GPT2LMHeadModel
-import torch
 import argparse
 
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description="Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned Distillation")
+import torch
+
+from transformers import GPT2LMHeadModel, RobertaForMaskedLM
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned Distillation"
+    )
     parser.add_argument("--model_type", default="roberta", choices=["roberta", "gpt2"])
-    parser.add_argument("--model_name", default='roberta-large', type=str)
-    parser.add_argument("--dump_checkpoint", default='serialization_dir/tf_roberta_048131723.pth', type=str)
-    parser.add_argument("--vocab_transform", action='store_true')
+    parser.add_argument("--model_name", default="roberta-large", type=str)
+    parser.add_argument("--dump_checkpoint", default="serialization_dir/tf_roberta_048131723.pth", type=str)
+    parser.add_argument("--vocab_transform", action="store_true")
     args = parser.parse_args()
 
-
-    if args.model_type == 'roberta':
+    if args.model_type == "roberta":
         model = RobertaForMaskedLM.from_pretrained(args.model_name)
-        prefix = 'roberta'
-    elif args.model_type == 'gpt2':
+        prefix = "roberta"
+    elif args.model_type == "gpt2":
         model = GPT2LMHeadModel.from_pretrained(args.model_name)
-        prefix = 'transformer'
+        prefix = "transformer"
 
     state_dict = model.state_dict()
     compressed_sd = {}
 
-    ### Embeddings ###
-    if args.model_type == 'gpt2':
-        for param_name in ['wte.weight', 'wpe.weight']:
-            compressed_sd[f'{prefix}.{param_name}'] = state_dict[f'{prefix}.{param_name}']
+    # Embeddings #
+    if args.model_type == "gpt2":
+        for param_name in ["wte.weight", "wpe.weight"]:
+            compressed_sd[f"{prefix}.{param_name}"] = state_dict[f"{prefix}.{param_name}"]
     else:
-        for w in ['word_embeddings', 'position_embeddings', 'token_type_embeddings']:
-            param_name = f'{prefix}.embeddings.{w}.weight'
+        for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
+            param_name = f"{prefix}.embeddings.{w}.weight"
             compressed_sd[param_name] = state_dict[param_name]
-        for w in ['weight', 'bias']:
-            param_name = f'{prefix}.embeddings.LayerNorm.{w}'
+        for w in ["weight", "bias"]:
+            param_name = f"{prefix}.embeddings.LayerNorm.{w}"
             compressed_sd[param_name] = state_dict[param_name]
 
-    ### Transformer Blocks ###
+    # Transformer Blocks #
     std_idx = 0
     for teacher_idx in [0, 2, 4, 7, 9, 11]:
-        if args.model_type == 'gpt2':
-            for layer in ['ln_1', 'attn.c_attn', 'attn.c_proj', 'ln_2', 'mlp.c_fc', 'mlp.c_proj']:
-                for w in ['weight', 'bias']:
-                    compressed_sd[f'{prefix}.h.{std_idx}.{layer}.{w}'] = \
-                        state_dict[f'{prefix}.h.{teacher_idx}.{layer}.{w}']
-            compressed_sd[f'{prefix}.h.{std_idx}.attn.bias'] = state_dict[f'{prefix}.h.{teacher_idx}.attn.bias']
+        if args.model_type == "gpt2":
+            for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
+                for w in ["weight", "bias"]:
+                    compressed_sd[f"{prefix}.h.{std_idx}.{layer}.{w}"] = state_dict[
+                        f"{prefix}.h.{teacher_idx}.{layer}.{w}"
+                    ]
+            compressed_sd[f"{prefix}.h.{std_idx}.attn.bias"] = state_dict[f"{prefix}.h.{teacher_idx}.attn.bias"]
         else:
-            for layer in ['attention.self.query', 'attention.self.key', 'attention.self.value',
-                        'attention.output.dense', 'attention.output.LayerNorm',
-                        'intermediate.dense', 'output.dense', 'output.LayerNorm']:
-                for w in ['weight', 'bias']:
-                    compressed_sd[f'{prefix}.encoder.layer.{std_idx}.{layer}.{w}'] = \
-                        state_dict[f'{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}']
+            for layer in [
+                "attention.self.query",
+                "attention.self.key",
+                "attention.self.value",
+                "attention.output.dense",
+                "attention.output.LayerNorm",
+                "intermediate.dense",
+                "output.dense",
+                "output.LayerNorm",
+            ]:
+                for w in ["weight", "bias"]:
+                    compressed_sd[f"{prefix}.encoder.layer.{std_idx}.{layer}.{w}"] = state_dict[
+                        f"{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}"
+                    ]
         std_idx += 1
 
-    ### Language Modeling Head ###s
-    if args.model_type == 'roberta':
-        for layer in ['lm_head.decoder.weight', 'lm_head.bias']:
-            compressed_sd[f'{layer}'] = state_dict[f'{layer}']
+    # Language Modeling Head ###s
+    if args.model_type == "roberta":
+        for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
+            compressed_sd[f"{layer}"] = state_dict[f"{layer}"]
         if args.vocab_transform:
-            for w in ['weight', 'bias']:
-                compressed_sd[f'lm_head.dense.{w}'] = state_dict[f'lm_head.dense.{w}']
-                compressed_sd[f'lm_head.layer_norm.{w}'] = state_dict[f'lm_head.layer_norm.{w}']
-    elif args.model_type == 'gpt2':
-        for w in ['weight', 'bias']:
-            compressed_sd[f'{prefix}.ln_f.{w}'] = state_dict[f'{prefix}.ln_f.{w}']
-        compressed_sd[f'lm_head.weight'] = state_dict[f'lm_head.weight']
+            for w in ["weight", "bias"]:
+                compressed_sd[f"lm_head.dense.{w}"] = state_dict[f"lm_head.dense.{w}"]
+                compressed_sd[f"lm_head.layer_norm.{w}"] = state_dict[f"lm_head.layer_norm.{w}"]
+    elif args.model_type == "gpt2":
+        for w in ["weight", "bias"]:
+            compressed_sd[f"{prefix}.ln_f.{w}"] = state_dict[f"{prefix}.ln_f.{w}"]
+        compressed_sd["lm_head.weight"] = state_dict["lm_head.weight"]
 
-    print(f'N layers selected for distillation: {std_idx}')
-    print(f'Number of params transfered for distillation: {len(compressed_sd.keys())}')
+    print(f"N layers selected for distillation: {std_idx}")
+    print(f"Number of params transfered for distillation: {len(compressed_sd.keys())}")
 
-    print(f'Save transfered checkpoint to {args.dump_checkpoint}.')
+    print(f"Save transfered checkpoint to {args.dump_checkpoint}.")
     torch.save(compressed_sd, args.dump_checkpoint)

examples/distillation/scripts/extract_distilbert.py

@@ -16,67 +16,77 @@
 Preprocessing script before training DistilBERT.
 Specific to BERT -> DistilBERT.
 """
-from transformers import BertForMaskedLM, RobertaForMaskedLM
-import torch
 import argparse
 
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description="Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned Distillation")
+import torch
+
+from transformers import BertForMaskedLM
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned Distillation"
+    )
     parser.add_argument("--model_type", default="bert", choices=["bert"])
-    parser.add_argument("--model_name", default='bert-base-uncased', type=str)
-    parser.add_argument("--dump_checkpoint", default='serialization_dir/tf_bert-base-uncased_0247911.pth', type=str)
-    parser.add_argument("--vocab_transform", action='store_true')
+    parser.add_argument("--model_name", default="bert-base-uncased", type=str)
+    parser.add_argument("--dump_checkpoint", default="serialization_dir/tf_bert-base-uncased_0247911.pth", type=str)
+    parser.add_argument("--vocab_transform", action="store_true")
     args = parser.parse_args()
 
-
-    if args.model_type == 'bert':
+    if args.model_type == "bert":
         model = BertForMaskedLM.from_pretrained(args.model_name)
-        prefix = 'bert'
+        prefix = "bert"
     else:
-        raise ValueError(f'args.model_type should be "bert".')
+        raise ValueError('args.model_type should be "bert".')
 
     state_dict = model.state_dict()
     compressed_sd = {}
 
-    for w in ['word_embeddings', 'position_embeddings']:
-        compressed_sd[f'distilbert.embeddings.{w}.weight'] = \
-            state_dict[f'{prefix}.embeddings.{w}.weight']
-    for w in ['weight', 'bias']:
-        compressed_sd[f'distilbert.embeddings.LayerNorm.{w}'] = \
-            state_dict[f'{prefix}.embeddings.LayerNorm.{w}']
+    for w in ["word_embeddings", "position_embeddings"]:
+        compressed_sd[f"distilbert.embeddings.{w}.weight"] = state_dict[f"{prefix}.embeddings.{w}.weight"]
+    for w in ["weight", "bias"]:
+        compressed_sd[f"distilbert.embeddings.LayerNorm.{w}"] = state_dict[f"{prefix}.embeddings.LayerNorm.{w}"]
 
     std_idx = 0
     for teacher_idx in [0, 2, 4, 7, 9, 11]:
-        for w in ['weight', 'bias']:
-            compressed_sd[f'distilbert.transformer.layer.{std_idx}.attention.q_lin.{w}'] = \
-                state_dict[f'{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}']
-            compressed_sd[f'distilbert.transformer.layer.{std_idx}.attention.k_lin.{w}'] = \
-                state_dict[f'{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}']
-            compressed_sd[f'distilbert.transformer.layer.{std_idx}.attention.v_lin.{w}'] = \
-                state_dict[f'{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}']
+        for w in ["weight", "bias"]:
+            compressed_sd[f"distilbert.transformer.layer.{std_idx}.attention.q_lin.{w}"] = state_dict[
+                f"{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"
+            ]
+            compressed_sd[f"distilbert.transformer.layer.{std_idx}.attention.k_lin.{w}"] = state_dict[
+                f"{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"
+            ]
+            compressed_sd[f"distilbert.transformer.layer.{std_idx}.attention.v_lin.{w}"] = state_dict[
+                f"{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"
+            ]
 
-            compressed_sd[f'distilbert.transformer.layer.{std_idx}.attention.out_lin.{w}'] = \
-                state_dict[f'{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}']
-            compressed_sd[f'distilbert.transformer.layer.{std_idx}.sa_layer_norm.{w}'] = \
-                state_dict[f'{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}']
+            compressed_sd[f"distilbert.transformer.layer.{std_idx}.attention.out_lin.{w}"] = state_dict[
+                f"{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"
+            ]
+            compressed_sd[f"distilbert.transformer.layer.{std_idx}.sa_layer_norm.{w}"] = state_dict[
+                f"{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"
+            ]
 
-            compressed_sd[f'distilbert.transformer.layer.{std_idx}.ffn.lin1.{w}'] = \
-                state_dict[f'{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}']
-            compressed_sd[f'distilbert.transformer.layer.{std_idx}.ffn.lin2.{w}'] = \
-                state_dict[f'{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}']
-            compressed_sd[f'distilbert.transformer.layer.{std_idx}.output_layer_norm.{w}'] = \
-                state_dict[f'{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}']
+            compressed_sd[f"distilbert.transformer.layer.{std_idx}.ffn.lin1.{w}"] = state_dict[
+                f"{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"
+            ]
+            compressed_sd[f"distilbert.transformer.layer.{std_idx}.ffn.lin2.{w}"] = state_dict[
+                f"{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"
+            ]
+            compressed_sd[f"distilbert.transformer.layer.{std_idx}.output_layer_norm.{w}"] = state_dict[
+                f"{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"
+            ]
         std_idx += 1
 
-    compressed_sd[f'vocab_projector.weight'] = state_dict[f'cls.predictions.decoder.weight']
-    compressed_sd[f'vocab_projector.bias'] = state_dict[f'cls.predictions.bias']
+    compressed_sd["vocab_projector.weight"] = state_dict["cls.predictions.decoder.weight"]
+    compressed_sd["vocab_projector.bias"] = state_dict["cls.predictions.bias"]
     if args.vocab_transform:
-        for w in ['weight', 'bias']:
-            compressed_sd[f'vocab_transform.{w}'] = state_dict[f'cls.predictions.transform.dense.{w}']
-            compressed_sd[f'vocab_layer_norm.{w}'] = state_dict[f'cls.predictions.transform.LayerNorm.{w}']
+        for w in ["weight", "bias"]:
+            compressed_sd[f"vocab_transform.{w}"] = state_dict[f"cls.predictions.transform.dense.{w}"]
+            compressed_sd[f"vocab_layer_norm.{w}"] = state_dict[f"cls.predictions.transform.LayerNorm.{w}"]
 
-    print(f'N layers selected for distillation: {std_idx}')
-    print(f'Number of params transfered for distillation: {len(compressed_sd.keys())}')
+    print(f"N layers selected for distillation: {std_idx}")
+    print(f"Number of params transfered for distillation: {len(compressed_sd.keys())}")
 
-    print(f'Save transfered checkpoint to {args.dump_checkpoint}.')
+    print(f"Save transfered checkpoint to {args.dump_checkpoint}.")
     torch.save(compressed_sd, args.dump_checkpoint)

examples/distillation/scripts/token_counts.py

@@ -15,37 +15,42 @@
 """
 Preprocessing script before training the distilled model.
 """
-from collections import Counter
 import argparse
-import pickle
 import logging
+import pickle
+from collections import Counter
 
-logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s',
-                    datefmt = '%m/%d/%Y %H:%M:%S',
-                    level = logging.INFO)
+
+logging.basicConfig(
+    format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
+)
 logger = logging.getLogger(__name__)
 
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description="Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)")
-    parser.add_argument("--data_file", type=str, default="data/dump.bert-base-uncased.pickle",
-                        help="The binarized dataset.")
-    parser.add_argument("--token_counts_dump", type=str, default="data/token_counts.bert-base-uncased.pickle",
-                        help="The dump file.")
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)"
+    )
+    parser.add_argument(
+        "--data_file", type=str, default="data/dump.bert-base-uncased.pickle", help="The binarized dataset."
+    )
+    parser.add_argument(
+        "--token_counts_dump", type=str, default="data/token_counts.bert-base-uncased.pickle", help="The dump file."
+    )
     parser.add_argument("--vocab_size", default=30522, type=int)
     args = parser.parse_args()
 
-    logger.info(f'Loading data from {args.data_file}')
-    with open(args.data_file, 'rb') as fp:
+    logger.info(f"Loading data from {args.data_file}")
+    with open(args.data_file, "rb") as fp:
         data = pickle.load(fp)
 
-    logger.info('Counting occurences for MLM.')
+    logger.info("Counting occurences for MLM.")
     counter = Counter()
     for tk_ids in data:
         counter.update(tk_ids)
-    counts = [0]*args.vocab_size
+    counts = [0] * args.vocab_size
     for k, v in counter.items():
         counts[k] = v
 
-    logger.info(f'Dump to {args.token_counts_dump}')
-    with open(args.token_counts_dump, 'wb') as handle:
+    logger.info(f"Dump to {args.token_counts_dump}")
+    with open(args.token_counts_dump, "wb") as handle:
         pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)

examples/distillation/train.py

@@ -16,272 +16,304 @@
 Training the distilled model.
 Supported architectures include: BERT -> DistilBERT, RoBERTa -> DistilRoBERTa, GPT2 -> DistilGPT2.
 """
-import os
 import argparse
-import pickle
 import json
+import os
+import pickle
 import shutil
+
 import numpy as np
 import torch
 
-from transformers import BertConfig, BertForMaskedLM, BertTokenizer
-from transformers import RobertaConfig, RobertaForMaskedLM, RobertaTokenizer
-from transformers import DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer
-from transformers import GPT2Config, GPT2LMHeadModel, GPT2Tokenizer
-
 from distiller import Distiller
-from utils import git_log, logger, init_gpu_params, set_seed
 from lm_seqs_dataset import LmSeqsDataset
+from transformers import (
+    BertConfig,
+    BertForMaskedLM,
+    BertTokenizer,
+    DistilBertConfig,
+    DistilBertForMaskedLM,
+    DistilBertTokenizer,
+    GPT2Config,
+    GPT2LMHeadModel,
+    GPT2Tokenizer,
+    RobertaConfig,
+    RobertaForMaskedLM,
+    RobertaTokenizer,
+)
+from utils import git_log, init_gpu_params, logger, set_seed
 
 
 MODEL_CLASSES = {
-    'distilbert': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
-    'roberta': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
-    'bert': (BertConfig, BertForMaskedLM, BertTokenizer),
-    'gpt2': (GPT2Config, GPT2LMHeadModel, GPT2Tokenizer)
+    "distilbert": (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
+    "roberta": (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
+    "bert": (BertConfig, BertForMaskedLM, BertTokenizer),
+    "gpt2": (GPT2Config, GPT2LMHeadModel, GPT2Tokenizer),
 }
 
+
 def sanity_checks(args):
     """
     A bunch of args sanity checks to perform even starting...
     """
-    assert (args.mlm and args.alpha_mlm > 0.) or (not args.mlm and args.alpha_mlm == 0.)
-    assert (args.alpha_mlm > 0. and args.alpha_clm == 0.) or (args.alpha_mlm == 0. and args.alpha_clm > 0.)
+    assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
+    assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
     if args.mlm:
         assert os.path.isfile(args.token_counts)
-        assert (args.student_type in ['roberta', 'distilbert']) and (args.teacher_type in ['roberta', 'bert'])
+        assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
     else:
-        assert (args.student_type in ['gpt2']) and (args.teacher_type in ['gpt2'])
+        assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])
 
-    assert args.teacher_type == args.student_type or (args.student_type=='distilbert' and args.teacher_type=='bert')
+    assert args.teacher_type == args.student_type or (
+        args.student_type == "distilbert" and args.teacher_type == "bert"
+    )
     assert os.path.isfile(args.student_config)
     if args.student_pretrained_weights is not None:
         assert os.path.isfile(args.student_pretrained_weights)
 
-    if args.freeze_token_type_embds: assert args.student_type in ['roberta']
+    if args.freeze_token_type_embds:
+        assert args.student_type in ["roberta"]
+
+    assert args.alpha_ce >= 0.0
+    assert args.alpha_mlm >= 0.0
+    assert args.alpha_clm >= 0.0
+    assert args.alpha_mse >= 0.0
+    assert args.alpha_cos >= 0.0
+    assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0
 
-    assert args.alpha_ce >= 0.
-    assert args.alpha_mlm >= 0.
-    assert args.alpha_clm >= 0.
-    assert args.alpha_mse >= 0.
-    assert args.alpha_cos >= 0.
-    assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.
 
 def freeze_pos_embeddings(student, args):
-    if args.student_type == 'roberta':
+    if args.student_type == "roberta":
         student.roberta.embeddings.position_embeddings.weight.requires_grad = False
-    elif args.student_type == 'gpt2':
+    elif args.student_type == "gpt2":
         student.transformer.wpe.weight.requires_grad = False
 
+
 def freeze_token_type_embeddings(student, args):
-    if args.student_type == 'roberta':
+    if args.student_type == "roberta":
         student.roberta.embeddings.token_type_embeddings.weight.requires_grad = False
 
+
 def main():
     parser = argparse.ArgumentParser(description="Training")
-    parser.add_argument("--force", action='store_true',
-                        help="Overwrite dump_path if it already exists.")
+    parser.add_argument("--force", action="store_true", help="Overwrite dump_path if it already exists.")
 
-    parser.add_argument("--dump_path", type=str, required=True,
-                        help="The output directory (log, checkpoints, parameters, etc.)")
-    parser.add_argument("--data_file", type=str, required=True,
-                        help="The binarized file (tokenized + tokens_to_ids) and grouped by sequence.")
+    parser.add_argument(
+        "--dump_path", type=str, required=True, help="The output directory (log, checkpoints, parameters, etc.)"
+    )
+    parser.add_argument(
+        "--data_file",
+        type=str,
+        required=True,
+        help="The binarized file (tokenized + tokens_to_ids) and grouped by sequence.",
+    )
 
-    parser.add_argument("--student_type", type=str, choices=["distilbert", "roberta", "gpt2"], required=True,
-                        help="The student type (DistilBERT, RoBERTa).")
-    parser.add_argument("--student_config", type=str, required=True,
-                        help="Path to the student configuration.")
-    parser.add_argument("--student_pretrained_weights", default=None, type=str,
-                        help="Load student initialization checkpoint.")
+    parser.add_argument(
+        "--student_type",
+        type=str,
+        choices=["distilbert", "roberta", "gpt2"],
+        required=True,
+        help="The student type (DistilBERT, RoBERTa).",
+    )
+    parser.add_argument("--student_config", type=str, required=True, help="Path to the student configuration.")
+    parser.add_argument(
+        "--student_pretrained_weights", default=None, type=str, help="Load student initialization checkpoint."
+    )
 
-    parser.add_argument("--teacher_type", choices=["bert", "roberta", "gpt2"], required=True,
-                        help="Teacher type (BERT, RoBERTa).")
-    parser.add_argument("--teacher_name", type=str, required=True,
-                        help="The teacher model.")
+    parser.add_argument(
+        "--teacher_type", choices=["bert", "roberta", "gpt2"], required=True, help="Teacher type (BERT, RoBERTa)."
+    )
+    parser.add_argument("--teacher_name", type=str, required=True, help="The teacher model.")
 
-    parser.add_argument("--temperature", default=2., type=float,
-                        help="Temperature for the softmax temperature.")
-    parser.add_argument("--alpha_ce", default=0.5, type=float,
-                        help="Linear weight for the distillation loss. Must be >=0.")
-    parser.add_argument("--alpha_mlm", default=0.0, type=float,
-                        help="Linear weight for the MLM loss. Must be >=0. Should be used in coonjunction with `mlm` flag.")
-    parser.add_argument("--alpha_clm", default=0.5, type=float,
-                        help="Linear weight for the CLM loss. Must be >=0.")
-    parser.add_argument("--alpha_mse", default=0.0, type=float,
-                        help="Linear weight of the MSE loss. Must be >=0.")
-    parser.add_argument("--alpha_cos", default=0.0, type=float,
-                        help="Linear weight of the cosine embedding loss. Must be >=0.")
+    parser.add_argument("--temperature", default=2.0, type=float, help="Temperature for the softmax temperature.")
+    parser.add_argument(
+        "--alpha_ce", default=0.5, type=float, help="Linear weight for the distillation loss. Must be >=0."
+    )
+    parser.add_argument(
+        "--alpha_mlm",
+        default=0.0,
+        type=float,
+        help="Linear weight for the MLM loss. Must be >=0. Should be used in coonjunction with `mlm` flag.",
+    )
+    parser.add_argument("--alpha_clm", default=0.5, type=float, help="Linear weight for the CLM loss. Must be >=0.")
+    parser.add_argument("--alpha_mse", default=0.0, type=float, help="Linear weight of the MSE loss. Must be >=0.")
+    parser.add_argument(
+        "--alpha_cos", default=0.0, type=float, help="Linear weight of the cosine embedding loss. Must be >=0."
+    )
 
-    parser.add_argument("--mlm", action="store_true",
-                        help="The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM.")
-    parser.add_argument("--mlm_mask_prop", default=0.15, type=float,
-                        help="Proportion of tokens for which we need to make a prediction.")
-    parser.add_argument("--word_mask", default=0.8, type=float,
-                        help="Proportion of tokens to mask out.")
-    parser.add_argument("--word_keep", default=0.1, type=float,
-                        help="Proportion of tokens to keep.")
-    parser.add_argument("--word_rand", default=0.1, type=float,
-                        help="Proportion of tokens to randomly replace.")
-    parser.add_argument("--mlm_smoothing", default=0.7, type=float,
-                        help="Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).")
-    parser.add_argument("--token_counts", type=str,
-                        help="The token counts in the data_file for MLM.")
+    parser.add_argument(
+        "--mlm", action="store_true", help="The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM."
+    )
+    parser.add_argument(
+        "--mlm_mask_prop",
+        default=0.15,
+        type=float,
+        help="Proportion of tokens for which we need to make a prediction.",
+    )
+    parser.add_argument("--word_mask", default=0.8, type=float, help="Proportion of tokens to mask out.")
+    parser.add_argument("--word_keep", default=0.1, type=float, help="Proportion of tokens to keep.")
+    parser.add_argument("--word_rand", default=0.1, type=float, help="Proportion of tokens to randomly replace.")
+    parser.add_argument(
+        "--mlm_smoothing",
+        default=0.7,
+        type=float,
+        help="Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).",
+    )
+    parser.add_argument("--token_counts", type=str, help="The token counts in the data_file for MLM.")
 
-    parser.add_argument("--restrict_ce_to_mask", action='store_true',
-                        help="If true, compute the distilation loss only the [MLM] prediction distribution.")
-    parser.add_argument("--freeze_pos_embs", action="store_true",
-                        help="Freeze positional embeddings during distillation. For student_type in ['roberta', 'gpt2'] only.")
-    parser.add_argument("--freeze_token_type_embds", action="store_true",
-                        help="Freeze token type embeddings during distillation if existent. For student_type in ['roberta'] only.")
+    parser.add_argument(
+        "--restrict_ce_to_mask",
+        action="store_true",
+        help="If true, compute the distilation loss only the [MLM] prediction distribution.",
+    )
+    parser.add_argument(
+        "--freeze_pos_embs",
+        action="store_true",
+        help="Freeze positional embeddings during distillation. For student_type in ['roberta', 'gpt2'] only.",
+    )
+    parser.add_argument(
+        "--freeze_token_type_embds",
+        action="store_true",
+        help="Freeze token type embeddings during distillation if existent. For student_type in ['roberta'] only.",
+    )
 
-    parser.add_argument("--n_epoch", type=int, default=3,
-                        help="Number of pass on the whole dataset.")
-    parser.add_argument("--batch_size", type=int, default=5,
-                        help="Batch size (for each process).")
-    parser.add_argument("--group_by_size", action='store_false',
-                        help="If true, group sequences that have similar length into the same batch. Default is true.")
+    parser.add_argument("--n_epoch", type=int, default=3, help="Number of pass on the whole dataset.")
+    parser.add_argument("--batch_size", type=int, default=5, help="Batch size (for each process).")
+    parser.add_argument(
+        "--group_by_size",
+        action="store_false",
+        help="If true, group sequences that have similar length into the same batch. Default is true.",
+    )
 
-    parser.add_argument("--gradient_accumulation_steps", type=int, default=50,
-                        help="Gradient accumulation for larger training batches.")
-    parser.add_argument("--warmup_prop", default=0.05, type=float,
-                        help="Linear warmup proportion.")
-    parser.add_argument("--weight_decay", default=0.0, type=float,
-                        help="Weight deay if we apply some.")
-    parser.add_argument("--learning_rate", default=5e-4, type=float,
-                        help="The initial learning rate for Adam.")
-    parser.add_argument("--adam_epsilon", default=1e-6, type=float,
-                        help="Epsilon for Adam optimizer.")
-    parser.add_argument("--max_grad_norm", default=5.0, type=float,
-                        help="Max gradient norm.")
-    parser.add_argument("--initializer_range", default=0.02, type=float,
-                        help="Random initialization range.")
+    parser.add_argument(
+        "--gradient_accumulation_steps",
+        type=int,
+        default=50,
+        help="Gradient accumulation for larger training batches.",
+    )
+    parser.add_argument("--warmup_prop", default=0.05, type=float, help="Linear warmup proportion.")
+    parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight deay if we apply some.")
+    parser.add_argument("--learning_rate", default=5e-4, type=float, help="The initial learning rate for Adam.")
+    parser.add_argument("--adam_epsilon", default=1e-6, type=float, help="Epsilon for Adam optimizer.")
+    parser.add_argument("--max_grad_norm", default=5.0, type=float, help="Max gradient norm.")
+    parser.add_argument("--initializer_range", default=0.02, type=float, help="Random initialization range.")
 
-    parser.add_argument('--fp16', action='store_true',
-                        help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit")
-    parser.add_argument('--fp16_opt_level', type=str, default='O1',
-                        help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
-                             "See details at https://nvidia.github.io/apex/amp.html")
-    parser.add_argument("--n_gpu", type=int, default=1,
-                        help="Number of GPUs in the node.")
-    parser.add_argument("--local_rank", type=int, default=-1,
-                        help="Distributed training - Local rank")
-    parser.add_argument("--seed", type=int, default=56,
-                        help="Random seed")
+    parser.add_argument(
+        "--fp16",
+        action="store_true",
+        help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit",
+    )
+    parser.add_argument(
+        "--fp16_opt_level",
+        type=str,
+        default="O1",
+        help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
+        "See details at https://nvidia.github.io/apex/amp.html",
+    )
+    parser.add_argument("--n_gpu", type=int, default=1, help="Number of GPUs in the node.")
+    parser.add_argument("--local_rank", type=int, default=-1, help="Distributed training - Local rank")
+    parser.add_argument("--seed", type=int, default=56, help="Random seed")
 
-    parser.add_argument("--log_interval", type=int, default=500,
-                        help="Tensorboard logging interval.")
-    parser.add_argument("--checkpoint_interval", type=int, default=4000,
-                        help="Checkpoint interval.")
+    parser.add_argument("--log_interval", type=int, default=500, help="Tensorboard logging interval.")
+    parser.add_argument("--checkpoint_interval", type=int, default=4000, help="Checkpoint interval.")
     args = parser.parse_args()
     sanity_checks(args)
 
-
-    ## ARGS ##
+    # ARGS #
     init_gpu_params(args)
     set_seed(args)
     if args.is_master:
         if os.path.exists(args.dump_path):
             if not args.force:
-                raise ValueError(f'Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite it'
-                                   'Use `--force` if you want to overwrite it')
+                raise ValueError(
+                    f"Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite it"
+                    "Use `--force` if you want to overwrite it"
+                )
             else:
                 shutil.rmtree(args.dump_path)
 
         if not os.path.exists(args.dump_path):
             os.makedirs(args.dump_path)
-        logger.info(f'Experiment will be dumped and logged in {args.dump_path}')
+        logger.info(f"Experiment will be dumped and logged in {args.dump_path}")
 
-
-        ### SAVE PARAMS ###
-        logger.info(f'Param: {args}')
-        with open(os.path.join(args.dump_path, 'parameters.json'), 'w') as f:
+        # SAVE PARAMS #
+        logger.info(f"Param: {args}")
+        with open(os.path.join(args.dump_path, "parameters.json"), "w") as f:
             json.dump(vars(args), f, indent=4)
         git_log(args.dump_path)
 
     student_config_class, student_model_class, _ = MODEL_CLASSES[args.student_type]
     teacher_config_class, teacher_model_class, teacher_tokenizer_class = MODEL_CLASSES[args.teacher_type]
 
-    ### TOKENIZER ###
+    # TOKENIZER #
     tokenizer = teacher_tokenizer_class.from_pretrained(args.teacher_name)
     special_tok_ids = {}
     for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
         idx = tokenizer.all_special_tokens.index(tok_symbol)
         special_tok_ids[tok_name] = tokenizer.all_special_ids[idx]
-    logger.info(f'Special tokens {special_tok_ids}')
+    logger.info(f"Special tokens {special_tok_ids}")
     args.special_tok_ids = special_tok_ids
     args.max_model_input_size = tokenizer.max_model_input_sizes[args.teacher_name]
 
-
-    ## DATA LOADER ##
-    logger.info(f'Loading data from {args.data_file}')
-    with open(args.data_file, 'rb') as fp:
+    # DATA LOADER #
+    logger.info(f"Loading data from {args.data_file}")
+    with open(args.data_file, "rb") as fp:
         data = pickle.load(fp)
 
-
     if args.mlm:
-        logger.info(f'Loading token counts from {args.token_counts} (already pre-computed)')
-        with open(args.token_counts, 'rb') as fp:
+        logger.info(f"Loading token counts from {args.token_counts} (already pre-computed)")
+        with open(args.token_counts, "rb") as fp:
             counts = pickle.load(fp)
-        
+
         token_probs = np.maximum(counts, 1) ** -args.mlm_smoothing
         for idx in special_tok_ids.values():
-            token_probs[idx] = 0.  # do not predict special tokens
+            token_probs[idx] = 0.0  # do not predict special tokens
         token_probs = torch.from_numpy(token_probs)
     else:
         token_probs = None
 
-
     train_lm_seq_dataset = LmSeqsDataset(params=args, data=data)
-    logger.info(f'Data loader created.')
+    logger.info("Data loader created.")
 
-
-    ## STUDENT ##
-    logger.info(f'Loading student config from {args.student_config}')
+    # STUDENT #
+    logger.info(f"Loading student config from {args.student_config}")
     stu_architecture_config = student_config_class.from_pretrained(args.student_config)
     stu_architecture_config.output_hidden_states = True
 
     if args.student_pretrained_weights is not None:
-        logger.info(f'Loading pretrained weights from {args.student_pretrained_weights}')
-        student = student_model_class.from_pretrained(args.student_pretrained_weights,
-                                                      config=stu_architecture_config)
+        logger.info(f"Loading pretrained weights from {args.student_pretrained_weights}")
+        student = student_model_class.from_pretrained(args.student_pretrained_weights, config=stu_architecture_config)
     else:
         student = student_model_class(stu_architecture_config)
 
-
     if args.n_gpu > 0:
-        student.to(f'cuda:{args.local_rank}')
-    logger.info(f'Student loaded.')
+        student.to(f"cuda:{args.local_rank}")
+    logger.info("Student loaded.")
 
-
-    ## TEACHER ##
+    # TEACHER #
     teacher = teacher_model_class.from_pretrained(args.teacher_name, output_hidden_states=True)
     if args.n_gpu > 0:
-        teacher.to(f'cuda:{args.local_rank}')
-    logger.info(f'Teacher loaded from {args.teacher_name}.')
+        teacher.to(f"cuda:{args.local_rank}")
+    logger.info(f"Teacher loaded from {args.teacher_name}.")
 
-
-    ## FREEZING ##
+    # FREEZING #
     if args.freeze_pos_embs:
         freeze_pos_embeddings(student, args)
     if args.freeze_token_type_embds:
         freeze_token_type_embeddings(student, args)
 
-
-    ## SANITY CHECKS ##
+    # SANITY CHECKS #
     assert student.config.vocab_size == teacher.config.vocab_size
     assert student.config.hidden_size == teacher.config.hidden_size
     assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
     if args.mlm:
         assert token_probs.size(0) == stu_architecture_config.vocab_size
 
-
-    ## DISTILLER ##
+    # DISTILLER #
     torch.cuda.empty_cache()
-    distiller = Distiller(params=args,
-                          dataset=train_lm_seq_dataset,
-                          token_probs=token_probs,
-                          student=student,
-                          teacher=teacher)
+    distiller = Distiller(
+        params=args, dataset=train_lm_seq_dataset, token_probs=token_probs, student=student, teacher=teacher
+    )
     distiller.train()
     logger.info("Let's go get some drinks.")
 

examples/distillation/training_configs/distilbert-base-cased.json

@@ -0,0 +1,15 @@
+{
+	"activation": "gelu",
+	"attention_dropout": 0.1,
+	"dim": 768,
+	"dropout": 0.1,
+	"hidden_dim": 3072,
+	"initializer_range": 0.02,
+	"max_position_embeddings": 512,
+	"n_heads": 12,
+	"n_layers": 6,
+	"sinusoidal_pos_embds": true,
+	"tie_weights_": true,
+	"vocab_size": 28996
+  }
+