using black -t py35

optional dependency
code formating
2019-12-27 18:40:41 +01:00 · 2019-12-27 15:06:16 +01:00 · 2019-12-27 13:14:46 +01:00 · 2019-12-27 13:14:38 +01:00 · 2019-12-27 13:06:36 +01:00 · 2019-12-27 13:06:21 +01:00
423 changed files with 18138 additions and 35186 deletions
@@ -3,18 +3,31 @@ jobs:
    run_tests_torch_and_tf:
        working_directory: ~/transformers
        docker:
-            - image: circleci/python:3.6
+            - image: circleci/python:3.5
        environment:
            OMP_NUM_THREADS: 1
        resource_class: xlarge
        parallelism: 1
        steps:
            - checkout
-            - run: sudo pip install .[sklearn,tf-cpu,torch,testing]
+            - run: sudo pip install .[sklearn,tf,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:
@@ -39,14 +52,14 @@ jobs:
        parallelism: 1
        steps:
            - checkout
-            - run: sudo pip install .[sklearn,tf-cpu,testing]
+            - run: sudo pip install .[sklearn,tf,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:3.6
+            - image: circleci/python:3.5
        environment:
            RUN_CUSTOM_TOKENIZERS: yes
        steps:
@@ -56,7 +69,7 @@ jobs:
    run_examples_torch:
        working_directory: ~/transformers
        docker:
-            - image: circleci/python:3.6
+            - image: circleci/python:3.5
        environment:
            OMP_NUM_THREADS: 1
        resource_class: xlarge
@@ -69,7 +82,7 @@ jobs:
    deploy_doc:
        working_directory: ~/transformers
        docker:
-            - image: circleci/python:3.6
+            - image: circleci/python:3.5
        steps:
            - add_ssh_keys:
                fingerprints:
@@ -88,13 +101,13 @@ jobs:
            # we need a version of isort with https://github.com/timothycrosley/isort/pull/1000
            - run: sudo pip install git+git://github.com/timothycrosley/isort.git@e63ae06ec7d70b06df9e528357650281a3d3ec22#egg=isort
            - run: sudo pip install .[tf,torch,quality]
-            - run: black --check --line-length 119 --target-version py35 examples templates tests src utils
+            - run: black --check --line-length 119 examples templates tests src utils
            - run: isort --check-only --recursive examples templates tests src utils
            - run: flake8 examples templates tests src utils
    check_repository_consistency:
        working_directory: ~/transformers
        docker:
-            - image: circleci/python:3.6
+            - image: circleci/python:3.5
        resource_class: small
        parallelism: 1
        steps:
@@ -118,3 +131,13 @@ workflows:
            - run_tests_torch
            - run_tests_tf
            - 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
@@ -25,5 +25,3 @@ 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
@@ -1,17 +1,17 @@
 ---
-name: "\U0001F5A5 New benchmark"
-about: Benchmark a part of this library and share your results
+name: "\U0001F5A5 New Benchmark"
+about: You benchmark a part of this library and would like to share your results
 title: "[Benchmark]"
 labels: ''
 assignees: ''

 ---

-# 🖥 Benchmarking `transformers`
+# Benchmarking Transformers

 ## Benchmark

-Which part of `transformers` did you benchmark?
+Which part of Transformers did you benchmark?

 ## Set-up

@@ -1,5 +1,5 @@
 ---
-name: "\U0001F31F New model addition"
+name: "\U0001F31FNew model addition"
 about: Submit a proposal/request to implement a new Transformer-based model
 title: ''
 labels: ''
@@ -7,14 +7,18 @@ assignees: ''

 ---

-# 🌟 New model addition
+# 🌟New model addition

 ## Model description

 <!-- Important information -->

-## Open source status
+## Open Source status

 * [ ] the model implementation is available: (give details)
 * [ ] the model weights are available: (give details)
-* [ ] who are the authors: (mention them, if possible by @gh-username)
+* [ ] who are the authors: (mention them)
+
+## Additional context
+
+<!-- Add any other context about the problem here. -->
@@ -1,29 +1,29 @@
 ---
 name: "\U0001F41B Bug Report"
-about: Submit a bug report to help us improve transformers
+about: Submit a bug report to help us improve PyTorch Transformers
 title: ''
 labels: ''
 assignees: ''

 ---

-# 🐛 Bug
+## 🐛 Bug

-## Information
+<!-- Important information -->

-Model I am using (Bert, XLNet ...):
+Model I am using (Bert, XLNet....):

-Language I am using the model on (English, Chinese ...):
+Language I am using the model on (English, Chinese....):

-The problem arises when using:
-* [ ] the official example scripts: (give details below)
-* [ ] my own modified scripts: (give details below)
+The problem arise when using:
+* [ ] the official example scripts: (give details)
+* [ ] my own modified scripts: (give details)

 The tasks I am working on is:
 * [ ] an official GLUE/SQUaD task: (give the name)
-* [ ] my own task or dataset: (give details below)
+* [ ] my own task or dataset: (give details)

-## To reproduce
+## To Reproduce

 Steps to reproduce the behavior:

@@ -31,22 +31,22 @@ Steps to reproduce the behavior:
 2.
 3.

-<!-- If you have code snippets, error messages, stack traces please provide them here as well.
-     Important! Use code tags to correctly format your code. See https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting
-     Do not use screenshots, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.-->
+<!-- If you have a code sample, error messages, stack traces, please provide it here as well. -->

 ## Expected behavior

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

-## 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?:
+## Environment
+
+* OS:
+* Python version:
+* PyTorch version:
+* PyTorch Transformers version (or branch):
+* Using GPU ?
+* Distributed of parallel setup ?
+* Any other relevant information:
+
+## Additional context
+
+<!-- Add any other context about the problem here. -->
@@ -1,25 +1,20 @@
 ---
-name: "\U0001F680 Feature request"
-about: Submit a proposal/request for a new transformers feature
+name: "\U0001F680 Feature Request"
+about: Submit a proposal/request for a new PyTorch Transformers feature
 title: ''
 labels: ''
 assignees: ''

 ---

-# 🚀 Feature request
+## 🚀 Feature

-<!-- A clear and concise description of the feature proposal.
-     Please provide a link to the paper and code in case they exist. -->
+<!-- A clear and concise description of the feature proposal. Please provide a link to the paper and code in case they exist. -->

 ## Motivation

-<!-- Please outline the motivation for the proposal. Is your feature request
-     related to a problem? e.g., I'm always frustrated when [...]. If this is related
-     to another GitHub issue, please link here too. -->
+<!-- Please outline the motivation for the proposal. Is your feature request related to a problem? e.g., I'm always frustrated when [...]. If this is related to another GitHub issue, please link here too. -->

-## Your contribution
+## Additional context

-<!-- Is there any way that you could help, e.g. by submitting a PR?
-     Make sure to read the CONTRIBUTING.MD readme:
-     https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md -->
+<!-- Add any other context or screenshots about the feature request here. -->
@@ -1,57 +1,47 @@
 ---
-name: "\U0001F4DA Migration from pytorch-pretrained-bert or pytorch-transformers"
-about: Report a problem when migrating from pytorch-pretrained-bert or pytorch-transformers to transformers
+name: "\U0001F4DA Migration from PyTorch-pretrained-Bert"
+about: Report a problem when migrating from PyTorch-pretrained-Bert to Transformers
 title: ''
 labels: ''
 assignees: ''

 ---

-# 📚 Migration
-
-## Information
+## 📚 Migration

 <!-- Important information -->

-Model I am using (Bert, XLNet ...):
+Model I am using (Bert, XLNet....):

-Language I am using the model on (English, Chinese ...):
+Language I am using the model on (English, Chinese....):

-The problem arises when using:
-* [ ] the official example scripts: (give details below)
-* [ ] my own modified scripts: (give details below)
+The problem arise when using:
+* [ ] the official example scripts: (give details)
+* [ ] my own modified scripts: (give details)

 The tasks I am working on is:
 * [ ] an official GLUE/SQUaD task: (give the name)
-* [ ] my own task or dataset: (give details below)
+* [ ] my own task or dataset: (give details)

-## Details
+Details of the issue:

-<!-- A clear and concise description of the migration issue.
-    If you have code snippets, please provide it here as well.
-    Important! Use code tags to correctly format your code. See https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting
-    Do not use screenshots, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.
-    -->
+<!-- A clear and concise description of the migration issue. If you have code snippets, please provide it here as well. -->

-## Environment info
-<!-- You can run the command `python transformers-cli env` and copy-and-paste its output below.
-     Don't forget to fill out the missing fields in that output! -->
- 
- `transformers` version:
- Platform:
- Python version:
- PyTorch version (GPU?):
- Tensorflow version (GPU?):
- Using GPU in script?:
- Using distributed or parallel set-up in script?:
-
-<!-- IMPORTANT: which version of the former library do you use? -->
-* `pytorch-transformers` or `pytorch-pretrained-bert` version (or branch):
+## Environment

+* OS:
+* Python version:
+* PyTorch version:
+* PyTorch Transformers version (or branch):
+* Using GPU ?
+* Distributed of parallel setup ?
+* Any other relevant information:

 ## Checklist

 - [ ] I have read the migration guide in the readme.
- ([pytorch-transformers](https://github.com/huggingface/transformers#migrating-from-pytorch-transformers-to-transformers);
-  [pytorch-pretrained-bert](https://github.com/huggingface/transformers#migrating-from-pytorch-pretrained-bert-to-transformers))
 - [ ] I checked if a related official extension example runs on my machine.
+
+## Additional context
+
+<!-- Add any other context about the problem here. -->
@@ -1,29 +1,12 @@
 ---
-name: "❓ Questions & Help"
-about: Post your general questions on Stack Overflow tagged huggingface-transformers
+name: "❓Questions & Help"
+about: Start a general discussion related to PyTorch Transformers
 title: ''
 labels: ''
 assignees: ''

 ---

-# ❓ Questions & Help
+## ❓ Questions & Help

-<!-- The GitHub issue tracker is primarly intended for bugs, feature requests,
-     new models and benchmarks, and migration questions. For all other questions,
-     we direct you to Stack Overflow (SO) where a whole community of PyTorch and
-     Tensorflow enthusiast can help you out. Make sure to tag your question with the
-     right deep learning framework as well as the huggingface-transformers tag: 
-     https://stackoverflow.com/questions/tagged/huggingface-transformers 
-     
-     If your question wasn't answered after a period of time on Stack Overflow, you
-     can always open a question on GitHub. You should then link to the SO question 
-     that you posted.
-     -->
-
-## Details
-<!-- Description of your issue -->
-
-<!-- You should first ask your question on SO, and only if
-     you didn't get an answer ask it here on GitHub. -->
-**A link to original question on Stack Overflow**: 
+<!-- A clear and concise description of the question. -->
@@ -1,19 +0,0 @@
-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]
-
-
-
@@ -1,50 +0,0 @@
-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/
@@ -1,51 +0,0 @@
-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/
-        
@@ -139,9 +139,3 @@ serialization_dir
 # emacs
 *.*~
 debug.env
-
-# vim
-.*.swp
-
-#ctags
-tags
@@ -41,10 +41,14 @@ 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 automatically, you can run the following command:
+To get the OS and software versions, execute the following code and copy-paste
+the output:

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

 ### Do you want to implement a new model?
@@ -198,13 +202,11 @@ 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 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. 
+4. Make sure pre-existing tests still pass;
+5. Add high-coverage tests. No quality test, no merge;
 6. All public methods must have informative docstrings;

+
 ### Tests

 You can run 🤗 Transformers tests with `unittest` or `pytest`.
@@ -3,14 +3,14 @@
 # Check that source code meets quality standards

 quality:
-	black --check --line-length 119 --target-version py35 examples templates tests src utils
+	black --check --line-length 119 examples templates tests src utils
 	isort --check-only --recursive examples templates tests src utils
 	flake8 examples templates tests src utils

 # Format source code automatically

 style:
-	black --line-length 119 --target-version py35 examples templates tests src utils
+	black --line-length 119 examples templates tests src utils
 	isort --recursive examples templates tests src utils

 # Run tests for the library
@@ -24,8 +24,6 @@

 🤗 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
@@ -62,11 +60,11 @@ 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][(v2.5.0)](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 |
+| [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 |

 ## Installation

-This repo is tested on Python 3.6+, PyTorch 1.0.0+ and TensorFlow 2.0.0-rc1
+This repo is tested on Python 3.5+, 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/).

@@ -162,10 +160,7 @@ At some point in the future, you'll be able to seamlessly move from pre-training
 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.
 14. **[MMBT](https://github.com/facebookresearch/mmbt/)** (from Facebook), released together with the paper a [Supervised Multimodal Bitransformers for Classifying Images and Text](https://arxiv.org/pdf/1909.02950.pdf) by Douwe Kiela, Suvrat Bhooshan, Hamed Firooz, Davide Testuggine.
-15. **[FlauBERT](https://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.
-16. **[BART](https://github.com/pytorch/fairseq/tree/master/examples/bart)** (from Facebook) released with the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/pdf/1910.13461.pdf) by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer.
-17. **[Other community models](https://huggingface.co/models)**, contributed by the [community](https://huggingface.co/users).
-18. 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. 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).

@@ -196,7 +191,7 @@ 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-cased'),
+          (DistilBertModel, DistilBertTokenizer, 'distilbert-base-uncased'),
          (RobertaModel,    RobertaTokenizer,    'roberta-base'),
          (XLMRobertaModel, XLMRobertaTokenizer, 'xlm-roberta-base'),
         ]
@@ -470,9 +465,9 @@ python ./examples/run_generation.py \

 ## Quick tour of model sharing

-Starting with `v2.2.2`, you can now upload and share your fine-tuned models with the community, using the <abbr title="Command-line interface">CLI</abbr> that's built-in to the library.
+New in `v2.2.2`: you can now upload and share your fine-tuned models with the community, using the <abbr title="Command-line interface">CLI</abbr> that's built-in to the library.

-**First, create an account on [https://huggingface.co/join](https://huggingface.co/join)**. Optionally, join an existing organization or create a new one. Then:
+**First, create an account on [https://huggingface.co/join](https://huggingface.co/join)**. Then:

 ```shell
 transformers-cli login
@@ -491,37 +486,21 @@ transformers-cli upload ./config.json [--filename folder/foobar.json]
 # (you can optionally override its filename, which can be nested inside a folder)
 ```

-If you want your model to be namespaced by your organization name rather than your username, add the following flag to any command:
-```shell
--organization organization_name
-```
-
-Your model will then be accessible through its identifier, a concatenation of your username (or organization name) and the folder name above:
+Your model will then be accessible through its identifier, a concatenation of your username and the folder name above:
 ```python
-"username/pretrained_model"
-# or if an org:
-"organization_name/pretrained_model"
+"username/model_name"
 ```

-**Please add a README.md model card** to the repo under `model_cards/` with: model description, training params (dataset, preprocessing, hardware used, hyperparameters), evaluation results, intended uses & limitations, etc.
-
-Your model now has a page on huggingface.co/models 🔥
-
 Anyone can load it from code:
 ```python
-tokenizer = AutoTokenizer.from_pretrained("namespace/pretrained_model")
-model = AutoModel.from_pretrained("namespace/pretrained_model")
+tokenizer = AutoTokenizer.from_pretrained("username/pretrained_model")
+model = AutoModel.from_pretrained("username/pretrained_model")
 ```

-List all your files on S3:
+Finally, list all your files on S3:
 ```shell
-transformers-cli s3 ls
-```
-
-You can also delete unneeded files:
-
-```shell
-transformers-cli s3 rm …
+transformers-cli ls
+# List all your S3 objects.
 ```

 ## Quick tour of pipelines
@@ -534,9 +513,8 @@ You can create `Pipeline` objects for the following down-stream tasks:
 - `feature-extraction`: Generates a tensor representation for the input sequence
 - `ner`: Generates named entity mapping for each word in the input sequence.
 - `sentiment-analysis`: Gives the polarity (positive / negative) of the whole input sequence.
- - `text-classification`: Initialize a `TextClassificationPipeline` directly, or see `sentiment-analysis` for an example.
- - `question-answering`: Provided some context and a question refering to the context, it will extract the answer to the question in the context.
- - `fill-mask`: Takes an input sequence containing a masked token (e.g. `<mask>`) and return list of most probable filled sequences, with their probabilities.
+ - `question-answering`: Provided some context and a question refering to the context, it will extract the answer to the question
+ in the context.

 ```python
 from transformers import pipeline
@@ -686,7 +664,7 @@ 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},
@@ -0,0 +1,7 @@
+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
@@ -1,26 +0,0 @@
-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"]
@@ -1,26 +0,0 @@
-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"]
@@ -1,25 +0,0 @@
-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"]
@@ -1,25 +0,0 @@
-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"]
@@ -1,25 +0,0 @@
-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"]
@@ -1,25 +0,0 @@
-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"]
@@ -1,25 +1,3 @@
-/* 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;
@@ -216,41 +194,3 @@ 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;
-    }
-}
@@ -58,84 +58,6 @@ 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
@@ -152,8 +74,6 @@ function onLoad() {
    addCustomFooter();
    addGithubButton();
    parseGithubButtons();
-    addHfMenu();
-    platformToggle();
 }

 window.addEventListener("load", onLoad);
@@ -20,13 +20,13 @@ sys.path.insert(0, os.path.abspath('../../src'))
 # -- Project information -----------------------------------------------------

 project = u'transformers'
-copyright = u'2020, huggingface'
+copyright = u'2019, huggingface'
 author = u'huggingface'

 # The short X.Y version
 version = u''
 # The full version, including alpha/beta/rc tags
-release = u'2.6.0'
+release = u'2.3.0'


 # -- General configuration ---------------------------------------------------
@@ -105,12 +105,6 @@ 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 ---------------------------------------------

@@ -3,12 +3,6 @@ Converting Tensorflow Checkpoints

 A command-line interface is provided to convert original Bert/GPT/GPT-2/Transformer-XL/XLNet/XLM checkpoints in models than be loaded using the ``from_pretrained`` methods of the library.

-.. note::
-    Since 2.3.0 the conversion script is now part of the transformers CLI (**transformers-cli**)
-    available in any transformers >= 2.3.0 installation.
-
-    The documentation below reflects the **transformers-cli convert** command format.
-
 BERT
 ^^^^

@@ -26,10 +20,10 @@ Here is an example of the conversion process for a pre-trained ``BERT-Base Uncas

   export BERT_BASE_DIR=/path/to/bert/uncased_L-12_H-768_A-12

-   transformers-cli convert --model_type bert \
-     --tf_checkpoint $BERT_BASE_DIR/bert_model.ckpt \
-     --config $BERT_BASE_DIR/bert_config.json \
-     --pytorch_dump_output $BERT_BASE_DIR/pytorch_model.bin
+   transformers bert \
+     $BERT_BASE_DIR/bert_model.ckpt \
+     $BERT_BASE_DIR/bert_config.json \
+     $BERT_BASE_DIR/pytorch_model.bin

 You can download Google's pre-trained models for the conversion `here <https://github.com/google-research/bert#pre-trained-models>`__.

@@ -42,12 +36,10 @@ Here is an example of the conversion process for a pre-trained OpenAI GPT model,

   export OPENAI_GPT_CHECKPOINT_FOLDER_PATH=/path/to/openai/pretrained/numpy/weights

-   transformers-cli convert --model_type gpt \
-     --tf_checkpoint $OPENAI_GPT_CHECKPOINT_FOLDER_PATH \
-     --pytorch_dump_output $PYTORCH_DUMP_OUTPUT \
-     [--config OPENAI_GPT_CONFIG] \
-     [--finetuning_task_name OPENAI_GPT_FINETUNED_TASK] \
-
+   transformers gpt \
+     $OPENAI_GPT_CHECKPOINT_FOLDER_PATH \
+     $PYTORCH_DUMP_OUTPUT \
+     [OPENAI_GPT_CONFIG]

 OpenAI GPT-2
 ^^^^^^^^^^^^
@@ -58,11 +50,10 @@ Here is an example of the conversion process for a pre-trained OpenAI GPT-2 mode

   export OPENAI_GPT2_CHECKPOINT_PATH=/path/to/gpt2/pretrained/weights

-   transformers-cli convert --model_type gpt2 \
-     --tf_checkpoint $OPENAI_GPT2_CHECKPOINT_PATH \
-     --pytorch_dump_output $PYTORCH_DUMP_OUTPUT \
-     [--config OPENAI_GPT2_CONFIG] \
-     [--finetuning_task_name OPENAI_GPT2_FINETUNED_TASK]
+   transformers gpt2 \
+     $OPENAI_GPT2_CHECKPOINT_PATH \
+     $PYTORCH_DUMP_OUTPUT \
+     [OPENAI_GPT2_CONFIG]

 Transformer-XL
 ^^^^^^^^^^^^^^
@@ -73,28 +64,27 @@ Here is an example of the conversion process for a pre-trained Transformer-XL mo

   export TRANSFO_XL_CHECKPOINT_FOLDER_PATH=/path/to/transfo/xl/checkpoint

-   transformers-cli convert --model_type transfo_xl \
-     --tf_checkpoint $TRANSFO_XL_CHECKPOINT_FOLDER_PATH \
-     --pytorch_dump_output $PYTORCH_DUMP_OUTPUT \
-     [--config TRANSFO_XL_CONFIG] \
-     [--finetuning_task_name TRANSFO_XL_FINETUNED_TASK]
+   transformers transfo_xl \
+     $TRANSFO_XL_CHECKPOINT_FOLDER_PATH \
+     $PYTORCH_DUMP_OUTPUT \
+     [TRANSFO_XL_CONFIG]


 XLNet
 ^^^^^

-Here is an example of the conversion process for a pre-trained XLNet model:
+Here is an example of the conversion process for a pre-trained XLNet model, fine-tuned on STS-B using the TensorFlow script:

 .. code-block:: shell

   export TRANSFO_XL_CHECKPOINT_PATH=/path/to/xlnet/checkpoint
   export TRANSFO_XL_CONFIG_PATH=/path/to/xlnet/config

-   transformers-cli convert --model_type xlnet \
-     --tf_checkpoint $TRANSFO_XL_CHECKPOINT_PATH \
-     --config $TRANSFO_XL_CONFIG_PATH \
-     --pytorch_dump_output $PYTORCH_DUMP_OUTPUT \
-     [--finetuning_task_name XLNET_FINETUNED_TASK] \
+   transformers xlnet \
+     $TRANSFO_XL_CHECKPOINT_PATH \
+     $TRANSFO_XL_CONFIG_PATH \
+     $PYTORCH_DUMP_OUTPUT \
+     STS-B \


 XLM
@@ -106,8 +96,6 @@ Here is an example of the conversion process for a pre-trained XLM model:

   export XLM_CHECKPOINT_PATH=/path/to/xlm/checkpoint

-   transformers-cli convert --model_type xlm \
-     --tf_checkpoint $XLM_CHECKPOINT_PATH \
-     --pytorch_dump_output $PYTORCH_DUMP_OUTPUT
-    [--config XML_CONFIG] \
-    [--finetuning_task_name XML_FINETUNED_TASK]
+   transformers xlm \
+     $XLM_CHECKPOINT_PATH \
+     $PYTORCH_DUMP_OUTPUT \
@@ -1,145 +0,0 @@
-Glossary
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Every model is different yet bears similarities with the others. Therefore most models use the same inputs, which are
-detailed here alongside usage examples.
-
-Input IDs
--------------------------
-
-The input ids are often the only required parameters to be passed to the model as input. *They are token indices,
-numerical representations of tokens building the sequences that will be used as input by the model*.
-
-Each tokenizer works differently but the underlying mechanism remains the same. Here's an example using the BERT
-tokenizer, which is a `WordPiece <https://arxiv.org/pdf/1609.08144.pdf>`__ tokenizer:
-
-::
-
-    from transformers import BertTokenizer
-    tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
-
-    sequence = "A Titan RTX has 24GB of VRAM"
-
-The tokenizer takes care of splitting the sequence into tokens available in the tokenizer vocabulary.
-
-::
-
-    # Continuation of the previous script
-    tokenized_sequence = tokenizer.tokenize(sequence)
-    assert tokenized_sequence == ['A', 'Titan', 'R', '##T', '##X', 'has', '24', '##GB', 'of', 'V', '##RA', '##M']
-
-These tokens can then be converted into IDs which are understandable by the model. Several methods are available for
-this, the recommended being `encode` or `encode_plus`, which leverage the Rust implementation of
-`huggingface/tokenizers <https://github.com/huggingface/tokenizers>`__ for peak performance.
-
-::
-
-    # Continuation of the previous script
-    encoded_sequence = tokenizer.encode(sequence)
-    assert encoded_sequence == [101, 138, 18696, 155, 1942, 3190, 1144, 1572, 13745, 1104, 159, 9664, 2107, 102]
-
-The `encode` and `encode_plus` methods automatically add "special tokens" which are special IDs the model uses.
-
-Attention mask
--------------------------
-
-The attention mask is an optional argument used when batching sequences together. This argument indicates to the
-model which tokens should be attended to, and which should not.
-
-For example, consider these two sequences:
-
-::
-
-    from transformers import BertTokenizer
-    tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
-
-    sequence_a = "This is a short sequence."
-    sequence_b = "This is a rather long sequence. It is at least longer than the sequence A."
-
-    encoded_sequence_a = tokenizer.encode(sequence_a)
-    assert len(encoded_sequence_a) == 8
-
-    encoded_sequence_b = tokenizer.encode(sequence_b)
-    assert len(encoded_sequence_b) == 19
-
-These two sequences have different lengths and therefore can't be put together in a same tensor as-is. The first
-sequence needs to be padded up to the length of the second one, or the second one needs to be truncated down to
-the length of the first one.
-
-In the first case, the list of IDs will be extended by the padding indices:
-
-::
-
-    # Continuation of the previous script
-    padded_sequence_a = tokenizer.encode(sequence_a, max_length=19, pad_to_max_length=True)
-
-    assert padded_sequence_a == [101, 1188, 1110, 170, 1603, 4954,  119, 102,    0,    0,    0,    0,    0,    0,    0,    0,   0,   0,   0]
-    assert encoded_sequence_b == [101, 1188, 1110, 170, 1897, 1263, 4954, 119, 1135, 1110, 1120, 1655, 2039, 1190, 1103, 4954, 138, 119, 102]
-
-These can then be converted into a tensor in PyTorch or TensorFlow. The attention mask is a binary tensor indicating
-the position of the padded indices so that the model does not attend to them. For the
-:class:`~transformers.BertTokenizer`, :obj:`1` indicate a value that should be attended to while :obj:`0` indicate
-a padded value.
-
-The method :func:`~transformers.PreTrainedTokenizer.encode_plus` may be used to obtain the attention mask directly:
-
-::
-
-    # Continuation of the previous script
-    sequence_a_dict = tokenizer.encode_plus(sequence_a, max_length=19, pad_to_max_length=True)
-
-    assert sequence_a_dict['input_ids'] == [101, 1188, 1110, 170, 1603, 4954, 119, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
-    assert sequence_a_dict['attention_mask'] == [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
-
-
-Token Type IDs
--------------------------
-
-Some models' purpose is to do sequence classification or question answering. These require two different sequences to
-be encoded in the same input IDs. They are usually separated by special tokens, such as the classifier and separator
-tokens. For example, the BERT model builds its two sequence input as such:
-
-::
-
-    from transformers import BertTokenizer
-    tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
-
-    # [CLS] SEQ_A [SEP] SEQ_B [SEP]
-
-    sequence_a = "HuggingFace is based in NYC"
-    sequence_b = "Where is HuggingFace based?"
-
-    encoded_sequence = tokenizer.encode(sequence_a, sequence_b)
-    assert tokenizer.decode(encoded_sequence) == "[CLS] HuggingFace is based in NYC [SEP] Where is HuggingFace based? [SEP]"
-
-This is enough for some models to understand where one sequence ends and where another begins. However, other models
-such as BERT have an additional mechanism, which are the segment IDs. The Token Type IDs are a binary mask identifying
-the different sequences in the model.
-
-We can leverage :func:`~transformers.PreTrainedTokenizer.encode_plus` to output the Token Type IDs for us:
-
-::
-
-    # Continuation of the previous script
-    encoded_dict = tokenizer.encode_plus(sequence_a, sequence_b)
-
-    assert encoded_dict['input_ids'] == [101, 20164, 10932, 2271, 7954, 1110, 1359, 1107, 17520, 102, 2777, 1110, 20164, 10932, 2271, 7954, 1359, 136, 102]
-    assert encoded_dict['token_type_ids'] == [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]
-
-The first sequence, the "context" used for the question, has all its tokens represented by :obj:`0`, whereas the
-question has all its tokens represented by :obj:`1`. Some models, like :class:`~transformers.XLNetModel` use an
-additional token represented by a :obj:`2`.
-
-
-Position IDs
--------------------------
-
-The position IDs are used by the model to identify which token is at which position. Contrary to RNNs that have the
-position of each token embedded within them, transformers are unaware of the position of each token. The position
-IDs are created for this purpose.
-
-They are an optional parameter. If no position IDs are passed to the model, they are automatically created as absolute
-positional embeddings.
-
-Absolute positional embeddings are selected in the range ``[0, config.max_position_embeddings - 1]``. Some models
-use other types of positional embeddings, such as sinusoidal position embeddings or relative position embeddings.
@@ -51,7 +51,6 @@ 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
@@ -59,9 +58,7 @@ The library currently contains PyTorch and Tensorflow implementations, pre-train

    installation
    quickstart
-    glossary
    pretrained_models
-    usage
    model_sharing
    examples
    notebooks
@@ -80,7 +77,6 @@ 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

@@ -100,6 +96,3 @@ The library currently contains PyTorch and Tensorflow implementations, pre-train
    model_doc/ctrl
    model_doc/camembert
    model_doc/albert
-    model_doc/xlmroberta
-    model_doc/flaubert
-    model_doc/bart
@@ -1,6 +1,6 @@
 # Installation

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

 ## With pip

@@ -20,12 +20,14 @@ The ``.optimization`` module provides:
    :members:

 .. autofunction:: transformers.create_optimizer
+    :members:

 Schedules
 ----------------------------------------------------

 Learning Rate Schedules
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
 .. autofunction:: transformers.get_constant_schedule


@@ -37,6 +39,7 @@ Learning Rate Schedules


 .. autofunction:: transformers.get_cosine_schedule_with_warmup
+    :members:

 .. image:: /imgs/warmup_cosine_schedule.png
    :target: /imgs/warmup_cosine_schedule.png
@@ -60,7 +63,7 @@ Learning Rate Schedules
 ``Warmup``
 ~~~~~~~~~~~~~~~~

-.. autoclass:: transformers.WarmUp
+.. autoclass:: transformers.Warmup
    :members:

 Gradient Strategies
@@ -1,68 +0,0 @@
-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-resource language such as English and low-resource languages such as Swahili).
+annotations for 15 different languages (including both high-ressource language such as English and low-ressource languages such as Swahili).

 It was released together with the paper
 `XNLI: Evaluating Cross-lingual Sentence Representations <https://arxiv.org/abs/1809.05053>`__
@@ -1,93 +1,63 @@
 ALBERT
 ----------------------------------------------------

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

 .. autoclass:: transformers.AlbertConfig
    :members:


-AlbertTokenizer
+``AlbertTokenizer``
 ~~~~~~~~~~~~~~~~~~~~~

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


-AlbertModel
+``AlbertModel``
 ~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.AlbertModel
    :members:


-AlbertForMaskedLM
+``AlbertForMaskedLM``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.AlbertForMaskedLM
    :members:


-AlbertForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+``AlbertForSequenceClassification``
+~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.AlbertForSequenceClassification
    :members:


-AlbertForQuestionAnswering
+``AlbertForQuestionAnswering``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.AlbertForQuestionAnswering
    :members:


-TFAlbertModel
+``TFAlbertModel``
 ~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFAlbertModel
    :members:


-TFAlbertForMaskedLM
+``TFAlbertForMaskedLM``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFAlbertForMaskedLM
    :members:


-TFAlbertForSequenceClassification
+``TFAlbertForSequenceClassification``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFAlbertForSequenceClassification
@@ -3,7 +3,7 @@ AutoModels

 In many cases, the architecture you want to use can be guessed from the name or the path of the pretrained model you are supplying to the ``from_pretrained`` method.

-AutoClasses are here to do this job for you so that you automatically retrieve the relevant model given the name/path to the pretrained weights/config/vocabulary:
+AutoClasses are here to do this job for you so that you automatically retreive the relevant model given the name/path to the pretrained weights/config/vocabulary:

 Instantiating one of ``AutoModel``, ``AutoConfig`` and ``AutoTokenizer`` will directly create a class of the relevant architecture (ex: ``model = AutoModel.from_pretrained('bert-base-cased')`` will create a instance of ``BertModel``).

@@ -15,13 +15,6 @@ Instantiating one of ``AutoModel``, ``AutoConfig`` and ``AutoTokenizer`` will di
    :members:


-``AutoTokenizer``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AutoTokenizer
-    :members:
-
-
 ``AutoModel``
 ~~~~~~~~~~~~~~~~~~~~~

@@ -29,37 +22,8 @@ Instantiating one of ``AutoModel``, ``AutoConfig`` and ``AutoTokenizer`` will di
    :members:


-``AutoModelForPreTraining``
-~~~~~~~~~~~~~~~~~~~~~
+``AutoTokenizer``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

-.. autoclass:: transformers.AutoModelForPreTraining
+.. autoclass:: transformers.AutoTokenizer
    :members:
-
-
-``AutoModelWithLMHead``
-~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AutoModelWithLMHead
-    :members:
-
-
-``AutoModelForSequenceClassification``
-~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AutoModelForSequenceClassification
-    :members:
-
-
-``AutoModelForQuestionAnswering``
-~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AutoModelForQuestionAnswering
-    :members:
-
-
-``AutoModelForTokenClassification``
-~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AutoModelForTokenClassification
-    :members:
-
@@ -1,56 +0,0 @@
-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:
-
@@ -1,161 +1,126 @@
 BERT
 ----------------------------------------------------

-Overview
-~~~~~~~~~~~~~~~~~~~~~
-
-The BERT model was proposed in `BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding <https://arxiv.org/abs/1810.04805>`__
-by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova. It's a bidirectional transformer
-pre-trained using a combination of masked language modeling objective and next sentence prediction
-on a large corpus comprising the Toronto Book Corpus and Wikipedia.
-
-The abstract from the paper is the following:
-
-*We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations
-from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional
-representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result,
-the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models
-for a wide range of tasks, such as question answering and language inference, without substantial task-specific
-architecture modifications.*
-
-*BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural
-language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI
-accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute
-improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).*
-
-Tips:
-
- BERT is a model with absolute position embeddings so it's usually advised to pad the inputs on
-  the right rather than the left.
- BERT was trained with a masked language modeling (MLM) objective. It is therefore efficient at predicting masked
-  tokens and at NLU in general, but is not optimal for text generation. Models trained with a causal language
-  modeling (CLM) objective are better in that regard.
- Alongside MLM, BERT was trained using a next sentence prediction (NSP) objective using the [CLS] token as a sequence
-  approximate. The user may use this token (the first token in a sequence built with special tokens) to get a sequence
-  prediction rather than a token prediction. However, averaging over the sequence may yield better results than using
-  the [CLS] token.
-
-BertConfig
+``BertConfig``
 ~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.BertConfig
    :members:


-BertTokenizer
+``BertTokenizer``
 ~~~~~~~~~~~~~~~~~~~~~

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


-BertModel
+``BertModel``
 ~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.BertModel
    :members:


-BertForPreTraining
+``BertForPreTraining``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.BertForPreTraining
    :members:


-BertForMaskedLM
+``BertForMaskedLM``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.BertForMaskedLM
    :members:


-BertForNextSentencePrediction
+``BertForNextSentencePrediction``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.BertForNextSentencePrediction
    :members:


-BertForSequenceClassification
+``BertForSequenceClassification``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.BertForSequenceClassification
    :members:


-BertForMultipleChoice
+``BertForMultipleChoice``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.BertForMultipleChoice
    :members:


-BertForTokenClassification
+``BertForTokenClassification``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.BertForTokenClassification
    :members:


-BertForQuestionAnswering
+``BertForQuestionAnswering``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.BertForQuestionAnswering
    :members:


-TFBertModel
+``TFBertModel``
 ~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFBertModel
    :members:


-TFBertForPreTraining
+``TFBertForPreTraining``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFBertForPreTraining
    :members:


-TFBertForMaskedLM
+``TFBertForMaskedLM``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFBertForMaskedLM
    :members:


-TFBertForNextSentencePrediction
+``TFBertForNextSentencePrediction``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFBertForNextSentencePrediction
    :members:


-TFBertForSequenceClassification
+``TFBertForSequenceClassification``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFBertForSequenceClassification
    :members:


-TFBertForMultipleChoice
+``TFBertForMultipleChoice``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFBertForMultipleChoice
    :members:


-TFBertForTokenClassification
+``TFBertForTokenClassification``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFBertForTokenClassification
    :members:


-TFBertForQuestionAnswering
+``TFBertForQuestionAnswering``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFBertForQuestionAnswering
@@ -1,100 +1,50 @@
 CamemBERT
 ----------------------------------------------------

-The CamemBERT model was proposed in `CamemBERT: a Tasty French Language Model <https://arxiv.org/abs/1911.03894>`__
-by Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suárez, Yoann Dupont, Laurent Romary, Éric Villemonte de la
-Clergerie, Djamé Seddah, and Benoît Sagot. It is based on Facebook's RoBERTa model released in 2019. It is a model
-trained on 138GB of French text.
-
-The abstract from the paper is the following:
-
-*Pretrained language models are now ubiquitous in Natural Language Processing. Despite their success,
-most available models have either been trained on English data or on the concatenation of data in multiple
-languages. This makes practical use of such models --in all languages except English-- very limited. Aiming
-to address this issue for French, we release CamemBERT, a French version of the Bi-directional Encoders for
-Transformers (BERT). We measure the performance of CamemBERT compared to multilingual models in multiple
-downstream tasks, namely part-of-speech tagging, dependency parsing, named-entity recognition, and natural
-language inference. CamemBERT improves the state of the art for most of the tasks considered. We release the
-pretrained model for CamemBERT hoping to foster research and downstream applications for French NLP.*
-
-Tips:
-
- This implementation is the same as RoBERTa. Refer to the `documentation of RoBERTa <./roberta.html>`__ for usage
-  examples as well as the information relative to the inputs and outputs.
-
-CamembertConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+``CamembertConfig``
+~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.CamembertConfig
    :members:


-CamembertTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+``CamembertTokenizer``
+~~~~~~~~~~~~~~~~~~~~~

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


-CamembertModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+``CamembertModel``
+~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.CamembertModel
    :members:


-CamembertForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+``CamembertForMaskedLM``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.CamembertForMaskedLM
    :members:


-CamembertForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+``CamembertForSequenceClassification``
+~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.CamembertForSequenceClassification
    :members:


-CamembertForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+``CamembertForMultipleChoice``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.CamembertForMultipleChoice
    :members:


-CamembertForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+``CamembertForTokenClassification``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.CamembertForTokenClassification
    :members:
-
-
-TFCamembertModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFCamembertModel
-    :members:
-
-
-TFCamembertForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFCamembertForMaskedLM
-    :members:
-
-
-TFCamembertForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFCamembertForSequenceClassification
-    :members:
-
-
-TFCamembertForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFCamembertForTokenClassification
-    :members:
@@ -1,73 +1,47 @@
 CTRL
 ----------------------------------------------------

-CTRL model was proposed in `CTRL: A Conditional Transformer Language Model for Controllable Generation <https://arxiv.org/abs/1909.05858>`_
-by Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and Richard Socher.
-It's a causal (unidirectional) transformer pre-trained using language modeling on a very large
-corpus of ~140 GB of text data with the first token reserved as a control code (such as Links, Books, Wikipedia etc.).
-
-The abstract from the paper is the following:
-
-*Large-scale language models show promising text generation capabilities, but users cannot easily control particular
-aspects of the generated text. We release CTRL, a 1.63 billion-parameter conditional transformer language model,
-trained to condition on control codes that govern style, content, and task-specific behavior. Control codes were
-derived from structure that naturally co-occurs with raw text, preserving the advantages of unsupervised learning
-while providing more explicit control over text generation. These codes also allow CTRL to predict which parts of
-the training data are most likely given a sequence. This provides a potential method for analyzing large amounts
-of data via model-based source attribution.*
-
-Tips:
-
- CTRL makes use of control codes to generate text: it requires generations to be started by certain words, sentences
-  or links to generate coherent text. Refer to the `original implementation <https://github.com/salesforce/ctrl>`__
-  for more information.
- CTRL is a model with absolute position embeddings so it's usually advised to pad the inputs on
-  the right rather than the left.
- CTRL was trained with a causal language modeling (CLM) objective and is therefore powerful at predicting the next
-  token in a sequence. Leveraging this feature allows CTRL to generate syntactically coherent text as
-  it can be observed in the `run_generation.py` example script.
- The PyTorch models can take the `past` as input, which is the previously computed key/value attention pairs. Using
-  this `past` value prevents the model from re-computing pre-computed values in the context of text generation.
-  See `reusing the past in generative models <../quickstart.html#using-the-past>`_ for more information on the usage
-  of this argument.
+Note: if you fine-tune a CTRL model using the Salesforce code (https://github.com/salesforce/ctrl),
+you'll be able to convert from TF to our HuggingFace/Transformers format using the 
+``convert_tf_to_huggingface_pytorch.py`` script (see `issue #1654 <https://github.com/huggingface/transformers/issues/1654>`_).


-CTRLConfig
+``CTRLConfig``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.CTRLConfig
    :members:


-CTRLTokenizer
+``CTRLTokenizer``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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


-CTRLModel
+``CTRLModel``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.CTRLModel
    :members:


-CTRLLMHeadModel
+``CTRLLMHeadModel``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.CTRLLMHeadModel
    :members:


-TFCTRLModel
+``TFCTRLModel``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFCTRLModel
    :members:


-TFCTRLLMHeadModel
+``TFCTRLLMHeadModel``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFCTRLLMHeadModel
@@ -1,96 +1,69 @@
 DistilBERT
 ----------------------------------------------------

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

 .. autoclass:: transformers.DistilBertConfig
    :members:


-DistilBertTokenizer
+``DistilBertTokenizer``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.DistilBertTokenizer
    :members:


-DistilBertModel
+``DistilBertModel``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.DistilBertModel
    :members:


-DistilBertForMaskedLM
+``DistilBertForMaskedLM``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.DistilBertForMaskedLM
    :members:


-DistilBertForSequenceClassification
+``DistilBertForSequenceClassification``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.DistilBertForSequenceClassification
    :members:


-DistilBertForQuestionAnswering
+``DistilBertForQuestionAnswering``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.DistilBertForQuestionAnswering
    :members:

-TFDistilBertModel
+``TFDistilBertModel``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFDistilBertModel
    :members:


-TFDistilBertForMaskedLM
+``TFDistilBertForMaskedLM``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFDistilBertForMaskedLM
    :members:


-TFDistilBertForSequenceClassification
+``TFDistilBertForSequenceClassification``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFDistilBertForSequenceClassification
    :members:


-TFDistilBertForQuestionAnswering
+``TFDistilBertForQuestionAnswering``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFDistilBertForQuestionAnswering
@@ -1,72 +0,0 @@
-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.*
-
-
-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:
-
-
@@ -1,91 +1,56 @@
 OpenAI GPT
 ----------------------------------------------------

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

 .. autoclass:: transformers.OpenAIGPTConfig
    :members:


-OpenAIGPTTokenizer
+``OpenAIGPTTokenizer``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~

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


-OpenAIGPTModel
+``OpenAIGPTModel``
 ~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.OpenAIGPTModel
    :members:


-OpenAIGPTLMHeadModel
+``OpenAIGPTLMHeadModel``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.OpenAIGPTLMHeadModel
    :members:


-OpenAIGPTDoubleHeadsModel
+``OpenAIGPTDoubleHeadsModel``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.OpenAIGPTDoubleHeadsModel
    :members:


-TFOpenAIGPTModel
+``TFOpenAIGPTModel``
 ~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFOpenAIGPTModel
    :members:


-TFOpenAIGPTLMHeadModel
+``TFOpenAIGPTLMHeadModel``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFOpenAIGPTLMHeadModel
    :members:


-TFOpenAIGPTDoubleHeadsModel
+``TFOpenAIGPTDoubleHeadsModel``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFOpenAIGPTDoubleHeadsModel
@@ -1,90 +1,56 @@
 OpenAI GPT2
 ----------------------------------------------------

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

 .. autoclass:: transformers.GPT2Config
    :members:


-GPT2Tokenizer
+``GPT2Tokenizer``
 ~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.GPT2Tokenizer
-    :members: save_vocabulary
+    :members:


-GPT2Model
+``GPT2Model``
 ~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.GPT2Model
    :members:


-GPT2LMHeadModel
+``GPT2LMHeadModel``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.GPT2LMHeadModel
    :members:


-GPT2DoubleHeadsModel
+``GPT2DoubleHeadsModel``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.GPT2DoubleHeadsModel
    :members:


-TFGPT2Model
+``TFGPT2Model``
 ~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFGPT2Model
    :members:


-TFGPT2LMHeadModel
+``TFGPT2LMHeadModel``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFGPT2LMHeadModel
    :members:


-TFGPT2DoubleHeadsModel
+``TFGPT2DoubleHeadsModel``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFGPT2DoubleHeadsModel
@@ -1,98 +1,57 @@
 RoBERTa
 ----------------------------------------------------

-The RoBERTa model was proposed in `RoBERTa: A Robustly Optimized BERT Pretraining Approach <https://arxiv.org/abs/1907.11692>`_
-by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer,
-Veselin Stoyanov. It is based on Google's BERT model released in 2018.
-
-It builds on BERT and modifies key hyperparameters, removing the next-sentence pretraining
-objective and training with much larger mini-batches and learning rates.
-
-The abstract from the paper is the following:
-
-*Language model pretraining has led to significant performance gains but careful comparison between different
-approaches is challenging. Training is computationally expensive, often done on private datasets of different sizes,
-and, as we will show, hyperparameter choices have significant impact on the final results. We present a replication
-study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and
-training data size. We find that BERT was significantly undertrained, and can match or exceed the performance of
-every model published after it. Our best model achieves state-of-the-art results on GLUE, RACE and SQuAD. These
-results highlight the importance of previously overlooked design choices, and raise questions about the source
-of recently reported improvements. We release our models and code.*
-
-Tips:
-
- This implementation is the same as :class:`~transformers.BertModel` with a tiny embeddings tweak as well as a
-  setup for Roberta pretrained models.
- RoBERTa has the same architecture as BERT, but uses a byte-level BPE as a tokenizer (same as GPT-2) and uses a
-  different pre-training scheme.
- RoBERTa doesn't have `token_type_ids`, you don't need to indicate which token belongs to which segment. Just separate your segments with the separation token `tokenizer.sep_token` (or `</s>`)
- `Camembert <./camembert.html>`__ is a wrapper around RoBERTa. Refer to this page for usage examples.
-
-RobertaConfig
+``RobertaConfig``
 ~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.RobertaConfig
    :members:


-RobertaTokenizer
+``RobertaTokenizer``
 ~~~~~~~~~~~~~~~~~~~~~

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


-RobertaModel
+``RobertaModel``
 ~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.RobertaModel
    :members:


-RobertaForMaskedLM
+``RobertaForMaskedLM``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.RobertaForMaskedLM
    :members:


-RobertaForSequenceClassification
+``RobertaForSequenceClassification``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.RobertaForSequenceClassification
    :members:


-RobertaForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.RobertaForTokenClassification
-    :members:
-
-TFRobertaModel
+``TFRobertaModel``
 ~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFRobertaModel
    :members:


-TFRobertaForMaskedLM
+``TFRobertaForMaskedLM``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFRobertaForMaskedLM
    :members:


-TFRobertaForSequenceClassification
+``TFRobertaForSequenceClassification``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFRobertaForSequenceClassification
    :members:
-
-
-TFRobertaForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFRobertaForTokenClassification
-    :members:
@@ -1,72 +1,43 @@
 Transformer XL
 ----------------------------------------------------

-Overview
-~~~~~~~~~~~~~~~~~~~~~

-The Transformer-XL model was proposed in
-`Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context <https://arxiv.org/abs/1901.02860>`__
-by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
-It's a causal (uni-directional) transformer with relative positioning (sinusoïdal) embeddings which can reuse
-previously computed hidden-states to attend to longer context (memory).
-This model also uses adaptive softmax inputs and outputs (tied).
-
-The abstract from the paper is the following:
-
-*Transformers have a potential of learning longer-term dependency, but are limited by a fixed-length context in the
-setting of language modeling. We propose a novel neural architecture Transformer-XL that enables learning dependency
-beyond a fixed length without disrupting temporal coherence. It consists of a segment-level recurrence mechanism and
-a novel positional encoding scheme. Our method not only enables capturing longer-term dependency, but also resolves
-the context fragmentation problem. As a result, Transformer-XL learns dependency that is 80% longer than RNNs and
-450% longer than vanilla Transformers, achieves better performance on both short and long sequences, and is up
-to 1,800+ times faster than vanilla Transformers during evaluation. Notably, we improve the state-of-the-art results
-of bpc/perplexity to 0.99 on enwiki8, 1.08 on text8, 18.3 on WikiText-103, 21.8 on One Billion Word, and 54.5 on
-Penn Treebank (without finetuning). When trained only on WikiText-103, Transformer-XL manages to generate reasonably
-coherent, novel text articles with thousands of tokens.*
-
-Tips:
-
- Transformer-XL uses relative sinusoidal positional embeddings. Padding can be done on the left or on the right.
-  The original implementation trains on SQuAD with padding on the left, therefore the padding defaults are set to left.
- Transformer-XL is one of the few models that has no sequence length limit.
-
-
-TransfoXLConfig
+``TransfoXLConfig``
 ~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TransfoXLConfig
    :members:


-TransfoXLTokenizer
+``TransfoXLTokenizer``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~

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


-TransfoXLModel
+``TransfoXLModel``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TransfoXLModel
    :members:


-TransfoXLLMHeadModel
+``TransfoXLLMHeadModel``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TransfoXLLMHeadModel
    :members:


-TFTransfoXLModel
+``TFTransfoXLModel``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFTransfoXLModel
    :members:


-TFTransfoXLLMHeadModel
+``TFTransfoXLLMHeadModel``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFTransfoXLLMHeadModel
@@ -1,106 +1,68 @@
 XLM
 ----------------------------------------------------

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

 .. autoclass:: transformers.XLMConfig
    :members:

-XLMTokenizer
+``XLMTokenizer``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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

-XLMModel
+``XLMModel``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.XLMModel
    :members:


-XLMWithLMHeadModel
+``XLMWithLMHeadModel``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.XLMWithLMHeadModel
    :members:


-XLMForSequenceClassification
+``XLMForSequenceClassification``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.XLMForSequenceClassification
    :members:


-XLMForQuestionAnsweringSimple
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.XLMForQuestionAnsweringSimple
-    :members:
-
-
-XLMForQuestionAnswering
+``XLMForQuestionAnswering``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.XLMForQuestionAnswering
    :members:


-TFXLMModel
+``TFXLMModel``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFXLMModel
    :members:


-TFXLMWithLMHeadModel
+``TFXLMWithLMHeadModel``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFXLMWithLMHeadModel
    :members:


-TFXLMForSequenceClassification
+``TFXLMForSequenceClassification``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFXLMForSequenceClassification
    :members:


-TFXLMForQuestionAnsweringSimple
+``TFXLMForQuestionAnsweringSimple``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFXLMForQuestionAnsweringSimple
@@ -1,106 +0,0 @@
-XLM-RoBERTa
------------------------------------------
-
-The XLM-RoBERTa model was proposed in `Unsupervised Cross-lingual Representation Learning at Scale <https://arxiv.org/abs/1911.02116>`__
-by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán,
-Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov. It is based on Facebook's RoBERTa model released in 2019.
-It is a large multi-lingual language model, trained on 2.5TB of filtered CommonCrawl data.
-
-The abstract from the paper is the following:
-
-*This paper shows that pretraining multilingual language models at scale leads to significant performance gains for
-a wide range of cross-lingual transfer tasks. We train a Transformer-based masked language model on one hundred
-languages, using more than two terabytes of filtered CommonCrawl data. Our model, dubbed XLM-R, significantly
-outperforms multilingual BERT (mBERT) on a variety of cross-lingual benchmarks, including +13.8% average accuracy
-on XNLI, +12.3% average F1 score on MLQA, and +2.1% average F1 score on NER. XLM-R performs particularly well on
-low-resource languages, improving 11.8% in XNLI accuracy for Swahili and 9.2% for Urdu over the previous XLM model.
-We also present a detailed empirical evaluation of the key factors that are required to achieve these gains,
-including the trade-offs between (1) positive transfer and capacity dilution and (2) the performance of high and
-low resource languages at scale. Finally, we show, for the first time, the possibility of multilingual modeling
-without sacrificing per-language performance; XLM-Ris very competitive with strong monolingual models on the GLUE
-and XNLI benchmarks. We will make XLM-R code, data, and models publicly available.*
-
-Tips:
-
- XLM-R is a multilingual model trained on 100 different languages. Unlike some XLM multilingual models, it does
-  not require `lang` tensors to understand which language is used, and should be able to determine the correct
-  language from the input ids.
- This implementation is the same as RoBERTa. Refer to the `documentation of RoBERTa <./roberta.html>`__ for usage
-  examples as well as the information relative to the inputs and outputs.
-
-XLMRobertaConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.XLMRobertaConfig
-    :members:
-
-
-XLMRobertaTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.XLMRobertaTokenizer
-    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
-        create_token_type_ids_from_sequences, save_vocabulary
-
-
-XLMRobertaModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.XLMRobertaModel
-    :members:
-
-
-XLMRobertaForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.XLMRobertaForMaskedLM
-    :members:
-
-
-XLMRobertaForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.XLMRobertaForSequenceClassification
-    :members:
-
-
-XLMRobertaForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.XLMRobertaForMultipleChoice
-    :members:
-
-
-XLMRobertaForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.XLMRobertaForTokenClassification
-    :members:
-
-
-TFXLMRobertaModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFXLMRobertaModel
-    :members:
-
-
-TFXLMRobertaForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFXLMRobertaForMaskedLM
-    :members:
-
-
-TFXLMRobertaForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFXLMRobertaForSequenceClassification
-    :members:
-
-
-TFXLMRobertaForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFXLMRobertaForTokenClassification
-    :members:
@@ -1,124 +1,70 @@
 XLNet
 ----------------------------------------------------

-Overview
-~~~~~~~~~~~~~~~~~~~~~
-
-The XLNet model was proposed in `XLNet: Generalized Autoregressive Pretraining for Language Understanding <https://arxiv.org/abs/1906.08237>`_
-by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
-XLnet is an extension of the Transformer-XL model pre-trained using an autoregressive method
-to learn bidirectional contexts by maximizing the expected likelihood over all permutations
-of the input sequence factorization order.
-
-The abstract from the paper is the following:
-
-*With the capability of modeling bidirectional contexts, denoising autoencoding based pretraining like BERT achieves
-better performance than pretraining approaches based on autoregressive language modeling. However, relying on
-corrupting the input with masks, BERT neglects dependency between the masked positions and suffers from a
-pretrain-finetune discrepancy. In light of these pros and cons, we propose XLNet, a generalized autoregressive
-pretraining method that (1) enables learning bidirectional contexts by maximizing the expected likelihood over
-all permutations of the factorization order and (2) overcomes the limitations of BERT thanks to its autoregressive
-formulation. Furthermore, XLNet integrates ideas from Transformer-XL, the state-of-the-art autoregressive model,
-into pretraining. Empirically, under comparable experiment settings, XLNet outperforms BERT on 20 tasks, often by
-a large margin, including question answering, natural language inference, sentiment analysis, and document ranking.*
-
-Tips:
-
- The specific attention pattern can be controlled at training and test time using the `perm_mask` input.
- Due to the difficulty of training a fully auto-regressive model over various factorization order,
-  XLNet is pretrained using only a sub-set of the output tokens as target which are selected
-  with the `target_mapping` input.
- To use XLNet for sequential decoding (i.e. not in fully bi-directional setting), use the `perm_mask` and
-  `target_mapping` inputs to control the attention span and outputs (see examples in `examples/run_generation.py`)
- XLNet is one of the few models that has no sequence length limit.
-
-
-XLNetConfig
+``XLNetConfig``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.XLNetConfig
    :members:


-XLNetTokenizer
+``XLNetTokenizer``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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


-XLNetModel
+``XLNetModel``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.XLNetModel
    :members:


-XLNetLMHeadModel
+``XLNetLMHeadModel``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.XLNetLMHeadModel
    :members:


-XLNetForSequenceClassification
+``XLNetForSequenceClassification``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.XLNetForSequenceClassification
    :members:


-XLNetForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.XLNetForTokenClassification
-    :members:
-
-
-XLNetForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.XLNetForMultipleChoice
-    :members:
-
-
-XLNetForQuestionAnsweringSimple
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.XLNetForQuestionAnsweringSimple
-    :members:
-
-
-XLNetForQuestionAnswering
+``XLNetForQuestionAnswering``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.XLNetForQuestionAnswering
    :members:


-TFXLNetModel
+``TFXLNetModel``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFXLNetModel
    :members:


-TFXLNetLMHeadModel
+``TFXLNetLMHeadModel``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFXLNetLMHeadModel
    :members:


-TFXLNetForSequenceClassification
+``TFXLNetForSequenceClassification``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFXLNetForSequenceClassification
    :members:


-TFXLNetForQuestionAnsweringSimple
+``TFXLNetForQuestionAnsweringSimple``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.TFXLNetForQuestionAnsweringSimple
@@ -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)**. Optionally, join an existing organization or create a new one. Then:
+**First, create an account on [https://huggingface.co/join](https://huggingface.co/join)**. Then:

 ```shell
 transformers-cli login
@@ -21,35 +21,20 @@ transformers-cli upload ./config.json [--filename folder/foobar.json]
 # (you can optionally override its filename, which can be nested inside a folder)
 ```

-If you want your model to be namespaced by your organization name rather than your username, add the following flag to any command:
-```shell
--organization organization_name
-```
-
-Your model will then be accessible through its identifier, a concatenation of your username (or organization name) and the folder name above:
+Your model will then be accessible through its identifier, a concatenation of your username and the folder name above:
 ```python
 "username/pretrained_model"
-# or if an org:
-"organization_name/pretrained_model"
 ```

-**Please add a README.md model card** to the repo under `model_cards/` with: model description, training params (dataset, preprocessing, hardware used, hyperparameters), evaluation results, intended uses & limitations, etc.
-
-Your model now has a page on huggingface.co/models 🔥
-
 Anyone can load it from code:
 ```python
-tokenizer = AutoTokenizer.from_pretrained("namespace/pretrained_model")
-model = AutoModel.from_pretrained("namespace/pretrained_model")
+tokenizer = AutoTokenizer.from_pretrained("username/pretrained_model")
+model = AutoModel.from_pretrained("username/pretrained_model")
 ```

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

-You can also delete unneeded files:
-
-```shell
-transformers-cli s3 rm …
-```
@@ -47,7 +47,6 @@ 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}


@@ -55,7 +54,6 @@ 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


@@ -64,7 +62,6 @@ 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])

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

 .. code-block::

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


@@ -88,10 +88,6 @@ For a list that includes community-uploaded models, refer to `https://huggingfac
 |                   | ``bert-base-finnish-uncased-v1``                           | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
 |                   |                                                            | | Trained on uncased Finnish text.                                                                                                    |
 |                   |                                                            | (see `details on turkunlp.org <http://turkunlp.org/FinBERT/>`__).                                                                     |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``bert-base-dutch-cased``                                  | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
-|                   |                                                            | | Trained on cased Dutch text.                                                                                                        |
-|                   |                                                            | (see `details on wietsedv repository <https://github.com/wietsedv/bertje/>`__).                                                       |
 +-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 | GPT               | ``openai-gpt``                                             | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
 |                   |                                                            | | OpenAI GPT English model                                                                                                            |
@@ -179,14 +175,6 @@ 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>`__)                                     |
@@ -259,32 +247,6 @@ 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                                                          |
 +-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| 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                                                                    |
-+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+


 .. <https://huggingface.co/transformers/examples.html>`__
@@ -209,7 +209,7 @@ past = None
 for i in range(100):
    print(i)
    output, past = model(context, past=past)
-    token = torch.argmax(output[..., -1, :])
+    token = torch.argmax(output[0, :])

    generated += [token.tolist()]
    context = token.unsqueeze(0)
@@ -220,3 +220,96 @@ 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'
+```
@@ -1,597 +0,0 @@
-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)
-
-    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"
-    generated = tokenizer.encode(sequence)
-
-    for i in range(50):
-        predictions = model(tf.constant([generated]))[0]
-        token = tf.argmax(predictions[0], axis=1)[-1].numpy()
-        generated += [token]
-
-    resulting_string = tokenizer.decode(generated)
-    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')]
@@ -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:

@@ -15,16 +15,15 @@ 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 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. |
+|----------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| [TensorFlow 2.0 models on GLUE](#TensorFlow-2.0-Bert-models-on-GLUE) | Examples running BERT TensorFlow 2.0 model on the GLUE tasks. 
+| [Language Model fine-tuning](#language-model-fine-tuning) | Fine-tuning the library models for language modeling on a text dataset. Causal language modeling for GPT/GPT-2, masked language modeling for BERT/RoBERTa. |
+| [Language Generation](#language-generation) | Conditional text generation using the auto-regressive models of the library: GPT, GPT-2, Transformer-XL and XLNet.                                         |
+| [GLUE](#glue) | Examples running BERT/XLM/XLNet/RoBERTa on the 9 GLUE tasks. Examples feature distributed training as well as half-precision.                              |
+| [SQuAD](#squad) | Using BERT/RoBERTa/XLNet/XLM for question answering, examples with distributed training.                                                                                  |
+| [Multiple Choice](#multiple-choice) | Examples running BERT/XLNet/RoBERTa on the SWAG/RACE/ARC tasks. 
+| [Named Entity Recognition](#named-entity-recognition) | Using BERT for Named Entity Recognition (NER) on the CoNLL 2003 dataset, examples with distributed training.                                                                                  |
 | [XNLI](#xnli) | Examples running BERT/XLM on the XNLI benchmark. |
-| [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

@@ -48,16 +47,16 @@ 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 training
+## Language model fine-tuning

-Based on the script [`run_language_modeling.py`](https://github.com/huggingface/transformers/blob/master/examples/run_language_modeling.py).
+Based on the script [`run_lm_finetuning.py`](https://github.com/huggingface/transformers/blob/master/examples/run_lm_finetuning.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 
+Fine-tuning the library models for language modeling on a text dataset for GPT, GPT-2, BERT and RoBERTa (DistilBERT 
 to be added soon). GPT and GPT-2 are fine-tuned using a causal language modeling (CLM) loss while BERT and RoBERTa 
 are fine-tuned using a masked language modeling (MLM) loss.

 Before running the following example, you should get a file that contains text on which the language model will be
-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/).
+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.
@@ -71,7 +70,7 @@ 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_language_modeling.py \
+python run_lm_finetuning.py \
    --output_dir=output \
    --model_type=gpt2 \
    --model_name_or_path=gpt2 \
@@ -88,7 +87,7 @@ 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).
@@ -99,7 +98,7 @@ 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_language_modeling.py \
+python run_lm_finetuning.py \
    --output_dir=output \
    --model_type=roberta \
    --model_name_or_path=roberta-base \
@@ -130,30 +129,30 @@ 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
-batch sizes between 16 and 64. Some of these tasks have a small dataset and training can lead to high variance in the results
+uncased  BERT base model (the checkpoint `bert-base-uncased`). All experiments ran on 8 V100 GPUs with a total train
+batch size of 24. Some of these tasks have a small dataset and training can lead to high variance in the results
 between different runs. We report the median on 5 runs (with different seeds) for each of the metrics.

 | Task  | Metric                       | Result      |
 |-------|------------------------------|-------------|
-| CoLA  | Matthew's corr               | 49.23       |
-| SST-2 | Accuracy                     | 91.97       |
-| MRPC  | F1/Accuracy                  | 89.47/85.29 |
-| STS-B | Person/Spearman corr.        | 83.95/83.70 |
-| QQP   | Accuracy/F1                  | 88.40/84.31 |
-| MNLI  | Matched acc./Mismatched acc. | 80.61/81.08 |
-| QNLI  | Accuracy                     | 87.46       |
-| RTE   | Accuracy                     | 61.73       |
-| WNLI  | Accuracy                     | 45.07       |
+| CoLA  | Matthew's corr               | 48.87       |
+| SST-2 | Accuracy                     | 91.74       |
+| MRPC  | F1/Accuracy                  | 90.70/86.27 |
+| STS-B | Person/Spearman corr.        | 91.39/91.04 |
+| QQP   | Accuracy/F1                  | 90.79/87.66 |
+| MNLI  | Matched acc./Mismatched acc. | 83.70/84.83 |
+| QNLI  | Accuracy                     | 89.31       |
+| RTE   | Accuracy                     | 71.43       |
+| WNLI  | Accuracy                     | 43.66       |

 Some of these results are significantly different from the ones reported on the test set
 of GLUE benchmark on the website. For QQP and WNLI, please refer to [FAQ #12](https://gluebenchmark.com/faq) on the webite.

-Before running any one of these GLUE tasks you should download the
+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`.
@@ -179,23 +178,23 @@ 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 any one of these GLUE tasks you should download the
+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`.
@@ -219,12 +218,12 @@ 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
@@ -360,8 +359,8 @@ 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)
@@ -379,7 +378,7 @@ export SQUAD_DIR=/path/to/SQUAD

 python run_squad.py \
  --model_type bert \
-  --model_name_or_path bert-base-uncased \
+  --model_name_or_path bert-base-cased \
  --do_train \
  --do_eval \
  --do_lower_case \
@@ -403,12 +402,12 @@ 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 SQuAD1.1:
+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:

 ```bash
-python -m torch.distributed.launch --nproc_per_node=8 ./examples/run_squad.py \
+python -m torch.distributed.launch --nproc_per_node=8 run_squad.py \
    --model_type bert \
-    --model_name_or_path bert-large-uncased-whole-word-masking \
+    --model_name_or_path bert-base-cased \
    --do_train \
    --do_eval \
    --do_lower_case \
@@ -418,9 +417,9 @@ python -m torch.distributed.launch --nproc_per_node=8 ./examples/run_squad.py \
    --num_train_epochs 2 \
    --max_seq_length 384 \
    --doc_stride 128 \
-    --output_dir ./examples/models/wwm_uncased_finetuned_squad/ \
-    --per_gpu_eval_batch_size=3   \
-    --per_gpu_train_batch_size=3   \
+    --output_dir ../models/wwm_uncased_finetuned_squad/ \
+    --per_gpu_train_batch_size 24 \
+    --gradient_accumulation_steps 12
 ```

 Training with the previously defined hyper-parameters yields the following results:
@@ -442,14 +441,14 @@ This example code fine-tunes XLNet on both SQuAD1.0 and SQuAD2.0 dataset. See ab
 ```bash
 export SQUAD_DIR=/path/to/SQUAD

-python run_squad.py \
+python /data/home/hlu/transformers/examples/run_squad.py \
    --model_type xlnet \
    --model_name_or_path xlnet-large-cased \
    --do_train \
    --do_eval \
    --do_lower_case \
-    --train_file $SQUAD_DIR/train-v1.1.json \
-    --predict_file $SQUAD_DIR/dev-v1.1.json \
+    --train_file /data/home/hlu/notebooks/NLP/examples/question_answering/train-v1.1.json \
+    --predict_file /data/home/hlu/notebooks/NLP/examples/question_answering/dev-v1.1.json \
    --learning_rate 3e-5 \
    --num_train_epochs 2 \
    --max_seq_length 384 \
@@ -516,17 +515,196 @@ 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-resource language such as English and low-resource 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-ressource language such as English and low-ressource languages such as Swahili).

 #### Fine-tuning on XNLI

 This example code fine-tunes mBERT (multi-lingual BERT) on the XNLI dataset. It runs in 106 mins
-on a single tesla V100 16GB. The data for XNLI can be downloaded with the following links and should be both saved (and un-zipped) in a
+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)
@@ -580,42 +758,4 @@ python run_mmimdb.py \
    --patience 5
 ```

-## Adversarial evaluation of model performances

-Here is an example on evaluating a model using adversarial evaluation of natural language inference with the Heuristic Analysis for NLI Systems (HANS) dataset [McCoy et al., 2019](https://arxiv.org/abs/1902.01007). The example was gracefully provided by [Nafise Sadat Moosavi](https://github.com/ns-moosavi).
-
-The HANS dataset can be downloaded from [this location](https://github.com/tommccoy1/hans).
-
-This is an example of using test_hans.py:
-
-```bash
-export HANS_DIR=path-to-hans
-export MODEL_TYPE=type-of-the-model-e.g.-bert-roberta-xlnet-etc
-export MODEL_PATH=path-to-the-model-directory-that-is-trained-on-NLI-e.g.-by-using-run_glue.py
-
-python examples/hans/test_hans.py \
-        --task_name hans \
-        --model_type $MODEL_TYPE \
-        --do_eval \
-        --do_lower_case \
-        --data_dir $HANS_DIR \
-        --model_name_or_path $MODEL_PATH \
-        --max_seq_length 128 \
-        --output_dir $MODEL_PATH \
-```
-
-This will create the hans_predictions.txt file in MODEL_PATH, which can then be evaluated using hans/evaluate_heur_output.py from the HANS dataset.
-
-The results of the BERT-base model that is trained on MNLI using batch size 8 and the random seed 42 on the HANS dataset is as follows:
-
-```bash
-Heuristic entailed results:
-lexical_overlap: 0.9702
-subsequence: 0.9942
-constituent: 0.9962
-
-Heuristic non-entailed results:
-lexical_overlap: 0.199
-subsequence: 0.0396
-constituent: 0.118
-```
@@ -24,15 +24,7 @@ import timeit
 from time import time
 from typing import List

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


 if is_tf_available():
@@ -258,21 +250,15 @@ 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_memory_filename: str = f"memory_{round(time())}.csv",
 ):
    if xla:
        tf.config.optimizer.set_jit(True)
@@ -281,25 +267,11 @@ def create_setup_and_compute(

    if tensorflow:
        dictionary = {model_name: {} for model_name in model_names}
-        results = _compute_tensorflow(
-            model_names, batch_sizes, slice_sizes, dictionary, average_over, amp, no_speed, no_memory, verbose
-        )
+        results = _compute_tensorflow(model_names, dictionary, average_over, amp)
    else:
        device = "cuda" if (gpu and torch.cuda.is_available()) else "cpu"
        dictionary = {model_name: {} for model_name in model_names}
-        results = _compute_pytorch(
-            model_names,
-            batch_sizes,
-            slice_sizes,
-            dictionary,
-            average_over,
-            device,
-            torchscript,
-            fp16,
-            no_speed,
-            no_memory,
-            verbose,
-        )
+        results = _compute_pytorch(model_names, dictionary, average_over, device, torchscript, fp16)

    print("=========== RESULTS ===========")
    for model_name in model_names:
@@ -308,19 +280,13 @@ def create_setup_and_compute(
            print("\t\t" + f"===== BATCH SIZE: {batch_size} =====")
            for slice_size in results[model_name]["ss"]:
                result = results[model_name]["results"][batch_size][slice_size]
-                memory = results[model_name]["memory"][batch_size][slice_size]
                if isinstance(result, str):
-                    print(f"\t\t{model_name}/{batch_size}/{slice_size}: " f"{result} " f"{memory}")
+                    print(f"\t\t{model_name}/{batch_size}/{slice_size}: " f"{result}")
                else:
-                    print(
-                        f"\t\t{model_name}/{batch_size}/{slice_size}: "
-                        f"{(round(1000 * result) / 1000)}"
-                        f"s "
-                        f"{memory}"
-                    )
+                    print(f"\t\t{model_name}/{batch_size}/{slice_size}: " f"{(round(1000 * result) / 1000)}" f"s")

    if save_to_csv:
-        with open(csv_filename, mode="w") as csv_file, open(csv_memory_filename, mode="w") as csv_memory_file:
+        with open(csv_filename, mode="w") as csv_file:
            fieldnames = [
                "model",
                "1x8",
@@ -351,8 +317,6 @@ def create_setup_and_compute(

            writer = csv.DictWriter(csv_file, fieldnames=fieldnames)
            writer.writeheader()
-            memory_writer = csv.DictWriter(csv_memory_file, fieldnames=fieldnames)
-            memory_writer.writeheader()

            for model_name in model_names:
                model_results = {
@@ -362,59 +326,8 @@ def create_setup_and_compute(
                }
                writer.writerow({"model": model_name, **model_results})

-                model_memory_results = {
-                    f"{bs}x{ss}": results[model_name]["memory"][bs][ss]
-                    for bs in results[model_name]["memory"]
-                    for ss in results[model_name]["memory"][bs]
-                }
-                memory_writer.writerow({"model": model_name, **model_memory_results})

-
-def print_summary_statistics(summary: MemorySummary):
-    print(
-        "\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.relative_mem_list
-        )
-    )
-    print(
-        "\nLines with top memory increase:\n"
-        + "\n".join(
-            f"=> {state.frame.filename}:{state.frame.line_number}: mem {state.cpu_gpu_with_units}: {state.frame.line_text}"
-            for state in summary.relative_mem_sorted[:6]
-        )
-    )
-    print(
-        "\nLines with lowest memory increase:\n"
-        + "\n".join(
-            f"=> {state.frame.filename}:{state.frame.line_number}: mem {state.cpu_gpu_with_units}: {state.frame.line_text}"
-            for state in summary.relative_mem_sorted[-6:]
-        )
-    )
-    print(
-        "\nLines with peak memory used:\n"
-        + "\n".join(
-            f"=> {state.frame.filename}:{state.frame.line_number}: mem {state.cpu_gpu_with_units}: {state.frame.line_text}"
-            for state in summary.absolute_mem_sorted[:6]
-        )
-    )
-    print(f"\nTotal memory increase: {summary.relative_mem_total.cpu_gpu_with_units}")
-
-
-def _compute_pytorch(
-    model_names,
-    batch_sizes,
-    slice_sizes,
-    dictionary,
-    average_over,
-    device,
-    torchscript,
-    fp16,
-    no_speed,
-    no_memory,
-    verbose,
-):
+def _compute_pytorch(model_names, dictionary, average_over, device, torchscript, fp16):
    for c, model_name in enumerate(model_names):
        print(f"{c + 1} / {len(model_names)}")
        config = AutoConfig.from_pretrained(model_name, torchscript=torchscript)
@@ -424,17 +337,17 @@ def _compute_pytorch(
        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": {}, "memory": {}}
+        dictionary[model_name] = {"bs": batch_sizes, "ss": slice_sizes, "results": {}}
        dictionary[model_name]["results"] = {i: {} for i in batch_sizes}
-        dictionary[model_name]["memory"] = {i: {} for i in batch_sizes}

        for batch_size in batch_sizes:
            if fp16:
                model.half()
            model.to(device)
            model.eval()
-
            for slice_size in slice_sizes:
                if max_input_size is not None and slice_size > max_input_size:
                    dictionary[model_name]["results"][batch_size][slice_size] = "N/A"
@@ -449,40 +362,18 @@ def _compute_pytorch(
                            inference = model
                            inference(sequence)

-                        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)
-
-                            dictionary[model_name]["memory"][batch_size][slice_size] = summary.relative_mem_total.cpu_gpu_with_units
-                        else:
-                            dictionary[model_name]["memory"][batch_size][slice_size] = "N/A"
-
-                        if not no_speed:
-                            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
-                        else:
-                            dictionary[model_name]["results"][batch_size][slice_size] = "N/A"
-
+                        print("Going through model with sequence of shape", sequence.shape)
+                        runtimes = timeit.repeat(lambda: inference(sequence), repeat=average_over, number=3)
+                        average_time = sum(runtimes) / float(len(runtimes)) / 3.0
+                        dictionary[model_name]["results"][batch_size][slice_size] = average_time
                    except RuntimeError as e:
                        print("Doesn't fit on GPU.", e)
                        torch.cuda.empty_cache()
                        dictionary[model_name]["results"][batch_size][slice_size] = "N/A"
-                        dictionary[model_name]["memory"][batch_size][slice_size] = "N/A"
    return dictionary


-def _compute_tensorflow(
-    model_names, batch_sizes, slice_sizes, dictionary, average_over, amp, no_speed, no_memory, verbose
-):
+def _compute_tensorflow(model_names, dictionary, average_over, amp):
    for c, model_name in enumerate(model_names):
        print(f"{c + 1} / {len(model_names)}")
        config = AutoConfig.from_pretrained(model_name)
@@ -492,10 +383,11 @@ def _compute_tensorflow(
        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": {}, "memory": {}}
+        dictionary[model_name] = {"bs": batch_sizes, "ss": slice_sizes, "results": {}}
        dictionary[model_name]["results"] = {i: {} for i in batch_sizes}
-        dictionary[model_name]["memory"] = {i: {} for i in batch_sizes}

        print("Using model", model)

@@ -517,30 +409,13 @@ def _compute_tensorflow(
                        # To make sure that the model is traced + that the tensors are on the appropriate device
                        inference(sequence)

-                        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)
-
-                            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]["results"][batch_size][slice_size] = average_time
-                        else:
-                            dictionary[model_name]["results"][batch_size][slice_size] = "N/A"
-
+                        runtimes = timeit.repeat(lambda: inference(sequence), repeat=average_over, number=3)
+                        average_time = sum(runtimes) / float(len(runtimes)) / 3.0
+                        dictionary[model_name]["results"][batch_size][slice_size] = average_time
                    except tf.errors.ResourceExhaustedError as e:
                        print("Doesn't fit on GPU.", e)
+                        torch.cuda.empty_cache()
                        dictionary[model_name]["results"][batch_size][slice_size] = "N/A"
-                        dictionary[model_name]["memory"][batch_size][slice_size] = "N/A"
    return dictionary


@@ -558,9 +433,6 @@ 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"
    )
@@ -605,8 +477,6 @@ def main():
    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":
@@ -631,8 +501,6 @@ def main():
        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,
@@ -640,9 +508,6 @@ def main():
                save_to_csv=args.save_to_csv,
                csv_filename=args.csv_filename,
                average_over=args.average_over,
-                no_speed=args.no_speed,
-                no_memory=args.no_memory,
-                verbose=args.verbose,
            )
        else:
            raise ImportError("Trying to run a PyTorch benchmark but PyTorch was not found in the environment.")
@@ -651,17 +516,12 @@ 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,
                average_over=args.average_over,
-                no_speed=args.no_speed,
-                no_memory=args.no_memory,
-                verbose=args.verbose,
            )
        else:
            raise ImportError("Trying to run a TensorFlow benchmark but TensorFlow was not found in the environment.")
@@ -30,7 +30,7 @@ def fill_mask(masked_input, model, tokenizer, topk=5):
            )
        else:
            topk_filled_outputs.append(
-                (masked_input.replace(masked_token, predicted_token), values[index].item(), predicted_token,)
+                (masked_input.replace(masked_token, predicted_token), values[index].item(), predicted_token)
            )
    return topk_filled_outputs

@@ -83,7 +83,7 @@ def pre_process_datasets(encoded_datasets, input_len, cap_length, start_token, d
        mc_token_ids = np.zeros((n_batch, 2), dtype=np.int64)
        lm_labels = np.full((n_batch, 2, input_len), fill_value=-100, dtype=np.int64)
        mc_labels = np.zeros((n_batch,), dtype=np.int64)
-        for i, (story, cont1, cont2, mc_label), in enumerate(dataset):
+        for i, (story, cont1, cont2, mc_label) in enumerate(dataset):
            with_cont1 = [start_token] + story[:cap_length] + [delimiter_token] + cont1[:cap_length] + [clf_token]
            with_cont2 = [start_token] + story[:cap_length] + [delimiter_token] + cont2[:cap_length] + [clf_token]
            input_ids[i, 0, : len(with_cont1)] = with_cont1
@@ -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()
-                optimizer.step()
                scheduler.step()
+                optimizer.step()
                optimizer.zero_grad()
                tr_loss += loss.item()
                exp_average_loss = (
@@ -51,9 +51,7 @@ logger = logging.getLogger(__name__)

 ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) for conf in [BertConfig]), ())

-MODEL_CLASSES = {
-    "bert": (BertConfig, BertForMultipleChoice, BertTokenizer),
-}
+MODEL_CLASSES = {"bert": (BertConfig, BertForMultipleChoice, BertTokenizer)}


 class SwagExample(object):
@@ -63,12 +61,7 @@ class SwagExample(object):
        self.swag_id = swag_id
        self.context_sentence = context_sentence
        self.start_ending = start_ending
-        self.endings = [
-            ending_0,
-            ending_1,
-            ending_2,
-            ending_3,
-        ]
+        self.endings = [ending_0, ending_1, ending_2, ending_3]
        self.label = label

    def __str__(self):
@@ -622,7 +615,7 @@ 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 = 0 if args.no_cuda else torch.cuda.device_count()
+        args.n_gpu = 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)
@@ -2,17 +2,15 @@

 This folder contains the original code used to train Distil* as well as examples showcasing how to use DistilBERT, DistilRoBERTa and DistilGPT2.

-**January 20, 2020 - Bug fixing** We have recently discovered and fixed [a bug](https://github.com/huggingface/transformers/commit/48cbf267c988b56c71a2380f748a3e6092ccaed3) in the evaluation of our `run_*.py` scripts that caused the reported metrics to be over-estimated on average. We have updated all the metrics with the latest runs.
+**December 6th, 2019 - Update** We release **DistilmBERT**: 92% of `bert-base-multilingual-cased` on XNLI. The model supports 104 different languages listed [here](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages).

-**December 6, 2019 - Update** We release **DistilmBERT**: 92% of `bert-base-multilingual-cased` on XNLI. The model supports 104 different languages listed [here](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages).
+**November 19th, 2019 - Update** We release German **DistilBERT**: 98.8% of `bert-base-german-dbmdz-cased` on NER tasks.

-**November 19, 2019 - Update** We release German **DistilBERT**: 98.8% of `bert-base-german-dbmdz-cased` on NER tasks.
+**October 23rd, 2019 - Update** We release **DistilRoBERTa**: 95% of `RoBERTa-base`'s performance on GLUE, twice as fast as RoBERTa while being 35% smaller.

-**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 3rd, 2019 - Update** We release our [NeurIPS workshop paper](https://arxiv.org/abs/1910.01108) explaining our approach on **DistilBERT**. It includes updated results and further experiments. We applied the same method to GPT2 and release the weights of **DistilGPT2**. DistilGPT2 is two times faster and 33% smaller than GPT2. **The paper superseeds our [previous blogpost](https://medium.com/huggingface/distilbert-8cf3380435b5) with a different distillation loss and better performances. Please use the paper as a reference when comparing/reporting results on DistilBERT.**

-**October 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!
+**September 19th, 2019 - Update:** We fixed bugs in the code and released an upadted version of the weights trained with a modification of the distillation loss. DistilBERT now reaches 97% of `BERT-base`'s performance on GLUE, and 86.9 F1 score on SQuAD v1.1 dev set (compared to 88.5 for `BERT-base`). We will publish a formal write-up of our approach in the near future!


 ## What is Distil*
@@ -20,7 +18,7 @@ This folder contains the original code used to train Distil* as well as examples
 Distil* is a class of compressed models that started with DistilBERT. DistilBERT stands for Distillated-BERT. DistilBERT is a small, fast, cheap and light Transformer model based on Bert architecture. It has 40% less parameters than `bert-base-uncased`, runs 60% faster while preserving 97% of BERT's performances as measured on the GLUE language understanding benchmark. DistilBERT is trained using knowledge distillation, a technique to compress a large model called the teacher into a smaller model called the student. By distillating Bert, we obtain a smaller Transformer model that bears a lot of similarities with the original BERT model while being lighter, smaller and faster to run. DistilBERT is thus an interesting option to put large-scaled trained Transformer model into production.

 We have applied the same method to other Transformer architectures and released the weights:
- GPT2: on the [WikiText-103](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/) benchmark, GPT2 reaches a perplexity on the test set of 16.3 compared to 21.1 for **DistilGPT2** (after fine-tuning on the train set).
+- GPT2: on the [WikiText-103](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/) benchmark, GPT2 reaches a perplexity on the test set of 15.0 compared to 18.5 for **DistilGPT2** (after fine-tuning on the train set).
 - RoBERTa: **DistilRoBERTa** reaches 95% of `RoBERTa-base`'s performance on GLUE while being twice faster and 35% smaller.
 - German BERT: **German DistilBERT** reaches 99% of `bert-base-german-dbmdz-cased`'s performance on German NER (CoNLL-2003).
 - Multilingual BERT: **DistilmBERT** reaches 92% of Multilingual BERT's performance on XNLI while being twice faster and 25% smaller. The model supports 104 languages listed [here](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages).
@@ -31,15 +29,13 @@ 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         |  **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              |
+| BERT-base                 |  **77.6**                      | 48.9 | 84.3 | 88.6 | 89.3 | 89.5 | 71.3 | 91.7 | 91.2 | 43.7              |
+| DistilBERT                |  **76.8**                      | 49.1 | 81.8 | 90.2 | 90.2 | 89.2 | 62.9 | 92.7 | 90.7 | 44.4              |
 | ---                       |    ---                         |  --- |  --- |  --- |  --- |  --- |  --- |  --- |  --- |  ---              |
 | 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              |
+| DistilRoBERTa<sup>1</sup> |  **79.0**/**82.3**<sup>2</sup> | 59.4 | 83.9 | 86.6 | 90.8 | 89.4 | 67.9 | 92.5 | 88.3 | 52.1              |

-<sup>1</sup> We did not use the MNLI checkpoint for fine-tuning but directly perform transfer learning on the pre-trained DistilRoBERTa.
+<sup>1</sup> We did not use the MNLI checkpoint for fine-tuning but directy perform transfer learning on the pre-trained DistilRoBERTa.

 <sup>2</sup> Macro-score computed without WNLI.

@@ -65,9 +61,7 @@ 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) 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-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-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.
@@ -76,8 +70,8 @@ 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-cased')
-model = DistilBertModel.from_pretrained('distilbert-base-cased')
+tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
+model = DistilBertModel.from_pretrained('distilbert-base-uncased')

 input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)
 outputs = model(input_ids)
@@ -85,7 +79,6 @@ 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')`
@@ -111,7 +104,7 @@ 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 occurrences 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 occurences of each tokens in the data:

 ```bash
 python scripts/token_counts.py \
@@ -179,7 +172,7 @@ Happy distillation!

 ## Citation

-If you find the resource useful, you should cite the following paper:
+If you find the ressource useful, you should cite the following paper:

 ```
@inproceedings{sanh2019distilbert,
@@ -42,7 +42,6 @@ class LmSeqsDataset(Dataset):
        self.check()
        self.remove_long_sequences()
        self.remove_empty_sequences()
-        self.remove_unknown_sequences()
        self.check()
        self.print_statistics()

@@ -110,22 +109,6 @@ class LmSeqsDataset(Dataset):
        new_size = len(self)
        logger.info(f"Remove {init_size - new_size} too short (<=11 tokens) sequences.")

-    def remove_unknown_sequences(self):
-        """
-        Remove sequences with a (too) high level of unknown tokens.
-        """
-        if "unk_token" not in self.params.special_tok_ids:
-            return
-        else:
-            unk_token_id = self.params.special_tok_ids["unk_token"]
-        init_size = len(self)
-        unk_occs = np.array([np.count_nonzero(a == unk_token_id) for a in self.token_ids])
-        indices = (unk_occs / self.lengths) < 0.5
-        self.token_ids = self.token_ids[indices]
-        self.lengths = self.lengths[indices]
-        new_size = len(self)
-        logger.info(f"Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).")
-
    def print_statistics(self):
        """
        Print some statistics on the corpus. Only the master process.
@@ -3,5 +3,5 @@ transformers
 gitpython==3.0.2
 tensorboard>=1.14.0
 tensorboardX==1.8
-psutil==5.6.6
+psutil==5.6.3
 scipy==1.3.1
@@ -13,20 +13,20 @@
 # 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.
-""" This is the exact same script as `examples/run_squad.py` (as of 2020, January 8th) with an additional and optional step of distillation."""
+""" This is the exact same script as `examples/run_squad.py` (as of 2019, October 4th) with an additional and optional step of distillation."""
+

 import argparse
 import glob
 import logging
 import os
 import random
-import timeit

 import numpy as np
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
+from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
 from torch.utils.data.distributed import DistributedSampler
 from tqdm import tqdm, trange

@@ -39,9 +39,6 @@ from transformers import (
    DistilBertConfig,
    DistilBertForQuestionAnswering,
    DistilBertTokenizer,
-    RobertaConfig,
-    RobertaForQuestionAnswering,
-    RobertaTokenizer,
    XLMConfig,
    XLMForQuestionAnswering,
    XLMTokenizer,
@@ -49,14 +46,22 @@ from transformers import (
    XLNetForQuestionAnswering,
    XLNetTokenizer,
    get_linear_schedule_with_warmup,
-    squad_convert_examples_to_features,
 )
-from transformers.data.metrics.squad_metrics import (
-    compute_predictions_log_probs,
-    compute_predictions_logits,
-    squad_evaluate,
+
+from ..utils_squad import (
+    RawResult,
+    RawResultExtended,
+    convert_examples_to_features,
+    read_squad_examples,
+    write_predictions,
+    write_predictions_extended,
 )
-from transformers.data.processors.squad import SquadResult, SquadV1Processor, SquadV2Processor
+
+# The follwing import is the official SQuAD evaluation script (2.0).
+# You can remove it from the dependencies if you are using this script outside of the library
+# We've added it here for automated tests (see examples/test_examples.py file)
+from ..utils_squad_evaluate import EVAL_OPTS
+from ..utils_squad_evaluate import main as evaluate_on_squad


 try:
@@ -76,7 +81,6 @@ MODEL_CLASSES = {
    "xlnet": (XLNetConfig, XLNetForQuestionAnswering, XLNetTokenizer),
    "xlm": (XLMConfig, XLMForQuestionAnswering, XLMTokenizer),
    "distilbert": (DistilBertConfig, DistilBertForQuestionAnswering, DistilBertTokenizer),
-    "roberta": (RobertaConfig, RobertaForQuestionAnswering, RobertaTokenizer),
 }


@@ -120,21 +124,11 @@ def train(args, train_dataset, model, tokenizer, teacher=None):
    scheduler = get_linear_schedule_with_warmup(
        optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total
    )
-
-    # Check if saved optimizer or scheduler states exist
-    if os.path.isfile(os.path.join(args.model_name_or_path, "optimizer.pt")) and os.path.isfile(
-        os.path.join(args.model_name_or_path, "scheduler.pt")
-    ):
-        # Load in optimizer and scheduler states
-        optimizer.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "optimizer.pt")))
-        scheduler.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "scheduler.pt")))
-
    if args.fp16:
        try:
            from apex import amp
        except ImportError:
            raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
-
        model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)

    # multi-gpu training (should be after apex fp16 initialization)
@@ -161,47 +155,18 @@ def train(args, train_dataset, model, tokenizer, teacher=None):
    logger.info("  Gradient Accumulation steps = %d", args.gradient_accumulation_steps)
    logger.info("  Total optimization steps = %d", t_total)

-    global_step = 1
-    epochs_trained = 0
-    steps_trained_in_current_epoch = 0
-    # Check if continuing training from a checkpoint
-    if os.path.exists(args.model_name_or_path):
-        try:
-            # set global_step to gobal_step of last saved checkpoint from model path
-            checkpoint_suffix = args.model_name_or_path.split("-")[-1].split("/")[0]
-            global_step = int(checkpoint_suffix)
-            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)
-
-            logger.info("  Continuing training from checkpoint, will skip to saved global_step")
-            logger.info("  Continuing training from epoch %d", epochs_trained)
-            logger.info("  Continuing training from global step %d", global_step)
-            logger.info("  Will skip the first %d steps in the first epoch", steps_trained_in_current_epoch)
-        except ValueError:
-            logger.info("  Starting fine-tuning.")
-
+    global_step = 0
    tr_loss, logging_loss = 0.0, 0.0
    model.zero_grad()
-    train_iterator = trange(
-        epochs_trained, int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0]
-    )
-    # Added here for reproductibility
-    set_seed(args)
-
+    train_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0])
+    set_seed(args)  # Added here for reproductibility
    for _ in train_iterator:
        epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])
        for step, batch in enumerate(epoch_iterator):
-
-            # Skip past any already trained steps if resuming training
-            if steps_trained_in_current_epoch > 0:
-                steps_trained_in_current_epoch -= 1
-                continue
-
            model.train()
            if teacher is not None:
                teacher.eval()
            batch = tuple(t.to(args.device) for t in batch)
-
            inputs = {
                "input_ids": batch[0],
                "attention_mask": batch[1],
@@ -212,8 +177,6 @@ def train(args, train_dataset, model, tokenizer, teacher=None):
                inputs["token_type_ids"] = None if args.model_type == "xlm" else batch[2]
            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]})
            outputs = model(**inputs)
            loss, start_logits_stu, end_logits_stu = outputs

@@ -251,25 +214,23 @@ def train(args, train_dataset, model, tokenizer, teacher=None):
            if args.fp16:
                with amp.scale_loss(loss, optimizer) as scaled_loss:
                    scaled_loss.backward()
+                torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
            else:
                loss.backward()
+                torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)

            tr_loss += loss.item()
            if (step + 1) % args.gradient_accumulation_steps == 0:
-                if args.fp16:
-                    torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
-                else:
-                    torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
-
                optimizer.step()
                scheduler.step()  # Update learning rate schedule
                model.zero_grad()
                global_step += 1

-                # Log metrics
                if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
-                    # Only evaluate when single GPU otherwise metrics may not average well
-                    if args.local_rank == -1 and args.evaluate_during_training:
+                    # Log metrics
+                    if (
+                        args.local_rank == -1 and args.evaluate_during_training
+                    ):  # Only evaluate when single GPU otherwise metrics may not average well
                        results = evaluate(args, model, tokenizer)
                        for key, value in results.items():
                            tb_writer.add_scalar("eval_{}".format(key), value, global_step)
@@ -286,15 +247,9 @@ def train(args, train_dataset, model, tokenizer, teacher=None):
                        model.module if hasattr(model, "module") else model
                    )  # Take care of distributed/parallel training
                    model_to_save.save_pretrained(output_dir)
-                    tokenizer.save_pretrained(output_dir)
-
                    torch.save(args, os.path.join(output_dir, "training_args.bin"))
                    logger.info("Saving model checkpoint to %s", output_dir)

-                    torch.save(optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt"))
-                    torch.save(scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt"))
-                    logger.info("Saving optimizer and scheduler states to %s", output_dir)
-
            if args.max_steps > 0 and global_step > args.max_steps:
                epoch_iterator.close()
                break
@@ -315,27 +270,18 @@ def evaluate(args, model, tokenizer, prefix=""):
        os.makedirs(args.output_dir)

    args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
-
    # Note that DistributedSampler samples randomly
-    eval_sampler = SequentialSampler(dataset)
+    eval_sampler = SequentialSampler(dataset) if args.local_rank == -1 else DistributedSampler(dataset)
    eval_dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)

-    # multi-gpu evaluate
-    if args.n_gpu > 1 and not isinstance(model, torch.nn.DataParallel):
-        model = torch.nn.DataParallel(model)
-
    # Eval!
    logger.info("***** Running evaluation {} *****".format(prefix))
    logger.info("  Num examples = %d", len(dataset))
    logger.info("  Batch size = %d", args.eval_batch_size)
-
    all_results = []
-    start_time = timeit.default_timer()
-
    for batch in tqdm(eval_dataloader, desc="Evaluating"):
        model.eval()
        batch = tuple(t.to(args.device) for t in batch)
-
        with torch.no_grad():
            inputs = {"input_ids": batch[0], "attention_mask": batch[1]}
            if args.model_type != "distilbert":
@@ -343,46 +289,30 @@ def evaluate(args, model, tokenizer, prefix=""):
            example_indices = batch[3]
            if args.model_type in ["xlnet", "xlm"]:
                inputs.update({"cls_index": batch[4], "p_mask": batch[5]})
-
            outputs = model(**inputs)

        for i, example_index in enumerate(example_indices):
            eval_feature = features[example_index.item()]
            unique_id = int(eval_feature.unique_id)
-
-            output = [to_list(output[i]) for output in outputs]
-
-            # Some models (XLNet, XLM) use 5 arguments for their predictions, while the other "simpler"
-            # models only use two.
-            if len(output) >= 5:
-                start_logits = output[0]
-                start_top_index = output[1]
-                end_logits = output[2]
-                end_top_index = output[3]
-                cls_logits = output[4]
-
-                result = SquadResult(
-                    unique_id,
-                    start_logits,
-                    end_logits,
-                    start_top_index=start_top_index,
-                    end_top_index=end_top_index,
-                    cls_logits=cls_logits,
+            if args.model_type in ["xlnet", "xlm"]:
+                # XLNet uses a more complex post-processing procedure
+                result = RawResultExtended(
+                    unique_id=unique_id,
+                    start_top_log_probs=to_list(outputs[0][i]),
+                    start_top_index=to_list(outputs[1][i]),
+                    end_top_log_probs=to_list(outputs[2][i]),
+                    end_top_index=to_list(outputs[3][i]),
+                    cls_logits=to_list(outputs[4][i]),
                )
-
            else:
-                start_logits, end_logits = output
-                result = SquadResult(unique_id, start_logits, end_logits)
-
+                result = RawResult(
+                    unique_id=unique_id, start_logits=to_list(outputs[0][i]), end_logits=to_list(outputs[1][i])
+                )
            all_results.append(result)

-    evalTime = timeit.default_timer() - start_time
-    logger.info("  Evaluation done in total %f secs (%f sec per example)", evalTime, evalTime / len(dataset))
-
    # Compute predictions
    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:
@@ -390,7 +320,7 @@ def evaluate(args, model, tokenizer, prefix=""):

    if args.model_type in ["xlnet", "xlm"]:
        # XLNet uses a more complex post-processing procedure
-        predictions = compute_predictions_log_probs(
+        write_predictions_extended(
            examples,
            features,
            all_results,
@@ -399,6 +329,7 @@ def evaluate(args, model, tokenizer, prefix=""):
            output_prediction_file,
            output_nbest_file,
            output_null_log_odds_file,
+            args.predict_file,
            model.config.start_n_top,
            model.config.end_n_top,
            args.version_2_with_negative,
@@ -406,7 +337,7 @@ def evaluate(args, model, tokenizer, prefix=""):
            args.verbose_logging,
        )
    else:
-        predictions = compute_predictions_logits(
+        write_predictions(
            examples,
            features,
            all_results,
@@ -419,70 +350,76 @@ def evaluate(args, model, tokenizer, prefix=""):
            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)
+    # Evaluate with the official SQuAD script
+    evaluate_options = EVAL_OPTS(
+        data_file=args.predict_file, pred_file=output_prediction_file, na_prob_file=output_null_log_odds_file
+    )
+    results = evaluate_on_squad(evaluate_options)
    return results


 def load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=False):
    if args.local_rank not in [-1, 0] and not evaluate:
-        # Make sure only the first process in distributed training process the dataset, and the others will use the cache
-        torch.distributed.barrier()
+        torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache

    # Load data features from cache or dataset file
    input_file = args.predict_file if evaluate else args.train_file
    cached_features_file = os.path.join(
        os.path.dirname(input_file),
-        "cached_distillation_{}_{}_{}".format(
+        "cached_{}_{}_{}".format(
            "dev" if evaluate else "train",
            list(filter(None, args.model_name_or_path.split("/"))).pop(),
            str(args.max_seq_length),
        ),
    )
-    if os.path.exists(cached_features_file) and not args.overwrite_cache:
+    if os.path.exists(cached_features_file) and not args.overwrite_cache and not output_examples:
        logger.info("Loading features from cached file %s", cached_features_file)
-        features_and_dataset = torch.load(cached_features_file)
-
-        try:
-            features, dataset, examples = (
-                features_and_dataset["features"],
-                features_and_dataset["dataset"],
-                features_and_dataset["examples"],
-            )
-        except KeyError:
-            raise DeprecationWarning(
-                "You seem to be loading features from an older version of this script please delete the "
-                "file %s in order for it to be created again" % cached_features_file
-            )
+        features = torch.load(cached_features_file)
    else:
        logger.info("Creating features from dataset file at %s", input_file)
-        processor = SquadV2Processor() if args.version_2_with_negative else SquadV1Processor()
-        if evaluate:
-            examples = processor.get_dev_examples(args.data_dir, filename=args.predict_file)
-        else:
-            examples = processor.get_train_examples(args.data_dir, filename=args.train_file)
-
-        features, dataset = squad_convert_examples_to_features(
+        examples = read_squad_examples(
+            input_file=input_file, is_training=not evaluate, version_2_with_negative=args.version_2_with_negative
+        )
+        features = convert_examples_to_features(
            examples=examples,
            tokenizer=tokenizer,
            max_seq_length=args.max_seq_length,
            doc_stride=args.doc_stride,
            max_query_length=args.max_query_length,
            is_training=not evaluate,
-            return_dataset="pt",
-            threads=args.threads,
        )
-
        if args.local_rank in [-1, 0]:
            logger.info("Saving features into cached file %s", cached_features_file)
-            torch.save({"features": features, "dataset": dataset, "examples": examples}, cached_features_file)
+            torch.save(features, cached_features_file)

    if args.local_rank == 0 and not evaluate:
-        # Make sure only the first process in distributed training process the dataset, and the others will use the cache
-        torch.distributed.barrier()
+        torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache
+
+    # Convert to Tensors and build dataset
+    all_input_ids = torch.tensor([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_cls_index = torch.tensor([f.cls_index for f in features], dtype=torch.long)
+    all_p_mask = torch.tensor([f.p_mask for f in features], dtype=torch.float)
+    if evaluate:
+        all_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long)
+        dataset = TensorDataset(
+            all_input_ids, all_input_mask, all_segment_ids, all_example_index, all_cls_index, all_p_mask
+        )
+    else:
+        all_start_positions = torch.tensor([f.start_position for f in features], dtype=torch.long)
+        all_end_positions = torch.tensor([f.end_position for f in features], dtype=torch.long)
+        dataset = TensorDataset(
+            all_input_ids,
+            all_input_mask,
+            all_segment_ids,
+            all_start_positions,
+            all_end_positions,
+            all_cls_index,
+            all_p_mask,
+        )

    if output_examples:
        return dataset, examples, features
@@ -493,6 +430,16 @@ def main():
    parser = argparse.ArgumentParser()

    # Required parameters
+    parser.add_argument(
+        "--train_file", default=None, type=str, required=True, help="SQuAD json for training. E.g., train-v1.1.json"
+    )
+    parser.add_argument(
+        "--predict_file",
+        default=None,
+        type=str,
+        required=True,
+        help="SQuAD json for predictions. E.g., dev-v1.1.json or test-v1.1.json",
+    )
    parser.add_argument(
        "--model_type",
        default=None,
@@ -539,27 +486,6 @@ def main():
    )

    # Other parameters
-    parser.add_argument(
-        "--data_dir",
-        default=None,
-        type=str,
-        help="The input data dir. Should contain the .json files for the task."
-        + "If no data dir or train/predict files are specified, will run with tensorflow_datasets.",
-    )
-    parser.add_argument(
-        "--train_file",
-        default=None,
-        type=str,
-        help="The input training file. If a data dir is specified, will look for the file there"
-        + "If no data dir or train/predict files are specified, will run with tensorflow_datasets.",
-    )
-    parser.add_argument(
-        "--predict_file",
-        default=None,
-        type=str,
-        help="The input evaluation file. If a data dir is specified, will look for the file there"
-        + "If no data dir or train/predict files are specified, will run with tensorflow_datasets.",
-    )
    parser.add_argument(
        "--config_name", default="", type=str, help="Pretrained config name or path if not the same as model_name"
    )
@@ -628,7 +554,7 @@ def main():
        default=1,
        help="Number of updates steps to accumulate before performing a backward/update pass.",
    )
-    parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.")
+    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(
@@ -692,8 +618,6 @@ def main():
    )
    parser.add_argument("--server_ip", type=str, default="", help="Can be used for distant debugging.")
    parser.add_argument("--server_port", type=str, default="", help="Can be used for distant debugging.")
-
-    parser.add_argument("--threads", type=int, default=1, help="multiple threads for converting example to features")
    args = parser.parse_args()

    if (
@@ -720,7 +644,7 @@ 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 = 0 if args.no_cuda else torch.cuda.device_count()
+        args.n_gpu = 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)
@@ -748,8 +672,7 @@ def main():

    # Load pretrained model and tokenizer
    if args.local_rank not in [-1, 0]:
-        # Make sure only the first process in distributed training will download model & vocab
-        torch.distributed.barrier()
+        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]
@@ -786,24 +709,12 @@ def main():
        teacher = None

    if args.local_rank == 0:
-        # Make sure only the first process in distributed training will download model & vocab
-        torch.distributed.barrier()
+        torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab

    model.to(args.device)

    logger.info("Training/evaluation parameters %s", args)

-    # Before we do anything with models, we want to ensure that we get fp16 execution of torch.einsum if args.fp16 is set.
-    # Otherwise it'll default to "promote" mode, and we'll get fp32 operations. Note that running `--fp16_opt_level="O2"` will
-    # remove the need for this code, but it is still valid.
-    if args.fp16:
-        try:
-            import apex
-
-            apex.amp.register_half_function(torch, "einsum")
-        except ImportError:
-            raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
-
    # Training
    if args.do_train:
        train_dataset = load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=False)
@@ -829,15 +740,15 @@ 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, do_lower_case=args.do_lower_case)
+        model = model_class.from_pretrained(args.output_dir, cache_dir=args.cache_dir if args.cache_dir else None)
+        tokenizer = tokenizer_class.from_pretrained(
+            args.output_dir, do_lower_case=args.do_lower_case, cache_dir=args.cache_dir if args.cache_dir else None
+        )
        model.to(args.device)

    # Evaluation - we can ask to evaluate all the checkpoints (sub-directories) in a directory
    results = {}
    if args.do_eval and args.local_rank in [-1, 0]:
-        if args.do_train:
-            logger.info("Loading checkpoints saved during training for evaluation")
        checkpoints = [args.output_dir]
        if args.eval_all_checkpoints:
            checkpoints = list(
@@ -850,7 +761,7 @@ 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)
+            model = model_class.from_pretrained(checkpoint, cache_dir=args.cache_dir if args.cache_dir else None)
            model.to(args.device)

            # Evaluate
@@ -75,17 +75,13 @@ def main():
        iter += 1
        if iter % interval == 0:
            end = time.time()
-            logger.info(f"{iter} examples processed. - {(end-start):.2f}s/{interval}expl")
+            logger.info(f"{iter} examples processed. - {(end-start)/interval:.2f}s/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"
-    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]
+    rslt_ = [np.uint16(d) for d in rslt]
    random.shuffle(rslt_)
    logger.info(f"Dump to {dp_file}")
    with open(dp_file, "wb") as handle:
@@ -1,15 +0,0 @@
-{
-	"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
-  }
-  
@@ -1,15 +0,0 @@
-{
-	"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": 119547
-  }
-  
@@ -1,14 +0,0 @@
-{
-    "vocab_size": 50265,
-    "hidden_size": 768,
-    "num_hidden_layers": 6,
-    "num_attention_heads": 12,
-    "intermediate_size": 3072,
-    "hidden_act": "gelu",
-    "hidden_dropout_prob": 0.1,
-    "attention_probs_dropout_prob": 0.1,
-    "max_position_embeddings": 514,
-    "type_vocab_size": 1,
-    "initializer_range": 0.02,
-    "layer_norm_eps": 0.00001
-}
@@ -117,9 +117,7 @@ def init_gpu_params(params):
    # initialize multi-GPU
    if params.multi_gpu:
        logger.info("Initializing PyTorch distributed")
-        torch.distributed.init_process_group(
-            init_method="env://", backend="nccl",
-        )
+        torch.distributed.init_process_group(init_method="env://", backend="nccl")


 def set_seed(args):
@@ -1,9 +0,0 @@
-# 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}`
@@ -1,38 +0,0 @@
-# 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
@@ -1,196 +0,0 @@
-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,
-                    task=args.task,
-                    label_list=self.labels,
-                    output_mode=args.glue_output_mode,
-                    pad_on_left=bool(args.model_type in ["xlnet"]),  # pad on the left for xlnet
-                    pad_token=self.tokenizer.convert_tokens_to_ids([self.tokenizer.pad_token])[0],
-                    pad_token_segment_id=4 if args.model_type in ["xlnet"] else 0,
-                )
-                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)))
-        GLUETransformer.load_from_checkpoint(checkpoints[-1])
-        trainer.test(model)
@@ -1,221 +0,0 @@
-# coding=utf-8
-# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
-# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" GLUE processors and helpers """
-
-import logging
-import os
-
-from transformers.file_utils import is_tf_available
-from utils_hans import DataProcessor, InputExample, InputFeatures
-
-
-if is_tf_available():
-    import tensorflow as tf
-
-logger = logging.getLogger(__name__)
-
-
-def hans_convert_examples_to_features(
-    examples,
-    tokenizer,
-    max_length=512,
-    task=None,
-    label_list=None,
-    output_mode=None,
-    pad_on_left=False,
-    pad_token=0,
-    pad_token_segment_id=0,
-    mask_padding_with_zero=True,
-):
-    """
-    Loads a data file into a list of ``InputFeatures``
-
-    Args:
-        examples: List of ``InputExamples`` or ``tf.data.Dataset`` containing the examples.
-        tokenizer: Instance of a tokenizer that will tokenize the examples
-        max_length: Maximum example length
-        task: HANS
-        label_list: List of labels. Can be obtained from the processor using the ``processor.get_labels()`` method
-        output_mode: String indicating the output mode. Either ``regression`` or ``classification``
-        pad_on_left: If set to ``True``, the examples will be padded on the left rather than on the right (default)
-        pad_token: Padding token
-        pad_token_segment_id: The segment ID for the padding token (It is usually 0, but can vary such as for XLNet where it is 4)
-        mask_padding_with_zero: If set to ``True``, the attention mask will be filled by ``1`` for actual values
-            and by ``0`` for padded values. If set to ``False``, inverts it (``1`` for padded values, ``0`` for
-            actual values)
-
-    Returns:
-        If the ``examples`` input is a ``tf.data.Dataset``, will return a ``tf.data.Dataset``
-        containing the task-specific features. If the input is a list of ``InputExamples``, will return
-        a list of task-specific ``InputFeatures`` which can be fed to the model.
-
-    """
-    is_tf_dataset = False
-    if is_tf_available() and isinstance(examples, tf.data.Dataset):
-        is_tf_dataset = True
-
-    if task is not None:
-        processor = glue_processors[task]()
-        if label_list is None:
-            label_list = processor.get_labels()
-            logger.info("Using label list %s for task %s" % (label_list, task))
-        if output_mode is None:
-            output_mode = glue_output_modes[task]
-            logger.info("Using output mode %s for task %s" % (output_mode, task))
-
-    label_map = {label: i for i, label in enumerate(label_list)}
-
-    features = []
-    for (ex_index, example) in enumerate(examples):
-        if ex_index % 10000 == 0:
-            logger.info("Writing example %d" % (ex_index))
-        if is_tf_dataset:
-            example = processor.get_example_from_tensor_dict(example)
-            example = processor.tfds_map(example)
-
-        inputs = tokenizer.encode_plus(example.text_a, example.text_b, add_special_tokens=True, max_length=max_length,)
-        input_ids, token_type_ids = inputs["input_ids"], inputs["token_type_ids"]
-
-        # The mask has 1 for real tokens and 0 for padding tokens. Only real
-        # tokens are attended to.
-        attention_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)
-
-        # Zero-pad up to the sequence length.
-        padding_length = max_length - len(input_ids)
-        if pad_on_left:
-            input_ids = ([pad_token] * padding_length) + input_ids
-            attention_mask = ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
-            token_type_ids = ([pad_token_segment_id] * padding_length) + token_type_ids
-        else:
-            input_ids = input_ids + ([pad_token] * padding_length)
-            attention_mask = attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)
-            token_type_ids = token_type_ids + ([pad_token_segment_id] * padding_length)
-
-        assert len(input_ids) == max_length, "Error with input length {} vs {}".format(len(input_ids), max_length)
-        assert len(attention_mask) == max_length, "Error with input length {} vs {}".format(
-            len(attention_mask), max_length
-        )
-        assert len(token_type_ids) == max_length, "Error with input length {} vs {}".format(
-            len(token_type_ids), max_length
-        )
-
-        if output_mode == "classification":
-            label = label_map[example.label] if example.label in label_map else 0
-        elif output_mode == "regression":
-            label = float(example.label)
-        else:
-            raise KeyError(output_mode)
-        pairID = str(example.pairID)
-
-        if ex_index < 10:
-            logger.info("*** Example ***")
-            logger.info("text_a: %s" % (example.text_a))
-            logger.info("text_b: %s" % (example.text_b))
-            logger.info("guid: %s" % (example.guid))
-            logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
-            logger.info("attention_mask: %s" % " ".join([str(x) for x in attention_mask]))
-            logger.info("token_type_ids: %s" % " ".join([str(x) for x in token_type_ids]))
-            logger.info("label: %s (id = %d)" % (example.label, label))
-
-        features.append(
-            InputFeatures(
-                input_ids=input_ids,
-                attention_mask=attention_mask,
-                token_type_ids=token_type_ids,
-                label=label,
-                pairID=pairID,
-            )
-        )
-
-    if is_tf_available() and is_tf_dataset:
-
-        def gen():
-            for ex in features:
-                yield (
-                    {
-                        "input_ids": ex.input_ids,
-                        "attention_mask": ex.attention_mask,
-                        "token_type_ids": ex.token_type_ids,
-                    },
-                    ex.label,
-                )
-
-        return tf.data.Dataset.from_generator(
-            gen,
-            ({"input_ids": tf.int32, "attention_mask": tf.int32, "token_type_ids": tf.int32}, tf.int64),
-            (
-                {
-                    "input_ids": tf.TensorShape([None]),
-                    "attention_mask": tf.TensorShape([None]),
-                    "token_type_ids": tf.TensorShape([None]),
-                },
-                tf.TensorShape([]),
-            ),
-        )
-
-    return features
-
-
-class HansProcessor(DataProcessor):
-    """Processor for the HANS data set."""
-
-    def get_example_from_tensor_dict(self, tensor_dict):
-        """See base class."""
-        return InputExample(
-            tensor_dict["idx"].numpy(),
-            tensor_dict["premise"].numpy().decode("utf-8"),
-            tensor_dict["hypothesis"].numpy().decode("utf-8"),
-            str(tensor_dict["label"].numpy()),
-        )
-
-    def get_train_examples(self, data_dir):
-        """See base class."""
-        return self._create_examples(self._read_tsv(os.path.join(data_dir, "heuristics_train_set.txt")), "train")
-
-    def get_dev_examples(self, data_dir):
-        """See base class."""
-        return self._create_examples(self._read_tsv(os.path.join(data_dir, "heuristics_evaluation_set.txt")), "dev")
-
-    def get_labels(self):
-        """See base class."""
-        return ["contradiction", "entailment", "neutral"]
-
-    def _create_examples(self, lines, set_type):
-        """Creates examples for the training and dev sets."""
-        examples = []
-        for (i, line) in enumerate(lines):
-            if i == 0:
-                continue
-            guid = "%s-%s" % (set_type, line[0])
-            text_a = line[5]
-            text_b = line[6]
-            pairID = line[7][2:] if line[7].startswith("ex") else line[7]
-            label = line[-1]
-            examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label, pairID=pairID))
-        return examples
-
-
-glue_tasks_num_labels = {
-    "hans": 3,
-}
-
-glue_processors = {
-    "hans": HansProcessor,
-}
-
-glue_output_modes = {
-    "hans": "classification",
-}
@@ -1,643 +0,0 @@
-# coding=utf-8
-# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
-# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" Finetuning the library models for sequence classification on GLUE (Bert, XLM, 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
-from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
-from torch.utils.data.distributed import DistributedSampler
-from tqdm import tqdm, trange
-
-from hans_processors import glue_output_modes as output_modes
-from hans_processors import glue_processors as processors
-from hans_processors import hans_convert_examples_to_features as convert_examples_to_features
-from transformers import (
-    WEIGHTS_NAME,
-    AdamW,
-    AlbertConfig,
-    AlbertForSequenceClassification,
-    AlbertTokenizer,
-    BertConfig,
-    BertForSequenceClassification,
-    BertTokenizer,
-    DistilBertConfig,
-    DistilBertForSequenceClassification,
-    DistilBertTokenizer,
-    RobertaConfig,
-    RobertaForSequenceClassification,
-    RobertaTokenizer,
-    XLMConfig,
-    XLMForSequenceClassification,
-    XLMTokenizer,
-    XLNetConfig,
-    XLNetForSequenceClassification,
-    XLNetTokenizer,
-    get_linear_schedule_with_warmup,
-)
-
-
-try:
-    from torch.utils.tensorboard import SummaryWriter
-except ImportError:
-    from tensorboardX 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),
-    "albert": (AlbertConfig, AlbertForSequenceClassification, AlbertTokenizer),
-}
-
-
-def set_seed(args):
-    random.seed(args.seed)
-    np.random.seed(args.seed)
-    torch.manual_seed(args.seed)
-    if args.n_gpu > 0:
-        torch.cuda.manual_seed_all(args.seed)
-
-
-def train(args, train_dataset, model, tokenizer):
-    """ Train the model """
-    if args.local_rank in [-1, 0]:
-        tb_writer = SummaryWriter()
-
-    args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
-    train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset)
-    train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size)
-
-    if args.max_steps > 0:
-        t_total = args.max_steps
-        args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1
-    else:
-        t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
-
-    # Prepare optimizer and schedule (linear warmup and decay)
-    no_decay = ["bias", "LayerNorm.weight"]
-    optimizer_grouped_parameters = [
-        {
-            "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
-            "weight_decay": args.weight_decay,
-        },
-        {"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0},
-    ]
-
-    optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
-    scheduler = get_linear_schedule_with_warmup(
-        optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total
-    )
-    if args.fp16:
-        try:
-            from apex import amp
-        except ImportError:
-            raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
-        model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)
-
-    # multi-gpu training (should be after apex fp16 initialization)
-    if args.n_gpu > 1:
-        model = torch.nn.DataParallel(model)
-
-    # Distributed training (should be after apex fp16 initialization)
-    if args.local_rank != -1:
-        model = torch.nn.parallel.DistributedDataParallel(
-            model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True
-        )
-
-    # Train!
-    logger.info("***** Running training *****")
-    logger.info("  Num examples = %d", len(train_dataset))
-    logger.info("  Num Epochs = %d", args.num_train_epochs)
-    logger.info("  Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size)
-    logger.info(
-        "  Total train batch size (w. parallel, distributed & accumulation) = %d",
-        args.train_batch_size
-        * args.gradient_accumulation_steps
-        * (torch.distributed.get_world_size() if args.local_rank != -1 else 1),
-    )
-    logger.info("  Gradient Accumulation steps = %d", args.gradient_accumulation_steps)
-    logger.info("  Total optimization steps = %d", t_total)
-
-    global_step = 0
-    tr_loss, logging_loss = 0.0, 0.0
-    model.zero_grad()
-    train_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0])
-    set_seed(args)  # Added here for reproductibility (even between python 2 and 3)
-    for _ in train_iterator:
-        epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])
-        for step, batch in enumerate(epoch_iterator):
-            model.train()
-            batch = tuple(t.to(args.device) for t in batch)
-            inputs = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
-            if args.model_type != "distilbert":
-                inputs["token_type_ids"] = (
-                    batch[2] if args.model_type in ["bert", "xlnet"] else None
-                )  # XLM, DistilBERT and RoBERTa don't use segment_ids
-            outputs = model(**inputs)
-            loss = outputs[0]  # model outputs are always tuple in transformers (see doc)
-
-            if args.n_gpu > 1:
-                loss = loss.mean()  # mean() to average on multi-gpu parallel training
-            if args.gradient_accumulation_steps > 1:
-                loss = loss / args.gradient_accumulation_steps
-
-            if args.fp16:
-                with amp.scale_loss(loss, optimizer) as scaled_loss:
-                    scaled_loss.backward()
-            else:
-                loss.backward()
-
-            tr_loss += loss.item()
-            if (step + 1) % args.gradient_accumulation_steps == 0:
-                if args.fp16:
-                    torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
-                else:
-                    torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
-
-                optimizer.step()
-                scheduler.step()  # Update learning rate schedule
-                model.zero_grad()
-                global_step += 1
-
-                if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
-                    logs = {}
-                    if (
-                        args.local_rank == -1 and args.evaluate_during_training
-                    ):  # Only evaluate when single GPU otherwise metrics may not average well
-                        results = evaluate(args, model, tokenizer)
-                        for key, value in results.items():
-                            eval_key = "eval_{}".format(key)
-                            logs[eval_key] = value
-
-                    loss_scalar = (tr_loss - logging_loss) / args.logging_steps
-                    learning_rate_scalar = scheduler.get_lr()[0]
-                    logs["learning_rate"] = learning_rate_scalar
-                    logs["loss"] = loss_scalar
-                    logging_loss = tr_loss
-
-                    for key, value in logs.items():
-                        tb_writer.add_scalar(key, value, global_step)
-                    # print(json.dumps({**logs, **{'step': global_step}}))
-
-                if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
-                    # Save model checkpoint
-                    output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(global_step))
-                    if not os.path.exists(output_dir):
-                        os.makedirs(output_dir)
-                    model_to_save = (
-                        model.module if hasattr(model, "module") else model
-                    )  # Take care of distributed/parallel training
-                    model_to_save.save_pretrained(output_dir)
-                    torch.save(args, os.path.join(output_dir, "training_args.bin"))
-                    logger.info("Saving model checkpoint to %s", output_dir)
-
-            if args.max_steps > 0 and global_step > args.max_steps:
-                epoch_iterator.close()
-                break
-        if args.max_steps > 0 and global_step > args.max_steps:
-            train_iterator.close()
-            break
-
-    if args.local_rank in [-1, 0]:
-        tb_writer.close()
-
-    return global_step, tr_loss / global_step
-
-
-def evaluate(args, model, tokenizer, prefix=""):
-    # Loop to handle MNLI double evaluation (matched, mis-matched)
-    eval_task_names = ("mnli", "mnli-mm") if args.task_name == "mnli" else (args.task_name,)
-    eval_outputs_dirs = (args.output_dir, args.output_dir + "-MM") if args.task_name == "mnli" else (args.output_dir,)
-
-    results = {}
-    for eval_task, eval_output_dir in zip(eval_task_names, eval_outputs_dirs):
-        eval_dataset, label_list = load_and_cache_examples(args, eval_task, tokenizer, evaluate=True)
-
-        if not os.path.exists(eval_output_dir) and args.local_rank in [-1, 0]:
-            os.makedirs(eval_output_dir)
-
-        args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
-        # Note that DistributedSampler samples randomly
-        eval_sampler = SequentialSampler(eval_dataset)
-        eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
-
-        # multi-gpu eval
-        if args.n_gpu > 1:
-            model = torch.nn.DataParallel(model)
-
-        # Eval!
-        logger.info("***** Running evaluation {} *****".format(prefix))
-        logger.info("  Num examples = %d", len(eval_dataset))
-        logger.info("  Batch size = %d", args.eval_batch_size)
-        eval_loss = 0.0
-        nb_eval_steps = 0
-        preds = None
-        out_label_ids = None
-        for batch in tqdm(eval_dataloader, desc="Evaluating"):
-            model.eval()
-            batch = tuple(t.to(args.device) for t in batch)
-
-            with torch.no_grad():
-                inputs = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
-                if args.model_type != "distilbert":
-                    inputs["token_type_ids"] = (
-                        batch[2] if args.model_type in ["bert", "xlnet"] else None
-                    )  # XLM, DistilBERT and RoBERTa don't use segment_ids
-                outputs = model(**inputs)
-                tmp_eval_loss, logits = outputs[:2]
-
-                eval_loss += tmp_eval_loss.mean().item()
-            nb_eval_steps += 1
-            if preds is None:
-                preds = logits.detach().cpu().numpy()
-                out_label_ids = inputs["labels"].detach().cpu().numpy()
-                pair_ids = batch[4].detach().cpu().numpy()
-            else:
-                preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)
-                out_label_ids = np.append(out_label_ids, inputs["labels"].detach().cpu().numpy(), axis=0)
-                pair_ids = np.append(pair_ids, batch[4].detach().cpu().numpy(), axis=0)
-
-        eval_loss = eval_loss / nb_eval_steps
-        if args.output_mode == "classification":
-            preds = np.argmax(preds, axis=1)
-        elif args.output_mode == "regression":
-            preds = np.squeeze(preds)
-
-        output_eval_file = os.path.join(eval_output_dir, "hans_predictions.txt")
-        with open(output_eval_file, "w") as writer:
-            writer.write("pairID,gld_label\n")
-            for pid, pred in zip(pair_ids, preds):
-                writer.write("ex" + str(pid) + "," + label_list[int(pred)] + "\n")
-
-    return results
-
-
-def load_and_cache_examples(args, task, tokenizer, evaluate=False):
-    if args.local_rank not in [-1, 0] and not evaluate:
-        torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache
-
-    processor = processors[task]()
-    output_mode = output_modes[task]
-    # Load data features from cache or dataset file
-    cached_features_file = os.path.join(
-        args.data_dir,
-        "cached_{}_{}_{}_{}".format(
-            "dev" if evaluate else "train",
-            list(filter(None, args.model_name_or_path.split("/"))).pop(),
-            str(args.max_seq_length),
-            str(task),
-        ),
-    )
-
-    label_list = processor.get_labels()
-
-    if os.path.exists(cached_features_file) and not args.overwrite_cache:
-        logger.info("Loading features from cached file %s", cached_features_file)
-        features = torch.load(cached_features_file)
-    else:
-        logger.info("Creating features from dataset file at %s", args.data_dir)
-        if task in ["mnli", "mnli-mm"] and args.model_type in ["roberta"]:
-            # HACK(label indices are swapped in RoBERTa pretrained model)
-            label_list[1], label_list[2] = label_list[2], label_list[1]
-        examples = (
-            processor.get_dev_examples(args.data_dir) if evaluate else processor.get_train_examples(args.data_dir)
-        )
-        features = convert_examples_to_features(
-            examples,
-            tokenizer,
-            label_list=label_list,
-            max_length=args.max_seq_length,
-            output_mode=output_mode,
-            pad_on_left=bool(args.model_type in ["xlnet"]),  # pad on the left for xlnet
-            pad_token=tokenizer.convert_tokens_to_ids([tokenizer.pad_token])[0],
-            pad_token_segment_id=4 if args.model_type in ["xlnet"] else 0,
-        )
-        if args.local_rank in [-1, 0]:
-            logger.info("Saving features into cached file %s", cached_features_file)
-            torch.save(features, cached_features_file)
-
-    if args.local_rank == 0 and not evaluate:
-        torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache
-
-    # Convert to Tensors and build dataset
-    all_input_ids = torch.tensor([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)
-    all_pair_ids = torch.tensor([int(f.pairID) for f in features], dtype=torch.long)
-
-    dataset = TensorDataset(all_input_ids, all_attention_mask, all_token_type_ids, all_labels, all_pair_ids)
-    return dataset, label_list
-
-
-def main():
-    parser = argparse.ArgumentParser()
-
-    # Required parameters
-    parser.add_argument(
-        "--data_dir",
-        default=None,
-        type=str,
-        required=True,
-        help="The input data dir. Should contain the .tsv files (or other data files) for the task.",
-    )
-    parser.add_argument(
-        "--model_type",
-        default=None,
-        type=str,
-        required=True,
-        help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()),
-    )
-    parser.add_argument(
-        "--model_name_or_path",
-        default=None,
-        type=str,
-        required=True,
-        help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS),
-    )
-    parser.add_argument(
-        "--task_name",
-        default=None,
-        type=str,
-        required=True,
-        help="The name of the task to train selected in the list: " + ", ".join(processors.keys()),
-    )
-    parser.add_argument(
-        "--output_dir",
-        default=None,
-        type=str,
-        required=True,
-        help="The output directory where the model predictions and checkpoints will be written.",
-    )
-
-    # Other parameters
-    parser.add_argument(
-        "--config_name", default="", type=str, help="Pretrained config name or path if not the same as model_name"
-    )
-    parser.add_argument(
-        "--tokenizer_name",
-        default="",
-        type=str,
-        help="Pretrained tokenizer name or path if not the same as model_name",
-    )
-    parser.add_argument(
-        "--cache_dir",
-        default="",
-        type=str,
-        help="Where do you want to store the pre-trained models downloaded from s3",
-    )
-    parser.add_argument(
-        "--max_seq_length",
-        default=128,
-        type=int,
-        help="The maximum total input sequence length after tokenization. Sequences longer "
-        "than this will be truncated, sequences shorter will be padded.",
-    )
-    parser.add_argument("--do_train", action="store_true", help="Whether to run training.")
-    parser.add_argument("--do_eval", action="store_true", help="Whether to run eval on the dev set.")
-    parser.add_argument(
-        "--evaluate_during_training", action="store_true", help="Rul evaluation during training at each logging step."
-    )
-    parser.add_argument(
-        "--do_lower_case", action="store_true", help="Set this flag if you are using an uncased model."
-    )
-
-    parser.add_argument("--per_gpu_train_batch_size", default=8, type=int, help="Batch size per GPU/CPU for training.")
-    parser.add_argument(
-        "--per_gpu_eval_batch_size", default=8, type=int, help="Batch size per GPU/CPU for evaluation."
-    )
-    parser.add_argument(
-        "--gradient_accumulation_steps",
-        type=int,
-        default=1,
-        help="Number of updates steps to accumulate before performing a backward/update pass.",
-    )
-    parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.")
-    parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.")
-    parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.")
-    parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
-    parser.add_argument(
-        "--num_train_epochs", default=3.0, type=float, help="Total number of training epochs to perform."
-    )
-    parser.add_argument(
-        "--max_steps",
-        default=-1,
-        type=int,
-        help="If > 0: set total number of training steps to perform. Override num_train_epochs.",
-    )
-    parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.")
-
-    parser.add_argument("--logging_steps", type=int, default=50, help="Log every X updates steps.")
-    parser.add_argument("--save_steps", type=int, default=50, help="Save checkpoint every X updates steps.")
-    parser.add_argument(
-        "--eval_all_checkpoints",
-        action="store_true",
-        help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number",
-    )
-    parser.add_argument("--no_cuda", action="store_true", help="Avoid using CUDA when available")
-    parser.add_argument(
-        "--overwrite_output_dir", action="store_true", help="Overwrite the content of the output directory"
-    )
-    parser.add_argument(
-        "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets"
-    )
-    parser.add_argument("--seed", type=int, default=42, help="random seed for initialization")
-
-    parser.add_argument(
-        "--fp16",
-        action="store_true",
-        help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit",
-    )
-    parser.add_argument(
-        "--fp16_opt_level",
-        type=str,
-        default="O1",
-        help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
-        "See details at https://nvidia.github.io/apex/amp.html",
-    )
-    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
-    parser.add_argument("--server_ip", type=str, default="", help="For distant debugging.")
-    parser.add_argument("--server_port", type=str, default="", help="For distant debugging.")
-    args = parser.parse_args()
-
-    if (
-        os.path.exists(args.output_dir)
-        and os.listdir(args.output_dir)
-        and args.do_train
-        and not args.overwrite_output_dir
-    ):
-        raise ValueError(
-            "Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format(
-                args.output_dir
-            )
-        )
-
-    # Setup distant debugging if needed
-    if args.server_ip and args.server_port:
-        # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
-        import ptvsd
-
-        print("Waiting for debugger attach")
-        ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
-        ptvsd.wait_for_attach()
-
-    # Setup CUDA, GPU & distributed training
-    if args.local_rank == -1 or args.no_cuda:
-        device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
-        args.n_gpu = 0 if args.no_cuda else torch.cuda.device_count()
-    else:  # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
-        torch.cuda.set_device(args.local_rank)
-        device = torch.device("cuda", args.local_rank)
-        torch.distributed.init_process_group(backend="nccl")
-        args.n_gpu = 1
-    args.device = device
-
-    # Setup logging
-    logging.basicConfig(
-        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
-        datefmt="%m/%d/%Y %H:%M:%S",
-        level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN,
-    )
-    logger.warning(
-        "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
-        args.local_rank,
-        device,
-        args.n_gpu,
-        bool(args.local_rank != -1),
-        args.fp16,
-    )
-
-    # Set seed
-    set_seed(args)
-
-    # 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)
-
-    # Load pretrained model and tokenizer
-    if args.local_rank not in [-1, 0]:
-        torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab
-
-    args.model_type = args.model_type.lower()
-    config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
-    config = config_class.from_pretrained(
-        args.config_name if args.config_name else args.model_name_or_path,
-        num_labels=num_labels,
-        finetuning_task=args.task_name,
-        cache_dir=args.cache_dir if args.cache_dir else None,
-    )
-    tokenizer = tokenizer_class.from_pretrained(
-        args.tokenizer_name if args.tokenizer_name else args.model_name_or_path,
-        do_lower_case=args.do_lower_case,
-        cache_dir=args.cache_dir if args.cache_dir else None,
-    )
-    model = model_class.from_pretrained(
-        args.model_name_or_path,
-        from_tf=bool(".ckpt" in args.model_name_or_path),
-        config=config,
-        cache_dir=args.cache_dir if args.cache_dir else None,
-    )
-
-    if args.local_rank == 0:
-        torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab
-
-    model.to(args.device)
-
-    logger.info("Training/evaluation parameters %s", args)
-
-    # Training
-    if args.do_train:
-        train_dataset, _ = load_and_cache_examples(args, args.task_name, tokenizer, evaluate=False)
-        global_step, tr_loss = train(args, train_dataset, model, tokenizer)
-        logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)
-
-    # Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
-    if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
-        # Create output directory if needed
-        if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
-            os.makedirs(args.output_dir)
-
-        logger.info("Saving model checkpoint to %s", args.output_dir)
-        # Save a trained model, configuration and tokenizer using `save_pretrained()`.
-        # They can then be reloaded using `from_pretrained()`
-        model_to_save = (
-            model.module if hasattr(model, "module") else model
-        )  # Take care of distributed/parallel training
-        model_to_save.save_pretrained(args.output_dir)
-        tokenizer.save_pretrained(args.output_dir)
-
-        # Good practice: save your training arguments together with the trained model
-        torch.save(args, os.path.join(args.output_dir, "training_args.bin"))
-
-        # Load a trained model and vocabulary that you have fine-tuned
-        model = model_class.from_pretrained(args.output_dir)
-        tokenizer = tokenizer_class.from_pretrained(args.output_dir)
-        model.to(args.device)
-
-    # Evaluation
-    results = {}
-    if args.do_eval and args.local_rank in [-1, 0]:
-        tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
-        checkpoints = [args.output_dir]
-        if args.eval_all_checkpoints:
-            checkpoints = list(
-                os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + "/**/" + WEIGHTS_NAME, recursive=True))
-            )
-            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)
-            result = dict((k + "_{}".format(global_step), v) for k, v in result.items())
-            results.update(result)
-
-    return results
-
-
-if __name__ == "__main__":
-    main()
@@ -1,121 +0,0 @@
-# coding=utf-8
-# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
-# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import copy
-import csv
-import json
-
-
-class InputExample(object):
-    """
-    A single training/test example for simple sequence classification.
-
-    Args:
-        guid: Unique id for the example.
-        text_a: string. The untokenized text of the first sequence. For single
-        sequence tasks, only this sequence must be specified.
-        text_b: (Optional) string. The untokenized text of the second sequence.
-        Only must be specified for sequence pair tasks.
-        label: (Optional) string. The label of the example. This should be
-        specified for train and dev examples, but not for test examples.
-    """
-
-    def __init__(self, guid, text_a, text_b=None, label=None, pairID=None):
-        self.guid = guid
-        self.text_a = text_a
-        self.text_b = text_b
-        self.label = label
-        self.pairID = pairID
-
-    def __repr__(self):
-        return str(self.to_json_string())
-
-    def to_dict(self):
-        """Serializes this instance to a Python dictionary."""
-        output = copy.deepcopy(self.__dict__)
-        return output
-
-    def to_json_string(self):
-        """Serializes this instance to a JSON string."""
-        return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
-
-
-class InputFeatures(object):
-    """
-    A single set of features of data.
-
-    Args:
-        input_ids: Indices of input sequence tokens in the vocabulary.
-        attention_mask: Mask to avoid performing attention on padding token indices.
-            Mask values selected in ``[0, 1]``:
-            Usually  ``1`` for tokens that are NOT MASKED, ``0`` for MASKED (padded) tokens.
-        token_type_ids: Segment token indices to indicate first and second portions of the inputs.
-        label: Label corresponding to the input
-    """
-
-    def __init__(self, input_ids, attention_mask, token_type_ids, label, pairID=None):
-        self.input_ids = input_ids
-        self.attention_mask = attention_mask
-        self.token_type_ids = token_type_ids
-        self.label = label
-        self.pairID = pairID
-
-    def __repr__(self):
-        return str(self.to_json_string())
-
-    def to_dict(self):
-        """Serializes this instance to a Python dictionary."""
-        output = copy.deepcopy(self.__dict__)
-        return output
-
-    def to_json_string(self):
-        """Serializes this instance to a JSON string."""
-        return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
-
-
-class DataProcessor(object):
-    """Base class for data converters for sequence classification data sets."""
-
-    def get_example_from_tensor_dict(self, tensor_dict):
-        """Gets an example from a dict with tensorflow tensors
-
-        Args:
-            tensor_dict: Keys and values should match the corresponding Glue
-                tensorflow_dataset examples.
-        """
-        raise NotImplementedError()
-
-    def get_train_examples(self, data_dir):
-        """Gets a collection of `InputExample`s for the train set."""
-        raise NotImplementedError()
-
-    def get_dev_examples(self, data_dir):
-        """Gets a collection of `InputExample`s for the dev set."""
-        raise NotImplementedError()
-
-    def get_labels(self):
-        """Gets the list of labels for this data set."""
-        raise NotImplementedError()
-
-    @classmethod
-    def _read_tsv(cls, input_file, quotechar=None):
-        """Reads a tab separated value file."""
-        with open(input_file, "r", encoding="utf-8-sig") as f:
-            reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
-            lines = []
-            for line in reader:
-                lines.append(line)
-            return lines
@@ -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 = 0 if args.no_cuda else torch.cuda.device_count()
+        args.n_gpu = 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)
@@ -31,7 +31,7 @@ POOLING_BREAKDOWN = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (

 class ImageEncoder(nn.Module):
    def __init__(self, args):
-        super().__init__()
+        super(ImageEncoder, self).__init__()
        model = torchvision.models.resnet152(pretrained=True)
        modules = list(model.children())[:-2]
        self.model = nn.Sequential(*modules)
@@ -138,6 +138,6 @@ def get_image_transforms():
            transforms.Resize(256),
            transforms.CenterCrop(224),
            transforms.ToTensor(),
-            transforms.Normalize(mean=[0.46777044, 0.44531429, 0.40661017], std=[0.12221994, 0.12145835, 0.14380469],),
+            transforms.Normalize(mean=[0.46777044, 0.44531429, 0.40661017], std=[0.12221994, 0.12145835, 0.14380469]),
        ]
    )
@@ -1,186 +0,0 @@
-## 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
-```
@@ -1,32 +0,0 @@
-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
@@ -1,43 +0,0 @@
-#!/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
@@ -1,196 +0,0 @@
-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.convert_tokens_to_ids([self.tokenizer.pad_token])[0],
-                    pad_token_segment_id=4 if args.model_type in ["xlnet"] else 0,
-                    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)))
-        NERTransformer.load_from_checkpoint(checkpoints[-1])
-        trainer.test(model)
@@ -5,7 +5,7 @@ class ClassificationHead(torch.nn.Module):
    """Classification Head for  transformer encoders"""

    def __init__(self, class_size, embed_size):
-        super().__init__()
+        super(ClassificationHead, self).__init__()
        self.class_size = class_size
        self.embed_size = embed_size
        # self.mlp1 = torch.nn.Linear(embed_size, embed_size)
@@ -344,7 +344,6 @@ def full_text_generation(
    gamma=1.5,
    gm_scale=0.9,
    kl_scale=0.01,
-    repetition_penalty=1.0,
    **kwargs
 ):
    classifier, class_id = get_classifier(discrim, class_label, device)
@@ -369,14 +368,7 @@ def full_text_generation(
        raise Exception("Specify either a bag of words or a discriminator")

    unpert_gen_tok_text, _, _ = generate_text_pplm(
-        model=model,
-        tokenizer=tokenizer,
-        context=context,
-        device=device,
-        length=length,
-        sample=sample,
-        perturb=False,
-        repetition_penalty=repetition_penalty,
+        model=model, tokenizer=tokenizer, context=context, device=device, length=length, sample=sample, perturb=False
    )
    if device == "cuda":
        torch.cuda.empty_cache()
@@ -409,7 +401,6 @@ def full_text_generation(
            gamma=gamma,
            gm_scale=gm_scale,
            kl_scale=kl_scale,
-            repetition_penalty=repetition_penalty,
        )
        pert_gen_tok_texts.append(pert_gen_tok_text)
        if classifier is not None:
@@ -446,7 +437,6 @@ def generate_text_pplm(
    gamma=1.5,
    gm_scale=0.9,
    kl_scale=0.01,
-    repetition_penalty=1.0,
 ):
    output_so_far = None
    if context:
@@ -518,13 +508,6 @@ def generate_text_pplm(

        pert_logits, past, pert_all_hidden = model(last, past=pert_past)
        pert_logits = pert_logits[:, -1, :] / temperature  # + SMALL_CONST
-
-        for token_idx in set(output_so_far[0].tolist()):
-            if pert_logits[0, token_idx] < 0:
-                pert_logits[0, token_idx] *= repetition_penalty
-            else:
-                pert_logits[0, token_idx] /= repetition_penalty
-
        pert_probs = F.softmax(pert_logits, dim=-1)

        if classifier is not None:
@@ -605,7 +588,6 @@ def run_pplm_example(
    seed=0,
    no_cuda=False,
    colorama=False,
-    repetition_penalty=1.0,
 ):
    # set Random seed
    torch.manual_seed(seed)
@@ -673,7 +655,6 @@ def run_pplm_example(
        gamma=gamma,
        gm_scale=gm_scale,
        kl_scale=kl_scale,
-        repetition_penalty=repetition_penalty,
    )

    # untokenize unperturbed text
@@ -737,7 +718,7 @@ if __name__ == "__main__":
    parser.add_argument("--cond_text", type=str, default="The lake", help="Prefix texts to condition on")
    parser.add_argument("--uncond", action="store_true", help="Generate from end-of-text as prefix")
    parser.add_argument(
-        "--num_samples", type=int, default=1, help="Number of samples to generate from the modified latents",
+        "--num_samples", type=int, default=1, help="Number of samples to generate from the modified latents"
    )
    parser.add_argument(
        "--bag_of_words",
@@ -760,9 +741,7 @@ if __name__ == "__main__":
    parser.add_argument(
        "--discrim_meta", type=str, default=None, help="Meta information for the generic discriminator"
    )
-    parser.add_argument(
-        "--class_label", type=int, default=-1, help="Class label used for the discriminator",
-    )
+    parser.add_argument("--class_label", type=int, default=-1, help="Class label used for the discriminator")
    parser.add_argument("--length", type=int, default=100)
    parser.add_argument("--stepsize", type=float, default=0.02)
    parser.add_argument("--temperature", type=float, default=1.0)
@@ -776,9 +755,7 @@ if __name__ == "__main__":
        default=0,
        help="Length of past which is being optimized; " "0 corresponds to infinite window length",
    )
-    parser.add_argument(
-        "--horizon_length", type=int, default=1, help="Length of future to optimize over",
-    )
+    parser.add_argument("--horizon_length", type=int, default=1, help="Length of future to optimize over")
    parser.add_argument("--decay", action="store_true", help="whether to decay or not")
    parser.add_argument("--gamma", type=float, default=1.5)
    parser.add_argument("--gm_scale", type=float, default=0.9)
@@ -786,9 +763,6 @@ if __name__ == "__main__":
    parser.add_argument("--seed", type=int, default=0)
    parser.add_argument("--no_cuda", action="store_true", help="no cuda")
    parser.add_argument("--colorama", action="store_true", help="colors keywords")
-    parser.add_argument(
-        "--repetition_penalty", type=float, default=1.0, help="Penalize repetition. More than 1.0 -> less repetition",
-    )

    args = parser.parse_args()
    run_pplm_example(**vars(args))
@@ -46,7 +46,7 @@ class Discriminator(torch.nn.Module):
    """Transformer encoder followed by a Classification Head"""

    def __init__(self, class_size, pretrained_model="gpt2-medium", cached_mode=False, device="cpu"):
-        super().__init__()
+        super(Discriminator, self).__init__()
        self.tokenizer = GPT2Tokenizer.from_pretrained(pretrained_model)
        self.encoder = GPT2LMHeadModel.from_pretrained(pretrained_model)
        self.embed_size = self.encoder.transformer.config.hidden_size
@@ -242,7 +242,7 @@ def train_discriminator(

        text = torchtext_data.Field()
        label = torchtext_data.Field(sequential=False)
-        train_data, val_data, test_data = datasets.SST.splits(text, label, fine_grained=True, train_subtrees=True,)
+        train_data, val_data, test_data = datasets.SST.splits(text, label, fine_grained=True, train_subtrees=True)

        x = []
        y = []
@@ -2,4 +2,3 @@ tensorboardX
 tensorboard
 scikit-learn
 seqeval
-psutil
@@ -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 = 0 if args.no_cuda else torch.cuda.device_count()
+        args.n_gpu = torch.cuda.device_count()
    else:
        torch.cuda.set_device(args.local_rank)
        args.device = torch.device("cuda", args.local_rank)
@@ -41,7 +41,7 @@ from transformers import (


 logging.basicConfig(
-    format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO,
+    format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
 )
 logger = logging.getLogger(__name__)

@@ -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,
@@ -106,8 +106,6 @@ 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
@@ -121,12 +119,12 @@ 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 = {
@@ -185,11 +183,10 @@ 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 = 0 if args.no_cuda else torch.cuda.device_count()
+    args.n_gpu = torch.cuda.device_count()

    set_seed(args)

@@ -213,50 +210,26 @@ def main():
    requires_preprocessing = args.model_type in PREPROCESSING_FUNCTIONS.keys()
    if requires_preprocessing:
        prepare_input = PREPROCESSING_FUNCTIONS.get(args.model_type)
-        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)
+        prompt_text = prepare_input(args, model, tokenizer, prompt_text)
+    encoded_prompt = tokenizer.encode(prompt_text, add_special_tokens=False, return_tensors="pt")

    output_sequences = model.generate(
        input_ids=encoded_prompt,
-        max_length=args.length + len(encoded_prompt[0]),
+        max_length=args.length,
        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,
    )

-    # Remove the batch dimension when returning multiple sequences
-    if len(output_sequences.shape) > 2:
-        output_sequences.squeeze_()
+    # 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]

-    generated_sequences = []
+    print(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
+    return text


 if __name__ == "__main__":
@@ -13,7 +13,7 @@
 # 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, Albert, XLM-RoBERTa)."""
+""" Finetuning the library models for sequence classification on GLUE (Bert, XLM, XLNet, RoBERTa)."""


 import argparse
@@ -30,12 +30,29 @@ from torch.utils.data.distributed import DistributedSampler
 from tqdm import tqdm, trange

 from transformers import (
-    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    WEIGHTS_NAME,
    AdamW,
-    AutoConfig,
-    AutoModelForSequenceClassification,
-    AutoTokenizer,
+    AlbertConfig,
+    AlbertForSequenceClassification,
+    AlbertTokenizer,
+    BertConfig,
+    BertForSequenceClassification,
+    BertTokenizer,
+    DistilBertConfig,
+    DistilBertForSequenceClassification,
+    DistilBertTokenizer,
+    RobertaConfig,
+    RobertaForSequenceClassification,
+    RobertaTokenizer,
+    XLMConfig,
+    XLMForSequenceClassification,
+    XLMRobertaConfig,
+    XLMRobertaForSequenceClassification,
+    XLMRobertaTokenizer,
+    XLMTokenizer,
+    XLNetConfig,
+    XLNetForSequenceClassification,
+    XLNetTokenizer,
    get_linear_schedule_with_warmup,
 )
 from transformers import glue_compute_metrics as compute_metrics
@@ -52,10 +69,23 @@ except ImportError:

 logger = logging.getLogger(__name__)

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

-ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) 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),
+}


 def set_seed(args):
@@ -118,7 +148,7 @@ def train(args, train_dataset, model, tokenizer):
    # Distributed training (should be after apex fp16 initialization)
    if args.local_rank != -1:
        model = torch.nn.parallel.DistributedDataParallel(
-            model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True,
+            model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True
        )

    # Train!
@@ -140,11 +170,8 @@ 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 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
+        # 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])
        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)

@@ -156,7 +183,7 @@ def train(args, train_dataset, model, tokenizer):
    tr_loss, logging_loss = 0.0, 0.0
    model.zero_grad()
    train_iterator = trange(
-        epochs_trained, int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0],
+        epochs_trained, int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0]
    )
    set_seed(args)  # Added here for reproductibility
    for _ in train_iterator:
@@ -173,8 +200,8 @@ def train(args, train_dataset, model, tokenizer):
            inputs = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
            if args.model_type != "distilbert":
                inputs["token_type_ids"] = (
-                    batch[2] if args.model_type in ["bert", "xlnet", "albert"] else None
-                )  # XLM, DistilBERT, RoBERTa, and XLM-RoBERTa don't use segment_ids
+                    batch[2] if args.model_type in ["bert", "xlnet"] else None
+                )  # XLM, DistilBERT and RoBERTa don't use segment_ids
            outputs = model(**inputs)
            loss = outputs[0]  # model outputs are always tuple in transformers (see doc)

@@ -190,11 +217,7 @@ def train(args, train_dataset, model, tokenizer):
                loss.backward()

            tr_loss += loss.item()
-            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 (step + 1) % args.gradient_accumulation_steps == 0:
                if args.fp16:
                    torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
                else:
@@ -274,7 +297,7 @@ 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 and not isinstance(model, torch.nn.DataParallel):
+        if args.n_gpu > 1:
            model = torch.nn.DataParallel(model)

        # Eval!
@@ -293,8 +316,8 @@ def evaluate(args, model, tokenizer, prefix=""):
                inputs = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
                if args.model_type != "distilbert":
                    inputs["token_type_ids"] = (
-                        batch[2] if args.model_type in ["bert", "xlnet", "albert"] else None
-                    )  # XLM, DistilBERT, RoBERTa, and XLM-RoBERTa don't use segment_ids
+                        batch[2] if args.model_type in ["bert", "xlnet"] else None
+                    )  # XLM, DistilBERT and RoBERTa don't use segment_ids
                outputs = model(**inputs)
                tmp_eval_loss, logits = outputs[:2]

@@ -399,7 +422,7 @@ def main():
        default=None,
        type=str,
        required=True,
-        help="Model type selected in the list: " + ", ".join(MODEL_TYPES),
+        help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()),
    )
    parser.add_argument(
        "--model_name_or_path",
@@ -425,7 +448,7 @@ def main():

    # Other parameters
    parser.add_argument(
-        "--config_name", default="", type=str, help="Pretrained config name or path if not the same as model_name",
+        "--config_name", default="", type=str, help="Pretrained config name or path if not the same as model_name"
    )
    parser.add_argument(
        "--tokenizer_name",
@@ -449,17 +472,15 @@ def main():
    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="Run evaluation during training at each logging step.",
+        "--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.",
+        "--do_lower_case", action="store_true", help="Set this flag if you are using an uncased model."
    )

+    parser.add_argument("--per_gpu_train_batch_size", default=8, type=int, help="Batch size per GPU/CPU for training.")
    parser.add_argument(
-        "--per_gpu_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.",
+        "--per_gpu_eval_batch_size", default=8, type=int, help="Batch size per GPU/CPU for evaluation."
    )
    parser.add_argument(
        "--gradient_accumulation_steps",
@@ -472,7 +493,7 @@ def main():
    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.",
+        "--num_train_epochs", default=3.0, type=float, help="Total number of training epochs to perform."
    )
    parser.add_argument(
        "--max_steps",
@@ -482,8 +503,8 @@ def main():
    )
    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("--logging_steps", type=int, default=50, help="Log every X updates steps.")
+    parser.add_argument("--save_steps", type=int, default=50, help="Save checkpoint every X updates steps.")
    parser.add_argument(
        "--eval_all_checkpoints",
        action="store_true",
@@ -491,10 +512,10 @@ def main():
    )
    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",
+        "--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",
+        "--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")

@@ -539,7 +560,7 @@ 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 = 0 if args.no_cuda else torch.cuda.device_count()
+        args.n_gpu = 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 +600,19 @@ 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 = AutoConfig.from_pretrained(
+    config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
+    config = config_class.from_pretrained(
        args.config_name if args.config_name else args.model_name_or_path,
        num_labels=num_labels,
        finetuning_task=args.task_name,
        cache_dir=args.cache_dir if args.cache_dir else None,
    )
-    tokenizer = AutoTokenizer.from_pretrained(
+    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 = AutoModelForSequenceClassification.from_pretrained(
+    model = model_class.from_pretrained(
        args.model_name_or_path,
        from_tf=bool(".ckpt" in args.model_name_or_path),
        config=config,
@@ -629,14 +651,14 @@ 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 = AutoModelForSequenceClassification.from_pretrained(args.output_dir)
-        tokenizer = AutoTokenizer.from_pretrained(args.output_dir)
+        model = model_class.from_pretrained(args.output_dir)
+        tokenizer = tokenizer_class.from_pretrained(args.output_dir)
        model.to(args.device)

    # Evaluation
    results = {}
    if args.do_eval and args.local_rank in [-1, 0]:
-        tokenizer = AutoTokenizer.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
+        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(
@@ -648,7 +670,7 @@ def main():
            global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else ""
            prefix = checkpoint.split("/")[-1] if checkpoint.find("checkpoint") != -1 else ""

-            model = AutoModelForSequenceClassification.from_pretrained(checkpoint)
+            model = model_class.from_pretrained(checkpoint)
            model.to(args.device)
            result = evaluate(args, model, tokenizer, prefix=prefix)
            result = dict((k + "_{}".format(global_step), v) for k, v in result.items())
@@ -28,24 +28,34 @@ import pickle
 import random
 import re
 import shutil
-from typing import Dict, List, Tuple

 import numpy as np
 import torch
-from torch.nn.utils.rnn import pad_sequence
 from torch.utils.data import DataLoader, Dataset, RandomSampler, SequentialSampler
 from torch.utils.data.distributed import DistributedSampler
 from tqdm import tqdm, trange

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

@@ -59,19 +69,22 @@ except ImportError:
 logger = logging.getLogger(__name__)


-MODEL_CONFIG_CLASSES = list(MODEL_WITH_LM_HEAD_MAPPING.keys())
-MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
+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),
+}


 class TextDataset(Dataset):
-    def __init__(self, tokenizer: PreTrainedTokenizer, args, file_path: str, block_size=512):
+    def __init__(self, tokenizer, args, file_path="train", block_size=512):
        assert os.path.isfile(file_path)
-
-        block_size = block_size - (tokenizer.max_len - tokenizer.max_len_single_sentence)
-
        directory, filename = os.path.split(file_path)
        cached_features_file = os.path.join(
-            directory, args.model_type + "_cached_lm_" + str(block_size) + "_" + filename
+            directory, args.model_name_or_path + "_cached_lm_" + str(block_size) + "_" + filename
        )

        if os.path.exists(cached_features_file) and not args.overwrite_cache:
@@ -101,35 +114,17 @@ class TextDataset(Dataset):
        return len(self.examples)

    def __getitem__(self, item):
-        return torch.tensor(self.examples[item], dtype=torch.long)
-
-
-class LineByLineTextDataset(Dataset):
-    def __init__(self, tokenizer: PreTrainedTokenizer, args, file_path: str, block_size=512):
-        assert os.path.isfile(file_path)
-        # Here, we do not cache the features, operating under the assumption
-        # that we will soon use fast multithreaded tokenizers from the
-        # `tokenizers` repo everywhere =)
-        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 and not line.isspace())]
-
-        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], dtype=torch.long)
+        return torch.tensor(self.examples[item])


 def load_and_cache_examples(args, tokenizer, evaluate=False):
-    file_path = args.eval_data_file if evaluate else args.train_data_file
-    if args.line_by_line:
-        return LineByLineTextDataset(tokenizer, args, file_path=file_path, block_size=args.block_size)
-    else:
-        return TextDataset(tokenizer, args, file_path=file_path, block_size=args.block_size)
+    dataset = TextDataset(
+        tokenizer,
+        args,
+        file_path=args.eval_data_file if evaluate else args.train_data_file,
+        block_size=args.block_size,
+    )
+    return dataset


 def set_seed(args):
@@ -140,11 +135,18 @@ def set_seed(args):
        torch.cuda.manual_seed_all(args.seed)


-def _sorted_checkpoints(args, checkpoint_prefix="checkpoint", use_mtime=False) -> List[str]:
-    ordering_and_checkpoint_path = []
+def _rotate_checkpoints(args, checkpoint_prefix, use_mtime=False):
+    if not args.save_total_limit:
+        return
+    if args.save_total_limit <= 0:
+        return

+    # Check if we should delete older checkpoint(s)
    glob_checkpoints = glob.glob(os.path.join(args.output_dir, "{}-*".format(checkpoint_prefix)))
+    if len(glob_checkpoints) <= args.save_total_limit:
+        return

+    ordering_and_checkpoint_path = []
    for path in glob_checkpoints:
        if use_mtime:
            ordering_and_checkpoint_path.append((os.path.getmtime(path), path))
@@ -155,20 +157,6 @@ def _sorted_checkpoints(args, checkpoint_prefix="checkpoint", use_mtime=False) -

    checkpoints_sorted = sorted(ordering_and_checkpoint_path)
    checkpoints_sorted = [checkpoint[1] for checkpoint in checkpoints_sorted]
-    return checkpoints_sorted
-
-
-def _rotate_checkpoints(args, checkpoint_prefix="checkpoint", use_mtime=False) -> None:
-    if not args.save_total_limit:
-        return
-    if args.save_total_limit <= 0:
-        return
-
-    # Check if we should delete older checkpoint(s)
-    checkpoints_sorted = _sorted_checkpoints(args, checkpoint_prefix, use_mtime)
-    if len(checkpoints_sorted) <= args.save_total_limit:
-        return
-
    number_of_checkpoints_to_delete = max(0, len(checkpoints_sorted) - args.save_total_limit)
    checkpoints_to_be_deleted = checkpoints_sorted[:number_of_checkpoints_to_delete]
    for checkpoint in checkpoints_to_be_deleted:
@@ -176,14 +164,8 @@ def _rotate_checkpoints(args, checkpoint_prefix="checkpoint", use_mtime=False) -
        shutil.rmtree(checkpoint)


-def mask_tokens(inputs: torch.Tensor, tokenizer: PreTrainedTokenizer, args) -> Tuple[torch.Tensor, torch.Tensor]:
+def mask_tokens(inputs, tokenizer, args):
    """ 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)
@@ -191,9 +173,6 @@ def mask_tokens(inputs: torch.Tensor, tokenizer: PreTrainedTokenizer, args) -> T
        tokenizer.get_special_tokens_mask(val, already_has_special_tokens=True) for val in labels.tolist()
    ]
    probability_matrix.masked_fill_(torch.tensor(special_tokens_mask, dtype=torch.bool), value=0.0)
-    if tokenizer._pad_token is not None:
-        padding_mask = labels.eq(tokenizer.pad_token_id)
-        probability_matrix.masked_fill_(padding_mask, value=0.0)
    masked_indices = torch.bernoulli(probability_matrix).bool()
    labels[~masked_indices] = -100  # We only compute loss on masked tokens

@@ -210,22 +189,14 @@ def mask_tokens(inputs: torch.Tensor, tokenizer: PreTrainedTokenizer, args) -> T
    return inputs, labels


-def train(args, train_dataset, model: PreTrainedModel, tokenizer: PreTrainedTokenizer) -> Tuple[int, float]:
+def train(args, train_dataset, model, tokenizer):
    """ Train the model """
    if args.local_rank in [-1, 0]:
        tb_writer = SummaryWriter()

    args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
-
-    def collate(examples: List[torch.Tensor]):
-        if tokenizer._pad_token is None:
-            return pad_sequence(examples, batch_first=True)
-        return pad_sequence(examples, batch_first=True, padding_value=tokenizer.pad_token_id)
-
    train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset)
-    train_dataloader = DataLoader(
-        train_dataset, sampler=train_sampler, batch_size=args.train_batch_size, collate_fn=collate
-    )
+    train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size)

    if args.max_steps > 0:
        t_total = args.max_steps
@@ -248,10 +219,8 @@ def train(args, train_dataset, model: PreTrainedModel, tokenizer: PreTrainedToke
    )

    # Check if saved optimizer or scheduler states exist
-    if (
-        args.model_name_or_path
-        and os.path.isfile(os.path.join(args.model_name_or_path, "optimizer.pt"))
-        and os.path.isfile(os.path.join(args.model_name_or_path, "scheduler.pt"))
+    if os.path.isfile(os.path.join(args.model_name_or_path, "optimizer.pt")) and os.path.isfile(
+        os.path.join(args.model_name_or_path, "scheduler.pt")
    ):
        # Load in optimizer and scheduler states
        optimizer.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "optimizer.pt")))
@@ -292,20 +261,16 @@ def train(args, train_dataset, model: PreTrainedModel, tokenizer: PreTrainedToke
    epochs_trained = 0
    steps_trained_in_current_epoch = 0
    # Check if continuing training from a checkpoint
-    if args.model_name_or_path and os.path.exists(args.model_name_or_path):
-        try:
-            # set global_step to gobal_step of last saved checkpoint from model path
-            checkpoint_suffix = args.model_name_or_path.split("-")[-1].split("/")[0]
-            global_step = int(checkpoint_suffix)
-            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)
+    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])
+        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)

-            logger.info("  Continuing training from checkpoint, will skip to saved global_step")
-            logger.info("  Continuing training from epoch %d", epochs_trained)
-            logger.info("  Continuing training from global step %d", global_step)
-            logger.info("  Will skip the first %d steps in the first epoch", steps_trained_in_current_epoch)
-        except ValueError:
-            logger.info("  Starting fine-tuning.")
+        logger.info("  Continuing training from checkpoint, will skip to saved global_step")
+        logger.info("  Continuing training from epoch %d", epochs_trained)
+        logger.info("  Continuing training from global step %d", global_step)
+        logger.info("  Will skip the first %d steps in the first epoch", steps_trained_in_current_epoch)

    tr_loss, logging_loss = 0.0, 0.0

@@ -371,7 +336,8 @@ def train(args, train_dataset, model: PreTrainedModel, tokenizer: PreTrainedToke
                    checkpoint_prefix = "checkpoint"
                    # Save model checkpoint
                    output_dir = os.path.join(args.output_dir, "{}-{}".format(checkpoint_prefix, global_step))
-                    os.makedirs(output_dir, exist_ok=True)
+                    if not os.path.exists(output_dir):
+                        os.makedirs(output_dir)
                    model_to_save = (
                        model.module if hasattr(model, "module") else model
                    )  # Take care of distributed/parallel training
@@ -400,27 +366,19 @@ def train(args, train_dataset, model: PreTrainedModel, tokenizer: PreTrainedToke
    return global_step, tr_loss / global_step


-def evaluate(args, model: PreTrainedModel, tokenizer: PreTrainedTokenizer, prefix="") -> Dict:
+def evaluate(args, model, tokenizer, prefix=""):
    # Loop to handle MNLI double evaluation (matched, mis-matched)
    eval_output_dir = args.output_dir

    eval_dataset = load_and_cache_examples(args, tokenizer, evaluate=True)

-    if args.local_rank in [-1, 0]:
-        os.makedirs(eval_output_dir, exist_ok=True)
+    if not os.path.exists(eval_output_dir) and args.local_rank in [-1, 0]:
+        os.makedirs(eval_output_dir)

    args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
    # Note that DistributedSampler samples randomly
-
-    def collate(examples: List[torch.Tensor]):
-        if tokenizer._pad_token is None:
-            return pad_sequence(examples, batch_first=True)
-        return pad_sequence(examples, batch_first=True, padding_value=tokenizer.pad_token_id)
-
    eval_sampler = SequentialSampler(eval_dataset)
-    eval_dataloader = DataLoader(
-        eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size, collate_fn=collate
-    )
+    eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)

    # multi-gpu evaluate
    if args.n_gpu > 1:
@@ -469,13 +427,11 @@ def main():
    )
    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(
-        "--model_type", type=str, required=True, help="The model architecture to be trained or fine-tuned.",
-    )

    # Other parameters
    parser.add_argument(
@@ -484,19 +440,13 @@ def main():
        type=str,
        help="An optional input evaluation data file to evaluate the perplexity on (a text file).",
    )
-    parser.add_argument(
-        "--line_by_line",
-        action="store_true",
-        help="Whether distinct lines of text in the dataset are to be handled as distinct sequences.",
-    )
-    parser.add_argument(
-        "--should_continue", action="store_true", help="Whether to continue from latest checkpoint in output_dir"
-    )
+
+    parser.add_argument("--model_type", default="bert", type=str, help="The model architecture to be fine-tuned.")
    parser.add_argument(
        "--model_name_or_path",
-        default=None,
+        default="bert-base-cased",
        type=str,
-        help="The model checkpoint for weights initialization. Leave None if you want to train a model from scratch.",
+        help="The model checkpoint for weights initialization.",
    )

    parser.add_argument(
@@ -508,21 +458,21 @@ def main():

    parser.add_argument(
        "--config_name",
-        default=None,
+        default="",
        type=str,
-        help="Optional pretrained config name or path if not the same as model_name_or_path. If both are None, initialize a new config.",
+        help="Optional pretrained config name or path if not the same as model_name_or_path",
    )
    parser.add_argument(
        "--tokenizer_name",
-        default=None,
+        default="",
        type=str,
-        help="Optional pretrained tokenizer name or path if not the same as model_name_or_path. If both are None, initialize a new tokenizer.",
+        help="Optional pretrained tokenizer name or path if not the same as model_name_or_path",
    )
    parser.add_argument(
        "--cache_dir",
-        default=None,
+        default="",
        type=str,
-        help="Optional directory to store the pre-trained models downloaded from s3 (instead of the default one)",
+        help="Optional directory to store the pre-trained models downloaded from s3 (instread of the default one)",
    )
    parser.add_argument(
        "--block_size",
@@ -537,6 +487,9 @@ def main():
    parser.add_argument(
        "--evaluate_during_training", action="store_true", help="Run evaluation during training at each logging step."
    )
+    parser.add_argument(
+        "--do_lower_case", action="store_true", help="Set this flag if you are using an uncased model."
+    )

    parser.add_argument("--per_gpu_train_batch_size", default=4, type=int, help="Batch size per GPU/CPU for training.")
    parser.add_argument(
@@ -563,8 +516,8 @@ def main():
    )
    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("--logging_steps", type=int, default=50, help="Log every X updates steps.")
+    parser.add_argument("--save_steps", type=int, default=50, help="Save checkpoint every X updates steps.")
    parser.add_argument(
        "--save_total_limit",
        type=int,
@@ -604,7 +557,7 @@ def main():

    if args.model_type in ["bert", "roberta", "distilbert", "camembert"] and not args.mlm:
        raise ValueError(
-            "BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the --mlm "
+            "BERT and RoBERTa do not have LM heads but masked LM heads. They must be run using the --mlm "
            "flag (masked language modeling)."
        )
    if args.eval_data_file is None and args.do_eval:
@@ -612,12 +565,6 @@ def main():
            "Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file "
            "or remove the --do_eval argument."
        )
-    if args.should_continue:
-        sorted_checkpoints = _sorted_checkpoints(args)
-        if len(sorted_checkpoints) == 0:
-            raise ValueError("Used --should_continue but no checkpoint was found in --output_dir.")
-        else:
-            args.model_name_or_path = sorted_checkpoints[-1]

    if (
        os.path.exists(args.output_dir)
@@ -643,7 +590,7 @@ 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 = 0 if args.no_cuda else torch.cuda.device_count()
+        args.n_gpu = 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)
@@ -673,45 +620,27 @@ 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

-    if args.config_name:
-        config = AutoConfig.from_pretrained(args.config_name, cache_dir=args.cache_dir)
-    elif args.model_name_or_path:
-        config = AutoConfig.from_pretrained(args.model_name_or_path, cache_dir=args.cache_dir)
-    else:
-        # 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 = AutoTokenizer.from_pretrained(args.tokenizer_name, cache_dir=args.cache_dir)
-    elif args.model_name_or_path:
-        tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, cache_dir=args.cache_dir)
-    else:
-        raise ValueError(
-            "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"
-        )
-
+    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,
+    )
+    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,
+    )
    if args.block_size <= 0:
-        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)
-
-    if args.model_name_or_path:
-        model = AutoModelWithLMHead.from_pretrained(
-            args.model_name_or_path,
-            from_tf=bool(".ckpt" in args.model_name_or_path),
-            config=config,
-            cache_dir=args.cache_dir,
-        )
-    else:
-        logger.info("Training new model from scratch")
-        model = AutoModelWithLMHead.from_config(config)
-
+        args.block_size = (
+            tokenizer.max_len_single_sentence
+        )  # Our input block size will be the max possible for the model
+    args.block_size = min(args.block_size, tokenizer.max_len_single_sentence)
+    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,
+    )
    model.to(args.device)

    if args.local_rank == 0:
@@ -735,8 +664,8 @@ def main():
    # Saving best-practices: if you use save_pretrained for the model and tokenizer, you can reload them using from_pretrained()
    if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
        # Create output directory if needed
-        if args.local_rank in [-1, 0]:
-            os.makedirs(args.output_dir, exist_ok=True)
+        if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
+            os.makedirs(args.output_dir)

        logger.info("Saving model checkpoint to %s", args.output_dir)
        # Save a trained model, configuration and tokenizer using `save_pretrained()`.
@@ -751,8 +680,8 @@ 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 = AutoModelWithLMHead.from_pretrained(args.output_dir)
-        tokenizer = AutoTokenizer.from_pretrained(args.output_dir)
+        model = model_class.from_pretrained(args.output_dir)
+        tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
        model.to(args.device)

    # Evaluation
@@ -769,7 +698,7 @@ def main():
            global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else ""
            prefix = checkpoint.split("/")[-1] if checkpoint.find("checkpoint") != -1 else ""

-            model = AutoModelWithLMHead.from_pretrained(checkpoint)
+            model = model_class.from_pretrained(checkpoint)
            model.to(args.device)
            result = evaluate(args, model, tokenizer, prefix=prefix)
            result = dict((k + "_{}".format(global_step), v) for k, v in result.items())
@@ -478,8 +478,8 @@ def main():
    )
    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("--logging_steps", type=int, default=50, help="Log every X updates steps.")
+    parser.add_argument("--save_steps", type=int, default=50, help="Save checkpoint every X updates steps.")
    parser.add_argument(
        "--eval_all_checkpoints",
        action="store_true",
@@ -535,7 +535,7 @@ 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 = 0 if args.no_cuda else torch.cuda.device_count()
+        args.n_gpu = 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)
@@ -31,12 +31,23 @@ from torch.utils.data.distributed import DistributedSampler
 from tqdm import tqdm, trange

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

 logger = logging.getLogger(__name__)

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

-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"]
+MODEL_CLASSES = {
+    "bert": (BertConfig, BertForTokenClassification, BertTokenizer),
+    "roberta": (RobertaConfig, RobertaForTokenClassification, RobertaTokenizer),
+    "distilbert": (DistilBertConfig, DistilBertForTokenClassification, DistilBertTokenizer),
+    "camembert": (CamembertConfig, CamembertForTokenClassification, CamembertTokenizer),
+    "xlmroberta": (XLMRobertaConfig, XLMRobertaForTokenClassification, XLMRobertaTokenizer),
+}


 def set_seed(args):
@@ -140,10 +160,7 @@ 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
-        try:
-            global_step = int(args.model_name_or_path.split("-")[-1].split("/")[0])
-        except ValueError:
-            global_step = 0
+        global_step = int(args.model_name_or_path.split("-")[-1].split("/")[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)

@@ -196,8 +213,8 @@ 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)

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

@@ -385,7 +402,7 @@ def main():
        default=None,
        type=str,
        required=True,
-        help="Model type selected in the list: " + ", ".join(MODEL_TYPES),
+        help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()),
    )
    parser.add_argument(
        "--model_name_or_path",
@@ -442,13 +459,7 @@ 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."
@@ -474,8 +485,8 @@ def main():
    )
    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("--logging_steps", type=int, default=50, help="Log every X updates steps.")
+    parser.add_argument("--save_steps", type=int, default=50, help="Save checkpoint every X updates steps.")
    parser.add_argument(
        "--eval_all_checkpoints",
        action="store_true",
@@ -531,7 +542,7 @@ 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 = 0 if args.no_cuda else torch.cuda.device_count()
+        args.n_gpu = 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)
@@ -568,21 +579,18 @@ 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 = AutoConfig.from_pretrained(
+    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,
-        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_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(
+    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_args,
    )
-    model = AutoModelForTokenClassification.from_pretrained(
+    model = model_class.from_pretrained(
        args.model_name_or_path,
        from_tf=bool(".ckpt" in args.model_name_or_path),
        config=config,
@@ -623,7 +631,7 @@ def main():
    # Evaluation
    results = {}
    if args.do_eval and args.local_rank in [-1, 0]:
-        tokenizer = AutoTokenizer.from_pretrained(args.output_dir, **tokenizer_args)
+        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(
@@ -633,7 +641,7 @@ 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 = AutoModelForTokenClassification.from_pretrained(checkpoint)
+            model = model_class.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:
@@ -645,8 +653,8 @@ def main():
                writer.write("{} = {}\n".format(key, str(results[key])))

    if args.do_predict and args.local_rank in [-1, 0]:
-        tokenizer = AutoTokenizer.from_pretrained(args.output_dir, **tokenizer_args)
-        model = AutoModelForTokenClassification.from_pretrained(args.output_dir)
+        tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
+        model = model_class.from_pretrained(args.output_dir)
        model.to(args.device)
        result, predictions = evaluate(args, model, tokenizer, labels, pad_token_label_id, mode="test")
        # Save results
@@ -30,12 +30,26 @@ from torch.utils.data.distributed import DistributedSampler
 from tqdm import tqdm, trange

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

 logger = logging.getLogger(__name__)

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

-ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) 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),
+}


 def set_seed(args):
@@ -147,19 +170,15 @@ 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):
-        try:
-            # set global_step to gobal_step of last saved checkpoint from model path
-            checkpoint_suffix = args.model_name_or_path.split("-")[-1].split("/")[0]
-            global_step = int(checkpoint_suffix)
-            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)
+        # 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])
+        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)

-            logger.info("  Continuing training from checkpoint, will skip to saved global_step")
-            logger.info("  Continuing training from epoch %d", epochs_trained)
-            logger.info("  Continuing training from global step %d", global_step)
-            logger.info("  Will skip the first %d steps in the first epoch", steps_trained_in_current_epoch)
-        except ValueError:
-            logger.info("  Starting fine-tuning.")
+        logger.info("  Continuing training from checkpoint, will skip to saved global_step")
+        logger.info("  Continuing training from epoch %d", epochs_trained)
+        logger.info("  Continuing training from global step %d", global_step)
+        logger.info("  Will skip the first %d steps in the first epoch", steps_trained_in_current_epoch)

    tr_loss, logging_loss = 0.0, 0.0
    model.zero_grad()
@@ -184,23 +203,15 @@ def train(args, train_dataset, model, tokenizer):
            inputs = {
                "input_ids": batch[0],
                "attention_mask": batch[1],
-                "token_type_ids": batch[2],
+                "token_type_ids": None if args.model_type in ["xlm", "roberta", "distilbert"] else batch[2],
                "start_positions": batch[3],
                "end_positions": batch[4],
            }

-            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]
@@ -301,22 +312,13 @@ def evaluate(args, model, tokenizer, prefix=""):
            inputs = {
                "input_ids": batch[0],
                "attention_mask": batch[1],
-                "token_type_ids": batch[2],
+                "token_type_ids": None if args.model_type in ["xlm", "roberta", "distilbert"] else batch[2],
            }
-
-            if args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
-                del inputs["token_type_ids"]
-
            example_indices = batch[3]

            # 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)

@@ -421,14 +423,10 @@ def load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=Fal
    )

    # Init features and dataset from cache if it exists
-    if os.path.exists(cached_features_file) and not args.overwrite_cache:
+    if os.path.exists(cached_features_file) and not args.overwrite_cache and not output_examples:
        logger.info("Loading features from cached file %s", cached_features_file)
        features_and_dataset = torch.load(cached_features_file)
-        features, dataset, examples = (
-            features_and_dataset["features"],
-            features_and_dataset["dataset"],
-            features_and_dataset["examples"],
-        )
+        features, dataset = features_and_dataset["features"], features_and_dataset["dataset"]
    else:
        logger.info("Creating features from dataset file at %s", input_dir)

@@ -463,7 +461,7 @@ def load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=Fal

        if args.local_rank in [-1, 0]:
            logger.info("Saving features into cached file %s", cached_features_file)
-            torch.save({"features": features, "dataset": dataset, "examples": examples}, cached_features_file)
+            torch.save({"features": features, "dataset": dataset}, cached_features_file)

    if args.local_rank == 0 and not evaluate:
        # Make sure only the first process in distributed training process the dataset, and the others will use the cache
@@ -483,7 +481,7 @@ def main():
        default=None,
        type=str,
        required=True,
-        help="Model type selected in the list: " + ", ".join(MODEL_TYPES),
+        help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()),
    )
    parser.add_argument(
        "--model_name_or_path",
@@ -573,7 +571,7 @@ def main():
    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="Run evaluation during training at each logging step."
+        "--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."
@@ -622,15 +620,9 @@ 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.")
+    parser.add_argument("--logging_steps", type=int, default=50, help="Log every X updates steps.")
+    parser.add_argument("--save_steps", type=int, default=50, help="Save checkpoint every X updates steps.")
    parser.add_argument(
        "--eval_all_checkpoints",
        action="store_true",
@@ -664,13 +656,6 @@ def main():
    parser.add_argument("--threads", type=int, default=1, help="multiple threads for converting example to features")
    args = parser.parse_args()

-    if args.doc_stride >= args.max_seq_length - args.max_query_length:
-        logger.warning(
-            "WARNING - You've set a doc stride which may be superior to the document length in some "
-            "examples. This could result in errors when building features from the examples. Please reduce the doc "
-            "stride or increase the maximum length to ensure the features are correctly built."
-        )
-
    if (
        os.path.exists(args.output_dir)
        and os.listdir(args.output_dir)
@@ -695,7 +680,7 @@ 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 = 0 if args.no_cuda else torch.cuda.device_count()
+        args.n_gpu = 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)
@@ -727,16 +712,17 @@ def main():
        torch.distributed.barrier()

    args.model_type = args.model_type.lower()
-    config = AutoConfig.from_pretrained(
+    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,
    )
-    tokenizer = AutoTokenizer.from_pretrained(
+    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 = AutoModelForQuestionAnswering.from_pretrained(
+    model = model_class.from_pretrained(
        args.model_name_or_path,
        from_tf=bool(".ckpt" in args.model_name_or_path),
        config=config,
@@ -786,8 +772,8 @@ 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 = AutoModelForQuestionAnswering.from_pretrained(args.output_dir)  # , force_download=True)
-        tokenizer = AutoTokenizer.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
+        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.to(args.device)

    # Evaluation - we can ask to evaluate all the checkpoints (sub-directories) in a directory
@@ -811,7 +797,7 @@ def main():
        for checkpoint in checkpoints:
            # Reload the model
            global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else ""
-            model = AutoModelForQuestionAnswering.from_pretrained(checkpoint)  # , force_download=True)
+            model = model_class.from_pretrained(checkpoint, force_download=True)
            model.to(args.device)

            # Evaluate
@@ -99,6 +99,9 @@ if TASK == "mrpc":
    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")

+    del inputs_1["special_tokens_mask"]
+    del inputs_2["special_tokens_mask"]
+
    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")
@@ -9,37 +9,42 @@ import re
 import numpy as np
 import tensorflow as tf
 from absl import app, flags, logging
+from fastprogress import master_bar, progress_bar
 from seqeval import metrics

 from transformers import (
    TF2_WEIGHTS_NAME,
-    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
-    AutoConfig,
-    AutoTokenizer,
+    BertConfig,
+    BertTokenizer,
+    DistilBertConfig,
+    DistilBertTokenizer,
    GradientAccumulator,
-    TFAutoModelForTokenClassification,
+    RobertaConfig,
+    RobertaTokenizer,
+    TFBertForTokenClassification,
+    TFDistilBertForTokenClassification,
+    TFRobertaForTokenClassification,
    create_optimizer,
 )
 from utils_ner import convert_examples_to_features, get_labels, read_examples_from_file


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


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

 flags.DEFINE_string(
    "model_name_or_path",
@@ -499,7 +504,8 @@ def main(_):
    labels = get_labels(args["labels"])
    num_labels = len(labels) + 1
    pad_token_label_id = 0
-    config = AutoConfig.from_pretrained(
+    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,
        cache_dir=args["cache_dir"] if args["cache_dir"] else None,
@@ -509,14 +515,14 @@ def main(_):

    # Training
    if args["do_train"]:
-        tokenizer = AutoTokenizer.from_pretrained(
+        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 = TFAutoModelForTokenClassification.from_pretrained(
+            model = model_class.from_pretrained(
                args["model_name_or_path"],
                from_pt=bool(".bin" in args["model_name_or_path"]),
                config=config,
@@ -551,7 +557,7 @@ def main(_):

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

@@ -573,7 +579,7 @@ def main(_):
            global_step = checkpoint.split("-")[-1] if re.match(".*checkpoint-[0-9]", checkpoint) else "final"

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

            y_true, y_pred, eval_loss = evaluate(
                args, strategy, model, tokenizer, labels, pad_token_label_id, mode="dev"
@@ -600,8 +606,8 @@ def main(_):
                        writer.write("\n")

    if args["do_predict"]:
-        tokenizer = AutoTokenizer.from_pretrained(args["output_dir"], do_lower_case=args["do_lower_case"])
-        model = TFAutoModelForTokenClassification.from_pretrained(args["output_dir"])
+        tokenizer = tokenizer_class.from_pretrained(args["output_dir"], do_lower_case=args["do_lower_case"])
+        model = model_class.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"
@@ -459,7 +459,7 @@ 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 decay if we apply some.")
+    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(
@@ -473,8 +473,8 @@ def main():
    )
    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("--logging_steps", type=int, default=50, help="Log every X updates steps.")
+    parser.add_argument("--save_steps", type=int, default=50, help="Save checkpoint every X updates steps.")
    parser.add_argument(
        "--eval_all_checkpoints",
        action="store_true",
@@ -530,7 +530,7 @@ 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 = 0 if args.no_cuda else torch.cuda.device_count()
+        args.n_gpu = 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)
@@ -15,7 +15,7 @@ pip install nltk py-rouge
 cd examples/summarization
 ```

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

 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:

@@ -1,59 +0,0 @@
-### Get the 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
-tar -xvf cnn_stories.tgz && tar -xvf dailymail_stories.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.
-
-### Usage
-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
-
-
-
-After downloading the CNN and Daily Mail datasets, preprocess the dataset:
-```commandline
-git clone https://github.com/artmatsak/cnn-dailymail
-cd cnn-dailymail && python make_datafiles.py ../cnn/stories/ ../dailymail/stories/
-```
-
-Run the training script: `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')
-```
@@ -1,66 +0,0 @@
-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(lns, out_file, batch_size=8, device=DEFAULT_DEVICE):
-    fout = Path(out_file).open("w")
-    model = BartForConditionalGeneration.from_pretrained("bart-large-cnn", output_past=True,).to(device)
-    tokenizer = BartTokenizer.from_pretrained("bart-large")
-
-    max_length = 140
-    min_length = 55
-
-    for batch in tqdm(list(chunks(lns, 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(
-        "--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()
-    lns = [" " + x.rstrip() for x in open(args.source_path).readlines()]
-    generate_summaries(lns, args.output_path, batch_size=args.bs, device=args.device)
-
-
-if __name__ == "__main__":
-    _run_generate()
@@ -1,172 +0,0 @@
-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
-from utils import SummarizationDataset
-
-
-logger = logging.getLogger(__name__)
-
-
-class BartSystem(BaseTransformer):
-
-    mode = "language-modeling"
-
-    def __init__(self, hparams):
-        super(BartSystem, self).__init__(hparams, num_labels=None, mode=self.mode)
-
-    def forward(
-        self, input_ids, attention_mask=None, decoder_input_ids=None, decoder_attention_mask=None, lm_labels=None
-    ):
-        return self.model(
-            input_ids,
-            attention_mask=attention_mask,
-            decoder_input_ids=decoder_input_ids,
-            decoder_attention_mask=decoder_attention_mask,
-            lm_labels=lm_labels,
-        )
-
-    def _step(self, batch):
-        y = batch["target_ids"]
-        y_ids = y[:, :-1].contiguous()
-        lm_labels = y[:, 1:].clone()
-        lm_labels[y[:, 1:] == self.tokenizer.pad_token_id] = -100
-        outputs = self(
-            input_ids=batch["source_ids"],
-            attention_mask=batch["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):
-        generated_ids = self.model.generate(
-            batch["source_ids"],
-            attention_mask=batch["source_mask"],
-            num_beams=1,
-            max_length=80,
-            repetition_penalty=2.5,
-            length_penalty=1.0,
-            early_stopping=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 batch["target_ids"]
-        ]
-        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 train_dataloader(self):
-        train_dataset = SummarizationDataset(
-            self.tokenizer, data_dir=self.hparams.data_dir, type_path="train", block_size=self.hparams.max_seq_length
-        )
-        dataloader = DataLoader(train_dataset, 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):
-        val_dataset = SummarizationDataset(
-            self.tokenizer, data_dir=self.hparams.data_dir, type_path="val", block_size=self.hparams.max_seq_length
-        )
-        return DataLoader(val_dataset, batch_size=self.hparams.eval_batch_size)
-
-    def test_dataloader(self):
-        test_dataset = SummarizationDataset(
-            self.tokenizer, data_dir=self.hparams.data_dir, type_path="test", block_size=self.hparams.max_seq_length
-        )
-        return DataLoader(test_dataset, 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_seq_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(
-            "--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
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    add_generic_args(parser, os.getcwd())
-    parser = BartSystem.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 = BartSystem(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)))
-        BartSystem.load_from_checkpoint(checkpoints[-1])
-        trainer.test(model)
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
thomwolf	e29dfbafac	using black -t py35	2019-12-27 18:40:41 +01:00
thomwolf	8fc584f030	optional dependency	2019-12-27 15:06:16 +01:00
thomwolf	94d5e1c886	code formating	2019-12-27 13:14:46 +01:00
thomwolf	3fa40b744b	really optional	2019-12-27 13:14:38 +01:00
thomwolf	4d97de84d0	code style formating	2019-12-27 13:06:36 +01:00
thomwolf	367e3c3ec5	move tokenizers to be optional dep	2019-12-27 13:06:21 +01:00