refactor

refactor prepare inputs for Bert
make style
2020-04-20 19:41:08 +02:00 · 2020-04-20 19:38:38 +02:00 · 2020-04-20 19:22:23 +02:00 · 2020-04-20 19:02:49 +02:00 · 2020-04-20 18:44:33 +02:00 · 2020-04-20 18:44:15 +02:00
464 changed files with 39506 additions and 14762 deletions
@@ -3,31 +3,18 @@ jobs:
    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 .[sklearn,tf,torch,testing]
+            - 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
-    run_all_tests_torch_and_tf:
-        working_directory: ~/transformers
-        docker:
-            - image: circleci/python:3.5
-        environment:
-            OMP_NUM_THREADS: 1
-            RUN_SLOW: yes
-            RUN_CUSTOM_TOKENIZERS: yes
-        resource_class: xlarge
-        parallelism: 1
-        steps:
-            - checkout
-            - run: sudo pip install .[mecab,sklearn,tf,torch,testing]
-            - run: python -m pytest -n 8 --dist=loadfile -s -v ./tests/
+
    run_tests_torch:
        working_directory: ~/transformers
        docker:
@@ -52,14 +39,14 @@ jobs:
        parallelism: 1
        steps:
            - checkout
-            - run: sudo pip install .[sklearn,tf,testing]
+            - 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
    run_tests_custom_tokenizers:
        working_directory: ~/transformers
        docker:
-            - image: circleci/python:3.5
+            - image: circleci/python:3.6
        environment:
            RUN_CUSTOM_TOKENIZERS: yes
        steps:
@@ -69,7 +56,7 @@ jobs:
    run_examples_torch:
        working_directory: ~/transformers
        docker:
-            - image: circleci/python:3.5
+            - image: circleci/python:3.6
        environment:
            OMP_NUM_THREADS: 1
        resource_class: xlarge
@@ -79,10 +66,20 @@ jobs:
            - 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:3.6
+        steps:
+            - checkout
+            - 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:
@@ -107,7 +104,7 @@ jobs:
    check_repository_consistency:
        working_directory: ~/transformers
        docker:
-            - image: circleci/python:3.5
+            - image: circleci/python:3.6
        resource_class: small
        parallelism: 1
        steps:
@@ -130,14 +127,5 @@ workflows:
            - run_tests_torch_and_tf
            - run_tests_torch
            - run_tests_tf
+            - build_doc
            - deploy_doc: *workflow_filters
-    run_slow_tests:
-        triggers:
-            - schedule:
-                cron: "0 4 * * 1"
-                filters:
-                    branches:
-                        only:
-                            - master
-        jobs:
-            - run_all_tests_torch_and_tf
@@ -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"
@@ -17,7 +17,7 @@ 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
@@ -25,3 +25,5 @@ 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
@@ -39,12 +39,14 @@ Steps to reproduce the behavior:

 <!-- A clear and concise description of what you would expect to happen. -->

-## Environment
-
-* OS:
-* Python version:
-* PyTorch version:
-* `transformers` version (or branch):
-* Using GPU ?
-* Distributed or parallel setup ?
-* Any other relevant information:
+## 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?:
@@ -33,16 +33,21 @@ The tasks I am working on is:
    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?:

-* OS:
-* Python version:
-* PyTorch version:
+<!-- IMPORTANT: which version of the former library do you use? -->
 * `pytorch-transformers` or `pytorch-pretrained-bert` version (or branch):
-* `transformers` version (or branch):
-* Using GPU?
-* Distributed or parallel setup?
-* Any other relevant information:
+

 ## Checklist

@@ -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]
+
+
+
@@ -0,0 +1,50 @@
+name: Self-hosted runner (push)
+
+on: 
+  # push:
+  #   branches:
+  #     - master
+  # 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 .[sklearn,tf,torch,testing]
+        pip uninstall -y tensorflow
+
+    - 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/
@@ -0,0 +1,51 @@
+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,tf,torch,testing]
+
+    - name: Are GPUs recognized by our DL frameworks
+      run: |
+        source .env/bin/activate
+        python -c "import torch; print(torch.cuda.is_available())"
+        python -c "import tensorflow as tf; print(tf.test.is_built_with_cuda(), tf.config.list_physical_devices('GPU'))"
+
+    - 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/
+        
@@ -139,3 +139,9 @@ serialization_dir
 # emacs
 *.*~
 debug.env
+
+# vim
+.*.swp
+
+#ctags
+tags
@@ -41,14 +41,10 @@ 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:

-```
-import platform; print("Platform", platform.platform())
-import sys; print("Python", sys.version)
-import torch; print("PyTorch", torch.__version__)
-import tensorflow; print("Tensorflow", tensorflow.__version__)
+```bash
+python transformers-cli env
 ```

 ### Do you want to implement a new model?
@@ -202,11 +198,13 @@ 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;
+4. Make sure existing tests pass;
+5. Add high-coverage tests. No quality test, 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`. 
+CircleCI does not run them. 
 6. All public methods must have informative docstrings;

-
 ### Tests

 You can run 🤗 Transformers tests with `unittest` or `pytest`.
@@ -24,6 +24,8 @@

 🤗 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.

+[![](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
@@ -60,11 +62,11 @@ Choose the right framework for every part of a model's lifetime
 | [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][(v2.3.0)](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 |
+| [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 3.5+, PyTorch 1.0.0+ and TensorFlow 2.0.0-rc1
+This repo is tested on Python 3.6+, PyTorch 1.0.0+ and TensorFlow 2.0.0-rc1

 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/).

@@ -146,22 +148,26 @@ At some point in the future, you'll be able to seamlessly move from pre-training

 🤗 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.
-12. **[T5](https://github.com/google-research/text-to-text-transfer-transformer)** (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://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.
+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. **[Other community models](https://huggingface.co/models)**, contributed by the [community](https://huggingface.co/users).
-16. 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.
+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.
+18. **[Other community models](https://huggingface.co/models)**, contributed by the [community](https://huggingface.co/users).
+19. 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).

@@ -192,7 +198,7 @@ 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'),
+          (DistilBertModel, DistilBertTokenizer, 'distilbert-base-cased'),
          (RobertaModel,    RobertaTokenizer,    'roberta-base'),
          (XLMRobertaModel, XLMRobertaTokenizer, 'xlm-roberta-base'),
         ]
@@ -332,7 +338,6 @@ python ./examples/run_glue.py \
    --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   \
@@ -386,7 +391,6 @@ python -m torch.distributed.launch --nproc_per_node 8 ./examples/run_glue.py   \
    --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   \
@@ -419,7 +423,6 @@ python -m torch.distributed.launch --nproc_per_node=8 ./examples/run_squad.py \
    --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 \
@@ -468,7 +471,7 @@ python ./examples/run_generation.py \

 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)**. Then:
+**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
@@ -487,24 +490,34 @@ transformers-cli upload ./config.json [--filename folder/foobar.json]
 # (you can optionally override its filename, which can be nested inside a folder)
 ```

-Your model will then be accessible through its identifier, a concatenation of your username and the folder name above:
+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("username/pretrained_model")
-model = AutoModel.from_pretrained("username/pretrained_model")
+tokenizer = AutoTokenizer.from_pretrained("namespace/pretrained_model")
+model = AutoModel.from_pretrained("namespace/pretrained_model")
 ```

-Finally, list all your files on S3:
+List all your files on S3:
 ```shell
 transformers-cli s3 ls
-# List all your S3 objects.
 ```

-You can also delete files:
+You can also delete unneeded files:

 ```shell
 transformers-cli s3 rm …
@@ -520,8 +533,11 @@ 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.
- - `question-answering`: Provided some context and a question refering to the context, it will extract the answer to the question
- in the context.
+ - `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
@@ -671,7 +687,7 @@ 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},
@@ -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
@@ -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"]
@@ -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"]
@@ -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"]
@@ -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"]
@@ -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"]
@@ -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"]
@@ -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**

@@ -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;
+    }
+}
@@ -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);
@@ -1,47 +1 @@
-<svg width="95px" height="88px" viewBox="0 0 95 88" version="1.1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
-    <!-- Generator: Sketch 43.2 (39069) - http://www.bohemiancoding.com/sketch -->
-    <title>icon</title>
-    <desc>Created with Sketch.</desc>
-    <defs>
-        <path d="M13,14.7890193 C22.8284801,14.7890193 26,6.02605902 26,1.5261751 C26,-0.812484109 24.4279133,-0.0763570998 21.9099482,1.17020987 C19.5830216,2.32219957 16.4482998,3.91011313 13,3.91011313 C5.82029825,3.91011313 0,-2.97370882 0,1.5261751 C0,6.02605902 3.17151989,14.7890193 13,14.7890193 Z" id="path-1"></path>
-    </defs>
-    <g id="Page-1" stroke="none" stroke-width="1" fill="none" fill-rule="evenodd">
-        <g id="icon_desktop">
-            <g id="icon">
-                <g id="icon_desktop">
-                    <g id="Group-2">
-                        <g id="Group">
-                            <path d="M93.7930402,70.08 C94.5430402,72.24 94.3630402,74.54 93.3630402,76.54 C92.6430402,78 91.6130402,79.13 90.3530402,80.14 C88.8330402,81.34 86.9430402,82.36 84.6630402,83.34 C81.9430402,84.5 78.6230402,85.59 77.1030402,85.99 C73.2130402,87 69.4730402,87.64 65.6830402,87.67 C60.2630402,87.72 55.5930402,86.44 52.2730402,83.17 C50.5530402,83.38 48.8130402,83.5 47.0630402,83.5 C45.4030402,83.5 43.7630402,83.4 42.1330402,83.2 C38.8030402,86.45 34.1530402,87.72 28.7530402,87.67 C24.9630402,87.64 21.2230402,87 17.3230402,85.99 C15.8130402,85.59 12.4930402,84.5 9.77304019,83.34 C7.49304019,82.36 5.60304019,81.34 4.09304019,80.14 C2.82304019,79.13 1.79304019,78 1.07304019,76.54 C0.0830401858,74.54 -0.106959814,72.24 0.653040186,70.08 C-0.0469598142,68.43 -0.226959814,66.54 0.323040186,64.45 C0.573040186,63.5 0.983040186,62.62 1.50304019,61.84 C1.39304019,61.43 1.30304019,61.01 1.24304019,60.55 C0.863040186,57.81 1.81304019,55.31 3.60304019,53.37 C4.48304019,52.4 5.43304019,51.73 6.42304019,51.3 C5.69304019,48.2 5.31304019,45.01 5.31304019,41.75 C5.31304019,18.69 24.0030402,0 47.0630402,0 C54.9830402,0 62.3930402,2.2 68.7130402,6.04 C69.8530402,6.74 70.9730402,7.49 72.0430402,8.29 C72.5730402,8.69 73.1030402,9.1 73.6130402,9.53 C74.1330402,9.95 74.6430402,10.39 75.1330402,10.84 C76.6130402,12.19 78.0030402,13.64 79.2730402,15.19 C79.7030402,15.7 80.1130402,16.23 80.5130402,16.77 C81.3230402,17.84 82.0730402,18.95 82.7630402,20.1 C83.8130402,21.82 84.7330402,23.62 85.5330402,25.49 C86.0630402,26.74 86.5230402,28.02 86.9330402,29.33 C87.5430402,31.29 88.0130402,33.31 88.3330402,35.39 C88.4330402,36.08 88.5230402,36.78 88.5930402,37.48 C88.7330402,38.88 88.8130402,40.3 88.8130402,41.75 C88.8130402,44.97 88.4330402,48.13 87.7230402,51.18 C88.8230402,51.61 89.8630402,52.31 90.8330402,53.37 C92.6230402,55.31 93.5730402,57.82 93.1930402,60.56 C93.1330402,61.01 93.0430402,61.43 92.9330402,61.84 C93.4530402,62.62 93.8630402,63.5 94.1130402,64.45 C94.6630402,66.54 94.4830402,68.43 93.7930402,70.08" id="Fill-1" fill="#FFFFFF" fill-rule="nonzero"></path>
-                            <circle id="Oval" fill="#FFD21E" fill-rule="nonzero" cx="46.75" cy="41.75" r="34.75"></circle>
-                            <path d="M81.5,41.75 C81.5,22.5581049 65.9418951,7 46.75,7 C27.5581049,7 12,22.5581049 12,41.75 C12,60.9418951 27.5581049,76.5 46.75,76.5 C65.9418951,76.5 81.5,60.9418951 81.5,41.75 Z M8,41.75 C8,20.3489659 25.3489659,3 46.75,3 C68.1510341,3 85.5,20.3489659 85.5,41.75 C85.5,63.1510341 68.1510341,80.5 46.75,80.5 C25.3489659,80.5 8,63.1510341 8,41.75 Z" id="Oval" fill="#FFAC03" fill-rule="nonzero"></path>
-                            <path d="M57.1723547,31.7151181 C58.0863134,32.7107502 57.3040427,35.2620959 58.7620957,35.2620959 C61.5235194,35.2620959 63.7620957,33.0235196 63.7620957,30.2620959 C63.7620957,27.5006721 61.5235194,25.2620959 58.7620957,25.2620959 C56.0006719,25.2620959 53.7620957,27.5006721 53.7620957,30.2620959 C53.7620957,31.5654666 56.3553563,30.8251108 57.1723547,31.7151181 Z" id="Oval-2" fill="#3A3B45" fill-rule="nonzero" transform="translate(58.762096, 30.262096) rotate(-28.000000) translate(-58.762096, -30.262096) "></path>
-                            <path d="M32.1723553,31.7151181 C33.086314,32.7107502 32.3040433,35.2620959 33.7620963,35.2620959 C36.52352,35.2620959 38.7620963,33.0235196 38.7620963,30.2620959 C38.7620963,27.5006721 36.52352,25.2620959 33.7620963,25.2620959 C31.0006725,25.2620959 28.7620963,27.5006721 28.7620963,30.2620959 C28.7620963,31.5654666 31.3553569,30.8251108 32.1723553,31.7151181 Z" id="Oval-2" fill="#3A3B45" fill-rule="nonzero" transform="translate(33.762096, 30.262096) scale(-1, 1) rotate(-28.000000) translate(-33.762096, -30.262096) "></path>
-                            <g id="Oval-4" transform="translate(33.500000, 41.500000)">
-                                <g id="Mask" fill-rule="nonzero" fill="#3A3B45">
-                                    <path d="M13,14.7890193 C22.8284801,14.7890193 26,6.02605902 26,1.5261751 C26,-0.812484109 24.4279133,-0.0763570998 21.9099482,1.17020987 C19.5830216,2.32219957 16.4482998,3.91011313 13,3.91011313 C5.82029825,3.91011313 0,-2.97370882 0,1.5261751 C0,6.02605902 3.17151989,14.7890193 13,14.7890193 Z" id="path-1"></path>
-                                </g>
-                                <g id="Clipped">
-                                    <mask id="mask-2" fill="white">
-                                        <use xlink:href="#path-1"></use>
-                                    </mask>
-                                    <g id="path-1"></g>
-                                    <path d="M13.25,25 C18.0399291,25 21.9229338,21.1169953 21.9229338,16.3270662 C21.9229338,12.5962324 19.5672252,9.41560375 16.2620987,8.19147116 C16.1404592,8.14641904 16.0175337,8.10401696 15.8933923,8.06433503 C15.0599892,7.79793679 14.1717882,10.6623144 13.25,10.6623144 C12.3886883,10.6623144 11.5567012,7.77968641 10.7713426,8.01349068 C7.18916268,9.07991937 4.57706621,12.3984489 4.57706621,16.3270662 C4.57706621,21.1169953 8.46007093,25 13.25,25 Z" id="Shape" fill="#EF4E4E" fill-rule="nonzero" mask="url(#mask-2)"></path>
-                                </g>
-                            </g>
-                            <circle id="Oval-3" fill="#FFD21E" fill-rule="nonzero" style="mix-blend-mode: multiply;" cx="70.25" cy="33.75" r="3.25"></circle>
-                            <circle id="Oval-3" fill="#FFD21E" fill-rule="nonzero" style="mix-blend-mode: multiply;" cx="23.75" cy="33.75" r="3.25"></circle>
-                        </g>
-                    </g>
-                </g>
-                <g id="Group-4" transform="translate(3.000000, 48.000000)" fill-rule="nonzero">
-                    <path d="M14.0619453,0 L14.0619453,0 C12.4429453,0 10.9959453,0.665 9.98694534,1.871 C9.36294534,2.618 8.71094534,3.822 8.65794534,5.625 C7.97894534,5.43 7.32594534,5.321 6.71594534,5.321 C5.16594534,5.321 3.76594534,5.915 2.77594534,6.994 C1.50394534,8.379 0.938945345,10.081 1.18494534,11.784 C1.30194534,12.595 1.57294534,13.322 1.97794534,13.995 C1.12394534,14.686 0.494945345,15.648 0.190945345,16.805 C-0.0470546551,17.712 -0.291054655,19.601 0.982945345,21.547 C0.901945345,21.674 0.825945345,21.806 0.754945345,21.941 C-0.0110546551,23.395 -0.0600546551,25.038 0.615945345,26.568 C1.64094534,28.887 4.18794534,30.714 9.13394534,32.675 C12.2109453,33.895 15.0259453,34.675 15.0509453,34.682 C19.1189453,35.737 22.7979453,36.273 25.9829453,36.273 C31.8369453,36.273 36.0279453,34.48 38.4399453,30.944 C42.3219453,25.25 41.7669453,20.042 36.7439453,15.022 C33.9639453,12.244 32.1159453,8.148 31.7309453,7.249 C30.9549453,4.587 28.9029453,1.628 25.4919453,1.628 L25.4909453,1.628 C25.2039453,1.628 24.9139453,1.651 24.6279453,1.696 C23.1339453,1.931 21.8279453,2.791 20.8949453,4.085 C19.8879453,2.833 18.9099453,1.837 18.0249453,1.275 C16.6909453,0.429 15.3579453,0 14.0619453,0 M14.0619453,4 C14.5719453,4 15.1949453,4.217 15.8819453,4.653 C18.0149453,6.006 22.1309453,13.081 23.6379453,15.833 C24.1429453,16.755 25.0059453,17.145 25.7829453,17.145 C27.3249453,17.145 28.5289453,15.612 25.9239453,13.664 C22.0069453,10.733 23.3809453,5.942 25.2509453,5.647 C25.3329453,5.634 25.4139453,5.628 25.4919453,5.628 C27.1919453,5.628 27.9419453,8.558 27.9419453,8.558 C27.9419453,8.558 30.1399453,14.078 33.9159453,17.851 C37.6919453,21.625 37.8869453,24.654 35.1349453,28.69 C33.2579453,31.442 29.6649453,32.273 25.9829453,32.273 C22.1639453,32.273 18.2489453,31.379 16.0549453,30.81 C15.9469453,30.782 2.60394534,27.013 4.29394534,23.805 C4.57794534,23.266 5.04594534,23.05 5.63494534,23.05 C8.01494534,23.05 12.3439453,26.592 14.2049453,26.592 C14.6209453,26.592 14.9139453,26.415 15.0339453,25.983 C15.8269453,23.138 2.97694534,21.942 4.05994534,17.821 C4.25094534,17.092 4.76894534,16.796 5.49694534,16.797 C8.64194534,16.797 15.6979453,22.328 17.1769453,22.328 C17.2899453,22.328 17.3709453,22.295 17.4149453,22.225 C18.1559453,21.029 17.7499453,20.194 12.5269453,17.033 C7.30394534,13.871 3.63794534,11.969 5.72294534,9.699 C5.96294534,9.437 6.30294534,9.321 6.71594534,9.321 C9.88694534,9.322 17.3789453,16.14 17.3789453,16.14 C17.3789453,16.14 19.4009453,18.243 20.6239453,18.243 C20.9049453,18.243 21.1439453,18.132 21.3059453,17.858 C22.1729453,16.396 13.2529453,9.636 12.7499453,6.847 C12.4089453,4.957 12.9889453,4 14.0619453,4" id="Fill-1" fill="#FFAC03"></path>
-                    <path d="M35.1348,28.6899 C37.8868,24.6539 37.6918,21.6249 33.9158,17.8509 C30.1398,14.0779 27.9418,8.5579 27.9418,8.5579 C27.9418,8.5579 27.1208,5.3519 25.2508,5.6469 C23.3808,5.9419 22.0078,10.7329 25.9248,13.6639 C29.8418,16.5939 25.1448,18.5849 23.6378,15.8329 C22.1308,13.0809 18.0158,6.0059 15.8818,4.6529 C13.7488,3.2999 12.2468,4.0579 12.7498,6.8469 C13.2528,9.6359 22.1738,16.3959 21.3058,17.8589 C20.4378,19.3209 17.3788,16.1399 17.3788,16.1399 C17.3788,16.1399 7.8068,7.4289 5.7228,9.6989 C3.6388,11.9689 7.3038,13.8709 12.5268,17.0329 C17.7508,20.1939 18.1558,21.0289 17.4148,22.2249 C16.6728,23.4209 5.1428,13.6999 4.0598,17.8209 C2.9778,21.9419 15.8268,23.1379 15.0338,25.9829 C14.2408,28.8289 5.9828,20.5979 4.2938,23.8049 C2.6038,27.0129 15.9468,30.7819 16.0548,30.8099 C20.3648,31.9279 31.3108,34.2969 35.1348,28.6899" id="Fill-4" fill="#FFD21E"></path>
-                </g>
-                <g id="Group-4" transform="translate(70.500000, 66.500000) scale(-1, 1) translate(-70.500000, -66.500000) translate(50.000000, 48.000000)" fill-rule="nonzero">
-                    <path d="M14.0619453,0 L14.0619453,0 C12.4429453,0 10.9959453,0.665 9.98694534,1.871 C9.36294534,2.618 8.71094534,3.822 8.65794534,5.625 C7.97894534,5.43 7.32594534,5.321 6.71594534,5.321 C5.16594534,5.321 3.76594534,5.915 2.77594534,6.994 C1.50394534,8.379 0.938945345,10.081 1.18494534,11.784 C1.30194534,12.595 1.57294534,13.322 1.97794534,13.995 C1.12394534,14.686 0.494945345,15.648 0.190945345,16.805 C-0.0470546551,17.712 -0.291054655,19.601 0.982945345,21.547 C0.901945345,21.674 0.825945345,21.806 0.754945345,21.941 C-0.0110546551,23.395 -0.0600546551,25.038 0.615945345,26.568 C1.64094534,28.887 4.18794534,30.714 9.13394534,32.675 C12.2109453,33.895 15.0259453,34.675 15.0509453,34.682 C19.1189453,35.737 22.7979453,36.273 25.9829453,36.273 C31.8369453,36.273 36.0279453,34.48 38.4399453,30.944 C42.3219453,25.25 41.7669453,20.042 36.7439453,15.022 C33.9639453,12.244 32.1159453,8.148 31.7309453,7.249 C30.9549453,4.587 28.9029453,1.628 25.4919453,1.628 L25.4909453,1.628 C25.2039453,1.628 24.9139453,1.651 24.6279453,1.696 C23.1339453,1.931 21.8279453,2.791 20.8949453,4.085 C19.8879453,2.833 18.9099453,1.837 18.0249453,1.275 C16.6909453,0.429 15.3579453,0 14.0619453,0 M14.0619453,4 C14.5719453,4 15.1949453,4.217 15.8819453,4.653 C18.0149453,6.006 22.1309453,13.081 23.6379453,15.833 C24.1429453,16.755 25.0059453,17.145 25.7829453,17.145 C27.3249453,17.145 28.5289453,15.612 25.9239453,13.664 C22.0069453,10.733 23.3809453,5.942 25.2509453,5.647 C25.3329453,5.634 25.4139453,5.628 25.4919453,5.628 C27.1919453,5.628 27.9419453,8.558 27.9419453,8.558 C27.9419453,8.558 30.1399453,14.078 33.9159453,17.851 C37.6919453,21.625 37.8869453,24.654 35.1349453,28.69 C33.2579453,31.442 29.6649453,32.273 25.9829453,32.273 C22.1639453,32.273 18.2489453,31.379 16.0549453,30.81 C15.9469453,30.782 2.60394534,27.013 4.29394534,23.805 C4.57794534,23.266 5.04594534,23.05 5.63494534,23.05 C8.01494534,23.05 12.3439453,26.592 14.2049453,26.592 C14.6209453,26.592 14.9139453,26.415 15.0339453,25.983 C15.8269453,23.138 2.97694534,21.942 4.05994534,17.821 C4.25094534,17.092 4.76894534,16.796 5.49694534,16.797 C8.64194534,16.797 15.6979453,22.328 17.1769453,22.328 C17.2899453,22.328 17.3709453,22.295 17.4149453,22.225 C18.1559453,21.029 17.7499453,20.194 12.5269453,17.033 C7.30394534,13.871 3.63794534,11.969 5.72294534,9.699 C5.96294534,9.437 6.30294534,9.321 6.71594534,9.321 C9.88694534,9.322 17.3789453,16.14 17.3789453,16.14 C17.3789453,16.14 19.4009453,18.243 20.6239453,18.243 C20.9049453,18.243 21.1439453,18.132 21.3059453,17.858 C22.1729453,16.396 13.2529453,9.636 12.7499453,6.847 C12.4089453,4.957 12.9889453,4 14.0619453,4" id="Fill-1" fill="#FFAC03"></path>
-                    <path d="M35.1348,28.6899 C37.8868,24.6539 37.6918,21.6249 33.9158,17.8509 C30.1398,14.0779 27.9418,8.5579 27.9418,8.5579 C27.9418,8.5579 27.1208,5.3519 25.2508,5.6469 C23.3808,5.9419 22.0078,10.7329 25.9248,13.6639 C29.8418,16.5939 25.1448,18.5849 23.6378,15.8329 C22.1308,13.0809 18.0158,6.0059 15.8818,4.6529 C13.7488,3.2999 12.2468,4.0579 12.7498,6.8469 C13.2528,9.6359 22.1738,16.3959 21.3058,17.8589 C20.4378,19.3209 17.3788,16.1399 17.3788,16.1399 C17.3788,16.1399 7.8068,7.4289 5.7228,9.6989 C3.6388,11.9689 7.3038,13.8709 12.5268,17.0329 C17.7508,20.1939 18.1558,21.0289 17.4148,22.2249 C16.6728,23.4209 5.1428,13.6999 4.0598,17.8209 C2.9778,21.9419 15.8268,23.1379 15.0338,25.9829 C14.2408,28.8289 5.9828,20.5979 4.2938,23.8049 C2.6038,27.0129 15.9468,30.7819 16.0548,30.8099 C20.3648,31.9279 31.3108,34.2969 35.1348,28.6899" id="Fill-4" fill="#FFD21E"></path>
-                </g>
-            </g>
-        </g>
-    </g>
-</svg>
+<svg clip-rule="evenodd" fill-rule="evenodd" stroke-linejoin="round" stroke-miterlimit="2" viewBox="0 0 127 118" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><clipPath id="a"><path clip-rule="nonzero" d="m62 75.052c13.105 0 17.333-11.684 17.333-17.684 0-3.118-2.096-2.136-5.453-.474-3.103 1.536-7.282 3.653-11.88 3.653-9.573 0-17.333-9.179-17.333-3.179s4.228 17.684 17.333 17.684z"/></clipPath><path d="m125.057 93.44c1 2.88.76 5.947-.573 8.613-.96 1.947-2.333 3.454-4.013 4.8-2.027 1.6-4.547 2.96-7.587 4.267-3.627 1.547-8.053 3-10.08 3.533-5.187 1.347-10.173 2.2-15.227 2.24-7.226.067-13.453-1.64-17.88-6-2.293.28-4.613.44-6.946.44-2.214 0-4.4-.133-6.574-.4-4.44 4.334-10.64 6.027-17.84 5.96-5.053-.04-10.04-.893-15.24-2.24-2.013-.533-6.44-1.986-10.066-3.533-3.04-1.307-5.56-2.667-7.574-4.267-1.693-1.346-3.066-2.853-4.026-4.8-1.32-2.666-1.574-5.733-.56-8.613-.934-2.2-1.174-4.72-.44-7.507.333-1.266.88-2.44 1.573-3.48-.147-.546-.267-1.106-.347-1.72-.506-3.653.76-6.986 3.147-9.573 1.173-1.293 2.44-2.187 3.76-2.76-.973-4.133-1.48-8.387-1.48-12.733 0-30.747 24.92-55.667 55.667-55.667 10.56 0 20.44 2.933 28.866 8.053 1.52.934 3.014 1.934 4.44 3 .707.534 1.414 1.08 2.094 1.654.693.56 1.373 1.146 2.026 1.746 1.974 1.8 3.827 3.734 5.52 5.8.574.68 1.12 1.387 1.654 2.107 1.08 1.427 2.08 2.907 3 4.44 1.4 2.293 2.626 4.693 3.693 7.187.707 1.666 1.32 3.373 1.867 5.12.813 2.613 1.44 5.306 1.866 8.08.134.92.254 1.853.347 2.786.187 1.867.293 3.76.293 5.694 0 4.293-.506 8.506-1.453 12.573 1.467.573 2.853 1.507 4.147 2.92 2.386 2.587 3.653 5.933 3.146 9.587-.08.6-.2 1.16-.346 1.706.693 1.04 1.24 2.214 1.573 3.48.733 2.787.493 5.307-.427 7.507" fill="#fff" fill-rule="nonzero"/><circle cx="62.333" cy="55.667" fill="#ffd21e" r="46.333"/><g fill-rule="nonzero"><path d="m108.667 55.667c0-25.59-20.744-46.334-46.334-46.334-25.589 0-46.333 20.744-46.333 46.334 0 25.589 20.744 46.333 46.333 46.333 25.59 0 46.334-20.744 46.334-46.333zm-98 0c0-28.535 23.132-51.667 51.666-51.667 28.535 0 51.667 23.132 51.667 51.667 0 28.534-23.132 51.666-51.667 51.666-28.534 0-51.666-23.132-51.666-51.666z" fill="#ffac03"/><path d="m77.387 43.055c1.7.6 2.376 4.093 4.092 3.181 3.251-1.729 4.485-5.765 2.757-9.016-1.729-3.251-5.765-4.485-9.016-2.757-3.251 1.729-4.485 5.765-2.757 9.016.816 1.535 3.406-.96 4.924-.424z" fill="#3a3b45"/><path d="m45.978 43.055c-1.699.6-2.375 4.093-4.092 3.181-3.251-1.729-4.485-5.765-2.756-9.016 1.728-3.251 5.765-4.485 9.016-2.757 3.251 1.729 4.485 5.765 2.756 9.016-.815 1.535-3.405-.96-4.924-.424z" fill="#3a3b45"/><path d="m62 75.052c13.105 0 17.333-11.684 17.333-17.684 0-3.118-2.096-2.136-5.453-.474-3.103 1.536-7.282 3.653-11.88 3.653-9.573 0-17.333-9.179-17.333-3.179s4.228 17.684 17.333 17.684z" fill="#3a3b45"/></g><g clip-path="url(#a)"><path d="m62.333 88.667c6.387 0 11.564-5.178 11.564-11.564 0-4.975-3.141-9.216-7.548-10.848-.162-.06-.326-.116-.491-.169-1.111-.355-2.296 3.464-3.525 3.464-1.148 0-2.257-3.844-3.305-3.532-4.776 1.422-8.259 5.847-8.259 11.085 0 6.386 5.178 11.564 11.564 11.564z" fill="#ef4e4e" fill-rule="nonzero"/></g><circle cx="93.667" cy="45" fill="#ffd21e" r="4.333"/><circle cx="31.667" cy="45" fill="#ffd21e" r="4.333"/><path d="m22.749 64c-2.158 0-4.088.887-5.433 2.495-.832.996-1.701 2.601-1.772 5.005-.905-.26-1.776-.405-2.589-.405-2.067 0-3.934.792-5.254 2.23-1.696 1.847-2.449 4.116-2.121 6.387.156 1.081.517 2.051 1.057 2.948-1.138.921-1.977 2.204-2.382 3.747-.318 1.209-.643 3.728 1.056 6.322-.108.17-.21.346-.304.526-1.022 1.938-1.087 4.129-.186 6.169 1.367 3.092 4.763 5.528 11.358 8.143 4.102 1.626 7.856 2.666 7.889 2.676 5.424 1.406 10.329 2.121 14.576 2.121 7.805 0 13.393-2.391 16.609-7.105 5.176-7.592 4.436-14.536-2.261-21.23-3.707-3.704-6.171-9.165-6.684-10.364-1.035-3.549-3.771-7.494-8.319-7.494h-.001c-.383 0-.769.03-1.151.09-1.992.314-3.733 1.46-4.977 3.186-1.343-1.67-2.647-2.998-3.827-3.747-1.778-1.128-3.556-1.7-5.284-1.7m0 5.333c.68 0 1.511.29 2.427.871 2.844 1.804 8.332 11.237 10.341 14.907.674 1.229 1.824 1.749 2.86 1.749 2.056 0 3.662-2.044.188-4.641-5.222-3.908-3.39-10.296-.897-10.69.109-.017.217-.025.321-.025 2.267 0 3.267 3.907 3.267 3.907s2.931 7.36 7.965 12.39c5.035 5.032 5.295 9.071 1.626 14.452-2.503 3.67-7.294 4.778-12.203 4.778-5.092 0-10.312-1.192-13.237-1.951-.144-.037-17.935-5.063-15.682-9.34.379-.719 1.003-1.007 1.788-1.007 3.174 0 8.946 4.723 11.427 4.723.555 0 .945-.236 1.105-.812 1.058-3.793-16.076-5.388-14.632-10.883.255-.972.946-1.366 1.916-1.365 4.194 0 13.602 7.375 15.574 7.375.15 0 .258-.044.317-.138.988-1.594.447-2.708-6.517-6.922-6.964-4.216-11.852-6.752-9.072-9.779.32-.349.773-.504 1.324-.504 4.228.001 14.217 9.092 14.217 9.092s2.696 2.804 4.327 2.804c.374 0 .693-.148.909-.513 1.156-1.95-10.737-10.963-11.408-14.682-.455-2.52.319-3.796 1.749-3.796" fill="#ffac03" fill-rule="nonzero"/><path d="m50.846 102.253c3.67-5.381 3.41-9.42-1.625-14.452-5.035-5.03-7.965-12.39-7.965-12.39s-1.095-4.275-3.588-3.882c-2.494.394-4.324 6.782.898 10.69 5.223 3.906-1Line truncated
@@ -20,13 +20,13 @@ 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'2.3.0'
+release = u'2.8.0'


 # -- 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 ---------------------------------------------

@@ -51,6 +51,7 @@ The library currently contains PyTorch and Tensorflow implementations, pre-train
 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
@@ -60,6 +61,7 @@ The library currently contains PyTorch and Tensorflow implementations, pre-train
    quickstart
    glossary
    pretrained_models
+    usage
    model_sharing
    examples
    notebooks
@@ -78,6 +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

@@ -97,4 +100,9 @@ The library currently contains PyTorch and Tensorflow implementations, pre-train
    model_doc/ctrl
    model_doc/camembert
    model_doc/albert
-    model_doc/xlmroberta
+    model_doc/xlmroberta
+    model_doc/flaubert
+    model_doc/bart
+    model_doc/t5
+    model_doc/electra
+    model_doc/dialogpt
@@ -1,6 +1,6 @@
 # Installation

-Transformers is tested on Python 3.5+ and PyTorch 1.1.0
+Transformers is tested on Python 3.6+ and PyTorch 1.1.0

 ## With pip

@@ -0,0 +1,68 @@
+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
@@ -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>`__
@@ -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:
@@ -27,7 +27,7 @@ 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
@@ -30,6 +30,8 @@ Tips:
  similar to a BERT-like architecture with the same number of hidden layers as it has to iterate through the same
  number of (repeating) layers.

+The original code can be found `here <https://github.com/google-research/ALBERT>`_.
+
 AlbertConfig
 ~~~~~~~~~~~~~~~~~~~~~

@@ -41,7 +43,8 @@ AlbertTokenizer
 ~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.AlbertTokenizer
-    :members:
+    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
+        create_token_type_ids_from_sequences, save_vocabulary


 AlbertModel
@@ -0,0 +1,56 @@
+Bart
+----------------------------------------------------
+**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>`__ 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 ``"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:
+
@@ -35,6 +35,8 @@ Tips:
  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
 ~~~~~~~~~~~~~~~~~~~~~

@@ -46,6 +48,14 @@ 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:


@@ -22,6 +22,8 @@ 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
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -33,7 +35,8 @@ CamembertTokenizer
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.CamembertTokenizer
-    :members:
+    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
+        create_token_type_ids_from_sequences, save_vocabulary


 CamembertModel
@@ -69,3 +72,31 @@ 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:
@@ -31,6 +31,8 @@ Tips:
  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
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -43,7 +45,7 @@ CTRLTokenizer
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.CTRLTokenizer
-    :members:
+    :members: save_vocabulary


 CTRLModel
@@ -0,0 +1,29 @@
+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>`_.
+ 
+
+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>`_.
@@ -27,6 +27,8 @@ 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
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -42,6 +44,13 @@ DistilBertTokenizer
    :members:


+DistilBertTokenizerFast
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.DistilBertTokenizerFast
+    :members:
+
+
 DistilBertModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -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:
@@ -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:
+
+
@@ -36,6 +36,9 @@ Tips:
 `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
 ~~~~~~~~~~~~~~~~~~~~~

@@ -47,6 +50,13 @@ OpenAIGPTTokenizer
 ~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.OpenAIGPTTokenizer
+    :members: save_vocabulary
+
+
+OpenAIGPTTokenizerFast
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.OpenAIGPTTokenizerFast
    :members:


@@ -5,7 +5,7 @@ Overview
 ~~~~~~~~~~~~~~~~~~~~~

 OpenAI GPT-2 model was proposed in
-`Language Models are Unsupervised Multitask Learners`_
+`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.
@@ -34,6 +34,8 @@ Tips:
 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
 ~~~~~~~~~~~~~~~~~~~~~
@@ -46,6 +48,13 @@ GPT2Tokenizer
 ~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.GPT2Tokenizer
+    :members: save_vocabulary
+
+
+GPT2TokenizerFast
+~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.GPT2TokenizerFast
    :members:


@@ -23,8 +23,14 @@ 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
 ~~~~~~~~~~~~~~~~~~~~~

@@ -36,7 +42,15 @@ RobertaTokenizer
 ~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.RobertaTokenizer
-    :members:
+    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
+        create_token_type_ids_from_sequences, save_vocabulary
+
+
+RobertaTokenizerFast
+~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.RobertaTokenizerFast
+    :members: build_inputs_with_special_tokens


 RobertaModel
@@ -0,0 +1,103 @@
+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.* perprended 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 tokens represents a unique mask token for this sentence and should start with ``<extra_id_1>``, ``<extrac_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:
@@ -30,6 +30,8 @@ Tips:
  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
 ~~~~~~~~~~~~~~~~~~~~~
@@ -42,6 +44,13 @@ TransfoXLTokenizer
 ~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TransfoXLTokenizer
+    :members: save_vocabulary
+
+
+TransfoXLTokenizerFast
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TransfoXLTokenizerFast
    :members:


@@ -30,6 +30,8 @@ Tips:
 - 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
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -41,7 +43,8 @@ XLMTokenizer
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.XLMTokenizer
-    :members:
+    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
+        create_token_type_ids_from_sequences, save_vocabulary

 XLMModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -22,9 +22,15 @@ and XNLI benchmarks. We will make XLM-R code, data, and models publicly availabl

 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
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -36,7 +42,8 @@ XLMRobertaTokenizer
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.XLMRobertaTokenizer
-    :members:
+    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
+        create_token_type_ids_from_sequences, save_vocabulary


 XLMRobertaModel
@@ -73,3 +80,30 @@ 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:
@@ -32,6 +32,8 @@ Tips:
  `target_mapping` inputs to control the attention span and outputs (see examples in `examples/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
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -44,7 +46,8 @@ XLNetTokenizer
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.XLNetTokenizer
-    :members:
+    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
+        create_token_type_ids_from_sequences, save_vocabulary


 XLNetModel
@@ -2,7 +2,7 @@

 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)**. Then:
+**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
@@ -21,25 +21,35 @@ transformers-cli upload ./config.json [--filename folder/foobar.json]
 # (you can optionally override its filename, which can be nested inside a folder)
 ```

-Your model will then be accessible through its identifier, a concatenation of your username and the folder name above:
+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("username/pretrained_model")
-model = AutoModel.from_pretrained("username/pretrained_model")
+tokenizer = AutoTokenizer.from_pretrained("namespace/pretrained_model")
+model = AutoModel.from_pretrained("namespace/pretrained_model")
 ```

-Finally, list all your files on S3:
+List all your files on S3:
 ```shell
 transformers-cli s3 ls
-# List all your S3 objects.
 ```

-You can also delete files:
+You can also delete unneeded files:

 ```shell
 transformers-cli s3 rm …
-```
+```
@@ -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])

@@ -73,6 +76,7 @@ You can then feed it all as input to your model:

 .. code-block::

+    # Continuation of the previous script
    outputs = model(input_ids, langs=langs)


@@ -0,0 +1 @@
+../../notebooks/README.md
@@ -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.
@@ -179,6 +179,14 @@ For a list that includes community-uploaded models, refer to `https://huggingfac
 |                   |                                                            | | 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>`__)                                     |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                   | ``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>`__)                                     |
+|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                   | ``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>`__)                                     |
@@ -251,6 +259,40 @@ For a list that includes community-uploaded models, refer to `https://huggingfac
 |                   | ``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                                                          |
 +-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-
-
-.. <https://huggingface.co/transformers/examples.html>`__
+| FlauBERT          | ``flaubert-small-cased``                                   | | 6-layer, 512-hidden, 8-heads, 54M parameters                                                                                        |
+|                   |                                                            | | FlauBERT small architecture                                                                                                         |
+|                   |                                                            | (see `details <https://github.com/getalp/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-base-cased``                                    | | 12-layer, 768-hidden, 12-heads, 138M parameters                                                                                     |
+|                   |                                                            | | FlauBERT base architecture with cased vocabulary                                                                                    |
+|                   |                                                            | (see `details <https://github.com/getalp/Flaubert>`__)                                                                                |
+|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                   | ``flaubert-large-cased``                                   | | 24-layer, 1024-hidden, 16-heads, 373M parameters                                                                                    |
+|                   |                                                            | | FlauBERT large architecture                                                                                                         |
+|                   |                                                            | (see `details <https://github.com/getalp/Flaubert>`__)                                                                                |
+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+| Bart              | ``bart-large``                                             | | 12-layer, 1024-hidden, 16-heads, 406M parameters                                                                                    |
+|                   |                                                            | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/bart>`_)                                                       |
+|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                   | ``bart-large-mnli``                                        | | Adds a 2 layer classification head with 1 million parameters                                                                        |
+|                   |                                                            | | bart-large base architecture with a classification head, finetuned on MNLI                                                          |
+|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                   | ``bart-large-cnn``                                         | | 12-layer, 1024-hidden, 16-heads, 406M parameters       (same as base)                                                               |
+|                   |                                                            | | bart-large base architecture finetuned on cnn summarization task                                                                    |
+|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                   | ``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.                                                    |
+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
@@ -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)
@@ -220,96 +220,3 @@ print(sequence)
 ```

 The model only requires a single token as input as all the previous tokens' key/value pairs are contained in the `past`.
-
-### Model2Model example
-
-Encoder-decoder architectures require two tokenized inputs: one for the encoder and the other one for the decoder. Let's assume that we want to use `Model2Model` for generative question answering, and start by tokenizing the question and answer that will be fed to the model.
-
-```python
-import torch
-from transformers import BertTokenizer, Model2Model
-
-# OPTIONAL: if you want to have more information on what's happening under the hood, activate the logger as follows
-import logging
-logging.basicConfig(level=logging.INFO)
-
-# Load pre-trained model tokenizer (vocabulary)
-tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-
-# Encode the input to the encoder (the question)
-question = "Who was Jim Henson?"
-encoded_question = tokenizer.encode(question)
-
-# Encode the input to the decoder (the answer)
-answer = "Jim Henson was a puppeteer"
-encoded_answer = tokenizer.encode(answer)
-
-# Convert inputs to PyTorch tensors
-question_tensor = torch.tensor([encoded_question])
-answer_tensor = torch.tensor([encoded_answer])
-```
-
-Let's see how we can use `Model2Model` to get the value of the loss associated with this (question, answer) pair:
-
-```python
-# In order to compute the loss we need to provide language model
-# labels (the token ids that the model should have produced) to
-# the decoder.
-lm_labels =  encoded_answer
-labels_tensor = torch.tensor([lm_labels])
-
-# Load pre-trained model (weights)
-model = Model2Model.from_pretrained('bert-base-uncased')
-
-# Set the model in evaluation mode to deactivate the DropOut modules
-# This is IMPORTANT to have reproducible results during evaluation!
-model.eval()
-
-# If you have a GPU, put everything on cuda
-question_tensor = question_tensor.to('cuda')
-answer_tensor = answer_tensor.to('cuda')
-labels_tensor = labels_tensor.to('cuda')
-model.to('cuda')
-
-# Predict hidden states features for each layer
-with torch.no_grad():
-    # See the models docstrings for the detail of the inputs
-    outputs = model(question_tensor, answer_tensor, decoder_lm_labels=labels_tensor)
-    # Transformers models always output tuples.
-    # See the models docstrings for the detail of all the outputs
-    # In our case, the first element is the value of the LM loss 
-    lm_loss = outputs[0]
-```
-
-This loss can be used to fine-tune `Model2Model` on the question answering task. Assuming that we fine-tuned the model, let us now see how to generate an answer:
-
-```python
-# Let's re-use the previous question
-question = "Who was Jim Henson?"
-encoded_question = tokenizer.encode(question)
-question_tensor = torch.tensor([encoded_question])
-
-# This time we try to generate the answer, so we start with an empty sequence
-answer = "[CLS]"
-encoded_answer = tokenizer.encode(answer, add_special_tokens=False)
-answer_tensor = torch.tensor([encoded_answer])
-
-# Load pre-trained model (weights)
-model = Model2Model.from_pretrained('fine-tuned-weights')
-model.eval()
-
-# If you have a GPU, put everything on cuda
-question_tensor = encoded_question.to('cuda')
-answer_tensor = encoded_answer.to('cuda')
-model.to('cuda')
-
-# Predict all tokens
-with torch.no_grad():
-    outputs = model(question_tensor, answer_tensor)
-    predictions = outputs[0]
-
-# confirm we were able to predict 'jim'
-predicted_index = torch.argmax(predictions[0, -1]).item()
-predicted_token = tokenizer.convert_ids_to_tokens([predicted_index])[0]
-assert predicted_token == 'jim'
-```
@@ -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)
@@ -0,0 +1,727 @@
+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/run_glue.py>`_ or
+`run_tf_glue.py <https://github.com/huggingface/transformers/tree/master/examples/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.
+
+There is currently no pipeline to do causal language modeling/generation.
+
+Here is an example using the tokenizer and model. leveraging the :func:`~transformers.PreTrainedModel.generate` method
+to generate the tokens following the initial sequence in PyTorch, and creating a simple loop in TensorFlow.
+
+::
+
+    ## PYTORCH CODE
+    from transformers import AutoModelWithLMHead, AutoTokenizer
+
+    tokenizer = AutoTokenizer.from_pretrained("gpt2")
+    model = AutoModelWithLMHead.from_pretrained("gpt2")
+
+    sequence = f"Hugging Face is based in DUMBO, New York City, and is"
+
+    input = tokenizer.encode(sequence, return_tensors="pt")
+    generated = model.generate(input, max_length=50, do_sample=True)
+
+    resulting_string = tokenizer.decode(generated.tolist()[0])
+    print(resulting_string)
+    ## TENSORFLOW CODE
+    from transformers import TFAutoModelWithLMHead, AutoTokenizer
+    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 is"
+    input = tokenizer.encode(sequence, return_tensors="tf")
+    generated = model.generate(input, max_length=50, do_sample=True)
+
+    resulting_string = tokenizer.decode(generated.tolist()[0])
+    print(resulting_string)
+
+
+This outputs a (hopefully) coherent string from the original sequence, as the
+:func:`~transformers.PreTrainedModel.generate` samples from a top_p/tok_k distribution:
+
+::
+
+    Hugging Face is based in DUMBO, New York City, and is a live-action TV series based on the novel by John
+    Carpenter, and its producers, David Kustlin and Steve Pichar. The film is directed by!
+
+
+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)
@@ -3,7 +3,7 @@
 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.

-**Important**  
+**Important**
 To run the latest versions of the examples, you have to install from source and install some specific requirements for the examples.
 Execute the following steps in a new virtual environment:

@@ -15,17 +15,17 @@ 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.                                                                                  |
+|----------------------------|------------------------------------------------------------------------------------------------------------------------------------------
+| [TensorFlow 2.0 models on GLUE](#TensorFlow-2.0-Bert-models-on-GLUE) | Examples running BERT TensorFlow 2.0 model on the GLUE tasks. |
+| [Running on TPUs](#running-on-tpus) | Examples on running fine-tuning tasks on Google TPUs to accelerate workloads. |
+| [Language Model training](#language-model-training) | Fine-tuning (or training from scratch) 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](https://github.com/huggingface/transformers/tree/master/examples/ner) | 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. |
-| [Adversarial evaluation of model performances](#adversarial-evaluation-of-model-performances) | Testing a model with adversarial evaluation of natural language
-inference on the Heuristic Analysis for NLI Systems (HANS) dataset (McCoy et al., 2019.) |
+| [Adversarial evaluation of model performances](#adversarial-evaluation-of-model-performances) | Testing a model with adversarial evaluation of natural language inference on the Heuristic Analysis for NLI Systems (HANS) dataset (McCoy et al., 2019.) |

 ## TensorFlow 2.0 Bert models on GLUE

@@ -49,16 +49,58 @@ Quick benchmarks from the script (no other modifications):

 Mixed precision (AMP) reduces the training time considerably for the same hardware and hyper-parameters (same batch size was used).

-## Language model fine-tuning
+## Running on TPUs

-Based on the script [`run_lm_finetuning.py`](https://github.com/huggingface/transformers/blob/master/examples/run_lm_finetuning.py).
+You can accelerate your workloads on Google's TPUs. For information on how to setup your TPU environment refer to this
+[README](https://github.com/pytorch/xla/blob/master/README.md).

-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 
+The following are some examples of running the `*_tpu.py` finetuning scripts on TPUs. All steps for data preparation are
+identical to your normal GPU + Huggingface setup.
+
+### GLUE
+
+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 `$GLUE_DIR`.
+
+For running your GLUE task on MNLI dataset you can run something like the following:
+
+```
+export XRT_TPU_CONFIG="tpu_worker;0;$TPU_IP_ADDRESS:8470"
+export GLUE_DIR=/path/to/glue
+export TASK_NAME=MNLI
+
+python run_glue_tpu.py \
+  --model_type bert \
+  --model_name_or_path bert-base-cased \
+  --task_name $TASK_NAME \
+  --do_train \
+  --do_eval \
+  --data_dir $GLUE_DIR/$TASK_NAME \
+  --max_seq_length 128 \
+  --train_batch_size 32 \
+  --learning_rate 3e-5 \
+  --num_train_epochs 3.0 \
+  --output_dir /tmp/$TASK_NAME \
+  --overwrite_output_dir \
+  --logging_steps 50 \
+  --save_steps 200 \
+  --num_cores=8 \
+  --only_log_master
+```
+
+
+## Language model training
+
+Based on the script [`run_language_modeling.py`](https://github.com/huggingface/transformers/blob/master/examples/run_language_modeling.py).
+
+Fine-tuning (or training from scratch) 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/).
+trained or 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.
@@ -72,7 +114,7 @@ the tokenization). The loss here is that of causal language modeling.
 export TRAIN_FILE=/path/to/dataset/wiki.train.raw
 export TEST_FILE=/path/to/dataset/wiki.test.raw

-python run_lm_finetuning.py \
+python run_language_modeling.py \
    --output_dir=output \
    --model_type=gpt2 \
    --model_name_or_path=gpt2 \
@@ -89,7 +131,7 @@ a score of ~20 perplexity once fine-tuned on the dataset.

 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. 
+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).
@@ -100,7 +142,7 @@ We use the `--mlm` flag so that the script may change its loss function.
 export TRAIN_FILE=/path/to/dataset/wiki.train.raw
 export TEST_FILE=/path/to/dataset/wiki.test.raw

-python run_lm_finetuning.py \
+python run_language_modeling.py \
    --output_dir=output \
    --model_type=roberta \
    --model_name_or_path=roberta-base \
@@ -131,8 +173,8 @@ python run_generation.py \

 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. 
+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 single V100 GPUs with a total train
@@ -154,7 +196,7 @@ between different runs. We report the median on 5 runs (with different seeds) fo
 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
+Before running any one 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`.
@@ -169,7 +211,6 @@ python run_glue.py \
  --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 \
@@ -180,23 +221,23 @@ python run_glue.py \

 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 
+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, 
+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 
+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
+Before running any one 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`.
@@ -210,7 +251,6 @@ python run_glue.py \
  --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 \
@@ -220,12 +260,12 @@ python run_glue.py \
 ```

 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 
+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 
+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
@@ -237,7 +277,6 @@ python run_glue.py \
  --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 \
@@ -262,7 +301,6 @@ python -m torch.distributed.launch \
    --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 \
@@ -296,7 +334,6 @@ python -m torch.distributed.launch \
    --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 \
@@ -337,7 +374,6 @@ python ./examples/run_multiple_choice.py \
 --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 \
@@ -361,8 +397,8 @@ Based on the script [`run_squad.py`](https://github.com/huggingface/transformers

 #### Fine-tuning BERT on SQuAD1.0

-This example code fine-tunes BERT on the SQuAD1.0 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 
+This example code fine-tunes BERT on the SQuAD1.0 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)
@@ -380,10 +416,9 @@ export SQUAD_DIR=/path/to/SQUAD

 python run_squad.py \
  --model_type bert \
-  --model_name_or_path bert-base-cased \
+  --model_name_or_path bert-base-uncased \
  --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 \
@@ -404,24 +439,23 @@ 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 SQuAD1.0:
+Here is an example using distributed training on 8 V100 GPUs and Bert Whole Word Masking uncased model to reach a F1 > 93 on SQuAD1.1:

 ```bash
-python -m torch.distributed.launch --nproc_per_node=8 run_squad.py \
+python -m torch.distributed.launch --nproc_per_node=8 ./examples/run_squad.py \
    --model_type bert \
-    --model_name_or_path bert-base-cased \
+    --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 \
    --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
+    --output_dir ./examples/models/wwm_uncased_finetuned_squad/ \
+    --per_gpu_eval_batch_size=3   \
+    --per_gpu_train_batch_size=3   \
 ```

 Training with the previously defined hyper-parameters yields the following results:
@@ -443,14 +477,13 @@ This example code fine-tunes XLNet on both SQuAD1.0 and SQuAD2.0 dataset. See ab
 ```bash
 export SQUAD_DIR=/path/to/SQUAD

-python /data/home/hlu/transformers/examples/run_squad.py \
+python 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 \
+    --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 \
@@ -517,196 +550,17 @@ Larger batch size may improve the performance while costing more memory.



-## 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).
+[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-resource language such as English and low-resource 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 
+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)
@@ -773,15 +627,14 @@ 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/test_hans.py \
+python examples/hans/test_hans.py \
        --task_name hans \
        --model_type $MODEL_TYPE \
        --do_eval \
-        --do_lower_case \
        --data_dir $HANS_DIR \
        --model_name_or_path $MODEL_PATH \
        --max_seq_length 128 \
-        -output_dir $MODEL_PATH \
+        --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.
@@ -20,11 +20,20 @@

 import argparse
 import csv
+import logging
 import timeit
 from time import time
-from typing import List
+from typing import Callable, List

-from transformers import AutoConfig, AutoTokenizer, is_tf_available, is_torch_available
+from transformers import (
+    AutoConfig,
+    AutoTokenizer,
+    MemorySummary,
+    is_tf_available,
+    is_torch_available,
+    start_memory_tracing,
+    stop_memory_tracing,
+)


 if is_tf_available():
@@ -38,10 +47,8 @@ if is_torch_available():

 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
@@ -250,15 +257,22 @@ as they entered."""

 def create_setup_and_compute(
    model_names: List[str],
+    batch_sizes: List[int],
+    slice_sizes: List[int],
    gpu: bool = True,
    tensorflow: bool = False,
    average_over: int = 3,
+    no_speed: bool = False,
+    no_memory: bool = False,
+    verbose: bool = False,
    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",
+    csv_time_filename: str = f"time_{round(time())}.csv",
+    csv_memory_filename: str = f"memory_{round(time())}.csv",
+    print_fn: Callable[[str], None] = print,
 ):
    if xla:
        tf.config.optimizer.set_jit(True)
@@ -267,69 +281,152 @@ def create_setup_and_compute(

    if tensorflow:
        dictionary = {model_name: {} for model_name in model_names}
-        results = _compute_tensorflow(model_names, dictionary, average_over, amp)
+        results = _compute_tensorflow(
+            model_names,
+            batch_sizes,
+            slice_sizes,
+            dictionary,
+            average_over,
+            amp,
+            no_speed,
+            no_memory,
+            verbose,
+            print_fn,
+        )
    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)
+        results = _compute_pytorch(
+            model_names,
+            batch_sizes,
+            slice_sizes,
+            dictionary,
+            average_over,
+            device,
+            torchscript,
+            fp16,
+            no_speed,
+            no_memory,
+            verbose,
+            print_fn,
+        )

-    print("=========== RESULTS ===========")
+    print_fn("=========== RESULTS ===========")
    for model_name in model_names:
-        print("\t" + f"======= MODEL CHECKPOINT: {model_name} =======")
+        print_fn("\t" + f"======= MODEL CHECKPOINT: {model_name} =======")
        for batch_size in results[model_name]["bs"]:
-            print("\t\t" + f"===== BATCH SIZE: {batch_size} =====")
+            print_fn("\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}")
+                time = results[model_name]["time"][batch_size][slice_size]
+                memory = results[model_name]["memory"][batch_size][slice_size]
+                if isinstance(time, str):
+                    print_fn(f"\t\t{model_name}/{batch_size}/{slice_size}: " f"{time} " f"{memory}")
                else:
-                    print(f"\t\t{model_name}/{batch_size}/{slice_size}: " f"{(round(1000 * result) / 1000)}" f"s")
+                    print_fn(
+                        f"\t\t{model_name}/{batch_size}/{slice_size}: "
+                        f"{(round(1000 * time) / 1000)}"
+                        f"s "
+                        f"{memory}"
+                    )

    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",
-            ]
+        with open(csv_time_filename, mode="w") as csv_time_file, open(
+            csv_memory_filename, mode="w"
+        ) as csv_memory_file:

-            writer = csv.DictWriter(csv_file, fieldnames=fieldnames)
-            writer.writeheader()
+            assert len(model_names) > 0, "At least 1 model should be defined, but got {}".format(model_names)
+
+            fieldnames = ["model", "batch_size", "sequence_length"]
+            time_writer = csv.DictWriter(csv_time_file, fieldnames=fieldnames + ["time_in_s"])
+            time_writer.writeheader()
+            memory_writer = csv.DictWriter(csv_memory_file, fieldnames=fieldnames + ["memory"])
+            memory_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})
+                time_dict = results[model_name]["time"]
+                memory_dict = results[model_name]["memory"]
+                for bs in time_dict:
+                    for ss in time_dict[bs]:
+                        time_writer.writerow(
+                            {
+                                "model": model_name,
+                                "batch_size": bs,
+                                "sequence_length": ss,
+                                "time_in_s": "{:.4f}".format(time_dict[bs][ss]),
+                            }
+                        )
+
+                for bs in memory_dict:
+                    for ss in time_dict[bs]:
+                        memory_writer.writerow(
+                            {
+                                "model": model_name,
+                                "batch_size": bs,
+                                "sequence_length": ss,
+                                "memory": memory_dict[bs][ss],
+                            }
+                        )


-def _compute_pytorch(model_names, dictionary, average_over, device, torchscript, fp16):
+def print_summary_statistics(summary: MemorySummary, print_fn: Callable[[str], None]):
+    print_fn(
+        "\nLines by line memory consumption:\n"
+        + "\n".join(
+            f"{state.frame.filename}:{state.frame.line_number}: mem {state.cpu_gpu}: {state.frame.line_text}"
+            for state in summary.sequential
+        )
+    )
+    print_fn(
+        "\nLines with top memory consumption:\n"
+        + "\n".join(
+            f"=> {state.frame.filename}:{state.frame.line_number}: mem {state.cpu_gpu}: {state.frame.line_text}"
+            for state in summary.cumulative[:6]
+        )
+    )
+    print_fn(
+        "\nLines with lowest memory consumption:\n"
+        + "\n".join(
+            f"=> {state.frame.filename}:{state.frame.line_number}: mem {state.cpu_gpu}: {state.frame.line_text}"
+            for state in summary.cumulative[-6:]
+        )
+    )
+    print_fn(f"\nTotal memory increase: {summary.total}")
+
+
+def get_print_function(save_print_log, log_filename):
+    if save_print_log:
+        logging.basicConfig(
+            level=logging.DEBUG,
+            filename=log_filename,
+            filemode="a+",
+            format="%(asctime)-15s %(levelname)-8s %(message)s",
+        )
+
+        def print_with_print_log(*args):
+            logging.info(*args)
+            print(*args)
+
+        return print_with_print_log
+    else:
+        return print
+
+
+def _compute_pytorch(
+    model_names,
+    batch_sizes,
+    slice_sizes,
+    dictionary,
+    average_over,
+    device,
+    torchscript,
+    fp16,
+    no_speed,
+    no_memory,
+    verbose,
+    print_fn,
+):
    for c, model_name in enumerate(model_names):
-        print(f"{c + 1} / {len(model_names)}")
+        print_fn(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)
@@ -337,45 +434,70 @@ def _compute_pytorch(model_names, dictionary, average_over, device, torchscript,
        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}
+        dictionary[model_name] = {"bs": batch_sizes, "ss": slice_sizes, "time": {}, "memory": {}}
+        dictionary[model_name]["time"] = {i: {} for i in batch_sizes}
+        dictionary[model_name]["memory"] = {i: {} for i in batch_sizes}
+
+        print_fn("Using model {}".format(model))
+        print_fn("Number of all parameters {}".format(model.num_parameters()))

        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"
+                    dictionary[model_name]["time"][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)
+                            print_fn("Tracing model with sequence size {}".format(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
+                        if not no_memory:
+                            # model.add_memory_hooks()  # Forward method tracing (only for PyTorch models)
+
+                            # Line by line memory tracing (all code in the module `transformers`) works for all models/arbitrary code
+                            trace = start_memory_tracing("transformers")
+                            inference(sequence)
+                            summary = stop_memory_tracing(trace)
+
+                            if verbose:
+                                print_summary_statistics(summary, print_fn)
+
+                            dictionary[model_name]["memory"][batch_size][slice_size] = str(summary.total)
+                        else:
+                            dictionary[model_name]["memory"][batch_size][slice_size] = "N/A"
+
+                        if not no_speed:
+                            print_fn("Going through model with sequence of shape".format(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]["time"][batch_size][slice_size] = average_time
+                        else:
+                            dictionary[model_name]["time"][batch_size][slice_size] = "N/A"
+
                    except RuntimeError as e:
-                        print("Doesn't fit on GPU.", e)
+                        print_fn("Doesn't fit on GPU. {}".format(e))
                        torch.cuda.empty_cache()
-                        dictionary[model_name]["results"][batch_size][slice_size] = "N/A"
+                        dictionary[model_name]["time"][batch_size][slice_size] = "N/A"
+                        dictionary[model_name]["memory"][batch_size][slice_size] = "N/A"
    return dictionary


-def _compute_tensorflow(model_names, dictionary, average_over, amp):
+def _compute_tensorflow(
+    model_names, batch_sizes, slice_sizes, dictionary, average_over, amp, no_speed, no_memory, verbose, print_fn
+):
    for c, model_name in enumerate(model_names):
-        print(f"{c + 1} / {len(model_names)}")
+        print_fn(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)
@@ -383,13 +505,13 @@ def _compute_tensorflow(model_names, dictionary, average_over, amp):
        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}
+        dictionary[model_name] = {"bs": batch_sizes, "ss": slice_sizes, "time": {}, "memory": {}}
+        dictionary[model_name]["time"] = {i: {} for i in batch_sizes}
+        dictionary[model_name]["memory"] = {i: {} for i in batch_sizes}

-        print("Using model", model)
+        print_fn("Using model {}".format(model))
+        print_fn("Number of all parameters {}".format(model.num_parameters()))

        @tf.function
        def inference(inputs):
@@ -398,24 +520,41 @@ def _compute_tensorflow(model_names, dictionary, average_over, amp):
        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"
+                    dictionary[model_name]["time"][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)
+                        print_fn("Going through model with sequence of shape {}".format(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
+                        if not no_memory:
+                            # Line by line memory tracing (all code in the module `transformers`) works for all models/arbitrary code
+                            trace = start_memory_tracing("transformers")
+                            inference(sequence)
+                            summary = stop_memory_tracing(trace)
+
+                            if verbose:
+                                print_summary_statistics(summary, print_fn)
+
+                            dictionary[model_name]["memory"][batch_size][slice_size] = str(summary.total)
+                        else:
+                            dictionary[model_name]["memory"][batch_size][slice_size] = "N/A"
+
+                        if not no_speed:
+                            runtimes = timeit.repeat(lambda: inference(sequence), repeat=average_over, number=3)
+                            average_time = sum(runtimes) / float(len(runtimes)) / 3.0
+                            dictionary[model_name]["time"][batch_size][slice_size] = average_time
+                        else:
+                            dictionary[model_name]["time"][batch_size][slice_size] = "N/A"
+
                    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"
+                        print_fn("Doesn't fit on GPU. {}".format(e))
+                        dictionary[model_name]["time"][batch_size][slice_size] = "N/A"
+                        dictionary[model_name]["memory"][batch_size][slice_size] = "N/A"
    return dictionary


@@ -433,6 +572,9 @@ def main():
        "of all available model "
        "architectures.",
    )
+    parser.add_argument("--verbose", required=False, action="store_true", help="Verbose memory tracing")
+    parser.add_argument("--no_speed", required=False, action="store_true", help="Don't perform speed measurments")
+    parser.add_argument("--no_memory", required=False, action="store_true", help="Don't perform memory measurments")
    parser.add_argument(
        "--torch", required=False, action="store_true", help="Benchmark the Pytorch version of the " "models"
    )
@@ -472,11 +614,31 @@ def main():
    )
    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."
+        "--log_print", required=False, action="store_true", help="Save all print statements in log file."
+    )
+    parser.add_argument(
+        "--csv_time_filename",
+        required=False,
+        default=f"time_{round(time())}.csv",
+        help="CSV filename used if saving time results to csv.",
+    )
+    parser.add_argument(
+        "--csv_memory_filename",
+        required=False,
+        default=f"memory_{round(time())}.csv",
+        help="CSV filename used if saving memory results to csv.",
+    )
+    parser.add_argument(
+        "--log_filename",
+        required=False,
+        default=f"log_{round(time())}.txt",
+        help="Log filename used if print statements are saved in log.",
    )
    parser.add_argument(
        "--average_over", required=False, default=30, type=int, help="Times an experiment will be run."
    )
+    parser.add_argument("--batch_sizes", nargs="+", type=int, default=[1, 2, 4, 8])
+    parser.add_argument("--slice_sizes", nargs="+", type=int, default=[8, 64, 128, 256, 512, 1024])

    args = parser.parse_args()
    if args.models == "all":
@@ -491,23 +653,33 @@ def main():
            "distilgpt2",
            "roberta-base",
            "ctrl",
+            "t5-base",
+            "bart-large",
        ]
    else:
        args.models = args.models.split()

-    print("Running with arguments", args)
+    print_fn = get_print_function(args.log_print, args.log_filename)
+    print_fn("Running with arguments: {}".format(args))

    if args.torch:
        if is_torch_available():
            create_setup_and_compute(
                model_names=args.models,
+                batch_sizes=args.batch_sizes,
+                slice_sizes=args.slice_sizes,
                tensorflow=False,
                gpu=args.torch_cuda,
                torchscript=args.torchscript,
                fp16=args.fp16,
                save_to_csv=args.save_to_csv,
-                csv_filename=args.csv_filename,
+                csv_time_filename=args.csv_time_filename,
+                csv_memory_filename=args.csv_memory_filename,
                average_over=args.average_over,
+                no_speed=args.no_speed,
+                no_memory=args.no_memory,
+                verbose=args.verbose,
+                print_fn=print_fn,
            )
        else:
            raise ImportError("Trying to run a PyTorch benchmark but PyTorch was not found in the environment.")
@@ -516,12 +688,19 @@ def main():
        if is_tf_available():
            create_setup_and_compute(
                model_names=args.models,
+                batch_sizes=args.batch_sizes,
+                slice_sizes=args.slice_sizes,
                tensorflow=True,
                xla=args.xla,
                amp=args.amp,
                save_to_csv=args.save_to_csv,
-                csv_filename=args.csv_filename,
+                csv_time_filename=args.csv_time_filename,
+                csv_memory_filename=args.csv_memory_filename,
                average_over=args.average_over,
+                no_speed=args.no_speed,
+                no_memory=args.no_memory,
+                verbose=args.verbose,
+                print_fn=print_fn,
            )
        else:
            raise ImportError("Trying to run a TensorFlow benchmark but TensorFlow was not found in the environment.")
@@ -249,8 +249,8 @@ 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 = (
@@ -622,7 +622,7 @@ 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)
@@ -10,14 +10,14 @@ This folder contains the original code used to train Distil* as well as examples

 **October 23, 2019 - Update** We release **DistilRoBERTa**: 95% of `RoBERTa-base`'s performance on GLUE, twice as fast as RoBERTa while being 35% smaller.

-**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 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 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*

-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 99% 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.
+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 16.3 compared to 21.1 for **DistilGPT2** (after fine-tuning on the train set).
@@ -31,13 +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-uncased         |  **77.6**                      | 49.2 | 80.8 | 87.4 | 87.5 | 86.4 | 61.7 | 92.0 | 83.8 | 45.1              |
-| DistilBERT-base-uncased   |  **76.8**                      | 43.6 | 79.0 | 87.5 | 85.3 | 84.9 | 59.9 | 90.7 | 81.2 | 56.3              |
+| 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> | 59.3 | 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.

@@ -63,7 +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.
@@ -72,8 +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)
@@ -81,6 +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')`
@@ -106,7 +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 \
@@ -174,7 +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,
@@ -3,5 +3,5 @@ transformers
 gitpython==3.0.2
 tensorboard>=1.14.0
 tensorboardX==1.8
-psutil==5.6.3
+psutil==5.6.6
 scipy==1.3.1
@@ -39,6 +39,9 @@ from transformers import (
    DistilBertConfig,
    DistilBertForQuestionAnswering,
    DistilBertTokenizer,
+    RobertaConfig,
+    RobertaForQuestionAnswering,
+    RobertaTokenizer,
    XLMConfig,
    XLMForQuestionAnswering,
    XLMTokenizer,
@@ -73,6 +76,7 @@ MODEL_CLASSES = {
    "xlnet": (XLNetConfig, XLNetForQuestionAnswering, XLNetTokenizer),
    "xlm": (XLMConfig, XLMForQuestionAnswering, XLMTokenizer),
    "distilbert": (DistilBertConfig, DistilBertForQuestionAnswering, DistilBertTokenizer),
+    "roberta": (RobertaConfig, RobertaForQuestionAnswering, RobertaTokenizer),
 }


@@ -716,7 +720,7 @@ 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)
@@ -75,13 +75,17 @@ def main():
        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.")

    dp_file = f"{args.dump_file}.{args.tokenizer_name}.pickle"
-    rslt_ = [np.uint16(d) for d in rslt]
+    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:
@@ -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
+  }
+  
@@ -0,0 +1,9 @@
+# GLUE Benchmark
+
+Based on the script [`run_glue.py`](https://github.com/huggingface/transformers/blob/master/examples/run_glue.py).
+
+#### Run PyTorch version using PyTorch-Lightning
+
+Run `bash run_pl.sh` from the `glue` directory. This will also install `pytorch-lightning` and the requirements in `examples/requirements.txt`. It is a shell pipeline that will automatically download, pre-process the data and run the specified models. Logs are saved in `lightning_logs` directory.
+
+Pass `--n_gpu` flag to change the number of GPUs. Default uses 1. At the end, the expected results are: `TEST RESULTS {'val_loss': tensor(0.0707), 'precision': 0.852427800698191, 'recall': 0.869537067011978, 'f1': 0.8608974358974358}`
@@ -0,0 +1,38 @@
+# Install newest ptl.
+pip install -U git+http://github.com/PyTorchLightning/pytorch-lightning/
+# Install example requirements
+pip install -r ../requirements.txt
+
+# Download glue data
+python3 ../../utils/download_glue_data.py
+
+export TASK=mrpc
+export DATA_DIR=./glue_data/MRPC/
+export MAX_LENGTH=128
+export LEARNING_RATE=2e-5
+export BERT_MODEL=bert-base-cased
+export MODEL_TYPE=bert
+export BATCH_SIZE=32
+export NUM_EPOCHS=3
+export SEED=2
+export OUTPUT_DIR_NAME=mrpc-pl-bert
+export CURRENT_DIR=${PWD}
+export OUTPUT_DIR=${CURRENT_DIR}/${OUTPUT_DIR_NAME}
+
+# Make output directory if it doesn't exist
+mkdir -p $OUTPUT_DIR
+# Add parent directory to python path to access transformer_base.py
+export PYTHONPATH="../":"${PYTHONPATH}"
+
+python3 run_pl_glue.py --data_dir $DATA_DIR \
+--model_type $MODEL_TYPE \
+--task $TASK \
+--model_name_or_path $BERT_MODEL \
+--output_dir $OUTPUT_DIR \
+--max_seq_length  $MAX_LENGTH \
+--learning_rate $LEARNING_RATE \
+--num_train_epochs $NUM_EPOCHS \
+--train_batch_size $BATCH_SIZE \
+--seed $SEED \
+--do_train \
+--do_predict
@@ -0,0 +1,192 @@
+import argparse
+import glob
+import logging
+import os
+import time
+
+import numpy as np
+import torch
+from torch.utils.data import DataLoader, TensorDataset
+
+from transformer_base import BaseTransformer, add_generic_args, generic_train
+from transformers import glue_compute_metrics as compute_metrics
+from transformers import glue_convert_examples_to_features as convert_examples_to_features
+from transformers import glue_output_modes
+from transformers import glue_processors as processors
+from transformers import glue_tasks_num_labels
+
+
+logger = logging.getLogger(__name__)
+
+
+class GLUETransformer(BaseTransformer):
+
+    mode = "sequence-classification"
+
+    def __init__(self, hparams):
+        hparams.glue_output_mode = glue_output_modes[hparams.task]
+        num_labels = glue_tasks_num_labels[hparams.task]
+
+        super().__init__(hparams, num_labels, self.mode)
+
+    def forward(self, **inputs):
+        return self.model(**inputs)
+
+    def training_step(self, batch, batch_idx):
+        inputs = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
+
+        if self.hparams.model_type != "distilbert":
+            inputs["token_type_ids"] = batch[2] if self.hparams.model_type in ["bert", "xlnet", "albert"] else None
+
+        outputs = self(**inputs)
+        loss = outputs[0]
+
+        tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
+        return {"loss": loss, "log": tensorboard_logs}
+
+    def prepare_data(self):
+        "Called to initialize data. Use the call to construct features"
+        args = self.hparams
+        processor = processors[args.task]()
+        self.labels = processor.get_labels()
+
+        for mode in ["train", "dev"]:
+            cached_features_file = self._feature_file(mode)
+            if not os.path.exists(cached_features_file) and not args.overwrite_cache:
+                logger.info("Creating features from dataset file at %s", args.data_dir)
+                examples = (
+                    processor.get_dev_examples(args.data_dir)
+                    if mode == "dev"
+                    else processor.get_train_examples(args.data_dir)
+                )
+                features = convert_examples_to_features(
+                    examples,
+                    self.tokenizer,
+                    max_length=args.max_seq_length,
+                    label_list=self.labels,
+                    output_mode=args.glue_output_mode,
+                )
+                logger.info("Saving features into cached file %s", cached_features_file)
+                torch.save(features, cached_features_file)
+
+    def load_dataset(self, mode, batch_size):
+        "Load datasets. Called after prepare data."
+
+        # We test on dev set to compare to benchmarks without having to submit to GLUE server
+        mode = "dev" if mode == "test" else mode
+
+        cached_features_file = self._feature_file(mode)
+        logger.info("Loading features from cached file %s", cached_features_file)
+        features = torch.load(cached_features_file)
+        all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
+        all_attention_mask = torch.tensor([f.attention_mask for f in features], dtype=torch.long)
+        all_token_type_ids = torch.tensor([f.token_type_ids for f in features], dtype=torch.long)
+        if self.hparams.glue_output_mode == "classification":
+            all_labels = torch.tensor([f.label for f in features], dtype=torch.long)
+        elif self.hparams.glue_output_mode == "regression":
+            all_labels = torch.tensor([f.label for f in features], dtype=torch.float)
+
+        return DataLoader(
+            TensorDataset(all_input_ids, all_attention_mask, all_token_type_ids, all_labels),
+            batch_size=batch_size,
+            shuffle=True,
+        )
+
+    def validation_step(self, batch, batch_idx):
+        inputs = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
+
+        if self.hparams.model_type != "distilbert":
+            inputs["token_type_ids"] = batch[2] if self.hparams.model_type in ["bert", "xlnet", "albert"] else None
+
+        outputs = self(**inputs)
+        tmp_eval_loss, logits = outputs[:2]
+        preds = logits.detach().cpu().numpy()
+        out_label_ids = inputs["labels"].detach().cpu().numpy()
+
+        return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
+
+    def _eval_end(self, outputs):
+        val_loss_mean = torch.stack([x["val_loss"] for x in outputs]).mean().detach().cpu().item()
+        preds = np.concatenate([x["pred"] for x in outputs], axis=0)
+
+        if self.hparams.glue_output_mode == "classification":
+            preds = np.argmax(preds, axis=1)
+        elif self.hparams.glue_output_mode == "regression":
+            preds = np.squeeze(preds)
+
+        out_label_ids = np.concatenate([x["target"] for x in outputs], axis=0)
+        out_label_list = [[] for _ in range(out_label_ids.shape[0])]
+        preds_list = [[] for _ in range(out_label_ids.shape[0])]
+
+        results = {**{"val_loss": val_loss_mean}, **compute_metrics(self.hparams.task, preds, out_label_ids)}
+
+        ret = {k: v for k, v in results.items()}
+        ret["log"] = results
+        return ret, preds_list, out_label_list
+
+    def validation_end(self, outputs: list) -> dict:
+        ret, preds, targets = self._eval_end(outputs)
+        logs = ret["log"]
+        return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
+
+    def test_epoch_end(self, outputs):
+        # updating to test_epoch_end instead of deprecated test_end
+        ret, predictions, targets = self._eval_end(outputs)
+
+        # Converting to the dic required by pl
+        # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
+        # pytorch_lightning/trainer/logging.py#L139
+        logs = ret["log"]
+        # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
+        return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
+
+    @staticmethod
+    def add_model_specific_args(parser, root_dir):
+        # Add NER specific options
+        BaseTransformer.add_model_specific_args(parser, root_dir)
+        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(
+            "--task", default="", type=str, required=True, help="The GLUE task to run",
+        )
+
+        parser.add_argument(
+            "--data_dir",
+            default=None,
+            type=str,
+            required=True,
+            help="The input data dir. Should contain the training files for the CoNLL-2003 NER task.",
+        )
+
+        parser.add_argument(
+            "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets"
+        )
+
+        return parser
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    add_generic_args(parser, os.getcwd())
+    parser = GLUETransformer.add_model_specific_args(parser, os.getcwd())
+    args = parser.parse_args()
+
+    # If output_dir not provided, a folder will be generated in pwd
+    if args.output_dir is None:
+        args.output_dir = os.path.join("./results", f"{args.task}_{args.model_type}_{time.strftime('%Y%m%d_%H%M%S')}",)
+        os.makedirs(args.output_dir)
+
+    model = GLUETransformer(args)
+    trainer = generic_train(model, args)
+
+    # Optionally, predict on dev set and write to output_dir
+    if args.do_predict:
+        checkpoints = list(sorted(glob.glob(os.path.join(args.output_dir, "checkpointepoch=*.ckpt"), recursive=True)))
+        model = model.load_from_checkpoint(checkpoints[-1])
+        trainer.test(model)
@@ -342,8 +342,8 @@ def load_and_cache_examples(args, task, tokenizer, evaluate=False):
            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,
+            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)
@@ -520,7 +520,7 @@ 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)
@@ -492,7 +492,7 @@ 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)
@@ -0,0 +1,186 @@
+## Named Entity Recognition
+
+Based on the scripts [`run_ner.py`](https://github.com/huggingface/transformers/blob/master/examples/ner/run_ner.py) for Pytorch and
+[`run_tf_ner.py`](https://github.com/huggingface/transformers/blob/master/examples/ner/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 PyTorch version using PyTorch-Lightning
+
+Run `bash run_pl.sh` from the `ner` directory. This would also install `pytorch-lightning` and the `examples/requirements.txt`. It is a shell pipeline which would automatically download, pre-process the data and run the models in `germeval-model` directory. Logs are saved in `lightning_logs` directory.
+
+Pass `--n_gpu` flag to change the number of GPUs. Default uses 1. At the end, the expected results are: `TEST RESULTS {'val_loss': tensor(0.0707), 'precision': 0.852427800698191, 'recall': 0.869537067011978, 'f1': 0.8608974358974358}`
+
+
+### 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
+```
@@ -0,0 +1,32 @@
+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
+ wget "https://raw.githubusercontent.com/stefan-it/fine-tuned-berts-seq/master/scripts/preprocess.py"
+export MAX_LENGTH=128
+export BERT_MODEL=bert-base-multilingual-cased
+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 $MAX_LENGTH > test.txt
+cat train.txt dev.txt test.txt | cut -d " " -f 2 | grep -v "^$"| sort | uniq > labels.txt
+export OUTPUT_DIR=germeval-model
+export BATCH_SIZE=32
+export NUM_EPOCHS=3
+export SAVE_STEPS=750
+export SEED=1
+
+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
@@ -31,23 +31,12 @@ from torch.utils.data.distributed import DistributedSampler
 from tqdm import tqdm, trange

 from transformers import (
+    MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
    WEIGHTS_NAME,
    AdamW,
-    BertConfig,
-    BertForTokenClassification,
-    BertTokenizer,
-    CamembertConfig,
-    CamembertForTokenClassification,
-    CamembertTokenizer,
-    DistilBertConfig,
-    DistilBertForTokenClassification,
-    DistilBertTokenizer,
-    RobertaConfig,
-    RobertaForTokenClassification,
-    RobertaTokenizer,
-    XLMRobertaConfig,
-    XLMRobertaForTokenClassification,
-    XLMRobertaTokenizer,
+    AutoConfig,
+    AutoModelForTokenClassification,
+    AutoTokenizer,
    get_linear_schedule_with_warmup,
 )
 from utils_ner import convert_examples_to_features, get_labels, read_examples_from_file
@@ -61,21 +50,12 @@ except ImportError:

 logger = logging.getLogger(__name__)

-ALL_MODELS = sum(
-    (
-        tuple(conf.pretrained_config_archive_map.keys())
-        for conf in (BertConfig, RobertaConfig, DistilBertConfig, CamembertConfig, XLMRobertaConfig)
-    ),
-    (),
-)
+MODEL_CONFIG_CLASSES = list(MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.keys())
+MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)

-MODEL_CLASSES = {
-    "bert": (BertConfig, BertForTokenClassification, BertTokenizer),
-    "roberta": (RobertaConfig, RobertaForTokenClassification, RobertaTokenizer),
-    "distilbert": (DistilBertConfig, DistilBertForTokenClassification, DistilBertTokenizer),
-    "camembert": (CamembertConfig, CamembertForTokenClassification, CamembertTokenizer),
-    "xlmroberta": (XLMRobertaConfig, XLMRobertaForTokenClassification, XLMRobertaTokenizer),
-}
+ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) for conf in MODEL_CONFIG_CLASSES), ())
+
+TOKENIZER_ARGS = ["do_lower_case", "strip_accents", "keep_accents", "use_fast"]


 def set_seed(args):
@@ -160,7 +140,10 @@ def train(args, train_dataset, model, tokenizer, labels, pad_token_label_id):
    # Check if continuing training from a checkpoint
    if os.path.exists(args.model_name_or_path):
        # set global_step to gobal_step of last saved checkpoint from model path
-        global_step = int(args.model_name_or_path.split("-")[-1].split("/")[0])
+        try:
+            global_step = int(args.model_name_or_path.split("-")[-1].split("/")[0])
+        except ValueError:
+            global_step = 0
        epochs_trained = global_step // (len(train_dataloader) // args.gradient_accumulation_steps)
        steps_trained_in_current_epoch = global_step % (len(train_dataloader) // args.gradient_accumulation_steps)

@@ -213,8 +196,8 @@ def train(args, train_dataset, model, tokenizer, labels, pad_token_label_id):
                else:
                    torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)

-                scheduler.step()  # Update learning rate schedule
                optimizer.step()
+                scheduler.step()  # Update learning rate schedule
                model.zero_grad()
                global_step += 1

@@ -365,8 +348,8 @@ def load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, mode):
            # roberta uses an extra separator b/w pairs of sentences, cf. github.com/pytorch/fairseq/commit/1684e166e3da03f5b600dbb7855cb98ddfcd0805
            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,
+            pad_token=tokenizer.pad_token_id,
+            pad_token_segment_id=tokenizer.pad_token_type_id,
            pad_token_label_id=pad_token_label_id,
        )
        if args.local_rank in [-1, 0]:
@@ -402,7 +385,7 @@ def main():
        default=None,
        type=str,
        required=True,
-        help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()),
+        help="Model type selected in the list: " + ", ".join(MODEL_TYPES),
    )
    parser.add_argument(
        "--model_name_or_path",
@@ -459,7 +442,13 @@ def main():
    parser.add_argument(
        "--do_lower_case", action="store_true", help="Set this flag if you are using an uncased model."
    )
-
+    parser.add_argument(
+        "--keep_accents", action="store_const", const=True, help="Set this flag if model is trained with accents."
+    )
+    parser.add_argument(
+        "--strip_accents", action="store_const", const=True, help="Set this flag if model is trained without accents."
+    )
+    parser.add_argument("--use_fast", action="store_const", const=True, help="Set this flag to use fast tokenization.")
    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."
@@ -542,7 +531,7 @@ 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)
@@ -579,18 +568,21 @@ def main():
        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(
+    config = AutoConfig.from_pretrained(
        args.config_name if args.config_name else args.model_name_or_path,
        num_labels=num_labels,
+        id2label={str(i): label for i, label in enumerate(labels)},
+        label2id={label: i for i, label in enumerate(labels)},
        cache_dir=args.cache_dir if args.cache_dir else None,
    )
-    tokenizer = tokenizer_class.from_pretrained(
+    tokenizer_args = {k: v for k, v in vars(args).items() if v is not None and k in TOKENIZER_ARGS}
+    logger.info("Tokenizer arguments: %s", tokenizer_args)
+    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 if args.cache_dir else None,
+        **tokenizer_args,
    )
-    model = model_class.from_pretrained(
+    model = AutoModelForTokenClassification.from_pretrained(
        args.model_name_or_path,
        from_tf=bool(".ckpt" in args.model_name_or_path),
        config=config,
@@ -631,7 +623,7 @@ def main():
    # 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)
+        tokenizer = AutoTokenizer.from_pretrained(args.output_dir, **tokenizer_args)
        checkpoints = [args.output_dir]
        if args.eval_all_checkpoints:
            checkpoints = list(
@@ -641,7 +633,7 @@ def main():
        logger.info("Evaluate the following checkpoints: %s", checkpoints)
        for checkpoint in checkpoints:
            global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else ""
-            model = model_class.from_pretrained(checkpoint)
+            model = AutoModelForTokenClassification.from_pretrained(checkpoint)
            model.to(args.device)
            result, _ = evaluate(args, model, tokenizer, labels, pad_token_label_id, mode="dev", prefix=global_step)
            if global_step:
@@ -653,8 +645,8 @@ def main():
                writer.write("{} = {}\n".format(key, str(results[key])))

    if args.do_predict and args.local_rank in [-1, 0]:
-        tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
-        model = model_class.from_pretrained(args.output_dir)
+        tokenizer = AutoTokenizer.from_pretrained(args.output_dir, **tokenizer_args)
+        model = AutoModelForTokenClassification.from_pretrained(args.output_dir)
        model.to(args.device)
        result, predictions = evaluate(args, model, tokenizer, labels, pad_token_label_id, mode="test")
        # Save results
@@ -0,0 +1,43 @@
+#!/usr/bin/env bash
+
+# Install newest ptl.
+pip install -U git+http://github.com/PyTorchLightning/pytorch-lightning/
+# for seqeval metrics import
+pip install -r ../requirements.txt
+
+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
+ wget "https://raw.githubusercontent.com/stefan-it/fine-tuned-berts-seq/master/scripts/preprocess.py"
+export MAX_LENGTH=128
+export BERT_MODEL=bert-base-multilingual-cased
+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 $MAX_LENGTH > test.txt
+cat train.txt dev.txt test.txt | cut -d " " -f 2 | grep -v "^$"| sort | uniq > labels.txt
+export BATCH_SIZE=32
+export NUM_EPOCHS=3
+export SEED=1
+
+export OUTPUT_DIR_NAME=germeval-model
+export CURRENT_DIR=${PWD}
+export OUTPUT_DIR=${CURRENT_DIR}/${OUTPUT_DIR_NAME}
+mkdir -p $OUTPUT_DIR
+
+# Add parent directory to python path to access transformer_base.py
+export PYTHONPATH="../":"${PYTHONPATH}"
+
+python3 run_pl_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 \
+--train_batch_size $BATCH_SIZE \
+--seed $SEED \
+--do_train \
+--do_predict
@@ -0,0 +1,196 @@
+import argparse
+import glob
+import logging
+import os
+
+import numpy as np
+import torch
+from seqeval.metrics import f1_score, precision_score, recall_score
+from torch.nn import CrossEntropyLoss
+from torch.utils.data import DataLoader, TensorDataset
+
+from transformer_base import BaseTransformer, add_generic_args, generic_train
+from utils_ner import convert_examples_to_features, get_labels, read_examples_from_file
+
+
+logger = logging.getLogger(__name__)
+
+
+class NERTransformer(BaseTransformer):
+    """
+    A training module for NER. See BaseTransformer for the core options.
+    """
+
+    mode = "token-classification"
+
+    def __init__(self, hparams):
+        self.labels = get_labels(hparams.labels)
+        num_labels = len(self.labels)
+        self.pad_token_label_id = CrossEntropyLoss().ignore_index
+        super(NERTransformer, self).__init__(hparams, num_labels, self.mode)
+
+    def forward(self, **inputs):
+        return self.model(**inputs)
+
+    def training_step(self, batch, batch_num):
+        "Compute loss and log."
+        inputs = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
+        if self.hparams.model_type != "distilbert":
+            inputs["token_type_ids"] = (
+                batch[2] if self.hparams.model_type in ["bert", "xlnet"] else None
+            )  # XLM and RoBERTa don"t use segment_ids
+
+        outputs = self(**inputs)
+        loss = outputs[0]
+        tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
+        return {"loss": loss, "log": tensorboard_logs}
+
+    def prepare_data(self):
+        "Called to initialize data. Use the call to construct features"
+        args = self.hparams
+        for mode in ["train", "dev", "test"]:
+            cached_features_file = self._feature_file(mode)
+            if not os.path.exists(cached_features_file):
+                logger.info("Creating features from dataset file at %s", args.data_dir)
+                examples = read_examples_from_file(args.data_dir, mode)
+                features = convert_examples_to_features(
+                    examples,
+                    self.labels,
+                    args.max_seq_length,
+                    self.tokenizer,
+                    cls_token_at_end=bool(args.model_type in ["xlnet"]),
+                    cls_token=self.tokenizer.cls_token,
+                    cls_token_segment_id=2 if args.model_type in ["xlnet"] else 0,
+                    sep_token=self.tokenizer.sep_token,
+                    sep_token_extra=bool(args.model_type in ["roberta"]),
+                    pad_on_left=bool(args.model_type in ["xlnet"]),
+                    pad_token=self.tokenizer.pad_token_id,
+                    pad_token_segment_id=self.tokenizer.pad_token_type_id,
+                    pad_token_label_id=self.pad_token_label_id,
+                )
+                logger.info("Saving features into cached file %s", cached_features_file)
+                torch.save(features, cached_features_file)
+
+    def load_dataset(self, mode, batch_size):
+        "Load datasets. Called after prepare data."
+        cached_features_file = self._feature_file(mode)
+        logger.info("Loading features from cached file %s", cached_features_file)
+        features = torch.load(cached_features_file)
+        all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
+        all_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long)
+        all_segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long)
+        all_label_ids = torch.tensor([f.label_ids for f in features], dtype=torch.long)
+        return DataLoader(
+            TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids), batch_size=batch_size
+        )
+
+    def validation_step(self, batch, batch_nb):
+        "Compute validation"
+
+        inputs = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
+        if self.hparams.model_type != "distilbert":
+            inputs["token_type_ids"] = (
+                batch[2] if self.hparams.model_type in ["bert", "xlnet"] else None
+            )  # XLM and RoBERTa don"t use segment_ids
+        outputs = self(**inputs)
+        tmp_eval_loss, logits = outputs[:2]
+        preds = logits.detach().cpu().numpy()
+        out_label_ids = inputs["labels"].detach().cpu().numpy()
+        return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
+
+    def _eval_end(self, outputs):
+        "Evaluation called for both Val and Test"
+        val_loss_mean = torch.stack([x["val_loss"] for x in outputs]).mean()
+        preds = np.concatenate([x["pred"] for x in outputs], axis=0)
+        preds = np.argmax(preds, axis=2)
+        out_label_ids = np.concatenate([x["target"] for x in outputs], axis=0)
+
+        label_map = {i: label for i, label in enumerate(self.labels)}
+        out_label_list = [[] for _ in range(out_label_ids.shape[0])]
+        preds_list = [[] for _ in range(out_label_ids.shape[0])]
+
+        for i in range(out_label_ids.shape[0]):
+            for j in range(out_label_ids.shape[1]):
+                if out_label_ids[i, j] != self.pad_token_label_id:
+                    out_label_list[i].append(label_map[out_label_ids[i][j]])
+                    preds_list[i].append(label_map[preds[i][j]])
+
+        results = {
+            "val_loss": val_loss_mean,
+            "precision": precision_score(out_label_list, preds_list),
+            "recall": recall_score(out_label_list, preds_list),
+            "f1": f1_score(out_label_list, preds_list),
+        }
+
+        ret = {k: v for k, v in results.items()}
+        ret["log"] = results
+        return ret, preds_list, out_label_list
+
+    def validation_end(self, outputs):
+        # todo: update to validation_epoch_end instead of deprecated validation_end
+        # when stable
+        ret, preds, targets = self._eval_end(outputs)
+        logs = ret["log"]
+        return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
+
+    def test_epoch_end(self, outputs):
+        # updating to test_epoch_end instead of deprecated test_end
+        ret, predictions, targets = self._eval_end(outputs)
+
+        # Converting to the dict required by pl
+        # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
+        # pytorch_lightning/trainer/logging.py#L139
+        logs = ret["log"]
+        # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
+        return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
+
+    @staticmethod
+    def add_model_specific_args(parser, root_dir):
+        # Add NER specific options
+        BaseTransformer.add_model_specific_args(parser, root_dir)
+        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(
+            "--labels",
+            default="",
+            type=str,
+            help="Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.",
+        )
+
+        parser.add_argument(
+            "--data_dir",
+            default=None,
+            type=str,
+            required=True,
+            help="The input data dir. Should contain the training files for the CoNLL-2003 NER task.",
+        )
+
+        parser.add_argument(
+            "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets"
+        )
+
+        return parser
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    add_generic_args(parser, os.getcwd())
+    parser = NERTransformer.add_model_specific_args(parser, os.getcwd())
+    args = parser.parse_args()
+    model = NERTransformer(args)
+    trainer = generic_train(model, args)
+
+    if args.do_predict:
+        # See https://github.com/huggingface/transformers/issues/3159
+        # pl use this format to create a checkpoint:
+        # https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
+        # /pytorch_lightning/callbacks/model_checkpoint.py#L169
+        checkpoints = list(sorted(glob.glob(os.path.join(args.output_dir, "checkpointepoch=*.ckpt"), recursive=True)))
+        model = model.load_from_checkpoint(checkpoints[-1])
+        trainer.test(model)
@@ -13,16 +13,11 @@ from seqeval import metrics

 from transformers import (
    TF2_WEIGHTS_NAME,
-    BertConfig,
-    BertTokenizer,
-    DistilBertConfig,
-    DistilBertTokenizer,
+    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
+    AutoConfig,
+    AutoTokenizer,
    GradientAccumulator,
-    RobertaConfig,
-    RobertaTokenizer,
-    TFBertForTokenClassification,
-    TFDistilBertForTokenClassification,
-    TFRobertaForTokenClassification,
+    TFAutoModelForTokenClassification,
    create_optimizer,
 )
 from utils_ner import convert_examples_to_features, get_labels, read_examples_from_file
@@ -34,22 +29,17 @@ except ImportError:
    from fastprogress.fastprogress import master_bar, progress_bar


-ALL_MODELS = sum(
-    (tuple(conf.pretrained_config_archive_map.keys()) for conf in (BertConfig, RobertaConfig, DistilBertConfig)), ()
-)
+MODEL_CONFIG_CLASSES = list(TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.keys())
+MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)

-MODEL_CLASSES = {
-    "bert": (BertConfig, TFBertForTokenClassification, BertTokenizer),
-    "roberta": (RobertaConfig, TFRobertaForTokenClassification, RobertaTokenizer),
-    "distilbert": (DistilBertConfig, TFDistilBertForTokenClassification, DistilBertTokenizer),
-}
+ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) for conf in MODEL_CONFIG_CLASSES), (),)


 flags.DEFINE_string(
    "data_dir", None, "The input data dir. Should contain the .conll files (or other data files) " "for the task."
 )

-flags.DEFINE_string("model_type", None, "Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()))
+flags.DEFINE_string("model_type", None, "Model type selected in the list: " + ", ".join(MODEL_TYPES))

 flags.DEFINE_string(
    "model_name_or_path",
@@ -167,7 +157,9 @@ def train(
    writer = tf.summary.create_file_writer("/tmp/mylogs")

    with strategy.scope():
-        loss_fct = tf.keras.losses.SparseCategoricalCrossentropy(reduction=tf.keras.losses.Reduction.NONE)
+        loss_fct = tf.keras.losses.SparseCategoricalCrossentropy(
+            from_logits=True, reduction=tf.keras.losses.Reduction.NONE
+        )
        optimizer = create_optimizer(args["learning_rate"], num_train_steps, args["warmup_steps"])

        if args["fp16"]:
@@ -215,11 +207,9 @@ def train(

            with tf.GradientTape() as tape:
                logits = model(train_features["input_ids"], **inputs)[0]
-                logits = tf.reshape(logits, (-1, len(labels) + 1))
-                active_loss = tf.reshape(train_features["input_mask"], (-1,))
-                active_logits = tf.boolean_mask(logits, active_loss)
-                train_labels = tf.reshape(train_labels, (-1,))
-                active_labels = tf.boolean_mask(train_labels, active_loss)
+                active_loss = tf.reshape(train_labels, (-1,)) != pad_token_label_id
+                active_logits = tf.boolean_mask(tf.reshape(logits, (-1, len(labels))), active_loss)
+                active_labels = tf.boolean_mask(tf.reshape(train_labels, (-1,)), active_loss)
                cross_entropy = loss_fct(active_labels, active_logits)
                loss = tf.reduce_sum(cross_entropy) * (1.0 / train_batch_size)
                grads = tape.gradient(loss, model.trainable_variables)
@@ -339,11 +329,9 @@ def evaluate(args, strategy, model, tokenizer, labels, pad_token_label_id, mode)

        with strategy.scope():
            logits = model(eval_features["input_ids"], **inputs)[0]
-            tmp_logits = tf.reshape(logits, (-1, len(labels) + 1))
-            active_loss = tf.reshape(eval_features["input_mask"], (-1,))
-            active_logits = tf.boolean_mask(tmp_logits, active_loss)
-            tmp_eval_labels = tf.reshape(eval_labels, (-1,))
-            active_labels = tf.boolean_mask(tmp_eval_labels, active_loss)
+            active_loss = tf.reshape(eval_labels, (-1,)) != pad_token_label_id
+            active_logits = tf.boolean_mask(tf.reshape(logits, (-1, len(labels))), active_loss)
+            active_labels = tf.boolean_mask(tf.reshape(eval_labels, (-1,)), active_loss)
            cross_entropy = loss_fct(active_labels, active_logits)
            loss += tf.reduce_sum(cross_entropy) * (1.0 / eval_batch_size)

@@ -446,8 +434,8 @@ def load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, batch_s
            # roberta uses an extra separator b/w pairs of sentences, cf. github.com/pytorch/fairseq/commit/1684e166e3da03f5b600dbb7855cb98ddfcd0805
            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,
+            pad_token=tokenizer.pad_token_id,
+            pad_token_segment_id=tokenizer.pad_token_type_id,
            pad_token_label_id=pad_token_label_id,
        )
        logging.info("Saving features into cached file %s", cached_features_file)
@@ -507,10 +495,9 @@ def main(_):
    )

    labels = get_labels(args["labels"])
-    num_labels = len(labels) + 1
-    pad_token_label_id = 0
-    config_class, model_class, tokenizer_class = MODEL_CLASSES[args["model_type"]]
-    config = config_class.from_pretrained(
+    num_labels = len(labels)
+    pad_token_label_id = -1
+    config = AutoConfig.from_pretrained(
        args["config_name"] if args["config_name"] else args["model_name_or_path"],
        num_labels=num_labels,
        cache_dir=args["cache_dir"] if args["cache_dir"] else None,
@@ -520,20 +507,19 @@ def main(_):

    # Training
    if args["do_train"]:
-        tokenizer = tokenizer_class.from_pretrained(
+        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"] if args["cache_dir"] else None,
        )

        with strategy.scope():
-            model = model_class.from_pretrained(
+            model = TFAutoModelForTokenClassification.from_pretrained(
                args["model_name_or_path"],
                from_pt=bool(".bin" in args["model_name_or_path"]),
                config=config,
                cache_dir=args["cache_dir"] if args["cache_dir"] else None,
            )
-            model.layers[-1].activation = tf.keras.activations.softmax

        train_batch_size = args["per_device_train_batch_size"] * args["n_device"]
        train_dataset, num_train_examples = load_and_cache_examples(
@@ -562,7 +548,7 @@ def main(_):

    # Evaluation
    if args["do_eval"]:
-        tokenizer = tokenizer_class.from_pretrained(args["output_dir"], do_lower_case=args["do_lower_case"])
+        tokenizer = AutoTokenizer.from_pretrained(args["output_dir"], do_lower_case=args["do_lower_case"])
        checkpoints = []
        results = []

@@ -584,7 +570,7 @@ def main(_):
            global_step = checkpoint.split("-")[-1] if re.match(".*checkpoint-[0-9]", checkpoint) else "final"

            with strategy.scope():
-                model = model_class.from_pretrained(checkpoint)
+                model = TFAutoModelForTokenClassification.from_pretrained(checkpoint)

            y_true, y_pred, eval_loss = evaluate(
                args, strategy, model, tokenizer, labels, pad_token_label_id, mode="dev"
@@ -611,8 +597,8 @@ def main(_):
                        writer.write("\n")

    if args["do_predict"]:
-        tokenizer = tokenizer_class.from_pretrained(args["output_dir"], do_lower_case=args["do_lower_case"])
-        model = model_class.from_pretrained(args["output_dir"])
+        tokenizer = AutoTokenizer.from_pretrained(args["output_dir"], do_lower_case=args["do_lower_case"])
+        model = TFAutoModelForTokenClassification.from_pretrained(args["output_dir"])
        eval_batch_size = args["per_device_eval_batch_size"] * args["n_device"]
        predict_dataset, _ = load_and_cache_examples(
            args, tokenizer, labels, pad_token_label_id, eval_batch_size, mode="test"
@@ -73,7 +73,7 @@ def read_examples_from_file(data_dir, mode):
                    # Examples could have no label for mode = "test"
                    labels.append("O")
        if words:
-            examples.append(InputExample(guid="%s-%d".format(mode, guid_index), words=words, labels=labels))
+            examples.append(InputExample(guid="{}-{}".format(mode, guid_index), words=words, labels=labels))
    return examples


@@ -112,12 +112,15 @@ def convert_examples_to_features(
        label_ids = []
        for word, label in zip(example.words, example.labels):
            word_tokens = tokenizer.tokenize(word)
-            tokens.extend(word_tokens)
-            # Use the real label id for the first token of the word, and padding ids for the remaining tokens
-            label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(word_tokens) - 1))
+
+            # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
+            if len(word_tokens) > 0:
+                tokens.extend(word_tokens)
+                # Use the real label id for the first token of the word, and padding ids for the remaining tokens
+                label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(word_tokens) - 1))

        # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
-        special_tokens_count = 3 if sep_token_extra else 2
+        special_tokens_count = tokenizer.num_added_tokens()
        if len(tokens) > max_seq_length - special_tokens_count:
            tokens = tokens[: (max_seq_length - special_tokens_count)]
            label_ids = label_ids[: (max_seq_length - special_tokens_count)]
@@ -2,3 +2,8 @@ tensorboardX
 tensorboard
 scikit-learn
 seqeval
+psutil
+sacrebleu
+rouge-score
+tensorflow_datasets
+pytorch-lightning==0.7.3  # April 10, 2020 release
@@ -338,7 +338,7 @@ 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)
@@ -59,7 +59,7 @@ MODEL_CLASSES = {
 # Padding text to help Transformer-XL and XLNet with short prompts as proposed by Aman Rusia
 # in https://github.com/rusiaaman/XLNet-gen#methodology
 # and https://medium.com/@amanrusia/xlnet-speaks-comparison-to-gpt-2-ea1a4e9ba39e
-PADDING_TEXT = """ In 1991, the remains of Russian Tsar Nicholas II and his family
+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,
@@ -106,6 +106,8 @@ def prepare_xlm_input(args, model, tokenizer, prompt_text):
            language = None
            while language not in available_languages:
                language = input("Using XLM. Select language in " + str(list(available_languages)) + " >>> ")
+
+        model.config.lang_id = model.config.lang2id[language]
        # kwargs["language"] = tokenizer.lang2id[language]

    # TODO fix mask_token_id setup when configurations will be synchronized between models and tokenizers
@@ -119,12 +121,12 @@ def prepare_xlm_input(args, model, tokenizer, prompt_text):

 def prepare_xlnet_input(args, _, tokenizer, prompt_text):
    prompt_text = (args.padding_text if args.padding_text else PADDING_TEXT) + prompt_text
-    return prompt_text, {}
+    return prompt_text


 def prepare_transfoxl_input(args, _, tokenizer, prompt_text):
    prompt_text = (args.padding_text if args.padding_text else PADDING_TEXT) + prompt_text
-    return prompt_text, {}
+    return prompt_text


 PREPROCESSING_FUNCTIONS = {
@@ -183,10 +185,11 @@ def main():

    parser.add_argument("--seed", type=int, default=42, help="random seed for initialization")
    parser.add_argument("--no_cuda", action="store_true", help="Avoid using CUDA when available")
+    parser.add_argument("--num_return_sequences", type=int, default=1, help="The number of samples to generate.")
    args = parser.parse_args()

    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()

    set_seed(args)

@@ -210,27 +213,50 @@ def main():
    requires_preprocessing = args.model_type in PREPROCESSING_FUNCTIONS.keys()
    if requires_preprocessing:
        prepare_input = PREPROCESSING_FUNCTIONS.get(args.model_type)
-        prompt_text = prepare_input(args, model, tokenizer, prompt_text)
-    encoded_prompt = tokenizer.encode(prompt_text, add_special_tokens=False, return_tensors="pt")
+        preprocessed_prompt_text = prepare_input(args, model, tokenizer, prompt_text)
+        encoded_prompt = tokenizer.encode(
+            preprocessed_prompt_text, add_special_tokens=False, return_tensors="pt", add_space_before_punct_symbol=True
+        )
+    else:
+        encoded_prompt = tokenizer.encode(prompt_text, add_special_tokens=False, return_tensors="pt")
    encoded_prompt = encoded_prompt.to(args.device)

    output_sequences = model.generate(
        input_ids=encoded_prompt,
-        max_length=args.length,
+        max_length=args.length + len(encoded_prompt[0]),
        temperature=args.temperature,
        top_k=args.k,
        top_p=args.p,
        repetition_penalty=args.repetition_penalty,
+        do_sample=True,
+        num_return_sequences=args.num_return_sequences,
    )

-    # Batch size == 1. to add more examples please use num_return_sequences > 1
-    generated_sequence = output_sequences[0].tolist()
-    text = tokenizer.decode(generated_sequence, clean_up_tokenization_spaces=True)
-    text = text[: text.find(args.stop_token) if args.stop_token else None]
+    # Remove the batch dimension when returning multiple sequences
+    if len(output_sequences.shape) > 2:
+        output_sequences.squeeze_()

-    print(text)
+    generated_sequences = []

-    return text
+    for generated_sequence_idx, generated_sequence in enumerate(output_sequences):
+        print("=== GENERATED SEQUENCE {} ===".format(generated_sequence_idx + 1))
+        generated_sequence = generated_sequence.tolist()
+
+        # Decode text
+        text = tokenizer.decode(generated_sequence, clean_up_tokenization_spaces=True)
+
+        # Remove all text after the stop token
+        text = text[: text.find(args.stop_token) if args.stop_token else None]
+
+        # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
+        total_sequence = (
+            prompt_text + text[len(tokenizer.decode(encoded_prompt[0], clean_up_tokenization_spaces=True)) :]
+        )
+
+        generated_sequences.append(total_sequence)
+        print(total_sequence)
+
+    return generated_sequences


 if __name__ == "__main__":
@@ -22,6 +22,8 @@ import json
 import logging
 import os
 import random
+from dataclasses import dataclass, field
+from typing import Optional

 import numpy as np
 import torch
@@ -30,29 +32,14 @@ from torch.utils.data.distributed import DistributedSampler
 from tqdm import tqdm, trange

 from transformers import (
+    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    WEIGHTS_NAME,
    AdamW,
-    AlbertConfig,
-    AlbertForSequenceClassification,
-    AlbertTokenizer,
-    BertConfig,
-    BertForSequenceClassification,
-    BertTokenizer,
-    DistilBertConfig,
-    DistilBertForSequenceClassification,
-    DistilBertTokenizer,
-    RobertaConfig,
-    RobertaForSequenceClassification,
-    RobertaTokenizer,
-    XLMConfig,
-    XLMForSequenceClassification,
-    XLMRobertaConfig,
-    XLMRobertaForSequenceClassification,
-    XLMRobertaTokenizer,
-    XLMTokenizer,
-    XLNetConfig,
-    XLNetForSequenceClassification,
-    XLNetTokenizer,
+    AutoConfig,
+    AutoModelForSequenceClassification,
+    AutoTokenizer,
+    HfArgumentParser,
+    TrainingArguments,
    get_linear_schedule_with_warmup,
 )
 from transformers import glue_compute_metrics as compute_metrics
@@ -69,31 +56,10 @@ except ImportError:

 logger = logging.getLogger(__name__)

-ALL_MODELS = sum(
-    (
-        tuple(conf.pretrained_config_archive_map.keys())
-        for conf in (
-            BertConfig,
-            XLNetConfig,
-            XLMConfig,
-            RobertaConfig,
-            DistilBertConfig,
-            AlbertConfig,
-            XLMRobertaConfig,
-        )
-    ),
-    (),
-)
+MODEL_CONFIG_CLASSES = list(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING.keys())
+MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)

-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),
-    "xlmroberta": (XLMRobertaConfig, XLMRobertaForSequenceClassification, XLMRobertaTokenizer),
-}
+ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) for conf in MODEL_CONFIG_CLASSES), (),)


 def set_seed(args):
@@ -178,8 +144,11 @@ def train(args, train_dataset, model, tokenizer):
    steps_trained_in_current_epoch = 0
    # Check if continuing training from a checkpoint
    if os.path.exists(args.model_name_or_path):
-        # set global_step to gobal_step of last saved checkpoint from model path
-        global_step = int(args.model_name_or_path.split("-")[-1].split("/")[0])
+        # set global_step to global_step of last saved checkpoint from model path
+        try:
+            global_step = int(args.model_name_or_path.split("-")[-1].split("/")[0])
+        except ValueError:
+            global_step = 0
        epochs_trained = global_step // (len(train_dataloader) // args.gradient_accumulation_steps)
        steps_trained_in_current_epoch = global_step % (len(train_dataloader) // args.gradient_accumulation_steps)

@@ -193,7 +162,7 @@ def train(args, train_dataset, model, tokenizer):
    train_iterator = trange(
        epochs_trained, int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0],
    )
-    set_seed(args)  # Added here for reproductibility
+    set_seed(args)  # Added here for reproducibility
    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):
@@ -225,7 +194,11 @@ def train(args, train_dataset, model, tokenizer):
                loss.backward()

            tr_loss += loss.item()
-            if (step + 1) % args.gradient_accumulation_steps == 0:
+            if (step + 1) % args.gradient_accumulation_steps == 0 or (
+                # last step in epoch but step is always smaller than gradient_accumulation_steps
+                len(epoch_iterator) <= args.gradient_accumulation_steps
+                and (step + 1) == len(epoch_iterator)
+            ):
                if args.fp16:
                    torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
                else:
@@ -305,7 +278,7 @@ 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!
@@ -385,14 +358,7 @@ def load_and_cache_examples(args, task, tokenizer, evaluate=False):
            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,
+            examples, tokenizer, max_length=args.max_seq_length, label_list=label_list, output_mode=output_mode,
        )
        if args.local_rank in [-1, 0]:
            logger.info("Saving features into cached file %s", cached_features_file)
@@ -414,137 +380,54 @@ def load_and_cache_examples(args, task, tokenizer, evaluate=False):
    return dataset


-def main():
-    parser = argparse.ArgumentParser()
+@dataclass
+class ModelArguments:
+    """
+    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
+    """

-    # 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.",
+    model_name_or_path: str = field(
+        metadata={"help": "Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS)}
    )
-    parser.add_argument(
-        "--model_type",
-        default=None,
-        type=str,
-        required=True,
-        help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()),
+    model_type: str = field(metadata={"help": "Model type selected in the list: " + ", ".join(MODEL_TYPES)})
+    config_name: Optional[str] = field(
+        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
    )
-    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),
+    tokenizer_name: Optional[str] = field(
+        default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
    )
-    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.",
+    cache_dir: Optional[str] = field(
+        default=None, metadata={"help": "Where do you want to store the pre-trained models downloaded from s3"}
    )

-    # Other parameters
-    parser.add_argument(
-        "--config_name", default="", type=str, help="Pretrained config name or path if not the same as model_name",
+
+@dataclass
+class DataProcessingArguments:
+    task_name: str = field(
+        metadata={"help": "The name of the task to train selected in the list: " + ", ".join(processors.keys())}
    )
-    parser.add_argument(
-        "--tokenizer_name",
-        default="",
-        type=str,
-        help="Pretrained tokenizer name or path if not the same as model_name",
+    data_dir: str = field(
+        metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}
    )
-    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",
+    max_seq_length: int = field(
        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.",
+        metadata={
+            "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.",
+    overwrite_cache: bool = field(
+        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
    )

-    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=500, help="Log every X updates steps.")
-    parser.add_argument("--save_steps", type=int, default=500, 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")
+def main():
+    parser = HfArgumentParser((ModelArguments, DataProcessingArguments, TrainingArguments))
+    model_args, dataprocessing_args, training_args = parser.parse_args_into_dataclasses()

-    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()
+    # For now, let's merge all the sets of args into one,
+    # but soon, we'll keep distinct sets of args, with a cleaner separation of concerns.
+    args = argparse.Namespace(**vars(model_args), **vars(dataprocessing_args), **vars(training_args))

    if (
        os.path.exists(args.output_dir)
@@ -553,24 +436,13 @@ def main():
        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
-            )
+            f"Output directory ({args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome."
        )

-    # 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 = 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)
@@ -610,23 +482,20 @@ def main():
        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(
+    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,
-        cache_dir=args.cache_dir if args.cache_dir else None,
+        cache_dir=args.cache_dir,
    )
-    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,
+    tokenizer = AutoTokenizer.from_pretrained(
+        args.tokenizer_name if args.tokenizer_name else args.model_name_or_path, cache_dir=args.cache_dir,
    )
-    model = model_class.from_pretrained(
+    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 if args.cache_dir else None,
+        cache_dir=args.cache_dir,
    )

    if args.local_rank == 0:
@@ -661,14 +530,14 @@ def main():
        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 = AutoModelForSequenceClassification.from_pretrained(args.output_dir)
+        tokenizer = AutoTokenizer.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)
+        tokenizer = AutoTokenizer.from_pretrained(args.output_dir)
        checkpoints = [args.output_dir]
        if args.eval_all_checkpoints:
            checkpoints = list(
@@ -680,7 +549,7 @@ def main():
            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 = AutoModelForSequenceClassification.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())
@@ -38,28 +38,14 @@ from torch.utils.data.distributed import DistributedSampler
 from tqdm import tqdm, trange

 from transformers import (
+    MODEL_WITH_LM_HEAD_MAPPING,
    WEIGHTS_NAME,
    AdamW,
-    BertConfig,
-    BertForMaskedLM,
-    BertTokenizer,
-    CamembertConfig,
-    CamembertForMaskedLM,
-    CamembertTokenizer,
-    DistilBertConfig,
-    DistilBertForMaskedLM,
-    DistilBertTokenizer,
-    GPT2Config,
-    GPT2LMHeadModel,
-    GPT2Tokenizer,
-    OpenAIGPTConfig,
-    OpenAIGPTLMHeadModel,
-    OpenAIGPTTokenizer,
+    AutoConfig,
+    AutoModelWithLMHead,
+    AutoTokenizer,
    PreTrainedModel,
    PreTrainedTokenizer,
-    RobertaConfig,
-    RobertaForMaskedLM,
-    RobertaTokenizer,
    get_linear_schedule_with_warmup,
 )

@@ -73,19 +59,16 @@ except ImportError:
 logger = logging.getLogger(__name__)


-MODEL_CLASSES = {
-    "gpt2": (GPT2Config, GPT2LMHeadModel, GPT2Tokenizer),
-    "openai-gpt": (OpenAIGPTConfig, OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
-    "bert": (BertConfig, BertForMaskedLM, BertTokenizer),
-    "roberta": (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
-    "distilbert": (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
-    "camembert": (CamembertConfig, CamembertForMaskedLM, CamembertTokenizer),
-}
+MODEL_CONFIG_CLASSES = list(MODEL_WITH_LM_HEAD_MAPPING.keys())
+MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)


 class TextDataset(Dataset):
    def __init__(self, tokenizer: PreTrainedTokenizer, args, file_path: str, block_size=512):
        assert os.path.isfile(file_path)
+
+        block_size = block_size - tokenizer.num_special_tokens_to_add(pair=False)
+
        directory, filename = os.path.split(file_path)
        cached_features_file = os.path.join(
            directory, args.model_type + "_cached_lm_" + str(block_size) + "_" + filename
@@ -118,7 +101,7 @@ class TextDataset(Dataset):
        return len(self.examples)

    def __getitem__(self, item):
-        return torch.tensor(self.examples[item])
+        return torch.tensor(self.examples[item], dtype=torch.long)


 class LineByLineTextDataset(Dataset):
@@ -130,15 +113,15 @@ class LineByLineTextDataset(Dataset):
        logger.info("Creating features from dataset file at %s", file_path)

        with open(file_path, encoding="utf-8") as f:
-            lines = [line for line in f.read().splitlines() if len(line) > 0]
+            lines = [line for line in f.read().splitlines() if (len(line) > 0 and not line.isspace())]

-        self.examples = tokenizer.batch_encode_plus(lines, max_length=block_size)["input_ids"]
+        self.examples = tokenizer.batch_encode_plus(lines, add_special_tokens=True, max_length=block_size)["input_ids"]

    def __len__(self):
        return len(self.examples)

    def __getitem__(self, i):
-        return torch.tensor(self.examples[i])
+        return torch.tensor(self.examples[i], dtype=torch.long)


 def load_and_cache_examples(args, tokenizer, evaluate=False):
@@ -195,6 +178,12 @@ def _rotate_checkpoints(args, checkpoint_prefix="checkpoint", use_mtime=False) -

 def mask_tokens(inputs: torch.Tensor, tokenizer: PreTrainedTokenizer, args) -> Tuple[torch.Tensor, torch.Tensor]:
    """ Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original. """
+
+    if tokenizer.mask_token is None:
+        raise ValueError(
+            "This tokenizer does not have a mask token which is necessary for masked language modeling. Remove the --mlm flag if you want to use this tokenizer."
+        )
+
    labels = inputs.clone()
    # We sample a few tokens in each sequence for masked-LM training (with probability args.mlm_probability defaults to 0.15 in Bert/RoBERTa)
    probability_matrix = torch.full(labels.shape, args.mlm_probability)
@@ -244,6 +233,9 @@ def train(args, train_dataset, model: PreTrainedModel, tokenizer: PreTrainedToke
    else:
        t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs

+    model = model.module if hasattr(model, "module") else model  # Take care of distributed/parallel training
+    model.resize_token_embeddings(len(tokenizer))
+
    # Prepare optimizer and schedule (linear warmup and decay)
    no_decay = ["bias", "LayerNorm.weight"]
    optimizer_grouped_parameters = [
@@ -320,16 +312,17 @@ def train(args, train_dataset, model: PreTrainedModel, tokenizer: PreTrainedToke

    tr_loss, logging_loss = 0.0, 0.0

-    model_to_resize = model.module if hasattr(model, "module") else model  # Take care of distributed/parallel training
-    model_to_resize.resize_token_embeddings(len(tokenizer))
-
    model.zero_grad()
    train_iterator = trange(
        epochs_trained, int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0]
    )
    set_seed(args)  # Added here for reproducibility
-    for _ in train_iterator:
+    for epoch in train_iterator:
        epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])
+
+        if args.local_rank != -1:
+            train_sampler.set_epoch(epoch)
+
        for step, batch in enumerate(epoch_iterator):

            # Skip past any already trained steps if resuming training
@@ -635,6 +628,7 @@ def main():
        and os.listdir(args.output_dir)
        and args.do_train
        and not args.overwrite_output_dir
+        and not args.should_continue
    ):
        raise ValueError(
            "Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format(
@@ -654,7 +648,7 @@ 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)
@@ -684,33 +678,36 @@ def main():
    if args.local_rank not in [-1, 0]:
        torch.distributed.barrier()  # Barrier to make sure only the first process in distributed training download model & vocab

-    config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
-
    if args.config_name:
-        config = config_class.from_pretrained(args.config_name, cache_dir=args.cache_dir)
+        config = AutoConfig.from_pretrained(args.config_name, cache_dir=args.cache_dir)
    elif args.model_name_or_path:
-        config = config_class.from_pretrained(args.model_name_or_path, cache_dir=args.cache_dir)
+        config = AutoConfig.from_pretrained(args.model_name_or_path, cache_dir=args.cache_dir)
    else:
-        config = config_class()
+        # When we release a pip version exposing CONFIG_MAPPING,
+        # we can do `config = CONFIG_MAPPING[args.model_type]()`.
+        raise ValueError(
+            "You are instantiating a new config instance from scratch. This is not supported, but you can do it from another script, save it,"
+            "and load it from here, using --config_name"
+        )

    if args.tokenizer_name:
-        tokenizer = tokenizer_class.from_pretrained(args.tokenizer_name, cache_dir=args.cache_dir)
+        tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, cache_dir=args.cache_dir)
    elif args.model_name_or_path:
-        tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path, cache_dir=args.cache_dir)
+        tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, cache_dir=args.cache_dir)
    else:
        raise ValueError(
-            "You are instantiating a new {} tokenizer. This is not supported, but you can do it from another script, save it,"
-            "and load it from here, using --tokenizer_name".format(tokenizer_class.__name__)
+            "You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another script, save it,"
+            "and load it from here, using --tokenizer_name"
        )

    if args.block_size <= 0:
-        args.block_size = tokenizer.max_len_single_sentence
+        args.block_size = tokenizer.max_len
        # Our input block size will be the max possible for the model
    else:
-        args.block_size = min(args.block_size, tokenizer.max_len_single_sentence)
+        args.block_size = min(args.block_size, tokenizer.max_len)

    if args.model_name_or_path:
-        model = model_class.from_pretrained(
+        model = AutoModelWithLMHead.from_pretrained(
            args.model_name_or_path,
            from_tf=bool(".ckpt" in args.model_name_or_path),
            config=config,
@@ -718,7 +715,7 @@ def main():
        )
    else:
        logger.info("Training new model from scratch")
-        model = model_class(config=config)
+        model = AutoModelWithLMHead.from_config(config)

    model.to(args.device)

@@ -759,8 +756,8 @@ def main():
        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 = AutoModelWithLMHead.from_pretrained(args.output_dir)
+        tokenizer = AutoTokenizer.from_pretrained(args.output_dir)
        model.to(args.device)

    # Evaluation
@@ -777,7 +774,7 @@ def main():
            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 = AutoModelWithLMHead.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())
@@ -361,7 +361,8 @@ def load_and_cache_examples(args, task, tokenizer, evaluate=False, test=False):
            args.max_seq_length,
            tokenizer,
            pad_on_left=bool(args.model_type in ["xlnet"]),  # pad on the left for xlnet
-            pad_token_segment_id=4 if args.model_type in ["xlnet"] else 0,
+            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)
@@ -535,7 +536,7 @@ 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)
@@ -30,26 +30,12 @@ from torch.utils.data.distributed import DistributedSampler
 from tqdm import tqdm, trange

 from transformers import (
+    MODEL_FOR_QUESTION_ANSWERING_MAPPING,
    WEIGHTS_NAME,
    AdamW,
-    AlbertConfig,
-    AlbertForQuestionAnswering,
-    AlbertTokenizer,
-    BertConfig,
-    BertForQuestionAnswering,
-    BertTokenizer,
-    DistilBertConfig,
-    DistilBertForQuestionAnswering,
-    DistilBertTokenizer,
-    RobertaConfig,
-    RobertaForQuestionAnswering,
-    RobertaTokenizer,
-    XLMConfig,
-    XLMForQuestionAnswering,
-    XLMTokenizer,
-    XLNetConfig,
-    XLNetForQuestionAnswering,
-    XLNetTokenizer,
+    AutoConfig,
+    AutoModelForQuestionAnswering,
+    AutoTokenizer,
    get_linear_schedule_with_warmup,
    squad_convert_examples_to_features,
 )
@@ -69,19 +55,10 @@ except ImportError:

 logger = logging.getLogger(__name__)

-ALL_MODELS = sum(
-    (tuple(conf.pretrained_config_archive_map.keys()) for conf in (BertConfig, RobertaConfig, XLNetConfig, XLMConfig)),
-    (),
-)
+MODEL_CONFIG_CLASSES = list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
+MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)

-MODEL_CLASSES = {
-    "bert": (BertConfig, BertForQuestionAnswering, BertTokenizer),
-    "roberta": (RobertaConfig, RobertaForQuestionAnswering, RobertaTokenizer),
-    "xlnet": (XLNetConfig, XLNetForQuestionAnswering, XLNetTokenizer),
-    "xlm": (XLMConfig, XLMForQuestionAnswering, XLMTokenizer),
-    "distilbert": (DistilBertConfig, DistilBertForQuestionAnswering, DistilBertTokenizer),
-    "albert": (AlbertConfig, AlbertForQuestionAnswering, AlbertTokenizer),
-}
+ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) for conf in MODEL_CONFIG_CLASSES), (),)


 def set_seed(args):
@@ -212,13 +189,18 @@ def train(args, train_dataset, model, tokenizer):
                "end_positions": batch[4],
            }

-            if args.model_type in ["xlm", "roberta", "distilbert"]:
+            if args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
                del inputs["token_type_ids"]

            if args.model_type in ["xlnet", "xlm"]:
                inputs.update({"cls_index": batch[5], "p_mask": batch[6]})
                if args.version_2_with_negative:
                    inputs.update({"is_impossible": batch[7]})
+                if hasattr(model, "config") and hasattr(model.config, "lang2id"):
+                    inputs.update(
+                        {"langs": (torch.ones(batch[0].shape, dtype=torch.int64) * args.lang_id).to(args.device)}
+                    )
+
            outputs = model(**inputs)
            # model outputs are always tuple in transformers (see doc)
            loss = outputs[0]
@@ -322,7 +304,7 @@ def evaluate(args, model, tokenizer, prefix=""):
                "token_type_ids": batch[2],
            }

-            if args.model_type in ["xlm", "roberta", "distilbert"]:
+            if args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
                del inputs["token_type_ids"]

            example_indices = batch[3]
@@ -330,6 +312,11 @@ def evaluate(args, model, tokenizer, prefix=""):
            # XLNet and XLM use more arguments for their predictions
            if args.model_type in ["xlnet", "xlm"]:
                inputs.update({"cls_index": batch[4], "p_mask": batch[5]})
+                # for lang_id-sensitive xlm models
+                if hasattr(model, "config") and hasattr(model.config, "lang2id"):
+                    inputs.update(
+                        {"langs": (torch.ones(batch[0].shape, dtype=torch.int64) * args.lang_id).to(args.device)}
+                    )

            outputs = model(**inputs)

@@ -496,7 +483,7 @@ def main():
        default=None,
        type=str,
        required=True,
-        help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()),
+        help="Model type selected in the list: " + ", ".join(MODEL_TYPES),
    )
    parser.add_argument(
        "--model_name_or_path",
@@ -635,6 +622,12 @@ def main():
        help="If true, all of the warnings related to data processing will be printed. "
        "A number of warnings are expected for a normal SQuAD evaluation.",
    )
+    parser.add_argument(
+        "--lang_id",
+        default=0,
+        type=int,
+        help="language id of input for language-specific xlm models (see tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)",
+    )

    parser.add_argument("--logging_steps", type=int, default=500, help="Log every X updates steps.")
    parser.add_argument("--save_steps", type=int, default=500, help="Save checkpoint every X updates steps.")
@@ -702,7 +695,7 @@ 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)
@@ -734,17 +727,16 @@ def main():
        torch.distributed.barrier()

    args.model_type = args.model_type.lower()
-    config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
-    config = config_class.from_pretrained(
+    config = AutoConfig.from_pretrained(
        args.config_name if args.config_name else args.model_name_or_path,
        cache_dir=args.cache_dir if args.cache_dir else None,
    )
-    tokenizer = tokenizer_class.from_pretrained(
+    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 if args.cache_dir else None,
    )
-    model = model_class.from_pretrained(
+    model = AutoModelForQuestionAnswering.from_pretrained(
        args.model_name_or_path,
        from_tf=bool(".ckpt" in args.model_name_or_path),
        config=config,
@@ -794,8 +786,8 @@ def main():
        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)  # , force_download=True)
-        tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
+        model = AutoModelForQuestionAnswering.from_pretrained(args.output_dir)  # , force_download=True)
+        tokenizer = AutoTokenizer.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
        model.to(args.device)

    # Evaluation - we can ask to evaluate all the checkpoints (sub-directories) in a directory
@@ -819,7 +811,7 @@ def main():
        for checkpoint in checkpoints:
            # Reload the model
            global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else ""
-            model = model_class.from_pretrained(checkpoint)  # , force_download=True)
+            model = AutoModelForQuestionAnswering.from_pretrained(checkpoint)  # , force_download=True)
            model.to(args.device)

            # Evaluate
@@ -1,22 +1,3 @@
-# coding=utf-8
-# Copyright 2018 The 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 sequence classification on GLUE (Bert, XLM, XLNet, RoBERTa)."""
-
-
-import argparse
-import logging
 import os

 import tensorflow as tf
@@ -24,309 +5,101 @@ import tensorflow_datasets

 from transformers import (
    BertConfig,
+    BertForSequenceClassification,
    BertTokenizer,
-    DistilBertConfig,
-    DistilBertTokenizer,
-    RobertaConfig,
-    RobertaTokenizer,
    TFBertForSequenceClassification,
-    TFDistilBertForSequenceClassification,
-    TFRobertaForSequenceClassification,
-    TFXLMForSequenceClassification,
-    TFXLNetForSequenceClassification,
-    XLMConfig,
-    XLMTokenizer,
-    XLNetConfig,
-    XLNetTokenizer,
-)
-from transformers import glue_convert_examples_to_features as convert_examples_to_features
-from transformers import glue_output_modes as output_modes
-from transformers import glue_processors as processors
-
-
-logger = logging.getLogger(__name__)
-
-ALL_MODELS = sum(
-    (
-        tuple(conf.pretrained_config_archive_map.keys())
-        for conf in (BertConfig, XLNetConfig, XLMConfig, RobertaConfig, DistilBertConfig)
-    ),
-    (),
+    glue_convert_examples_to_features,
+    glue_processors,
 )

-MODEL_CLASSES = {
-    "bert": (BertConfig, TFBertForSequenceClassification, BertTokenizer),
-    "xlnet": (XLNetConfig, TFXLNetForSequenceClassification, XLNetTokenizer),
-    "xlm": (XLMConfig, TFXLMForSequenceClassification, XLMTokenizer),
-    "roberta": (RobertaConfig, TFRobertaForSequenceClassification, RobertaTokenizer),
-    "distilbert": (DistilBertConfig, TFDistilBertForSequenceClassification, DistilBertTokenizer),
-}
+
+# script parameters
+BATCH_SIZE = 32
+EVAL_BATCH_SIZE = BATCH_SIZE * 2
+USE_XLA = False
+USE_AMP = False
+EPOCHS = 3
+
+TASK = "mrpc"
+
+if TASK == "sst-2":
+    TFDS_TASK = "sst2"
+elif TASK == "sts-b":
+    TFDS_TASK = "stsb"
+else:
+    TFDS_TASK = TASK
+
+num_labels = len(glue_processors[TASK]().get_labels())
+print(num_labels)
+
+tf.config.optimizer.set_jit(USE_XLA)
+tf.config.optimizer.set_experimental_options({"auto_mixed_precision": USE_AMP})
+
+# Load tokenizer and model from pretrained model/vocabulary. Specify the number of labels to classify (2+: classification, 1: regression)
+config = BertConfig.from_pretrained("bert-base-cased", num_labels=num_labels)
+tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
+model = TFBertForSequenceClassification.from_pretrained("bert-base-cased", config=config)
+
+# Load dataset via TensorFlow Datasets
+data, info = tensorflow_datasets.load(f"glue/{TFDS_TASK}", with_info=True)
+train_examples = info.splits["train"].num_examples
+
+# MNLI expects either validation_matched or validation_mismatched
+valid_examples = info.splits["validation"].num_examples
+
+# Prepare dataset for GLUE as a tf.data.Dataset instance
+train_dataset = glue_convert_examples_to_features(data["train"], tokenizer, max_length=128, task=TASK)
+
+# MNLI expects either validation_matched or validation_mismatched
+valid_dataset = glue_convert_examples_to_features(data["validation"], tokenizer, max_length=128, task=TASK)
+train_dataset = train_dataset.shuffle(128).batch(BATCH_SIZE).repeat(-1)
+valid_dataset = valid_dataset.batch(EVAL_BATCH_SIZE)
+
+# Prepare training: Compile tf.keras model with optimizer, loss and learning rate schedule
+opt = tf.keras.optimizers.Adam(learning_rate=3e-5, epsilon=1e-08)
+if USE_AMP:
+    # loss scaling is currently required when using mixed precision
+    opt = tf.keras.mixed_precision.experimental.LossScaleOptimizer(opt, "dynamic")


-def load_and_cache_examples(args, data, task, tokenizer, split):
-    if task == "mnli" and split == "validation":
-        split = "validation_matched"
+if num_labels == 1:
+    loss = tf.keras.losses.MeanSquaredError()
+else:
+    loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)

-    features_output_dir = os.path.join(args.output_dir, "features")
-    cached_features_file = os.path.join(
-        features_output_dir,
-        "cached_{}_{}_{}_{}.tfrecord".format(
-            split, list(filter(None, args.model_name_or_path.split("/"))).pop(), str(args.max_seq_length), str(task)
-        ),
-    )
+metric = tf.keras.metrics.SparseCategoricalAccuracy("accuracy")
+model.compile(optimizer=opt, loss=loss, metrics=[metric])

-    if not os.path.exists(cached_features_file) or args.overwrite_cache:
-        logger.info("Converting examples to features")
-        dataset = convert_examples_to_features(data[split], tokenizer, args.max_seq_length, task)
+# Train and evaluate using tf.keras.Model.fit()
+train_steps = train_examples // BATCH_SIZE
+valid_steps = valid_examples // EVAL_BATCH_SIZE

-        if not os.path.exists(features_output_dir):
-            os.makedirs(features_output_dir)
+history = model.fit(
+    train_dataset,
+    epochs=EPOCHS,
+    steps_per_epoch=train_steps,
+    validation_data=valid_dataset,
+    validation_steps=valid_steps,
+)

-        with tf.compat.v1.python_io.TFRecordWriter(cached_features_file) as tfwriter:
-            for feature in dataset:
-                example, label = feature
-                feature_key_value_pair = {
-                    "input_ids": tf.train.Feature(int64_list=tf.train.Int64List(value=example["input_ids"])),
-                    "attention_mask": tf.train.Feature(int64_list=tf.train.Int64List(value=example["attention_mask"])),
-                    "token_type_ids": tf.train.Feature(int64_list=tf.train.Int64List(value=example["token_type_ids"])),
-                    "label": tf.train.Feature(int64_list=tf.train.Int64List(value=[label])),
-                }
-                features = tf.train.Features(feature=feature_key_value_pair)
-                example = tf.train.Example(features=features)
+# Save TF2 model
+os.makedirs("./save/", exist_ok=True)
+model.save_pretrained("./save/")

-                tfwriter.write(example.SerializeToString())
+if TASK == "mrpc":
+    # Load the TensorFlow model in PyTorch for inspection
+    # This is to demo the interoperability between the two frameworks, you don't have to
+    # do this in real life (you can run the inference on the TF model).
+    pytorch_model = BertForSequenceClassification.from_pretrained("./save/", from_tf=True)

-        logger.info("Features saved to cache")
+    # Quickly test a few predictions - MRPC is a paraphrasing task, let's see if our model learned the task
+    sentence_0 = "This research was consistent with his findings."
+    sentence_1 = "His findings were compatible with this research."
+    sentence_2 = "His findings were not compatible with this research."
+    inputs_1 = tokenizer.encode_plus(sentence_0, sentence_1, add_special_tokens=True, return_tensors="pt")
+    inputs_2 = tokenizer.encode_plus(sentence_0, sentence_2, add_special_tokens=True, return_tensors="pt")

-    features = {
-        "input_ids": tf.io.FixedLenFeature([args.max_seq_length], tf.int64),
-        "attention_mask": tf.io.FixedLenFeature([args.max_seq_length], tf.int64),
-        "token_type_ids": tf.io.FixedLenFeature([args.max_seq_length], tf.int64),
-        "label": tf.io.FixedLenFeature([], tf.int64),
-    }
-
-    def select_data_from_record(record):
-        record = tf.io.parse_single_example(record, features)
-        x = {
-            "input_ids": record["input_ids"],
-            "attention_mask": record["attention_mask"],
-            "token_type_ids": record["token_type_ids"],
-        }
-        y = record["label"]
-        return (x, y)
-
-    dataset = tf.data.TFRecordDataset(cached_features_file)
-    dataset = dataset.map(select_data_from_record)
-
-    logger.info("Created dataset %s from TFRecord" % split)
-    return dataset
-
-
-def main():
-    parser = argparse.ArgumentParser()
-
-    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.",
-    )
-    parser.add_argument(
-        "--overwrite_output_dir", action="store_true", help="Overwrite the content of the output directory"
-    )
-
-    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("--train_batch_size", default=8, type=int, help="Batch size per GPU/CPU for training.")
-    parser.add_argument(
-        "--valid_batch_size", default=8, type=int, help="Batch size per GPU/CPU for validation during training."
-    )
-    parser.add_argument(
-        "--test_batch_size", default=8, type=int, help="Batch size per GPU/CPU for evaluation after training."
-    )
-    parser.add_argument(
-        "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets"
-    )
-
-    parser.add_argument("--num_train_epochs", default=3, type=int, help="Total number of training epochs to perform.")
-    parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.")
-    parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.")
-    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_lower_case", action="store_true", help="Set this flag if you are using an uncased model."
-    )
-
-    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("--xla", action="store_true", help="Whether to use XLA (Accelerated Linear Algebra).")
-    parser.add_argument("--amp", action="store_true", help="Whether to use AMP (Automatic Mixed Precision).")
-    parser.add_argument(
-        "--force_download",
-        action="store_true",
-        help="Whether to force download the weights from S3 (useful if the file is corrupted).",
-    )
-    args = parser.parse_args()
-
-    # Setup logging
-    logging.basicConfig(
-        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
-        datefmt="%m/%d/%Y %H:%M:%S",
-        level=logging.INFO,
-    )
-
-    if os.path.exists(args.output_dir) and args.do_train:
-        if not args.overwrite_output_dir and bool(
-            [file for file in os.listdir(args.output_dir) if "features" not in file]
-        ):
-            raise ValueError(
-                "Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format(
-                    args.output_dir
-                )
-            )
-
-    args.model_type = args.model_type.lower()
-    config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
-
-    TASK = args.task_name.lower()
-
-    if TASK not in processors:
-        raise ValueError("Task not found: %s" % (TASK))
-
-    if TASK == "sst-2":
-        TFDS_TASK = "sst2"
-    elif TASK == "sts-b":
-        TFDS_TASK = "stsb"
-    else:
-        TFDS_TASK = TASK
-
-    num_labels = len(processors[TASK]().get_labels())
-    print(num_labels)
-
-    tf.config.optimizer.set_jit(args.xla)
-    tf.config.optimizer.set_experimental_options({"auto_mixed_precision": args.amp})
-
-    # Load tokenizer and model from pretrained model/vocabulary. Specify the number of labels to classify (2+: classification, 1: regression)
-    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,
-        force_download=args.force_download,
-    )
-
-    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,
-        force_download=args.force_download,
-    )
-
-    model = model_class.from_pretrained(
-        args.model_name_or_path,
-        config=config,
-        cache_dir=args.cache_dir if args.cache_dir else None,
-        force_download=args.force_download,
-    )
-
-    # Load dataset via TensorFlow Datasets
-    data, info = tensorflow_datasets.load("glue/%s" % TFDS_TASK, with_info=True)
-
-    # Prepare training: Compile tf.keras model with optimizer, loss and learning rate schedule
-    opt = tf.keras.optimizers.Adam(learning_rate=args.learning_rate, epsilon=args.adam_epsilon)
-
-    if args.amp:
-        # loss scaling is currently required when using mixed precision
-        opt = tf.keras.mixed_precision.experimental.LossScaleOptimizer(opt, "dynamic")
-
-    if num_labels == 1:
-        loss = tf.keras.losses.MeanSquaredError()
-    else:
-        loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
-
-    metric = tf.keras.metrics.SparseCategoricalAccuracy("accuracy")
-    model.compile(optimizer=opt, loss=loss, metrics=[metric])
-
-    class save_model(tf.keras.callbacks.Callback):
-        def on_epoch_end(self, epoch, logs=None):
-            print("Saving model at epoch {}".format(epoch))
-            output_dir = os.path.join(args.output_dir, "checkpoint-epoch-{}".format(epoch))
-            if not os.path.exists(output_dir):
-                os.makedirs(output_dir)
-            self.model.save_pretrained(output_dir)
-
-    if args.do_train:
-        train_dataset = load_and_cache_examples(args, data=data, task=TASK, tokenizer=tokenizer, split="train")
-        train_dataset = train_dataset.batch(args.train_batch_size).repeat(args.num_train_epochs)
-        train_examples = info.splits["train"].num_examples / args.train_batch_size
-
-        validation_identifier = "validation_mismatched" if TASK == "mnli" else "validation"
-        valid_dataset = load_and_cache_examples(
-            args, data=data, task=TASK, tokenizer=tokenizer, split=validation_identifier
-        )
-        valid_dataset = valid_dataset.batch(args.valid_batch_size)
-        valid_examples = info.splits[validation_identifier].num_examples / args.valid_batch_size
-
-        history = model.fit(
-            train_dataset,
-            steps_per_epoch=train_examples,
-            epochs=args.num_train_epochs,
-            validation_data=valid_dataset if args.evaluate_during_training else None,
-            validation_steps=valid_examples if args.evaluate_during_training else None,
-            callbacks=[save_model()],
-        )
-
-    if args.do_eval:
-        test_dataset = load_and_cache_examples(args, data=data, task=TASK, tokenizer=tokenizer, split="test")
-        test_dataset = test_dataset.batch(args.test_batch_size)
-        test_examples = info.splits["test"].num_examples / args.test_batch_size
-
-        results = model.evaluate(test_dataset, steps=test_examples)
-
-
-if __name__ == "__main__":
-    main()
+    pred_1 = pytorch_model(**inputs_1)[0].argmax().item()
+    pred_2 = pytorch_model(**inputs_2)[0].argmax().item()
+    print("sentence_1 is", "a paraphrase" if pred_1 else "not a paraphrase", "of sentence_0")
+    print("sentence_2 is", "a paraphrase" if pred_2 else "not a paraphrase", "of sentence_0")
@@ -1,699 +0,0 @@
-# Copyright 2018 The 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 sequence classification on GLUE (Bert, XLM, XLNet, RoBERTa)."""
-from __future__ import absolute_import, division, print_function
-
-import argparse
-import datetime
-import glob
-import logging
-import math
-import os
-import pickle
-
-import regex as re
-import tensorflow as tf
-from fastprogress import master_bar, progress_bar
-from seqeval import metrics
-from tqdm import tqdm, trange
-
-from transformers import (
-    TF2_WEIGHTS_NAME,
-    BertConfig,
-    BertTokenizer,
-    DistilBertConfig,
-    DistilBertTokenizer,
-    GradientAccumulator,
-    SquadV1Processor,
-    SquadV2Processor,
-    TFBertForQuestionAnswering,
-    TFDistilBertForQuestionAnswering,
-    TFXLMForQuestionAnsweringSimple,
-    TFXLNetForQuestionAnsweringSimple,
-    XLMConfig,
-    XLMTokenizer,
-    XLNetConfig,
-    XLNetTokenizer,
-    create_optimizer,
-    squad_convert_examples_to_features,
-)
-from transformers.data.metrics.squad_metrics import compute_predictions_logits, squad_evaluate
-from transformers.data.processors.squad import SquadResult
-
-
-logger = logging.getLogger(__name__)
-
-ALL_MODELS = sum(
-    (
-        tuple(conf.pretrained_config_archive_map.keys())
-        for conf in (BertConfig, XLNetConfig, XLMConfig, DistilBertConfig)
-    ),
-    (),
-)
-
-MODEL_CLASSES = {
-    "bert": (BertConfig, TFBertForQuestionAnswering, BertTokenizer),
-    "xlnet": (XLNetConfig, TFXLNetForQuestionAnsweringSimple, XLNetTokenizer),
-    "xlm": (XLMConfig, TFXLMForQuestionAnsweringSimple, XLMTokenizer),
-    "distilbert": (DistilBertConfig, TFDistilBertForQuestionAnswering, DistilBertTokenizer),
-}
-
-
-def train(args, strategy, train_dataset, tokenizer, model, num_train_examples, train_batch_size):
-    if args.max_steps > 0:
-        num_train_steps = args.max_steps * args.gradient_accumulation_steps
-        args.num_train_epochs = 1
-    else:
-        num_train_steps = (
-            math.ceil(num_train_examples / train_batch_size)
-            // args.gradient_accumulation_steps
-            * args.num_train_epochs
-        )
-
-    writer = tf.summary.create_file_writer("/tmp/mylogs")
-
-    with strategy.scope():
-        loss_fct = tf.keras.losses.SparseCategoricalCrossentropy(
-            reduction=tf.keras.losses.Reduction.NONE, from_logits=True
-        )
-        optimizer = create_optimizer(args.learning_rate, num_train_steps, args.warmup_steps)
-
-        if args.xla:
-            tf.config.optimizer.set_jit(True)
-
-        if args.amp:
-            optimizer = tf.keras.mixed_precision.experimental.LossScaleOptimizer(optimizer, "dynamic")
-
-        loss_metric = tf.keras.metrics.Mean(name="loss", dtype=tf.float32)
-        gradient_accumulator = GradientAccumulator()
-
-    logging.info("***** Running training *****")
-    logging.info("  Num examples = %d", num_train_examples)
-    logging.info("  Num Epochs = %d", args.num_train_epochs)
-    logging.info("  Instantaneous batch size per device = %d", args.per_device_train_batch_size)
-    logging.info(
-        "  Total train batch size (w. parallel, distributed & accumulation) = %d",
-        train_batch_size * args.gradient_accumulation_steps,
-    )
-    logging.info("  Gradient Accumulation steps = %d", args.gradient_accumulation_steps)
-    logging.info("  Total training steps = %d", num_train_steps)
-
-    model.summary()
-
-    @tf.function
-    def apply_gradients():
-        grads_and_vars = []
-
-        for gradient, variable in zip(gradient_accumulator.gradients, model.trainable_variables):
-            if gradient is not None:
-                scaled_gradient = gradient / (args.n_device * args.gradient_accumulation_steps)
-                grads_and_vars.append((scaled_gradient, variable))
-            else:
-                grads_and_vars.append((gradient, variable))
-
-        optimizer.apply_gradients(grads_and_vars, args.max_grad_norm)
-        gradient_accumulator.reset()
-
-    @tf.function
-    def train_step(train_features, train_labels):
-        def step_fn(train_features, train_labels):
-            with tf.GradientTape() as tape:
-                start_logits, end_logits = model(train_features)
-                start_logits = tf.multiply(
-                    start_logits, tf.dtypes.cast((train_features["attention_mask"]), tf.float32)
-                )
-                end_logits = tf.multiply(end_logits, tf.dtypes.cast((train_features["attention_mask"]), tf.float32))
-                start_loss = loss_fct(train_labels["start_position"], start_logits)
-                end_loss = loss_fct(train_labels["end_position"], end_logits)
-                total_loss = (start_loss + end_loss) / 2
-
-                loss = tf.reduce_sum(total_loss) * (1.0 / train_batch_size)
-                grads = tape.gradient(loss, model.trainable_variables)
-
-                gradient_accumulator(grads)
-
-            return total_loss
-
-        per_example_losses = strategy.experimental_run_v2(step_fn, args=(train_features, train_labels))
-        mean_loss = strategy.reduce(tf.distribute.ReduceOp.MEAN, per_example_losses, axis=0)
-
-        return mean_loss
-
-    current_time = datetime.datetime.now()
-    train_iterator = master_bar(range(args.num_train_epochs))
-    global_step = 0
-    logging_loss = 0.0
-
-    for epoch in train_iterator:
-        epoch_iterator = progress_bar(
-            train_dataset,
-            total=num_train_steps / args.num_train_epochs,
-            parent=train_iterator,
-            display=args.n_device > 1,
-        )
-        step = 1
-
-        with strategy.scope():
-            for train_features, train_labels in tqdm(
-                epoch_iterator,
-                desc="Training, epoch {}".format(epoch),
-                total=int(num_train_steps / args.num_train_epochs),
-            ):
-                loss = train_step(train_features, train_labels)
-
-                if step % args.gradient_accumulation_steps == 0:
-                    strategy.experimental_run_v2(apply_gradients)
-
-                    loss_metric(loss)
-
-                    global_step += 1
-
-                    if args.logging_steps > 0 and global_step % args.logging_steps == 0:
-                        # Log metrics
-                        if args.n_device == 1 and args.evaluate_during_training:
-                            # Only evaluate when single GPU otherwise metrics may not average well
-                            results = evaluate(args, strategy, model, tokenizer, global_step)
-
-                            with writer.as_default():
-                                tf.summary.scalar("exact", results[0], global_step)
-                                tf.summary.scalar("f1", results[1], global_step)
-
-                        lr = optimizer.learning_rate
-                        learning_rate = lr(step)
-
-                        with writer.as_default():
-                            tf.summary.scalar("lr", learning_rate, global_step)
-                            tf.summary.scalar(
-                                "loss", (loss_metric.result() - logging_loss) / args.logging_steps, global_step
-                            )
-
-                        logging_loss = loss_metric.result()
-
-                    with writer.as_default():
-                        tf.summary.scalar("loss", loss_metric.result(), step=step)
-
-                    if 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.save_pretrained(output_dir)
-                        logging.info("Saving model checkpoint to %s", output_dir)
-
-                train_iterator.child.comment = f"loss : {loss_metric.result()}"
-                step += 1
-
-        train_iterator.write(f"loss epoch {epoch + 1}: {loss_metric.result()}")
-
-        loss_metric.reset_states()
-
-    logging.info("  Training took time = {}".format(datetime.datetime.now() - current_time))
-
-
-def evaluate(args, strategy, model, tokenizer, prefix):
-    eval_batch_size = args.per_device_eval_batch_size * args.n_device
-    eval_dataset, size, examples, features = load_and_cache_examples(
-        args, tokenizer, evaluate=True, output_examples=True
-    )
-    eval_dataset = eval_dataset.batch(args.per_device_eval_batch_size)
-    eval_dataset = strategy.experimental_distribute_dataset(eval_dataset)
-    num_eval_steps = math.ceil(size / eval_batch_size)
-    master = master_bar(range(1))
-    eval_iterator = progress_bar(eval_dataset, total=num_eval_steps, parent=master, display=args.n_device > 1)
-
-    logging.info("***** Running evaluation *****")
-    logging.info("  Num examples = %d", size)
-    logging.info("  Batch size = %d", eval_batch_size)
-
-    all_results = []
-    for index, (eval_features, eval_labels) in tqdm(
-        enumerate(eval_iterator), total=size / args.per_device_eval_batch_size
-    ):
-        with strategy.scope():
-            start_logits, end_logits = model(eval_features)
-
-            for sample_index in range(args.per_device_eval_batch_size):
-                if index * args.per_device_eval_batch_size + sample_index < len(features):
-                    result = SquadResult(
-                        features[index * args.per_device_eval_batch_size + sample_index].unique_id,
-                        start_logits[sample_index],
-                        end_logits[sample_index],
-                    )
-                    all_results.append(result)
-
-    output_prediction_file = os.path.join(args.output_dir, "predictions_{}.json".format(prefix))
-    output_nbest_file = os.path.join(args.output_dir, "nbest_predictions_{}.json".format(prefix))
-
-    if args.version_2_with_negative:
-        output_null_log_odds_file = os.path.join(args.output_dir, "null_odds_{}.json".format(prefix))
-    else:
-        output_null_log_odds_file = None
-
-    predictions = compute_predictions_logits(
-        examples,
-        features,
-        all_results,
-        args.n_best_size,
-        args.max_answer_length,
-        args.do_lower_case,
-        output_prediction_file,
-        output_nbest_file,
-        output_null_log_odds_file,
-        args.verbose_logging,
-        args.version_2_with_negative,
-        args.null_score_diff_threshold,
-        tokenizer,
-    )
-
-    # Compute the F1 and exact scores.
-    results = squad_evaluate(examples, predictions)
-    return results
-
-
-def load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=False):
-    features_output_dir = os.path.join(args.output_dir, "features")
-    cached_features_file = os.path.join(
-        features_output_dir,
-        "cached_{}_{}_{}.tfrecord".format(
-            "dev" if evaluate else "train",
-            list(filter(None, args.model_name_or_path.split("/"))).pop(),
-            str(args.max_seq_length),
-        ),
-    )
-
-    if not os.path.exists(cached_features_file) or args.overwrite_cache:
-        if args.version_2_with_negative:
-            processor = SquadV2Processor()
-        else:
-            processor = SquadV1Processor()
-
-        if args.data_dir:
-            directory = args.directory
-            examples = processor.get_dev_examples(directory) if evaluate else processor.get_train_examples(directory)
-        else:
-            try:
-                import tensorflow_datasets as tfds
-            except ImportError:
-                raise ImportError("If not data_dir is specified, tensorflow_datasets needs to be installed.")
-
-            if args.version_2_with_negative:
-                logger.warning("tensorflow_datasets does not handle version 2 of SQuAD.")
-
-            examples = processor.get_examples_from_dataset(tfds.load("squad"), evaluate=evaluate)
-            examples = examples
-
-        logger.info("Converting examples to features")
-        features, dataset = squad_convert_examples_to_features(
-            examples,
-            tokenizer,
-            args.max_seq_length,
-            args.doc_stride,
-            args.max_query_length,
-            is_training=not evaluate,
-            return_dataset="tf",
-        )
-
-        if not os.path.exists(features_output_dir):
-            os.makedirs(features_output_dir)
-
-        with tf.compat.v1.python_io.TFRecordWriter(cached_features_file) as tfwriter:
-            for feature in tqdm(dataset, desc="Building tfrecord dataset", total=len(features)):
-                example, result = feature
-                feature_key_value_pair = {
-                    "input_ids": tf.train.Feature(int64_list=tf.train.Int64List(value=example["input_ids"])),
-                    "attention_mask": tf.train.Feature(int64_list=tf.train.Int64List(value=example["attention_mask"])),
-                    "token_type_ids": tf.train.Feature(int64_list=tf.train.Int64List(value=example["token_type_ids"])),
-                    "start_position": tf.train.Feature(
-                        int64_list=tf.train.Int64List(value=[result["start_position"]])
-                    ),
-                    "end_position": tf.train.Feature(int64_list=tf.train.Int64List(value=[result["end_position"]])),
-                    "cls_index": tf.train.Feature(int64_list=tf.train.Int64List(value=[result["cls_index"]])),
-                    "p_mask": tf.train.Feature(int64_list=tf.train.Int64List(value=result["p_mask"])),
-                }
-                feature_skeleton = tf.train.Features(feature=feature_key_value_pair)
-                example = tf.train.Example(features=feature_skeleton)
-
-                tfwriter.write(example.SerializeToString())
-
-            with open("{}.pickle".format(cached_features_file.replace(".tfrecord", "_examples")), "wb") as handle:
-                pickle.dump(examples, handle, protocol=pickle.HIGHEST_PROTOCOL)
-
-            with open("{}.pickle".format(cached_features_file.replace(".tfrecord", "_features")), "wb") as handle:
-                pickle.dump(features, handle, protocol=pickle.HIGHEST_PROTOCOL)
-
-        logger.info("Features saved to cache")
-
-    feature_skeleton = {
-        "input_ids": tf.io.FixedLenFeature([args.max_seq_length], tf.int64),
-        "attention_mask": tf.io.FixedLenFeature([args.max_seq_length], tf.int64),
-        "token_type_ids": tf.io.FixedLenFeature([args.max_seq_length], tf.int64),
-        "start_position": tf.io.FixedLenFeature([], tf.int64),
-        "end_position": tf.io.FixedLenFeature([], tf.int64),
-        "cls_index": tf.io.FixedLenFeature([], tf.int64),
-        "p_mask": tf.io.FixedLenFeature([args.max_seq_length], tf.int64),
-    }
-
-    def select_data_from_record(record):
-        record = tf.io.parse_single_example(record, feature_skeleton)
-        x = {
-            "input_ids": record["input_ids"],
-            "attention_mask": record["attention_mask"],
-            "token_type_ids": record["token_type_ids"],
-        }
-        y = {
-            "start_position": record["start_position"],
-            "end_position": record["end_position"],
-            "cls_index": record["cls_index"],
-            "p_mask": record["p_mask"],
-        }
-        return x, y
-
-    dataset = tf.data.TFRecordDataset(cached_features_file)
-    dataset = dataset.map(select_data_from_record)
-
-    with open("{}.pickle".format(cached_features_file.replace(".tfrecord", "_examples")), "rb") as handle:
-        examples = pickle.load(handle)
-
-    with open("{}.pickle".format(cached_features_file.replace(".tfrecord", "_features")), "rb") as handle:
-        features = pickle.load(handle)
-
-    logger.info("Created dataset %s from TFRecord" % "dev" if evaluate else "train")
-
-    if output_examples:
-        return dataset, len(list(dataset.__iter__())), examples, features
-    return dataset, len(list(dataset.__iter__()))
-
-
-def main():
-    parser = argparse.ArgumentParser()
-
-    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 predictions and checkpoints will be written.",
-    )
-    parser.add_argument(
-        "--data_dir",
-        default=None,
-        type=str,
-        required=False,
-        help="The input data directory containing the .json files. If no data dir is specified, uses tensorflow_datasets to load the data."
-        + ", ".join(MODEL_CLASSES.keys()),
-    )
-    parser.add_argument(
-        "--overwrite_output_dir", action="store_true", help="Overwrite the content of the output directory"
-    )
-
-    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_device_train_batch_size", default=8, type=int, help="Batch size per GPU/CPU for training."
-    )
-    parser.add_argument(
-        "--per_device_eval_batch_size",
-        default=8,
-        type=int,
-        help="Batch size per GPU/CPU for validation during training.",
-    )
-    parser.add_argument(
-        "--per_device_test_batch_size",
-        default=8,
-        type=int,
-        help="Batch size per GPU/CPU for evaluation after training.",
-    )
-    parser.add_argument(
-        "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets"
-    )
-
-    parser.add_argument("--num_train_epochs", default=3, type=int, help="Total number of training epochs to perform.")
-    parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.")
-    parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.")
-    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(
-        "--doc_stride",
-        default=128,
-        type=int,
-        help="When splitting up a long document into chunks, how much stride to take between chunks.",
-    )
-    parser.add_argument(
-        "--max_query_length",
-        default=64,
-        type=int,
-        help="The maximum number of tokens for the question. Questions longer than this will "
-        "be truncated to this length.",
-    )
-    parser.add_argument(
-        "--do_lower_case", action="store_true", help="Set this flag if you are using an uncased model."
-    )
-
-    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("--xla", action="store_true", help="Whether to use XLA (Accelerated Linear Algebra).")
-    parser.add_argument("--amp", action="store_true", help="Whether to use AMP (Automatic Mixed Precision).")
-    parser.add_argument(
-        "--force_download",
-        action="store_true",
-        help="Whether to force download the weights from S3 (useful if the file is corrupted).",
-    )
-
-    parser.add_argument(
-        "--tpu",
-        default=None,
-        help="The Cloud TPU to use for training. This should be either the name "
-        "used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 "
-        "url.",
-    )
-    parser.add_argument("--num_tpu_cores", default="8", help="Total number of TPU cores to use.")
-    parser.add_argument(
-        "--gpus",
-        default="0",
-        help="Comma separated list of gpus devices. If only one, switch to single gpu strategy, if None takes all the gpus available.",
-    )
-    parser.add_argument("--no_cuda", action="store_true", help="Whether not to use CUDA when available")
-    parser.add_argument(
-        "--version_2_with_negative",
-        action="store_true",
-        help="If true, the SQuAD examples contain some that do not have an answer.",
-    )
-    parser.add_argument(
-        "--max_steps",
-        default=-1,
-        help="If > 0: set total number of training steps to perform. Override num_train_epochs.",
-    )
-    parser.add_argument(
-        "--gradient_accumulation_steps",
-        default=1,
-        help="Number of updates steps to accumulate before performing a backward/update pass.",
-    )
-    parser.add_argument(
-        "--warmup_steps", default=0, help="Linear warmup over warmup_steps.",
-    )
-    parser.add_argument("--max_grad_norm", default=1.0, help="Max gradient norm.")
-    parser.add_argument("--logging_steps", default=500, type=int, help="Log every X updates.")
-    parser.add_argument("--save_steps", default=500, type=int, help="Save checkpoint every X updates.")
-    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(
-        "--n_best_size",
-        default=20,
-        type=int,
-        help="The total number of n-best predictions to generate in the nbest_predictions.json output file.",
-    )
-    parser.add_argument(
-        "--max_answer_length",
-        default=30,
-        type=int,
-        help="The maximum length of an answer that can be generated. This is needed because the start "
-        "and end predictions are not conditioned on one another.",
-    )
-    parser.add_argument(
-        "--verbose_logging",
-        action="store_true",
-        help="If true, all of the warnings related to data processing will be printed. "
-        "A number of warnings are expected for a normal SQuAD evaluation.",
-    )
-    parser.add_argument(
-        "--null_score_diff_threshold",
-        type=float,
-        default=0.0,
-        help="If null_score - best_non_null is greater than the threshold predict null.",
-    )
-    args = parser.parse_args()
-
-    # Setup logging
-    logging.basicConfig(
-        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
-        datefmt="%m/%d/%Y %H:%M:%S",
-        level=logging.INFO,
-    )
-
-    if os.path.exists(args.output_dir) and args.do_train:
-        if not args.overwrite_output_dir and bool(
-            [file for file in os.listdir(args.output_dir) if "features" not in file]
-        ):
-            raise ValueError(
-                "Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format(
-                    args.output_dir
-                )
-            )
-
-        if args.amp:
-            tf.config.optimizer.set_experimental_options({"auto_mixed_precision": True})
-
-    if args.tpu:
-        resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu=args.tpu)
-        tf.config.experimental_connect_to_cluster(resolver)
-        tf.tpu.experimental.initialize_tpu_system(resolver)
-        strategy = tf.distribute.experimental.TPUStrategy(resolver)
-        args.n_device = args.num_tpu_cores
-    elif len(args.gpus.split(",")) > 1:
-        args.n_device = len([f"/gpu:{gpu}" for gpu in args.gpus.split(",")])
-        strategy = tf.distribute.MirroredStrategy(devices=[f"/gpu:{gpu}" for gpu in args.gpus.split(",")])
-    elif args.no_cuda:
-        args.n_device = 1
-        strategy = tf.distribute.OneDeviceStrategy(device="/cpu:0")
-    else:
-        args.n_device = len(args.gpus.split(","))
-        strategy = tf.distribute.OneDeviceStrategy(device="/gpu:" + args.gpus.split(",")[0])
-
-    logging.warning(
-        "n_device: %s, distributed training: %s, 16-bits training: %s",
-        args.n_device,
-        bool(args.n_device > 1),
-        args.amp,
-    )
-
-    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,
-        cache_dir=args.cache_dir if args.cache_dir else None,
-    )
-
-    logging.info("Training/evaluation parameters %s", args)
-
-    if args.do_train:
-        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,
-        )
-
-        with strategy.scope():
-            model = model_class.from_pretrained(
-                args.model_name_or_path,
-                from_pt=bool(".bin" in args.model_name_or_path),
-                config=config,
-                cache_dir=args.cache_dir if args.cache_dir else None,
-            )
-            model.layers[-1].activation = tf.keras.activations.softmax
-
-        train_batch_size = args.per_device_train_batch_size * args.n_device
-        train_dataset, num_train_examples = load_and_cache_examples(args, tokenizer, evaluate=False)
-
-        train_dataset = train_dataset.batch(train_batch_size)
-        train_dataset = train_dataset.prefetch(buffer_size=train_batch_size)
-        train_dataset = strategy.experimental_distribute_dataset(train_dataset)
-
-        train(
-            args, strategy, train_dataset, tokenizer, model, num_train_examples, train_batch_size,
-        )
-
-        if not os.path.exists(args.output_dir):
-            os.makedirs(args.output_dir)
-
-        logging.info("Saving model to %s", args.output_dir)
-
-        model.save_pretrained(args.output_dir)
-        tokenizer.save_pretrained(args.output_dir)
-
-    if args.do_eval:
-        tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
-        checkpoints = []
-        results = []
-
-        if args.eval_all_checkpoints:
-            checkpoints = list(
-                os.path.dirname(c)
-                for c in sorted(
-                    glob.glob(args.output_dir + "/**/" + TF2_WEIGHTS_NAME, recursive=True),
-                    key=lambda f: int("".join(filter(str.isdigit, f)) or -1),
-                )
-            )
-
-        logging.info("Evaluate the following checkpoints: %s", checkpoints)
-
-        if len(checkpoints) == 0:
-            checkpoints.append(args.output_dir)
-
-        for checkpoint in checkpoints:
-            global_step = checkpoint.split("-")[-1] if re.match(".*checkpoint-[0-9]", checkpoint) else "final"
-
-            with strategy.scope():
-                model = model_class.from_pretrained(checkpoint)
-                results = evaluate(args, strategy, model, tokenizer, prefix=global_step)
-
-            result = dict((k + ("_{}".format(global_step) if global_step else ""), v) for k, v in results.items())
-            results.update(result)
-
-        logger.info("Results: {}".format(results))
-
-
-if __name__ == "__main__":
-    main()
@@ -0,0 +1,611 @@
+# coding=utf-8
+# Copyright 2019 The Google AI Language Team Authors and The HuggingFace Inc. team.
+# Copyright (c) 2019, 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.
+""" Finetuning the library models for sequence classification on GLUE (Bert, DistilBert, XLNet, RoBERTa)."""
+
+from __future__ import absolute_import, division, print_function
+
+import argparse
+import glob
+import logging
+import os
+import random
+
+import numpy as np
+import torch
+import torch_xla.core.xla_model as xm
+import torch_xla.debug.metrics as met
+import torch_xla.distributed.parallel_loader as pl
+import torch_xla.distributed.xla_multiprocessing as xmp
+from torch.utils.data import DataLoader, RandomSampler, TensorDataset
+from torch.utils.data.distributed import DistributedSampler
+from tqdm import tqdm, trange
+
+from transformers import (
+    WEIGHTS_NAME,
+    AdamW,
+    BertConfig,
+    BertForSequenceClassification,
+    BertTokenizer,
+    DistilBertConfig,
+    DistilBertForSequenceClassification,
+    DistilBertTokenizer,
+    RobertaConfig,
+    RobertaForSequenceClassification,
+    RobertaTokenizer,
+    XLMConfig,
+    XLMForSequenceClassification,
+    XLMTokenizer,
+    XLNetConfig,
+    XLNetForSequenceClassification,
+    XLNetTokenizer,
+    get_linear_schedule_with_warmup,
+)
+from transformers import glue_compute_metrics as compute_metrics
+from transformers import glue_convert_examples_to_features as convert_examples_to_features
+from transformers import glue_output_modes as output_modes
+from transformers import glue_processors as processors
+
+
+try:
+    # Only tensorboardX supports writing directly to gs://
+    from tensorboardX import SummaryWriter
+except ImportError:
+    from torch.utils.tensorboard import SummaryWriter
+
+
+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),
+}
+
+
+def set_seed(seed):
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+
+
+def get_sampler(dataset):
+    if xm.xrt_world_size() <= 1:
+        return RandomSampler(dataset)
+    return DistributedSampler(dataset, num_replicas=xm.xrt_world_size(), rank=xm.get_ordinal())
+
+
+def train(args, train_dataset, model, tokenizer, disable_logging=False):
+    """ Train the model """
+    if xm.is_master_ordinal():
+        # Only master writes to Tensorboard
+        tb_writer = SummaryWriter(args.tensorboard_logdir)
+
+    train_sampler = get_sampler(train_dataset)
+    dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size)
+
+    if args.max_steps > 0:
+        t_total = args.max_steps
+        args.num_train_epochs = args.max_steps // (len(dataloader) // args.gradient_accumulation_steps) + 1
+    else:
+        t_total = len(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,
+    )
+
+    # Train!
+    logger.info("***** Running training *****")
+    logger.info("  Num examples = %d", len(dataloader) * args.train_batch_size)
+    logger.info("  Num Epochs = %d", args.num_train_epochs)
+    logger.info("  Instantaneous batch size per TPU core = %d", args.train_batch_size)
+    logger.info(
+        "  Total train batch size (w. parallel, distributed & accumulation) = %d",
+        (args.train_batch_size * args.gradient_accumulation_steps * xm.xrt_world_size()),
+    )
+    logger.info("  Gradient Accumulation steps = %d", args.gradient_accumulation_steps)
+    logger.info("  Total optimization steps = %d", t_total)
+
+    global_step = 0
+    loss = None
+    model.zero_grad()
+    train_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=disable_logging)
+    set_seed(args.seed)  # Added here for reproductibility (even between python 2 and 3)
+    for epoch in train_iterator:
+        # tpu-comment: Get TPU parallel loader which sends data to TPU in background.
+        train_dataloader = pl.ParallelLoader(dataloader, [args.device]).per_device_loader(args.device)
+        epoch_iterator = tqdm(train_dataloader, desc="Iteration", total=len(dataloader), disable=disable_logging)
+        for step, batch in enumerate(epoch_iterator):
+
+            # Save model checkpoint.
+            if args.save_steps > 0 and global_step % args.save_steps == 0:
+                output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(global_step))
+                logger.info("Saving model checkpoint to %s", output_dir)
+
+                if xm.is_master_ordinal():
+                    if not os.path.exists(output_dir):
+                        os.makedirs(output_dir)
+                    torch.save(args, os.path.join(output_dir, "training_args.bin"))
+
+                # Barrier to wait for saving checkpoint.
+                xm.rendezvous("mid_training_checkpoint")
+                # model.save_pretrained needs to be called by all ordinals
+                model.save_pretrained(output_dir)
+
+            model.train()
+            inputs = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
+            if args.model_type != "distilbert":
+                # XLM, DistilBERT and RoBERTa don't use segment_ids
+                inputs["token_type_ids"] = batch[2] if args.model_type in ["bert", "xlnet"] else None
+            outputs = model(**inputs)
+            loss = outputs[0]  # model outputs are always tuple in transformers (see doc)
+
+            if args.gradient_accumulation_steps > 1:
+                loss = loss / args.gradient_accumulation_steps
+
+            loss.backward()
+
+            if (step + 1) % args.gradient_accumulation_steps == 0:
+                torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
+
+                xm.optimizer_step(optimizer)
+                scheduler.step()  # Update learning rate schedule
+                model.zero_grad()
+                global_step += 1
+
+                if args.logging_steps > 0 and global_step % args.logging_steps == 0:
+                    # Log metrics.
+                    results = {}
+                    if args.evaluate_during_training:
+                        results = evaluate(args, model, tokenizer, disable_logging=disable_logging)
+                    loss_scalar = loss.item()
+                    logger.info(
+                        "global_step: {global_step}, lr: {lr:.6f}, loss: {loss:.3f}".format(
+                            global_step=global_step, lr=scheduler.get_lr()[0], loss=loss_scalar
+                        )
+                    )
+                    if xm.is_master_ordinal():
+                        # tpu-comment: All values must be in CPU and not on TPU device
+                        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", loss_scalar, global_step)
+
+            if args.max_steps > 0 and global_step > args.max_steps:
+                epoch_iterator.close()
+                break
+        if args.metrics_debug:
+            # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
+            xm.master_print(met.metrics_report())
+        if args.max_steps > 0 and global_step > args.max_steps:
+            train_iterator.close()
+            break
+
+    if xm.is_master_ordinal():
+        tb_writer.close()
+    return global_step, loss.item()
+
+
+def evaluate(args, model, tokenizer, prefix="", disable_logging=False):
+    """Evaluate the model"""
+    if xm.is_master_ordinal():
+        # Only master writes to Tensorboard
+        tb_writer = SummaryWriter(args.tensorboard_logdir)
+
+    # 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,)
+
+    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_sampler = get_sampler(eval_dataset)
+
+        if not os.path.exists(eval_output_dir):
+            os.makedirs(eval_output_dir)
+
+        dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size, shuffle=False)
+        eval_dataloader = pl.ParallelLoader(dataloader, [args.device]).per_device_loader(args.device)
+
+        # Eval!
+        logger.info("***** Running evaluation {} *****".format(prefix))
+        logger.info("  Num examples = %d", len(dataloader) * args.eval_batch_size)
+        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", disable=disable_logging):
+            model.eval()
+
+            with torch.no_grad():
+                inputs = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
+                if args.model_type != "distilbert":
+                    # XLM, DistilBERT and RoBERTa don't use segment_ids
+                    inputs["token_type_ids"] = batch[2] if args.model_type in ["bert", "xlnet"] else None
+                outputs = model(**inputs)
+                batch_eval_loss, logits = outputs[:2]
+
+                eval_loss += batch_eval_loss
+            nb_eval_steps += 1
+            if preds is None:
+                preds = logits.detach().cpu().numpy()
+                out_label_ids = inputs["labels"].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)
+
+        # tpu-comment: Get all predictions and labels from all worker shards of eval dataset
+        preds = xm.mesh_reduce("eval_preds", preds, np.concatenate)
+        out_label_ids = xm.mesh_reduce("eval_out_label_ids", out_label_ids, np.concatenate)
+
+        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)
+        results["eval_loss"] = eval_loss.item()
+
+        output_eval_file = os.path.join(eval_output_dir, prefix, "eval_results.txt")
+        if xm.is_master_ordinal():
+            with open(output_eval_file, "w") as writer:
+                logger.info("***** Eval results {} *****".format(prefix))
+                for key in sorted(results.keys()):
+                    logger.info("  %s = %s", key, str(results[key]))
+                    writer.write("%s = %s\n" % (key, str(results[key])))
+                    tb_writer.add_scalar(f"{eval_task}/{key}", results[key])
+
+    if args.metrics_debug:
+        # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
+        xm.master_print(met.metrics_report())
+
+    if xm.is_master_ordinal():
+        tb_writer.close()
+
+    return results
+
+
+def load_and_cache_examples(args, task, tokenizer, evaluate=False):
+    if not xm.is_master_ordinal():
+        xm.rendezvous("load_and_cache_examples")
+
+    processor = processors[task]()
+    output_mode = output_modes[task]
+    cached_features_file = os.path.join(
+        args.cache_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),
+        ),
+    )
+
+    # Load data features from cache or dataset file
+    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"]:
+            # 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, max_length=args.max_seq_length, label_list=label_list, output_mode=output_mode,
+        )
+        logger.info("Saving features into cached file %s", cached_features_file)
+        torch.save(features, cached_features_file)
+
+    if xm.is_master_ordinal():
+        xm.rendezvous("load_and_cache_examples")
+
+    # Convert to Tensors and build dataset
+    all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
+    all_attention_mask = torch.tensor([f.attention_mask for f in features], dtype=torch.long)
+    all_token_type_ids = torch.tensor([f.token_type_ids for f in features], dtype=torch.long)
+    if output_mode == "classification":
+        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
+
+
+def main(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)
+        )
+
+    # tpu-comment: Get TPU/XLA Device
+    args.device = xm.xla_device()
+
+    # Setup logging
+    logging.basicConfig(
+        format="[xla:{}] %(asctime)s - %(levelname)s - %(name)s -   %(message)s".format(xm.get_ordinal()),
+        datefmt="%m/%d/%Y %H:%M:%S",
+        level=logging.INFO,
+    )
+    disable_logging = False
+    if not xm.is_master_ordinal() and args.only_log_master:
+        # Disable all non-master loggers below CRITICAL.
+        logging.disable(logging.CRITICAL)
+        disable_logging = True
+    logger.warning("Process rank: %s, device: %s, num_cores: %s", xm.get_ordinal(), args.device, args.num_cores)
+
+    # Set seed to have same initialization
+    set_seed(args.seed)
+
+    # Prepare GLUE task
+    args.task_name = args.task_name.lower()
+    if args.task_name not in processors:
+        raise ValueError("Task not found: %s" % (args.task_name))
+    processor = processors[args.task_name]()
+    args.output_mode = output_modes[args.task_name]
+    label_list = processor.get_labels()
+    num_labels = len(label_list)
+
+    if not xm.is_master_ordinal():
+        xm.rendezvous(
+            "download_only_once"
+        )  # Make sure only the first process in distributed training will download model & vocab
+
+    # Load pretrained model and tokenizer
+    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,
+        xla_device=True,
+    )
+    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 xm.is_master_ordinal():
+        xm.rendezvous("download_only_once")
+
+    # Send model to TPU/XLA device.
+    model.to(args.device)
+
+    logger.info("Training/evaluation parameters %s", args)
+
+    if args.do_train:
+        # Train the model.
+        train_dataset = load_and_cache_examples(args, args.task_name, tokenizer, evaluate=False)
+        global_step, tr_loss = train(args, train_dataset, model, tokenizer, disable_logging=disable_logging)
+        logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)
+
+        if xm.is_master_ordinal():
+            # Save trained model.
+            # Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
+
+            # Create output directory if needed
+            if not os.path.exists(args.output_dir):
+                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()`
+            tokenizer.save_pretrained(args.output_dir)
+            # Good practice: save your training arguments together with the trained.
+            torch.save(args, os.path.join(args.output_dir, "training_args.bin"))
+
+        xm.rendezvous("post_training_checkpoint")
+        # model.save_pretrained needs to be called by all ordinals
+        model.save_pretrained(args.output_dir)
+
+        # 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:
+        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))
+            )
+            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 = model_class.from_pretrained(checkpoint)
+            model.to(args.device)
+            result = evaluate(args, model, tokenizer, prefix=prefix, disable_logging=disable_logging)
+            result = dict((k + "_{}".format(global_step), v) for k, v in result.items())
+            results.update(result)
+
+    return results
+
+
+def get_args():
+    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.",
+    )
+
+    # TPU Parameters
+    parser.add_argument("--num_cores", default=8, type=int, help="Number of TPU cores to use (1 or 8).")
+    parser.add_argument("--metrics_debug", action="store_true", help="Whether to print debug metrics.")
+
+    # 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 and features file generated",
+    )
+    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("--train_batch_size", default=8, type=int, help="Per core batch size for training.")
+    parser.add_argument("--eval_batch_size", default=8, type=int, help="Per core batch size 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(
+        "--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("--tensorboard_logdir", default="./runs", type=str, help="Where to write tensorboard metrics.")
+    parser.add_argument("--logging_steps", type=int, default=50, help="Log every X update steps.")
+    parser.add_argument("--only_log_master", action="store_true", help="Whether to log only from each hosts master.")
+    parser.add_argument("--save_steps", type=int, default=50, help="Save checkpoint every X update 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(
+        "--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")
+
+    return parser.parse_args()
+
+
+def _mp_fn(rank, args):
+    main(args)
+
+
+def main_cli():
+    args = get_args()
+    xmp.spawn(_mp_fn, args=(args,), nprocs=args.num_cores)
+
+
+if __name__ == "__main__":
+    main_cli()
@@ -344,14 +344,7 @@ def load_and_cache_examples(args, task, tokenizer, evaluate=False):
            processor.get_test_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=False,
-            pad_token=tokenizer.convert_tokens_to_ids([tokenizer.pad_token])[0],
-            pad_token_segment_id=0,
+            examples, tokenizer, max_length=args.max_seq_length, label_list=label_list, output_mode=output_mode,
        )
        if args.local_rank in [-1, 0]:
            logger.info("Saving features into cached file %s", cached_features_file)
@@ -459,7 +452,7 @@ def main():
        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("--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(
@@ -530,7 +523,7 @@ 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)
@@ -0,0 +1,52 @@
+### Get Preprocessed CNN Data
+To be able to reproduce the authors' results on the CNN/Daily Mail dataset you first need to download both CNN and Daily Mail datasets [from Kyunghyun Cho's website](https://cs.nyu.edu/~kcho/DMQA/) (the links next to "Stories") in the same folder. Then uncompress the archives by running:
+
+```bash
+wget https://s3.amazonaws.com/datasets.huggingface.co/summarization/cnn_dm.tgz
+tar -xzvf cnn_dm.tgz
+```
+
+this should make a directory called cnn_dm/ with files like `test.source`. 
+To use your own data, copy that files format. Each article to be summarized is on its own line.
+
+### Evaluation
+To create summaries for each article in dataset, run:
+```bash
+python evaluate_cnn.py <path_to_test.source> cnn_test_summaries.txt
+```
+the default batch size, 8, fits in 16GB GPU memory, but may need to be adjusted to fit your system.
+
+
+### Training
+Run/modify `run_train.sh`
+    
+### Where is the code?
+The core model is in `src/transformers/modeling_bart.py`. This directory only contains examples.
+
+## (WIP) Rouge Scores
+
+### Stanford CoreNLP Setup
+```
+ptb_tokenize () {
+    cat $1 | java edu.stanford.nlp.process.PTBTokenizer -ioFileList -preserveLines > $2
+}
+
+sudo apt install openjdk-8-jre-headless
+sudo apt-get install ant
+wget http://nlp.stanford.edu/software/stanford-corenlp-full-2018-10-05.zip
+unzip stanford-corenlp-full-2018-10-05.zip
+cd stanford-corenlp-full-2018-10-05
+export CLASSPATH=stanford-corenlp-3.9.2.jar:stanford-corenlp-3.9.2-models.jar
+```
+Then run `ptb_tokenize` on `test.target` and your generated hypotheses.
+### Rouge Setup
+Install `files2rouge` following the instructions at [here](https://github.com/pltrdy/files2rouge).
+I also needed to run `sudo apt-get install libxml-parser-perl`
+
+```python
+from files2rouge import files2rouge
+from files2rouge import settings
+files2rouge.run(<path_to_tokenized_hypo>,
+                <path_to_tokenized_target>,
+               saveto='rouge_output.txt')
+```
@@ -0,0 +1,71 @@
+import argparse
+from pathlib import Path
+
+import torch
+from tqdm import tqdm
+
+from transformers import BartForConditionalGeneration, BartTokenizer
+
+
+DEFAULT_DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+
+def chunks(lst, n):
+    """Yield successive n-sized chunks from lst."""
+    for i in range(0, len(lst), n):
+        yield lst[i : i + n]
+
+
+def generate_summaries(
+    examples: list, out_file: str, model_name: str, batch_size: int = 8, device: str = DEFAULT_DEVICE
+):
+    fout = Path(out_file).open("w")
+    model = BartForConditionalGeneration.from_pretrained(model_name).to(device)
+    tokenizer = BartTokenizer.from_pretrained("bart-large")
+
+    max_length = 140
+    min_length = 55
+
+    for batch in tqdm(list(chunks(examples, batch_size))):
+        dct = tokenizer.batch_encode_plus(batch, max_length=1024, return_tensors="pt", pad_to_max_length=True)
+        summaries = model.generate(
+            input_ids=dct["input_ids"].to(device),
+            attention_mask=dct["attention_mask"].to(device),
+            num_beams=4,
+            length_penalty=2.0,
+            max_length=max_length + 2,  # +2 from original because we start at step=1 and stop before max_length
+            min_length=min_length + 1,  # +1 from original because we start at step=1
+            no_repeat_ngram_size=3,
+            early_stopping=True,
+            decoder_start_token_id=model.config.eos_token_id,
+        )
+        dec = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in summaries]
+        for hypothesis in dec:
+            fout.write(hypothesis + "\n")
+            fout.flush()
+
+
+def run_generate():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "source_path", type=str, help="like cnn_dm/test.source",
+    )
+    parser.add_argument(
+        "output_path", type=str, help="where to save summaries",
+    )
+    parser.add_argument(
+        "model_name", type=str, default="bart-large-cnn", help="like bart-large-cnn",
+    )
+    parser.add_argument(
+        "--device", type=str, required=False, default=DEFAULT_DEVICE, help="cuda, cuda:1, cpu etc.",
+    )
+    parser.add_argument(
+        "--bs", type=int, default=8, required=False, help="batch size: how many to summarize at a time",
+    )
+    args = parser.parse_args()
+    examples = [" " + x.rstrip() for x in open(args.source_path).readlines()]
+    generate_summaries(examples, args.output_path, args.model_name, batch_size=args.bs, device=args.device)
+
+
+if __name__ == "__main__":
+    run_generate()
@@ -0,0 +1,184 @@
+import argparse
+import glob
+import logging
+import os
+import time
+
+import torch
+from torch.utils.data import DataLoader
+
+from transformer_base import BaseTransformer, add_generic_args, generic_train, get_linear_schedule_with_warmup
+
+
+try:
+    from .utils import SummarizationDataset
+except ImportError:
+    from utils import SummarizationDataset
+
+
+logger = logging.getLogger(__name__)
+
+
+class SummarizationTrainer(BaseTransformer):
+
+    mode = "language-modeling"
+
+    def __init__(self, hparams):
+        super().__init__(hparams, num_labels=None, mode=self.mode)
+        self.dataset_kwargs: dict = dict(
+            data_dir=self.hparams.data_dir,
+            max_source_length=self.hparams.max_source_length,
+            max_target_length=self.hparams.max_target_length,
+        )
+
+    def forward(self, input_ids, attention_mask=None, decoder_input_ids=None, lm_labels=None):
+        return self.model(
+            input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, lm_labels=lm_labels,
+        )
+
+    def _step(self, batch):
+        pad_token_id = self.tokenizer.pad_token_id
+        source_ids, source_mask, y = batch["source_ids"], batch["source_mask"], batch["target_ids"]
+        y_ids = y[:, :-1].contiguous()
+        lm_labels = y[:, 1:].clone()
+        lm_labels[y[:, 1:] == pad_token_id] = -100
+        outputs = self(source_ids, attention_mask=source_mask, decoder_input_ids=y_ids, lm_labels=lm_labels,)
+
+        loss = outputs[0]
+
+        return loss
+
+    def training_step(self, batch, batch_idx):
+        loss = self._step(batch)
+
+        tensorboard_logs = {"train_loss": loss}
+        return {"loss": loss, "log": tensorboard_logs}
+
+    def validation_step(self, batch, batch_idx):
+        loss = self._step(batch)
+        return {"val_loss": loss}
+
+    def validation_end(self, outputs):
+        avg_loss = torch.stack([x["val_loss"] for x in outputs]).mean()
+        tensorboard_logs = {"val_loss": avg_loss}
+        return {"avg_val_loss": avg_loss, "log": tensorboard_logs}
+
+    def test_step(self, batch, batch_idx):
+        pad_token_id = self.tokenizer.pad_token_id
+        source_ids, source_mask, y = SummarizationDataset.trim_seq2seq_batch(batch, pad_token_id)
+        # NOTE: the following kwargs get more speed and lower quality summaries than those in evaluate_cnn.py
+        generated_ids = self.model.generate(
+            input_ids=source_ids,
+            attention_mask=source_mask,
+            num_beams=1,
+            max_length=80,
+            repetition_penalty=2.5,
+            length_penalty=1.0,
+            early_stopping=True,
+            use_cache=True,
+        )
+        preds = [
+            self.tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True)
+            for g in generated_ids
+        ]
+        target = [self.tokenizer.decode(t, skip_special_tokens=True, clean_up_tokenization_spaces=True) for t in y]
+        loss = self._step(batch)
+
+        return {"val_loss": loss, "preds": preds, "target": target}
+
+    def test_end(self, outputs):
+        return self.validation_end(outputs)
+
+    def test_epoch_end(self, outputs):
+        output_test_predictions_file = os.path.join(self.hparams.output_dir, "test_predictions.txt")
+        output_test_targets_file = os.path.join(self.hparams.output_dir, "test_targets.txt")
+        # write predictions and targets for later rouge evaluation.
+        with open(output_test_predictions_file, "w+") as p_writer, open(output_test_targets_file, "w+") as t_writer:
+            for output_batch in outputs:
+                p_writer.writelines(s + "\n" for s in output_batch["preds"])
+                t_writer.writelines(s + "\n" for s in output_batch["target"])
+            p_writer.close()
+            t_writer.close()
+
+        return self.test_end(outputs)
+
+    def get_dataloader(self, type_path: str, batch_size: int) -> DataLoader:
+        dataset = SummarizationDataset(self.tokenizer, type_path=type_path, **self.dataset_kwargs)
+        dataloader = DataLoader(dataset, batch_size=batch_size, collate_fn=dataset.collate_fn)
+        return dataloader
+
+    def train_dataloader(self) -> DataLoader:
+        dataloader = self.get_dataloader("train", batch_size=self.hparams.train_batch_size)
+        t_total = (
+            (len(dataloader.dataset) // (self.hparams.train_batch_size * max(1, self.hparams.n_gpu)))
+            // self.hparams.gradient_accumulation_steps
+            * float(self.hparams.num_train_epochs)
+        )
+        scheduler = get_linear_schedule_with_warmup(
+            self.opt, num_warmup_steps=self.hparams.warmup_steps, num_training_steps=t_total
+        )
+        self.lr_scheduler = scheduler
+        return dataloader
+
+    def val_dataloader(self) -> DataLoader:
+        return self.get_dataloader("val", batch_size=self.hparams.eval_batch_size)
+
+    def test_dataloader(self) -> DataLoader:
+        return self.get_dataloader("test", batch_size=self.hparams.eval_batch_size)
+
+    @staticmethod
+    def add_model_specific_args(parser, root_dir):
+        BaseTransformer.add_model_specific_args(parser, root_dir)
+        # Add BART specific options
+        parser.add_argument(
+            "--max_source_length",
+            default=1024,
+            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(
+            "--max_target_length",
+            default=56,
+            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(
+            "--data_dir",
+            default=None,
+            type=str,
+            required=True,
+            help="The input data dir. Should contain the dataset files for the CNN/DM summarization task.",
+        )
+        return parser
+
+
+def main(args):
+
+    # If output_dir not provided, a folder will be generated in pwd
+    if not args.output_dir:
+        args.output_dir = os.path.join("./results", f"{args.task}_{args.model_type}_{time.strftime('%Y%m%d_%H%M%S')}",)
+        os.makedirs(args.output_dir)
+    model = SummarizationTrainer(args)
+    trainer = generic_train(model, args)
+
+    # Optionally, predict on dev set and write to output_dir
+    if args.do_predict:
+        # See https://github.com/huggingface/transformers/issues/3159
+        # pl use this format to create a checkpoint:
+        # https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
+        # /pytorch_lightning/callbacks/model_checkpoint.py#L169
+        checkpoints = list(sorted(glob.glob(os.path.join(args.output_dir, "checkpointepoch=*.ckpt"), recursive=True)))
+        model = model.load_from_checkpoint(checkpoints[-1])
+        trainer.test(model)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    add_generic_args(parser, os.getcwd())
+    parser = SummarizationTrainer.add_model_specific_args(parser, os.getcwd())
+    args = parser.parse_args()
+
+    main(args)
@@ -0,0 +1,19 @@
+export OUTPUT_DIR_NAME=bart_sum
+export CURRENT_DIR=${PWD}
+export OUTPUT_DIR=${CURRENT_DIR}/${OUTPUT_DIR_NAME}
+
+# Make output directory if it doesn't exist
+mkdir -p $OUTPUT_DIR
+
+# Add parent directory to python path to access transformer_base.py
+export PYTHONPATH="../../":"${PYTHONPATH}"
+
+python finetune.py \
+--data_dir=./cnn-dailymail/cnn_dm \
+--model_type=bart \
+--model_name_or_path=bart-large \
+--learning_rate=3e-5 \
+--train_batch_size=4 \
+--eval_batch_size=4 \
+--output_dir=$OUTPUT_DIR \
+--do_train  $@
@@ -0,0 +1,33 @@
+# Script for verifying that run_bart_sum can be invoked from its directory
+
+# Get tiny dataset with cnn_dm format (4 examples for train, val, test)
+wget https://s3.amazonaws.com/datasets.huggingface.co/summarization/cnn_tiny.tgz
+tar -xzvf cnn_tiny.tgz
+rm cnn_tiny.tgz
+
+export OUTPUT_DIR_NAME=bart_utest_output
+export CURRENT_DIR=${PWD}
+export OUTPUT_DIR=${CURRENT_DIR}/${OUTPUT_DIR_NAME}
+
+# Make output directory if it doesn't exist
+mkdir -p $OUTPUT_DIR
+
+# Add parent directory to python path to access transformer_base.py and utils.py
+export PYTHONPATH="../../":"${PYTHONPATH}"
+python finetune.py \
+--data_dir=cnn_tiny/ \
+--model_type=bart \
+--model_name_or_path=sshleifer/bart-tiny-random \
+--learning_rate=3e-5 \
+--train_batch_size=2 \
+--eval_batch_size=2 \
+--output_dir=$OUTPUT_DIR \
+--num_train_epochs=1  \
+--n_gpu=0 \
+--do_train $@
+
+rm -rf cnn_tiny
+rm -rf $OUTPUT_DIR
+
+
+
@@ -0,0 +1,148 @@
+import argparse
+import logging
+import os
+import sys
+import tempfile
+import unittest
+from pathlib import Path
+from unittest.mock import patch
+
+from torch.utils.data import DataLoader
+
+from transformers import BartTokenizer
+
+from .evaluate_cnn import run_generate
+from .finetune import main
+from .utils import SummarizationDataset
+
+
+logging.basicConfig(level=logging.DEBUG)
+
+logger = logging.getLogger()
+
+DEFAULT_ARGS = {
+    "output_dir": "",
+    "fp16": False,
+    "fp16_opt_level": "O1",
+    "n_gpu": 1,
+    "n_tpu_cores": 0,
+    "max_grad_norm": 1.0,
+    "do_train": True,
+    "do_predict": False,
+    "gradient_accumulation_steps": 1,
+    "server_ip": "",
+    "server_port": "",
+    "seed": 42,
+    "model_type": "bart",
+    "model_name_or_path": "sshleifer/bart-tiny-random",
+    "config_name": "",
+    "tokenizer_name": "",
+    "cache_dir": "",
+    "do_lower_case": False,
+    "learning_rate": 3e-05,
+    "weight_decay": 0.0,
+    "adam_epsilon": 1e-08,
+    "warmup_steps": 0,
+    "num_train_epochs": 1,
+    "train_batch_size": 2,
+    "eval_batch_size": 2,
+    "max_source_length": 12,
+    "max_target_length": 12,
+}
+
+
+def _dump_articles(path: Path, articles: list):
+    with path.open("w") as f:
+        f.write("\n".join(articles))
+
+
+def make_test_data_dir():
+    tmp_dir = Path(tempfile.gettempdir())
+    articles = [" Sam ate lunch today", "Sams lunch ingredients"]
+    summaries = ["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
+    for split in ["train", "val", "test"]:
+        _dump_articles((tmp_dir / f"{split}.source"), articles)
+        _dump_articles((tmp_dir / f"{split}.target"), summaries)
+    return tmp_dir
+
+
+class TestBartExamples(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        stream_handler = logging.StreamHandler(sys.stdout)
+        logger.addHandler(stream_handler)
+        logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks
+        return cls
+
+    def test_bart_cnn_cli(self):
+        tmp = Path(tempfile.gettempdir()) / "utest_generations_bart_sum.hypo"
+        output_file_name = Path(tempfile.gettempdir()) / "utest_output_bart_sum.hypo"
+        articles = [" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
+        _dump_articles(tmp, articles)
+        testargs = ["evaluate_cnn.py", str(tmp), str(output_file_name), "sshleifer/bart-tiny-random"]
+        with patch.object(sys, "argv", testargs):
+            run_generate()
+            self.assertTrue(Path(output_file_name).exists())
+            os.remove(Path(output_file_name))
+
+    def test_bart_run_sum_cli(self):
+        args_d: dict = DEFAULT_ARGS.copy()
+        tmp_dir = make_test_data_dir()
+        output_dir = tempfile.mkdtemp(prefix="output_")
+        args_d.update(
+            data_dir=tmp_dir, model_type="bart", train_batch_size=2, eval_batch_size=2, n_gpu=0, output_dir=output_dir,
+        )
+        main(argparse.Namespace(**args_d))
+        args_d.update({"do_train": False, "do_predict": True})
+
+        main(argparse.Namespace(**args_d))
+        contents = os.listdir(output_dir)
+        expected_contents = {
+            "checkpointepoch=0.ckpt",
+            "test_results.txt",
+        }
+        created_files = {os.path.basename(p) for p in contents}
+        self.assertSetEqual(expected_contents, created_files)
+
+    def test_t5_run_sum_cli(self):
+        args_d: dict = DEFAULT_ARGS.copy()
+        tmp_dir = make_test_data_dir()
+        output_dir = tempfile.mkdtemp(prefix="output_")
+        args_d.update(
+            data_dir=tmp_dir,
+            model_type="t5",
+            model_name_or_path="patrickvonplaten/t5-tiny-random",
+            train_batch_size=2,
+            eval_batch_size=2,
+            n_gpu=0,
+            output_dir=output_dir,
+            do_predict=True,
+        )
+        main(argparse.Namespace(**args_d))
+
+        # args_d.update({"do_train": False, "do_predict": True})
+        # main(argparse.Namespace(**args_d))
+
+    def test_bart_summarization_dataset(self):
+        tmp_dir = Path(tempfile.gettempdir())
+        articles = [" Sam ate lunch today", "Sams lunch ingredients"]
+        summaries = ["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
+        _dump_articles((tmp_dir / "train.source"), articles)
+        _dump_articles((tmp_dir / "train.target"), summaries)
+        tokenizer = BartTokenizer.from_pretrained("bart-large")
+        max_len_source = max(len(tokenizer.encode(a)) for a in articles)
+        max_len_target = max(len(tokenizer.encode(a)) for a in summaries)
+        trunc_target = 4
+        train_dataset = SummarizationDataset(
+            tokenizer, data_dir=tmp_dir, type_path="train", max_source_length=20, max_target_length=trunc_target,
+        )
+        dataloader = DataLoader(train_dataset, batch_size=2, collate_fn=train_dataset.collate_fn)
+        for batch in dataloader:
+            self.assertEqual(batch["source_mask"].shape, batch["source_ids"].shape)
+            # show that articles were trimmed.
+            self.assertEqual(batch["source_ids"].shape[1], max_len_source)
+            self.assertGreater(20, batch["source_ids"].shape[1])  # trimmed significantly
+
+            # show that targets were truncated
+            self.assertEqual(batch["target_ids"].shape[1], trunc_target)  # Truncated
+            self.assertGreater(max_len_target, trunc_target)  # Truncated
@@ -0,0 +1,56 @@
+import os
+
+import torch
+from torch.utils.data import Dataset
+
+from transformers.tokenization_utils import trim_batch
+
+
+def encode_file(tokenizer, data_path, max_length, pad_to_max_length=True, return_tensors="pt"):
+    examples = []
+    with open(data_path, "r") as f:
+        for text in f.readlines():
+            tokenized = tokenizer.batch_encode_plus(
+                [text], max_length=max_length, pad_to_max_length=pad_to_max_length, return_tensors=return_tensors,
+            )
+            examples.append(tokenized)
+    return examples
+
+
+class SummarizationDataset(Dataset):
+    def __init__(
+        self,
+        tokenizer,
+        data_dir="./cnn-dailymail/cnn_dm/",
+        type_path="train",
+        max_source_length=1024,
+        max_target_length=56,
+    ):
+        super().__init__()
+        self.tokenizer = tokenizer
+        self.source = encode_file(tokenizer, os.path.join(data_dir, type_path + ".source"), max_source_length)
+        self.target = encode_file(tokenizer, os.path.join(data_dir, type_path + ".target"), max_target_length)
+
+    def __len__(self):
+        return len(self.source)
+
+    def __getitem__(self, index):
+        source_ids = self.source[index]["input_ids"].squeeze()
+        target_ids = self.target[index]["input_ids"].squeeze()
+        src_mask = self.source[index]["attention_mask"].squeeze()
+        return {"source_ids": source_ids, "source_mask": src_mask, "target_ids": target_ids}
+
+    @staticmethod
+    def trim_seq2seq_batch(batch, pad_token_id):
+        y = trim_batch(batch["target_ids"], pad_token_id)
+        source_ids, source_mask = trim_batch(batch["source_ids"], pad_token_id, attention_mask=batch["source_mask"])
+        return source_ids, source_mask, y
+
+    def collate_fn(self, batch):
+        input_ids = torch.stack([x["source_ids"] for x in batch])
+        masks = torch.stack([x["source_mask"] for x in batch])
+        target_ids = torch.stack([x["target_ids"] for x in batch])
+        pad_token_id = self.tokenizer.pad_token_id
+        y = trim_batch(target_ids, pad_token_id)
+        source_ids, source_mask = trim_batch(input_ids, pad_token_id, attention_mask=masks)
+        return {"source_ids": source_ids, "source_mask": source_mask, "target_ids": y}
@@ -15,7 +15,7 @@ pip install nltk py-rouge
 cd examples/summarization
 ```

-## Reproduce the authors' results on ROUGE
+## Reproduce the authors'  ROUGE score

 To be able to reproduce the authors' results on the CNN/Daily Mail dataset you first need to download both CNN and Daily Mail datasets [from Kyunghyun Cho's website](https://cs.nyu.edu/~kcho/DMQA/) (the links next to "Stories") in the same folder. Then uncompress the archives by running:

@@ -23,7 +23,7 @@ logger = logging.getLogger(__name__)


 BERTABS_FINETUNED_CONFIG_MAP = {
-    "bertabs-finetuned-cnndm": "https://s3.amazonaws.com/models.huggingface.co/bert/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization-config.json",
+    "bertabs-finetuned-cnndm": "https://s3.amazonaws.com/models.huggingface.co/bert/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/config.json",
 }


@@ -157,7 +157,7 @@ def convert_bertabs_checkpoints(path_to_checkpoints, dump_path):
    # directory structure. We save the state_dict instead.
    logging.info("saving the model's state dictionary")
    torch.save(
-        new_model.state_dict(), "bertabs-finetuned-cnndm-extractive-abstractive-summarization-pytorch_model.bin"
+        new_model.state_dict(), "./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin"
    )


@@ -34,7 +34,7 @@ from transformers import BertConfig, BertModel, PreTrainedModel
 MAX_SIZE = 5000

 BERTABS_FINETUNED_MODEL_MAP = {
-    "bertabs-finetuned-cnndm": "https://s3.amazonaws.com/models.huggingface.co/bert/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization-pytorch_model.bin",
+    "bertabs-finetuned-cnndm": "https://s3.amazonaws.com/models.huggingface.co/bert/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin",
 }


@@ -303,7 +303,7 @@ class TransformerDecoderLayer(nn.Module):
        self.layer_norm_2 = nn.LayerNorm(d_model, eps=1e-6)
        self.drop = nn.Dropout(dropout)
        mask = self._get_attn_subsequent_mask(MAX_SIZE)
-        # Register self.mask as a buffer in TransformerDecoderLayer, so
+        # Register self.mask as a saved_state in TransformerDecoderLayer, so
        # it gets TransformerDecoderLayer's cuda behavior automatically.
        self.register_buffer("mask", mask)

@@ -844,7 +844,7 @@ class Translator(object):
            dec_out, dec_states = self.model.decoder(decoder_input, src_features, dec_states, step=step)

            # Generator forward.
-            log_probs = self.generator.forward(dec_out.transpose(0, 1).squeeze(0))
+            log_probs = self.generator(dec_out.transpose(0, 1).squeeze(0))
            vocab_size = log_probs.size(-1)

            if step < min_length: