update to store uncompressed model on S3

do not save model if no training
predict_fp16
2018-12-19 11:18:17 +01:00 · 2018-12-18 23:26:49 +01:00 · 2018-12-18 23:12:44 +01:00 · 2018-12-18 22:38:03 +01:00 · 2018-12-18 22:02:32 +01:00
298 changed files with 5260 additions and 67561 deletions
@@ -1,133 +0,0 @@
-version: 2
-jobs:
-    build_py3_torch_and_tf:
-        working_directory: ~/transformers
-        docker:
-            - image: circleci/python:3.5
-        resource_class: xlarge
-        parallelism: 1
-        steps:
-            - checkout
-            - run: sudo pip install torch
-            - run: sudo pip install tensorflow
-            - run: sudo pip install --progress-bar off .
-            - run: sudo pip install pytest codecov pytest-cov
-            - run: sudo pip install tensorboardX scikit-learn
-            - run: python -m pytest -sv ./transformers/tests/ --cov
-            - run: codecov
-    build_py3_torch:
-        working_directory: ~/transformers
-        docker:
-            - image: circleci/python:3.5
-        resource_class: xlarge
-        parallelism: 1
-        steps:
-            - checkout
-            - run: sudo pip install torch
-            - run: sudo pip install --progress-bar off .
-            - run: sudo pip install pytest codecov pytest-cov
-            - run: sudo pip install tensorboardX scikit-learn
-            - run: python -m pytest -sv ./transformers/tests/ --cov
-            - run: python -m pytest -sv ./examples/
-            - run: codecov
-    build_py3_tf:
-        working_directory: ~/transformers
-        docker:
-            - image: circleci/python:3.5
-        resource_class: xlarge
-        parallelism: 1
-        steps:
-            - checkout
-            - run: sudo pip install tensorflow
-            - run: sudo pip install --progress-bar off .
-            - run: sudo pip install pytest codecov pytest-cov
-            - run: sudo pip install tensorboardX scikit-learn
-            - run: python -m pytest -sv ./transformers/tests/ --cov
-            - run: codecov
-    build_py2_torch:
-        working_directory: ~/transformers
-        resource_class: large
-        parallelism: 1
-        docker:
-            - image: circleci/python:2.7
-        steps:
-            - checkout
-            - run: sudo pip install torch
-            - run: sudo pip install --progress-bar off .
-            - run: sudo pip install pytest codecov pytest-cov
-            - run: python -m pytest -sv ./transformers/tests/ --cov
-            - run: codecov
-    build_py2_tf:
-        working_directory: ~/transformers
-        resource_class: large
-        parallelism: 1
-        docker:
-            - image: circleci/python:2.7
-        steps:
-            - checkout
-            - run: sudo pip install tensorflow
-            - run: sudo pip install --progress-bar off .
-            - run: sudo pip install pytest codecov pytest-cov
-            - run: python -m pytest -sv ./transformers/tests/ --cov
-            - run: codecov
-    build_py3_custom_tokenizers:
-        working_directory: ~/transformers
-        docker:
-            - image: circleci/python:3.5
-        steps:
-            - checkout
-            - run: sudo pip install --progress-bar off .
-            - run: sudo pip install pytest
-            - run: sudo pip install mecab-python3
-            - run: RUN_CUSTOM_TOKENIZERS=1 python -m pytest -sv ./transformers/tests/tokenization_bert_japanese_test.py
-    build_py2_custom_tokenizers:
-        working_directory: ~/transformers
-        docker:
-            - image: circleci/python:2.7
-        steps:
-            - checkout
-            - run: sudo pip install --progress-bar off .
-            - run: sudo pip install pytest
-            - run: sudo apt-get -y install libmecab-dev mecab mecab-ipadic-utf8 swig
-            - run: sudo pip install mecab-python
-            - run: RUN_CUSTOM_TOKENIZERS=1 python -m pytest -sv ./transformers/tests/tokenization_bert_japanese_test.py
-    deploy_doc:
-        working_directory: ~/transformers
-        docker:
-            - image: circleci/python:3.5
-        steps:
-            - add_ssh_keys:
-                  fingerprints:
-                      - "5b:7a:95:18:07:8c:aa:76:4c:60:35:88:ad:60:56:71"
-            - checkout
-            - run: sudo pip install --progress-bar off -r docs/requirements.txt
-            - run: sudo pip install --progress-bar off -r requirements.txt
-            - run: ./.circleci/deploy.sh
-    repository_consistency:
-        working_directory: ~/transformers
-        docker:
-            - image: circleci/python:3.5
-        resource_class: small
-        parallelism: 1
-        steps:
-            - checkout
-            - run: sudo pip install requests
-            - run: python ./utils/link_tester.py
-workflow_filters: &workflow_filters
-    filters:
-        branches:
-            only:
-                - master
-workflows:
-    version: 2
-    build_and_test:
-        jobs:
-            - repository_consistency
-            - build_py3_custom_tokenizers
-            - build_py2_custom_tokenizers
-            - build_py3_torch_and_tf
-            - build_py3_torch
-            - build_py3_tf
-            - build_py2_torch
-            - build_py2_tf
-            - deploy_doc: *workflow_filters
@@ -1,26 +0,0 @@
-cd docs
-
-function deploy_doc(){
-	echo "Creating doc at commit $1 and pushing to folder $2"
-	git checkout $1
-	if [ ! -z "$2" ] 
-	then
-		if [ -d "$dir/$2" ]; then
-			echo "Directory" $2 "already exists"
-		else
-			echo "Pushing version" $2
-			make clean && make html && scp -r -oStrictHostKeyChecking=no _build/html $doc:$dir/$2
-		fi
-	else
-		echo "Pushing master"
-		make clean && make html && scp -r -oStrictHostKeyChecking=no _build/html/* $doc:$dir
-	fi
-}
-
-deploy_doc "master" 
-deploy_doc "b33a385" v1.0.0
-deploy_doc "fe02e45" v1.1.0
-deploy_doc "89fd345" v1.2.0
-deploy_doc "fc9faa8" v2.0.0
-deploy_doc "3ddce1d" v2.1.1
-deploy_doc "3616209" v2.2.0
@@ -1,12 +0,0 @@
-[run]
-source=transformers
-omit =
-    # skip convertion scripts from testing for now
-    */convert_*
-    */__main__.py
-[report]
-exclude_lines =
-    pragma: no cover
-    raise
-    except
-    register_parameter
@@ -1,22 +0,0 @@
---
-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
-
-## Benchmark
-
-Which part of Transformers did you benchmark?
-
-## Set-up
-
-What did you run your benchmarks on? Please include details, such as: CPU, GPU? If using multiple GPUs, which parallelization did you use?
-
-## Results
-
-Put your results here!
@@ -1,24 +0,0 @@
---
-name: "\U0001F31FNew model addition"
-about: Submit a proposal/request to implement a new Transformer-based model
-title: ''
-labels: ''
-assignees: ''
-
---
-
-# 🌟New model addition
-
-## Model description
-
-<!-- Important information -->
-
-## Open Source status
-
-* [ ] the model implementation is available: (give details)
-* [ ] the model weights are available: (give details)
-* [ ] who are the authors: (mention them)
-
-## Additional context
-
-<!-- Add any other context about the problem here. -->
@@ -1,52 +0,0 @@
---
-name: "\U0001F41B Bug Report"
-about: Submit a bug report to help us improve PyTorch Transformers
-title: ''
-labels: ''
-assignees: ''
-
---
-
-## 🐛 Bug
-
-<!-- Important information -->
-
-Model I am using (Bert, XLNet....):
-
-Language I am using the model on (English, Chinese....):
-
-The problem arise when using:
-* [ ] the official example scripts: (give details)
-* [ ] my own modified scripts: (give details)
-
-The tasks I am working on is:
-* [ ] an official GLUE/SQUaD task: (give the name)
-* [ ] my own task or dataset: (give details)
-
-## To Reproduce
-
-Steps to reproduce the behavior:
-
-1.
-2.
-3.
-
-<!-- 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 expected to happen. -->
-
-## Environment
-
-* OS:
-* Python version:
-* PyTorch version:
-* PyTorch Transformers version (or branch):
-* Using GPU ?
-* Distributed of parallel setup ?
-* Any other relevant information:
-
-## Additional context
-
-<!-- Add any other context about the problem here. -->
@@ -1,20 +0,0 @@
---
-name: "\U0001F680 Feature Request"
-about: Submit a proposal/request for a new PyTorch Transformers feature
-title: ''
-labels: ''
-assignees: ''
-
---
-
-## 🚀 Feature
-
-<!-- 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. -->
-
-## Additional context
-
-<!-- Add any other context or screenshots about the feature request here. -->
@@ -1,47 +0,0 @@
---
-name: "\U0001F4DA Migration from PyTorch-pretrained-Bert"
-about: Report a problem when migrating from PyTorch-pretrained-Bert to Transformers
-title: ''
-labels: ''
-assignees: ''
-
---
-
-## 📚 Migration
-
-<!-- Important information -->
-
-Model I am using (Bert, XLNet....):
-
-Language I am using the model on (English, Chinese....):
-
-The problem arise when using:
-* [ ] the official example scripts: (give details)
-* [ ] my own modified scripts: (give details)
-
-The tasks I am working on is:
-* [ ] an official GLUE/SQUaD task: (give the name)
-* [ ] my own task or dataset: (give 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. -->
-
-## 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.
- [ ] I checked if a related official extension example runs on my machine.
-
-## Additional context
-
-<!-- Add any other context about the problem here. -->
@@ -1,12 +0,0 @@
---
-name: "❓Questions & Help"
-about: Start a general discussion related to PyTorch Transformers
-title: ''
-labels: ''
-assignees: ''
-
---
-
-## ❓ Questions & Help
-
-<!-- A clear and concise description of the question. -->
@@ -1,17 +0,0 @@
-# Number of days of inactivity before an issue becomes stale
-daysUntilStale: 60
-# Number of days of inactivity before a stale issue is closed
-daysUntilClose: 7
-# Issues with these labels will never be considered stale
-exemptLabels:
-  - pinned
-  - security
-# Label to use when marking an issue as stale
-staleLabel: wontfix
-# Comment to post when marking an issue as stale. Set to `false` to disable
-markComment: >
-  This issue has been automatically marked as stale because it has not had
-  recent activity. It will be closed if no further activity occurs. Thank you
-  for your contributions.
-# Comment to post when closing a stale issue. Set to `false` to disable
-closeComment: false
@@ -118,24 +118,5 @@ dmypy.json
 # vscode
 .vscode

-# Pycharm
-.idea
-
 # TF code
-tensorflow_code
-
-# Models
-models
-proc_data
-
-# examples
-runs
-examples/runs
-
-# data
-/data
-serialization_dir
-
-# emacs
-*.*~
-debug.env
+tensorflow_code
@@ -1,179 +0,0 @@
-# How to contribute to transformers?
-
-Everyone is welcome to contribute, and we value everybody's contribution. Code
-is thus not the only way to help the community. Answering questions, helping
-others, reaching out and improving the documentations are immensely valuable to
-the community.
-
-It also helps us if you spread the word: reference the library from blog posts
-on the awesome projects it made possible, shout out on Twitter every time it has
-helped you, or simply star the repo to say "thank you".
-
-## You can contribute in so many ways!
-
-There are 4 ways you can contribute to transformers:
-* Fixing outstanding issues with the existing code;
-* Implementing new models;
-* Contributing to the examples or to the documentation;
-* Submitting issues related to bugs or desired new features.
-
-*All are equally valuable to the community.*
-
-## Submitting a new issue or feature request
-
-Do your best to follow these guidelines when submitting an issue or a feature
-request. It will make it easier for us to come back to you quickly and with good
-feedback.
-
-### Did you find a bug?
-
-The transformers are robust and reliable thanks to the users who notify us of
-the problems they encounter. So thank you for reporting an issue.
-
-First, we would really appreciate it if you could **make sure the bug was not
-already reported** (use the search bar on Github under Issues).
-
-Did not find it? :( So we can act quickly on it, please follow these steps:
-
-* Include your **OS type and version**, the versions of **Python**, **PyTorch** and
-  **Tensorflow** when applicable;
-* A short, self-contained, code snippet that allows us to reproduce the bug in
-  less than 30s;
-* Provide the *full* traceback if an exception is raised.
-
-To get the OS and software versions, execute the following code and copy-paste
-the output:
-
-```
-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?
-
-Awesome! Please provide the following information:
-
-* Short description of the model and link to the paper;
-* Link to the implementation if it is open-source;
-* Link to the model weights if they are available.
-
-If you are willing to contribute the model yourself, let us know so we can best
-guide you.
-
-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.
-
-### Do you want a new feature (that is not a model)?
-
-A world-class feature request addresses the following points:
-
-1. Motivation first:
-  * Is it related to a problem/frustration with the library? If so, please explain
-    why. Providing a code snippet that demonstrates the problem is best.
-  * Is it related to something you would need for a project? We'd love to hear
-    about it!
-  * Is it something you worked on and think could benefit the community?
-    Awesome! Tell us what problem it solved for you.
-2. Write a *full paragraph* describing the feature;
-3. Provide a **code snippet** that demonstrates its future use;
-4. In case this is related to a paper, please attach a link;
-5. Attach any additional information (drawings, screenshots, etc.) you think may help.
-
-If your issue is well written we're already 80% of the way there by the time you
-post it.
-
-We have added **templates** to guide you in the process of adding a new example script for training or testing the models in the library. You can find them in the [`templates`](./templates) folder.
-
-## Start contributing! (Pull Requests)
-
-Before writing code, we strongly advise you to search through the exising PRs or
-issues to make sure that nobody is already working on the same thing. If you are
-unsure, it is always a good idea to open an issue to get some feedback.
-
-You will need basic `git` proficiency to be able to contribute to
-`transformers`. `git` is not the easiest tool to use but it has the greatest
-manual. Type `git --help` in a shell and enjoy. If you prefer books, [Pro
-Git](https://git-scm.com/book/en/v2) is a very good reference.
-
-Follow these steps to start contributing:
-
-1. Fork the [repository](https://github.com/huggingface/transformers) by
-   clicking on the 'Fork' button on the repository's page. This creates a copy of the code
-   under your github user account.
-2. Clone your fork to your local disk, and add the base repository as a remote:
-   
-   ```bash
-   $ git clone git@github.com:<your Github handle>/transformers.git
-   $ cd transformers
-   $ git remote add upstream https://github.com/huggingface/transformers.git
-   ```
-
-3. Create a new branch to hold your development changes:
-
-   ```bash
-   $ git checkout -b a-descriptive-name-for-my-changes
-   ```
-   
-   **do not** work on the `master` branch.
-   
-4. Set up a development environment by running the following command in a virtual environment:
-
-   ```bash
-   $ pip install -r requirements-dev.txt
-   ```
-
-5. Develop the features on your branch. Add changed files using `git add` and
-   then `git commit` to record your changes locally:
-   
-   ```bash
-   $ git add modified_file.py
-   $ git commit
-   ```
-   
-   Please write [good commit
-   messages](https://chris.beams.io/posts/git-commit/). It
-   is a good idea to sync your copy of the code with the original repository
-   regularly. This way you can quickly account for changes:
-   
-   ```bash
-   $ git fetch upstream
-   $ git rebase upstream/master
-   ```
-   
-   Push the changes to your account using:
-   
-   ```bash
-   $ git push -u origin a-descriptive-name-for-my-changes
-   ```
-   
-6. Once you are satisfied (**and the checklist below is happy too**), go to the
-   webpage of your fork on Github. Click on 'Pull request' to send your changes
-   to the project maintainers for review.
-   
-7. It's ok if maintainers ask you for changes. It happens to core contributors
-   too! So everyone can see the changes in the Pull request, work in your local
-   branch and push the changes to your fork. They will automatically appear in
-   the pull request.
-
-
-### Checklist
-
-1. The title of your pull request should be a summary of its contribution;
-2. If your pull request adresses an issue, please mention the issue number in
-   the pull request description to make sure they are linked (and people
-   consulting the issue know you are working on it);
-3. To indicate a work in progress please prefix the title with `[WIP]`. These
-   are useful to avoid duplicated work, and to differentiate it from PRs ready
-   to be merged;
-4. Make sure pre-existing tests still pass;
-5. Add high-coverage tests. No quality test, no merge;
-6. All public methods must have informative doctrings;
-
-
-### Style guide
-
-For documentation strings, `transformers` follows the [google
-style](https://google.github.io/styleguide/pyguide.html).
-
-#### This guide was heavily inspired by the awesome [scikit-learn guide to contributing](https://github.com/scikit-learn/scikit-learn/blob/master/CONTRIBUTING.md)
@@ -1 +0,0 @@
-include LICENSE
@@ -0,0 +1,2 @@
+#!/bin/sh
+python -m pytorch_pretrained_bert "$@"
@@ -1,22 +0,0 @@
-cd docs
-
-function deploy_doc(){
-	echo "Creating doc at commit $1 and pushing to folder $2"
-	git checkout $1
-	if [ ! -z "$2" ] 
-	then
-		echo "Pushing version" $2
-		make clean && make html && scp -r -oStrictHostKeyChecking=no _build/html $doc:$dir/$2
-	else
-		echo "Pushing master"
-		make clean && make html && scp -r -oStrictHostKeyChecking=no _build/html/* $doc:$dir
-	fi
-}
-
-deploy_doc "master" 
-deploy_doc "b33a385" v1.0.0
-deploy_doc "fe02e45" v1.1.0
-deploy_doc "89fd345" v1.2.0
-deploy_doc "fc9faa8" v2.0.0
-deploy_doc "3ddce1d" v2.1.1
-deploy_doc "f2f3294" v2.2.0
@@ -2,6 +2,6 @@ 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
+RUN pip install pytorch-pretrained-bert

 WORKDIR /workspace
@@ -1,19 +0,0 @@
-# Minimal makefile for Sphinx documentation
-#
-
-# You can set these variables from the command line.
-SPHINXOPTS    =
-SPHINXBUILD   = sphinx-build
-SOURCEDIR     = source
-BUILDDIR      = _build
-
-# Put it first so that "make" without argument is like "make help".
-help:
-	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
-
-.PHONY: help Makefile
-
-# Catch-all target: route all unknown targets to Sphinx using the new
-# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
-%: Makefile
-	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
@@ -1,67 +0,0 @@
-# Generating the documentation
-
-To generate the documentation, you first have to build it. Several packages are necessary to build the doc,
-you can install them using:
-
-```bash
-pip install -r requirements.txt
-```
- 
-## Packages installed
-
-Here's an overview of all the packages installed. If you ran the previous command installing all packages from 
-`requirements.txt`, you do not need to run the following commands.
-
-Building it requires the package `sphinx` that you can 
-install using:
-
-```bash
-pip install -U sphinx
-```
-
-You would also need the custom installed [theme](https://github.com/readthedocs/sphinx_rtd_theme) by 
-[Read The Docs](https://readthedocs.org/). You can install it using the following command:
-
-```bash
-pip install sphinx_rtd_theme
-```
-
-The third necessary package is the `recommonmark` package to accept Markdown as well as Restructured text:
-
-```bash
-pip install recommonmark
-```
-
-## Building the documentation
-
-Make sure that there is a symlink from the `example` file (in /examples) inside the source folder. Run the following 
-command to generate it:
-
-```bash
-ln -s ../../examples/README.md examples.md
-```
-
-Once you have setup `sphinx`, you can build the documentation by running the following command in the `/docs` folder:
-
-```bash
-make html
-```
-
---
-**NOTE**
-
-If you are adding/removing elements from the toc-tree or from any structural item, it is recommended to clean the build
-directory before rebuilding. Run the following command to clean and build:
-
-```bash
-make clean && make html
-```
-
---
-
-It should build the static app that will be available under `/docs/_build/html`
-
-## Adding a new element to the tree (toc-tree)
-
-Accepted files are reStructuredText (.rst) and Markdown (.md). Create a file with its extension and put it
-in the source directory. You can then link it to the toc-tree by putting the filename without the extension.
@@ -1,32 +0,0 @@
-alabaster==0.7.12
-Babel==2.7.0
-certifi==2019.6.16
-chardet==3.0.4
-commonmark==0.9.0
-docutils==0.14
-future==0.17.1
-idna==2.8
-imagesize==1.1.0
-Jinja2==2.10.1
-MarkupSafe==1.1.1
-packaging==19.0
-Pygments==2.4.2
-pyparsing==2.4.0
-pytz==2019.1
-recommonmark==0.5.0
-requests==2.22.0
-six==1.12.0
-snowballstemmer==1.9.0
-Sphinx==2.1.2
-sphinx-rtd-theme==0.4.3
-sphinxcontrib-applehelp==1.0.1
-sphinxcontrib-devhelp==1.0.1
-sphinxcontrib-htmlhelp==1.0.2
-sphinxcontrib-jsmath==1.0.1
-sphinxcontrib-qthelp==1.0.2
-sphinxcontrib-serializinghtml==1.1.3
-urllib3==1.25.3
-sphinx-markdown-tables==0.0.9
-numpy==1.17.2
-tensorflow==2.0.0rc2
-torch==1.2.0
@@ -1,12 +0,0 @@
-
-.highlight .c1, .highlight .sd{
-    color: #999
-}
-
-.highlight .nn, .highlight .k, .highlight .s1, .highlight .nb, .highlight .bp, .highlight .kc {
-    color: #FB8D68;
-}
-
-.highlight .kn, .highlight .nv, .highlight .s2, .highlight .ow {
-    color: #6670FF;
-}
@@ -1,196 +0,0 @@
-/* The literal code blocks */
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@@ -1,54 +0,0 @@
-# Benchmarks
-
-This section is dedicated to the Benchmarks done by the library, both by maintainers, contributors and users. These 
-benchmark will help keep track of the preformance improvements that are brought to our models across versions.
-
-## Benchmarking all models for inference
-
-As of version 2.1 we have benchmarked all models for inference, across many different settings: using PyTorch, with
-and without TorchScript, using TensorFlow, with and without XLA. All of those tests were done across CPUs (except for
-TensorFlow XLA) and GPUs.
-
-The approach is detailed in the [following blogpost](https://medium.com/huggingface/benchmarking-transformers-pytorch-and-tensorflow-e2917fb891c2)
-
-The results are available [here](https://docs.google.com/spreadsheets/d/1sryqufw2D0XlUH4sq3e9Wnxu5EAQkaohzrJbd5HdQ_w/edit?usp=sharing).
-
-## TF2 with mixed precision, XLA, Distribution (@tlkh)
-
-This work was done by [Timothy Liu](https://github.com/tlkh).
-
-There are very positive results to be gained from the various TensorFlow 2.0 features:
-
- Automatic Mixed Precision (AMP)
- XLA compiler
- Distribution strategies (multi-GPU)
-
-The benefits are listed here (tested on CoLA, MRPC, SST-2):
-
- AMP: Between 1.4x to 1.6x decrease in overall time without change in batch size
- AMP+XLA: Up to 2.5x decrease in overall time on SST-2 (larger dataset)
- Distribution: Between 1.4x to 3.4x decrease in overall time on 4xV100
- Combined: Up to 5.7x decrease in overall training time, or 9.1x training throughput
-
-The model quality (measured by the validation accuracy) fluctuates slightly. Taking an average of 4 training runs 
-on a single GPU gives the following results:
-
- CoLA: AMP results in slighter lower acc (0.820 vs 0.824)
- MRPC: AMP results in lower acc (0.823 vs 0.835)
- SST-2: AMP results in slighter lower acc (0.918 vs 0.922)
-
-However, in a distributed setting with 4xV100 (4x batch size), AMP can yield in better results:
-
-CoLA: AMP results in higher acc (0.828 vs 0.812)
-MRPC: AMP results in lower acc (0.817 vs 0.827)
-SST-2: AMP results in slightly lower acc (0.926 vs 0.929)
-
-The benchmark script is available [here](https://github.com/NVAITC/benchmarking/blob/master/tf2/bert_dist.py).
-
-Note: on some tasks (e.g. MRPC), the dataset is too small. The overhead due to the model compilation with XLA as well
-as the distribution strategy setup does not speed things up. The XLA compile time is also the reason why although throughput 
-can increase a lot (e.g. 2.7x for single GPU), overall (end-to-end) training speed-up is not as fast (as low as 1.4x)
-
-The benefits as seen on SST-2 (larger dataset) is much clear.
-
-All results can be seen on this [Google Sheet](https://docs.google.com/spreadsheets/d/1538MN224EzjbRL239sqSiUy6YY-rAjHyXhTzz_Zptls/edit#gid=960868445).
@@ -1,18 +0,0 @@
-BERTology
---------
-
-There is a growing field of study concerned with investigating the inner working of large-scale transformers like BERT (that some call "BERTology"). Some good examples of this field are:
-
-
-* BERT Rediscovers the Classical NLP Pipeline by Ian Tenney, Dipanjan Das, Ellie Pavlick: https://arxiv.org/abs/1905.05950
-* Are Sixteen Heads Really Better than One? by Paul Michel, Omer Levy, Graham Neubig: https://arxiv.org/abs/1905.10650
-* What Does BERT Look At? An Analysis of BERT's Attention by Kevin Clark, Urvashi Khandelwal, Omer Levy, Christopher D. Manning: https://arxiv.org/abs/1906.04341
-
-In order to help this new field develop, we have included a few additional features in the BERT/GPT/GPT-2 models to help people access the inner representations, mainly adapted  from the great work of Paul Michel (https://arxiv.org/abs/1905.10650):
-
-
-* accessing all the hidden-states of BERT/GPT/GPT-2,
-* accessing all the attention weights for each head of BERT/GPT/GPT-2,
-* retrieving heads output values and gradients to be able to compute head importance score and prune head as explained in https://arxiv.org/abs/1905.10650.
-
-To help you understand and use these features, we have added a specific example script: `bertology.py <https://github.com/huggingface/transformers/blob/master/examples/run_bertology.py>`_ while extract information and prune a model pre-trained on GLUE.
@@ -1,188 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-# Configuration file for the Sphinx documentation builder.
-#
-# This file does only contain a selection of the most common options. For a
-# full list see the documentation:
-# http://www.sphinx-doc.org/en/master/config
-
-# -- Path setup --------------------------------------------------------------
-
-# If extensions (or modules to document with autodoc) are in another directory,
-# add these directories to sys.path here. If the directory is relative to the
-# documentation root, use os.path.abspath to make it absolute, like shown here.
-#
-import os
-import sys
-sys.path.insert(0, os.path.abspath('../..'))
-
-
-# -- Project information -----------------------------------------------------
-
-project = u'transformers'
-copyright = u'2019, huggingface'
-author = u'huggingface'
-
-# The short X.Y version
-version = u''
-# The full version, including alpha/beta/rc tags
-release = u'2.2.2'
-
-
-# -- General configuration ---------------------------------------------------
-
-# If your documentation needs a minimal Sphinx version, state it here.
-#
-# needs_sphinx = '1.0'
-
-# Add any Sphinx extension module names here, as strings. They can be
-# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
-# ones.
-extensions = [
-    'sphinx.ext.autodoc',
-    'sphinx.ext.coverage',
-    'sphinx.ext.napoleon',
-    'recommonmark',
-    'sphinx.ext.viewcode',
-    'sphinx_markdown_tables'
-]
-
-# Add any paths that contain templates here, relative to this directory.
-templates_path = ['_templates']
-
-# The suffix(es) of source filenames.
-# You can specify multiple suffix as a list of string:
-#
-source_suffix = ['.rst', '.md']
-# source_suffix = '.rst'
-
-# The master toctree document.
-master_doc = 'index'
-
-# The language for content autogenerated by Sphinx. Refer to documentation
-# for a list of supported languages.
-#
-# This is also used if you do content translation via gettext catalogs.
-# Usually you set "language" from the command line for these cases.
-language = None
-
-# List of patterns, relative to source directory, that match files and
-# directories to ignore when looking for source files.
-# This pattern also affects html_static_path and html_extra_path.
-exclude_patterns = [u'_build', 'Thumbs.db', '.DS_Store']
-
-# The name of the Pygments (syntax highlighting) style to use.
-pygments_style = None
-
-
-# -- Options for HTML output -------------------------------------------------
-
-# The theme to use for HTML and HTML Help pages.  See the documentation for
-# a list of builtin themes.
-#
-html_theme = 'sphinx_rtd_theme'
-
-# Theme options are theme-specific and customize the look and feel of a theme
-# further.  For a list of options available for each theme, see the
-# documentation.
-#
-html_theme_options = {
-    'analytics_id': 'UA-83738774-2'
-}
-
-# Add any paths that contain custom static files (such as style sheets) here,
-# relative to this directory. They are copied after the builtin static files,
-# so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ['_static']
-
-# Custom sidebar templates, must be a dictionary that maps document names
-# to template names.
-#
-# The default sidebars (for documents that don't match any pattern) are
-# defined by theme itself.  Builtin themes are using these templates by
-# default: ``['localtoc.html', 'relations.html', 'sourcelink.html',
-# 'searchbox.html']``.
-#
-# html_sidebars = {}
-
-
-# -- Options for HTMLHelp output ---------------------------------------------
-
-# Output file base name for HTML help builder.
-htmlhelp_basename = 'transformersdoc'
-
-
-# -- Options for LaTeX output ------------------------------------------------
-
-latex_elements = {
-    # The paper size ('letterpaper' or 'a4paper').
-    #
-    # 'papersize': 'letterpaper',
-
-    # The font size ('10pt', '11pt' or '12pt').
-    #
-    # 'pointsize': '10pt',
-
-    # Additional stuff for the LaTeX preamble.
-    #
-    # 'preamble': '',
-
-    # Latex figure (float) alignment
-    #
-    # 'figure_align': 'htbp',
-}
-
-# Grouping the document tree into LaTeX files. List of tuples
-# (source start file, target name, title,
-#  author, documentclass [howto, manual, or own class]).
-latex_documents = [
-    (master_doc, 'transformers.tex', u'transformers Documentation',
-     u'huggingface', 'manual'),
-]
-
-
-# -- Options for manual page output ------------------------------------------
-
-# One entry per manual page. List of tuples
-# (source start file, name, description, authors, manual section).
-man_pages = [
-    (master_doc, 'transformers', u'transformers Documentation',
-     [author], 1)
-]
-
-
-# -- Options for Texinfo output ----------------------------------------------
-
-# Grouping the document tree into Texinfo files. List of tuples
-# (source start file, target name, title, author,
-#  dir menu entry, description, category)
-texinfo_documents = [
-    (master_doc, 'transformers', u'transformers Documentation',
-     author, 'transformers', 'One line description of project.',
-     'Miscellaneous'),
-]
-
-
-# -- Options for Epub output -------------------------------------------------
-
-# Bibliographic Dublin Core info.
-epub_title = project
-
-# The unique identifier of the text. This can be a ISBN number
-# or the project homepage.
-#
-# epub_identifier = ''
-
-# A unique identification for the text.
-#
-# epub_uid = ''
-
-# A list of files that should not be packed into the epub file.
-epub_exclude_files = ['search.html']
-
-def setup(app):
-    app.add_stylesheet('css/huggingface.css')
-    app.add_stylesheet('css/code-snippets.css')
-    app.add_js_file('js/custom.js')
-
-# -- Extension configuration -------------------------------------------------
@@ -1,101 +0,0 @@
-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.
-
-BERT
-^^^^
-
-You can convert any TensorFlow checkpoint for BERT (in particular `the pre-trained models released by Google <https://github.com/google-research/bert#pre-trained-models>`_\ ) in a PyTorch save file by using the `convert_tf_checkpoint_to_pytorch.py <https://github.com/huggingface/transformers/blob/master/transformers/convert_tf_checkpoint_to_pytorch.py>`_ script.
-
-This CLI takes as input a TensorFlow checkpoint (three files starting with ``bert_model.ckpt``\ ) and the associated configuration file (\ ``bert_config.json``\ ), and creates a PyTorch model for this configuration, loads the weights from the TensorFlow checkpoint in the PyTorch model and saves the resulting model in a standard PyTorch save file that can be imported using ``torch.load()`` (see examples in `run_bert_extract_features.py <https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/examples/run_bert_extract_features.py>`_\ , `run_bert_classifier.py <https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/examples/run_bert_classifier.py>`_ and `run_bert_squad.py <https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/examples/run_bert_squad.py>`_\ ).
-
-You only need to run this conversion script **once** to get a PyTorch model. You can then disregard the TensorFlow checkpoint (the three files starting with ``bert_model.ckpt``\ ) but be sure to keep the configuration file (\ ``bert_config.json``\ ) and the vocabulary file (\ ``vocab.txt``\ ) as these are needed for the PyTorch model too.
-
-To run this specific conversion script you will need to have TensorFlow and PyTorch installed (\ ``pip install tensorflow``\ ). The rest of the repository only requires PyTorch.
-
-Here is an example of the conversion process for a pre-trained ``BERT-Base Uncased`` model:
-
-.. code-block:: shell
-
-   export BERT_BASE_DIR=/path/to/bert/uncased_L-12_H-768_A-12
-
-   transformers bert \
-     $BERT_BASE_DIR/bert_model.ckpt \
-     $BERT_BASE_DIR/bert_config.json \
-     $BERT_BASE_DIR/pytorch_model.bin
-
-You can download Google's pre-trained models for the conversion `here <https://github.com/google-research/bert#pre-trained-models>`__.
-
-OpenAI GPT
-^^^^^^^^^^
-
-Here is an example of the conversion process for a pre-trained OpenAI GPT model, assuming that your NumPy checkpoint save as the same format than OpenAI pretrained model (see `here <https://github.com/openai/finetune-transformer-lm>`__\ )
-
-.. code-block:: shell
-
-   export OPENAI_GPT_CHECKPOINT_FOLDER_PATH=/path/to/openai/pretrained/numpy/weights
-
-   transformers gpt \
-     $OPENAI_GPT_CHECKPOINT_FOLDER_PATH \
-     $PYTORCH_DUMP_OUTPUT \
-     [OPENAI_GPT_CONFIG]
-
-OpenAI GPT-2
-^^^^^^^^^^^^
-
-Here is an example of the conversion process for a pre-trained OpenAI GPT-2 model (see `here <https://github.com/openai/gpt-2>`__\ )
-
-.. code-block:: shell
-
-   export OPENAI_GPT2_CHECKPOINT_PATH=/path/to/gpt2/pretrained/weights
-
-   transformers gpt2 \
-     $OPENAI_GPT2_CHECKPOINT_PATH \
-     $PYTORCH_DUMP_OUTPUT \
-     [OPENAI_GPT2_CONFIG]
-
-Transformer-XL
-^^^^^^^^^^^^^^
-
-Here is an example of the conversion process for a pre-trained Transformer-XL model (see `here <https://github.com/kimiyoung/transformer-xl/tree/master/tf#obtain-and-evaluate-pretrained-sota-models>`__\ )
-
-.. code-block:: shell
-
-   export TRANSFO_XL_CHECKPOINT_FOLDER_PATH=/path/to/transfo/xl/checkpoint
-
-   transformers transfo_xl \
-     $TRANSFO_XL_CHECKPOINT_FOLDER_PATH \
-     $PYTORCH_DUMP_OUTPUT \
-     [TRANSFO_XL_CONFIG]
-
-
-XLNet
-^^^^^
-
-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 xlnet \
-     $TRANSFO_XL_CHECKPOINT_PATH \
-     $TRANSFO_XL_CONFIG_PATH \
-     $PYTORCH_DUMP_OUTPUT \
-     STS-B \
-
-
-XLM
-^^^
-
-Here is an example of the conversion process for a pre-trained XLM model:
-
-.. code-block:: shell
-
-   export XLM_CHECKPOINT_PATH=/path/to/xlm/checkpoint
-
-   transformers xlm \
-     $XLM_CHECKPOINT_PATH \
-     $PYTORCH_DUMP_OUTPUT \
@@ -1 +0,0 @@
-../../examples/README.md
@@ -1,97 +0,0 @@
-Transformers
-================================================================================================================================================
-
-🤗 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.
-
-This is the documentation of our repository `transformers <https://github.com/huggingface/transformers>`__.
-
-Features
---------------------------------------------------
-
- As easy to use as pytorch-transformers
- As powerful and concise as Keras
- High performance on NLU and NLG tasks
- Low barrier to entry for educators and practitioners
-
-State-of-the-art NLP for everyone:
-
- Deep learning researchers
- Hands-on practitioners
- AI/ML/NLP teachers and educators
-
-Lower compute costs, smaller carbon footprint:
-
- Researchers can share trained models instead of always retraining
- Practitioners can reduce compute time and production costs
- 8 architectures with over 30 pretrained models, some in more than 100 languages
-
-Choose the right framework for every part of a model's lifetime:
-
- Train state-of-the-art models in 3 lines of code
- Deep interoperability between TensorFlow 2.0 and PyTorch models
- Move a single model between TF2.0/PyTorch frameworks at will
- Seamlessly pick the right framework for training, evaluation, production
-
-Contents
---------------------------------
-
-The library currently contains PyTorch and Tensorflow implementations, pre-trained model weights, usage scripts and conversion utilities for the following models:
-
-1. `BERT <https://github.com/google-research/bert>`_ (from Google) released with the paper `BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding <https://arxiv.org/abs/1810.04805>`_ by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.
-2. `GPT <https://github.com/openai/finetune-transformer-lm>`_ (from OpenAI) released with the paper `Improving Language Understanding by Generative Pre-Training <https://blog.openai.com/language-unsupervised>`_ by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever.
-3. `GPT-2 <https://blog.openai.com/better-language-models>`_ (from OpenAI) released with the paper `Language Models are Unsupervised Multitask Learners <https://blog.openai.com/better-language-models>`_ by Alec Radford*, Jeffrey Wu*, Rewon Child, David Luan, Dario Amodei** and Ilya Sutskever**.
-4. `Transformer-XL <https://github.com/kimiyoung/transformer-xl>`_ (from Google/CMU) released with the paper `Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context <https://arxiv.org/abs/1901.02860>`_ by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
-5. `XLNet <https://github.com/zihangdai/xlnet>`_ (from Google/CMU) released with the paper `XLNet: Generalized Autoregressive Pretraining for Language Understanding <https://arxiv.org/abs/1906.08237>`_ by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
-6. `XLM <https://github.com/facebookresearch/XLM>`_ (from Facebook) released together with the paper `Cross-lingual Language Model Pretraining <https://arxiv.org/abs/1901.07291>`_ by Guillaume Lample and Alexis Conneau.
-7. `RoBERTa <https://github.com/pytorch/fairseq/tree/master/examples/roberta>`_ (from Facebook), released together with the paper a `Robustly Optimized BERT Pretraining Approach <https://arxiv.org/abs/1907.11692>`_ by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
-8. `DistilBERT <https://huggingface.co/transformers/model_doc/distilbert.html>`_ (from HuggingFace) released together with the paper `DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter <https://arxiv.org/abs/1910.01108>`_ by Victor Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into `DistilGPT2 <https://github.com/huggingface/transformers/tree/master/examples/distillation>`_.
-9. `CTRL <https://github.com/pytorch/fairseq/tree/master/examples/ctrl>`_ (from Salesforce), released together with the paper `CTRL: A Conditional Transformer Language Model for Controllable Generation <https://www.github.com/salesforce/ctrl>`_ by Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and Richard Socher.
-10. `CamemBERT <https://huggingface.co/transformers/model_doc/camembert.html>`_ (from FAIR, Inria, Sorbonne Université) released together with the paper `CamemBERT: a Tasty French Language Model <https://arxiv.org/abs/1911.03894>`_ by Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suarez, Yoann Dupont, Laurent Romary, Eric Villemonte de la Clergerie, Djame Seddah, and Benoît Sagot.
-11. `ALBERT <https://github.com/google-research/ALBERT>`_ (from Google Research), released together with the paper a `ALBERT: A Lite BERT for Self-supervised Learning of Language Representations <https://arxiv.org/abs/1909.11942>`_ by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.
-
-.. toctree::
-    :maxdepth: 2
-    :caption: Notes
-
-    installation
-    quickstart
-    pretrained_models
-    model_sharing
-    examples
-    notebooks
-    serialization
-    converting_tensorflow_models
-    migration
-    bertology
-    torchscript
-    multilingual
-    benchmarks
-
-.. toctree::
-    :maxdepth: 2
-    :caption: Main classes
-
-    main_classes/configuration
-    main_classes/model
-    main_classes/tokenizer
-    main_classes/optimizer_schedules
-    main_classes/processors
-
-.. toctree::
-    :maxdepth: 2
-    :caption: Package Reference
-
-    model_doc/auto
-    model_doc/bert
-    model_doc/gpt
-    model_doc/transformerxl
-    model_doc/gpt2
-    model_doc/xlm
-    model_doc/xlnet
-    model_doc/roberta
-    model_doc/distilbert
-    model_doc/ctrl
-    model_doc/camembert
-    model_doc/albert
@@ -1,67 +0,0 @@
-# Installation
-
-Transformers is tested on Python 2.7 and 3.5+ (examples are tested only on python 3.5+) and PyTorch 1.1.0
-
-## With pip
-
-PyTorch Transformers can be installed using pip as follows:
-
-``` bash
-pip install transformers
-```
-
-## From source
-
-To install from source, clone the repository and install with:
-
-``` bash
-git clone https://github.com/huggingface/transformers.git
-cd transformers
-pip install [--editable] .
-```
-
-## Tests
-
-An extensive test suite is included to test the library behavior and several examples. Library tests can be found in the [tests folder](https://github.com/huggingface/transformers/tree/master/transformers/tests) and examples tests in the [examples folder](https://github.com/huggingface/transformers/tree/master/examples).
-
-Tests can be run using `unittest` or `pytest` (install pytest if needed with `pip install pytest`).
-
-Run all the tests from the root of the cloned repository with the commands:
-
-```bash
-python -m unittest discover -s transformers/tests -p "*test.py" -t .
-python -m unittest discover -s examples -p "*test.py" -t examples
-```
-
-or
-
-``` bash
-python -m pytest -sv ./transformers/tests/
-python -m pytest -sv ./examples/
-```
-
-By default, slow tests are skipped. Set the `RUN_SLOW` environment variable to `yes` to run them.
-
-## OpenAI GPT original tokenization workflow
-
-If you want to reproduce the original tokenization process of the `OpenAI GPT` paper, you will need to install `ftfy` (use version 4.4.3 if you are using Python 2) and `SpaCy`:
-
-``` bash
-pip install spacy ftfy==4.4.3
-python -m spacy download en
-```
-
-If you don't install `ftfy` and `SpaCy`, the `OpenAI GPT` tokenizer will default to tokenize using BERT's `BasicTokenizer` followed by Byte-Pair Encoding (which should be fine for most usage, don't worry).
-
-## Note on model downloads (Continuous Integration or large-scale deployments)
-
-If you expect to be downloading large volumes of models (more than 1,000) from our hosted bucket (for instance through your CI setup, or a large-scale production deployment), please cache the model files on your end. It will be way faster, and cheaper. Feel free to contact us privately if you need any help.
-
-## Do you want to run a Transformer model on a mobile device?
-
-You should check out our [swift-coreml-transformers](https://github.com/huggingface/swift-coreml-transformers) repo.
-
-It contains a set of tools to convert PyTorch or TensorFlow 2.0 trained Transformer models (currently contains `GPT-2`, `DistilGPT-2`, `BERT`, and `DistilBERT`) to CoreML models that run on iOS devices.
-
-At some point in the future, you'll be able to seamlessly move from pre-training or fine-tuning models in PyTorch to productizing them in CoreML,
-or prototype a model or an app in CoreML then research its hyperparameters or architecture from PyTorch. Super exciting!
@@ -1,10 +0,0 @@
-Configuration
----------------------------------------------------
-
-The base class ``PretrainedConfig`` implements the common methods for loading/saving a configuration either from a local file or directory, or from a pretrained model configuration provided by the library (downloaded from HuggingFace's AWS S3 repository).
-
-``PretrainedConfig``
-~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.PretrainedConfig
-    :members:
@@ -1,21 +0,0 @@
-Models
----------------------------------------------------
-
-The base class ``PreTrainedModel`` implements the common methods for loading/saving a model either from a local file or directory, or from a pretrained model configuration provided by the library (downloaded from HuggingFace's AWS S3 repository).
-
-``PreTrainedModel`` also implements a few methods which are common among all the models to:
-
- resize the input token embeddings when new tokens are added to the vocabulary
- prune the attention heads of the model.
-
-``PreTrainedModel``
-~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.PreTrainedModel
-    :members:
-
-``TFPreTrainedModel``
-~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFPreTrainedModel
-    :members:
@@ -1,75 +0,0 @@
-Optimizer
----------------------------------------------------
-
-The ``.optimization`` module provides:
-
- an optimizer with weight decay fixed that can be used to fine-tuned models, and
- several schedules in the form of schedule objects that inherit from ``_LRSchedule``:
- a gradient accumulation class to accumulate the gradients of multiple batches
-
-``AdamW``
-~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AdamW
-    :members:
-
-``AdamWeightDecay``
-~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AdamWeightDecay
-    :members:
-
-.. autofunction:: transformers.create_optimizer
-    :members:
-
-Schedules
----------------------------------------------------
-
-Learning Rate Schedules
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. autofunction:: transformers.get_constant_schedule
-
-
-.. autofunction:: transformers.get_constant_schedule_with_warmup
-
-.. image:: /imgs/warmup_constant_schedule.png
-    :target: /imgs/warmup_constant_schedule.png
-    :alt:
-
-
-.. autofunction:: transformers.get_cosine_schedule_with_warmup
-    :members:
-
-.. image:: /imgs/warmup_cosine_schedule.png
-    :target: /imgs/warmup_cosine_schedule.png
-    :alt:
-
-
-.. autofunction:: transformers.get_cosine_with_hard_restarts_schedule_with_warmup
-
-.. image:: /imgs/warmup_cosine_hard_restarts_schedule.png
-    :target: /imgs/warmup_cosine_hard_restarts_schedule.png
-    :alt:
-
-
-
-.. autofunction:: transformers.get_linear_schedule_with_warmup
-
-.. image:: /imgs/warmup_linear_schedule.png
-    :target: /imgs/warmup_linear_schedule.png
-    :alt:
-
-``Warmup``
-~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.Warmup
-    :members:
-
-Gradient Strategies
----------------------------------------------------
-
-``GradientAccumulator``
-~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.GradientAccumulator
@@ -1,153 +0,0 @@
-Processors
----------------------------------------------------
-
-This library includes processors for several traditional tasks. These processors can be used to process a dataset into
-examples that can be fed to a model.
-
-Processors
-~~~~~~~~~~~~~~~~~~~~~
-
-All processors follow the same architecture which is that of the
-:class:`~transformers.data.processors.utils.DataProcessor`. The processor returns a list
-of :class:`~transformers.data.processors.utils.InputExample`. These
-:class:`~transformers.data.processors.utils.InputExample` can be converted to
-:class:`~transformers.data.processors.utils.InputFeatures` in order to be fed to the model.
-
-.. autoclass:: transformers.data.processors.utils.DataProcessor
-    :members:
-
-
-.. autoclass:: transformers.data.processors.utils.InputExample
-    :members:
-
-
-.. autoclass:: transformers.data.processors.utils.InputFeatures
-    :members:
-
-
-GLUE
-~~~~~~~~~~~~~~~~~~~~~
-
-`General Language Understanding Evaluation (GLUE) <https://gluebenchmark.com/>`__ is a benchmark that evaluates
-the performance of models across a diverse set of existing NLU tasks. It was released together with the paper
-`GLUE: A multi-task benchmark and analysis platform for natural language understanding <https://openreview.net/pdf?id=rJ4km2R5t7>`__
-
-This library hosts a total of 10 processors for the following tasks: MRPC, MNLI, MNLI (mismatched),
-CoLA, SST2, STSB, QQP, QNLI, RTE and WNLI.
-
-Those processors are:
-    - :class:`~transformers.data.processors.utils.MrpcProcessor`
-    - :class:`~transformers.data.processors.utils.MnliProcessor`
-    - :class:`~transformers.data.processors.utils.MnliMismatchedProcessor`
-    - :class:`~transformers.data.processors.utils.Sst2Processor`
-    - :class:`~transformers.data.processors.utils.StsbProcessor`
-    - :class:`~transformers.data.processors.utils.QqpProcessor`
-    - :class:`~transformers.data.processors.utils.QnliProcessor`
-    - :class:`~transformers.data.processors.utils.RteProcessor`
-    - :class:`~transformers.data.processors.utils.WnliProcessor`
-
-Additionally, the following method  can be used to load values from a data file and convert them to a list of
-:class:`~transformers.data.processors.utils.InputExample`.
-
-.. automethod:: transformers.data.processors.glue.glue_convert_examples_to_features
-
-Example usage
-^^^^^^^^^^^^^^^^^^^^^^^^^
-
-An example using these processors is given in the `run_glue.py <https://github.com/huggingface/pytorch-transformers/blob/master/examples/run_glue.py>`__ script.
-
-
-XNLI
-~~~~~~~~~~~~~~~~~~~~~
-
-`The Cross-Lingual NLI Corpus (XNLI) <https://www.nyu.edu/projects/bowman/xnli/>`__ is a benchmark that evaluates
-the quality of cross-lingual text representations. 
-XNLI is crowd-sourced dataset based on `MultiNLI <http://www.nyu.edu/projects/bowman/multinli/>`: pairs of text are labeled with textual entailment 
-annotations for 15 different languages (including both high-ressource language such as English and low-ressource languages such as Swahili).
-
-It was released together with the paper
-`XNLI: Evaluating Cross-lingual Sentence Representations <https://arxiv.org/abs/1809.05053>`__
-
-This library hosts the processor to load the XNLI data:
-    - :class:`~transformers.data.processors.utils.XnliProcessor`
-
-Please note that since the gold labels are available on the test set, evaluation is performed on the test set.
-
-An example using these processors is given in the
-`run_xnli.py <https://github.com/huggingface/pytorch-transformers/blob/master/examples/run_xnli.py>`__ script.
-
-
-SQuAD
-~~~~~~~~~~~~~~~~~~~~~
-
-`The Stanford Question Answering Dataset (SQuAD) <https://rajpurkar.github.io/SQuAD-explorer//>`__ is a benchmark that evaluates
-the performance of models on question answering. Two versions are available, v1.1 and v2.0. The first version (v1.1) was released together with the paper
-`SQuAD: 100,000+ Questions for Machine Comprehension of Text <https://arxiv.org/abs/1606.05250>`__. The second version (v2.0) was released alongside 
-the paper `Know What You Don't Know: Unanswerable Questions for SQuAD <https://arxiv.org/abs/1806.03822>`__.
-
-This library hosts a processor for each of the two versions:
-
-Processors
-^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Those processors are:
-    - :class:`~transformers.data.processors.utils.SquadV1Processor`
-    - :class:`~transformers.data.processors.utils.SquadV2Processor`
-
-They both inherit from the abstract class :class:`~transformers.data.processors.utils.SquadProcessor`
-
-.. autoclass:: transformers.data.processors.squad.SquadProcessor
-    :members:
-
-Additionally, the following method can be used to convert SQuAD examples into :class:`~transformers.data.processors.utils.SquadFeatures`
-that can be used as model inputs.
-
-.. automethod:: transformers.data.processors.squad.squad_convert_examples_to_features
-
-These processors as well as the aforementionned method can be used with files containing the data as well as with the `tensorflow_datasets` package.
-Examples are given below.
-
-
-Example usage
-^^^^^^^^^^^^^^^^^^^^^^^^^
-Here is an example using the processors as well as the conversion method using data files:
-
-Example::
-
-    # Loading a V2 processor
-    processor = SquadV2Processor()
-    examples = processor.get_dev_examples(squad_v2_data_dir)
-
-    # Loading a V1 processor
-    processor = SquadV1Processor()
-    examples = processor.get_dev_examples(squad_v1_data_dir)
-
-    features = squad_convert_examples_to_features( 
-        examples=examples,
-        tokenizer=tokenizer,
-        max_seq_length=max_seq_length,
-        doc_stride=args.doc_stride,
-        max_query_length=max_query_length,
-        is_training=not evaluate,
-    )
-
-Using `tensorflow_datasets` is as easy as using a data file:
-
-Example::
-
-    # tensorflow_datasets only handle Squad V1.
-    tfds_examples = tfds.load("squad")
-    examples = SquadV1Processor().get_examples_from_dataset(tfds_examples, evaluate=evaluate)
-
-    features = squad_convert_examples_to_features( 
-        examples=examples,
-        tokenizer=tokenizer,
-        max_seq_length=max_seq_length,
-        doc_stride=args.doc_stride,
-        max_query_length=max_query_length,
-        is_training=not evaluate,
-    )
-
-
-Another example using these processors is given in the
-`run_squad.py <https://github.com/huggingface/transformers/blob/master/examples/run_squad.py>`__ script.
@@ -1,16 +0,0 @@
-Tokenizer
----------------------------------------------------
-
-The base class ``PreTrainedTokenizer`` implements the common methods for loading/saving a tokenizer either from a local file or directory, or from a pretrained tokenizer provided by the library (downloaded from HuggingFace's AWS S3 repository).
-
-``PreTrainedTokenizer`` is the main entry point into tokenizers as it also implements the main methods for using all the tokenizers:
-
- tokenizing, converting tokens to ids and back and encoding/decoding,
- adding new tokens to the vocabulary in a way that is independant of the underlying structure (BPE, SentencePiece...),
- managing special tokens (adding them, assigning them to roles, making sure they are not split during tokenization)
-
-``PreTrainedTokenizer``
-~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.PreTrainedTokenizer
-    :members:
@@ -1,109 +0,0 @@
-# Migrating from pytorch-pretrained-bert
-
-
-Here is a quick summary of what you should take care of when migrating from `pytorch-pretrained-bert` to `transformers`
-
-### Models always output `tuples`
-
-The main breaking change when migrating from `pytorch-pretrained-bert` to `transformers` is that the models forward method always outputs a `tuple` with various elements depending on the model and the configuration parameters.
-
-The exact content of the tuples for each model are detailled in the models' docstrings and the [documentation](https://huggingface.co/transformers/).
-
-In pretty much every case, you will be fine by taking the first element of the output as the output you previously used in `pytorch-pretrained-bert`.
-
-Here is a `pytorch-pretrained-bert` to `transformers` conversion example for a `BertForSequenceClassification` classification model:
-
-```python
-# Let's load our model
-model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
-
-# If you used to have this line in pytorch-pretrained-bert:
-loss = model(input_ids, labels=labels)
-
-# Now just use this line in transformers to extract the loss from the output tuple:
-outputs = model(input_ids, labels=labels)
-loss = outputs[0]
-
-# In transformers you can also have access to the logits:
-loss, logits = outputs[:2]
-
-# And even the attention weigths if you configure the model to output them (and other outputs too, see the docstrings and documentation)
-model = BertForSequenceClassification.from_pretrained('bert-base-uncased', output_attentions=True)
-outputs = model(input_ids, labels=labels)
-loss, logits, attentions = outputs
-```
-
-### Serialization
-
-Breaking change in the `from_pretrained()`method:
-
-1. Models are now set in evaluation mode by default when instantiated with the `from_pretrained()` method. To train them don't forget to set them back in training mode (`model.train()`) to activate the dropout modules.
-
-2. The additional `*inputs` and `**kwargs` arguments supplied to the `from_pretrained()` method used to be directly passed to the underlying model's class `__init__()` method. They are now used to update the model configuration attribute first which can break derived model classes build based on the previous `BertForSequenceClassification` examples. More precisely, the positional arguments `*inputs` provided to `from_pretrained()` are directly forwarded the model `__init__()` method while the keyword arguments `**kwargs` (i) which match configuration class attributes are used to update said attributes (ii) which don't match any configuration class attributes are forwarded to the model `__init__()` method.
-
-Also, while not a breaking change, the serialization methods have been standardized and you probably should switch to the new method `save_pretrained(save_directory)` if you were using any other serialization method before.
-
-Here is an example:
-
-```python
-### Let's load a model and tokenizer
-model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
-tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-
-### Do some stuff to our model and tokenizer
-# Ex: add new tokens to the vocabulary and embeddings of our model
-tokenizer.add_tokens(['[SPECIAL_TOKEN_1]', '[SPECIAL_TOKEN_2]'])
-model.resize_token_embeddings(len(tokenizer))
-# Train our model
-train(model)
-
-### Now let's save our model and tokenizer to a directory
-model.save_pretrained('./my_saved_model_directory/')
-tokenizer.save_pretrained('./my_saved_model_directory/')
-
-### Reload the model and the tokenizer
-model = BertForSequenceClassification.from_pretrained('./my_saved_model_directory/')
-tokenizer = BertTokenizer.from_pretrained('./my_saved_model_directory/')
-```
-
-### Optimizers: BertAdam & OpenAIAdam are now AdamW, schedules are standard PyTorch schedules
-
-The two optimizers previously included, `BertAdam` and `OpenAIAdam`, have been replaced by a single `AdamW` optimizer which has a few differences:
-
- it only implements weights decay correction,
- schedules are now externals (see below),
- gradient clipping is now also external (see below).
-
-The new optimizer `AdamW` matches PyTorch `Adam` optimizer API and let you use standard PyTorch or apex methods for the schedule and clipping.
-
-The schedules are now standard [PyTorch learning rate schedulers](https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate) and not part of the optimizer anymore.
-
-Here is a conversion examples from `BertAdam` with a linear warmup and decay schedule to `AdamW` and the same schedule:
-
-```python
-# Parameters:
-lr = 1e-3
-max_grad_norm = 1.0
-num_training_steps = 1000
-num_warmup_steps = 100
-warmup_proportion = float(num_warmup_steps) / float(num_training_steps)  # 0.1
-
-### Previously BertAdam optimizer was instantiated like this:
-optimizer = BertAdam(model.parameters(), lr=lr, schedule='warmup_linear', warmup=warmup_proportion, num_training_steps=num_training_steps)
-### and used like this:
-for batch in train_data:
-    loss = model(batch)
-    loss.backward()
-    optimizer.step()
-
-### In Transformers, optimizer and schedules are splitted and instantiated like this:
-optimizer = AdamW(model.parameters(), lr=lr, correct_bias=False)  # To reproduce BertAdam specific behavior set correct_bias=False
-scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps)  # PyTorch scheduler
-### and used like this:
-for batch in train_data:
-    loss = model(batch)
-    loss.backward()
-    torch.nn.utils.clip_grad_norm_(model.parameters(), max_grad_norm)  # Gradient clipping is not in AdamW anymore (so you can use amp without issue)
-    optimizer.step()
-    scheduler.step()
-```
@@ -1,64 +0,0 @@
-ALBERT
----------------------------------------------------
-
-``AlbrtConfig``
-~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AlbertConfig
-    :members:
-
-
-``AlbertTokenizer``
-~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AlbertTokenizer
-    :members:
-
-
-``AlbertModel``
-~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AlbertModel
-    :members:
-
-
-``AlbertForMaskedLM``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AlbertForMaskedLM
-    :members:
-
-
-``AlbertForSequenceClassification``
-~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AlbertForSequenceClassification
-    :members:
-
-
-``AlbertForQuestionAnswering``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AlbertForQuestionAnswering
-    :members:
-
-
-``TFAlbertModel``
-~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFAlbertModel
-    :members:
-
-
-``TFAlbertForMaskedLM``
-~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFAlbertForMaskedLM
-    :members:
-
-
-``TFAlbertForSequenceClassification``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFAlbertForSequenceClassification
-    :members:
@@ -1,29 +0,0 @@
-AutoModels
-----------
-
-In many cases, the architecture you want to use can be guessed from the name or the path of the pretrained model you are supplying to the ``from_pretrained`` method.
-
-AutoClasses are here to do this job for you so that you automatically retreive the relevant model given the name/path to the pretrained weights/config/vocabulary:
-
-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``).
-
-
-``AutoConfig``
-~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AutoConfig
-    :members:
-
-
-``AutoModel``
-~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AutoModel
-    :members:
-
-
-``AutoTokenizer``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AutoTokenizer
-    :members:
@@ -1,128 +0,0 @@
-BERT
----------------------------------------------------
-
-``BertConfig``
-~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BertConfig
-    :members:
-
-
-``BertTokenizer``
-~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BertTokenizer
-    :members:
-
-
-``BertModel``
-~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BertModel
-    :members:
-
-
-``BertForPreTraining``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BertForPreTraining
-    :members:
-
-
-``BertForMaskedLM``
-~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BertForMaskedLM
-    :members:
-
-
-``BertForNextSentencePrediction``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BertForNextSentencePrediction
-    :members:
-
-
-``BertForSequenceClassification``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BertForSequenceClassification
-    :members:
-
-
-``BertForMultipleChoice``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BertForMultipleChoice
-    :members:
-
-
-``BertForTokenClassification``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BertForTokenClassification
-    :members:
-
-
-``BertForQuestionAnswering``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BertForQuestionAnswering
-    :members:
-
-
-``TFBertModel``
-~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFBertModel
-    :members:
-
-
-``TFBertForPreTraining``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFBertForPreTraining
-    :members:
-
-
-``TFBertForMaskedLM``
-~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFBertForMaskedLM
-    :members:
-
-
-``TFBertForNextSentencePrediction``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFBertForNextSentencePrediction
-    :members:
-
-
-``TFBertForSequenceClassification``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFBertForSequenceClassification
-    :members:
-
-
-``TFBertForMultipleChoice``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFBertForMultipleChoice
-    :members:
-
-
-``TFBertForTokenClassification``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFBertForTokenClassification
-    :members:
-
-
-``TFBertForQuestionAnswering``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFBertForQuestionAnswering
-    :members:
-
@@ -1,50 +0,0 @@
-CamemBERT
----------------------------------------------------
-
-``CamembertConfig``
-~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.CamembertConfig
-    :members:
-
-
-``CamembertTokenizer``
-~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.CamembertTokenizer
-    :members:
-
-
-``CamembertModel``
-~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.CamembertModel
-    :members:
-
-
-``CamembertForMaskedLM``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.CamembertForMaskedLM
-    :members:
-
-
-``CamembertForSequenceClassification``
-~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.CamembertForSequenceClassification
-    :members:
-
-
-``CamembertForMultipleChoice``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.CamembertForMultipleChoice
-    :members:
-
-
-``CamembertForTokenClassification``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.CamembertForTokenClassification
-    :members:
@@ -1,49 +0,0 @@
-CTRL
----------------------------------------------------
-
-Note: if you fine-tune a CTRL model using the Salesforce code (https://github.com/salesforce/ctrl),
-you'll be able to convert from TF to our HuggingFace/Transformers format using the 
-``convert_tf_to_huggingface_pytorch.py`` script (see `issue #1654 <https://github.com/huggingface/transformers/issues/1654>`_).
-
-
-``CTRLConfig``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.CTRLConfig
-    :members:
-
-
-``CTRLTokenizer``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.CTRLTokenizer
-    :members:
-
-
-``CTRLModel``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.CTRLModel
-    :members:
-
-
-``CTRLLMHeadModel``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.CTRLLMHeadModel
-    :members:
-
-
-``TFCTRLModel``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFCTRLModel
-    :members:
-
-
-``TFCTRLLMHeadModel``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFCTRLLMHeadModel
-    :members:
-
@@ -1,70 +0,0 @@
-DistilBERT
----------------------------------------------------
-
-``DistilBertConfig``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.DistilBertConfig
-    :members:
-
-
-``DistilBertTokenizer``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.DistilBertTokenizer
-    :members:
-
-
-``DistilBertModel``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.DistilBertModel
-    :members:
-
-
-``DistilBertForMaskedLM``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.DistilBertForMaskedLM
-    :members:
-
-
-``DistilBertForSequenceClassification``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.DistilBertForSequenceClassification
-    :members:
-
-
-``DistilBertForQuestionAnswering``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.DistilBertForQuestionAnswering
-    :members:
-
-``TFDistilBertModel``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFDistilBertModel
-    :members:
-
-
-``TFDistilBertForMaskedLM``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFDistilBertForMaskedLM
-    :members:
-
-
-``TFDistilBertForSequenceClassification``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFDistilBertForSequenceClassification
-    :members:
-
-
-``TFDistilBertForQuestionAnswering``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFDistilBertForQuestionAnswering
-    :members:
@@ -1,57 +0,0 @@
-OpenAI GPT
----------------------------------------------------
-
-``OpenAIGPTConfig``
-~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.OpenAIGPTConfig
-    :members:
-
-
-``OpenAIGPTTokenizer``
-~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.OpenAIGPTTokenizer
-    :members:
-
-
-``OpenAIGPTModel``
-~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.OpenAIGPTModel
-    :members:
-
-
-``OpenAIGPTLMHeadModel``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.OpenAIGPTLMHeadModel
-    :members:
-
-
-``OpenAIGPTDoubleHeadsModel``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.OpenAIGPTDoubleHeadsModel
-    :members:
-
-
-``TFOpenAIGPTModel``
-~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFOpenAIGPTModel
-    :members:
-
-
-``TFOpenAIGPTLMHeadModel``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFOpenAIGPTLMHeadModel
-    :members:
-
-
-``TFOpenAIGPTDoubleHeadsModel``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFOpenAIGPTDoubleHeadsModel
-    :members:
@@ -1,57 +0,0 @@
-OpenAI GPT2
----------------------------------------------------
-
-``GPT2Config``
-~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.GPT2Config
-    :members:
-
-
-``GPT2Tokenizer``
-~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.GPT2Tokenizer
-    :members:
-
-
-``GPT2Model``
-~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.GPT2Model
-    :members:
-
-
-``GPT2LMHeadModel``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.GPT2LMHeadModel
-    :members:
-
-
-``GPT2DoubleHeadsModel``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.GPT2DoubleHeadsModel
-    :members:
-
-
-``TFGPT2Model``
-~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFGPT2Model
-    :members:
-
-
-``TFGPT2LMHeadModel``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFGPT2LMHeadModel
-    :members:
-
-
-``TFGPT2DoubleHeadsModel``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFGPT2DoubleHeadsModel
-    :members:
@@ -1,57 +0,0 @@
-RoBERTa
----------------------------------------------------
-
-``RobertaConfig``
-~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.RobertaConfig
-    :members:
-
-
-``RobertaTokenizer``
-~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.RobertaTokenizer
-    :members:
-
-
-``RobertaModel``
-~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.RobertaModel
-    :members:
-
-
-``RobertaForMaskedLM``
-~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.RobertaForMaskedLM
-    :members:
-
-
-``RobertaForSequenceClassification``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.RobertaForSequenceClassification
-    :members:
-
-
-``TFRobertaModel``
-~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFRobertaModel
-    :members:
-
-
-``TFRobertaForMaskedLM``
-~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFRobertaForMaskedLM
-    :members:
-
-
-``TFRobertaForSequenceClassification``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFRobertaForSequenceClassification
-    :members:
@@ -1,44 +0,0 @@
-Transformer XL
----------------------------------------------------
-
-
-``TransfoXLConfig``
-~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TransfoXLConfig
-    :members:
-
-
-``TransfoXLTokenizer``
-~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TransfoXLTokenizer
-    :members:
-
-
-``TransfoXLModel``
-~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TransfoXLModel
-    :members:
-
-
-``TransfoXLLMHeadModel``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TransfoXLLMHeadModel
-    :members:
-
-
-``TFTransfoXLModel``
-~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFTransfoXLModel
-    :members:
-
-
-``TFTransfoXLLMHeadModel``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFTransfoXLLMHeadModel
-    :members:
@@ -1,69 +0,0 @@
-XLM
----------------------------------------------------
-
-``XLMConfig``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.XLMConfig
-    :members:
-
-``XLMTokenizer``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.XLMTokenizer
-    :members:
-
-``XLMModel``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.XLMModel
-    :members:
-
-
-``XLMWithLMHeadModel``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.XLMWithLMHeadModel
-    :members:
-
-
-``XLMForSequenceClassification``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.XLMForSequenceClassification
-    :members:
-
-
-``XLMForQuestionAnswering``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.XLMForQuestionAnswering
-    :members:
-
-
-``TFXLMModel``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFXLMModel
-    :members:
-
-
-``TFXLMWithLMHeadModel``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFXLMWithLMHeadModel
-    :members:
-
-
-``TFXLMForSequenceClassification``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFXLMForSequenceClassification
-    :members:
-
-
-``TFXLMForQuestionAnsweringSimple``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFXLMForQuestionAnsweringSimple
-    :members:
@@ -1,71 +0,0 @@
-XLNet
----------------------------------------------------
-
-``XLNetConfig``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.XLNetConfig
-    :members:
-
-
-``XLNetTokenizer``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.XLNetTokenizer
-    :members:
-
-
-``XLNetModel``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.XLNetModel
-    :members:
-
-
-``XLNetLMHeadModel``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.XLNetLMHeadModel
-    :members:
-
-
-``XLNetForSequenceClassification``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.XLNetForSequenceClassification
-    :members:
-
-
-``XLNetForQuestionAnswering``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.XLNetForQuestionAnswering
-    :members:
-
-
-``TFXLNetModel``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFXLNetModel
-    :members:
-
-
-``TFXLNetLMHeadModel``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFXLNetLMHeadModel
-    :members:
-
-
-``TFXLNetForSequenceClassification``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFXLNetForSequenceClassification
-    :members:
-
-
-``TFXLNetForQuestionAnsweringSimple``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFXLNetForQuestionAnsweringSimple
-    :members:
@@ -1,40 +0,0 @@
-# Model upload and sharing
-
-Starting with `v2.2.2`, you can now upload and share your fine-tuned models with the community, using the <abbr title="Command-line interface">CLI</abbr> that's built-in to the library.
-
-**First, create an account on [https://huggingface.co/join](https://huggingface.co/join)**. Then:
-
-```shell
-transformers-cli login
-# log in using the same credentials as on huggingface.co
-```
-Upload your model:
-```shell
-transformers-cli upload ./path/to/pretrained_model/
-
-# ^^ Upload folder containing weights/tokenizer/config
-# saved via `.save_pretrained()`
-
-transformers-cli upload ./config.json [--filename folder/foobar.json]
-
-# ^^ Upload a single file
-# (you can optionally override its filename, which can be nested inside a folder)
-```
-
-Your model will then be accessible through its identifier, a concatenation of your username and the folder name above:
-```python
-"username/pretrained_model"
-```
-
-Anyone can load it from code:
-```python
-tokenizer = AutoTokenizer.from_pretrained("username/pretrained_model")
-model = AutoModel.from_pretrained("username/pretrained_model")
-```
-
-Finally, list all your files on S3:
-```shell
-transformers-cli ls
-# List all your S3 objects.
-```
-
@@ -1,103 +0,0 @@
-Multi-lingual models
-================================================
-
-Most of the models available in this library are mono-lingual models (English, Chinese and German). A few
-multi-lingual models are available and have a different mechanisms than mono-lingual models.
-This page details the usage of these models.
-
-The two models that currently support multiple languages are BERT and XLM.
-
-XLM
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-XLM has a total of 10 different checkpoints, only one of which is mono-lingual. The 9 remaining model checkpoints can
-be split in two categories: the checkpoints that make use of language embeddings, and those that don't
-
-XLM & Language Embeddings
------------------------------------------------
-
-This section concerns the following checkpoints:
-
- ``xlm-mlm-ende-1024`` (Masked language modeling, English-German)
- ``xlm-mlm-enfr-1024`` (Masked language modeling, English-French)
- ``xlm-mlm-enro-1024`` (Masked language modeling, English-Romanian)
- ``xlm-mlm-xnli15-1024`` (Masked language modeling, XNLI languages)
- ``xlm-mlm-tlm-xnli15-1024`` (Masked language modeling + Translation, XNLI languages)
- ``xlm-clm-enfr-1024`` (Causal language modeling, English-French)
- ``xlm-clm-ende-1024`` (Causal language modeling, English-German)
-
-These checkpoints require language embeddings that will specify the language used at inference time. These language
-embeddings are represented as a tensor that is of the same shape as the input ids passed to the model. The values in
-these tensors depend on the language used and are identifiable using the ``lang2id`` and ``id2lang`` attributes
-from the tokenizer.
-
-Here is an example using the ``xlm-clm-enfr-1024`` checkpoint (Causal language modeling, English-French):
-
-
-.. code-block::
-
-    import torch
-    from transformers import XLMTokenizer, XLMWithLMHeadModel
-
-    tokenizer = XLMTokenizer.from_pretrained("xlm-clm-1024-enfr")
-
-
-The different languages this model/tokenizer handles, as well as the ids of these languages are visible using the
-``lang2id`` attribute:
-
-.. code-block::
-
-    print(tokenizer.lang2id)  # {'en': 0, 'fr': 1}
-
-
-These ids should be used when passing a language parameter during a model pass. Let's define our inputs:
-
-.. code-block::
-
-    input_ids = torch.tensor([tokenizer.encode("Wikipedia was used to")]) # batch size of 1
-
-
-We should now define the language embedding by using the previously defined language id. We want to create a tensor
-filled with the appropriate language ids, of the same size as input_ids. For english, the id is 0:
-
-.. code-block::
-
-    language_id = tokenizer.lang2id['en']  # 0
-    langs = torch.tensor([language_id] * input_ids.shape[1])  # torch.tensor([0, 0, 0, ..., 0])
-
-    # We reshape it to be of size (batch_size, sequence_length)
-    langs = langs.view(1, -1) # is now of shape [1, sequence_length] (we have a batch size of 1)
-
-
-You can then feed it all as input to your model:
-
-.. code-block::
-
-    outputs = model(input_ids, langs=langs)
-
-
-The example `run_generation.py <https://github.com/huggingface/transformers/blob/master/examples/run_generation.py>`__
-can generate text using the CLM checkpoints from XLM, using the language embeddings.
-
-XLM without Language Embeddings
------------------------------------------------
-
-This section concerns the following checkpoints:
-
- ``xlm-mlm-17-1280`` (Masked language modeling, 17 languages)
- ``xlm-mlm-100-1280`` (Masked language modeling, 100 languages)
-
-These checkpoints do not require language embeddings at inference time. These models are used to have generic
-sentence representations, differently from previously-mentioned XLM checkpoints.
-
-
-BERT
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-BERT has two checkpoints that can be used for multi-lingual tasks:
-
- ``bert-base-multilingual-uncased`` (Masked language modeling + Next sentence prediction, 102 languages)
- ``bert-base-multilingual-cased`` (Masked language modeling + Next sentence prediction, 104 languages)
-
-These checkpoints do not require language embeddings at inference time. They should identify the language
-used in the context and infer accordingly.
@@ -1,16 +0,0 @@
-Notebooks
-================================================
-
-We include `three Jupyter Notebooks <https://github.com/huggingface/transformers/tree/master/notebooks>`_ that can be used to check that the predictions of the PyTorch model are identical to the predictions of the original TensorFlow model.
-
-
-*
-  The first NoteBook (\ `Comparing-TF-and-PT-models.ipynb <https://github.com/huggingface/transformers/blob/master/notebooks/Comparing-TF-and-PT-models.ipynb>`_\ ) extracts the hidden states of a full sequence on each layers of the TensorFlow and the PyTorch models and computes the standard deviation between them. In the given example, we get a standard deviation of 1.5e-7 to 9e-7 on the various hidden state of the models.
-
-*
-  The second NoteBook (\ `Comparing-TF-and-PT-models-SQuAD.ipynb <https://github.com/huggingface/transformers/blob/master/notebooks/Comparing-TF-and-PT-models-SQuAD.ipynb>`_\ ) compares the loss computed by the TensorFlow and the PyTorch models for identical initialization of the fine-tuning layer of the ``BertForQuestionAnswering`` and computes the standard deviation between them. In the given example, we get a standard deviation of 2.5e-7 between the models.
-
-*
-  The third NoteBook (\ `Comparing-TF-and-PT-models-MLM-NSP.ipynb <https://github.com/huggingface/transformers/blob/master/notebooks/Comparing-TF-and-PT-models-MLM-NSP.ipynb>`_\ ) compares the predictions computed by the TensorFlow and the PyTorch models for masked token language modeling using the pre-trained masked language modeling model.
-
-Please follow the instructions given in the notebooks to run and modify them.
@@ -1,245 +0,0 @@
-Pretrained models
-================================================
-
-Here is the full list of the currently provided pretrained models together with a short presentation of each model.
-
-
-+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| Architecture      | Shortcut name                                              | Details of the model                                                                                                                  |
-+===================+============================================================+=======================================================================================================================================+
-| BERT              | ``bert-base-uncased``                                      | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
-|                   |                                                            | | Trained on lower-cased English text.                                                                                                |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``bert-large-uncased``                                     | | 24-layer, 1024-hidden, 16-heads, 340M parameters.                                                                                   |
-|                   |                                                            | | Trained on lower-cased English text.                                                                                                |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``bert-base-cased``                                        | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
-|                   |                                                            | | Trained on cased English text.                                                                                                      |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``bert-large-cased``                                       | | 24-layer, 1024-hidden, 16-heads, 340M parameters.                                                                                   |
-|                   |                                                            | | Trained on cased English text.                                                                                                      |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``bert-base-multilingual-uncased``                         | | (Original, not recommended) 12-layer, 768-hidden, 12-heads, 110M parameters.                                                        |
-|                   |                                                            | | Trained on lower-cased text in the top 102 languages with the largest Wikipedias                                                    |
-|                   |                                                            | (see `details <https://github.com/google-research/bert/blob/master/multilingual.md>`__).                                              |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``bert-base-multilingual-cased``                           | | (New, **recommended**) 12-layer, 768-hidden, 12-heads, 110M parameters.                                                             |
-|                   |                                                            | | Trained on cased text in the top 104 languages with the largest Wikipedias                                                          |
-|                   |                                                            | (see `details <https://github.com/google-research/bert/blob/master/multilingual.md>`__).                                              |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``bert-base-chinese``                                      | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
-|                   |                                                            | | Trained on cased Chinese Simplified and Traditional text.                                                                           |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``bert-base-german-cased``                                 | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
-|                   |                                                            | | Trained on cased German text by Deepset.ai                                                                                          |
-|                   |                                                            | (see `details on deepset.ai website <https://deepset.ai/german-bert>`__).                                                             |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``bert-large-uncased-whole-word-masking``                  | | 24-layer, 1024-hidden, 16-heads, 340M parameters.                                                                                   |
-|                   |                                                            | | Trained on lower-cased English text using Whole-Word-Masking                                                                        |
-|                   |                                                            | (see `details <https://github.com/google-research/bert/#bert>`__).                                                                    |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``bert-large-cased-whole-word-masking``                    | | 24-layer, 1024-hidden, 16-heads, 340M parameters.                                                                                   |
-|                   |                                                            | | Trained on cased English text using Whole-Word-Masking                                                                              |
-|                   |                                                            | (see `details <https://github.com/google-research/bert/#bert>`__).                                                                    |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``bert-large-uncased-whole-word-masking-finetuned-squad``  | | 24-layer, 1024-hidden, 16-heads, 340M parameters.                                                                                   |
-|                   |                                                            | | The ``bert-large-uncased-whole-word-masking`` model fine-tuned on SQuAD                                                             |
-|                   |                                                            | (see details of fine-tuning in the `example section <https://github.com/huggingface/transformers/tree/master/examples>`__).           |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``bert-large-cased-whole-word-masking-finetuned-squad``    | | 24-layer, 1024-hidden, 16-heads, 340M parameters                                                                                    |
-|                   |                                                            | | The ``bert-large-cased-whole-word-masking`` model fine-tuned on SQuAD                                                               |
-|                   |                                                            | (see `details of fine-tuning in the example section <https://huggingface.co/transformers/examples.html>`__)                           |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``bert-base-cased-finetuned-mrpc``                         | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
-|                   |                                                            | | The ``bert-base-cased`` model fine-tuned on MRPC                                                                                    |
-|                   |                                                            | (see `details of fine-tuning in the example section <https://huggingface.co/transformers/examples.html>`__)                           |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``bert-base-german-dbmdz-cased``                           | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
-|                   |                                                            | | Trained on cased German text by DBMDZ                                                                                               |
-|                   |                                                            | (see `details on dbmdz repository <https://github.com/dbmdz/german-bert>`__).                                                         |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``bert-base-german-dbmdz-uncased``                         | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
-|                   |                                                            | | Trained on uncased German text by DBMDZ                                                                                             |
-|                   |                                                            | (see `details on dbmdz repository <https://github.com/dbmdz/german-bert>`__).                                                         |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``bert-base-japanese``                                     | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
-|                   |                                                            | | Trained on Japanese text. Text is tokenized with MeCab and WordPiece.                                                               |
-|                   |                                                            | | `MeCab <https://taku910.github.io/mecab/>`__ is required for tokenization.                                                          |
-|                   |                                                            | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__).                                               |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``bert-base-japanese-whole-word-masking``                  | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
-|                   |                                                            | | Trained on Japanese text using Whole-Word-Masking. Text is tokenized with MeCab and WordPiece.                                      |
-|                   |                                                            | | `MeCab <https://taku910.github.io/mecab/>`__ is required for tokenization.                                                          |
-|                   |                                                            | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__).                                               |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``bert-base-japanese-char``                                | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
-|                   |                                                            | | Trained on Japanese text. Text is tokenized into characters.                                                                        |
-|                   |                                                            | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__).                                               |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``bert-base-japanese-char-whole-word-masking``             | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
-|                   |                                                            | | Trained on Japanese text using Whole-Word-Masking. Text is tokenized into characters.                                               |
-|                   |                                                            | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__).                                               |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``bert-base-finnish-cased-v1``                             | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
-|                   |                                                            | | Trained on cased Finnish text.                                                                                                      |
-|                   |                                                            | (see `details on turkunlp.org <http://turkunlp.org/FinBERT/>`__).                                                                     |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``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/>`__).                                                                     |
-+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| GPT               | ``openai-gpt``                                             | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
-|                   |                                                            | | OpenAI GPT English model                                                                                                            |
-+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| GPT-2             | ``gpt2``                                                   | | 12-layer, 768-hidden, 12-heads, 117M parameters.                                                                                    |
-|                   |                                                            | | OpenAI GPT-2 English model                                                                                                          |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``gpt2-medium``                                            | | 24-layer, 1024-hidden, 16-heads, 345M parameters.                                                                                   |
-|                   |                                                            | | OpenAI's Medium-sized GPT-2 English model                                                                                           |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``gpt2-large``                                             | | 36-layer, 1280-hidden, 20-heads, 774M parameters.                                                                                   |
-|                   |                                                            | | OpenAI's Large-sized GPT-2 English model                                                                                            |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``gpt2-xl``                                                | | 48-layer, 1600-hidden, 25-heads, 1558M parameters.                                                                                  |
-|                   |                                                            | | OpenAI's XL-sized GPT-2 English model                                                                                               |
-+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| Transformer-XL    | ``transfo-xl-wt103``                                       | | 18-layer, 1024-hidden, 16-heads, 257M parameters.                                                                                   |
-|                   |                                                            | | English model trained on wikitext-103                                                                                               |
-+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| XLNet             | ``xlnet-base-cased``                                       | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
-|                   |                                                            | | XLNet English model                                                                                                                 |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``xlnet-large-cased``                                      | | 24-layer, 1024-hidden, 16-heads, 340M parameters.                                                                                   |
-|                   |                                                            | | XLNet Large English model                                                                                                           |
-+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| XLM               | ``xlm-mlm-en-2048``                                        | | 12-layer, 2048-hidden, 16-heads                                                                                                     |
-|                   |                                                            | | XLM English model                                                                                                                   |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``xlm-mlm-ende-1024``                                      | | 6-layer, 1024-hidden, 8-heads                                                                                                       |
-|                   |                                                            | | XLM English-German model trained on the concatenation of English and German wikipedia                                               |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``xlm-mlm-enfr-1024``                                      | | 6-layer, 1024-hidden, 8-heads                                                                                                       |
-|                   |                                                            | | XLM English-French model trained on the concatenation of English and French wikipedia                                               |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``xlm-mlm-enro-1024``                                      | | 6-layer, 1024-hidden, 8-heads                                                                                                       |
-|                   |                                                            | | XLM English-Romanian Multi-language model                                                                                           |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``xlm-mlm-xnli15-1024``                                    | | 12-layer, 1024-hidden, 8-heads                                                                                                      |
-|                   |                                                            | | XLM Model pre-trained with MLM on the `15 XNLI languages <https://github.com/facebookresearch/XNLI>`__.                             |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``xlm-mlm-tlm-xnli15-1024``                                | | 12-layer, 1024-hidden, 8-heads                                                                                                      |
-|                   |                                                            | | XLM Model pre-trained with MLM + TLM on the `15 XNLI languages <https://github.com/facebookresearch/XNLI>`__.                       |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``xlm-clm-enfr-1024``                                      | | 6-layer, 1024-hidden, 8-heads                                                                                                       |
-|                   |                                                            | | XLM English-French model trained with CLM (Causal Language Modeling) on the concatenation of English and French wikipedia           |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``xlm-clm-ende-1024``                                      | | 6-layer, 1024-hidden, 8-heads                                                                                                       |
-|                   |                                                            | | XLM English-German model trained with CLM (Causal Language Modeling) on the concatenation of English and German wikipedia           |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``xlm-mlm-17-1280``                                        | | 16-layer, 1280-hidden, 16-heads                                                                                                     |
-|                   |                                                            | | XLM model trained with MLM (Masked Language Modeling) on 17 languages.                                                              |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``xlm-mlm-100-1280``                                       | | 16-layer, 1280-hidden, 16-heads                                                                                                     |
-|                   |                                                            | | XLM model trained with MLM (Masked Language Modeling) on 100 languages.                                                             |
-+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| RoBERTa           | ``roberta-base``                                           | | 12-layer, 768-hidden, 12-heads, 125M parameters                                                                                     |
-|                   |                                                            | | RoBERTa using the BERT-base architecture                                                                                            |
-|                   |                                                            | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/roberta>`__)                                                   |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``roberta-large``                                          | | 24-layer, 1024-hidden, 16-heads, 355M parameters                                                                                    |
-|                   |                                                            | | RoBERTa using the BERT-large architecture                                                                                           |
-|                   |                                                            | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/roberta>`__)                                                   |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``roberta-large-mnli``                                     | | 24-layer, 1024-hidden, 16-heads, 355M parameters                                                                                    |
-|                   |                                                            | | ``roberta-large`` fine-tuned on `MNLI <http://www.nyu.edu/projects/bowman/multinli/>`__.                                            |
-|                   |                                                            | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/roberta>`__)                                                   |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``roberta-base-openai-detector``                           | | 12-layer, 768-hidden, 12-heads, 125M parameters                                                                                     |
-|                   |                                                            | | ``roberta-base`` fine-tuned by OpenAI on the outputs of the 1.5B-parameter GPT-2 model.                                             |
-|                   |                                                            | (see `details <https://github.com/openai/gpt-2-output-dataset/tree/master/detector>`__)                                               |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``roberta-large-openai-detector``                          | | 24-layer, 1024-hidden, 16-heads, 355M parameters                                                                                    |
-|                   |                                                            | | ``roberta-large`` fine-tuned by OpenAI on the outputs of the 1.5B-parameter GPT-2 model.                                            |
-|                   |                                                            | (see `details <https://github.com/openai/gpt-2-output-dataset/tree/master/detector>`__)                                               |
-+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| DistilBERT        | ``distilbert-base-uncased``                                | | 6-layer, 768-hidden, 12-heads, 66M parameters                                                                                       |
-|                   |                                                            | | The DistilBERT model distilled from the BERT model `bert-base-uncased` checkpoint                                                   |
-|                   |                                                            | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__)                                     |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``distilbert-base-uncased-distilled-squad``                | | 6-layer, 768-hidden, 12-heads, 66M parameters                                                                                       |
-|                   |                                                            | | The DistilBERT model distilled from the BERT model `bert-base-uncased` checkpoint, with an additional linear layer.                 |
-|                   |                                                            | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__)                                     |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``distilgpt2``                                             | | 6-layer, 768-hidden, 12-heads, 82M parameters                                                                                       |
-|                   |                                                            | | The DistilGPT2 model distilled from the GPT2 model `gpt2` checkpoint.                                                               |
-|                   |                                                            | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__)                                     |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``distilroberta-base``                                     | | 6-layer, 768-hidden, 12-heads, 82M parameters                                                                                       |
-|                   |                                                            | | The DistilRoBERTa model distilled from the RoBERTa model `roberta-base` checkpoint.                                                 |
-|                   |                                                            | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__)                                     |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``distilbert-base-german-cased``                           | | 6-layer, 768-hidden, 12-heads, 66M parameters                                                                                       |
-|                   |                                                            | | The German DistilBERT model distilled from the German DBMDZ BERT model `bert-base-german-dbmdz-cased` checkpoint.                   |
-|                   |                                                            | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__)                                     |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``distilbert-base-multilingual-cased``                     | | 6-layer, 768-hidden, 12-heads, 134M parameters                                                                                      |
-|                   |                                                            | | The multilingual DistilBERT model distilled from the Multilingual BERT model `bert-base-multilingual-cased` checkpoint.             |
-|                   |                                                            | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__)                                     |
-+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| CTRL              | ``ctrl``                                                   | | 48-layer, 1280-hidden, 16-heads, 1.6B parameters                                                                                    |
-|                   |                                                            | | Salesforce's Large-sized CTRL English model                                                                                         |
-+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| CamemBERT         | ``camembert-base``                                         | | 12-layer, 768-hidden, 12-heads, 110M parameters                                                                                     |
-|                   |                                                            | | CamemBERT using the BERT-base architecture                                                                                          |
-|                   |                                                            | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/camembert>`__)                                                 |
-+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| ALBERT            | ``albert-base-v1``                                         | | 12 repeating layers, 128 embedding, 768-hidden, 12-heads, 11M parameters                                                            |
-|                   |                                                            | | ALBERT base model                                                                                                                   |
-|                   |                                                            | (see `details <https://github.com/google-research/ALBERT>`__)                                                                         |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``albert-large-v1``                                        | | 24 repeating layers, 128 embedding, 1024-hidden, 16-heads, 17M parameters                                                           |
-|                   |                                                            | | ALBERT large model                                                                                                                  |
-|                   |                                                            | (see `details <https://github.com/google-research/ALBERT>`__)                                                                         |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``albert-xlarge-v1``                                       | | 24 repeating layers, 128 embedding, 2048-hidden, 16-heads, 58M parameters                                                           |
-|                   |                                                            | | ALBERT xlarge model                                                                                                                 |
-|                   |                                                            | (see `details <https://github.com/google-research/ALBERT>`__)                                                                         |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``albert-xxlarge-v1``                                      | | 12 repeating layer, 128 embedding, 4096-hidden, 64-heads, 223M parameters                                                           |
-|                   |                                                            | | ALBERT xxlarge model                                                                                                                |
-|                   |                                                            | (see `details <https://github.com/google-research/ALBERT>`__)                                                                         |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``albert-base-v2``                                         | | 12 repeating layers, 128 embedding, 768-hidden, 12-heads, 11M parameters                                                            |
-|                   |                                                            | | ALBERT base model with no dropout, additional training data and longer training                                                     |
-|                   |                                                            | (see `details <https://github.com/google-research/ALBERT>`__)                                                                         |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``albert-large-v2``                                        | | 24 repeating layers, 128 embedding, 1024-hidden, 16-heads, 17M parameters                                                           |
-|                   |                                                            | | ALBERT large model with no dropout, additional training data and longer training                                                    |
-|                   |                                                            | (see `details <https://github.com/google-research/ALBERT>`__)                                                                         |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``albert-xlarge-v2``                                       | | 24 repeating layers, 128 embedding, 2048-hidden, 16-heads, 58M parameters                                                           |
-|                   |                                                            | | ALBERT xlarge model with no dropout, additional training data and longer training                                                   |
-|                   |                                                            | (see `details <https://github.com/google-research/ALBERT>`__)                                                                         |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``albert-xxlarge-v2``                                      | | 12 repeating layer, 128 embedding, 4096-hidden, 64-heads, 223M parameters                                                           |
-|                   |                                                            | | ALBERT xxlarge model with no dropout, additional training data and longer training                                                  |
-|                   |                                                            | (see `details <https://github.com/google-research/ALBERT>`__)                                                                         |
-+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| T5                | ``t5-small``                                               | | ~60M parameters with 6-layers, 512-hidden-state, 2048 feed-forward hidden-state, 8-heads,                                           |
-|                   |                                                            | | Trained on English text: the Colossal Clean Crawled Corpus (C4)                                                                     |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``t5-base``                                                | | ~220M parameters with 12-layers, 768-hidden-state, 3072 feed-forward hidden-state, 12-heads,                                        |
-|                   |                                                            | | Trained on English text: the Colossal Clean Crawled Corpus (C4)                                                                     |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``t5-large``                                               | | ~770M parameters with 24-layers, 1024-hidden-state, 4096 feed-forward hidden-state, 16-heads,                                       |
-|                   |                                                            | | Trained on English text: the Colossal Clean Crawled Corpus (C4)                                                                     |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``t5-3B``                                                  | | ~2.8B parameters with 24-layers, 1024-hidden-state, 16384 feed-forward hidden-state, 32-heads,                                      |
-|                   |                                                            | | Trained on English text: the Colossal Clean Crawled Corpus (C4)                                                                     |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``t5-11B``                                                 | | ~11B parameters with 24-layers, 1024-hidden-state, 65536 feed-forward hidden-state, 128-heads,                                      |
-|                   |                                                            | | Trained on English text: the Colossal Clean Crawled Corpus (C4)                                                                     |
-+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-
-
-.. <https://huggingface.co/transformers/examples.html>`__
@@ -1,315 +0,0 @@
-# Quickstart
-
-## Philosophy
-
-Transformers is an opinionated library built for NLP researchers seeking to use/study/extend large-scale transformers models.
-
-The library was designed with two strong goals in mind:
-
- be as easy and fast to use as possible:
-
-  - we strongly limited the number of user-facing abstractions to learn, in fact there are almost no abstractions, just three standard classes required to use each model: configuration, models and tokenizer,
-  - all of these classes can be initialized in a simple and unified way from pretrained instances by using a common `from_pretrained()` instantiation method which will take care of downloading (if needed), caching and loading the related class from a pretrained instance supplied in the library or your own saved instance.
-  - as a consequence, this library is NOT a modular toolbox of building blocks for neural nets. If you want to extend/build-upon the library, just use regular Python/PyTorch modules and inherit from the base classes of the library to reuse functionalities like model loading/saving.
-
- provide state-of-the-art models with performances as close as possible to the original models:
-
-  - we provide at least one example for each architecture which reproduces a result provided by the official authors of said architecture,
-  - the code is usually as close to the original code base as possible which means some PyTorch code may be not as *pytorchic* as it could be as a result of being converted TensorFlow code.
-
-A few other goals:
-
- expose the models' internals as consistently as possible:
-
-  - we give access, using a single API to the full hidden-states and attention weights,
-  - tokenizer and base model's API are standardized to easily switch between models.
-
- incorporate a subjective selection of promising tools for fine-tuning/investigating these models:
-
-  - a simple/consistent way to add new tokens to the vocabulary and embeddings for fine-tuning,
-  - simple ways to mask and prune transformer heads.
-
-## Main concepts
-
-The library is build around three type of classes for each models:
-
- **model classes** which are PyTorch models (`torch.nn.Modules`) of the 8 models architectures currently provided in the library, e.g. `BertModel`
- **configuration classes** which store all the parameters required to build a model, e.g. `BertConfig`. You don't always need to instantiate these your-self, in particular if you are using a pretrained model without any modification, creating the model will automatically take care of instantiating the configuration (which is part of the model)
- **tokenizer classes** which store the vocabulary for each model and provide methods for encoding/decoding strings in list of token embeddings indices to be fed to a model, e.g. `BertTokenizer`
-
-All these classes can be instantiated from pretrained instances and saved locally using two methods:
-
- `from_pretrained()` let you instantiate a model/configuration/tokenizer from a pretrained version either provided by the library itself (currently 27 models are provided as listed [here](https://huggingface.co/transformers/pretrained_models.html)) or stored locally (or on a server) by the user,
- `save_pretrained()` let you save a model/configuration/tokenizer locally so that it can be reloaded using `from_pretrained()`.
-
-We'll finish this quickstart tour by going through a few simple quick-start examples to see how we can instantiate and use these classes. The rest of the documentation is organized in two parts:
-
- the **MAIN CLASSES** section details the common functionalities/method/attributes of the three main type of classes (configuration, model, tokenizer) plus some optimization related classes provided as utilities for training,
- the **PACKAGE REFERENCE** section details all the variants of each class for each model architectures and in particular the input/output that you should expect when calling each of them.
-
-## Quick tour: Usage
-
-Here are two examples showcasing a few `Bert` and `GPT2` classes and pre-trained models.
-
-See full API reference for examples for each model class.
-
-### BERT example
-
-Let's start by preparing a tokenized input (a list of token embeddings indices to be fed to Bert) from a text string using `BertTokenizer`
-
-```python
-import torch
-from transformers import BertTokenizer, BertModel, BertForMaskedLM
-
-# 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')
-
-# Tokenize input
-text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
-tokenized_text = tokenizer.tokenize(text)
-
-# Mask a token that we will try to predict back with `BertForMaskedLM`
-masked_index = 8
-tokenized_text[masked_index] = '[MASK]'
-assert tokenized_text == ['[CLS]', 'who', 'was', 'jim', 'henson', '?', '[SEP]', 'jim', '[MASK]', 'was', 'a', 'puppet', '##eer', '[SEP]']
-
-# Convert token to vocabulary indices
-indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
-# Define sentence A and B indices associated to 1st and 2nd sentences (see paper)
-segments_ids = [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1]
-
-# Convert inputs to PyTorch tensors
-tokens_tensor = torch.tensor([indexed_tokens])
-segments_tensors = torch.tensor([segments_ids])
-```
-
-Let's see how we can use `BertModel` to encode our inputs in hidden-states:
-
-```python
-# Load pre-trained model (weights)
-model = BertModel.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
-tokens_tensor = tokens_tensor.to('cuda')
-segments_tensors = segments_tensors.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(tokens_tensor, token_type_ids=segments_tensors)
-    # Transformers models always output tuples.
-    # See the models docstrings for the detail of all the outputs
-    # In our case, the first element is the hidden state of the last layer of the Bert model
-    encoded_layers = outputs[0]
-# We have encoded our input sequence in a FloatTensor of shape (batch size, sequence length, model hidden dimension)
-assert tuple(encoded_layers.shape) == (1, len(indexed_tokens), model.config.hidden_size)
-```
-
-And how to use `BertForMaskedLM` to predict a masked token:
-
-```python
-# Load pre-trained model (weights)
-model = BertForMaskedLM.from_pretrained('bert-base-uncased')
-model.eval()
-
-# If you have a GPU, put everything on cuda
-tokens_tensor = tokens_tensor.to('cuda')
-segments_tensors = segments_tensors.to('cuda')
-model.to('cuda')
-
-# Predict all tokens
-with torch.no_grad():
-    outputs = model(tokens_tensor, token_type_ids=segments_tensors)
-    predictions = outputs[0]
-
-# confirm we were able to predict 'henson'
-predicted_index = torch.argmax(predictions[0, masked_index]).item()
-predicted_token = tokenizer.convert_ids_to_tokens([predicted_index])[0]
-assert predicted_token == 'henson'
-```
-
-### OpenAI GPT-2
-
-Here is a quick-start example using `GPT2Tokenizer` and `GPT2LMHeadModel` class with OpenAI's pre-trained model to predict the next token from a text prompt.
-
-First let's prepare a tokenized input from our text string using `GPT2Tokenizer`
-
-```python
-import torch
-from transformers import GPT2Tokenizer, GPT2LMHeadModel
-
-# OPTIONAL: if you want to have more information on what's happening, activate the logger as follows
-import logging
-logging.basicConfig(level=logging.INFO)
-
-# Load pre-trained model tokenizer (vocabulary)
-tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
-
-# Encode a text inputs
-text = "Who was Jim Henson ? Jim Henson was a"
-indexed_tokens = tokenizer.encode(text)
-
-# Convert indexed tokens in a PyTorch tensor
-tokens_tensor = torch.tensor([indexed_tokens])
-```
-
-Let's see how to use `GPT2LMHeadModel` to generate the next token following our text:
-
-```python
-# Load pre-trained model (weights)
-model = GPT2LMHeadModel.from_pretrained('gpt2')
-
-# 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
-tokens_tensor = tokens_tensor.to('cuda')
-model.to('cuda')
-
-# Predict all tokens
-with torch.no_grad():
-    outputs = model(tokens_tensor)
-    predictions = outputs[0]
-
-# get the predicted next sub-word (in our case, the word 'man')
-predicted_index = torch.argmax(predictions[0, -1, :]).item()
-predicted_text = tokenizer.decode(indexed_tokens + [predicted_index])
-assert predicted_text == 'Who was Jim Henson? Jim Henson was a man'
-```
-
-Examples for each model class of each model architecture (Bert, GPT, GPT-2, Transformer-XL, XLNet and XLM) can be found in the [documentation](#documentation).
-
-#### Using the past
-
-GPT-2 as well as some other models (GPT, XLNet, Transfo-XL, CTRL) make use of a `past` or `mems` attribute which can be used to prevent re-computing the key/value pairs when using sequential decoding. It is useful when generating sequences as a big part of the attention mechanism benefits from previous computations.
-
-Here is a fully-working example using the `past` with `GPT2LMHeadModel` and argmax decoding (which should only be used as an example, as argmax decoding introduces a lot of repetition):
-
-```python
-from transformers import GPT2LMHeadModel, GPT2Tokenizer
-import torch
-
-tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
-model = GPT2LMHeadModel.from_pretrained('gpt2')
-
-generated = tokenizer.encode("The Manhattan bridge")
-context = torch.tensor([generated])
-past = None
-
-for i in range(100):
-    print(i)
-    output, past = model(context, past=past)
-    token = torch.argmax(output[0, :])
-
-    generated += [token.tolist()]
-    context = token.unsqueeze(0)
-
-sequence = tokenizer.decode(generated)
-
-print(sequence)
-```
-
-The model only requires a single token as input as all the previous tokens' key/value pairs are contained in the `past`.
-
-### 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'
-```
@@ -1,190 +0,0 @@
-Loading Google AI or OpenAI pre-trained weights or PyTorch dump
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-``from_pretrained()`` method
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-To load one of Google AI's, OpenAI's pre-trained models or a PyTorch saved model (an instance of ``BertForPreTraining`` saved with ``torch.save()``\ ), the PyTorch model classes and the tokenizer can be instantiated using the ``from_pretrained()`` method:
-
-.. code-block:: python
-
-   model = BERT_CLASS.from_pretrained(PRE_TRAINED_MODEL_NAME_OR_PATH, cache_dir=None, from_tf=False, state_dict=None, *input, **kwargs)
-
-where
-
-
-* ``BERT_CLASS`` is either a tokenizer to load the vocabulary (\ ``BertTokenizer`` or ``OpenAIGPTTokenizer`` classes) or one of the eight BERT or three OpenAI GPT PyTorch model classes (to load the pre-trained weights): ``BertModel``\ , ``BertForMaskedLM``\ , ``BertForNextSentencePrediction``\ , ``BertForPreTraining``\ , ``BertForSequenceClassification``\ , ``BertForTokenClassification``\ , ``BertForMultipleChoice``\ , ``BertForQuestionAnswering``\ , ``OpenAIGPTModel``\ , ``OpenAIGPTLMHeadModel`` or ``OpenAIGPTDoubleHeadsModel``\ , and
-*
-  ``PRE_TRAINED_MODEL_NAME_OR_PATH`` is either:
-
-
-  *
-    the shortcut name of a Google AI's or OpenAI's pre-trained model selected in the list:
-
-
-    * ``bert-base-uncased``: 12-layer, 768-hidden, 12-heads, 110M parameters
-    * ``bert-large-uncased``: 24-layer, 1024-hidden, 16-heads, 340M parameters
-    * ``bert-base-cased``: 12-layer, 768-hidden, 12-heads , 110M parameters
-    * ``bert-large-cased``: 24-layer, 1024-hidden, 16-heads, 340M parameters
-    * ``bert-base-multilingual-uncased``: (Orig, not recommended) 102 languages, 12-layer, 768-hidden, 12-heads, 110M parameters
-    * ``bert-base-multilingual-cased``: **(New, recommended)** 104 languages, 12-layer, 768-hidden, 12-heads, 110M parameters
-    * ``bert-base-chinese``: Chinese Simplified and Traditional, 12-layer, 768-hidden, 12-heads, 110M parameters
-    * ``bert-base-german-cased``: Trained on German data only, 12-layer, 768-hidden, 12-heads, 110M parameters `Performance Evaluation <https://deepset.ai/german-bert>`__
-    * ``bert-large-uncased-whole-word-masking``: 24-layer, 1024-hidden, 16-heads, 340M parameters - Trained with Whole Word Masking (mask all of the the tokens corresponding to a word at once)
-    * ``bert-large-cased-whole-word-masking``: 24-layer, 1024-hidden, 16-heads, 340M parameters - Trained with Whole Word Masking (mask all of the the tokens corresponding to a word at once)
-    * ``bert-large-uncased-whole-word-masking-finetuned-squad``: The ``bert-large-uncased-whole-word-masking`` model finetuned on SQuAD (using the ``run_bert_squad.py`` examples). Results: *exact_match: 86.91579943235573, f1: 93.1532499015869*
-    * ``bert-base-german-dbmdz-cased``: Trained on German data only, 12-layer, 768-hidden, 12-heads, 110M parameters `Performance Evaluation <https://github.com/dbmdz/german-bert>`__
-    * ``bert-base-german-dbmdz-uncased``: Trained on (uncased) German data only, 12-layer, 768-hidden, 12-heads, 110M parameters `Performance Evaluation <https://github.com/dbmdz/german-bert>`__
-    * ``openai-gpt``: OpenAI GPT English model, 12-layer, 768-hidden, 12-heads, 110M parameters
-    * ``gpt2``: OpenAI GPT-2 English model, 12-layer, 768-hidden, 12-heads, 117M parameters
-    * ``gpt2-medium``: OpenAI GPT-2 English model, 24-layer, 1024-hidden, 16-heads, 345M parameters
-    * ``transfo-xl-wt103``: Transformer-XL English model trained on wikitext-103, 18-layer, 1024-hidden, 16-heads, 257M parameters
-
-  *
-    a path or url to a pretrained model archive containing:
-
-
-    * ``bert_config.json`` or ``openai_gpt_config.json`` a configuration file for the model, and
-    * ``pytorch_model.bin`` a PyTorch dump of a pre-trained instance of ``BertForPreTraining``\ , ``OpenAIGPTModel``\ , ``TransfoXLModel``\ , ``GPT2LMHeadModel`` (saved with the usual ``torch.save()``\ )
-
-  If ``PRE_TRAINED_MODEL_NAME_OR_PATH`` is a shortcut name, the pre-trained weights will be downloaded from AWS S3 (see the links `here <https://github.com/huggingface/transformers/blob/master/transformers/modeling_bert.py>`__\ ) and stored in a cache folder to avoid future download (the cache folder can be found at ``~/.pytorch_pretrained_bert/``\ ).
-
-*
-  ``cache_dir`` can be an optional path to a specific directory to download and cache the pre-trained model weights. This option is useful in particular when you are using distributed training: to avoid concurrent access to the same weights you can set for example ``cache_dir='./pretrained_model_{}'.format(args.local_rank)`` (see the section on distributed training for more information).
-
-* ``from_tf``\ : should we load the weights from a locally saved TensorFlow checkpoint
-* ``state_dict``\ : an optional state dictionary (collections.OrderedDict object) to use instead of Google pre-trained models
-* ``*inputs``\ , `**kwargs`: additional input for the specific Bert class (ex: num_labels for BertForSequenceClassification)
-
-``Uncased`` means that the text has been lowercased before WordPiece tokenization, e.g., ``John Smith`` becomes ``john smith``. The Uncased model also strips out any accent markers. ``Cased`` means that the true case and accent markers are preserved. Typically, the Uncased model is better unless you know that case information is important for your task (e.g., Named Entity Recognition or Part-of-Speech tagging). For information about the Multilingual and Chinese model, see the `Multilingual README <https://github.com/google-research/bert/blob/master/multilingual.md>`__ or the original TensorFlow repository.
-
-When using an ``uncased model``\ , make sure to pass ``--do_lower_case`` to the example training scripts (or pass ``do_lower_case=True`` to FullTokenizer if you're using your own script and loading the tokenizer your-self.).
-
-Examples:
-
-.. code-block:: python
-
-   # BERT
-   tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True, do_basic_tokenize=True)
-   model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
-
-   # OpenAI GPT
-   tokenizer = OpenAIGPTTokenizer.from_pretrained('openai-gpt')
-   model = OpenAIGPTModel.from_pretrained('openai-gpt')
-
-   # Transformer-XL
-   tokenizer = TransfoXLTokenizer.from_pretrained('transfo-xl-wt103')
-   model = TransfoXLModel.from_pretrained('transfo-xl-wt103')
-
-   # OpenAI GPT-2
-   tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
-   model = GPT2Model.from_pretrained('gpt2')
-
-Cache directory
-~~~~~~~~~~~~~~~
-
-``pytorch_pretrained_bert`` save the pretrained weights in a cache directory which is located at (in this order of priority):
-
-
-* ``cache_dir`` optional arguments to the ``from_pretrained()`` method (see above),
-* shell environment variable ``PYTORCH_PRETRAINED_BERT_CACHE``\ ,
-* PyTorch cache home + ``/pytorch_pretrained_bert/``
-  where PyTorch cache home is defined by (in this order):
-
-  * shell environment variable ``ENV_TORCH_HOME``
-  * shell environment variable ``ENV_XDG_CACHE_HOME`` + ``/torch/``\ )
-  * default: ``~/.cache/torch/``
-
-Usually, if you don't set any specific environment variable, ``pytorch_pretrained_bert`` cache will be at ``~/.cache/torch/pytorch_pretrained_bert/``.
-
-You can alsways safely delete ``pytorch_pretrained_bert`` cache but the pretrained model weights and vocabulary files wil have to be re-downloaded from our S3.
-
-Serialization best-practices
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-This section explain how you can save and re-load a fine-tuned model (BERT, GPT, GPT-2 and Transformer-XL).
-There are three types of files you need to save to be able to reload a fine-tuned model:
-
-
-* the model itself which should be saved following PyTorch serialization `best practices <https://pytorch.org/docs/stable/notes/serialization.html#best-practices>`__\ ,
-* the configuration file of the model which is saved as a JSON file, and
-* the vocabulary (and the merges for the BPE-based models GPT and GPT-2).
-
-The *default filenames* of these files are as follow:
-
-
-* the model weights file: ``pytorch_model.bin``\ ,
-* the configuration file: ``config.json``\ ,
-* the vocabulary file: ``vocab.txt`` for BERT and Transformer-XL, ``vocab.json`` for GPT/GPT-2 (BPE vocabulary),
-* for GPT/GPT-2 (BPE vocabulary) the additional merges file: ``merges.txt``.
-
-**If you save a model using these *default filenames*\ , you can then re-load the model and tokenizer using the ``from_pretrained()`` method.**
-
-Here is the recommended way of saving the model, configuration and vocabulary to an ``output_dir`` directory and reloading the model and tokenizer afterwards:
-
-.. code-block:: python
-
-   from transformers import WEIGHTS_NAME, CONFIG_NAME
-
-   output_dir = "./models/"
-
-   # Step 1: Save a model, configuration and vocabulary that you have fine-tuned
-
-   # If we have a distributed model, save only the encapsulated model
-   # (it was wrapped in PyTorch DistributedDataParallel or DataParallel)
-   model_to_save = model.module if hasattr(model, 'module') else model
-
-   # If we save using the predefined names, we can load using `from_pretrained`
-   output_model_file = os.path.join(output_dir, WEIGHTS_NAME)
-   output_config_file = os.path.join(output_dir, CONFIG_NAME)
-
-   torch.save(model_to_save.state_dict(), output_model_file)
-   model_to_save.config.to_json_file(output_config_file)
-   tokenizer.save_vocabulary(output_dir)
-
-   # Step 2: Re-load the saved model and vocabulary
-
-   # Example for a Bert model
-   model = BertForQuestionAnswering.from_pretrained(output_dir)
-   tokenizer = BertTokenizer.from_pretrained(output_dir, do_lower_case=args.do_lower_case)  # Add specific options if needed
-   # Example for a GPT model
-   model = OpenAIGPTDoubleHeadsModel.from_pretrained(output_dir)
-   tokenizer = OpenAIGPTTokenizer.from_pretrained(output_dir)
-
-Here is another way you can save and reload the model if you want to use specific paths for each type of files:
-
-.. code-block:: python
-
-   output_model_file = "./models/my_own_model_file.bin"
-   output_config_file = "./models/my_own_config_file.bin"
-   output_vocab_file = "./models/my_own_vocab_file.bin"
-
-   # Step 1: Save a model, configuration and vocabulary that you have fine-tuned
-
-   # If we have a distributed model, save only the encapsulated model
-   # (it was wrapped in PyTorch DistributedDataParallel or DataParallel)
-   model_to_save = model.module if hasattr(model, 'module') else model
-
-   torch.save(model_to_save.state_dict(), output_model_file)
-   model_to_save.config.to_json_file(output_config_file)
-   tokenizer.save_vocabulary(output_vocab_file)
-
-   # Step 2: Re-load the saved model and vocabulary
-
-   # We didn't save using the predefined WEIGHTS_NAME, CONFIG_NAME names, we cannot load using `from_pretrained`.
-   # Here is how to do it in this situation:
-
-   # Example for a Bert model
-   config = BertConfig.from_json_file(output_config_file)
-   model = BertForQuestionAnswering(config)
-   state_dict = torch.load(output_model_file)
-   model.load_state_dict(state_dict)
-   tokenizer = BertTokenizer(output_vocab_file, do_lower_case=args.do_lower_case)
-
-   # Example for a GPT model
-   config = OpenAIGPTConfig.from_json_file(output_config_file)
-   model = OpenAIGPTDoubleHeadsModel(config)
-   state_dict = torch.load(output_model_file)
-   model.load_state_dict(state_dict)
-   tokenizer = OpenAIGPTTokenizer(output_vocab_file)
-
@@ -1,135 +0,0 @@
-TorchScript
-================================================
-
-.. note::
-    This is the very beginning of our experiments with TorchScript and we are still exploring its capabilities
-    with variable-input-size models. It is a focus of interest to us and we will deepen our analysis in upcoming
-    releases, with more code examples, a more flexible implementation, and benchmarks comparing python-based codes
-    with compiled TorchScript.
-
-
-According to Pytorch's documentation: "TorchScript is a way to create serializable and optimizable models from PyTorch code".
-Pytorch's two modules `JIT and TRACE <https://pytorch.org/docs/stable/jit.html>`_ allow the developer to export
-their model to be re-used in other programs, such as efficiency-oriented C++ programs.
-
-We have provided an interface that allows the export of `transformers` models to TorchScript so that they can
-be reused in a different environment than a Pytorch-based python program. Here we explain how to use our models so that
-they can be exported, and what to be mindful of when using these models with TorchScript.
-
-Exporting a model needs two things:
-
-* dummy inputs to execute a model forward pass.
-* the model needs to be instantiated with the ``torchscript`` flag.
-
-These necessities imply several things developers should be careful about. These are detailed below.
-
-
-Implications
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-TorchScript flag and tied weights
------------------------------------------------
-This flag is necessary because most of the language models in this repository have tied weights between their
-``Embedding`` layer and their ``Decoding`` layer. TorchScript does not allow the export of models that have tied weights,
-it is therefore necessary to untie the weights beforehand.
-
-This implies that models instantiated with the ``torchscript`` flag have their ``Embedding`` layer and ``Decoding`` layer
-separate, which means that they should not be trained down the line. Training would de-synchronize the two layers,
-leading to unexpected results.
-
-This is not the case for models that do not have a Language Model head, as those do not have tied weights. These models
-can be safely exported without the ``torchscript`` flag.
-
-Dummy inputs and standard lengths
------------------------------------------------
-
-The dummy inputs are used to do a model forward pass. While the inputs' values are propagating through the layers,
-Pytorch keeps track of the different operations executed on each tensor. These recorded operations are then used
-to create the "trace" of the model.
-
-The trace is created relatively to the inputs' dimensions. It is therefore constrained by the dimensions of the dummy
-input, and will not work for any other sequence length or batch size. When trying with a different size, an error such
-as:
-
-``The expanded size of the tensor (3) must match the existing size (7) at non-singleton dimension 2``
-
-will be raised. It is therefore recommended to trace the model with a dummy input size at least as large as the largest
-input that will be fed to the model during inference. Padding can be performed to fill the missing values. As the model
-will have been traced with a large input size however, the dimensions of the different matrix will be large as well,
-resulting in more calculations.
-
-It is recommended to be careful of the total number of operations done on each input and to follow performance closely
-when exporting varying sequence-length models.
-
-Using TorchScript in Python
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Below are examples of using the Python to save, load models as well as how to use the trace for inference.
-
-Saving a model
------------------------------------------------
-
-This snippet shows how to use TorchScript to export a ``BertModel``. Here the ``BertModel`` is instantiated
-according to a ``BertConfig`` class and then saved to disk under the filename ``traced_bert.pt``
-
-.. code-block:: python
-
-    from transformers import BertModel, BertTokenizer, BertConfig
-    import torch
-
-    enc = BertTokenizer.from_pretrained("bert-base-uncased")
-
-    # Tokenizing input text
-    text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
-    tokenized_text = enc.tokenize(text)
-
-    # Masking one of the input tokens
-    masked_index = 8
-    tokenized_text[masked_index] = '[MASK]'
-    indexed_tokens = enc.convert_tokens_to_ids(tokenized_text)
-    segments_ids = [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1]
-
-    # Creating a dummy input
-    tokens_tensor = torch.tensor([indexed_tokens])
-    segments_tensors = torch.tensor([segments_ids])
-    dummy_input = [tokens_tensor, segments_tensors]
-
-    # Initializing the model with the torchscript flag
-    # Flag set to True even though it is not necessary as this model does not have an LM Head.
-    config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
-        num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, torchscript=True)
-
-    # Instantiating the model
-    model = BertModel(config)
-
-    # The model needs to be in evaluation mode
-    model.eval()
-
-    # If you are instantiating the model with `from_pretrained` you can also easily set the TorchScript flag
-    model = BertModel.from_pretrained("bert-base-uncased", torchscript=True)
-
-    # Creating the trace
-    traced_model = torch.jit.trace(model, [tokens_tensor, segments_tensors])
-    torch.jit.save(traced_model, "traced_bert.pt")
-
-Loading a model
------------------------------------------------
-
-This snippet shows how to load the ``BertModel`` that was previously saved to disk under the name ``traced_bert.pt``.
-We are re-using the previously initialised ``dummy_input``.
-
-.. code-block:: python
-
-    loaded_model = torch.jit.load("traced_model.pt")
-    loaded_model.eval()
-
-    all_encoder_layers, pooled_output = loaded_model(dummy_input)
-
-Using a traced model for inference
------------------------------------------------
-
-Using the traced model for inference is as simple as using its ``__call__`` dunder method:
-
-.. code-block:: python
-
-    traced_model(tokens_tensor, segments_tensors)
@@ -1,685 +0,0 @@
-# Examples
-
-In this section a few examples are put together. All of these examples work for several models, making use of the very
-similar API between the different models.
-
-**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:
-
-```bash
-git clone https://github.com/huggingface/transformers
-cd transformers
-pip install [--editable] .
-pip install -r ./examples/requirements.txt
-```
-
-| Section                    | Description                                                                                                                                                |
-|----------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| [TensorFlow 2.0 models on GLUE](#TensorFlow-2.0-Bert-models-on-GLUE) | Examples running BERT TensorFlow 2.0 model on the GLUE tasks. 
-| [Language Model fine-tuning](#language-model-fine-tuning) | Fine-tuning the library models for language modeling on a text dataset. Causal language modeling for GPT/GPT-2, masked language modeling for BERT/RoBERTa. |
-| [Language Generation](#language-generation) | Conditional text generation using the auto-regressive models of the library: GPT, GPT-2, Transformer-XL and XLNet.                                         |
-| [GLUE](#glue) | Examples running BERT/XLM/XLNet/RoBERTa on the 9 GLUE tasks. Examples feature distributed training as well as half-precision.                              |
-| [SQuAD](#squad) | Using BERT/RoBERTa/XLNet/XLM for question answering, examples with distributed training.                                                                                  |
-| [Multiple Choice](#multiple-choice) | Examples running BERT/XLNet/RoBERTa on the SWAG/RACE/ARC tasks. 
-| [Named Entity Recognition](#named-entity-recognition) | Using BERT for Named Entity Recognition (NER) on the CoNLL 2003 dataset, examples with distributed training.                                                                                  |
-| [XNLI](#xnli) | Examples running BERT/XLM on the XNLI benchmark. |
-
-## TensorFlow 2.0 Bert models on GLUE
-
-Based on the script [`run_tf_glue.py`](https://github.com/huggingface/transformers/blob/master/examples/run_tf_glue.py).
-
-Fine-tuning the library TensorFlow 2.0 Bert model for sequence classification on the  MRPC task of the GLUE benchmark: [General Language Understanding Evaluation](https://gluebenchmark.com/).
-
-This script has an option for mixed precision (Automatic Mixed Precision / AMP) to run models on Tensor Cores (NVIDIA Volta/Turing GPUs) and future hardware and an option for XLA, which uses the XLA compiler to reduce model runtime.
-Options are toggled using `USE_XLA` or `USE_AMP` variables in the script.
-These options and the below benchmark are provided by @tlkh.
-
-Quick benchmarks from the script (no other modifications):
-
-| GPU    | Mode | Time (2nd epoch) | Val Acc (3 runs) |
-| --------- | -------- | ----------------------- | ----------------------|
-| Titan V | FP32 | 41s | 0.8438/0.8281/0.8333 |
-| Titan V | AMP | 26s | 0.8281/0.8568/0.8411 |
-| V100    | FP32 | 35s | 0.8646/0.8359/0.8464 |
-| V100    | AMP | 22s | 0.8646/0.8385/0.8411 |
-| 1080 Ti | FP32 | 55s | - | 
-
-Mixed precision (AMP) reduces the training time considerably for the same hardware and hyper-parameters (same batch size was used).
-
-## Language model fine-tuning
-
-Based on the script [`run_lm_finetuning.py`](https://github.com/huggingface/transformers/blob/master/examples/run_lm_finetuning.py).
-
-Fine-tuning the library models for language modeling on a text dataset for GPT, GPT-2, BERT and RoBERTa (DistilBERT 
-to be added soon). GPT and GPT-2 are fine-tuned using a causal language modeling (CLM) loss while BERT and RoBERTa 
-are fine-tuned using a masked language modeling (MLM) loss.
-
-Before running the following example, you should get a file that contains text on which the language model will be
-fine-tuned. A good example of such text is the [WikiText-2 dataset](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/).
-
-We will refer to two different files: `$TRAIN_FILE`, which contains text for training, and `$TEST_FILE`, which contains
-text that will be used for evaluation.
-
-### GPT-2/GPT and causal language modeling
-
-The following example fine-tunes GPT-2 on WikiText-2. We're using the raw WikiText-2 (no tokens were replaced before
-the tokenization). The loss here is that of causal language modeling.
-
-```bash
-export TRAIN_FILE=/path/to/dataset/wiki.train.raw
-export TEST_FILE=/path/to/dataset/wiki.test.raw
-
-python run_lm_finetuning.py \
-    --output_dir=output \
-    --model_type=gpt2 \
-    --model_name_or_path=gpt2 \
-    --do_train \
-    --train_data_file=$TRAIN_FILE \
-    --do_eval \
-    --eval_data_file=$TEST_FILE
-```
-
-This takes about half an hour to train on a single K80 GPU and about one minute for the evaluation to run. It reaches
-a score of ~20 perplexity once fine-tuned on the dataset.
-
-### RoBERTa/BERT and masked language modeling
-
-The following example fine-tunes RoBERTa on WikiText-2. Here too, we're using the raw WikiText-2. The loss is different
-as BERT/RoBERTa have a bidirectional mechanism; we're therefore using the same loss that was used during their
-pre-training: masked language modeling. 
-
-In accordance to the RoBERTa paper, we use dynamic masking rather than static masking. The model may, therefore, converge
-slightly slower (over-fitting takes more epochs).
-
-We use the `--mlm` flag so that the script may change its loss function.
-
-```bash
-export TRAIN_FILE=/path/to/dataset/wiki.train.raw
-export TEST_FILE=/path/to/dataset/wiki.test.raw
-
-python run_lm_finetuning.py \
-    --output_dir=output \
-    --model_type=roberta \
-    --model_name_or_path=roberta-base \
-    --do_train \
-    --train_data_file=$TRAIN_FILE \
-    --do_eval \
-    --eval_data_file=$TEST_FILE \
-    --mlm
-```
-
-## Language generation
-
-Based on the script [`run_generation.py`](https://github.com/huggingface/transformers/blob/master/examples/run_generation.py).
-
-Conditional text generation using the auto-regressive models of the library: GPT, GPT-2, Transformer-XL, XLNet, CTRL.
-A similar script is used for our official demo [Write With Transfomer](https://transformer.huggingface.co), where you
-can try out the different models available in the library.
-
-Example usage:
-
-```bash
-python run_generation.py \
-    --model_type=gpt2 \
-    --model_name_or_path=gpt2
-```
-
-## GLUE
-
-Based on the script [`run_glue.py`](https://github.com/huggingface/transformers/blob/master/examples/run_glue.py).
-
-Fine-tuning the library models for sequence classification on the GLUE benchmark: [General Language Understanding 
-Evaluation](https://gluebenchmark.com/). This script can fine-tune the following models: BERT, XLM, XLNet and RoBERTa. 
-
-GLUE is made up of a total of 9 different tasks. We get the following results on the dev set of the benchmark with an
-uncased  BERT base model (the checkpoint `bert-base-uncased`). All experiments ran on 8 V100 GPUs with a total train
-batch size of 24. Some of these tasks have a small dataset and training can lead to high variance in the results
-between different runs. We report the median on 5 runs (with different seeds) for each of the metrics.
-
-| Task  | Metric                       | Result      |
-|-------|------------------------------|-------------|
-| CoLA  | Matthew's corr               | 48.87       |
-| SST-2 | Accuracy                     | 91.74       |
-| MRPC  | F1/Accuracy                  | 90.70/86.27 |
-| STS-B | Person/Spearman corr.        | 91.39/91.04 |
-| QQP   | Accuracy/F1                  | 90.79/87.66 |
-| MNLI  | Matched acc./Mismatched acc. | 83.70/84.83 |
-| QNLI  | Accuracy                     | 89.31       |
-| RTE   | Accuracy                     | 71.43       |
-| WNLI  | Accuracy                     | 43.66       |
-
-Some of these results are significantly different from the ones reported on the test set
-of GLUE benchmark on the website. For QQP and WNLI, please refer to [FAQ #12](https://gluebenchmark.com/faq) on the webite.
-
-Before running anyone of these GLUE tasks you should download the
-[GLUE data](https://gluebenchmark.com/tasks) by running
-[this script](https://gist.github.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e)
-and unpack it to some directory `$GLUE_DIR`.
-
-```bash
-export GLUE_DIR=/path/to/glue
-export TASK_NAME=MRPC
-
-python run_glue.py \
-  --model_type bert \
-  --model_name_or_path bert-base-cased \
-  --task_name $TASK_NAME \
-  --do_train \
-  --do_eval \
-  --do_lower_case \
-  --data_dir $GLUE_DIR/$TASK_NAME \
-  --max_seq_length 128 \
-  --per_gpu_train_batch_size 32 \
-  --learning_rate 2e-5 \
-  --num_train_epochs 3.0 \
-  --output_dir /tmp/$TASK_NAME/
-```
-
-where task name can be one of CoLA, SST-2, MRPC, STS-B, QQP, MNLI, QNLI, RTE, WNLI.
-
-The dev set results will be present within the text file `eval_results.txt` in the specified output_dir. 
-In case of MNLI, since there are two separate dev sets (matched and mismatched), there will be a separate 
-output folder called `/tmp/MNLI-MM/` in addition to `/tmp/MNLI/`.
-
-The code has not been tested with half-precision training with apex on any GLUE task apart from MRPC, MNLI, 
-CoLA, SST-2. The following section provides details on how to run half-precision training with MRPC. With that being 
-said, there shouldn’t be any issues in running half-precision training with the remaining GLUE tasks as well, 
-since the data processor for each task inherits from the base class DataProcessor.
-
-### MRPC
-
-#### Fine-tuning example
-
-The following examples fine-tune BERT on the Microsoft Research Paraphrase Corpus (MRPC) corpus and runs in less 
-than 10 minutes on a single K-80 and in 27 seconds (!) on single tesla V100 16GB with apex installed.
-
-Before running anyone of these GLUE tasks you should download the
-[GLUE data](https://gluebenchmark.com/tasks) by running
-[this script](https://gist.github.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e)
-and unpack it to some directory `$GLUE_DIR`.
-
-```bash
-export GLUE_DIR=/path/to/glue
-
-python run_glue.py \
-  --model_type bert \
-  --model_name_or_path bert-base-cased \
-  --task_name MRPC \
-  --do_train \
-  --do_eval \
-  --do_lower_case \
-  --data_dir $GLUE_DIR/MRPC/ \
-  --max_seq_length 128 \
-  --per_gpu_train_batch_size 32 \
-  --learning_rate 2e-5 \
-  --num_train_epochs 3.0 \
-  --output_dir /tmp/mrpc_output/
-```
-
-Our test ran on a few seeds with [the original implementation hyper-
-parameters](https://github.com/google-research/bert#sentence-and-sentence-pair-classification-tasks) gave evaluation 
-results between 84% and 88%.
-
-#### Using Apex and mixed-precision
-
-Using Apex and 16 bit precision, the fine-tuning on MRPC only takes 27 seconds. First install 
-[apex](https://github.com/NVIDIA/apex), then run the following example:
-
-```bash
-export GLUE_DIR=/path/to/glue
-
-python run_glue.py \
-  --model_type bert \
-  --model_name_or_path bert-base-cased \
-  --task_name MRPC \
-  --do_train \
-  --do_eval \
-  --do_lower_case \
-  --data_dir $GLUE_DIR/MRPC/ \
-  --max_seq_length 128 \
-  --per_gpu_train_batch_size 32 \
-  --learning_rate 2e-5 \
-  --num_train_epochs 3.0 \
-  --output_dir /tmp/mrpc_output/ \
-  --fp16
-```
-
-#### Distributed training
-
-Here is an example using distributed training on 8 V100 GPUs. The model used is the BERT whole-word-masking and it
-reaches F1 > 92 on MRPC.
-
-```bash
-export GLUE_DIR=/path/to/glue
-
-python -m torch.distributed.launch \
-    --nproc_per_node 8 run_glue.py \
-    --model_type bert \
-    --model_name_or_path bert-base-cased \
-    --task_name MRPC \
-    --do_train \
-    --do_eval \
-    --do_lower_case \
-    --data_dir $GLUE_DIR/MRPC/ \
-    --max_seq_length 128 \
-    --per_gpu_train_batch_size 8 \
-    --learning_rate 2e-5 \
-    --num_train_epochs 3.0 \
-    --output_dir /tmp/mrpc_output/
-```
-
-Training with these hyper-parameters gave us the following results:
-
-```bash
-acc = 0.8823529411764706
-acc_and_f1 = 0.901702786377709
-eval_loss = 0.3418912578906332
-f1 = 0.9210526315789473
-global_step = 174
-loss = 0.07231863956341798
-```
-
-### MNLI
-
-The following example uses the BERT-large, uncased, whole-word-masking model and fine-tunes it on the MNLI task.
-
-```bash
-export GLUE_DIR=/path/to/glue
-
-python -m torch.distributed.launch \
-    --nproc_per_node 8 run_glue.py \
-    --model_type bert \
-    --model_name_or_path bert-base-cased \
-    --task_name mnli \
-    --do_train \
-    --do_eval \
-    --do_lower_case \
-    --data_dir $GLUE_DIR/MNLI/ \
-    --max_seq_length 128 \
-    --per_gpu_train_batch_size 8 \
-    --learning_rate 2e-5 \
-    --num_train_epochs 3.0 \
-    --output_dir output_dir \
-```
-
-The results  are the following:
-
-```bash
-***** Eval results *****
-  acc = 0.8679706601466992
-  eval_loss = 0.4911287787382479
-  global_step = 18408
-  loss = 0.04755385363816904
-
-***** Eval results *****
-  acc = 0.8747965825874695
-  eval_loss = 0.45516540421714036
-  global_step = 18408
-  loss = 0.04755385363816904
-```
-
-## Multiple Choice
-
-Based on the script [`run_multiple_choice.py`]().
-
-#### Fine-tuning on SWAG
-Download [swag](https://github.com/rowanz/swagaf/tree/master/data) data
-
-```bash
-#training on 4 tesla V100(16GB) GPUS
-export SWAG_DIR=/path/to/swag_data_dir
-python ./examples/run_multiple_choice.py \
--model_type roberta \
--task_name swag \
--model_name_or_path roberta-base \
--do_train \
--do_eval \
--do_lower_case \
--data_dir $SWAG_DIR \
--learning_rate 5e-5 \
--num_train_epochs 3 \
--max_seq_length 80 \
--output_dir models_bert/swag_base \
--per_gpu_eval_batch_size=16 \
--per_gpu_train_batch_size=16 \
--gradient_accumulation_steps 2 \
--overwrite_output
-```
-Training with the defined hyper-parameters yields the following results:
-```
-***** Eval results *****
-eval_acc = 0.8338998300509847
-eval_loss = 0.44457291918821606
-```
-
-## SQuAD
-
-Based on the script [`run_squad.py`](https://github.com/huggingface/transformers/blob/master/examples/run_squad.py).
-
-#### Fine-tuning on SQuAD
-
-This example code fine-tunes BERT on the SQuAD dataset. It runs in 24 min (with BERT-base) or 68 min (with BERT-large) 
-on a single tesla V100 16GB. The data for SQuAD can be downloaded with the following links and should be saved in a 
-$SQUAD_DIR directory.
-
-* [train-v1.1.json](https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json)
-* [dev-v1.1.json](https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json)
-* [evaluate-v1.1.py](https://github.com/allenai/bi-att-flow/blob/master/squad/evaluate-v1.1.py)
-
-```bash
-export SQUAD_DIR=/path/to/SQUAD
-
-python run_squad.py \
-  --model_type bert \
-  --model_name_or_path bert-base-cased \
-  --do_train \
-  --do_eval \
-  --do_lower_case \
-  --train_file $SQUAD_DIR/train-v1.1.json \
-  --predict_file $SQUAD_DIR/dev-v1.1.json \
-  --per_gpu_train_batch_size 12 \
-  --learning_rate 3e-5 \
-  --num_train_epochs 2.0 \
-  --max_seq_length 384 \
-  --doc_stride 128 \
-  --output_dir /tmp/debug_squad/
-```
-
-Training with the previously defined hyper-parameters yields the following results:
-
-```bash
-f1 = 88.52
-exact_match = 81.22
-```
-
-#### Distributed training
-
-
-Here is an example using distributed training on 8 V100 GPUs and Bert Whole Word Masking uncased model to reach a F1 > 93 on SQuAD:
-
-```bash
-python -m torch.distributed.launch --nproc_per_node=8 run_squad.py \
-    --model_type bert \
-    --model_name_or_path bert-base-cased \
-    --do_train \
-    --do_eval \
-    --do_lower_case \
-    --train_file $SQUAD_DIR/train-v1.1.json \
-    --predict_file $SQUAD_DIR/dev-v1.1.json \
-    --learning_rate 3e-5 \
-    --num_train_epochs 2 \
-    --max_seq_length 384 \
-    --doc_stride 128 \
-    --output_dir ../models/wwm_uncased_finetuned_squad/ \
-    --per_gpu_train_batch_size 24 \
-    --gradient_accumulation_steps 12
-```
-
-Training with the previously defined hyper-parameters yields the following results:
-
-```bash
-f1 = 93.15
-exact_match = 86.91
-```
-
-This fine-tuned model is available as a checkpoint under the reference
-`bert-large-uncased-whole-word-masking-finetuned-squad`.
-
-#### Fine-tuning XLNet on SQuAD
-
-This example code fine-tunes XLNet on the SQuAD dataset. See above to download the data for SQuAD .
-
-```bash
-export SQUAD_DIR=/path/to/SQUAD
-
-python /data/home/hlu/transformers/examples/run_squad.py \
-    --model_type xlnet \
-    --model_name_or_path xlnet-large-cased \
-    --do_train \
-    --do_eval \
-    --do_lower_case \
-    --train_file /data/home/hlu/notebooks/NLP/examples/question_answering/train-v1.1.json \
-    --predict_file /data/home/hlu/notebooks/NLP/examples/question_answering/dev-v1.1.json \
-    --learning_rate 3e-5 \
-    --num_train_epochs 2 \
-    --max_seq_length 384 \
-    --doc_stride 128 \
-    --output_dir ./wwm_cased_finetuned_squad/ \
-    --per_gpu_eval_batch_size=4  \
-    --per_gpu_train_batch_size=4   \
-    --save_steps 5000
-```
-
-Training with the previously defined hyper-parameters yields the following results:
-
-```python
-{
-"exact": 85.45884578997162,
-"f1": 92.5974600601065,
-"total": 10570,
-"HasAns_exact": 85.45884578997162,
-"HasAns_f1": 92.59746006010651,
-"HasAns_total": 10570
-}
-```
-
-## Named Entity Recognition
-
-Based on the scripts [`run_ner.py`](https://github.com/huggingface/transformers/blob/master/examples/run_ner.py) for Pytorch and
-[`run_tf_ner.py`](https://github.com/huggingface/transformers/blob/master/examples/run_tf_ner.py) for Tensorflow 2.
-This example fine-tune Bert Multilingual on GermEval 2014 (German NER).
-Details and results for the fine-tuning provided by @stefan-it.
-
-### Data (Download and pre-processing steps)
-
-Data can be obtained from the [GermEval 2014](https://sites.google.com/site/germeval2014ner/data) shared task page.
-
-Here are the commands for downloading and pre-processing train, dev and test datasets. The original data format has four (tab-separated) columns, in a pre-processing step only the two relevant columns (token and outer span NER annotation) are extracted:
-
-```bash
-curl -L 'https://sites.google.com/site/germeval2014ner/data/NER-de-train.tsv?attredirects=0&d=1' \
-| grep -v "^#" | cut -f 2,3 | tr '\t' ' ' > train.txt.tmp
-curl -L 'https://sites.google.com/site/germeval2014ner/data/NER-de-dev.tsv?attredirects=0&d=1' \
-| grep -v "^#" | cut -f 2,3 | tr '\t' ' ' > dev.txt.tmp
-curl -L 'https://sites.google.com/site/germeval2014ner/data/NER-de-test.tsv?attredirects=0&d=1' \
-| grep -v "^#" | cut -f 2,3 | tr '\t' ' ' > test.txt.tmp
-```
-
-The GermEval 2014 dataset contains some strange "control character" tokens like `'\x96', '\u200e', '\x95', '\xad' or '\x80'`. One problem with these tokens is, that `BertTokenizer` returns an empty token for them, resulting in misaligned `InputExample`s. I wrote a script that a) filters these tokens and b) splits longer sentences into smaller ones (once the max. subtoken length is reached).
-
-```bash
-wget "https://raw.githubusercontent.com/stefan-it/fine-tuned-berts-seq/master/scripts/preprocess.py"
-```
-Let's define some variables that we need for further pre-processing steps and training the model:
-
-```bash
-export MAX_LENGTH=128
-export BERT_MODEL=bert-base-multilingual-cased
-```
-
-Run the pre-processing script on training, dev and test datasets:
-
-```bash
-python3 preprocess.py train.txt.tmp $BERT_MODEL $MAX_LENGTH > train.txt
-python3 preprocess.py dev.txt.tmp $BERT_MODEL $MAX_LENGTH > dev.txt
-python3 preprocess.py test.txt.tmp $BERT_MODEL $MAX_LENGTH > test.txt
-```
-
-The GermEval 2014 dataset has much more labels than CoNLL-2002/2003 datasets, so an own set of labels must be used:
-
-```bash
-cat train.txt dev.txt test.txt | cut -d " " -f 2 | grep -v "^$"| sort | uniq > labels.txt
-```
-
-### Prepare the run
-
-Additional environment variables must be set:
-
-```bash
-export OUTPUT_DIR=germeval-model
-export BATCH_SIZE=32
-export NUM_EPOCHS=3
-export SAVE_STEPS=750
-export SEED=1
-```
-
-### Run the Pytorch version
-
-To start training, just run:
-
-```bash
-python3 run_ner.py --data_dir ./ \
--model_type bert \
--labels ./labels.txt \
--model_name_or_path $BERT_MODEL \
--output_dir $OUTPUT_DIR \
--max_seq_length  $MAX_LENGTH \
--num_train_epochs $NUM_EPOCHS \
--per_gpu_train_batch_size $BATCH_SIZE \
--save_steps $SAVE_STEPS \
--seed $SEED \
--do_train \
--do_eval \
--do_predict
-```
-
-If your GPU supports half-precision training, just add the `--fp16` flag. After training, the model will be both evaluated on development and test datasets.
-
-#### Evaluation
-
-Evaluation on development dataset outputs the following for our example:
-
-```bash
-10/04/2019 00:42:06 - INFO - __main__ -   ***** Eval results  *****
-10/04/2019 00:42:06 - INFO - __main__ -     f1 = 0.8623348017621146
-10/04/2019 00:42:06 - INFO - __main__ -     loss = 0.07183869666975543
-10/04/2019 00:42:06 - INFO - __main__ -     precision = 0.8467916366258111
-10/04/2019 00:42:06 - INFO - __main__ -     recall = 0.8784592370979806
-```
-
-On the test dataset the following results could be achieved:
-
-```bash
-10/04/2019 00:42:42 - INFO - __main__ -   ***** Eval results  *****
-10/04/2019 00:42:42 - INFO - __main__ -     f1 = 0.8614389652384803
-10/04/2019 00:42:42 - INFO - __main__ -     loss = 0.07064602487454782
-10/04/2019 00:42:42 - INFO - __main__ -     precision = 0.8604651162790697
-10/04/2019 00:42:42 - INFO - __main__ -     recall = 0.8624150210424085
-```
-
-#### Comparing BERT (large, cased), RoBERTa (large, cased) and DistilBERT (base, uncased)
-
-Here is a small comparison between BERT (large, cased), RoBERTa (large, cased) and DistilBERT (base, uncased) with the same hyperparameters as specified in the [example documentation](https://huggingface.co/transformers/examples.html#named-entity-recognition) (one run):
-
-| Model | F-Score Dev | F-Score Test
-| --------------------------------- | ------- | --------
-| `bert-large-cased`            | 95.59 | 91.70
-| `roberta-large`                  | 95.96 | 91.87
-| `distilbert-base-uncased` | 94.34 | 90.32
-
-### Run the Tensorflow 2 version
-
-To start training, just run:
-
-```bash
-python3 run_tf_ner.py --data_dir ./ \
--model_type bert \
--labels ./labels.txt \
--model_name_or_path $BERT_MODEL \
--output_dir $OUTPUT_DIR \
--max_seq_length  $MAX_LENGTH \
--num_train_epochs $NUM_EPOCHS \
--per_device_train_batch_size $BATCH_SIZE \
--save_steps $SAVE_STEPS \
--seed $SEED \
--do_train \
--do_eval \
--do_predict
-```
-
-Such as the Pytorch version, if your GPU supports half-precision training, just add the `--fp16` flag. After training, the model will be both evaluated on development and test datasets.
-
-#### Evaluation
-
-Evaluation on development dataset outputs the following for our example:
-```bash
-           precision    recall  f1-score   support
-
- LOCderiv     0.7619    0.6154    0.6809        52
-  PERpart     0.8724    0.8997    0.8858      4057
-  OTHpart     0.9360    0.9466    0.9413       711
-  ORGpart     0.7015    0.6989    0.7002       269
-  LOCpart     0.7668    0.8488    0.8057       496
-      LOC     0.8745    0.9191    0.8963       235
- ORGderiv     0.7723    0.8571    0.8125        91
- OTHderiv     0.4800    0.6667    0.5581        18
-      OTH     0.5789    0.6875    0.6286        16
- PERderiv     0.5385    0.3889    0.4516        18
-      PER     0.5000    0.5000    0.5000         2
-      ORG     0.0000    0.0000    0.0000         3
-
-micro avg     0.8574    0.8862    0.8715      5968
-macro avg     0.8575    0.8862    0.8713      5968
-```
-
-On the test dataset the following results could be achieved:
-```bash
-           precision    recall  f1-score   support
-
-  PERpart     0.8847    0.8944    0.8896      9397
-  OTHpart     0.9376    0.9353    0.9365      1639
-  ORGpart     0.7307    0.7044    0.7173       697
-      LOC     0.9133    0.9394    0.9262       561
-  LOCpart     0.8058    0.8157    0.8107      1150
-      ORG     0.0000    0.0000    0.0000         8
- OTHderiv     0.5882    0.4762    0.5263        42
- PERderiv     0.6571    0.5227    0.5823        44
-      OTH     0.4906    0.6667    0.5652        39
- ORGderiv     0.7016    0.7791    0.7383       172
- LOCderiv     0.8256    0.6514    0.7282       109
-      PER     0.0000    0.0000    0.0000        11
-
-micro avg     0.8722    0.8774    0.8748     13869
-macro avg     0.8712    0.8774    0.8740     13869
-```
-
-## XNLI
-
-Based on the script [`run_xnli.py`](https://github.com/huggingface/transformers/blob/master/examples/run_xnli.py).
-
-[XNLI](https://www.nyu.edu/projects/bowman/xnli/) is crowd-sourced dataset based on [MultiNLI](http://www.nyu.edu/projects/bowman/multinli/). It is an evaluation benchmark for cross-lingual text representations. Pairs of text are labeled with textual entailment annotations for 15 different languages (including both high-ressource language such as English and low-ressource languages such as Swahili).
-
-#### Fine-tuning on XNLI
-
-This example code fine-tunes mBERT (multi-lingual BERT) on the XNLI dataset. It runs in 106 mins
-on a single tesla V100 16GB. The data for XNLI can be downloaded with the following links and should be both saved (and un-zipped) in a 
-`$XNLI_DIR` directory.
-
-* [XNLI 1.0](https://www.nyu.edu/projects/bowman/xnli/XNLI-1.0.zip)
-* [XNLI-MT 1.0](https://www.nyu.edu/projects/bowman/xnli/XNLI-MT-1.0.zip)
-
-```bash
-export XNLI_DIR=/path/to/XNLI
-
-python run_xnli.py \
-  --model_type bert \
-  --model_name_or_path bert-base-multilingual-cased \
-  --language de \
-  --train_language en \
-  --do_train \
-  --do_eval \
-  --data_dir $XNLI_DIR \
-  --per_gpu_train_batch_size 32 \
-  --learning_rate 5e-5 \
-  --num_train_epochs 2.0 \
-  --max_seq_length 128 \
-  --output_dir /tmp/debug_xnli/ \
-  --save_steps -1
-```
-
-Training with the previously defined hyper-parameters yields the following results on the **test** set:
-
-```bash
-acc = 0.7093812375249501
-```
@@ -1,477 +0,0 @@
-# coding=utf-8
-# Copyright 2018 The HuggingFace Inc. team.
-# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" Benchmarking the library on inference and training """
-
-# If checking the tensors placement
-# tf.debugging.set_log_device_placement(True)
-
-from typing import List
-import timeit
-from transformers import is_tf_available, is_torch_available
-from time import time
-import argparse
-import csv
-
-if is_tf_available():
-    import tensorflow as tf
-    from transformers import TFAutoModel
-
-if is_torch_available():
-    import torch
-    from transformers import AutoModel
-
-from transformers import AutoConfig, AutoTokenizer
-
-input_text = """Bent over their instruments, three hundred Fertilizers were plunged, as 
-the Director of Hatcheries and Conditioning entered the room, in the 
-
-
-
-scarcely breathing silence, the absent-minded, soliloquizing hum or 
-whistle, of absorbed concentration. A troop of newly arrived students, 
-very young, pink and callow, followed nervously, rather abjectly, at the 
-Director's heels. Each of them carried a notebook, in which, whenever 
-the great man spoke, he desperately scribbled. Straight from the 
-horse's mouth. It was a rare privilege. The D. H. C. for Central London 
-always made a point of personally conducting his new students round 
-the various departments. 
-
-"Just to give you a general idea," he would explain to them. For of 
-course some sort of general idea they must have, if they were to do 
-their work intelligently-though as little of one, if they were to be good 
-and happy members of society, as possible. For particulars, as every 
-one knows, make for virtue and happiness; generalities are intellectu- 
-ally necessary evils. Not philosophers but fret-sawyers and stamp col- 
-lectors compose the backbone of society. 
-
-"To-morrow," he would add, smiling at them with a slightly menacing 
-geniality, "you'll be settling down to serious work. You won't have time 
-for generalities. Meanwhile ..." 
-
-Meanwhile, it was a privilege. Straight from the horse's mouth into the 
-notebook. The boys scribbled like mad. 
-
-Tall and rather thin but upright, the Director advanced into the room. 
-He had a long chin and big rather prominent teeth, just covered, when 
-he was not talking, by his full, floridly curved lips. Old, young? Thirty? 
-Fifty? Fifty-five? It was hard to say. And anyhow the question didn't 
-arise; in this year of stability, A. F. 632, it didn't occur to you to ask it. 
-
-"I shall begin at the beginning," said the D.H.C. and the more zealous 
-students recorded his intention in their notebooks: Begin at the begin- 
-ning. "These," he waved his hand, "are the incubators." And opening 
-an insulated door he showed them racks upon racks of numbered test- 
-tubes. "The week's supply of ova. Kept," he explained, "at blood heat; 
-whereas the male gametes," and here he opened another door, "they 
-have to be kept at thirty-five instead of thirty-seven. Full blood heat 
-sterilizes." Rams wrapped in theremogene beget no lambs. 
-
-Still leaning against the incubators he gave them, while the pencils 
-scurried illegibly across the pages, a brief description of the modern 
-
-
-
-fertilizing process; spoke first, of course, of its surgical introduc- 
-tion-"the operation undergone voluntarily for the good of Society, not 
-to mention the fact that it carries a bonus amounting to six months' 
-salary"; continued with some account of the technique for preserving 
-the excised ovary alive and actively developing; passed on to a consid- 
-eration of optimum temperature, salinity, viscosity; referred to the liq- 
-uor in which the detached and ripened eggs were kept; and, leading 
-his charges to the work tables, actually showed them how this liquor 
-was drawn off from the test-tubes; how it was let out drop by drop 
-onto the specially warmed slides of the microscopes; how the eggs 
-which it contained were inspected for abnormalities, counted and 
-transferred to a porous receptacle; how (and he now took them to 
-watch the operation) this receptacle was immersed in a warm bouillon 
-containing free-swimming spermatozoa-at a minimum concentration 
-of one hundred thousand per cubic centimetre, he insisted; and how, 
-after ten minutes, the container was lifted out of the liquor and its 
-contents re-examined; how, if any of the eggs remained unfertilized, it 
-was again immersed, and, if necessary, yet again; how the fertilized 
-ova went back to the incubators; where the Alphas and Betas re- 
-mained until definitely bottled; while the Gammas, Deltas and Epsilons 
-were brought out again, after only thirty-six hours, to undergo Bo- 
-kanovsky's Process. 
-
-"Bokanovsky's Process," repeated the Director, and the students un- 
-derlined the words in their little notebooks. 
-
-One egg, one embryo, one adult-normality. But a bokanovskified egg 
-will bud, will proliferate, will divide. From eight to ninety-six buds, and 
-every bud will grow into a perfectly formed embryo, and every embryo 
-into a full-sized adult. Making ninety-six human beings grow where 
-only one grew before. Progress. 
-
-"Essentially," the D.H.C. concluded, "bokanovskification consists of a 
-series of arrests of development. We check the normal growth and, 
-paradoxically enough, the egg responds by budding." 
-
-Responds by budding. The pencils were busy. 
-
-He pointed. On a very slowly moving band a rack-full of test-tubes was 
-entering a large metal box, another, rack-full was emerging. Machinery 
-faintly purred. It took eight minutes for the tubes to go through, he 
-
-
-
-told them. Eight minutes of hard X-rays being about as much as an 
-egg can stand. A few died; of the rest, the least susceptible divided 
-into two; most put out four buds; some eight; all were returned to the 
-incubators, where the buds began to develop; then, after two days, 
-were suddenly chilled, chilled and checked. Two, four, eight, the buds 
-in their turn budded; and having budded were dosed almost to death 
-with alcohol; consequently burgeoned again and having budded-bud 
-out of bud out of bud-were thereafter-further arrest being generally 
-fatal-left to develop in peace. By which time the original egg was in a 
-fair way to becoming anything from eight to ninety-six embryos- a 
-prodigious improvement, you will agree, on nature. Identical twins-but 
-not in piddling twos and threes as in the old viviparous days, when an 
-egg would sometimes accidentally divide; actually by dozens, by 
-scores at a time. 
-
-"Scores," the Director repeated and flung out his arms, as though he 
-were distributing largesse. "Scores." 
-
-But one of the students was fool enough to ask where the advantage 
-lay. 
-
-"My good boy!" The Director wheeled sharply round on him. "Can't you 
-see? Can't you see?" He raised a hand; his expression was solemn. 
-"Bokanovsky's Process is one of the major instruments of social stabil- 
-ity!" 
-
-Major instruments of social stability. 
-
-Standard men and women; in uniform batches. The whole of a small 
-factory staffed with the products of a single bokanovskified egg. 
-
-"Ninety-six identical twins working ninety-six identical machines!" The 
-voice was almost tremulous with enthusiasm. "You really know where 
-you are. For the first time in history." He quoted the planetary motto. 
-"Community, Identity, Stability." Grand words. "If we could bo- 
-kanovskify indefinitely the whole problem would be solved." 
-
-Solved by standard Gammas, unvarying Deltas, uniform Epsilons. Mil- 
-lions of identical twins. The principle of mass production at last applied 
-to biology. 
-
-
-
-"But, alas," the Director shook his head, "we can't bokanovskify indefi- 
-nitely." 
-
-Ninety-six seemed to be the limit; seventy-two a good average. From 
-the same ovary and with gametes of the same male to manufacture as 
-many batches of identical twins as possible-that was the best (sadly a 
-second best) that they could do. And even that was difficult. 
-
-"For in nature it takes thirty years for two hundred eggs to reach ma- 
-turity. But our business is to stabilize the population at this moment, 
-here and now. Dribbling out twins over a quarter of a century-what 
-would be the use of that?" 
-
-Obviously, no use at all. But Podsnap's Technique had immensely ac- 
-celerated the process of ripening. They could make sure of at least a 
-hundred and fifty mature eggs within two years. Fertilize and bo- 
-kanovskify-in other words, multiply by seventy-two-and you get an 
-average of nearly eleven thousand brothers and sisters in a hundred 
-and fifty batches of identical twins, all within two years of the same 
-age. 
-
-"And in exceptional cases we can make one ovary yield us over fifteen 
-thousand adult individuals." 
-
-Beckoning to a fair-haired, ruddy young man who happened to be 
-passing at the moment. "Mr. Foster," he called. The ruddy young man 
-approached. "Can you tell us the record for a single ovary, Mr. Foster?" 
-
-"Sixteen thousand and twelve in this Centre," Mr. Foster replied with- 
-out hesitation. He spoke very quickly, had a vivacious blue eye, and 
-took an evident pleasure in quoting figures. "Sixteen thousand and 
-twelve; in one hundred and eighty-nine batches of identicals. But of 
-course they've done much better," he rattled on, "in some of the tropi- 
-cal Centres. Singapore has often produced over sixteen thousand five 
-hundred; and Mombasa has actually touched the seventeen thousand 
-mark. But then they have unfair advantages. You should see the way a 
-negro ovary responds to pituitary! It's quite astonishing, when you're 
-used to working with European material. Still," he added, with a laugh 
-(but the light of combat was in his eyes and the lift of his chin was 
-challenging), "still, we mean to beat them if we can. I'm working on a 
-wonderful Delta-Minus ovary at this moment. Only just eighteen 
-
-
-
-months old. Over twelve thousand seven hundred children already, ei- 
-ther decanted or in embryo. And still going strong. We'll beat them 
-yet." 
-
-"That's the spirit I like!" cried the Director, and clapped Mr. Foster on 
-the shoulder. "Come along with us, and give these boys the benefit of 
-your expert knowledge." 
-
-Mr. Foster smiled modestly. "With pleasure." They went. 
-In the Bottling Room all was harmonious bustle and ordered activity. 
-Flaps of fresh sow's peritoneum ready cut to the proper size came 
-shooting up in little lifts from the Organ Store in the sub-basement. 
-Whizz and then, click! the lift-hatches hew open; the bottle-liner had 
-only to reach out a hand, take the flap, insert, smooth-down, and be- 
-fore the lined bottle had had time to travel out of reach along the end- 
-less band, whizz, click! another flap of peritoneum had shot up from 
-the depths, ready to be slipped into yet another bottle, the next of that 
-slow interminable procession on the band. 
-
-Next to the Liners stood the Matriculators. The procession advanced; 
-one by one the eggs were transferred from their test-tubes to the 
-larger containers; deftly the peritoneal lining was slit, the morula 
-dropped into place, the saline solution poured in ... and already the 
-bottle had passed, and it was the turn of the labellers. Heredity, date 
-of fertilization, membership of Bokanovsky Group-details were trans- 
-ferred from test-tube to bottle. No longer anonymous, but named, 
-identified, the procession marched slowly on; on through an opening in 
-the wall, slowly on into the Social Predestination Room. 
-"Eighty-eight cubic metres of card-index," said Mr. Foster with relish, 
-as they entered."""
-
-
-def create_setup_and_compute(model_names: List[str],
-                             gpu: bool = True,
-                             tensorflow: bool = False,
-                             average_over: int = 3,
-                             torchscript: bool = False,
-                             xla: bool = False,
-                             amp: bool = False,
-                             fp16: bool = False,
-                             save_to_csv: bool = False,
-                             csv_filename: str = f"results_{round(time())}.csv"):
-    if xla:
-        tf.config.optimizer.set_jit(True)
-    if amp:
-        tf.config.optimizer.set_experimental_options({"auto_mixed_precision": True})
-
-    if tensorflow:
-        dictionary = {model_name: {} for model_name in model_names}
-        results = _compute_tensorflow(model_names, dictionary, average_over, amp)
-    else:
-        device = 'cuda' if (gpu and torch.cuda.is_available()) else 'cpu'
-        dictionary = {model_name: {} for model_name in model_names}
-        results = _compute_pytorch(model_names, dictionary, average_over, device, torchscript, fp16)
-
-    print("=========== RESULTS ===========")
-    for model_name in model_names:
-        print("\t" + f"======= MODEL CHECKPOINT: {model_name} =======")
-        for batch_size in results[model_name]["bs"]:
-            print("\t\t" + f"===== BATCH SIZE: {batch_size} =====")
-            for slice_size in results[model_name]["ss"]:
-                result = results[model_name]['results'][batch_size][slice_size]
-                if isinstance(result, str):
-                    print(f"\t\t{model_name}/{batch_size}/{slice_size}: "
-                          f"{result}")
-                else:
-                    print(f"\t\t{model_name}/{batch_size}/{slice_size}: "
-                          f"{(round(1000 * result) / 1000)}"
-                          f"s")
-
-    if save_to_csv:
-        with open(csv_filename, mode='w') as csv_file:
-            fieldnames = ['model',
-                          '1x8', '1x64', '1x128', '1x256', '1x512', '1x1024',
-                          '2x8', '2x64', '2x128', '2x256', '2x512', '2x1024',
-                          '4x8', '4x64', '4x128', '4x256', '4x512', '4x1024',
-                          '8x8', '8x64', '8x128', '8x256', '8x512', '8x1024',
-                          ]
-
-            writer = csv.DictWriter(csv_file, fieldnames=fieldnames)
-            writer.writeheader()
-
-            for model_name in model_names:
-                model_results = {
-                    f'{bs}x{ss}': results[model_name]['results'][bs][ss]
-                    for bs in results[model_name]["results"]
-                    for ss in results[model_name]['results'][bs]
-                }
-                writer.writerow({'model': model_name, **model_results})
-
-
-def _compute_pytorch(model_names, dictionary, average_over, device, torchscript, fp16):
-    for c, model_name in enumerate(model_names):
-        print(f"{c + 1} / {len(model_names)}")
-        config = AutoConfig.from_pretrained(model_name, torchscript=torchscript)
-        model = AutoModel.from_pretrained(model_name, config=config)
-        tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-        tokenized_sequence = tokenizer.encode(input_text, add_special_tokens=False)
-
-        max_input_size = tokenizer.max_model_input_sizes[model_name]
-        batch_sizes = [1, 2, 4, 8]
-        slice_sizes = [8, 64, 128, 256, 512, 1024]
-
-        dictionary[model_name] = {"bs": batch_sizes, "ss": slice_sizes, "results": {}}
-        dictionary[model_name]["results"] = {i: {} for i in batch_sizes}
-
-        for batch_size in batch_sizes:
-            if fp16:
-                model.half()
-            model.to(device)
-            model.eval()
-            for slice_size in slice_sizes:
-                if max_input_size is not None and slice_size > max_input_size:
-                    dictionary[model_name]["results"][batch_size][slice_size] = "N/A"
-                else:
-                    sequence = torch.tensor(tokenized_sequence[:slice_size], device=device).repeat(batch_size, 1)
-                    try:
-                        if torchscript:
-                            print("Tracing model with sequence size", sequence.shape)
-                            inference = torch.jit.trace(model, sequence)
-                            inference(sequence)
-                        else:
-                            inference = model
-                            inference(sequence)
-
-                        print("Going through model with sequence of shape", sequence.shape)
-                        runtimes = timeit.repeat(lambda: inference(sequence), repeat=average_over, number=3)
-                        average_time = sum(runtimes)/float(len(runtimes)) / 3.0
-                        dictionary[model_name]["results"][batch_size][slice_size] = average_time
-                    except RuntimeError as e:
-                        print("Doesn't fit on GPU.", e)
-                        torch.cuda.empty_cache()
-                        dictionary[model_name]["results"][batch_size][slice_size] = "N/A"
-    return dictionary
-
-
-def _compute_tensorflow(model_names, dictionary, average_over, amp):
-    for c, model_name in enumerate(model_names):
-        print(f"{c + 1} / {len(model_names)}")
-        config = AutoConfig.from_pretrained(model_name)
-        model = TFAutoModel.from_pretrained(model_name, config=config)
-        tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-        tokenized_sequence = tokenizer.encode(input_text, add_special_tokens=False)
-
-        max_input_size = tokenizer.max_model_input_sizes[model_name]
-        batch_sizes = [1, 2, 4, 8]
-        slice_sizes = [8, 64, 128, 256, 512, 1024]
-
-        dictionary[model_name] = {"bs": batch_sizes, "ss": slice_sizes, "results": {}}
-        dictionary[model_name]["results"] = {i: {} for i in batch_sizes}
-
-        print("Using model", model)
-
-        @tf.function
-        def inference(inputs):
-            return model(inputs)
-
-        for batch_size in batch_sizes:
-            for slice_size in slice_sizes:
-                if max_input_size is not None and slice_size > max_input_size:
-                    dictionary[model_name]["results"][batch_size][slice_size] = "N/A"
-                else:
-                    sequence = tf.stack([tf.squeeze(tf.constant(tokenized_sequence[:slice_size])[None, :])] * batch_size)
-
-                    try:
-                        print("Going through model with sequence of shape", sequence.shape)
-                        # To make sure that the model is traced + that the tensors are on the appropriate device
-                        inference(sequence)
-
-                        runtimes = timeit.repeat(lambda: inference(sequence), repeat=average_over, number=3)
-                        average_time = sum(runtimes)/float(len(runtimes)) / 3.0
-                        dictionary[model_name]["results"][batch_size][slice_size] = average_time
-                    except tf.errors.ResourceExhaustedError as e:
-                        print("Doesn't fit on GPU.", e)
-                        torch.cuda.empty_cache()
-                        dictionary[model_name]["results"][batch_size][slice_size] = "N/A"
-    return dictionary
-
-
-def main():
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument("--models", required=False, type=str, default='all', help="Model checkpoints to be provided "
-                                                                                  "to the AutoModel classes. Leave "
-                                                                                  "blank to benchmark the base version "
-                                                                                  "of all available model "
-                                                                                  "architectures.")
-    parser.add_argument("--torch", required=False, action="store_true", help="Benchmark the Pytorch version of the "
-                                                                             "models")
-    parser.add_argument("--torch_cuda", required=False, action="store_true", help="Pytorch only: run on available "
-                                                                                  "cuda devices")
-    parser.add_argument("--torchscript", required=False, action="store_true", help="Pytorch only: trace the models "
-                                                                                   "using torchscript")
-    parser.add_argument("--tensorflow", required=False, action="store_true", help="Benchmark the TensorFlow version "
-                                                                                  "of the models. Will run on GPU if "
-                                                                                  "the correct dependencies are "
-                                                                                  "installed")
-    parser.add_argument("--xla", required=False, action="store_true", help="TensorFlow only: use XLA acceleration.")
-    parser.add_argument("--amp", required=False, action="store_true", help="TensorFlow only: use automatic mixed precision acceleration.")
-    parser.add_argument("--fp16", required=False, action="store_true", help="PyTorch only: use FP16 to accelerate inference.")
-    parser.add_argument("--keras_predict", required=False, action="store_true", help="Whether to use model.predict "
-                                                                                     "instead of model() to do a "
-                                                                                     "forward pass.")
-    parser.add_argument("--save_to_csv", required=False, action="store_true", help="Save to a CSV file.")
-    parser.add_argument("--csv_filename", required=False, default=None, help="CSV filename used if saving results to csv.")
-    parser.add_argument("--average_over", required=False, default=30, type=int, help="Times an experiment will be run.")
-
-    args = parser.parse_args()
-    if args.models == 'all':
-        args.models = [
-            "gpt2",
-            "bert-base-cased",
-            "xlnet-base-cased",
-            "xlm-mlm-en-2048",
-            "transfo-xl-wt103",
-            "openai-gpt",
-            "distilbert-base-uncased",
-            "distilgpt2",
-            "roberta-base",
-            "ctrl"
-        ]
-    else:
-        args.models = args.models.split()
-
-    print("Running with arguments", args)
-
-    if args.torch:
-        if is_torch_available():
-            create_setup_and_compute(
-                model_names=args.models,
-                tensorflow=False,
-                gpu=args.torch_cuda,
-                torchscript=args.torchscript,
-                fp16=args.fp16,
-                save_to_csv=args.save_to_csv,
-                csv_filename=args.csv_filename,
-                average_over=args.average_over
-            )
-        else:
-            raise ImportError("Trying to run a PyTorch benchmark but PyTorch was not found in the environment.")
-
-    if args.tensorflow:
-        if is_tf_available():
-            create_setup_and_compute(
-                model_names=args.models,
-                tensorflow=True,
-                xla=args.xla,
-                amp=args.amp,
-                save_to_csv=args.save_to_csv,
-                csv_filename=args.csv_filename,
-                average_over=args.average_over
-            )
-        else:
-            raise ImportError("Trying to run a TensorFlow benchmark but TensorFlow was not found in the environment.")
-
-if __name__ == '__main__':
-    main()
-
@@ -1,5 +0,0 @@
-# Community contributed examples
-
-This folder contains examples which are not actively maintained (mostly contributed by the community).
-
-Using these examples together with a recent version of the library usually requires to make small (sometimes big) adaptations to get the scripts working.
@@ -1,48 +0,0 @@
-from pathlib import Path
-import tarfile
-import urllib.request
-
-import torch
-
-from transformers.tokenization_camembert import CamembertTokenizer
-from transformers.modeling_camembert import CamembertForMaskedLM
-
-
-def fill_mask(masked_input, model, tokenizer, topk=5):
-    # Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
-    assert masked_input.count('<mask>') == 1
-    input_ids = torch.tensor(tokenizer.encode(masked_input, add_special_tokens=True)).unsqueeze(0)  # Batch size 1
-    logits = model(input_ids)[0]  # The last hidden-state is the first element of the output tuple
-    masked_index = (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
-    logits = logits[0, masked_index, :]
-    prob = logits.softmax(dim=0)
-    values, indices = prob.topk(k=topk, dim=0)
-    topk_predicted_token_bpe = ' '.join([tokenizer.convert_ids_to_tokens(indices[i].item())
-                                         for i in range(len(indices))])
-    masked_token = tokenizer.mask_token
-    topk_filled_outputs = []
-    for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(' ')):
-        predicted_token = predicted_token_bpe.replace('\u2581', ' ')
-        if " {0}".format(masked_token) in masked_input:
-            topk_filled_outputs.append((
-                masked_input.replace(
-                    ' {0}'.format(masked_token), predicted_token
-                ),
-                values[index].item(),
-                predicted_token,
-            ))
-        else:
-            topk_filled_outputs.append((
-                masked_input.replace(masked_token, predicted_token),
-                values[index].item(),
-                predicted_token,
-            ))
-    return topk_filled_outputs
-
-
-tokenizer = CamembertTokenizer.from_pretrained('camembert-base')
-model = CamembertForMaskedLM.from_pretrained('camembert-base')
-model.eval()
-
-masked_input = "Le camembert est <mask> :)"
-print(fill_mask(masked_input, model, tokenizer, topk=3))
@@ -1,290 +0,0 @@
-# coding=utf-8
-# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University 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.
-""" OpenAI GPT model fine-tuning script.
-    Adapted from https://github.com/huggingface/pytorch-openai-transformer-lm/blob/master/train.py
-    It self adapted from https://github.com/openai/finetune-transformer-lm/blob/master/train.py
-
-    This script with default values fine-tunes and evaluate a pretrained OpenAI GPT on the RocStories dataset:
-        python run_openai_gpt.py \
-          --model_name openai-gpt \
-          --do_train \
-          --do_eval \
-          --train_dataset $ROC_STORIES_DIR/cloze_test_val__spring2016\ -\ cloze_test_ALL_val.csv \
-          --eval_dataset $ROC_STORIES_DIR/cloze_test_test__spring2016\ -\ cloze_test_ALL_test.csv \
-          --output_dir ../log \
-          --train_batch_size 16 \
-"""
-import argparse
-import os
-import csv
-import random
-import logging
-from tqdm import tqdm, trange
-
-import numpy as np
-import torch
-from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
-                              TensorDataset)
-
-from transformers import (OpenAIGPTDoubleHeadsModel, OpenAIGPTTokenizer,
-                                     AdamW, cached_path, WEIGHTS_NAME, CONFIG_NAME,
-                                     get_linear_schedule_with_warmup)
-
-ROCSTORIES_URL = "https://s3.amazonaws.com/datasets.huggingface.co/ROCStories.tar.gz"
-
-logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s',
-                    datefmt = '%m/%d/%Y %H:%M:%S',
-                    level = logging.INFO)
-logger = logging.getLogger(__name__)
-
-def accuracy(out, labels):
-    outputs = np.argmax(out, axis=1)
-    return np.sum(outputs == labels)
-
-def load_rocstories_dataset(dataset_path):
-    """ Output a list of tuples(story, 1st continuation, 2nd continuation, label) """
-    with open(dataset_path, encoding='utf_8') as f:
-        f = csv.reader(f)
-        output = []
-        next(f) # skip the first line
-        for line in tqdm(f):
-            output.append((' '.join(line[1:5]), line[5], line[6], int(line[-1])-1))
-    return output
-
-def pre_process_datasets(encoded_datasets, input_len, cap_length, start_token, delimiter_token, clf_token):
-    """ Pre-process datasets containing lists of tuples(story, 1st continuation, 2nd continuation, label)
-
-        To Transformer inputs of shape (n_batch, n_alternative, length) comprising for each batch, continuation:
-        input_ids[batch, alternative, :] = [start_token] + story[:cap_length] + [delimiter_token] + cont1[:cap_length] + [clf_token]
-    """
-    tensor_datasets = []
-    for dataset in encoded_datasets:
-        n_batch = len(dataset)
-        input_ids = np.zeros((n_batch, 2, input_len), dtype=np.int64)
-        mc_token_ids = np.zeros((n_batch, 2), dtype=np.int64)
-        lm_labels = np.full((n_batch, 2, input_len), fill_value=-1, dtype=np.int64)
-        mc_labels = np.zeros((n_batch,), dtype=np.int64)
-        for i, (story, cont1, cont2, mc_label), in enumerate(dataset):
-            with_cont1 = [start_token] + story[:cap_length] + [delimiter_token] + cont1[:cap_length] + [clf_token]
-            with_cont2 = [start_token] + story[:cap_length] + [delimiter_token] + cont2[:cap_length] + [clf_token]
-            input_ids[i, 0, :len(with_cont1)] = with_cont1
-            input_ids[i, 1, :len(with_cont2)] = with_cont2
-            mc_token_ids[i, 0] = len(with_cont1) - 1
-            mc_token_ids[i, 1] = len(with_cont2) - 1
-            lm_labels[i, 0, :len(with_cont1)] = with_cont1
-            lm_labels[i, 1, :len(with_cont2)] = with_cont2
-            mc_labels[i] = mc_label
-        all_inputs = (input_ids, mc_token_ids, lm_labels, mc_labels)
-        tensor_datasets.append(tuple(torch.tensor(t) for t in all_inputs))
-    return tensor_datasets
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--model_name', type=str, default='openai-gpt',
-                        help='pretrained model name')
-    parser.add_argument("--do_train", action='store_true', help="Whether to run training.")
-    parser.add_argument("--do_eval", action='store_true', help="Whether to run eval on the dev set.")
-    parser.add_argument("--output_dir", default=None, type=str, required=True,
-                        help="The output directory where the model predictions and checkpoints will be written.")
-    parser.add_argument('--train_dataset', type=str, default='')
-    parser.add_argument('--eval_dataset', type=str, default='')
-    parser.add_argument('--seed', type=int, default=42)
-    parser.add_argument('--num_train_epochs', type=int, default=3)
-    parser.add_argument('--train_batch_size', type=int, default=8)
-    parser.add_argument('--eval_batch_size', type=int, default=16)
-    parser.add_argument("--adam_epsilon", default=1e-8, type=float,
-                        help="Epsilon for Adam optimizer.")
-    parser.add_argument('--max_grad_norm', type=int, default=1)
-    parser.add_argument("--max_steps", default=-1, type=int,
-                        help="If > 0: set total number of training \
-                        steps to perform. Override num_train_epochs.")
-    parser.add_argument('--gradient_accumulation_steps', type=int, default=1,
-                        help="Number of updates steps to accumulate before\
-                        performing a backward/update pass.")
-    parser.add_argument('--learning_rate', type=float, default=6.25e-5)
-    parser.add_argument("--warmup_steps", default=0, type=int,
-                        help="Linear warmup over warmup_steps.")
-    parser.add_argument('--lr_schedule', type=str, default='warmup_linear')
-    parser.add_argument('--weight_decay', type=float, default=0.01)
-    parser.add_argument('--lm_coef', type=float, default=0.9)
-    parser.add_argument('--n_valid', type=int, default=374)
-
-    parser.add_argument('--server_ip', type=str, default='', help="Can be used for distant debugging.")
-    parser.add_argument('--server_port', type=str, default='', help="Can be used for distant debugging.")
-    args = parser.parse_args()
-    print(args)
-
-    if args.server_ip and args.server_port:
-        # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
-        import ptvsd
-        print("Waiting for debugger attach")
-        ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
-        ptvsd.wait_for_attach()
-
-    random.seed(args.seed)
-    np.random.seed(args.seed)
-    torch.manual_seed(args.seed)
-    torch.cuda.manual_seed_all(args.seed)
-
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    n_gpu = torch.cuda.device_count()
-    logger.info("device: {}, n_gpu {}".format(device, n_gpu))
-
-    if not args.do_train and not args.do_eval:
-        raise ValueError("At least one of `do_train` or `do_eval` must be True.")
-
-    if not os.path.exists(args.output_dir):
-        os.makedirs(args.output_dir)
-
-    # Load tokenizer and model
-    # This loading functions also add new tokens and embeddings called `special tokens`
-    # These new embeddings will be fine-tuned on the RocStories dataset
-    special_tokens = ['_start_', '_delimiter_', '_classify_']
-    tokenizer = OpenAIGPTTokenizer.from_pretrained(args.model_name)
-    tokenizer.add_tokens(special_tokens)
-    special_tokens_ids = tokenizer.convert_tokens_to_ids(special_tokens)
-    model = OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name)
-    model.resize_token_embeddings(len(tokenizer))
-    model.to(device)
-
-    # Load and encode the datasets
-    if not args.train_dataset and not args.eval_dataset:
-        roc_stories = cached_path(ROCSTORIES_URL)
-    def tokenize_and_encode(obj):
-        """ Tokenize and encode a nested object """
-        if isinstance(obj, str):
-            return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(obj))
-        elif isinstance(obj, int):
-            return obj
-        return list(tokenize_and_encode(o) for o in obj)
-    logger.info("Encoding dataset...")
-    train_dataset = load_rocstories_dataset(args.train_dataset)
-    eval_dataset = load_rocstories_dataset(args.eval_dataset)
-    datasets = (train_dataset, eval_dataset)
-    encoded_datasets = tokenize_and_encode(datasets)
-
-    # Compute the max input length for the Transformer
-    max_length = model.config.n_positions // 2 - 2
-    input_length = max(len(story[:max_length]) + max(len(cont1[:max_length]), len(cont2[:max_length])) + 3  \
-                           for dataset in encoded_datasets for story, cont1, cont2, _ in dataset)
-    input_length = min(input_length, model.config.n_positions)  # Max size of input for the pre-trained model
-
-    # Prepare inputs tensors and dataloaders
-    tensor_datasets = pre_process_datasets(encoded_datasets, input_length, max_length, *special_tokens_ids)
-    train_tensor_dataset, eval_tensor_dataset = tensor_datasets[0], tensor_datasets[1]
-
-    train_data = TensorDataset(*train_tensor_dataset)
-    train_sampler = RandomSampler(train_data)
-    train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size)
-
-    eval_data = TensorDataset(*eval_tensor_dataset)
-    eval_sampler = SequentialSampler(eval_data)
-    eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size)
-
-    # Prepare optimizer
-    if args.do_train:
-        if args.max_steps > 0:
-            t_total = args.max_steps
-            args.num_train_epochs = args.max_steps //\
-                (len(train_dataloader) // args.gradient_accumulation_steps) + 1
-        else:
-            t_total = len(train_dataloader)\
-                // args.gradient_accumulation_steps * args.num_train_epochs
-
-        param_optimizer = list(model.named_parameters())
-        no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
-        optimizer_grouped_parameters = [
-            {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': args.weight_decay},
-            {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
-            ]
-        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.do_train:
-        nb_tr_steps, tr_loss, exp_average_loss = 0, 0, None
-        model.train()
-        for _ in trange(int(args.num_train_epochs), desc="Epoch"):
-            tr_loss = 0
-            nb_tr_steps = 0
-            tqdm_bar = tqdm(train_dataloader, desc="Training")
-            for step, batch in enumerate(tqdm_bar):
-                batch = tuple(t.to(device) for t in batch)
-                input_ids, mc_token_ids, lm_labels, mc_labels = batch
-                losses = model(input_ids, mc_token_ids=mc_token_ids, lm_labels=lm_labels, mc_labels=mc_labels)
-                loss = args.lm_coef * losses[0] + losses[1]
-                loss.backward()
-                scheduler.step()
-                optimizer.step()
-                optimizer.zero_grad()
-                tr_loss += loss.item()
-                exp_average_loss = loss.item() if exp_average_loss is None else 0.7*exp_average_loss+0.3*loss.item()
-                nb_tr_steps += 1
-                tqdm_bar.desc = "Training loss: {:.2e} lr: {:.2e}".format(exp_average_loss, scheduler.get_lr()[0])
-
-    # Save a trained model
-    if args.do_train:
-        # Save a trained model, configuration and tokenizer
-        model_to_save = model.module if hasattr(model, 'module') else model  # Only save the model itself
-
-        # If we save using the predefined names, we can load using `from_pretrained`
-        output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME)
-        output_config_file = os.path.join(args.output_dir, CONFIG_NAME)
-
-        torch.save(model_to_save.state_dict(), output_model_file)
-        model_to_save.config.to_json_file(output_config_file)
-        tokenizer.save_vocabulary(args.output_dir)
-
-        # Load a trained model and vocabulary that you have fine-tuned
-        model = OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir)
-        tokenizer = OpenAIGPTTokenizer.from_pretrained(args.output_dir)
-        model.to(device)
-
-    if args.do_eval:
-        model.eval()
-        eval_loss, eval_accuracy = 0, 0
-        nb_eval_steps, nb_eval_examples = 0, 0
-        for batch in tqdm(eval_dataloader, desc="Evaluating"):
-            batch = tuple(t.to(device) for t in batch)
-            input_ids, mc_token_ids, lm_labels, mc_labels = batch
-            with torch.no_grad():
-               _, mc_loss, _, mc_logits = model(input_ids, mc_token_ids=mc_token_ids, lm_labels=lm_labels, mc_labels=mc_labels)
-
-            mc_logits = mc_logits.detach().cpu().numpy()
-            mc_labels = mc_labels.to('cpu').numpy()
-            tmp_eval_accuracy = accuracy(mc_logits, mc_labels)
-
-            eval_loss += mc_loss.mean().item()
-            eval_accuracy += tmp_eval_accuracy
-
-            nb_eval_examples += input_ids.size(0)
-            nb_eval_steps += 1
-
-        eval_loss = eval_loss / nb_eval_steps
-        eval_accuracy = eval_accuracy / nb_eval_examples
-        train_loss = tr_loss/nb_tr_steps if args.do_train else None
-        result = {'eval_loss': eval_loss,
-                  'eval_accuracy': eval_accuracy,
-                  'train_loss': train_loss}
-
-        output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
-        with open(output_eval_file, "w") as writer:
-            logger.info("***** Eval results *****")
-            for key in sorted(result.keys()):
-                logger.info("  %s = %s", key, str(result[key]))
-                writer.write("%s = %s\n" % (key, str(result[key])))
-
-if __name__ == '__main__':
-    main()
@@ -1,677 +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.
-"""BERT finetuning runner.
-   Finetuning the library models for multiple choice on SWAG (Bert).
-"""
-from __future__ import absolute_import, division, print_function
-
-import argparse
-import logging
-import csv
-import os
-import random
-import sys
-import glob
-
-import numpy as np
-import torch
-from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
-                              TensorDataset)
-from torch.utils.data.distributed import DistributedSampler
-
-try:
-    from torch.utils.tensorboard import SummaryWriter
-except:
-    from tensorboardX import SummaryWriter
-
-from tqdm import tqdm, trange
-
-from transformers import (WEIGHTS_NAME, BertConfig,
-                                  BertForMultipleChoice, BertTokenizer)
-
-from transformers import AdamW, get_linear_schedule_with_warmup
-
-logger = logging.getLogger(__name__)
-
-ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) \
-                  for conf in [BertConfig]), ())
-
-MODEL_CLASSES = {
-    'bert': (BertConfig, BertForMultipleChoice, BertTokenizer),
-}
-
-class SwagExample(object):
-    """A single training/test example for the SWAG dataset."""
-    def __init__(self,
-                 swag_id,
-                 context_sentence,
-                 start_ending,
-                 ending_0,
-                 ending_1,
-                 ending_2,
-                 ending_3,
-                 label = None):
-        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.label = label
-
-    def __str__(self):
-        return self.__repr__()
-
-    def __repr__(self):
-        l = [
-            "swag_id: {}".format(self.swag_id),
-            "context_sentence: {}".format(self.context_sentence),
-            "start_ending: {}".format(self.start_ending),
-            "ending_0: {}".format(self.endings[0]),
-            "ending_1: {}".format(self.endings[1]),
-            "ending_2: {}".format(self.endings[2]),
-            "ending_3: {}".format(self.endings[3]),
-        ]
-
-        if self.label is not None:
-            l.append("label: {}".format(self.label))
-
-        return ", ".join(l)
-
-class InputFeatures(object):
-    def __init__(self,
-                 example_id,
-                 choices_features,
-                 label
-
-    ):
-        self.example_id = example_id
-        self.choices_features = [
-            {
-                'input_ids': input_ids,
-                'input_mask': input_mask,
-                'segment_ids': segment_ids
-            }
-            for _, input_ids, input_mask, segment_ids in choices_features
-        ]
-        self.label = label
-
-def read_swag_examples(input_file, is_training=True):
-    with open(input_file, 'r', encoding='utf-8') as f:
-        reader = csv.reader(f)
-        lines = []
-        for line in reader:
-            if sys.version_info[0] == 2:
-                line = list(unicode(cell, 'utf-8') for cell in line)
-            lines.append(line)
-
-    if is_training and lines[0][-1] != 'label':
-        raise ValueError(
-            "For training, the input file must contain a label column."
-        )
-
-    examples = [
-        SwagExample(
-            swag_id = line[2],
-            context_sentence = line[4],
-            start_ending = line[5], # in the swag dataset, the
-                                         # common beginning of each
-                                         # choice is stored in "sent2".
-            ending_0 = line[7],
-            ending_1 = line[8],
-            ending_2 = line[9],
-            ending_3 = line[10],
-            label = int(line[11]) if is_training else None
-        ) for line in lines[1:] # we skip the line with the column names
-    ]
-
-    return examples
-
-def convert_examples_to_features(examples, tokenizer, max_seq_length,
-                                 is_training):
-    """Loads a data file into a list of `InputBatch`s."""
-
-    # Swag is a multiple choice task. To perform this task using Bert,
-    # we will use the formatting proposed in "Improving Language
-    # Understanding by Generative Pre-Training" and suggested by
-    # @jacobdevlin-google in this issue
-    # https://github.com/google-research/bert/issues/38.
-    #
-    # Each choice will correspond to a sample on which we run the
-    # inference. For a given Swag example, we will create the 4
-    # following inputs:
-    # - [CLS] context [SEP] choice_1 [SEP]
-    # - [CLS] context [SEP] choice_2 [SEP]
-    # - [CLS] context [SEP] choice_3 [SEP]
-    # - [CLS] context [SEP] choice_4 [SEP]
-    # The model will output a single value for each input. To get the
-    # final decision of the model, we will run a softmax over these 4
-    # outputs.
-    features = []
-    for example_index, example in tqdm(enumerate(examples)):
-        context_tokens = tokenizer.tokenize(example.context_sentence)
-        start_ending_tokens = tokenizer.tokenize(example.start_ending)
-
-        choices_features = []
-        for ending_index, ending in enumerate(example.endings):
-            # We create a copy of the context tokens in order to be
-            # able to shrink it according to ending_tokens
-            context_tokens_choice = context_tokens[:]
-            ending_tokens = start_ending_tokens + tokenizer.tokenize(ending)
-            # Modifies `context_tokens_choice` and `ending_tokens` in
-            # place so that the total length is less than the
-            # specified length.  Account for [CLS], [SEP], [SEP] with
-            # "- 3"
-            _truncate_seq_pair(context_tokens_choice, ending_tokens, max_seq_length - 3)
-
-            tokens = ["[CLS]"] + context_tokens_choice + ["[SEP]"] + ending_tokens + ["[SEP]"]
-            segment_ids = [0] * (len(context_tokens_choice) + 2) + [1] * (len(ending_tokens) + 1)
-
-            input_ids = tokenizer.convert_tokens_to_ids(tokens)
-            input_mask = [1] * len(input_ids)
-
-            # Zero-pad up to the sequence length.
-            padding = [0] * (max_seq_length - len(input_ids))
-            input_ids += padding
-            input_mask += padding
-            segment_ids += padding
-
-            assert len(input_ids) == max_seq_length
-            assert len(input_mask) == max_seq_length
-            assert len(segment_ids) == max_seq_length
-
-            choices_features.append((tokens, input_ids, input_mask, segment_ids))
-
-        label = example.label
-        if example_index < 5:
-            logger.info("*** Example ***")
-            logger.info("swag_id: {}".format(example.swag_id))
-            for choice_idx, (tokens, input_ids, input_mask, segment_ids) in enumerate(choices_features):
-                logger.info("choice: {}".format(choice_idx))
-                logger.info("tokens: {}".format(' '.join(tokens)))
-                logger.info("input_ids: {}".format(' '.join(map(str, input_ids))))
-                logger.info("input_mask: {}".format(' '.join(map(str, input_mask))))
-                logger.info("segment_ids: {}".format(' '.join(map(str, segment_ids))))
-            if is_training:
-                logger.info("label: {}".format(label))
-
-        features.append(
-            InputFeatures(
-                example_id = example.swag_id,
-                choices_features = choices_features,
-                label = label
-            )
-        )
-
-    return features
-
-def _truncate_seq_pair(tokens_a, tokens_b, max_length):
-    """Truncates a sequence pair in place to the maximum length."""
-
-    # This is a simple heuristic which will always truncate the longer sequence
-    # one token at a time. This makes more sense than truncating an equal percent
-    # of tokens from each, since if one sequence is very short then each token
-    # that's truncated likely contains more information than a longer sequence.
-    while True:
-        total_length = len(tokens_a) + len(tokens_b)
-        if total_length <= max_length:
-            break
-        if len(tokens_a) > len(tokens_b):
-            tokens_a.pop()
-        else:
-            tokens_b.pop()
-
-def accuracy(out, labels):
-    outputs = np.argmax(out, axis=1)
-    return np.sum(outputs == labels)
-
-def select_field(features, field):
-    return [
-        [
-            choice[field]
-            for choice in feature.choices_features
-        ]
-        for feature in features
-    ]
-
-
-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 load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=False):
-    if args.local_rank not in [-1, 0]:
-        torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache
-
-    # Load data features from cache or dataset file
-    input_file = args.predict_file if evaluate else args.train_file
-    cached_features_file = os.path.join(os.path.dirname(input_file), 'cached_{}_{}_{}'.format(
-        'dev' if evaluate else 'train',
-        list(filter(None, args.model_name_or_path.split('/'))).pop(),
-        str(args.max_seq_length)))
-    if os.path.exists(cached_features_file) and not args.overwrite_cache and not output_examples:
-        logger.info("Loading features from cached file %s", cached_features_file)
-        features = torch.load(cached_features_file)
-    else:
-        logger.info("Creating features from dataset file at %s", input_file)
-        examples = read_swag_examples(input_file)
-        features = convert_examples_to_features(
-            examples, tokenizer, args.max_seq_length, not evaluate)
-
-        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:
-        torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache
-
-    # Convert to Tensors and build dataset
-    all_input_ids = torch.tensor(select_field(features, 'input_ids'), dtype=torch.long)
-    all_input_mask = torch.tensor(select_field(features, 'input_mask'), dtype=torch.long)
-    all_segment_ids = torch.tensor(select_field(features, 'segment_ids'), dtype=torch.long)
-    all_label = torch.tensor([f.label for f in features], dtype=torch.long)
-
-    if evaluate:
-        dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids,
-                                all_label)
-    else:
-        dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids,
-                                all_label)
-
-    if output_examples:
-        return dataset, examples, features
-    return dataset
-def train(args, train_dataset, model, tokenizer):
-    """ Train the model """
-    if args.local_rank in [-1, 0]:
-        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],
-                      #'token_type_ids':  None if args.model_type == 'xlm' else batch[2],
-                      'token_type_ids': batch[2],
-                      'labels':         batch[3]}
-            # if args.model_type in ['xlnet', 'xlm']:
-            #     inputs.update({'cls_index': batch[5],
-            #                    'p_mask':       batch[6]})
-            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 (not distributed) 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()
-                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:
-                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:
-                    # 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)
-                    tb_writer.add_scalar('lr', scheduler.get_lr()[0], global_step)
-                    tb_writer.add_scalar('loss', (tr_loss - logging_loss)/args.logging_steps, global_step)
-                    logging_loss = tr_loss
-
-                if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
-                    # Save model checkpoint
-                    output_dir = os.path.join(args.output_dir, 'checkpoint-{}'.format(global_step))
-                    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)
-                    tokenizer.save_vocabulary(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=""):
-    dataset, examples, features = load_and_cache_examples(args, tokenizer, evaluate=True, output_examples=True)
-
-    if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
-        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) if args.local_rank == -1 else DistributedSampler(dataset)
-    eval_dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
-
-    # Eval!
-    logger.info("***** Running evaluation {} *****".format(prefix))
-    logger.info("  Num examples = %d", len(dataset))
-    logger.info("  Batch size = %d", args.eval_batch_size)
-
-
-    eval_loss, eval_accuracy = 0, 0
-    nb_eval_steps, nb_eval_examples = 0, 0
-
-    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],
-                      # 'token_type_ids': None if args.model_type == 'xlm' else batch[2]  # XLM don't use segment_ids
-                      'token_type_ids': batch[2],
-                      'labels':         batch[3]}
-
-            # if args.model_type in ['xlnet', 'xlm']:
-            #     inputs.update({'cls_index': batch[4],
-            #                    'p_mask':    batch[5]})
-            outputs = model(**inputs)
-            tmp_eval_loss, logits = outputs[:2]
-            eval_loss += tmp_eval_loss.mean().item()
-
-        logits = logits.detach().cpu().numpy()
-        label_ids = inputs['labels'].to('cpu').numpy()
-        tmp_eval_accuracy = accuracy(logits, label_ids)
-        eval_accuracy += tmp_eval_accuracy
-
-        nb_eval_steps += 1
-        nb_eval_examples += inputs['input_ids'].size(0)
-
-    eval_loss = eval_loss / nb_eval_steps
-    eval_accuracy = eval_accuracy / nb_eval_examples
-    result = {'eval_loss': eval_loss,
-              'eval_accuracy': eval_accuracy}
-
-    output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
-    with open(output_eval_file, "w") as writer:
-        logger.info("***** Eval results *****")
-        for key in sorted(result.keys()):
-            logger.info("%s = %s", key, str(result[key]))
-            writer.write("%s = %s\n" % (key, str(result[key])))
-
-    return result
-
-def main():
-    parser = argparse.ArgumentParser()
-
-    ## Required parameters
-    parser.add_argument("--train_file", default=None, type=str, required=True,
-                        help="SWAG csv for training. E.g., train.csv")
-    parser.add_argument("--predict_file", default=None, type=str, required=True,
-                        help="SWAG csv for predictions. E.g., val.csv or test.csv")
-    parser.add_argument("--model_type", default=None, type=str, required=True,
-                        help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()))
-    parser.add_argument("--model_name_or_path", default=None, type=str, required=True,
-                        help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS))
-    parser.add_argument("--output_dir", default=None, type=str, required=True,
-                        help="The output directory where the model checkpoints and predictions will be written.")
-
-    ## Other parameters
-    parser.add_argument("--config_name", default="", type=str,
-                        help="Pretrained config name or path if not the same as model_name")
-    parser.add_argument("--tokenizer_name", default="", type=str,
-                        help="Pretrained tokenizer name or path if not the same as model_name")
-    parser.add_argument("--max_seq_length", default=384, type=int,
-                        help="The maximum total input sequence length after tokenization. Sequences "
-                             "longer than this will be truncated, and sequences shorter than this will be padded.")
-    parser.add_argument("--do_train", action='store_true',
-                        help="Whether to run training.")
-    parser.add_argument("--do_eval", action='store_true',
-                        help="Whether to run eval on the dev set.")
-    parser.add_argument("--evaluate_during_training", action='store_true',
-                        help="Rul evaluation during training at each logging step.")
-    parser.add_argument("--do_lower_case", action='store_true',
-                        help="Set this flag if you are using an uncased model.")
-
-    parser.add_argument("--per_gpu_train_batch_size", default=8, type=int,
-                        help="Batch size per GPU/CPU for training.")
-    parser.add_argument("--per_gpu_eval_batch_size", default=8, type=int,
-                        help="Batch size per GPU/CPU for evaluation.")
-    parser.add_argument("--learning_rate", default=5e-5, type=float,
-                        help="The initial learning rate for Adam.")
-    parser.add_argument('--gradient_accumulation_steps', type=int, default=1,
-                        help="Number of updates steps to accumulate before performing a backward/update pass.")
-    parser.add_argument("--weight_decay", default=0.0, type=float,
-                        help="Weight deay if we apply some.")
-    parser.add_argument("--adam_epsilon", default=1e-8, type=float,
-                        help="Epsilon for Adam optimizer.")
-    parser.add_argument("--max_grad_norm", default=1.0, type=float,
-                        help="Max gradient norm.")
-    parser.add_argument("--num_train_epochs", default=3.0, type=float,
-                        help="Total number of training epochs to perform.")
-    parser.add_argument("--max_steps", default=-1, type=int,
-                        help="If > 0: set total number of training steps to perform. Override num_train_epochs.")
-    parser.add_argument("--warmup_steps", default=0, type=int,
-                        help="Linear warmup over warmup_steps.")
-
-    parser.add_argument('--logging_steps', type=int, default=50,
-                        help="Log every X updates steps.")
-    parser.add_argument('--save_steps', type=int, default=50,
-                        help="Save checkpoint every X updates steps.")
-    parser.add_argument("--eval_all_checkpoints", action='store_true',
-                        help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number")
-    parser.add_argument("--no_cuda", action='store_true',
-                        help="Whether not to use CUDA when available")
-    parser.add_argument('--overwrite_output_dir', action='store_true',
-                        help="Overwrite the content of the output directory")
-    parser.add_argument('--overwrite_cache', action='store_true',
-                        help="Overwrite the cached training and evaluation sets")
-    parser.add_argument('--seed', type=int, default=42,
-                        help="random seed for initialization")
-
-    parser.add_argument("--local_rank", type=int, default=-1,
-                        help="local_rank for distributed training on gpus")
-    parser.add_argument('--fp16', action='store_true',
-                        help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit")
-    parser.add_argument('--fp16_opt_level', type=str, default='O1',
-                        help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
-                             "See details at https://nvidia.github.io/apex/amp.html")
-    parser.add_argument('--server_ip', type=str, default='', help="Can be used for distant debugging.")
-    parser.add_argument('--server_port', type=str, default='', help="Can be used for distant debugging.")
-    args = parser.parse_args()
-
-    if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train and not args.overwrite_output_dir:
-        raise ValueError("Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format(args.output_dir))
-
-    # Setup distant debugging if needed
-    if args.server_ip and args.server_port:
-        # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
-        import ptvsd
-        print("Waiting for debugger attach")
-        ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
-        ptvsd.wait_for_attach()
-
-    # Setup CUDA, GPU & distributed training
-    if args.local_rank == -1 or args.no_cuda:
-        device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
-        args.n_gpu = torch.cuda.device_count()
-    else:  # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
-        torch.cuda.set_device(args.local_rank)
-        device = torch.device("cuda", args.local_rank)
-        torch.distributed.init_process_group(backend='nccl')
-        args.n_gpu = 1
-    args.device = device
-
-    # Setup logging
-    logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s',
-                        datefmt = '%m/%d/%Y %H:%M:%S',
-                        level = logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
-    logger.warning("Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
-                    args.local_rank, device, args.n_gpu, bool(args.local_rank != -1), args.fp16)
-
-    # Set seed
-    set_seed(args)
-
-    # Load pretrained model and tokenizer
-    if args.local_rank not in [-1, 0]:
-        torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab
-
-    args.model_type = args.model_type.lower()
-    config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
-    config = config_class.from_pretrained(args.config_name if args.config_name else args.model_name_or_path)
-    tokenizer = tokenizer_class.from_pretrained(args.tokenizer_name if args.tokenizer_name else args.model_name_or_path, do_lower_case=args.do_lower_case)
-    model = model_class.from_pretrained(args.model_name_or_path, from_tf=bool('.ckpt' in args.model_name_or_path), config=config)
-
-    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, tokenizer, evaluate=False, output_examples=False)
-        global_step, tr_loss = train(args, train_dataset, model, tokenizer)
-        logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)
-
-
-    # Save the trained model and the tokenizer
-    if args.local_rank == -1 or torch.distributed.get_rank() == 0:
-        # Create output directory if needed
-        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 - 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:
-            checkpoints = [args.output_dir]
-        else:
-            # if do_train is False and do_eval is true, load model directly from pretrained.
-            checkpoints = [args.model_name_or_path]
-
-        if args.eval_all_checkpoints:
-            checkpoints = list(os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME, recursive=True)))
-            logging.getLogger("transformers.modeling_utils").setLevel(logging.WARN)  # Reduce model loading logs
-
-        logger.info("Evaluate the following checkpoints: %s", checkpoints)
-
-        for checkpoint in checkpoints:
-            # Reload the model
-            global_step = checkpoint.split('-')[-1] if len(checkpoints) > 1 else ""
-            model = model_class.from_pretrained(checkpoint)
-            tokenizer = tokenizer_class.from_pretrained(checkpoint)
-            model.to(args.device)
-
-            # Evaluate
-            result = evaluate(args, model, tokenizer, prefix=global_step)
-
-            result = dict((k + ('_{}'.format(global_step) if global_step else ''), v) for k, v in result.items())
-            results.update(result)
-
-    logger.info("Results: {}".format(results))
-
-    return results
-
-
-if __name__ == "__main__":
-    main()
@@ -1,153 +0,0 @@
-# coding=utf-8
-# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University 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.
-""" PyTorch Transformer XL model evaluation script.
-    Adapted from https://github.com/kimiyoung/transformer-xl.
-    In particular https://github.com/kimiyoung/transformer-xl/blob/master/pytorch/eval.py
-
-    This script with default values evaluates a pretrained Transformer-XL on WikiText 103
-"""
-from __future__ import absolute_import, division, print_function, unicode_literals
-
-import argparse
-import logging
-import time
-import math
-
-import torch
-
-from transformers import TransfoXLLMHeadModel, TransfoXLCorpus, TransfoXLTokenizer
-
-logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s',
-                    datefmt = '%m/%d/%Y %H:%M:%S',
-                    level = logging.INFO)
-logger = logging.getLogger(__name__)
-
-def main():
-    parser = argparse.ArgumentParser(description='PyTorch Transformer Language Model')
-    parser.add_argument('--model_name', type=str, default='transfo-xl-wt103',
-                        help='pretrained model name')
-    parser.add_argument('--split', type=str, default='test',
-                        choices=['all', 'valid', 'test'],
-                        help='which split to evaluate')
-    parser.add_argument('--batch_size', type=int, default=10,
-                        help='batch size')
-    parser.add_argument('--tgt_len', type=int, default=128,
-                        help='number of tokens to predict')
-    parser.add_argument('--ext_len', type=int, default=0,
-                        help='length of the extended context')
-    parser.add_argument('--mem_len', type=int, default=1600,
-                        help='length of the retained previous heads')
-    parser.add_argument('--clamp_len', type=int, default=1000,
-                        help='max positional embedding index')
-    parser.add_argument('--no_cuda', action='store_true',
-                        help='Do not use CUDA even though CUA is available')
-    parser.add_argument('--work_dir', type=str, required=True,
-                        help='path to the work_dir')
-    parser.add_argument('--no_log', action='store_true',
-                        help='do not log the eval result')
-    parser.add_argument('--same_length', action='store_true',
-                        help='set same length attention with masking')
-    parser.add_argument('--server_ip', type=str, default='', help="Can be used for distant debugging.")
-    parser.add_argument('--server_port', type=str, default='', help="Can be used for distant debugging.")
-    args = parser.parse_args()
-    assert args.ext_len >= 0, 'extended context length must be non-negative'
-
-    if args.server_ip and args.server_port:
-        # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
-        import ptvsd
-        print("Waiting for debugger attach")
-        ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
-        ptvsd.wait_for_attach()
-
-    device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
-    logger.info("device: {}".format(device))
-
-    # Load a pre-processed dataset
-    # You can also build the corpus yourself using TransfoXLCorpus methods
-    # The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
-    # and tokenizing the dataset
-    # The pre-processed corpus is a convertion (using the conversion script )
-    tokenizer = TransfoXLTokenizer.from_pretrained(args.model_name)
-    corpus = TransfoXLCorpus.from_pretrained(args.model_name)
-    ntokens = len(corpus.vocab)
-
-    va_iter = corpus.get_iterator('valid', args.batch_size, args.tgt_len,
-        device=device, ext_len=args.ext_len)
-    te_iter = corpus.get_iterator('test', args.batch_size, args.tgt_len,
-        device=device, ext_len=args.ext_len)
-
-    # Load a pre-trained model
-    model = TransfoXLLMHeadModel.from_pretrained(args.model_name)
-    model = model.to(device)
-
-    logger.info('Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}'.format(
-        args.batch_size, args.tgt_len, args.ext_len, args.mem_len, args.clamp_len))
-
-    model.reset_length(args.tgt_len, args.ext_len, args.mem_len)
-    if args.clamp_len > 0:
-        model.clamp_len = args.clamp_len
-    if args.same_length:
-        model.same_length = True
-
-    ###############################################################################
-    # Evaluation code
-    ###############################################################################
-    def evaluate(eval_iter):
-        # Turn on evaluation mode which disables dropout.
-        model.eval()
-        total_len, total_loss = 0, 0.
-        start_time = time.time()
-        with torch.no_grad():
-            mems = None
-            for idx, (data, target, seq_len) in enumerate(eval_iter):
-                ret = model(data, lm_labels=target, mems=mems)
-                loss, _, mems = ret
-                loss = loss.mean()
-                total_loss += seq_len * loss.item()
-                total_len += seq_len
-            total_time = time.time() - start_time
-        logger.info('Time : {:.2f}s, {:.2f}ms/segment'.format(
-                total_time, 1000 * total_time / (idx+1)))
-        return total_loss / total_len
-
-    # Run on test data.
-    if args.split == 'all':
-        test_loss = evaluate(te_iter)
-        valid_loss = evaluate(va_iter)
-    elif args.split == 'valid':
-        valid_loss = evaluate(va_iter)
-        test_loss = None
-    elif args.split == 'test':
-        test_loss = evaluate(te_iter)
-        valid_loss = None
-
-    def format_log(loss, split):
-        log_str = '| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '.format(
-            split, loss, math.exp(loss))
-        return log_str
-
-    log_str = ''
-    if valid_loss is not None:
-        log_str += format_log(valid_loss, 'valid')
-    if test_loss is not None:
-        log_str += format_log(test_loss, 'test')
-
-    logger.info('=' * 100)
-    logger.info(log_str)
-    logger.info('=' * 100)
-
-if __name__ == '__main__':
-    main()
@@ -1,184 +0,0 @@
-# Distil*
-
-This folder contains the original code used to train Distil* as well as examples showcasing how to use DistilBERT, DistilRoBERTa and DistilGPT2.
-
-**December 6th, 2019 - Update** We release **DistilmBERT**: 92% of `bert-base-multilingual-cased` on XNLI. The model supports 104 different languages listed [here](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages).
-
-**November 19th, 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 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.**
-
-**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*
-
-Distil* is a class of compressed models that started with DistilBERT. DistilBERT stands for Distillated-BERT. DistilBERT is a small, fast, cheap and light Transformer model based on Bert architecture. It has 40% less parameters than `bert-base-uncased`, runs 60% faster while preserving 97% of BERT's performances as measured on the GLUE language understanding benchmark. DistilBERT is trained using knowledge distillation, a technique to compress a large model called the teacher into a smaller model called the student. By distillating Bert, we obtain a smaller Transformer model that bears a lot of similarities with the original BERT model while being lighter, smaller and faster to run. DistilBERT is thus an interesting option to put large-scaled trained Transformer model into production.
-
-We have applied the same method to other Transformer architectures and released the weights:
- GPT2: on the [WikiText-103](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/) benchmark, GPT2 reaches a perplexity on the test set of 15.0 compared to 18.5 for **DistilGPT2** (after fine-tuning on the train set).
- 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).
-
-For more information on DistilBERT, please refer to our [NeurIPS workshop paper](https://arxiv.org/abs/1910.01108).
-
-Here are the results on the dev sets of GLUE:
-
-| Model                     | Macro-score                    | CoLA | MNLI | MRPC | QNLI | QQP  | RTE  | SST-2| STS-B| WNLI              |
-| :---:                     |    :---:                       | :---:| :---:| :---:| :---:| :---:| :---:| :---:| :---:| :---:             |
-| BERT-base                 |  **77.6**                      | 48.9 | 84.3 | 88.6 | 89.3 | 89.5 | 71.3 | 91.7 | 91.2 | 43.7              |
-| DistilBERT                |  **76.8**                      | 49.1 | 81.8 | 90.2 | 90.2 | 89.2 | 62.9 | 92.7 | 90.7 | 44.4              |
-| ---                       |    ---                         |  --- |  --- |  --- |  --- |  --- |  --- |  --- |  --- |  ---              |
-| RoBERTa-base (reported)   |  **83.2**/**86.4**<sup>2</sup> | 63.6 | 87.6 | 90.2 | 92.8 | 91.9 | 78.7 | 94.8 | 91.2 | 57.7<sup>3</sup>  |
-| DistilRoBERTa<sup>1</sup> |  **79.0**/**82.3**<sup>2</sup> | 59.4 | 83.9 | 86.6 | 90.8 | 89.4 | 67.9 | 92.5 | 88.3 | 52.1              |
-
-<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.
-
-<sup>3</sup> We compute this score ourselves for completeness.
-
-Here are the results on the *test* sets for 6 of the languages available in XNLI. The results are computed in the zero shot setting (trained on the English portion and evaluated on the target language portion):
-
-| Model                        | English | Spanish | Chinese | German | Arabic  | Urdu |
-| :---:                        | :---:   | :---:   | :---:   | :---:  | :---:   | :---:|
-| mBERT base cased (computed)  | 82.1    | 74.6    | 69.1    | 72.3   | 66.4    | 58.5 |
-| mBERT base uncased (reported)| 81.4    | 74.3    | 63.8    | 70.5   | 62.1    | 58.3 |
-| DistilmBERT                  | 78.2    | 69.1    | 64.0    | 66.3   | 59.1    | 54.7 |
-
-## Setup
-
-This part of the library has only be tested with Python3.6+. There are few specific dependencies to install before launching a distillation, you can install them with the command `pip install -r requirements.txt`. 
-
-**Important note:** The training scripts have been updated to support PyTorch v1.2.0 (there are breakings changes compared to v1.1.0).
-
-
-## How to use DistilBERT
-
-Transformers includes five pre-trained Distil* models, currently only provided for English and German (we are investigating the possibility to train and release a multilingual version of DistilBERT):
-
- `distilbert-base-uncased`: DistilBERT English language model pretrained on the same data used to pretrain Bert (concatenation of the Toronto Book Corpus and full English Wikipedia) using distillation with the supervision of the `bert-base-uncased` version of Bert. The model has 6 layers, 768 dimension and 12 heads, totalizing 66M parameters.
- `distilbert-base-uncased-distilled-squad`: A finetuned version of `distilbert-base-uncased` finetuned using (a second step of) knwoledge distillation on SQuAD 1.0. This model reaches a F1 score of 86.9 on the dev set (for comparison, Bert `bert-base-uncased` version reaches a 88.5 F1 score).
- `distilbert-base-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.
- `distilbert-base-multilingual-cased`: DistilmBERT multilingual model pretrained with the supervision of `bert-base-multilingual-cased` on the concatenation of Wikipedia in 104 different languages. The model supports the 104 languages listed [here](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages). The model has 6 layers, 768 dimension and 12 heads, totalizing 134M parameters (compared to 177M parameters for mBERT-base). On average DistilmBERT is twice as fast as mBERT-base.
-
-Using DistilBERT is very similar to using BERT. DistilBERT share the same tokenizer as BERT's `bert-base-uncased` even though we provide a link to this tokenizer under the `DistilBertTokenizer` name to have a consistent naming between the library models.
-
-```python
-tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
-model = DistilBertModel.from_pretrained('distilbert-base-uncased')
-
-input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)
-outputs = model(input_ids)
-last_hidden_states = outputs[0]  # The last hidden-state is the first element of the output tuple
-```
-
-Similarly, using the other Distil* models simply consists in calling the base classes with a different pretrained checkpoint:
- DistilGPT2: `model = GPT2Model.from_pretrained('distilgpt2')`
- DistilRoBERTa: `model = RobertaModel.from_pretrained('distilroberta-base')`
- DistilmBERT: `model = DistilBertModel.from_pretrained('distilbert-base-multilingual-cased')`
-
-
-## How to train Distil*
-
-In the following, we will explain how you can train DistilBERT.
-
-### A. Preparing the data
-
-The weights we release are trained using a concatenation of Toronto Book Corpus and English Wikipedia (same training data as the English version of BERT).
-
-To avoid processing the data several time, we do it once and for all before the training. From now on, will suppose that you have a text file `dump.txt` which contains one sequence per line (a sequence being composed of one of several coherent sentences).
-
-First, we will binarize the data, i.e. tokenize the data and convert each token in an index in our model's vocabulary.
-
-```bash
-python scripts/binarized_data.py \
-    --file_path data/dump.txt \
-    --tokenizer_type bert \
-    --tokenizer_name bert-base-uncased \
-    --dump_file data/binarized_text
-```
-
-Our implementation of masked language modeling loss follows [XLM](https://github.com/facebookresearch/XLM)'s one and smoothes the probability of masking with a factor that put more emphasis on rare words. Thus we count the occurences of each tokens in the data:
-
-```bash
-python scripts/token_counts.py \
-    --data_file data/binarized_text.bert-base-uncased.pickle \
-    --token_counts_dump data/token_counts.bert-base-uncased.pickle \
-    --vocab_size 30522
-```
-
-### B. Training
-
-Training with distillation is really simple once you have pre-processed the data:
-
-```bash
-python train.py \
-    --student_type distilbert \
-    --student_config training_configs/distilbert-base-uncased.json \
-    --teacher_type bert \
-    --teacher_name bert-base-uncased \
-    --alpha_ce 5.0 --alpha_mlm 2.0 --alpha_cos 1.0 --alpha_clm 0.0 --mlm \
-    --freeze_pos_embs \
-    --dump_path serialization_dir/my_first_training \
-    --data_file data/binarized_text.bert-base-uncased.pickle \
-    --token_counts data/token_counts.bert-base-uncased.pickle \
-    --force # overwrites the `dump_path` if it already exists.
-```
-
-By default, this will launch a training on a single GPU (even if more are available on the cluster). Other parameters are available in the command line, please look in `train.py` or run `python train.py --help` to list them.
-
-We highly encourage you to use distributed training for training DistilBERT as the training corpus is quite large. Here's an example that runs a distributed training on a single node having 4 GPUs:
-
-```bash
-export NODE_RANK=0
-export N_NODES=1
-
-export N_GPU_NODE=4
-export WORLD_SIZE=4
-export MASTER_PORT=<AN_OPEN_PORT>
-export MASTER_ADDR=<I.P.>
-
-pkill -f 'python -u train.py'
-
-python -m torch.distributed.launch \
-    --nproc_per_node=$N_GPU_NODE \
-    --nnodes=$N_NODES \
-    --node_rank $NODE_RANK \
-    --master_addr $MASTER_ADDR \
-    --master_port $MASTER_PORT \
-    train.py \
-        --force \
-        --n_gpu $WORLD_SIZE \
-        --student_type distilbert \
-        --student_config training_configs/distilbert-base-uncased.json \
-        --teacher_type bert \
-        --teacher_name bert-base-uncased \
-        --alpha_ce 0.33 --alpha_mlm 0.33 --alpha_cos 0.33 --alpha_clm 0.0 --mlm \
-        --freeze_pos_embs \
-        --dump_path serialization_dir/my_first_training \
-        --data_file data/binarized_text.bert-base-uncased.pickle \
-        --token_counts data/token_counts.bert-base-uncased.pickle
-```
-
-**Tips:** Starting distillated training with good initialization of the model weights is crucial to reach decent performance. In our experiments, we initialized our model from a few layers of the teacher (Bert) itself! Please refer to `scripts/extract.py` and `scripts/extract_distilbert.py` to create a valid initialization checkpoint and use `--student_pretrained_weights` argument to use this initialization for the distilled training!
-
-Happy distillation!
-
-## Citation
-
-If you find the ressource useful, you should cite the following paper:
-
-```
-@inproceedings{sanh2019distilbert,
-  title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter},
-  author={Sanh, Victor and Debut, Lysandre and Chaumond, Julien and Wolf, Thomas},
-  booktitle={NeurIPS EMC^2 Workshop},
-  year={2019}
-}
-```
@@ -1,540 +0,0 @@
-# coding=utf-8
-# Copyright 2019-present, the HuggingFace Inc. team and Facebook, Inc.
-#
-# 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.
-""" The distiller to distil the student.
-    Adapted in part from Facebook, Inc XLM model (https://github.com/facebookresearch/XLM)
-"""
-import os
-import math
-import psutil
-import time
-from tqdm import trange, tqdm
-import numpy as np
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.optim import AdamW
-from torch.utils.data.distributed import DistributedSampler
-from torch.utils.data import RandomSampler, BatchSampler, DataLoader
-
-try:
-    from torch.utils.tensorboard import SummaryWriter
-except:
-    from tensorboardX import SummaryWriter
-
-from transformers import get_linear_schedule_with_warmup
-
-from utils import logger
-from lm_seqs_dataset import LmSeqsDataset
-from grouped_batch_sampler import GroupedBatchSampler, create_lengths_groups
-
-class Distiller:
-    def __init__(self,
-                 params: dict,
-                 dataset: LmSeqsDataset,
-                 token_probs: torch.tensor,
-                 student: nn.Module,
-                 teacher: nn.Module):
-        logger.info('Initializing Distiller')
-        self.params = params
-        self.dump_path = params.dump_path
-        self.multi_gpu = params.multi_gpu
-        self.fp16 = params.fp16
-
-        self.student = student
-        self.teacher = teacher
-
-        self.student_config = student.config
-        self.vocab_size = student.config.vocab_size
-
-        if params.n_gpu <= 1:
-            sampler = RandomSampler(dataset)
-        else:
-            sampler = DistributedSampler(dataset)
-
-        if params.group_by_size:
-            groups = create_lengths_groups(lengths=dataset.lengths, k=params.max_model_input_size)
-            sampler = GroupedBatchSampler(sampler=sampler, group_ids=groups, batch_size=params.batch_size)
-        else:
-            sampler = BatchSampler(sampler=sampler, batch_size=params.batch_size, drop_last=False)
-
-        self.dataloader = DataLoader(dataset=dataset,
-                                     batch_sampler=sampler,
-                                     collate_fn=dataset.batch_sequences)
-
-        self.temperature = params.temperature
-        assert self.temperature > 0.
-
-        self.alpha_ce = params.alpha_ce
-        self.alpha_mlm = params.alpha_mlm
-        self.alpha_clm = params.alpha_clm
-        self.alpha_mse = params.alpha_mse
-        self.alpha_cos = params.alpha_cos
-
-        self.mlm = params.mlm
-        if self.mlm:
-            logger.info(f'Using MLM loss for LM step.')
-            self.mlm_mask_prop = params.mlm_mask_prop
-            assert 0.0 <= self.mlm_mask_prop <= 1.0
-            assert params.word_mask + params.word_keep + params.word_rand == 1.0
-            self.pred_probs = torch.FloatTensor([params.word_mask, params.word_keep, params.word_rand])
-            self.pred_probs = self.pred_probs.to(f'cuda:{params.local_rank}') if params.n_gpu > 0 else self.pred_probs
-            self.token_probs = token_probs.to(f'cuda:{params.local_rank}') if params.n_gpu > 0 else token_probs
-            if self.fp16:
-                self.pred_probs = self.pred_probs.half()
-                self.token_probs = self.token_probs.half()
-        else:
-            logger.info(f'Using CLM loss for LM step.')
-
-        self.epoch = 0
-        self.n_iter = 0
-        self.n_total_iter = 0
-        self.n_sequences_epoch = 0
-        self.total_loss_epoch = 0
-        self.last_loss = 0
-        self.last_loss_ce = 0
-        self.last_loss_mlm = 0
-        self.last_loss_clm = 0
-        if self.alpha_mse > 0.: self.last_loss_mse = 0
-        if self.alpha_cos > 0.: self.last_loss_cos = 0
-        self.last_log = 0
-
-        self.ce_loss_fct = nn.KLDivLoss(reduction='batchmean')
-        self.lm_loss_fct = nn.CrossEntropyLoss(ignore_index=-1)
-        if self.alpha_mse > 0.:
-            self.mse_loss_fct = nn.MSELoss(reduction='sum')
-        if self.alpha_cos > 0.:
-            self.cosine_loss_fct = nn.CosineEmbeddingLoss(reduction='mean')
-
-        logger.info('--- Initializing model optimizer')
-        assert params.gradient_accumulation_steps >= 1
-        self.num_steps_epoch = len(self.dataloader)
-        num_train_optimization_steps = int(self.num_steps_epoch / params.gradient_accumulation_steps * params.n_epoch) + 1
-
-        no_decay = ['bias', 'LayerNorm.weight']
-        optimizer_grouped_parameters = [
-            {'params': [p for n, p in student.named_parameters() if not any(nd in n for nd in no_decay) and p.requires_grad], 'weight_decay': params.weight_decay},
-            {'params': [p for n, p in student.named_parameters() if any(nd in n for nd in no_decay) and p.requires_grad], 'weight_decay': 0.0}
-        ]
-        logger.info("------ Number of trainable parameters (student): %i" % sum([p.numel() for p in self.student.parameters() if p.requires_grad]))
-        logger.info("------ Number of parameters (student): %i" % sum([p.numel() for p in self.student.parameters()]))
-        self.optimizer = AdamW(optimizer_grouped_parameters,
-                               lr=params.learning_rate,
-                               eps=params.adam_epsilon,
-                               betas=(0.9, 0.98))
-
-        warmup_steps = math.ceil(num_train_optimization_steps * params.warmup_prop)
-        self.scheduler = get_linear_schedule_with_warmup(self.optimizer,
-                                                num_warmup_steps=warmup_steps,
-                                                num_training_steps=num_train_optimization_steps)
-
-        if self.fp16:
-            try:
-                from apex import amp
-            except ImportError:
-                raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
-            logger.info(f"Using fp16 training: {self.params.fp16_opt_level} level")
-            self.student, self.optimizer = amp.initialize(self.student,
-                                                          self.optimizer,
-                                                          opt_level=self.params.fp16_opt_level)
-            self.teacher = self.teacher.half()
-
-        if self.multi_gpu:
-            if self.fp16:
-                from apex.parallel import DistributedDataParallel
-                logger.info("Using apex.parallel.DistributedDataParallel for distributed training.")
-                self.student = DistributedDataParallel(self.student)
-            else:
-                from torch.nn.parallel import DistributedDataParallel
-                logger.info("Using nn.parallel.DistributedDataParallel for distributed training.")
-                self.student = DistributedDataParallel(self.student,
-                                                       device_ids=[params.local_rank],
-                                                       output_device=params.local_rank,
-                                                       find_unused_parameters=True)
-
-        self.is_master = params.is_master
-        if self.is_master:
-            logger.info('--- Initializing Tensorboard')
-            self.tensorboard = SummaryWriter(log_dir=os.path.join(self.dump_path, 'log', 'train'))
-            self.tensorboard.add_text(tag='config/training', text_string=str(self.params), global_step=0)
-            self.tensorboard.add_text(tag='config/student', text_string=str(self.student_config), global_step=0)
-
-    def prepare_batch_mlm(self,
-                          batch):
-        """
-        Prepare the batch: from the token_ids and the lenghts, compute the attention mask and the masked label for MLM.
-
-        Input:
-        ------
-            batch: `Tuple`
-                token_ids: `torch.tensor(bs, seq_length)` - The token ids for each of the sequence. It is padded.
-                lengths: `torch.tensor(bs)` - The lengths of each of the sequences in the batch.
-
-        Output:
-        -------
-            token_ids: `torch.tensor(bs, seq_length)` - The token ids after the modifications for MLM.
-            attn_mask: `torch.tensor(bs, seq_length)` - The attention mask for the self-attention.
-            mlm_labels: `torch.tensor(bs, seq_length)` - The masked languge modeling labels. There is a -1 where there is nothing to predict.
-        """
-        token_ids, lengths = batch
-        token_ids, lengths = self.round_batch(x=token_ids, lengths=lengths)
-        assert token_ids.size(0) == lengths.size(0)
-
-        attn_mask = (torch.arange(token_ids.size(1), dtype=torch.long, device=lengths.device) < lengths[:, None])
-
-        bs, max_seq_len = token_ids.size()
-        mlm_labels = token_ids.new(token_ids.size()).copy_(token_ids)
-
-        x_prob = self.token_probs[token_ids.flatten()]
-        n_tgt = math.ceil(self.mlm_mask_prop * lengths.sum().item())
-        tgt_ids = torch.multinomial(x_prob / x_prob.sum(), n_tgt, replacement=False)
-        pred_mask = torch.zeros(bs * max_seq_len, dtype=torch.bool, device=token_ids.device) # previously `dtype=torch.uint8`, cf pytorch 1.2.0 compatibility
-        pred_mask[tgt_ids] = 1
-        pred_mask = pred_mask.view(bs, max_seq_len)
-
-        pred_mask[token_ids == self.params.special_tok_ids['pad_token']] = 0
-
-        # mask a number of words == 0 [8] (faster with fp16)
-        if self.fp16:
-            n1 = pred_mask.sum().item()
-            if n1 > 8:
-                pred_mask = pred_mask.view(-1)
-                n2 = max(n1 % 8, 8 * (n1 // 8))
-                if n2 != n1:
-                    pred_mask[torch.nonzero(pred_mask).view(-1)[:n1-n2]] = 0
-                pred_mask = pred_mask.view(bs, max_seq_len)
-                assert pred_mask.sum().item() % 8 == 0, pred_mask.sum().item()
-
-        _token_ids_real = token_ids[pred_mask]
-        _token_ids_rand = _token_ids_real.clone().random_(self.vocab_size)
-        _token_ids_mask = _token_ids_real.clone().fill_(self.params.special_tok_ids['mask_token'])
-        probs = torch.multinomial(self.pred_probs, len(_token_ids_real), replacement=True)
-        _token_ids = _token_ids_mask * (probs == 0).long() + _token_ids_real * (probs == 1).long() + _token_ids_rand * (probs == 2).long()
-        token_ids = token_ids.masked_scatter(pred_mask, _token_ids)
-
-        mlm_labels[~pred_mask] = -1 # previously `mlm_labels[1-pred_mask] = -1`, cf pytorch 1.2.0 compatibility
-
-        # sanity checks
-        assert 0 <= token_ids.min() <= token_ids.max() < self.vocab_size
-
-        return token_ids, attn_mask, mlm_labels
-
-    def prepare_batch_clm(self,
-                          batch):
-        """
-        Prepare the batch: from the token_ids and the lenghts, compute the attention mask and the labels for CLM.
-
-        Input:
-        ------
-            batch: `Tuple`
-                token_ids: `torch.tensor(bs, seq_length)` - The token ids for each of the sequence. It is padded.
-                lengths: `torch.tensor(bs)` - The lengths of each of the sequences in the batch.
-
-        Output:
-        -------
-            token_ids: `torch.tensor(bs, seq_length)` - The token ids after the modifications for MLM.
-            attn_mask: `torch.tensor(bs, seq_length)` - The attention mask for the self-attention.
-            clm_labels: `torch.tensor(bs, seq_length)` - The causal languge modeling labels. There is a -1 where there is nothing to predict.
-        """
-        token_ids, lengths = batch
-        token_ids, lengths = self.round_batch(x=token_ids, lengths=lengths)
-        assert token_ids.size(0) == lengths.size(0)
-
-        attn_mask = (torch.arange(token_ids.size(1), dtype=torch.long, device=lengths.device) < lengths[:, None])
-        clm_labels = token_ids.new(token_ids.size()).copy_(token_ids)
-        clm_labels[~attn_mask] = -1 # previously `clm_labels[1-attn_mask] = -1`, cf pytorch 1.2.0 compatibility
-
-        # sanity checks
-        assert 0 <= token_ids.min() <= token_ids.max() < self.vocab_size
-
-        return token_ids, attn_mask, clm_labels
-
-    def round_batch(self,
-                    x: torch.tensor,
-                    lengths: torch.tensor):
-        """
-        For float16 only.
-        Sub-sample sentences in a batch, and add padding, so that each dimension is a multiple of 8.
-
-        Input:
-        ------
-            x: `torch.tensor(bs, seq_length)` - The token ids.
-            lengths: `torch.tensor(bs, seq_length)` - The lengths of each of the sequence in the batch.
-
-        Output:
-        -------
-            x:  `torch.tensor(new_bs, new_seq_length)` - The updated token ids.
-            lengths: `torch.tensor(new_bs, new_seq_length)` - The updated lengths.
-        """
-        if not self.fp16 or len(lengths) < 8:
-            return x, lengths
-
-        # number of sentences == 0 [8]
-        bs1 = len(lengths)
-        bs2 = 8 * (bs1 // 8)
-        assert bs2 > 0 and bs2 % 8 == 0
-        if bs1 != bs2:
-            idx = torch.randperm(bs1)[:bs2]
-            lengths = lengths[idx]
-            slen = lengths.max().item()
-            x = x[idx, :slen]
-        else:
-            idx = None
-
-        # sequence length == 0 [8]
-        ml1 = x.size(1)
-        if ml1 % 8 != 0:
-            pad = 8 - (ml1 % 8)
-            ml2 = ml1 + pad
-            if self.mlm:
-                pad_id = self.params.special_tok_ids['pad_token']
-            else:
-                pad_id = self.params.special_tok_ids['unk_token']
-            padding_tensor = torch.zeros(bs2, pad, dtype=torch.long, device=x.device).fill_(pad_id)
-            x = torch.cat([x, padding_tensor], 1)
-            assert x.size() == (bs2, ml2)
-
-        assert x.size(0) % 8 == 0
-        assert x.size(1) % 8 == 0
-        return x, lengths
-
-    def train(self):
-        """
-        The real training loop.
-        """
-        if self.is_master: logger.info('Starting training')
-        self.last_log = time.time()
-        self.student.train()
-        self.teacher.eval()
-
-        for _ in range(self.params.n_epoch):
-            if self.is_master: logger.info(f'--- Starting epoch {self.epoch}/{self.params.n_epoch-1}')
-            if self.multi_gpu:
-                torch.distributed.barrier()
-
-            iter_bar = tqdm(self.dataloader, desc="-Iter", disable=self.params.local_rank not in [-1, 0])
-            for batch in iter_bar:
-                if self.params.n_gpu > 0:
-                    batch = tuple(t.to(f'cuda:{self.params.local_rank}') for t in batch)
-
-                if self.mlm:
-                    token_ids, attn_mask, lm_labels = self.prepare_batch_mlm(batch=batch)
-                else:
-                    token_ids, attn_mask, lm_labels = self.prepare_batch_clm(batch=batch)
-                self.step(input_ids=token_ids, attention_mask=attn_mask, lm_labels=lm_labels)
-
-                iter_bar.update()
-                iter_bar.set_postfix({'Last_loss': f'{self.last_loss:.2f}',
-                                      'Avg_cum_loss': f'{self.total_loss_epoch/self.n_iter:.2f}'})
-            iter_bar.close()
-
-            if self.is_master: logger.info(f'--- Ending epoch {self.epoch}/{self.params.n_epoch-1}')
-            self.end_epoch()
-
-        if self.is_master:
-            logger.info(f'Save very last checkpoint as `pytorch_model.bin`.')
-            self.save_checkpoint(checkpoint_name=f'pytorch_model.bin')
-            logger.info('Training is finished')
-
-    def step(self,
-             input_ids: torch.tensor,
-             attention_mask: torch.tensor,
-             lm_labels: torch.tensor):
-        """
-        One optimization step: forward of student AND teacher, backward on the loss (for gradient accumulation),
-        and possibly a parameter update (depending on the gradient accumulation).
-
-        Input:
-        ------
-        input_ids: `torch.tensor(bs, seq_length)` - The token ids.
-        attention_mask: `torch.tensor(bs, seq_length)` - The attention mask for self attention.
-        lm_labels: `torch.tensor(bs, seq_length)` - The language modeling labels (mlm labels for MLM and clm labels for CLM).
-        """
-        if self.mlm:
-            s_logits, s_hidden_states = self.student(input_ids=input_ids, attention_mask=attention_mask)     # (bs, seq_length, voc_size)
-            with torch.no_grad():
-                t_logits, t_hidden_states = self.teacher(input_ids=input_ids, attention_mask=attention_mask) # (bs, seq_length, voc_size)
-        else:
-            s_logits, _, s_hidden_states = self.student(input_ids=input_ids, attention_mask=None)            # (bs, seq_length, voc_size)
-            with torch.no_grad():
-                t_logits, _, t_hidden_states = self.teacher(input_ids=input_ids, attention_mask=None)           # (bs, seq_length, voc_size)
-        assert s_logits.size() == t_logits.size()
-
-        #https://github.com/peterliht/knowledge-distillation-pytorch/blob/master/model/net.py#L100
-        #https://github.com/peterliht/knowledge-distillation-pytorch/issues/2
-        if self.params.restrict_ce_to_mask:
-            mask = (lm_labels>-1).unsqueeze(-1).expand_as(s_logits)    # (bs, seq_lenth, voc_size)
-        else:
-            mask = attention_mask.unsqueeze(-1).expand_as(s_logits)    # (bs, seq_lenth, voc_size)
-        s_logits_slct = torch.masked_select(s_logits, mask)            # (bs * seq_length * voc_size) modulo the 1s in mask
-        s_logits_slct = s_logits_slct.view(-1, s_logits.size(-1))      # (bs * seq_length, voc_size) modulo the 1s in mask
-        t_logits_slct = torch.masked_select(t_logits, mask)            # (bs * seq_length * voc_size) modulo the 1s in mask
-        t_logits_slct = t_logits_slct.view(-1, s_logits.size(-1))      # (bs * seq_length, voc_size) modulo the 1s in mask
-        assert t_logits_slct.size() == s_logits_slct.size()
-
-        loss_ce = self.ce_loss_fct(F.log_softmax(s_logits_slct/self.temperature, dim=-1),
-                                   F.softmax(t_logits_slct/self.temperature, dim=-1)) * (self.temperature)**2
-        loss = self.alpha_ce*loss_ce
-
-        if self.alpha_mlm > 0.:
-            loss_mlm = self.lm_loss_fct(s_logits.view(-1, s_logits.size(-1)), lm_labels.view(-1))
-            loss += self.alpha_mlm * loss_mlm
-        if self.alpha_clm > 0.:
-            shift_logits = s_logits[..., :-1, :].contiguous()
-            shift_labels = lm_labels[..., 1:].contiguous()
-            loss_clm = self.lm_loss_fct(shift_logits.view(-1, shift_logits.size(-1)),
-                                        shift_labels.view(-1))
-            loss += self.alpha_clm * loss_clm
-
-        if self.alpha_mse > 0.:
-            loss_mse = self.mse_loss_fct(s_logits_slct, t_logits_slct)/s_logits_slct.size(0) # Reproducing batchmean reduction
-            loss += self.alpha_mse * loss_mse
-        if self.alpha_cos > 0.:
-            s_hidden_states = s_hidden_states[-1]                              # (bs, seq_length, dim)
-            t_hidden_states = t_hidden_states[-1]                              # (bs, seq_length, dim)
-            mask = attention_mask.unsqueeze(-1).expand_as(s_hidden_states)     # (bs, seq_length, dim)
-            assert s_hidden_states.size() == t_hidden_states.size()
-            dim = s_hidden_states.size(-1)
-            
-            s_hidden_states_slct = torch.masked_select(s_hidden_states, mask)        # (bs * seq_length * dim)
-            s_hidden_states_slct = s_hidden_states_slct.view(-1, dim)                # (bs * seq_length, dim)
-            t_hidden_states_slct = torch.masked_select(t_hidden_states, mask)        # (bs * seq_length * dim)
-            t_hidden_states_slct = t_hidden_states_slct.view(-1, dim)                # (bs * seq_length, dim)
-        
-            target = s_hidden_states_slct.new(s_hidden_states_slct.size(0)).fill_(1) # (bs * seq_length,)
-            loss_cos = self.cosine_loss_fct(s_hidden_states_slct, t_hidden_states_slct, target)
-            loss += self.alpha_cos * loss_cos
-
-        self.total_loss_epoch += loss.item()
-        self.last_loss = loss.item()
-        self.last_loss_ce = loss_ce.item()
-        if self.alpha_mlm > 0.:
-            self.last_loss_mlm = loss_mlm.item()
-        if self.alpha_clm > 0.:
-            self.last_loss_clm = loss_clm.item()
-        if self.alpha_mse > 0.:
-            self.last_loss_mse = loss_mse.item()
-        if self.alpha_cos > 0.:
-            self.last_loss_cos = loss_cos.item()
-
-        self.optimize(loss)
-
-        self.n_sequences_epoch += input_ids.size(0)
-
-    def optimize(self,
-                 loss):
-        """
-        Normalization on the loss (gradient accumulation or distributed training), followed by
-        backward pass on the loss, possibly followed by a parameter update (depending on the gradient accumulation).
-        Also update the metrics for tensorboard.
-        """
-        # Check for NaN
-        if (loss != loss).data.any():
-            logger.error('NaN detected')
-            exit()
-
-        if self.multi_gpu:
-            loss = loss.mean()
-        if self.params.gradient_accumulation_steps > 1:
-            loss = loss / self.params.gradient_accumulation_steps
-
-        if self.fp16:
-            from apex import amp
-            with amp.scale_loss(loss, self.optimizer) as scaled_loss:
-                scaled_loss.backward()
-        else:
-            loss.backward()
-
-        self.iter()
-        if self.n_iter % self.params.gradient_accumulation_steps == 0:
-            if self.fp16:
-                torch.nn.utils.clip_grad_norm_(amp.master_params(self.optimizer), self.params.max_grad_norm)
-            else:
-                torch.nn.utils.clip_grad_norm_(self.student.parameters(), self.params.max_grad_norm)
-            self.optimizer.step()
-            self.optimizer.zero_grad()
-            self.scheduler.step()
-
-    def iter(self):
-        """
-        Update global counts, write to tensorboard and save checkpoint.
-        """
-        self.n_iter += 1
-        self.n_total_iter += 1
-
-        if self.n_total_iter % self.params.log_interval == 0:
-            self.log_tensorboard()
-            self.last_log = time.time()
-        if self.n_total_iter % self.params.checkpoint_interval == 0:
-            self.save_checkpoint()
-
-    def log_tensorboard(self):
-        """
-        Log into tensorboard. Only by the master process.
-        """
-        if not self.is_master:
-            return
-
-        for param_name, param in self.student.named_parameters():
-            self.tensorboard.add_scalar(tag='parameter_mean/' + param_name, scalar_value=param.data.mean(), global_step=self.n_total_iter)
-            self.tensorboard.add_scalar(tag='parameter_std/' + param_name, scalar_value=param.data.std(), global_step=self.n_total_iter)
-            if param.grad is None:
-                continue
-            self.tensorboard.add_scalar(tag="grad_mean/" + param_name, scalar_value=param.grad.data.mean(),global_step=self.n_total_iter)
-            self.tensorboard.add_scalar(tag="grad_std/" + param_name, scalar_value=param.grad.data.std(), global_step=self.n_total_iter)
-
-        self.tensorboard.add_scalar(tag="losses/cum_avg_loss_epoch", scalar_value=self.total_loss_epoch/self.n_iter, global_step=self.n_total_iter)
-        self.tensorboard.add_scalar(tag="losses/loss", scalar_value=self.last_loss, global_step=self.n_total_iter)
-        self.tensorboard.add_scalar(tag="losses/loss_ce", scalar_value=self.last_loss_ce, global_step=self.n_total_iter)
-        if self.alpha_mlm > 0.:
-            self.tensorboard.add_scalar(tag="losses/loss_mlm", scalar_value=self.last_loss_mlm, global_step=self.n_total_iter)
-        if self.alpha_clm > 0.:
-            self.tensorboard.add_scalar(tag="losses/loss_clm", scalar_value=self.last_loss_clm, global_step=self.n_total_iter)
-        if self.alpha_mse > 0.:
-            self.tensorboard.add_scalar(tag="losses/loss_mse", scalar_value=self.last_loss_mse, global_step=self.n_total_iter)
-        if self.alpha_cos > 0.:
-            self.tensorboard.add_scalar(tag="losses/loss_cos", scalar_value=self.last_loss_cos, global_step=self.n_total_iter)
-        self.tensorboard.add_scalar(tag="learning_rate/lr", scalar_value=self.scheduler.get_lr()[0], global_step=self.n_total_iter)
-        
-        self.tensorboard.add_scalar(tag="global/memory_usage", scalar_value=psutil.virtual_memory()._asdict()['used']/1_000_000, global_step=self.n_total_iter)
-        self.tensorboard.add_scalar(tag="global/speed", scalar_value=time.time()-self.last_log, global_step=self.n_total_iter)
-
-    def end_epoch(self):
-        """
-        Finally arrived at the end of epoch (full pass on dataset).
-        Do some tensorboard logging and checkpoint saving.
-        """
-        logger.info(f'{self.n_sequences_epoch} sequences have been trained during this epoch.')
-
-        if self.is_master:
-            self.save_checkpoint(checkpoint_name=f'model_epoch_{self.epoch}.pth')
-            self.tensorboard.add_scalar(tag='epoch/loss', scalar_value=self.total_loss_epoch/self.n_iter, global_step=self.epoch)
-
-        self.epoch += 1
-        self.n_sequences_epoch = 0
-        self.n_iter = 0
-        self.total_loss_epoch = 0
-
-    def save_checkpoint(self,
-                        checkpoint_name: str = 'checkpoint.pth'):
-        """
-        Save the current state. Only by the master process.
-        """
-        if not self.is_master:
-            return
-        mdl_to_save = self.student.module if hasattr(self.student, 'module') else self.student
-        mdl_to_save.config.save_pretrained(self.dump_path)
-        state_dict = mdl_to_save.state_dict()
-        torch.save(state_dict, os.path.join(self.dump_path, checkpoint_name))
@@ -1,105 +0,0 @@
-# coding=utf-8
-# Copyright 2019-present, the HuggingFace Inc. team and Facebook, Inc.
-#
-# 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.
-""" Adapted from PyTorch Vision (https://github.com/pytorch/vision/blob/master/references/detection/group_by_aspect_ratio.py)
-"""
-import bisect
-import copy
-from collections import defaultdict
-import numpy as np
-
-from torch.utils.data.sampler import BatchSampler, Sampler
-
-from utils import logger
-
-def _quantize(x, bins):
-    bins = copy.deepcopy(bins)
-    bins = sorted(bins)
-    quantized = list(map(lambda y: bisect.bisect_right(bins, y), x))
-    return quantized
-
-def create_lengths_groups(lengths, k=0):
-    bins = np.arange(start=3, stop=k, step=4).tolist() if k > 0 else [10]
-    groups = _quantize(lengths, bins)
-    # count number of elements per group
-    counts = np.unique(groups, return_counts=True)[1]
-    fbins = [0] + bins + [np.inf]
-    logger.info("Using {} as bins for aspect lengths quantization".format(fbins))
-    logger.info("Count of instances per bin: {}".format(counts))
-    return groups
-
-class GroupedBatchSampler(BatchSampler):
-    """
-    Wraps another sampler to yield a mini-batch of indices.
-    It enforces that the batch only contain elements from the same group.
-    It also tries to provide mini-batches which follows an ordering which is
-    as close as possible to the ordering from the original sampler.
-    Arguments:
-        sampler (Sampler): Base sampler.
-        group_ids (list[int]): If the sampler produces indices in range [0, N),
-            `group_ids` must be a list of `N` ints which contains the group id of each sample.
-            The group ids must be a continuous set of integers starting from
-            0, i.e. they must be in the range [0, num_groups).
-        batch_size (int): Size of mini-batch.
-    """
-    def __init__(self, sampler, group_ids, batch_size):
-        if not isinstance(sampler, Sampler):
-            raise ValueError(
-                "sampler should be an instance of "
-                "torch.utils.data.Sampler, but got sampler={}".format(sampler)
-            )
-        self.sampler = sampler
-        self.group_ids = group_ids
-        self.batch_size = batch_size
-
-    def __iter__(self):
-        buffer_per_group = defaultdict(list)
-        samples_per_group = defaultdict(list)
-
-        num_batches = 0
-        for idx in self.sampler:
-            group_id = self.group_ids[idx]
-            buffer_per_group[group_id].append(idx)
-            samples_per_group[group_id].append(idx)
-            if len(buffer_per_group[group_id]) == self.batch_size:
-                yield buffer_per_group[group_id] #TODO
-                num_batches += 1
-                del buffer_per_group[group_id]
-            assert len(buffer_per_group[group_id]) < self.batch_size
-
-        # now we have run out of elements that satisfy
-        # the group criteria, let's return the remaining
-        # elements so that the size of the sampler is
-        # deterministic
-        expected_num_batches = len(self)
-        num_remaining = expected_num_batches - num_batches
-        if num_remaining > 0:
-            # for the remaining batches, group the batches by similar lengths
-            batch_idx = []
-            for group_id, idxs in sorted(buffer_per_group.items(), key=lambda x: x[0]):
-                batch_idx.extend(idxs)
-                if len(batch_idx) >= self.batch_size:
-                    yield batch_idx[:self.batch_size]
-                    batch_idx = batch_idx[self.batch_size:]
-                    num_remaining -= 1
-            if len(batch_idx) > 0:
-                yield batch_idx
-                num_remaining -= 1
-        assert num_remaining == 0
-
-    def __len__(self):
-        """
-        Return the number of mini-batches rather than the number of samples.
-        """
-        return (len(self.sampler) + self.batch_size - 1) // self.batch_size
@@ -1,151 +0,0 @@
-# coding=utf-8
-# Copyright 2019-present, the HuggingFace Inc. team and Facebook, Inc.
-#
-# 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.
-""" Dataset to distilled models
-    adapted in part from Facebook, Inc XLM model (https://github.com/facebookresearch/XLM)
-"""
-import torch
-from torch.utils.data import Dataset
-
-import numpy as np
-from utils import logger
-
-class LmSeqsDataset(Dataset):
-    """Custom Dataset wrapping language modeling sequences.
-
-    Each sample will be retrieved by indexing the list of token_ids and their corresponding lengths.
-
-    Input:
-    ------
-        params: `NameSpace` parameters
-        data: `List[np.array[int]]
-    """
-
-    def __init__(self,
-                 params,
-                 data):
-        self.params = params
-
-        self.token_ids = np.array(data)
-        self.lengths = np.array([len(t) for t in data])
-
-        self.check()
-        self.remove_long_sequences()
-        self.remove_empty_sequences()
-        self.check()
-        self.print_statistics()
-
-    def __getitem__(self, index):
-        return (self.token_ids[index], self.lengths[index])
-
-    def __len__(self):
-        return len(self.lengths)
-
-    def check(self):
-        """
-        Some sanity checks
-        """
-        assert len(self.token_ids) == len(self.lengths)
-        assert all(self.lengths[i] == len(self.token_ids[i]) for i in range(len(self.lengths))) 
-
-    def remove_long_sequences(self):
-        """
-        Sequences that are too long are splitted by chunk of max_model_input_size.
-        """
-        max_len = self.params.max_model_input_size
-        indices = self.lengths > max_len
-        logger.info(f'Splitting {sum(indices)} too long sequences.')
-
-        def divide_chunks(l, n):
-            return [l[i:i + n] for i in range(0, len(l), n)]
-
-        new_tok_ids = []
-        new_lengths = []
-        if self.params.mlm:
-            cls_id, sep_id = self.params.special_tok_ids['cls_token'], self.params.special_tok_ids['sep_token']
-        else:
-            cls_id, sep_id = self.params.special_tok_ids['bos_token'], self.params.special_tok_ids['eos_token']
-
-        for seq_, len_ in zip(self.token_ids, self.lengths):
-            assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
-            if len_ <= max_len:
-                new_tok_ids.append(seq_)
-                new_lengths.append(len_)
-            else:
-                sub_seqs = []
-                for sub_s in divide_chunks(seq_, max_len-2):
-                    if sub_s[0] != cls_id:
-                        sub_s = np.insert(sub_s, 0, cls_id)
-                    if sub_s[-1] != sep_id:
-                        sub_s = np.insert(sub_s, len(sub_s), sep_id)
-                    assert len(sub_s) <= max_len
-                    assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
-                    sub_seqs.append(sub_s)
-
-                new_tok_ids.extend(sub_seqs)
-                new_lengths.extend([len(l) for l in sub_seqs])
-
-        self.token_ids = np.array(new_tok_ids)
-        self.lengths = np.array(new_lengths)
-
-    def remove_empty_sequences(self):
-        """
-        Too short sequences are simply removed. This could be tunedd.
-        """
-        init_size = len(self)
-        indices = self.lengths > 11
-        self.token_ids = self.token_ids[indices]
-        self.lengths = self.lengths[indices]
-        new_size = len(self)
-        logger.info(f'Remove {init_size - new_size} too short (<=11 tokens) sequences.')
-
-    def print_statistics(self):
-        """
-        Print some statistics on the corpus. Only the master process.
-        """
-        if not self.params.is_master:
-            return
-        logger.info(f'{len(self)} sequences')
-        # data_len = sum(self.lengths)
-        # nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
-        # logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')
-
-        # unk_idx = self.params.special_tok_ids['unk_token']
-        # nb_unkown = sum([(t==unk_idx).sum() for t in self.token_ids])
-        # logger.info(f'{nb_unkown} unknown tokens (covering {100*nb_unkown/data_len:.2f}% of the data)')
-
-    def batch_sequences(self,
-                        batch):
-        """
-        Do the padding and transform into torch.tensor.
-        """
-        token_ids = [t[0] for t in batch]
-        lengths = [t[1] for t in batch]
-        assert len(token_ids) == len(lengths)
-
-        # Max for paddings
-        max_seq_len_ = max(lengths)
-
-        # Pad token ids
-        if self.params.mlm:
-            pad_idx = self.params.special_tok_ids['pad_token']
-        else:
-            pad_idx = self.params.special_tok_ids['unk_token']
-        tk_ = [list(t.astype(int)) + [pad_idx]*(max_seq_len_-len(t)) for t in token_ids]
-        assert len(tk_) == len(token_ids)
-        assert all(len(t) == max_seq_len_ for t in tk_)
-
-        tk_t = torch.tensor(tk_)      # (bs, max_seq_len_)
-        lg_t = torch.tensor(lengths)  # (bs)
-        return tk_t, lg_t
@@ -1,6 +0,0 @@
-gitpython==3.0.2
-tensorboard>=1.14.0
-tensorboardX==1.8
-psutil==5.6.3
-scipy==1.3.1
-transformers
@@ -1,600 +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.
-""" This is the exact same script as `examples/run_squad.py` (as of 2019, October 4th) with an additional and optional step of distillation."""
-
-from __future__ import absolute_import, division, print_function
-
-import argparse
-import logging
-import os
-import random
-import glob
-
-import numpy as np
-import torch
-from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
-                              TensorDataset)
-from torch.utils.data.distributed import DistributedSampler
-import torch.nn.functional as F
-import torch.nn as nn
-
-try:
-    from torch.utils.tensorboard import SummaryWriter
-except:
-    from tensorboardX import SummaryWriter
-
-from tqdm import tqdm, trange
-
-from transformers import (WEIGHTS_NAME, BertConfig,
-                                  BertForQuestionAnswering, BertTokenizer,
-                                  XLMConfig, XLMForQuestionAnswering,
-                                  XLMTokenizer, XLNetConfig,
-                                  XLNetForQuestionAnswering,
-                                  XLNetTokenizer,
-                                  DistilBertConfig, DistilBertForQuestionAnswering, DistilBertTokenizer)
-
-from transformers import AdamW, get_linear_schedule_with_warmup
-
-from ..utils_squad import (read_squad_examples, convert_examples_to_features,
-                         RawResult, write_predictions,
-                         RawResultExtended, write_predictions_extended)
-
-# 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, main as evaluate_on_squad
-
-logger = logging.getLogger(__name__)
-
-ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) \
-                  for conf in (BertConfig, XLNetConfig, XLMConfig)), ())
-
-MODEL_CLASSES = {
-    'bert': (BertConfig, BertForQuestionAnswering, BertTokenizer),
-    'xlnet': (XLNetConfig, XLNetForQuestionAnswering, XLNetTokenizer),
-    'xlm': (XLMConfig, XLMForQuestionAnswering, XLMTokenizer),
-    'distilbert': (DistilBertConfig, DistilBertForQuestionAnswering, DistilBertTokenizer)
-}
-
-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 to_list(tensor):
-    return tensor.detach().cpu().tolist()
-
-def train(args, train_dataset, model, tokenizer, teacher=None):
-    """ 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()
-            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], 
-                      'start_positions': batch[3], 
-                      'end_positions':   batch[4]}
-            if args.model_type != 'distilbert':
-                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]})
-            outputs = model(**inputs)
-            loss, start_logits_stu, end_logits_stu = outputs
-
-            # Distillation loss
-            if teacher is not None:
-                if 'token_type_ids' not in inputs:
-                    inputs['token_type_ids'] = None if args.teacher_type == 'xlm' else batch[2]
-                with torch.no_grad():
-                    start_logits_tea, end_logits_tea = teacher(input_ids=inputs['input_ids'],
-                                                               token_type_ids=inputs['token_type_ids'],
-                                                               attention_mask=inputs['attention_mask'])
-                assert start_logits_tea.size() == start_logits_stu.size()
-                assert end_logits_tea.size() == end_logits_stu.size()
-                
-                loss_fct = nn.KLDivLoss(reduction='batchmean')
-                loss_start = loss_fct(F.log_softmax(start_logits_stu/args.temperature, dim=-1),
-                                      F.softmax(start_logits_tea/args.temperature, dim=-1)) * (args.temperature**2)
-                loss_end = loss_fct(F.log_softmax(end_logits_stu/args.temperature, dim=-1),
-                                    F.softmax(end_logits_tea/args.temperature, dim=-1)) * (args.temperature**2)
-                loss_ce = (loss_start + loss_end)/2.
-
-                loss = args.alpha_ce*loss_ce + args.alpha_squad*loss
-
-            if args.n_gpu > 1:
-                loss = loss.mean() # mean() to average on multi-gpu parallel (not distributed) training
-            if args.gradient_accumulation_steps > 1:
-                loss = loss / args.gradient_accumulation_steps
-
-            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:
-                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:
-                    # 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)
-                    tb_writer.add_scalar('lr', scheduler.get_lr()[0], global_step)
-                    tb_writer.add_scalar('loss', (tr_loss - logging_loss)/args.logging_steps, global_step)
-                    logging_loss = tr_loss
-
-                if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
-                    # Save model checkpoint
-                    output_dir = os.path.join(args.output_dir, 'checkpoint-{}'.format(global_step))
-                    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=""):
-    dataset, examples, features = load_and_cache_examples(args, tokenizer, evaluate=True, output_examples=True)
-
-    if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
-        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) if args.local_rank == -1 else DistributedSampler(dataset)
-    eval_dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
-
-    # 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 = []
-    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':
-                inputs['token_type_ids'] = None if args.model_type == 'xlm' else batch[2]  # XLM don't use segment_ids
-            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)
-            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:
-                result = RawResult(unique_id    = unique_id,
-                                   start_logits = to_list(outputs[0][i]),
-                                   end_logits   = to_list(outputs[1][i]))
-            all_results.append(result)
-
-    # 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:
-        output_null_log_odds_file = None
-
-    if args.model_type in ['xlnet', 'xlm']:
-        # XLNet uses a more complex post-processing procedure
-        write_predictions_extended(examples, features, all_results, args.n_best_size,
-                        args.max_answer_length, 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, tokenizer, args.verbose_logging)
-    else:
-        write_predictions(examples, features, all_results, args.n_best_size,
-                        args.max_answer_length, args.do_lower_case, output_prediction_file,
-                        output_nbest_file, output_null_log_odds_file, args.verbose_logging,
-                        args.version_2_with_negative, args.null_score_diff_threshold)
-
-    # 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:
-        torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache
-
-    # Load data features from cache or dataset file
-    input_file = args.predict_file if evaluate else args.train_file
-    cached_features_file = os.path.join(os.path.dirname(input_file), 'cached_{}_{}_{}'.format(
-        'dev' if evaluate else 'train',
-        list(filter(None, args.model_name_or_path.split('/'))).pop(),
-        str(args.max_seq_length)))
-    if os.path.exists(cached_features_file) and not args.overwrite_cache and not output_examples:
-        logger.info("Loading features from cached file %s", cached_features_file)
-        features = torch.load(cached_features_file)
-    else:
-        logger.info("Creating features from dataset file at %s", input_file)
-        examples = read_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)
-        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_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
-    return dataset
-
-
-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, type=str, required=True,
-                        help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()))
-    parser.add_argument("--model_name_or_path", default=None, type=str, required=True,
-                        help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS))
-    parser.add_argument("--output_dir", default=None, type=str, required=True,
-                        help="The output directory where the model checkpoints and predictions will be written.")
-
-    # Distillation parameters (optional)
-    parser.add_argument('--teacher_type', default=None, type=str,
-                        help="Teacher type. Teacher tokenizer and student (model) tokenizer must output the same tokenization. Only for distillation.")
-    parser.add_argument('--teacher_name_or_path', default=None, type=str,
-                        help="Path to the already SQuAD fine-tuned teacher model. Only for distillation.")
-    parser.add_argument('--alpha_ce', default=0.5, type=float,
-                        help="Distillation loss linear weight. Only for distillation.")
-    parser.add_argument('--alpha_squad', default=0.5, type=float,
-                        help="True SQuAD loss linear weight. Only for distillation.")
-    parser.add_argument('--temperature', default=2.0, type=float,
-                        help="Distillation temperature. Only for distillation.")
-
-    ## 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('--version_2_with_negative', action='store_true',
-                        help='If true, the SQuAD examples contain some that do not have an answer.')
-    parser.add_argument('--null_score_diff_threshold', type=float, default=0.0,
-                        help="If null_score - best_non_null is greater than the threshold predict null.")
-
-    parser.add_argument("--max_seq_length", default=384, type=int,
-                        help="The maximum total input sequence length after WordPiece tokenization. Sequences "
-                             "longer than this will be truncated, and sequences shorter than this will be padded.")
-    parser.add_argument("--doc_stride", default=128, type=int,
-                        help="When splitting up a long document into chunks, how much stride to take between chunks.")
-    parser.add_argument("--max_query_length", default=64, type=int,
-                        help="The maximum number of tokens for the question. Questions longer than this will "
-                             "be truncated to this length.")
-    parser.add_argument("--do_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("--learning_rate", default=5e-5, type=float,
-                        help="The initial learning rate for Adam.")
-    parser.add_argument('--gradient_accumulation_steps', type=int, default=1,
-                        help="Number of updates steps to accumulate before performing a backward/update pass.")
-    parser.add_argument("--weight_decay", default=0.0, type=float,
-                        help="Weight deay if we apply some.")
-    parser.add_argument("--adam_epsilon", default=1e-8, type=float,
-                        help="Epsilon for Adam optimizer.")
-    parser.add_argument("--max_grad_norm", default=1.0, type=float,
-                        help="Max gradient norm.")
-    parser.add_argument("--num_train_epochs", default=3.0, type=float,
-                        help="Total number of training epochs to perform.")
-    parser.add_argument("--max_steps", default=-1, type=int,
-                        help="If > 0: set total number of training steps to perform. Override num_train_epochs.")
-    parser.add_argument("--warmup_steps", default=0, type=int,
-                        help="Linear warmup over warmup_steps.")
-    parser.add_argument("--n_best_size", default=20, type=int,
-                        help="The total number of n-best predictions to generate in the nbest_predictions.json output file.")
-    parser.add_argument("--max_answer_length", default=30, type=int,
-                        help="The maximum length of an answer that can be generated. This is needed because the start "
-                             "and end predictions are not conditioned on one another.")
-    parser.add_argument("--verbose_logging", action='store_true',
-                        help="If true, all of the warnings related to data processing will be printed. "
-                             "A number of warnings are expected for a normal SQuAD evaluation.")
-
-    parser.add_argument('--logging_steps', type=int, default=50,
-                        help="Log every X updates steps.")
-    parser.add_argument('--save_steps', type=int, default=50,
-                        help="Save checkpoint every X updates steps.")
-    parser.add_argument("--eval_all_checkpoints", action='store_true',
-                        help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number")
-    parser.add_argument("--no_cuda", action='store_true',
-                        help="Whether not to use CUDA when available")
-    parser.add_argument('--overwrite_output_dir', action='store_true',
-                        help="Overwrite the content of the output directory")
-    parser.add_argument('--overwrite_cache', action='store_true',
-                        help="Overwrite the cached training and evaluation sets")
-    parser.add_argument('--seed', type=int, default=42,
-                        help="random seed for initialization")
-
-    parser.add_argument("--local_rank", type=int, default=-1,
-                        help="local_rank for distributed training on gpus")
-    parser.add_argument('--fp16', action='store_true',
-                        help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit")
-    parser.add_argument('--fp16_opt_level', type=str, default='O1',
-                        help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
-                             "See details at https://nvidia.github.io/apex/amp.html")
-    parser.add_argument('--server_ip', type=str, default='', help="Can be used for distant debugging.")
-    parser.add_argument('--server_port', type=str, default='', help="Can be used for distant debugging.")
-    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 = torch.cuda.device_count()
-    else:  # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
-        torch.cuda.set_device(args.local_rank)
-        device = torch.device("cuda", args.local_rank)
-        torch.distributed.init_process_group(backend='nccl')
-        args.n_gpu = 1
-    args.device = device
-
-    # Setup logging
-    logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s',
-                        datefmt = '%m/%d/%Y %H:%M:%S',
-                        level = logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
-    logger.warning("Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
-                    args.local_rank, device, args.n_gpu, bool(args.local_rank != -1), args.fp16)
-
-    # Set seed
-    set_seed(args)
-
-    # Load pretrained model and tokenizer
-    if args.local_rank not in [-1, 0]:
-        torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab
-
-    args.model_type = args.model_type.lower()
-    config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
-    config = config_class.from_pretrained(args.config_name if args.config_name else args.model_name_or_path,
-                                          cache_dir=args.cache_dir if args.cache_dir else None)
-    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.teacher_type is not None:
-        assert args.teacher_name_or_path is not None
-        assert args.alpha_ce > 0.
-        assert args.alpha_ce + args.alpha_squad > 0.
-        assert args.teacher_type != 'distilbert', "We constraint teachers not to be of type DistilBERT."
-        teacher_config_class, teacher_model_class, _ = MODEL_CLASSES[args.teacher_type]
-        teacher_config = teacher_config_class.from_pretrained(args.teacher_name_or_path,
-                                                              cache_dir=args.cache_dir if args.cache_dir else None)
-        teacher = teacher_model_class.from_pretrained(args.teacher_name_or_path,
-                                                      config=teacher_config,
-                                                      cache_dir=args.cache_dir if args.cache_dir else None)
-        teacher.to(args.device)
-    else:
-        teacher = 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, tokenizer, evaluate=False, output_examples=False)
-        global_step, tr_loss = train(args, train_dataset, model, tokenizer, teacher=teacher)
-        logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)
-
-
-    # Save the trained model and the tokenizer
-    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, 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]:
-        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 model loading logs
-
-        logger.info("Evaluate the following checkpoints: %s", checkpoints)
-
-        for checkpoint in checkpoints:
-            # Reload the model
-            global_step = checkpoint.split('-')[-1] if len(checkpoints) > 1 else ""
-            model = model_class.from_pretrained(checkpoint, cache_dir=args.cache_dir if args.cache_dir else None)
-            model.to(args.device)
-
-            # Evaluate
-            result = evaluate(args, model, tokenizer, prefix=global_step)
-
-            result = dict((k + ('_{}'.format(global_step) if global_step else ''), v) for k, v in result.items())
-            results.update(result)
-
-    logger.info("Results: {}".format(results))
-
-    return results
-
-
-if __name__ == "__main__":
-    main()
@@ -1,92 +0,0 @@
-# coding=utf-8
-# Copyright 2019-present, the HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-Preprocessing script before distillation.
-"""
-import argparse
-import pickle
-import random
-import time
-import numpy as np
-from transformers import BertTokenizer, RobertaTokenizer, GPT2Tokenizer
-import logging
-
-logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s',
-                    datefmt = '%m/%d/%Y %H:%M:%S',
-                    level = logging.INFO)
-logger = logging.getLogger(__name__)
-
-def main():
-    parser = argparse.ArgumentParser(description="Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).")
-    parser.add_argument('--file_path', type=str, default='data/dump.txt',
-                        help='The path to the data.')
-    parser.add_argument('--tokenizer_type', type=str, default='bert', choices=['bert', 'roberta', 'gpt2'])
-    parser.add_argument('--tokenizer_name', type=str, default='bert-base-uncased',
-                        help="The tokenizer to use.")
-    parser.add_argument('--dump_file', type=str, default='data/dump',
-                        help='The dump file prefix.')
-    args = parser.parse_args()
-
-
-    logger.info(f'Loading Tokenizer ({args.tokenizer_name})')
-    if args.tokenizer_type == 'bert':
-        tokenizer = BertTokenizer.from_pretrained(args.tokenizer_name)
-        bos = tokenizer.special_tokens_map['cls_token'] # `[CLS]`
-        sep = tokenizer.special_tokens_map['sep_token'] # `[SEP]`
-    elif args.tokenizer_type == 'roberta':
-        tokenizer = RobertaTokenizer.from_pretrained(args.tokenizer_name)
-        bos = tokenizer.special_tokens_map['cls_token'] # `<s>`
-        sep = tokenizer.special_tokens_map['sep_token'] # `</s>`
-    elif args.tokenizer_type == 'gpt2':
-        tokenizer = GPT2Tokenizer.from_pretrained(args.tokenizer_name)
-        bos = tokenizer.special_tokens_map['bos_token'] # `<|endoftext|>`
-        sep = tokenizer.special_tokens_map['eos_token'] # `<|endoftext|>`    
-
-    logger.info(f'Loading text from {args.file_path}')
-    with open(args.file_path, 'r', encoding='utf8') as fp:
-        data = fp.readlines()
-
-
-    logger.info(f'Start encoding')
-    logger.info(f'{len(data)} examples to process.')
-
-    rslt = []
-    iter = 0
-    interval = 10000
-    start = time.time()
-    for text in data:
-        text = f'{bos} {text.strip()} {sep}'
-        token_ids = tokenizer.encode(text, add_special_tokens=False)
-        rslt.append(token_ids)
-
-        iter += 1
-        if iter % interval == 0:
-            end = time.time()
-            logger.info(f'{iter} examples processed. - {(end-start)/interval:.2f}s/expl')
-            start = time.time()
-    logger.info('Finished binarization')
-    logger.info(f'{len(data)} examples processed.')
-
-
-    dp_file = f'{args.dump_file}.{args.tokenizer_name}.pickle'
-    rslt_ = [np.uint16(d) for d in rslt]
-    random.shuffle(rslt_)
-    logger.info(f'Dump to {dp_file}')
-    with open(dp_file, 'wb') as handle:
-        pickle.dump(rslt_, handle, protocol=pickle.HIGHEST_PROTOCOL)
-
-
-if __name__ == "__main__":
-    main()
@@ -1,89 +0,0 @@
-# coding=utf-8
-# Copyright 2019-present, the HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-Preprocessing script before training the distilled model.
-Specific to RoBERTa -> DistilRoBERTa and GPT2 -> DistilGPT2.
-"""
-from transformers import BertForMaskedLM, RobertaForMaskedLM, GPT2LMHeadModel
-import torch
-import argparse
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description="Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned Distillation")
-    parser.add_argument("--model_type", default="roberta", choices=["roberta", "gpt2"])
-    parser.add_argument("--model_name", default='roberta-large', type=str)
-    parser.add_argument("--dump_checkpoint", default='serialization_dir/tf_roberta_048131723.pth', type=str)
-    parser.add_argument("--vocab_transform", action='store_true')
-    args = parser.parse_args()
-
-
-    if args.model_type == 'roberta':
-        model = RobertaForMaskedLM.from_pretrained(args.model_name)
-        prefix = 'roberta'
-    elif args.model_type == 'gpt2':
-        model = GPT2LMHeadModel.from_pretrained(args.model_name)
-        prefix = 'transformer'
-
-    state_dict = model.state_dict()
-    compressed_sd = {}
-
-    ### Embeddings ###
-    if args.model_type == 'gpt2':
-        for param_name in ['wte.weight', 'wpe.weight']:
-            compressed_sd[f'{prefix}.{param_name}'] = state_dict[f'{prefix}.{param_name}']
-    else:
-        for w in ['word_embeddings', 'position_embeddings', 'token_type_embeddings']:
-            param_name = f'{prefix}.embeddings.{w}.weight'
-            compressed_sd[param_name] = state_dict[param_name]
-        for w in ['weight', 'bias']:
-            param_name = f'{prefix}.embeddings.LayerNorm.{w}'
-            compressed_sd[param_name] = state_dict[param_name]
-
-    ### Transformer Blocks ###
-    std_idx = 0
-    for teacher_idx in [0, 2, 4, 7, 9, 11]:
-        if args.model_type == 'gpt2':
-            for layer in ['ln_1', 'attn.c_attn', 'attn.c_proj', 'ln_2', 'mlp.c_fc', 'mlp.c_proj']:
-                for w in ['weight', 'bias']:
-                    compressed_sd[f'{prefix}.h.{std_idx}.{layer}.{w}'] = \
-                        state_dict[f'{prefix}.h.{teacher_idx}.{layer}.{w}']
-            compressed_sd[f'{prefix}.h.{std_idx}.attn.bias'] = state_dict[f'{prefix}.h.{teacher_idx}.attn.bias']
-        else:
-            for layer in ['attention.self.query', 'attention.self.key', 'attention.self.value',
-                        'attention.output.dense', 'attention.output.LayerNorm',
-                        'intermediate.dense', 'output.dense', 'output.LayerNorm']:
-                for w in ['weight', 'bias']:
-                    compressed_sd[f'{prefix}.encoder.layer.{std_idx}.{layer}.{w}'] = \
-                        state_dict[f'{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}']
-        std_idx += 1
-
-    ### Language Modeling Head ###s
-    if args.model_type == 'roberta':
-        for layer in ['lm_head.decoder.weight', 'lm_head.bias']:
-            compressed_sd[f'{layer}'] = state_dict[f'{layer}']
-        if args.vocab_transform:
-            for w in ['weight', 'bias']:
-                compressed_sd[f'lm_head.dense.{w}'] = state_dict[f'lm_head.dense.{w}']
-                compressed_sd[f'lm_head.layer_norm.{w}'] = state_dict[f'lm_head.layer_norm.{w}']
-    elif args.model_type == 'gpt2':
-        for w in ['weight', 'bias']:
-            compressed_sd[f'{prefix}.ln_f.{w}'] = state_dict[f'{prefix}.ln_f.{w}']
-        compressed_sd[f'lm_head.weight'] = state_dict[f'lm_head.weight']
-
-    print(f'N layers selected for distillation: {std_idx}')
-    print(f'Number of params transfered for distillation: {len(compressed_sd.keys())}')
-
-    print(f'Save transfered checkpoint to {args.dump_checkpoint}.')
-    torch.save(compressed_sd, args.dump_checkpoint)
@@ -1,82 +0,0 @@
-# coding=utf-8
-# Copyright 2019-present, the HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-Preprocessing script before training DistilBERT.
-Specific to BERT -> DistilBERT.
-"""
-from transformers import BertForMaskedLM, RobertaForMaskedLM
-import torch
-import argparse
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description="Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned Distillation")
-    parser.add_argument("--model_type", default="bert", choices=["bert"])
-    parser.add_argument("--model_name", default='bert-base-uncased', type=str)
-    parser.add_argument("--dump_checkpoint", default='serialization_dir/tf_bert-base-uncased_0247911.pth', type=str)
-    parser.add_argument("--vocab_transform", action='store_true')
-    args = parser.parse_args()
-
-
-    if args.model_type == 'bert':
-        model = BertForMaskedLM.from_pretrained(args.model_name)
-        prefix = 'bert'
-    else:
-        raise ValueError(f'args.model_type should be "bert".')
-
-    state_dict = model.state_dict()
-    compressed_sd = {}
-
-    for w in ['word_embeddings', 'position_embeddings']:
-        compressed_sd[f'distilbert.embeddings.{w}.weight'] = \
-            state_dict[f'{prefix}.embeddings.{w}.weight']
-    for w in ['weight', 'bias']:
-        compressed_sd[f'distilbert.embeddings.LayerNorm.{w}'] = \
-            state_dict[f'{prefix}.embeddings.LayerNorm.{w}']
-
-    std_idx = 0
-    for teacher_idx in [0, 2, 4, 7, 9, 11]:
-        for w in ['weight', 'bias']:
-            compressed_sd[f'distilbert.transformer.layer.{std_idx}.attention.q_lin.{w}'] = \
-                state_dict[f'{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}']
-            compressed_sd[f'distilbert.transformer.layer.{std_idx}.attention.k_lin.{w}'] = \
-                state_dict[f'{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}']
-            compressed_sd[f'distilbert.transformer.layer.{std_idx}.attention.v_lin.{w}'] = \
-                state_dict[f'{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}']
-
-            compressed_sd[f'distilbert.transformer.layer.{std_idx}.attention.out_lin.{w}'] = \
-                state_dict[f'{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}']
-            compressed_sd[f'distilbert.transformer.layer.{std_idx}.sa_layer_norm.{w}'] = \
-                state_dict[f'{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}']
-
-            compressed_sd[f'distilbert.transformer.layer.{std_idx}.ffn.lin1.{w}'] = \
-                state_dict[f'{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}']
-            compressed_sd[f'distilbert.transformer.layer.{std_idx}.ffn.lin2.{w}'] = \
-                state_dict[f'{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}']
-            compressed_sd[f'distilbert.transformer.layer.{std_idx}.output_layer_norm.{w}'] = \
-                state_dict[f'{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}']
-        std_idx += 1
-
-    compressed_sd[f'vocab_projector.weight'] = state_dict[f'cls.predictions.decoder.weight']
-    compressed_sd[f'vocab_projector.bias'] = state_dict[f'cls.predictions.bias']
-    if args.vocab_transform:
-        for w in ['weight', 'bias']:
-            compressed_sd[f'vocab_transform.{w}'] = state_dict[f'cls.predictions.transform.dense.{w}']
-            compressed_sd[f'vocab_layer_norm.{w}'] = state_dict[f'cls.predictions.transform.LayerNorm.{w}']
-
-    print(f'N layers selected for distillation: {std_idx}')
-    print(f'Number of params transfered for distillation: {len(compressed_sd.keys())}')
-
-    print(f'Save transfered checkpoint to {args.dump_checkpoint}.')
-    torch.save(compressed_sd, args.dump_checkpoint)
@@ -1,51 +0,0 @@
-# coding=utf-8
-# Copyright 2019-present, the HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-Preprocessing script before training the distilled model.
-"""
-from collections import Counter
-import argparse
-import pickle
-import logging
-
-logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s',
-                    datefmt = '%m/%d/%Y %H:%M:%S',
-                    level = logging.INFO)
-logger = logging.getLogger(__name__)
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description="Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)")
-    parser.add_argument("--data_file", type=str, default="data/dump.bert-base-uncased.pickle",
-                        help="The binarized dataset.")
-    parser.add_argument("--token_counts_dump", type=str, default="data/token_counts.bert-base-uncased.pickle",
-                        help="The dump file.")
-    parser.add_argument("--vocab_size", default=30522, type=int)
-    args = parser.parse_args()
-
-    logger.info(f'Loading data from {args.data_file}')
-    with open(args.data_file, 'rb') as fp:
-        data = pickle.load(fp)
-
-    logger.info('Counting occurences for MLM.')
-    counter = Counter()
-    for tk_ids in data:
-        counter.update(tk_ids)
-    counts = [0]*args.vocab_size
-    for k, v in counter.items():
-        counts[k] = v
-
-    logger.info(f'Dump to {args.token_counts_dump}')
-    with open(args.token_counts_dump, 'wb') as handle:
-        pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)
@@ -1,290 +0,0 @@
-# coding=utf-8
-# Copyright 2019-present, the HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-Training the distilled model.
-Supported architectures include: BERT -> DistilBERT, RoBERTa -> DistilRoBERTa, GPT2 -> DistilGPT2.
-"""
-import os
-import argparse
-import pickle
-import json
-import shutil
-import numpy as np
-import torch
-
-from transformers import BertConfig, BertForMaskedLM, BertTokenizer
-from transformers import RobertaConfig, RobertaForMaskedLM, RobertaTokenizer
-from transformers import DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer
-from transformers import GPT2Config, GPT2LMHeadModel, GPT2Tokenizer
-
-from distiller import Distiller
-from utils import git_log, logger, init_gpu_params, set_seed
-from lm_seqs_dataset import LmSeqsDataset
-
-
-MODEL_CLASSES = {
-    'distilbert': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
-    'roberta': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
-    'bert': (BertConfig, BertForMaskedLM, BertTokenizer),
-    'gpt2': (GPT2Config, GPT2LMHeadModel, GPT2Tokenizer)
-}
-
-def sanity_checks(args):
-    """
-    A bunch of args sanity checks to perform even starting...
-    """
-    assert (args.mlm and args.alpha_mlm > 0.) or (not args.mlm and args.alpha_mlm == 0.)
-    assert (args.alpha_mlm > 0. and args.alpha_clm == 0.) or (args.alpha_mlm == 0. and args.alpha_clm > 0.)
-    if args.mlm:
-        assert os.path.isfile(args.token_counts)
-        assert (args.student_type in ['roberta', 'distilbert']) and (args.teacher_type in ['roberta', 'bert'])
-    else:
-        assert (args.student_type in ['gpt2']) and (args.teacher_type in ['gpt2'])
-
-    assert args.teacher_type == args.student_type or (args.student_type=='distilbert' and args.teacher_type=='bert')
-    assert os.path.isfile(args.student_config)
-    if args.student_pretrained_weights is not None:
-        assert os.path.isfile(args.student_pretrained_weights)
-
-    if args.freeze_token_type_embds: assert args.student_type in ['roberta']
-
-    assert args.alpha_ce >= 0.
-    assert args.alpha_mlm >= 0.
-    assert args.alpha_clm >= 0.
-    assert args.alpha_mse >= 0.
-    assert args.alpha_cos >= 0.
-    assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.
-
-def freeze_pos_embeddings(student, args):
-    if args.student_type == 'roberta':
-        student.roberta.embeddings.position_embeddings.weight.requires_grad = False
-    elif args.student_type == 'gpt2':
-        student.transformer.wpe.weight.requires_grad = False
-
-def freeze_token_type_embeddings(student, args):
-    if args.student_type == 'roberta':
-        student.roberta.embeddings.token_type_embeddings.weight.requires_grad = False
-
-def main():
-    parser = argparse.ArgumentParser(description="Training")
-    parser.add_argument("--force", action='store_true',
-                        help="Overwrite dump_path if it already exists.")
-
-    parser.add_argument("--dump_path", type=str, required=True,
-                        help="The output directory (log, checkpoints, parameters, etc.)")
-    parser.add_argument("--data_file", type=str, required=True,
-                        help="The binarized file (tokenized + tokens_to_ids) and grouped by sequence.")
-
-    parser.add_argument("--student_type", type=str, choices=["distilbert", "roberta", "gpt2"], required=True,
-                        help="The student type (DistilBERT, RoBERTa).")
-    parser.add_argument("--student_config", type=str, required=True,
-                        help="Path to the student configuration.")
-    parser.add_argument("--student_pretrained_weights", default=None, type=str,
-                        help="Load student initialization checkpoint.")
-
-    parser.add_argument("--teacher_type", choices=["bert", "roberta", "gpt2"], required=True,
-                        help="Teacher type (BERT, RoBERTa).")
-    parser.add_argument("--teacher_name", type=str, required=True,
-                        help="The teacher model.")
-
-    parser.add_argument("--temperature", default=2., type=float,
-                        help="Temperature for the softmax temperature.")
-    parser.add_argument("--alpha_ce", default=0.5, type=float,
-                        help="Linear weight for the distillation loss. Must be >=0.")
-    parser.add_argument("--alpha_mlm", default=0.0, type=float,
-                        help="Linear weight for the MLM loss. Must be >=0. Should be used in coonjunction with `mlm` flag.")
-    parser.add_argument("--alpha_clm", default=0.5, type=float,
-                        help="Linear weight for the CLM loss. Must be >=0.")
-    parser.add_argument("--alpha_mse", default=0.0, type=float,
-                        help="Linear weight of the MSE loss. Must be >=0.")
-    parser.add_argument("--alpha_cos", default=0.0, type=float,
-                        help="Linear weight of the cosine embedding loss. Must be >=0.")
-
-    parser.add_argument("--mlm", action="store_true",
-                        help="The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM.")
-    parser.add_argument("--mlm_mask_prop", default=0.15, type=float,
-                        help="Proportion of tokens for which we need to make a prediction.")
-    parser.add_argument("--word_mask", default=0.8, type=float,
-                        help="Proportion of tokens to mask out.")
-    parser.add_argument("--word_keep", default=0.1, type=float,
-                        help="Proportion of tokens to keep.")
-    parser.add_argument("--word_rand", default=0.1, type=float,
-                        help="Proportion of tokens to randomly replace.")
-    parser.add_argument("--mlm_smoothing", default=0.7, type=float,
-                        help="Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).")
-    parser.add_argument("--token_counts", type=str,
-                        help="The token counts in the data_file for MLM.")
-
-    parser.add_argument("--restrict_ce_to_mask", action='store_true',
-                        help="If true, compute the distilation loss only the [MLM] prediction distribution.")
-    parser.add_argument("--freeze_pos_embs", action="store_true",
-                        help="Freeze positional embeddings during distillation. For student_type in ['roberta', 'gpt2'] only.")
-    parser.add_argument("--freeze_token_type_embds", action="store_true",
-                        help="Freeze token type embeddings during distillation if existent. For student_type in ['roberta'] only.")
-
-    parser.add_argument("--n_epoch", type=int, default=3,
-                        help="Number of pass on the whole dataset.")
-    parser.add_argument("--batch_size", type=int, default=5,
-                        help="Batch size (for each process).")
-    parser.add_argument("--group_by_size", action='store_false',
-                        help="If true, group sequences that have similar length into the same batch. Default is true.")
-
-    parser.add_argument("--gradient_accumulation_steps", type=int, default=50,
-                        help="Gradient accumulation for larger training batches.")
-    parser.add_argument("--warmup_prop", default=0.05, type=float,
-                        help="Linear warmup proportion.")
-    parser.add_argument("--weight_decay", default=0.0, type=float,
-                        help="Weight deay if we apply some.")
-    parser.add_argument("--learning_rate", default=5e-4, type=float,
-                        help="The initial learning rate for Adam.")
-    parser.add_argument("--adam_epsilon", default=1e-6, type=float,
-                        help="Epsilon for Adam optimizer.")
-    parser.add_argument("--max_grad_norm", default=5.0, type=float,
-                        help="Max gradient norm.")
-    parser.add_argument("--initializer_range", default=0.02, type=float,
-                        help="Random initialization range.")
-
-    parser.add_argument('--fp16', action='store_true',
-                        help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit")
-    parser.add_argument('--fp16_opt_level', type=str, default='O1',
-                        help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
-                             "See details at https://nvidia.github.io/apex/amp.html")
-    parser.add_argument("--n_gpu", type=int, default=1,
-                        help="Number of GPUs in the node.")
-    parser.add_argument("--local_rank", type=int, default=-1,
-                        help="Distributed training - Local rank")
-    parser.add_argument("--seed", type=int, default=56,
-                        help="Random seed")
-
-    parser.add_argument("--log_interval", type=int, default=500,
-                        help="Tensorboard logging interval.")
-    parser.add_argument("--checkpoint_interval", type=int, default=4000,
-                        help="Checkpoint interval.")
-    args = parser.parse_args()
-    sanity_checks(args)
-
-
-    ## ARGS ##
-    init_gpu_params(args)
-    set_seed(args)
-    if args.is_master:
-        if os.path.exists(args.dump_path):
-            if not args.force:
-                raise ValueError(f'Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite it'
-                                   'Use `--force` if you want to overwrite it')
-            else:
-                shutil.rmtree(args.dump_path)
-
-        if not os.path.exists(args.dump_path):
-            os.makedirs(args.dump_path)
-        logger.info(f'Experiment will be dumped and logged in {args.dump_path}')
-
-
-        ### SAVE PARAMS ###
-        logger.info(f'Param: {args}')
-        with open(os.path.join(args.dump_path, 'parameters.json'), 'w') as f:
-            json.dump(vars(args), f, indent=4)
-        git_log(args.dump_path)
-
-    student_config_class, student_model_class, _ = MODEL_CLASSES[args.student_type]
-    teacher_config_class, teacher_model_class, teacher_tokenizer_class = MODEL_CLASSES[args.teacher_type]
-
-    ### TOKENIZER ###
-    tokenizer = teacher_tokenizer_class.from_pretrained(args.teacher_name)
-    special_tok_ids = {}
-    for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
-        idx = tokenizer.all_special_tokens.index(tok_symbol)
-        special_tok_ids[tok_name] = tokenizer.all_special_ids[idx]
-    logger.info(f'Special tokens {special_tok_ids}')
-    args.special_tok_ids = special_tok_ids
-    args.max_model_input_size = tokenizer.max_model_input_sizes[args.teacher_name]
-
-
-    ## DATA LOADER ##
-    logger.info(f'Loading data from {args.data_file}')
-    with open(args.data_file, 'rb') as fp:
-        data = pickle.load(fp)
-
-
-    if args.mlm:
-        logger.info(f'Loading token counts from {args.token_counts} (already pre-computed)')
-        with open(args.token_counts, 'rb') as fp:
-            counts = pickle.load(fp)
-        
-        token_probs = np.maximum(counts, 1) ** -args.mlm_smoothing
-        for idx in special_tok_ids.values():
-            token_probs[idx] = 0.  # do not predict special tokens
-        token_probs = torch.from_numpy(token_probs)
-    else:
-        token_probs = None
-
-
-    train_lm_seq_dataset = LmSeqsDataset(params=args, data=data)
-    logger.info(f'Data loader created.')
-
-
-    ## STUDENT ##
-    logger.info(f'Loading student config from {args.student_config}')
-    stu_architecture_config = student_config_class.from_pretrained(args.student_config)
-    stu_architecture_config.output_hidden_states = True
-
-    if args.student_pretrained_weights is not None:
-        logger.info(f'Loading pretrained weights from {args.student_pretrained_weights}')
-        student = student_model_class.from_pretrained(args.student_pretrained_weights,
-                                                      config=stu_architecture_config)
-    else:
-        student = student_model_class(stu_architecture_config)
-
-
-    if args.n_gpu > 0:
-        student.to(f'cuda:{args.local_rank}')
-    logger.info(f'Student loaded.')
-
-
-    ## TEACHER ##
-    teacher = teacher_model_class.from_pretrained(args.teacher_name, output_hidden_states=True)
-    if args.n_gpu > 0:
-        teacher.to(f'cuda:{args.local_rank}')
-    logger.info(f'Teacher loaded from {args.teacher_name}.')
-
-
-    ## FREEZING ##
-    if args.freeze_pos_embs:
-        freeze_pos_embeddings(student, args)
-    if args.freeze_token_type_embds:
-        freeze_token_type_embeddings(student, args)
-
-
-    ## SANITY CHECKS ##
-    assert student.config.vocab_size == teacher.config.vocab_size
-    assert student.config.hidden_size == teacher.config.hidden_size
-    assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
-    if args.mlm:
-        assert token_probs.size(0) == stu_architecture_config.vocab_size
-
-
-    ## DISTILLER ##
-    torch.cuda.empty_cache()
-    distiller = Distiller(params=args,
-                          dataset=train_lm_seq_dataset,
-                          token_probs=token_probs,
-                          student=student,
-                          teacher=teacher)
-    distiller.train()
-    logger.info("Let's go get some drinks.")
-
-
-if __name__ == "__main__":
-    main()
@@ -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": 30522
-  }
-  
@@ -1,10 +0,0 @@
-{
-	"initializer_range": 0.02,
-	"layer_norm_epsilon": 0.00001,
-	"n_ctx": 1024,
-	"n_embd": 768,
-	"n_head": 12,
-	"n_layer": 6,
-	"n_positions": 1024,
-	"vocab_size": 50257
-}
@@ -1,129 +0,0 @@
-# coding=utf-8
-# Copyright 2019-present, the HuggingFace Inc. team and Facebook, Inc.
-#
-# 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.
-""" Utils to train DistilBERT
-    adapted in part from Facebook, Inc XLM model (https://github.com/facebookresearch/XLM)
-"""
-import git
-import json
-import os
-import socket
-import torch
-import numpy as np
-
-import logging
-logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s',
-                    datefmt = '%m/%d/%Y %H:%M:%S',
-                    level = logging.INFO)
-logger = logging.getLogger(__name__)
-
-
-def git_log(folder_path: str):
-    """
-    Log commit info.
-    """
-    repo = git.Repo(search_parent_directories=True)
-    repo_infos = {
-        'repo_id': str(repo),
-        'repo_sha': str(repo.head.object.hexsha),
-        'repo_branch': str(repo.active_branch)
-    }
-
-    with open(os.path.join(folder_path, 'git_log.json'), 'w') as f:
-        json.dump(repo_infos, f, indent=4)
-
-
-def init_gpu_params(params):
-    """
-    Handle single and multi-GPU / multi-node.
-    """
-    if params.n_gpu <= 0:
-        params.local_rank = 0
-        params.master_port = -1
-        params.is_master = True
-        params.multi_gpu = False
-        return
-
-    assert torch.cuda.is_available()
-
-    logger.info('Initializing GPUs')
-    if params.n_gpu > 1:
-        assert params.local_rank != -1
-
-        params.world_size = int(os.environ['WORLD_SIZE'])
-        params.n_gpu_per_node = int(os.environ['N_GPU_NODE'])
-        params.global_rank = int(os.environ['RANK'])
-
-        # number of nodes / node ID
-        params.n_nodes = params.world_size // params.n_gpu_per_node
-        params.node_id = params.global_rank // params.n_gpu_per_node
-        params.multi_gpu = True
-
-        assert params.n_nodes == int(os.environ['N_NODES'])
-        assert params.node_id == int(os.environ['NODE_RANK'])
-
-    # local job (single GPU)
-    else:
-        assert params.local_rank == -1
-
-        params.n_nodes = 1
-        params.node_id = 0
-        params.local_rank = 0
-        params.global_rank = 0
-        params.world_size = 1
-        params.n_gpu_per_node = 1
-        params.multi_gpu = False
-
-    # sanity checks
-    assert params.n_nodes >= 1
-    assert 0 <= params.node_id < params.n_nodes
-    assert 0 <= params.local_rank <= params.global_rank < params.world_size
-    assert params.world_size == params.n_nodes * params.n_gpu_per_node
-
-    # define whether this is the master process / if we are in multi-node distributed mode
-    params.is_master = params.node_id == 0 and params.local_rank == 0
-    params.multi_node = params.n_nodes > 1
-
-    # summary
-    PREFIX = f"--- Global rank: {params.global_rank} - "
-    logger.info(PREFIX + "Number of nodes: %i" % params.n_nodes)
-    logger.info(PREFIX + "Node ID        : %i" % params.node_id)
-    logger.info(PREFIX + "Local rank     : %i" % params.local_rank)
-    logger.info(PREFIX + "World size     : %i" % params.world_size)
-    logger.info(PREFIX + "GPUs per node  : %i" % params.n_gpu_per_node)
-    logger.info(PREFIX + "Master         : %s" % str(params.is_master))
-    logger.info(PREFIX + "Multi-node     : %s" % str(params.multi_node))
-    logger.info(PREFIX + "Multi-GPU      : %s" % str(params.multi_gpu))
-    logger.info(PREFIX + "Hostname       : %s" % socket.gethostname())
-
-    # set GPU device
-    torch.cuda.set_device(params.local_rank)
-
-    # initialize multi-GPU
-    if params.multi_gpu:
-        logger.info("Initializing PyTorch distributed")
-        torch.distributed.init_process_group(
-            init_method='env://',
-            backend='nccl',
-        )
-
-
-def set_seed(args):
-    """
-    Set the random seed.
-    """
-    np.random.seed(args.seed)
-    torch.manual_seed(args.seed)
-    if args.n_gpu > 0:
-        torch.cuda.manual_seed_all(args.seed)
@@ -0,0 +1,298 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Extract pre-computed feature vectors from a PyTorch BERT model."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+import collections
+import logging
+import json
+import re
+
+import torch
+from torch.utils.data import TensorDataset, DataLoader, SequentialSampler
+from torch.utils.data.distributed import DistributedSampler
+
+from pytorch_pretrained_bert.tokenization import BertTokenizer
+from pytorch_pretrained_bert.modeling import BertModel
+
+logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s', 
+                    datefmt = '%m/%d/%Y %H:%M:%S',
+                    level = logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+class InputExample(object):
+
+    def __init__(self, unique_id, text_a, text_b):
+        self.unique_id = unique_id
+        self.text_a = text_a
+        self.text_b = text_b
+
+
+class InputFeatures(object):
+    """A single set of features of data."""
+
+    def __init__(self, unique_id, tokens, input_ids, input_mask, input_type_ids):
+        self.unique_id = unique_id
+        self.tokens = tokens
+        self.input_ids = input_ids
+        self.input_mask = input_mask
+        self.input_type_ids = input_type_ids
+
+
+def convert_examples_to_features(examples, seq_length, tokenizer):
+    """Loads a data file into a list of `InputBatch`s."""
+
+    features = []
+    for (ex_index, example) in enumerate(examples):
+        tokens_a = tokenizer.tokenize(example.text_a)
+
+        tokens_b = None
+        if example.text_b:
+            tokens_b = tokenizer.tokenize(example.text_b)
+
+        if tokens_b:
+            # Modifies `tokens_a` and `tokens_b` in place so that the total
+            # length is less than the specified length.
+            # Account for [CLS], [SEP], [SEP] with "- 3"
+            _truncate_seq_pair(tokens_a, tokens_b, seq_length - 3)
+        else:
+            # Account for [CLS] and [SEP] with "- 2"
+            if len(tokens_a) > seq_length - 2:
+                tokens_a = tokens_a[0:(seq_length - 2)]
+
+        # The convention in BERT is:
+        # (a) For sequence pairs:
+        #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
+        #  type_ids: 0   0  0    0    0     0       0 0    1  1  1  1   1 1
+        # (b) For single sequences:
+        #  tokens:   [CLS] the dog is hairy . [SEP]
+        #  type_ids: 0   0   0   0  0     0 0
+        #
+        # Where "type_ids" are used to indicate whether this is the first
+        # sequence or the second sequence. The embedding vectors for `type=0` and
+        # `type=1` were learned during pre-training and are added to the wordpiece
+        # embedding vector (and position vector). This is not *strictly* necessary
+        # since the [SEP] token unambigiously separates the sequences, but it makes
+        # it easier for the model to learn the concept of sequences.
+        #
+        # For classification tasks, the first vector (corresponding to [CLS]) is
+        # used as as the "sentence vector". Note that this only makes sense because
+        # the entire model is fine-tuned.
+        tokens = []
+        input_type_ids = []
+        tokens.append("[CLS]")
+        input_type_ids.append(0)
+        for token in tokens_a:
+            tokens.append(token)
+            input_type_ids.append(0)
+        tokens.append("[SEP]")
+        input_type_ids.append(0)
+
+        if tokens_b:
+            for token in tokens_b:
+                tokens.append(token)
+                input_type_ids.append(1)
+            tokens.append("[SEP]")
+            input_type_ids.append(1)
+
+        input_ids = tokenizer.convert_tokens_to_ids(tokens)
+
+        # The mask has 1 for real tokens and 0 for padding tokens. Only real
+        # tokens are attended to.
+        input_mask = [1] * len(input_ids)
+
+        # Zero-pad up to the sequence length.
+        while len(input_ids) < seq_length:
+            input_ids.append(0)
+            input_mask.append(0)
+            input_type_ids.append(0)
+
+        assert len(input_ids) == seq_length
+        assert len(input_mask) == seq_length
+        assert len(input_type_ids) == seq_length
+
+        if ex_index < 5:
+            logger.info("*** Example ***")
+            logger.info("unique_id: %s" % (example.unique_id))
+            logger.info("tokens: %s" % " ".join([str(x) for x in tokens]))
+            logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
+            logger.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
+            logger.info(
+                "input_type_ids: %s" % " ".join([str(x) for x in input_type_ids]))
+
+        features.append(
+            InputFeatures(
+                unique_id=example.unique_id,
+                tokens=tokens,
+                input_ids=input_ids,
+                input_mask=input_mask,
+                input_type_ids=input_type_ids))
+    return features
+
+
+def _truncate_seq_pair(tokens_a, tokens_b, max_length):
+    """Truncates a sequence pair in place to the maximum length."""
+
+    # This is a simple heuristic which will always truncate the longer sequence
+    # one token at a time. This makes more sense than truncating an equal percent
+    # of tokens from each, since if one sequence is very short then each token
+    # that's truncated likely contains more information than a longer sequence.
+    while True:
+        total_length = len(tokens_a) + len(tokens_b)
+        if total_length <= max_length:
+            break
+        if len(tokens_a) > len(tokens_b):
+            tokens_a.pop()
+        else:
+            tokens_b.pop()
+
+
+def read_examples(input_file):
+    """Read a list of `InputExample`s from an input file."""
+    examples = []
+    unique_id = 0
+    with open(input_file, "r", encoding='utf-8') as reader:
+        while True:
+            line = reader.readline()
+            if not line:
+                break
+            line = line.strip()
+            text_a = None
+            text_b = None
+            m = re.match(r"^(.*) \|\|\| (.*)$", line)
+            if m is None:
+                text_a = line
+            else:
+                text_a = m.group(1)
+                text_b = m.group(2)
+            examples.append(
+                InputExample(unique_id=unique_id, text_a=text_a, text_b=text_b))
+            unique_id += 1
+    return examples
+
+
+def main():
+    parser = argparse.ArgumentParser()
+
+    ## Required parameters
+    parser.add_argument("--input_file", default=None, type=str, required=True)
+    parser.add_argument("--output_file", default=None, type=str, required=True)
+    parser.add_argument("--bert_model", default=None, type=str, required=True,
+                        help="Bert pre-trained model selected in the list: bert-base-uncased, "
+                             "bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese.")
+
+    ## Other parameters
+    parser.add_argument("--do_lower_case", default=False, action='store_true', help="Set this flag if you are using an uncased model.")
+    parser.add_argument("--layers", default="-1,-2,-3,-4", type=str)
+    parser.add_argument("--max_seq_length", default=128, type=int,
+                        help="The maximum total input sequence length after WordPiece tokenization. Sequences longer "
+                            "than this will be truncated, and sequences shorter than this will be padded.")
+    parser.add_argument("--batch_size", default=32, type=int, help="Batch size for predictions.")
+    parser.add_argument("--local_rank",
+                        type=int,
+                        default=-1,
+                        help = "local_rank for distributed training on gpus")
+    parser.add_argument("--no_cuda",
+                        default=False,
+                        action='store_true',
+                        help="Whether not to use CUDA when available")
+
+    args = parser.parse_args()
+
+    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")
+        n_gpu = torch.cuda.device_count()
+    else:
+        device = torch.device("cuda", args.local_rank)
+        n_gpu = 1
+        # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
+        torch.distributed.init_process_group(backend='nccl')
+    logger.info("device: {} n_gpu: {} distributed training: {}".format(device, n_gpu, bool(args.local_rank != -1)))
+
+    layer_indexes = [int(x) for x in args.layers.split(",")]
+
+    tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case)
+
+    examples = read_examples(args.input_file)
+
+    features = convert_examples_to_features(
+        examples=examples, seq_length=args.max_seq_length, tokenizer=tokenizer)
+
+    unique_id_to_feature = {}
+    for feature in features:
+        unique_id_to_feature[feature.unique_id] = feature
+
+    model = BertModel.from_pretrained(args.bert_model)
+    model.to(device)
+
+    if args.local_rank != -1:
+        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank],
+                                                          output_device=args.local_rank)
+    elif n_gpu > 1:
+        model = torch.nn.DataParallel(model)
+
+    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_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long)
+
+    eval_data = TensorDataset(all_input_ids, all_input_mask, all_example_index)
+    if args.local_rank == -1:
+        eval_sampler = SequentialSampler(eval_data)
+    else:
+        eval_sampler = DistributedSampler(eval_data)
+    eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.batch_size)
+
+    model.eval()
+    with open(args.output_file, "w", encoding='utf-8') as writer:
+        for input_ids, input_mask, example_indices in eval_dataloader:
+            input_ids = input_ids.to(device)
+            input_mask = input_mask.to(device)
+
+            all_encoder_layers, _ = model(input_ids, token_type_ids=None, attention_mask=input_mask)
+            all_encoder_layers = all_encoder_layers
+
+            for b, example_index in enumerate(example_indices):
+                feature = features[example_index.item()]
+                unique_id = int(feature.unique_id)
+                # feature = unique_id_to_feature[unique_id]
+                output_json = collections.OrderedDict()
+                output_json["linex_index"] = unique_id
+                all_out_features = []
+                for (i, token) in enumerate(feature.tokens):
+                    all_layers = []
+                    for (j, layer_index) in enumerate(layer_indexes):
+                        layer_output = all_encoder_layers[int(layer_index)].detach().cpu().numpy()
+                        layer_output = layer_output[b]
+                        layers = collections.OrderedDict()
+                        layers["index"] = layer_index
+                        layers["values"] = [
+                            round(x.item(), 6) for x in layer_output[i]
+                        ]
+                        all_layers.append(layers)
+                    out_features = collections.OrderedDict()
+                    out_features["token"] = token
+                    out_features["layers"] = all_layers
+                    all_out_features.append(out_features)
+                output_json["features"] = all_out_features
+                writer.write(json.dumps(output_json) + "\n")
+
+
+if __name__ == "__main__":
+    main()
@@ -1,54 +0,0 @@
-# Plug and Play Language Models: a Simple Approach to Controlled Text Generation
-
-Authors: [Sumanth Dathathri](https://dathath.github.io/), [Andrea Madotto](https://andreamad8.github.io/), Janice Lan, Jane Hung, Eric Frank, [Piero Molino](https://w4nderlu.st/), [Jason Yosinski](http://yosinski.com/), and [Rosanne Liu](http://www.rosanneliu.com/)
-
-This folder contains the original code used to run the Plug and Play Language Model (PPLM).
-
-Paper link: https://arxiv.org/abs/1912.02164
-
-Blog link: https://eng.uber.com/pplm
-
-Please check out the repo under uber-research for more information: https://github.com/uber-research/PPLM
-
-
-## Setup
-
-```bash
-git clone https://github.com/huggingface/transformers && cd transformers
-pip install [--editable] .
-pip install nltk torchtext # additional requirements.
-cd examples/pplm
-```
-
-## PPLM-BoW 
-
-### Example command for bag-of-words control
-
-```bash
-python run_pplm.py -B military --cond_text "The potato" --length 50 --gamma 1.5 --num_iterations 3 --num_samples 10 --stepsize 0.03 --window_length 5 --kl_scale 0.01 --gm_scale 0.99 --colorama --sample
-```
-
-### Tuning hyperparameters for bag-of-words control
-
-1. Increase `--stepsize` to intensify topic control, and decrease its value to soften the control. `--stepsize 0` recovers the original uncontrolled GPT-2 model. 
-
-2. If the language being generated is repetitive (For e.g. "science science experiment experiment"), there are several options to consider: </br>
-	a) Reduce the `--stepsize` </br>
-	b) Increase `--kl_scale` (the KL-loss coefficient) or decrease `--gm_scale` (the gm-scaling term) </br>
-	c) Add `--grad-length xx` where xx is an (integer <= length, e.g. `--grad-length 30`).</br>
-
-
-## PPLM-Discrim
-
-### Example command for discriminator based sentiment control
-
-```bash
-python run_pplm.py -D sentiment --class_label 2 --cond_text "My dog died" --length 50 --gamma 1.0 --num_iterations 10 --num_samples 10 --stepsize 0.04 --kl_scale 0.01 --gm_scale 0.95 --sample
-```
-
-### Tuning hyperparameters for discriminator control
-
-1. Increase `--stepsize` to intensify topic control, and decrease its value to soften the control. `--stepsize 0` recovers the original uncontrolled GPT-2 model. 
-
-2. Use `--class_label 3` for negative, and `--class_label 2` for positive
-
@@ -1,18 +0,0 @@
-import torch
-
-class ClassificationHead(torch.nn.Module):
-    """Classification Head for  transformer encoders"""
-
-    def __init__(self, class_size, embed_size):
-        super(ClassificationHead, self).__init__()
-        self.class_size = class_size
-        self.embed_size = embed_size
-        # self.mlp1 = torch.nn.Linear(embed_size, embed_size)
-        # self.mlp2 = (torch.nn.Linear(embed_size, class_size))
-        self.mlp = torch.nn.Linear(embed_size, class_size)
-
-    def forward(self, hidden_state):
-        # hidden_state = F.relu(self.mlp1(hidden_state))
-        # hidden_state = self.mlp2(hidden_state)
-        logits = self.mlp(hidden_state)
-        return logits
@@ -1,879 +0,0 @@
-#! /usr/bin/env python3
-# coding=utf-8
-
-#Copyright (c) 2019 Uber Technologies, Inc.
-#
-#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.
-
-"""
-Example command with bag of words:
-python examples/run_pplm.py -B space --cond_text "The president" --length 100 --gamma 1.5 --num_iterations 3 --num_samples 10 --stepsize 0.01 --window_length 5 --kl_scale 0.01 --gm_scale 0.95
-
-Example command with discriminator:
-python examples/run_pplm.py -D sentiment --class_label 3 --cond_text "The lake" --length 10 --gamma 1.0 --num_iterations 30 --num_samples 10 --stepsize 0.01 --kl_scale 0.01 --gm_scale 0.95
-"""
-
-import argparse
-import json
-from operator import add
-from typing import List, Optional, Tuple, Union
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-from torch.autograd import Variable
-from tqdm import trange
-
-from transformers import GPT2Tokenizer
-from transformers.file_utils import cached_path
-from transformers.modeling_gpt2 import GPT2LMHeadModel
-from pplm_classification_head import ClassificationHead
-
-PPLM_BOW = 1
-PPLM_DISCRIM = 2
-PPLM_BOW_DISCRIM = 3
-SMALL_CONST = 1e-15
-BIG_CONST = 1e10
-
-BAG_OF_WORDS_ARCHIVE_MAP = {
-    'legal': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/legal.txt",
-    'military': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/military.txt",
-    'politics': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/politics.txt",
-    'religion': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/religion.txt",
-    'science': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/science.txt",
-    'space': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/space.txt",
-    'technology': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/technology.txt",
-}
-
-DISCRIMINATOR_MODELS_PARAMS = {
-    "clickbait": {
-        "url": "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/discriminators/clickbait_classifier_head.pt",
-        "class_size": 2,
-        "embed_size": 1024,
-        "class_vocab": {"non_clickbait": 0, "clickbait": 1},
-        "default_class": 1,
-        "pretrained_model": "gpt2-medium",
-    },
-    "sentiment": {
-        "url": "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/discriminators/SST_classifier_head.pt",
-        "class_size": 5,
-        "embed_size": 1024,
-        "class_vocab": {"very_positive": 2, "very_negative": 3},
-        "default_class": 3,
-        "pretrained_model": "gpt2-medium",
-    },
-}
-
-
-def to_var(x, requires_grad=False, volatile=False, device='cuda'):
-    if torch.cuda.is_available() and device == 'cuda':
-        x = x.cuda()
-    elif device != 'cuda':
-        x = x.to(device)
-    return Variable(x, requires_grad=requires_grad, volatile=volatile)
-
-
-def top_k_filter(logits, k, probs=False):
-    """
-    Masks everything but the k top entries as -infinity (1e10).
-    Used to mask logits such that e^-infinity -> 0 won't contribute to the
-    sum of the denominator.
-    """
-    if k == 0:
-        return logits
-    else:
-        values = torch.topk(logits, k)[0]
-        batch_mins = values[:, -1].view(-1, 1).expand_as(logits)
-        if probs:
-            return torch.where(logits < batch_mins,
-                               torch.ones_like(logits) * 0.0, logits)
-        return torch.where(logits < batch_mins,
-                           torch.ones_like(logits) * -BIG_CONST,
-                           logits)
-
-
-def perturb_past(
-        past,
-        model,
-        last,
-        unpert_past=None,
-        unpert_logits=None,
-        accumulated_hidden=None,
-        grad_norms=None,
-        stepsize=0.01,
-        one_hot_bows_vectors=None,
-        classifier=None,
-        class_label=None,
-        loss_type=0,
-        num_iterations=3,
-        horizon_length=1,
-        window_length=0,
-        decay=False,
-        gamma=1.5,
-        kl_scale=0.01,
-        device='cuda',
-):
-    # Generate inital perturbed past
-    grad_accumulator = [
-        (np.zeros(p.shape).astype("float32"))
-        for p in past
-    ]
-
-    if accumulated_hidden is None:
-        accumulated_hidden = 0
-
-    if decay:
-        decay_mask = torch.arange(
-            0.,
-            1.0 + SMALL_CONST,
-            1.0 / (window_length)
-        )[1:]
-    else:
-        decay_mask = 1.0
-
-    # TODO fix this comment (SUMANTH)
-    # Generate a mask is gradient perturbated is based on a past window
-    _, _, _, curr_length, _ = past[0].shape
-
-    if curr_length > window_length and window_length > 0:
-        ones_key_val_shape = (
-                tuple(past[0].shape[:-2])
-                + tuple([window_length])
-                + tuple(past[0].shape[-1:])
-        )
-
-        zeros_key_val_shape = (
-                tuple(past[0].shape[:-2])
-                + tuple([curr_length - window_length])
-                + tuple(past[0].shape[-1:])
-        )
-
-        ones_mask = torch.ones(ones_key_val_shape)
-        ones_mask = decay_mask * ones_mask.permute(0, 1, 2, 4, 3)
-        ones_mask = ones_mask.permute(0, 1, 2, 4, 3)
-
-        window_mask = torch.cat(
-            (ones_mask, torch.zeros(zeros_key_val_shape)),
-            dim=-2
-        ).to(device)
-    else:
-        window_mask = torch.ones_like(past[0]).to(device)
-
-    # accumulate perturbations for num_iterations
-    loss_per_iter = []
-    new_accumulated_hidden = None
-    for i in range(num_iterations):
-        print("Iteration ", i + 1)
-        curr_perturbation = [
-            to_var(torch.from_numpy(p_), requires_grad=True, device=device)
-            for p_ in grad_accumulator
-        ]
-
-        # Compute hidden using perturbed past
-        perturbed_past = list(map(add, past, curr_perturbation))
-        _, _, _, curr_length, _ = curr_perturbation[0].shape
-        all_logits, _, all_hidden = model(last, past=perturbed_past)
-        hidden = all_hidden[-1]
-        new_accumulated_hidden = accumulated_hidden + torch.sum(
-            hidden,
-            dim=1
-        ).detach()
-        # TODO: Check the layer-norm consistency of this with trained discriminator (Sumanth)
-        logits = all_logits[:, -1, :]
-        probs = F.softmax(logits, dim=-1)
-
-        loss = 0.0
-        loss_list = []
-        if loss_type == PPLM_BOW or loss_type == PPLM_BOW_DISCRIM:
-            for one_hot_bow in one_hot_bows_vectors:
-                bow_logits = torch.mm(probs, torch.t(one_hot_bow))
-                bow_loss = -torch.log(torch.sum(bow_logits))
-                loss += bow_loss
-                loss_list.append(bow_loss)
-            print(" pplm_bow_loss:", loss.data.cpu().numpy())
-
-        if loss_type == 2 or loss_type == 3:
-            ce_loss = torch.nn.CrossEntropyLoss()
-            # TODO why we need to do this assignment and not just using unpert_past? (Sumanth)
-            curr_unpert_past = unpert_past
-            curr_probs = torch.unsqueeze(probs, dim=1)
-            wte = model.resize_token_embeddings()
-            for _ in range(horizon_length):
-                inputs_embeds = torch.matmul(curr_probs, wte.weight.data)
-                _, curr_unpert_past, curr_all_hidden = model(
-                    past=curr_unpert_past,
-                    inputs_embeds=inputs_embeds
-                )
-                curr_hidden = curr_all_hidden[-1]
-                new_accumulated_hidden = new_accumulated_hidden + torch.sum(
-                    curr_hidden, dim=1)
-
-            prediction = classifier(new_accumulated_hidden /
-                                    (curr_length + 1 + horizon_length))
-
-            label = torch.tensor(prediction.shape[0] * [class_label],
-                                 device=device,
-                                 dtype=torch.long)
-            discrim_loss = ce_loss(prediction, label)
-            print(" pplm_discrim_loss:", discrim_loss.data.cpu().numpy())
-            loss += discrim_loss
-            loss_list.append(discrim_loss)
-
-        kl_loss = 0.0
-        if kl_scale > 0.0:
-            unpert_probs = F.softmax(unpert_logits[:, -1, :], dim=-1)
-            unpert_probs = (
-                    unpert_probs + SMALL_CONST *
-                    (unpert_probs <= SMALL_CONST).float().to(device).detach()
-            )
-            correction = SMALL_CONST * (probs <= SMALL_CONST).float().to(
-                device).detach()
-            corrected_probs = probs + correction.detach()
-            kl_loss = kl_scale * (
-                (corrected_probs * (corrected_probs / unpert_probs).log()).sum()
-            )
-            print(' kl_loss', kl_loss.data.cpu().numpy())
-            loss += kl_loss
-
-        loss_per_iter.append(loss.data.cpu().numpy())
-        print(' pplm_loss', (loss - kl_loss).data.cpu().numpy())
-
-        # compute gradients
-        loss.backward()
-
-        # calculate gradient norms
-        if grad_norms is not None and loss_type == PPLM_BOW:
-            grad_norms = [
-                torch.max(grad_norms[index], torch.norm(p_.grad * window_mask))
-                for index, p_ in enumerate(curr_perturbation)
-            ]
-        else:
-            grad_norms = [
-                (torch.norm(p_.grad * window_mask) + SMALL_CONST)
-                for index, p_ in enumerate(curr_perturbation)
-            ]
-
-        # normalize gradients
-        grad = [
-            -stepsize *
-            (p_.grad * window_mask / grad_norms[
-                index] ** gamma).data.cpu().numpy()
-            for index, p_ in enumerate(curr_perturbation)
-        ]
-
-        # accumulate gradient
-        grad_accumulator = list(map(add, grad, grad_accumulator))
-
-        # reset gradients, just to make sure
-        for p_ in curr_perturbation:
-            p_.grad.data.zero_()
-
-        # removing past from the graph
-        new_past = []
-        for p_ in past:
-            new_past.append(p_.detach())
-        past = new_past
-
-    # apply the accumulated perturbations to the past
-    grad_accumulator = [
-        to_var(torch.from_numpy(p_), requires_grad=True, device=device)
-        for p_ in grad_accumulator
-    ]
-    pert_past = list(map(add, past, grad_accumulator))
-
-    return pert_past, new_accumulated_hidden, grad_norms, loss_per_iter
-
-
-def get_classifier(
-        name: Optional[str], class_label: Union[str, int],
-        device: str
-) -> Tuple[Optional[ClassificationHead], Optional[int]]:
-    if name is None:
-        return None, None
-
-    params = DISCRIMINATOR_MODELS_PARAMS[name]
-    classifier = ClassificationHead(
-        class_size=params['class_size'],
-        embed_size=params['embed_size']
-    ).to(device)
-    if "url" in params:
-        resolved_archive_file = cached_path(params["url"])
-    elif "path" in params:
-        resolved_archive_file = params["path"]
-    else:
-        raise ValueError("Either url or path have to be specified "
-                         "in the discriminator model parameters")
-    classifier.load_state_dict(
-        torch.load(resolved_archive_file, map_location=device))
-    classifier.eval()
-
-    if isinstance(class_label, str):
-        if class_label in params["class_vocab"]:
-            label_id = params["class_vocab"][class_label]
-        else:
-            label_id = params["default_class"]
-            print("class_label {} not in class_vocab".format(class_label))
-            print("available values are: {}".format(params["class_vocab"]))
-            print("using default class {}".format(label_id))
-
-    elif isinstance(class_label, int):
-        if class_label in set(params["class_vocab"].values()):
-            label_id = class_label
-        else:
-            label_id = params["default_class"]
-            print("class_label {} not in class_vocab".format(class_label))
-            print("available values are: {}".format(params["class_vocab"]))
-            print("using default class {}".format(label_id))
-
-    else:
-        label_id = params["default_class"]
-
-    return classifier, label_id
-
-
-def get_bag_of_words_indices(bag_of_words_ids_or_paths: List[str], tokenizer) -> \
-        List[List[List[int]]]:
-    bow_indices = []
-    for id_or_path in bag_of_words_ids_or_paths:
-        if id_or_path in BAG_OF_WORDS_ARCHIVE_MAP:
-            filepath = cached_path(BAG_OF_WORDS_ARCHIVE_MAP[id_or_path])
-        else:
-            filepath = id_or_path
-        with open(filepath, "r") as f:
-            words = f.read().strip().split("\n")
-        bow_indices.append(
-            [tokenizer.encode(word.strip(), add_prefix_space=True) for word in
-             words])
-    return bow_indices
-
-
-def build_bows_one_hot_vectors(bow_indices, tokenizer, device='cuda'):
-    if bow_indices is None:
-        return None
-
-    one_hot_bows_vectors = []
-    for single_bow in bow_indices:
-        single_bow = list(filter(lambda x: len(x) <= 1, single_bow))
-        single_bow = torch.tensor(single_bow).to(device)
-        num_words = single_bow.shape[0]
-        one_hot_bow = torch.zeros(num_words, tokenizer.vocab_size).to(device)
-        one_hot_bow.scatter_(1, single_bow, 1)
-        one_hot_bows_vectors.append(one_hot_bow)
-    return one_hot_bows_vectors
-
-
-def full_text_generation(
-        model,
-        tokenizer,
-        context=None,
-        num_samples=1,
-        device="cuda",
-        bag_of_words=None,
-        discrim=None,
-        class_label=None,
-        length=100,
-        stepsize=0.02,
-        temperature=1.0,
-        top_k=10,
-        sample=False,
-        num_iterations=3,
-        grad_length=10000,
-        horizon_length=1,
-        window_length=0,
-        decay=False,
-        gamma=1.5,
-        gm_scale=0.9,
-        kl_scale=0.01,
-        **kwargs
-):
-    classifier, class_id = get_classifier(
-        discrim,
-        class_label,
-        device
-    )
-
-    bow_indices = []
-    if bag_of_words:
-        bow_indices = get_bag_of_words_indices(bag_of_words.split(";"),
-                                               tokenizer)
-
-    if bag_of_words and classifier:
-        print("Both PPLM-BoW and PPLM-Discrim are on. This is not optimized.")
-        loss_type = PPLM_BOW_DISCRIM
-
-    elif bag_of_words:
-        loss_type = PPLM_BOW
-        print("Using PPLM-BoW")
-
-    elif classifier is not None:
-        loss_type = PPLM_DISCRIM
-        print("Using PPLM-Discrim")
-
-    else:
-        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
-    )
-    if device == 'cuda':
-        torch.cuda.empty_cache()
-
-    pert_gen_tok_texts = []
-    discrim_losses = []
-    losses_in_time = []
-
-    for i in range(num_samples):
-        pert_gen_tok_text, discrim_loss, loss_in_time = generate_text_pplm(
-            model=model,
-            tokenizer=tokenizer,
-            context=context,
-            device=device,
-            perturb=True,
-            bow_indices=bow_indices,
-            classifier=classifier,
-            class_label=class_id,
-            loss_type=loss_type,
-            length=length,
-            stepsize=stepsize,
-            temperature=temperature,
-            top_k=top_k,
-            sample=sample,
-            num_iterations=num_iterations,
-            grad_length=grad_length,
-            horizon_length=horizon_length,
-            window_length=window_length,
-            decay=decay,
-            gamma=gamma,
-            gm_scale=gm_scale,
-            kl_scale=kl_scale,
-        )
-        pert_gen_tok_texts.append(pert_gen_tok_text)
-        if classifier is not None:
-            discrim_losses.append(discrim_loss.data.cpu().numpy())
-        losses_in_time.append(loss_in_time)
-
-    if device == 'cuda':
-        torch.cuda.empty_cache()
-
-    return unpert_gen_tok_text, pert_gen_tok_texts, discrim_losses, losses_in_time
-
-
-def generate_text_pplm(
-        model,
-        tokenizer,
-        context=None,
-        past=None,
-        device="cuda",
-        perturb=True,
-        bow_indices=None,
-        classifier=None,
-        class_label=None,
-        loss_type=0,
-        length=100,
-        stepsize=0.02,
-        temperature=1.0,
-        top_k=10,
-        sample=False,
-        num_iterations=3,
-        grad_length=10000,
-        horizon_length=1,
-        window_length=0,
-        decay=False,
-        gamma=1.5,
-        gm_scale=0.9,
-        kl_scale=0.01,
-):
-    output_so_far = None
-    if context:
-        context_t = torch.tensor(context, device=device, dtype=torch.long)
-        while len(context_t.shape) < 2:
-            context_t = context_t.unsqueeze(0)
-        output_so_far = context_t
-
-    # collect one hot vectors for bags of words
-    one_hot_bows_vectors = build_bows_one_hot_vectors(bow_indices, tokenizer,
-                                                      device)
-
-    grad_norms = None
-    last = None
-    unpert_discrim_loss = 0
-    loss_in_time = []
-    for i in trange(length, ascii=True):
-
-        # Get past/probs for current output, except for last word
-        # Note that GPT takes 2 inputs: past + current_token
-
-        # run model forward to obtain unperturbed
-        if past is None and output_so_far is not None:
-            last = output_so_far[:, -1:]
-            if output_so_far.shape[1] > 1:
-                _, past, _ = model(output_so_far[:, :-1])
-
-        unpert_logits, unpert_past, unpert_all_hidden = model(output_so_far)
-        unpert_last_hidden = unpert_all_hidden[-1]
-
-        # check if we are abowe grad max length
-        if i >= grad_length:
-            current_stepsize = stepsize * 0
-        else:
-            current_stepsize = stepsize
-
-        # modify the past if necessary
-        if not perturb or num_iterations == 0:
-            pert_past = past
-
-        else:
-            accumulated_hidden = unpert_last_hidden[:, :-1, :]
-            accumulated_hidden = torch.sum(accumulated_hidden, dim=1)
-
-            if past is not None:
-                pert_past, _, grad_norms, loss_this_iter = perturb_past(
-                    past,
-                    model,
-                    last,
-                    unpert_past=unpert_past,
-                    unpert_logits=unpert_logits,
-                    accumulated_hidden=accumulated_hidden,
-                    grad_norms=grad_norms,
-                    stepsize=current_stepsize,
-                    one_hot_bows_vectors=one_hot_bows_vectors,
-                    classifier=classifier,
-                    class_label=class_label,
-                    loss_type=loss_type,
-                    num_iterations=num_iterations,
-                    horizon_length=horizon_length,
-                    window_length=window_length,
-                    decay=decay,
-                    gamma=gamma,
-                    kl_scale=kl_scale,
-                    device=device,
-                )
-                loss_in_time.append(loss_this_iter)
-            else:
-                pert_past = past
-
-        pert_logits, past, pert_all_hidden = model(last, past=pert_past)
-        pert_logits = pert_logits[:, -1, :] / temperature  # + SMALL_CONST
-        pert_probs = F.softmax(pert_logits, dim=-1)
-
-        if classifier is not None:
-            ce_loss = torch.nn.CrossEntropyLoss()
-            prediction = classifier(torch.mean(unpert_last_hidden, dim=1))
-            label = torch.tensor([class_label], device=device,
-                                 dtype=torch.long)
-            unpert_discrim_loss = ce_loss(prediction, label)
-            print(
-                "unperturbed discrim loss",
-                unpert_discrim_loss.data.cpu().numpy()
-            )
-        else:
-            unpert_discrim_loss = 0
-
-        # Fuse the modified model and original model
-        if perturb:
-
-            unpert_probs = F.softmax(unpert_logits[:, -1, :], dim=-1)
-
-            pert_probs = ((pert_probs ** gm_scale) * (
-                    unpert_probs ** (1 - gm_scale)))  # + SMALL_CONST
-            pert_probs = top_k_filter(pert_probs, k=top_k,
-                                      probs=True)  # + SMALL_CONST
-
-            # rescale
-            if torch.sum(pert_probs) <= 1:
-                pert_probs = pert_probs / torch.sum(pert_probs)
-
-        else:
-            pert_logits = top_k_filter(pert_logits, k=top_k)  # + SMALL_CONST
-            pert_probs = F.softmax(pert_logits, dim=-1)
-
-        # sample or greedy
-        if sample:
-            last = torch.multinomial(pert_probs, num_samples=1)
-
-        else:
-            _, last = torch.topk(pert_probs, k=1, dim=-1)
-
-        # update context/output_so_far appending the new token
-        output_so_far = (
-            last if output_so_far is None
-            else torch.cat((output_so_far, last), dim=1)
-        )
-
-        print(tokenizer.decode(output_so_far.tolist()[0]))
-
-    return output_so_far, unpert_discrim_loss, loss_in_time
-
-
-def set_generic_model_params(discrim_weights, discrim_meta):
-    if discrim_weights is None:
-        raise ValueError('When using a generic discriminator, '
-                         'discrim_weights need to be specified')
-    if discrim_meta is None:
-        raise ValueError('When using a generic discriminator, '
-                         'discrim_meta need to be specified')
-
-    with open(discrim_meta, 'r') as discrim_meta_file:
-        meta = json.load(discrim_meta_file)
-    meta['path'] = discrim_weights
-    DISCRIMINATOR_MODELS_PARAMS['generic'] = meta
-
-
-def run_pplm_example(
-        pretrained_model="gpt2-medium",
-        cond_text="",
-        uncond=False,
-        num_samples=1,
-        bag_of_words=None,
-        discrim=None,
-        discrim_weights=None,
-        discrim_meta=None,
-        class_label=-1,
-        length=100,
-        stepsize=0.02,
-        temperature=1.0,
-        top_k=10,
-        sample=False,
-        num_iterations=3,
-        grad_length=10000,
-        horizon_length=1,
-        window_length=0,
-        decay=False,
-        gamma=1.5,
-        gm_scale=0.9,
-        kl_scale=0.01,
-        seed=0,
-        no_cuda=False,
-        colorama=False
-):
-    # set Random seed
-    torch.manual_seed(seed)
-    np.random.seed(seed)
-
-    # set the device
-    device = "cuda" if torch.cuda.is_available() and not no_cuda else "cpu"
-
-    if discrim == 'generic':
-        set_generic_model_params(discrim_weights, discrim_meta)
-
-    if discrim is not None:
-        pretrained_model = DISCRIMINATOR_MODELS_PARAMS[discrim][
-            "pretrained_model"
-        ]
-        print("discrim = {}, pretrained_model set "
-              "to discriminator's = {}".format(discrim, pretrained_model))
-
-    # load pretrained model
-    model = GPT2LMHeadModel.from_pretrained(
-        pretrained_model,
-        output_hidden_states=True
-    )
-    model.to(device)
-    model.eval()
-
-    # load tokenizer
-    tokenizer = GPT2Tokenizer.from_pretrained(pretrained_model)
-
-    # Freeze GPT-2 weights
-    for param in model.parameters():
-        param.requires_grad = False
-
-    # figure out conditioning text
-    if uncond:
-        tokenized_cond_text = tokenizer.encode(
-            [tokenizer.bos_token]
-        )
-    else:
-        raw_text = cond_text
-        while not raw_text:
-            print("Did you forget to add `--cond_text`? ")
-            raw_text = input("Model prompt >>> ")
-        tokenized_cond_text = tokenizer.encode(tokenizer.bos_token + raw_text)
-
-    print("= Prefix of sentence =")
-    print(tokenizer.decode(tokenized_cond_text))
-    print()
-
-    # generate unperturbed and perturbed texts
-
-    # full_text_generation returns:
-    # unpert_gen_tok_text, pert_gen_tok_texts, discrim_losses, losses_in_time
-    unpert_gen_tok_text, pert_gen_tok_texts, _, _ = full_text_generation(
-        model=model,
-        tokenizer=tokenizer,
-        context=tokenized_cond_text,
-        device=device,
-        num_samples=num_samples,
-        bag_of_words=bag_of_words,
-        discrim=discrim,
-        class_label=class_label,
-        length=length,
-        stepsize=stepsize,
-        temperature=temperature,
-        top_k=top_k,
-        sample=sample,
-        num_iterations=num_iterations,
-        grad_length=grad_length,
-        horizon_length=horizon_length,
-        window_length=window_length,
-        decay=decay,
-        gamma=gamma,
-        gm_scale=gm_scale,
-        kl_scale=kl_scale,
-    )
-
-    # untokenize unperturbed text
-    unpert_gen_text = tokenizer.decode(unpert_gen_tok_text.tolist()[0])
-
-    print("=" * 80)
-    print("= Unperturbed generated text =")
-    print(unpert_gen_text)
-    print()
-
-    generated_texts = []
-
-    bow_word_ids = set()
-    if bag_of_words and colorama:
-        bow_indices = get_bag_of_words_indices(bag_of_words.split(";"),
-                                               tokenizer)
-        for single_bow_list in bow_indices:
-            # filtering all words in the list composed of more than 1 token
-            filtered = list(filter(lambda x: len(x) <= 1, single_bow_list))
-            # w[0] because we are sure w has only 1 item because previous fitler
-            bow_word_ids.update(w[0] for w in filtered)
-
-    # iterate through the perturbed texts
-    for i, pert_gen_tok_text in enumerate(pert_gen_tok_texts):
-        try:
-            # untokenize unperturbed text
-            if colorama:
-                import colorama
-
-                pert_gen_text = ''
-                for word_id in pert_gen_tok_text.tolist()[0]:
-                    if word_id in bow_word_ids:
-                        pert_gen_text += '{}{}{}'.format(
-                            colorama.Fore.RED,
-                            tokenizer.decode([word_id]),
-                            colorama.Style.RESET_ALL
-                        )
-                    else:
-                        pert_gen_text += tokenizer.decode([word_id])
-            else:
-                pert_gen_text = tokenizer.decode(pert_gen_tok_text.tolist()[0])
-
-            print("= Perturbed generated text {} =".format(i + 1))
-            print(pert_gen_text)
-            print()
-        except:
-            pass
-
-        # keep the prefix, perturbed seq, original seq for each index
-        generated_texts.append(
-            (tokenized_cond_text, pert_gen_tok_text, unpert_gen_tok_text)
-        )
-
-    return
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--pretrained_model",
-        "-M",
-        type=str,
-        default="gpt2-medium",
-        help="pretrained model name or path to local checkpoint",
-    )
-    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",
-    )
-    parser.add_argument(
-        "--bag_of_words",
-        "-B",
-        type=str,
-        default=None,
-        help="Bags of words used for PPLM-BoW. "
-             "Either a BOW id (see list in code) or a filepath. "
-             "Multiple BoWs separated by ;",
-    )
-    parser.add_argument(
-        "--discrim",
-        "-D",
-        type=str,
-        default=None,
-        choices=("clickbait", "sentiment", "toxicity", "generic"),
-        help="Discriminator to use",
-    )
-    parser.add_argument('--discrim_weights', type=str, default=None,
-                        help='Weights for the generic discriminator')
-    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("--length", type=int, default=100)
-    parser.add_argument("--stepsize", type=float, default=0.02)
-    parser.add_argument("--temperature", type=float, default=1.0)
-    parser.add_argument("--top_k", type=int, default=10)
-    parser.add_argument(
-        "--sample", action="store_true",
-        help="Generate from end-of-text as prefix"
-    )
-    parser.add_argument("--num_iterations", type=int, default=3)
-    parser.add_argument("--grad_length", type=int, default=10000)
-    parser.add_argument(
-        "--window_length",
-        type=int,
-        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("--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)
-    parser.add_argument("--kl_scale", type=float, default=0.01)
-    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")
-
-    args = parser.parse_args()
-    run_pplm_example(**vars(args))
@@ -1,588 +0,0 @@
-#! /usr/bin/env python3
-# coding=utf-8
-
-#Copyright (c) 2019 Uber Technologies, Inc.
-#
-#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 argparse
-import csv
-import json
-import math
-import time
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-import torch.optim
-import torch.optim as optim
-import torch.utils.data as data
-from nltk.tokenize.treebank import TreebankWordDetokenizer
-from torchtext import data as torchtext_data
-from torchtext import datasets
-from tqdm import tqdm, trange
-
-from transformers import GPT2Tokenizer, GPT2LMHeadModel
-from pplm_classification_head import ClassificationHead
-
-torch.manual_seed(0)
-np.random.seed(0)
-EPSILON = 1e-10
-example_sentence = "This is incredible! I love it, this is the best chicken I have ever had."
-max_length_seq = 100
-
-
-
-
-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(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
-        self.classifier_head = ClassificationHead(
-            class_size=class_size,
-            embed_size=self.embed_size
-        )
-        self.cached_mode = cached_mode
-        self.device = device
-
-    def get_classifier(self):
-        return self.classifier_head
-
-    def train_custom(self):
-        for param in self.encoder.parameters():
-            param.requires_grad = False
-        self.classifier_head.train()
-
-    def avg_representation(self, x):
-        mask = x.ne(0).unsqueeze(2).repeat(
-            1, 1, self.embed_size
-        ).float().to(self.device).detach()
-        hidden, _ = self.encoder.transformer(x)
-        masked_hidden = hidden * mask
-        avg_hidden = torch.sum(masked_hidden, dim=1) / (
-                torch.sum(mask, dim=1).detach() + EPSILON
-        )
-        return avg_hidden
-
-    def forward(self, x):
-        if self.cached_mode:
-            avg_hidden = x.to(self.device)
-        else:
-            avg_hidden = self.avg_representation(x.to(self.device))
-
-        logits = self.classifier_head(avg_hidden)
-        probs = F.log_softmax(logits, dim=-1)
-
-        return probs
-
-
-class Dataset(data.Dataset):
-    def __init__(self, X, y):
-        """Reads source and target sequences from txt files."""
-        self.X = X
-        self.y = y
-
-    def __len__(self):
-        return len(self.X)
-
-    def __getitem__(self, index):
-        """Returns one data pair (source and target)."""
-        data = {}
-        data["X"] = self.X[index]
-        data["y"] = self.y[index]
-        return data
-
-
-def collate_fn(data):
-    def pad_sequences(sequences):
-        lengths = [len(seq) for seq in sequences]
-
-        padded_sequences = torch.zeros(
-            len(sequences),
-            max(lengths)
-        ).long()  # padding value = 0
-
-        for i, seq in enumerate(sequences):
-            end = lengths[i]
-            padded_sequences[i, :end] = seq[:end]
-
-        return padded_sequences, lengths
-
-    item_info = {}
-    for key in data[0].keys():
-        item_info[key] = [d[key] for d in data]
-
-    x_batch, _ = pad_sequences(item_info["X"])
-    y_batch = torch.tensor(item_info["y"], dtype=torch.long)
-
-    return x_batch, y_batch
-
-
-def cached_collate_fn(data):
-    item_info = {}
-    for key in data[0].keys():
-        item_info[key] = [d[key] for d in data]
-
-    x_batch = torch.cat(item_info["X"], 0)
-    y_batch = torch.tensor(item_info["y"], dtype=torch.long)
-
-    return x_batch, y_batch
-
-
-def train_epoch(data_loader, discriminator, optimizer,
-                epoch=0, log_interval=10, device='cpu'):
-    samples_so_far = 0
-    discriminator.train_custom()
-    for batch_idx, (input_t, target_t) in enumerate(data_loader):
-        input_t, target_t = input_t.to(device), target_t.to(device)
-
-        optimizer.zero_grad()
-
-        output_t = discriminator(input_t)
-        loss = F.nll_loss(output_t, target_t)
-        loss.backward(retain_graph=True)
-        optimizer.step()
-
-        samples_so_far += len(input_t)
-
-        if batch_idx % log_interval == 0:
-            print(
-                "Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(
-                    epoch + 1,
-                    samples_so_far, len(data_loader.dataset),
-                    100 * samples_so_far / len(data_loader.dataset), loss.item()
-                )
-            )
-
-
-def evaluate_performance(data_loader, discriminator, device='cpu'):
-    discriminator.eval()
-    test_loss = 0
-    correct = 0
-    with torch.no_grad():
-        for input_t, target_t in data_loader:
-            input_t, target_t = input_t.to(device), target_t.to(device)
-            output_t = discriminator(input_t)
-            # sum up batch loss
-            test_loss += F.nll_loss(output_t, target_t, reduction="sum").item()
-            # get the index of the max log-probability
-            pred_t = output_t.argmax(dim=1, keepdim=True)
-            correct += pred_t.eq(target_t.view_as(pred_t)).sum().item()
-
-    test_loss /= len(data_loader.dataset)
-
-    print(
-        "Performance on test set: "
-        "Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)".format(
-            test_loss, correct, len(data_loader.dataset),
-            100. * correct / len(data_loader.dataset)
-        )
-    )
-
-
-def predict(input_sentence, model, classes, cached=False, device='cpu'):
-    input_t = model.tokenizer.encode(input_sentence)
-    input_t = torch.tensor([input_t], dtype=torch.long, device=device)
-    if cached:
-        input_t = model.avg_representation(input_t)
-
-    log_probs = model(input_t).data.cpu().numpy().flatten().tolist()
-    print("Input sentence:", input_sentence)
-    print("Predictions:", ", ".join(
-        "{}: {:.4f}".format(c, math.exp(log_prob)) for c, log_prob in
-        zip(classes, log_probs)
-    ))
-
-
-def get_cached_data_loader(dataset, batch_size, discriminator,
-                           shuffle=False, device='cpu'):
-    data_loader = torch.utils.data.DataLoader(dataset=dataset,
-                                              batch_size=batch_size,
-                                              collate_fn=collate_fn)
-
-    xs = []
-    ys = []
-    for batch_idx, (x, y) in enumerate(tqdm(data_loader, ascii=True)):
-        with torch.no_grad():
-            x = x.to(device)
-            avg_rep = discriminator.avg_representation(x).cpu().detach()
-            avg_rep_list = torch.unbind(avg_rep.unsqueeze(1))
-            xs += avg_rep_list
-            ys += y.cpu().numpy().tolist()
-
-    data_loader = torch.utils.data.DataLoader(
-        dataset=Dataset(xs, ys),
-        batch_size=batch_size,
-        shuffle=shuffle,
-        collate_fn=cached_collate_fn)
-
-    return data_loader
-
-
-def train_discriminator(
-        dataset, dataset_fp=None, pretrained_model="gpt2-medium",
-        epochs=10, batch_size=64, log_interval=10,
-        save_model=False, cached=False, no_cuda=False):
-    device = "cuda" if torch.cuda.is_available() and not no_cuda else "cpu"
-
-    print("Preprocessing {} dataset...".format(dataset))
-    start = time.time()
-
-    if dataset == "SST":
-        idx2class = ["positive", "negative", "very positive", "very negative",
-                     "neutral"]
-        class2idx = {c: i for i, c in enumerate(idx2class)}
-
-        discriminator = Discriminator(
-            class_size=len(idx2class),
-            pretrained_model=pretrained_model,
-            cached_mode=cached,
-            device=device
-        ).to(device)
-
-        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,
-        )
-
-        x = []
-        y = []
-        for i in trange(len(train_data), ascii=True):
-            seq = TreebankWordDetokenizer().detokenize(
-                vars(train_data[i])["text"]
-            )
-            seq = discriminator.tokenizer.encode(seq)
-            seq = torch.tensor([50256] + seq, device=device, dtype=torch.long)
-            x.append(seq)
-            y.append(class2idx[vars(train_data[i])["label"]])
-        train_dataset = Dataset(x, y)
-
-        test_x = []
-        test_y = []
-        for i in trange(len(test_data), ascii=True):
-            seq = TreebankWordDetokenizer().detokenize(
-                vars(test_data[i])["text"]
-            )
-            seq = discriminator.tokenizer.encode(seq)
-            seq = torch.tensor([50256] + seq, device=device, dtype=torch.long)
-            test_x.append(seq)
-            test_y.append(class2idx[vars(test_data[i])["label"]])
-        test_dataset = Dataset(test_x, test_y)
-
-        discriminator_meta = {
-            "class_size": len(idx2class),
-            "embed_size": discriminator.embed_size,
-            "pretrained_model": pretrained_model,
-            "class_vocab": class2idx,
-            "default_class": 2,
-        }
-
-    elif dataset == "clickbait":
-        idx2class = ["non_clickbait", "clickbait"]
-        class2idx = {c: i for i, c in enumerate(idx2class)}
-
-        discriminator = Discriminator(
-            class_size=len(idx2class),
-            pretrained_model=pretrained_model,
-            cached_mode=cached,
-            device=device
-        ).to(device)
-
-        with open("datasets/clickbait/clickbait_train_prefix.txt") as f:
-            data = []
-            for i, line in enumerate(f):
-                try:
-                    data.append(eval(line))
-                except:
-                    print("Error evaluating line {}: {}".format(
-                        i, line
-                    ))
-                    continue
-        x = []
-        y = []
-        with open("datasets/clickbait/clickbait_train_prefix.txt") as f:
-            for i, line in enumerate(tqdm(f, ascii=True)):
-                try:
-                    d = eval(line)
-                    seq = discriminator.tokenizer.encode(d["text"])
-
-                    if len(seq) < max_length_seq:
-                        seq = torch.tensor(
-                            [50256] + seq, device=device, dtype=torch.long
-                        )
-                    else:
-                        print("Line {} is longer than maximum length {}".format(
-                            i, max_length_seq
-                        ))
-                        continue
-                    x.append(seq)
-                    y.append(d["label"])
-                except:
-                    print("Error evaluating / tokenizing"
-                          " line {}, skipping it".format(i))
-                    pass
-
-        full_dataset = Dataset(x, y)
-        train_size = int(0.9 * len(full_dataset))
-        test_size = len(full_dataset) - train_size
-        train_dataset, test_dataset = torch.utils.data.random_split(
-            full_dataset, [train_size, test_size]
-        )
-
-        discriminator_meta = {
-            "class_size": len(idx2class),
-            "embed_size": discriminator.embed_size,
-            "pretrained_model": pretrained_model,
-            "class_vocab": class2idx,
-            "default_class": 1,
-        }
-
-    elif dataset == "toxic":
-        idx2class = ["non_toxic", "toxic"]
-        class2idx = {c: i for i, c in enumerate(idx2class)}
-
-        discriminator = Discriminator(
-            class_size=len(idx2class),
-            pretrained_model=pretrained_model,
-            cached_mode=cached,
-            device=device
-        ).to(device)
-
-        x = []
-        y = []
-        with open("datasets/toxic/toxic_train.txt") as f:
-            for i, line in enumerate(tqdm(f, ascii=True)):
-                try:
-                    d = eval(line)
-                    seq = discriminator.tokenizer.encode(d["text"])
-
-                    if len(seq) < max_length_seq:
-                        seq = torch.tensor(
-                            [50256] + seq, device=device, dtype=torch.long
-                        )
-                    else:
-                        print("Line {} is longer than maximum length {}".format(
-                            i, max_length_seq
-                        ))
-                        continue
-                    x.append(seq)
-                    y.append(int(np.sum(d["label"]) > 0))
-                except:
-                    print("Error evaluating / tokenizing"
-                          " line {}, skipping it".format(i))
-                    pass
-
-        full_dataset = Dataset(x, y)
-        train_size = int(0.9 * len(full_dataset))
-        test_size = len(full_dataset) - train_size
-        train_dataset, test_dataset = torch.utils.data.random_split(
-            full_dataset, [train_size, test_size]
-        )
-
-        discriminator_meta = {
-            "class_size": len(idx2class),
-            "embed_size": discriminator.embed_size,
-            "pretrained_model": pretrained_model,
-            "class_vocab": class2idx,
-            "default_class": 0,
-        }
-
-    else:  # if dataset == "generic":
-        # This assumes the input dataset is a TSV with the following structure:
-        # class \t text
-
-        if dataset_fp is None:
-            raise ValueError("When generic dataset is selected, "
-                             "dataset_fp needs to be specified aswell.")
-
-        classes = set()
-        with open(dataset_fp) as f:
-            csv_reader = csv.reader(f, delimiter="\t")
-            for row in tqdm(csv_reader, ascii=True):
-                if row:
-                    classes.add(row[0])
-
-        idx2class = sorted(classes)
-        class2idx = {c: i for i, c in enumerate(idx2class)}
-
-        discriminator = Discriminator(
-            class_size=len(idx2class),
-            pretrained_model=pretrained_model,
-            cached_mode=cached,
-            device=device
-        ).to(device)
-
-        x = []
-        y = []
-        with open(dataset_fp) as f:
-            csv_reader = csv.reader(f, delimiter="\t")
-            for i, row in enumerate(tqdm(csv_reader, ascii=True)):
-                if row:
-                    label = row[0]
-                    text = row[1]
-
-                    try:
-                        seq = discriminator.tokenizer.encode(text)
-                        if (len(seq) < max_length_seq):
-                            seq = torch.tensor(
-                                [50256] + seq,
-                                device=device,
-                                dtype=torch.long
-                            )
-
-                        else:
-                            print(
-                                "Line {} is longer than maximum length {}".format(
-                                    i, max_length_seq
-                                ))
-                            continue
-
-                        x.append(seq)
-                        y.append(class2idx[label])
-
-                    except:
-                        print("Error tokenizing line {}, skipping it".format(i))
-                        pass
-
-        full_dataset = Dataset(x, y)
-        train_size = int(0.9 * len(full_dataset))
-        test_size = len(full_dataset) - train_size
-        train_dataset, test_dataset = torch.utils.data.random_split(
-            full_dataset,
-            [train_size, test_size]
-        )
-
-        discriminator_meta = {
-            "class_size": len(idx2class),
-            "embed_size": discriminator.embed_size,
-            "pretrained_model": pretrained_model,
-            "class_vocab": class2idx,
-            "default_class": 0,
-        }
-
-    end = time.time()
-    print("Preprocessed {} data points".format(
-        len(train_dataset) + len(test_dataset))
-    )
-    print("Data preprocessing took: {:.3f}s".format(end - start))
-
-    if cached:
-        print("Building representation cache...")
-
-        start = time.time()
-
-        train_loader = get_cached_data_loader(
-            train_dataset, batch_size, discriminator,
-            shuffle=True, device=device
-        )
-
-        test_loader = get_cached_data_loader(
-            test_dataset, batch_size, discriminator, device=device
-        )
-
-        end = time.time()
-        print("Building representation cache took: {:.3f}s".format(end - start))
-
-    else:
-        train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
-                                                   batch_size=batch_size,
-                                                   shuffle=True,
-                                                   collate_fn=collate_fn)
-        test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
-                                                  batch_size=batch_size,
-                                                  collate_fn=collate_fn)
-
-    if save_model:
-        with open("{}_classifier_head_meta.json".format(dataset),
-                  "w") as meta_file:
-            json.dump(discriminator_meta, meta_file)
-
-    optimizer = optim.Adam(discriminator.parameters(), lr=0.0001)
-
-    for epoch in range(epochs):
-        start = time.time()
-        print("\nEpoch", epoch + 1)
-
-        train_epoch(
-            discriminator=discriminator,
-            data_loader=train_loader,
-            optimizer=optimizer,
-            epoch=epoch,
-            log_interval=log_interval,
-            device=device
-        )
-        evaluate_performance(
-            data_loader=test_loader,
-            discriminator=discriminator,
-            device=device
-        )
-
-        end = time.time()
-        print("Epoch took: {:.3f}s".format(end - start))
-
-        print("\nExample prediction")
-        predict(example_sentence, discriminator, idx2class,
-                cached=cached, device=device)
-
-        if save_model:
-            # torch.save(discriminator.state_dict(),
-            #           "{}_discriminator_{}.pt".format(
-            #               args.dataset, epoch + 1
-            #               ))
-            torch.save(discriminator.get_classifier().state_dict(),
-                       "{}_classifier_head_epoch_{}.pt".format(dataset,
-                                                               epoch + 1))
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(
-        description="Train a discriminator on top of GPT-2 representations")
-    parser.add_argument("--dataset", type=str, default="SST",
-                        choices=("SST", "clickbait", "toxic", "generic"),
-                        help="dataset to train the discriminator on."
-                             "In case of generic, the dataset is expected"
-                             "to be a TSBV file with structure: class \\t text")
-    parser.add_argument("--dataset_fp", type=str, default="",
-                        help="File path of the dataset to use. "
-                             "Needed only in case of generic datadset")
-    parser.add_argument("--pretrained_model", type=str, default="gpt2-medium",
-                        help="Pretrained model to use as encoder")
-    parser.add_argument("--epochs", type=int, default=10, metavar="N",
-                        help="Number of training epochs")
-    parser.add_argument("--batch_size", type=int, default=64, metavar="N",
-                        help="input batch size for training (default: 64)")
-    parser.add_argument("--log_interval", type=int, default=10, metavar="N",
-                        help="how many batches to wait before logging training status")
-    parser.add_argument("--save_model", action="store_true",
-                        help="whether to save the model")
-    parser.add_argument("--cached", action="store_true",
-                        help="whether to cache the input representations")
-    parser.add_argument("--no_cuda", action="store_true",
-                        help="use to turn off cuda")
-    args = parser.parse_args()
-
-    train_discriminator(**(vars(args)))
@@ -1,4 +0,0 @@
-tensorboardX
-tensorboard
-scikit-learn
-seqeval
@@ -1,357 +0,0 @@
-#!/usr/bin/env python3
-# Copyright 2018 CMU and The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" Bertology: this script shows how you can explore the internals of the models in the library to:
-    - compute the entropy of the head attentions
-    - compute the importance of each head
-    - prune (remove) the low importance head.
-    Some parts of this script are adapted from the code of Michel et al. (http://arxiv.org/abs/1905.10650)
-    which is available at https://github.com/pmichel31415/are-16-heads-really-better-than-1
-"""
-import os
-import argparse
-import logging
-from datetime import timedelta, datetime
-from tqdm import tqdm
-
-import numpy as np
-
-import torch
-from torch.utils.data import DataLoader, SequentialSampler, TensorDataset, Subset
-from torch.utils.data.distributed import DistributedSampler
-from torch.nn import CrossEntropyLoss, MSELoss
-
-from transformers import (WEIGHTS_NAME,
-                                  BertConfig, BertForSequenceClassification, BertTokenizer,
-                                  XLMConfig, XLMForSequenceClassification, XLMTokenizer,
-                                  XLNetConfig, XLNetForSequenceClassification, XLNetTokenizer)
-
-from run_glue import set_seed, load_and_cache_examples, ALL_MODELS, MODEL_CLASSES
-
-from transformers import glue_compute_metrics as compute_metrics
-from transformers import glue_output_modes as output_modes
-from transformers import glue_processors as processors
-
-logger = logging.getLogger(__name__)
-
-
-def entropy(p):
-    """ Compute the entropy of a probability distribution """
-    plogp = p * torch.log(p)
-    plogp[p == 0] = 0
-    return -plogp.sum(dim=-1)
-
-
-def print_2d_tensor(tensor):
-    """ Print a 2D tensor """
-    logger.info("lv, h >\t" + "\t".join(f"{x + 1}" for x in range(len(tensor))))
-    for row in range(len(tensor)):
-        if tensor.dtype != torch.long:
-            logger.info(f"layer {row + 1}:\t" + "\t".join(f"{x:.5f}" for x in tensor[row].cpu().data))
-        else:
-            logger.info(f"layer {row + 1}:\t" + "\t".join(f"{x:d}" for x in tensor[row].cpu().data))
-
-
-def compute_heads_importance(args, model, eval_dataloader, compute_entropy=True, compute_importance=True, head_mask=None):
-    """ This method shows how to compute:
-        - head attention entropy
-        - head importance scores according to http://arxiv.org/abs/1905.10650
-    """
-    # Prepare our tensors
-    n_layers, n_heads = model.bert.config.num_hidden_layers, model.bert.config.num_attention_heads
-    head_importance = torch.zeros(n_layers, n_heads).to(args.device)
-    attn_entropy = torch.zeros(n_layers, n_heads).to(args.device)
-
-    if head_mask is None:
-        head_mask = torch.ones(n_layers, n_heads).to(args.device)
-    head_mask.requires_grad_(requires_grad=True)
-    preds = None
-    labels = None
-    tot_tokens = 0.0
-
-    for step, batch in enumerate(tqdm(eval_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])):
-        batch = tuple(t.to(args.device) for t in batch)
-        input_ids, input_mask, segment_ids, label_ids = batch
-
-        # Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
-        outputs = model(input_ids, token_type_ids=segment_ids, attention_mask=input_mask, labels=label_ids, head_mask=head_mask)
-        loss, logits, all_attentions = outputs[0], outputs[1], outputs[-1]  # Loss and logits are the first, attention the last
-        loss.backward()  # Backpropagate to populate the gradients in the head mask
-
-        if compute_entropy:
-            for layer, attn in enumerate(all_attentions):
-                masked_entropy = entropy(attn.detach()) * input_mask.float().unsqueeze(1)
-                attn_entropy[layer] += masked_entropy.sum(-1).sum(0).detach()
-
-        if compute_importance:
-            head_importance += head_mask.grad.abs().detach()
-
-        # Also store our logits/labels if we want to compute metrics afterwards
-        if preds is None:
-            preds = logits.detach().cpu().numpy()
-            labels = label_ids.detach().cpu().numpy()
-        else:
-            preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)
-            labels = np.append(labels, label_ids.detach().cpu().numpy(), axis=0)
-
-        tot_tokens += input_mask.float().detach().sum().data
-
-    # Normalize
-    attn_entropy /= tot_tokens
-    head_importance /= tot_tokens
-    # Layerwise importance normalization
-    if not args.dont_normalize_importance_by_layer:
-        exponent = 2
-        norm_by_layer = torch.pow(torch.pow(head_importance, exponent).sum(-1), 1/exponent)
-        head_importance /= norm_by_layer.unsqueeze(-1) + 1e-20
-
-    if not args.dont_normalize_global_importance:
-        head_importance = (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())
-
-    # Print/save matrices
-    np.save(os.path.join(args.output_dir, 'attn_entropy.npy'), attn_entropy.detach().cpu().numpy())
-    np.save(os.path.join(args.output_dir, 'head_importance.npy'), head_importance.detach().cpu().numpy())
-
-    logger.info("Attention entropies")
-    print_2d_tensor(attn_entropy)
-    logger.info("Head importance scores")
-    print_2d_tensor(head_importance)
-    logger.info("Head ranked by importance scores")
-    head_ranks = torch.zeros(head_importance.numel(), dtype=torch.long, device=args.device)
-    head_ranks[head_importance.view(-1).sort(descending=True)[1]] = torch.arange(head_importance.numel(), device=args.device)
-    head_ranks = head_ranks.view_as(head_importance)
-    print_2d_tensor(head_ranks)
-
-    return attn_entropy, head_importance, preds, labels
-
-
-def mask_heads(args, model, eval_dataloader):
-    """ This method shows how to mask head (set some heads to zero), to test the effect on the network,
-        based on the head importance scores, as described in Michel et al. (http://arxiv.org/abs/1905.10650)
-    """
-    _, head_importance, preds, labels = compute_heads_importance(args, model, eval_dataloader, compute_entropy=False)
-    preds = np.argmax(preds, axis=1) if args.output_mode == "classification" else np.squeeze(preds)
-    original_score = compute_metrics(args.task_name, preds, labels)[args.metric_name]
-    logger.info("Pruning: original score: %f, threshold: %f", original_score, original_score * args.masking_threshold)
-
-    new_head_mask = torch.ones_like(head_importance)
-    num_to_mask = max(1, int(new_head_mask.numel() * args.masking_amount))
-
-    current_score = original_score
-    while current_score >= original_score * args.masking_threshold:
-        head_mask = new_head_mask.clone() # save current head mask
-        # heads from least important to most - keep only not-masked heads
-        head_importance[head_mask == 0.0] = float('Inf')
-        current_heads_to_mask = head_importance.view(-1).sort()[1]
-
-        if len(current_heads_to_mask) <= num_to_mask:
-            break
-
-        # mask heads
-        current_heads_to_mask = current_heads_to_mask[:num_to_mask]
-        logger.info("Heads to mask: %s", str(current_heads_to_mask.tolist()))
-        new_head_mask = new_head_mask.view(-1)
-        new_head_mask[current_heads_to_mask] = 0.0
-        new_head_mask = new_head_mask.view_as(head_mask)
-        print_2d_tensor(new_head_mask)
-
-        # Compute metric and head importance again
-        _, head_importance, preds, labels = compute_heads_importance(args, model, eval_dataloader, compute_entropy=False, head_mask=new_head_mask)
-        preds = np.argmax(preds, axis=1) if args.output_mode == "classification" else np.squeeze(preds)
-        current_score = compute_metrics(args.task_name, preds, labels)[args.metric_name]
-        logger.info("Masking: current score: %f, remaning heads %d (%.1f percents)", current_score, new_head_mask.sum(), new_head_mask.sum()/new_head_mask.numel() * 100)
-
-    logger.info("Final head mask")
-    print_2d_tensor(head_mask)
-    np.save(os.path.join(args.output_dir, 'head_mask.npy'), head_mask.detach().cpu().numpy())
-
-    return head_mask
-
-
-def prune_heads(args, model, eval_dataloader, head_mask):
-    """ This method shows how to prune head (remove heads weights) based on
-        the head importance scores as described in Michel et al. (http://arxiv.org/abs/1905.10650)
-    """
-    # Try pruning and test time speedup
-    # Pruning is like masking but we actually remove the masked weights
-    before_time = datetime.now()
-    _, _, preds, labels = compute_heads_importance(args, model, eval_dataloader,
-                                                   compute_entropy=False, compute_importance=False, head_mask=head_mask)
-    preds = np.argmax(preds, axis=1) if args.output_mode == "classification" else np.squeeze(preds)
-    score_masking = compute_metrics(args.task_name, preds, labels)[args.metric_name]
-    original_time = datetime.now() - before_time
-
-    original_num_params = sum(p.numel() for p in model.parameters())
-    heads_to_prune = dict((layer, (1 - head_mask[layer].long()).nonzero().tolist()) for layer in range(len(head_mask)))
-    assert sum(len(h) for h in heads_to_prune.values()) == (1 - head_mask.long()).sum().item()
-    model.prune_heads(heads_to_prune)
-    pruned_num_params = sum(p.numel() for p in model.parameters())
-
-    before_time = datetime.now()
-    _, _, preds, labels = compute_heads_importance(args, model, eval_dataloader,
-                                                    compute_entropy=False, compute_importance=False, head_mask=None)
-    preds = np.argmax(preds, axis=1) if args.output_mode == "classification" else np.squeeze(preds)
-    score_pruning = compute_metrics(args.task_name, preds, labels)[args.metric_name]
-    new_time = datetime.now() - before_time
-
-    logger.info("Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)", original_num_params, pruned_num_params, pruned_num_params/original_num_params * 100)
-    logger.info("Pruning: score with masking: %f score with pruning: %f", score_masking, score_pruning)
-    logger.info("Pruning: speed ratio (new timing / original timing): %f percents", original_time/new_time * 100)
-
-
-def main():
-    parser = argparse.ArgumentParser()
-    ## Required parameters
-    parser.add_argument("--data_dir", default=None, type=str, required=True,
-                        help="The input data dir. Should contain the .tsv files (or other data files) for the task.")
-    parser.add_argument("--model_name_or_path", default=None, type=str, required=True,
-                        help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(
-                            ALL_MODELS))
-    parser.add_argument("--task_name", default=None, type=str, required=True,
-                        help="The name of the task to train selected in the list: " + ", ".join(processors.keys()))
-    parser.add_argument("--output_dir", default=None, type=str, required=True,
-                        help="The output directory where the model predictions and checkpoints will be written.")
-
-    ## Other parameters
-    parser.add_argument("--config_name", default="", type=str,
-                        help="Pretrained config name or path if not the same as model_name_or_path")
-    parser.add_argument("--tokenizer_name", default="", type=str,
-                        help="Pretrained tokenizer name or path if not the same as model_name_or_path")
-    parser.add_argument("--cache_dir", default="", type=str,
-                        help="Where do you want to store the pre-trained models downloaded from s3")
-    parser.add_argument("--data_subset", type=int, default=-1,
-                        help="If > 0: limit the data to a subset of data_subset instances.")
-    parser.add_argument("--overwrite_output_dir", action='store_true',
-                        help="Whether to overwrite data in output directory")
-    parser.add_argument('--overwrite_cache', action='store_true',
-                        help="Overwrite the cached training and evaluation sets")
-
-    parser.add_argument("--dont_normalize_importance_by_layer", action='store_true',
-                        help="Don't normalize importance score by layers")
-    parser.add_argument("--dont_normalize_global_importance", action='store_true',
-                        help="Don't normalize all importance scores between 0 and 1")
-
-    parser.add_argument("--try_masking", action='store_true',
-                        help="Whether to try to mask head until a threshold of accuracy.")
-    parser.add_argument("--masking_threshold", default=0.9, type=float,
-                        help="masking threshold in term of metrics (stop masking when metric < threshold * original metric value).")
-    parser.add_argument("--masking_amount", default=0.1, type=float,
-                        help="Amount to heads to masking at each masking step.")
-    parser.add_argument("--metric_name", default="acc", type=str,
-                        help="Metric to use for head masking.")
-
-    parser.add_argument("--max_seq_length", default=128, type=int,
-                        help="The maximum total input sequence length after WordPiece tokenization. \n"
-                             "Sequences longer than this will be truncated, sequences shorter padded.")
-    parser.add_argument("--batch_size", default=1, type=int, help="Batch size.")
-
-    parser.add_argument("--seed", type=int, default=42)
-    parser.add_argument("--local_rank", type=int, default=-1, help="local_rank for distributed training on gpus")
-    parser.add_argument("--no_cuda", action='store_true', help="Whether not to use CUDA when available")
-    parser.add_argument('--server_ip', type=str, default='', help="Can be used for distant debugging.")
-    parser.add_argument('--server_port', type=str, default='', help="Can be used for distant debugging.")
-    args = parser.parse_args()
-
-    if args.server_ip and args.server_port:
-        # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
-        import ptvsd
-        print("Waiting for debugger attach")
-        ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
-        ptvsd.wait_for_attach()
-
-    # Setup devices and distributed training
-    if args.local_rank == -1 or args.no_cuda:
-        args.device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
-        args.n_gpu = torch.cuda.device_count()
-    else:
-        torch.cuda.set_device(args.local_rank)
-        args.device = torch.device("cuda", args.local_rank)
-        args.n_gpu = 1
-        torch.distributed.init_process_group(backend='nccl')  # Initializes the distributed backend
-
-    # Setup logging
-    logging.basicConfig(level = logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
-    logger.info("device: {} n_gpu: {}, distributed: {}".format(args.device, args.n_gpu, bool(args.local_rank != -1)))
-
-    # Set seeds
-    set_seed(args)
-
-    # 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 = ""
-    for key in MODEL_CLASSES:
-        if key in args.model_name_or_path.lower():
-            args.model_type = key  # take the first match in model types
-            break
-    config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
-    config = config_class.from_pretrained(args.config_name if args.config_name else args.model_name_or_path,
-                                          num_labels=num_labels,
-                                          finetuning_task=args.task_name,
-                                          output_attentions=True,
-                                          cache_dir=args.cache_dir if args.cache_dir else None)
-    tokenizer = tokenizer_class.from_pretrained(args.tokenizer_name if args.tokenizer_name else args.model_name_or_path,
-                                                cache_dir=args.cache_dir if args.cache_dir else None)
-    model = model_class.from_pretrained(args.model_name_or_path,
-                                        from_tf=bool('.ckpt' in args.model_name_or_path),
-                                        config=config,
-                                        cache_dir=args.cache_dir if args.cache_dir else None)
-
-    if args.local_rank == 0:
-        torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab
-
-    # Distributed and parallel training
-    model.to(args.device)
-    if args.local_rank != -1:
-        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank],
-                                                          output_device=args.local_rank,
-                                                          find_unused_parameters=True)
-    elif args.n_gpu > 1:
-        model = torch.nn.DataParallel(model)
-
-    # Print/save training arguments
-    torch.save(args, os.path.join(args.output_dir, 'run_args.bin'))
-    logger.info("Training/evaluation parameters %s", args)
-
-    # Prepare dataset for the GLUE task
-    eval_data = load_and_cache_examples(args, args.task_name, tokenizer, evaluate=True)
-    if args.data_subset > 0:
-        eval_data = Subset(eval_data, list(range(min(args.data_subset, len(eval_data)))))
-    eval_sampler = SequentialSampler(eval_data) if args.local_rank == -1 else DistributedSampler(eval_data)
-    eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.batch_size)
-
-
-    # Compute head entropy and importance score
-    compute_heads_importance(args, model, eval_dataloader)
-
-
-    # Try head masking (set heads to zero until the score goes under a threshole)
-    # and head pruning (remove masked heads and see the effect on the network)
-    if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
-        head_mask = mask_heads(args, model, eval_dataloader)
-        prune_heads(args, model, eval_dataloader, head_mask)
-
-
-if __name__ == '__main__':
-    main()
@@ -0,0 +1,625 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors and The HugginFace 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.
+"""BERT finetuning runner."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import csv
+import os
+import logging
+import argparse
+import random
+from datetime import datetime
+from tqdm import tqdm, trange
+
+import numpy as np
+import torch
+from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
+from torch.utils.data.distributed import DistributedSampler
+
+from pytorch_pretrained_bert.tokenization import BertTokenizer
+from pytorch_pretrained_bert.modeling import BertForSequenceClassification
+from pytorch_pretrained_bert.optimization import BertAdam
+from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE
+
+logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s',
+                    datefmt = '%m/%d/%Y %H:%M:%S',
+                    level = logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+class InputExample(object):
+    """A single training/test example for simple sequence classification."""
+
+    def __init__(self, guid, text_a, text_b=None, label=None):
+        """Constructs a InputExample.
+
+        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.
+        """
+        self.guid = guid
+        self.text_a = text_a
+        self.text_b = text_b
+        self.label = label
+
+
+class InputFeatures(object):
+    """A single set of features of data."""
+
+    def __init__(self, input_ids, input_mask, segment_ids, label_id):
+        self.input_ids = input_ids
+        self.input_mask = input_mask
+        self.segment_ids = segment_ids
+        self.label_id = label_id
+
+
+class DataProcessor(object):
+    """Base class for data converters for sequence classification data sets."""
+
+    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') as f:
+            reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
+            lines = []
+            for line in reader:
+                lines.append(line)
+            return lines
+
+
+class MrpcProcessor(DataProcessor):
+    """Processor for the MRPC data set (GLUE version)."""
+
+    def get_train_examples(self, data_dir):
+        """See base class."""
+        logger.info("LOOKING AT {}".format(os.path.join(data_dir, "train.tsv")))
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
+
+    def get_dev_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
+
+    def get_labels(self):
+        """See base class."""
+        return ["0", "1"]
+
+    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, i)
+            text_a = line[3]
+            text_b = line[4]
+            label = line[0]
+            examples.append(
+                InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
+        return examples
+
+
+class MnliProcessor(DataProcessor):
+    """Processor for the MultiNLI data set (GLUE version)."""
+
+    def get_train_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
+
+    def get_dev_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "dev_matched.tsv")),
+            "dev_matched")
+
+    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[8]
+            text_b = line[9]
+            label = line[-1]
+            examples.append(
+                InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
+        return examples
+
+
+class ColaProcessor(DataProcessor):
+    """Processor for the CoLA data set (GLUE version)."""
+
+    def get_train_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
+
+    def get_dev_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
+
+    def get_labels(self):
+        """See base class."""
+        return ["0", "1"]
+
+    def _create_examples(self, lines, set_type):
+        """Creates examples for the training and dev sets."""
+        examples = []
+        for (i, line) in enumerate(lines):
+            guid = "%s-%s" % (set_type, i)
+            text_a = line[3]
+            label = line[1]
+            examples.append(
+                InputExample(guid=guid, text_a=text_a, text_b=None, label=label))
+        return examples
+
+
+def convert_examples_to_features(examples, label_list, max_seq_length, tokenizer):
+    """Loads a data file into a list of `InputBatch`s."""
+
+    label_map = {label : i for i, label in enumerate(label_list)}
+
+    features = []
+    for (ex_index, example) in enumerate(examples):
+        tokens_a = tokenizer.tokenize(example.text_a)
+
+        tokens_b = None
+        if example.text_b:
+            tokens_b = tokenizer.tokenize(example.text_b)
+            # Modifies `tokens_a` and `tokens_b` in place so that the total
+            # length is less than the specified length.
+            # Account for [CLS], [SEP], [SEP] with "- 3"
+            _truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
+        else:
+            # Account for [CLS] and [SEP] with "- 2"
+            if len(tokens_a) > max_seq_length - 2:
+                tokens_a = tokens_a[:(max_seq_length - 2)]
+
+        # The convention in BERT is:
+        # (a) For sequence pairs:
+        #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
+        #  type_ids: 0   0  0    0    0     0       0 0    1  1  1  1   1 1
+        # (b) For single sequences:
+        #  tokens:   [CLS] the dog is hairy . [SEP]
+        #  type_ids: 0   0   0   0  0     0 0
+        #
+        # Where "type_ids" are used to indicate whether this is the first
+        # sequence or the second sequence. The embedding vectors for `type=0` and
+        # `type=1` were learned during pre-training and are added to the wordpiece
+        # embedding vector (and position vector). This is not *strictly* necessary
+        # since the [SEP] token unambigiously separates the sequences, but it makes
+        # it easier for the model to learn the concept of sequences.
+        #
+        # For classification tasks, the first vector (corresponding to [CLS]) is
+        # used as as the "sentence vector". Note that this only makes sense because
+        # the entire model is fine-tuned.
+        tokens = ["[CLS]"] + tokens_a + ["[SEP]"]
+        segment_ids = [0] * len(tokens)
+
+        if tokens_b:
+            tokens += tokens_b + ["[SEP]"]
+            segment_ids += [1] * (len(tokens_b) + 1)
+
+        input_ids = tokenizer.convert_tokens_to_ids(tokens)
+
+        # The mask has 1 for real tokens and 0 for padding tokens. Only real
+        # tokens are attended to.
+        input_mask = [1] * len(input_ids)
+
+        # Zero-pad up to the sequence length.
+        padding = [0] * (max_seq_length - len(input_ids))
+        input_ids += padding
+        input_mask += padding
+        segment_ids += padding
+
+        assert len(input_ids) == max_seq_length
+        assert len(input_mask) == max_seq_length
+        assert len(segment_ids) == max_seq_length
+
+        label_id = label_map[example.label]
+        if ex_index < 5:
+            logger.info("*** Example ***")
+            logger.info("guid: %s" % (example.guid))
+            logger.info("tokens: %s" % " ".join(
+                    [str(x) for x in tokens]))
+            logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
+            logger.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
+            logger.info(
+                    "segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
+            logger.info("label: %s (id = %d)" % (example.label, label_id))
+
+        features.append(
+                InputFeatures(input_ids=input_ids,
+                              input_mask=input_mask,
+                              segment_ids=segment_ids,
+                              label_id=label_id))
+    return features
+
+
+def _truncate_seq_pair(tokens_a, tokens_b, max_length):
+    """Truncates a sequence pair in place to the maximum length."""
+
+    # This is a simple heuristic which will always truncate the longer sequence
+    # one token at a time. This makes more sense than truncating an equal percent
+    # of tokens from each, since if one sequence is very short then each token
+    # that's truncated likely contains more information than a longer sequence.
+    while True:
+        total_length = len(tokens_a) + len(tokens_b)
+        if total_length <= max_length:
+            break
+        if len(tokens_a) > len(tokens_b):
+            tokens_a.pop()
+        else:
+            tokens_b.pop()
+
+def accuracy(out, labels):
+    outputs = np.argmax(out, axis=1)
+    return np.sum(outputs == labels)
+
+def warmup_linear(x, warmup=0.002):
+    if x < warmup:
+        return x/warmup
+    return 1.0 - x
+
+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("--bert_model", default=None, type=str, required=True,
+                        help="Bert pre-trained model selected in the list: bert-base-uncased, "
+                             "bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese.")
+    parser.add_argument("--task_name",
+                        default=None,
+                        type=str,
+                        required=True,
+                        help="The name of the task to train.")
+    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("--max_seq_length",
+                        default=128,
+                        type=int,
+                        help="The maximum total input sequence length after WordPiece tokenization. \n"
+                             "Sequences longer than this will be truncated, and sequences shorter \n"
+                             "than this will be padded.")
+    parser.add_argument("--do_train",
+                        default=False,
+                        action='store_true',
+                        help="Whether to run training.")
+    parser.add_argument("--do_eval",
+                        default=False,
+                        action='store_true',
+                        help="Whether to run eval on the dev set.")
+    parser.add_argument("--do_lower_case",
+                        default=False,
+                        action='store_true',
+                        help="Set this flag if you are using an uncased model.")
+    parser.add_argument("--train_batch_size",
+                        default=32,
+                        type=int,
+                        help="Total batch size for training.")
+    parser.add_argument("--eval_batch_size",
+                        default=8,
+                        type=int,
+                        help="Total batch size for eval.")
+    parser.add_argument("--learning_rate",
+                        default=5e-5,
+                        type=float,
+                        help="The initial learning rate for Adam.")
+    parser.add_argument("--num_train_epochs",
+                        default=3.0,
+                        type=float,
+                        help="Total number of training epochs to perform.")
+    parser.add_argument("--warmup_proportion",
+                        default=0.1,
+                        type=float,
+                        help="Proportion of training to perform linear learning rate warmup for. "
+                             "E.g., 0.1 = 10%% of training.")
+    parser.add_argument("--no_cuda",
+                        default=False,
+                        action='store_true',
+                        help="Whether not to use CUDA when available")
+    parser.add_argument("--local_rank",
+                        type=int,
+                        default=-1,
+                        help="local_rank for distributed training on gpus")
+    parser.add_argument('--seed',
+                        type=int,
+                        default=42,
+                        help="random seed for initialization")
+    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('--fp16',
+                        default=False,
+                        action='store_true',
+                        help="Whether to use 16-bit float precision instead of 32-bit")
+    parser.add_argument('--loss_scale',
+                        type=float, default=0,
+                        help="Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
+                             "0 (default value): dynamic loss scaling.\n"
+                             "Positive power of 2: static loss scaling value.\n")
+
+    args = parser.parse_args()
+
+    processors = {
+        "cola": ColaProcessor,
+        "mnli": MnliProcessor,
+        "mrpc": MrpcProcessor,
+    }
+
+    num_labels_task = {
+        "cola": 2,
+        "mnli": 3,
+        "mrpc": 2,
+    }
+
+    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")
+        n_gpu = torch.cuda.device_count()
+    else:
+        torch.cuda.set_device(args.local_rank)
+        device = torch.device("cuda", args.local_rank)
+        n_gpu = 1
+        # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
+        torch.distributed.init_process_group(backend='nccl')
+    logger.info("device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".format(
+        device, n_gpu, bool(args.local_rank != -1), args.fp16))
+
+    if args.gradient_accumulation_steps < 1:
+        raise ValueError("Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format(
+                            args.gradient_accumulation_steps))
+
+    args.train_batch_size = int(args.train_batch_size / args.gradient_accumulation_steps)
+
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    if n_gpu > 0:
+        torch.cuda.manual_seed_all(args.seed)
+
+    if not args.do_train and not args.do_eval:
+        raise ValueError("At least one of `do_train` or `do_eval` must be True.")
+
+    if os.path.exists(args.output_dir) and os.listdir(args.output_dir):
+        raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir))
+    os.makedirs(args.output_dir, exist_ok=True)
+
+    task_name = args.task_name.lower()
+
+    if task_name not in processors:
+        raise ValueError("Task not found: %s" % (task_name))
+
+    processor = processors[task_name]()
+    num_labels = num_labels_task[task_name]
+    label_list = processor.get_labels()
+
+    tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case)
+
+    train_examples = None
+    num_train_steps = None
+    if args.do_train:
+        train_examples = processor.get_train_examples(args.data_dir)
+        num_train_steps = int(
+            len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps * args.num_train_epochs)
+
+    # Prepare model
+    model = BertForSequenceClassification.from_pretrained(args.bert_model,
+              cache_dir=PYTORCH_PRETRAINED_BERT_CACHE / 'distributed_{}'.format(args.local_rank),
+              num_labels = num_labels)
+    if args.fp16:
+        model.half()
+    model.to(device)
+    if args.local_rank != -1:
+        try:
+            from apex.parallel import DistributedDataParallel as DDP
+        except ImportError:
+            raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.")
+
+        model = DDP(model)
+    elif n_gpu > 1:
+        model = torch.nn.DataParallel(model)
+
+    # Prepare optimizer
+    param_optimizer = list(model.named_parameters())
+    no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
+    optimizer_grouped_parameters = [
+        {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01},
+        {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
+        ]
+    t_total = num_train_steps
+    if args.local_rank != -1:
+        t_total = t_total // torch.distributed.get_world_size()
+    if args.fp16:
+        try:
+            from apex.optimizers import FP16_Optimizer
+            from apex.optimizers import FusedAdam
+        except ImportError:
+            raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.")
+
+        optimizer = FusedAdam(optimizer_grouped_parameters,
+                              lr=args.learning_rate,
+                              bias_correction=False,
+                              max_grad_norm=1.0)
+        if args.loss_scale == 0:
+            optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True)
+        else:
+            optimizer = FP16_Optimizer(optimizer, static_loss_scale=args.loss_scale)
+
+    else:
+        optimizer = BertAdam(optimizer_grouped_parameters,
+                             lr=args.learning_rate,
+                             warmup=args.warmup_proportion,
+                             t_total=t_total)
+
+    global_step = 0
+    if args.do_train:
+        train_features = convert_examples_to_features(
+            train_examples, label_list, args.max_seq_length, tokenizer)
+        logger.info("***** Running training *****")
+        logger.info("  Num examples = %d", len(train_examples))
+        logger.info("  Batch size = %d", args.train_batch_size)
+        logger.info("  Num steps = %d", num_train_steps)
+        all_input_ids = torch.tensor([f.input_ids for f in train_features], dtype=torch.long)
+        all_input_mask = torch.tensor([f.input_mask for f in train_features], dtype=torch.long)
+        all_segment_ids = torch.tensor([f.segment_ids for f in train_features], dtype=torch.long)
+        all_label_ids = torch.tensor([f.label_id for f in train_features], dtype=torch.long)
+        train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)
+        if args.local_rank == -1:
+            train_sampler = RandomSampler(train_data)
+        else:
+            train_sampler = DistributedSampler(train_data)
+        train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size)
+
+        model.train()
+        for _ in trange(int(args.num_train_epochs), desc="Epoch"):
+            tr_loss = 0
+            nb_tr_examples, nb_tr_steps = 0, 0
+            for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration")):
+                batch = tuple(t.to(device) for t in batch)
+                input_ids, input_mask, segment_ids, label_ids = batch
+                loss = model(input_ids, segment_ids, input_mask, label_ids)
+                if n_gpu > 1:
+                    loss = loss.mean() # mean() to average on multi-gpu.
+                if args.gradient_accumulation_steps > 1:
+                    loss = loss / args.gradient_accumulation_steps
+
+                if args.fp16:
+                    optimizer.backward(loss)
+                else:
+                    loss.backward()
+
+                tr_loss += loss.item()
+                nb_tr_examples += input_ids.size(0)
+                nb_tr_steps += 1
+                if (step + 1) % args.gradient_accumulation_steps == 0:
+                    # modify learning rate with special warm up BERT uses
+                    lr_this_step = args.learning_rate * warmup_linear(global_step/t_total, args.warmup_proportion)
+                    for param_group in optimizer.param_groups:
+                        param_group['lr'] = lr_this_step
+                    optimizer.step()
+                    optimizer.zero_grad()
+                    global_step += 1
+
+    # Save a trained model
+    model_to_save = model.module if hasattr(model, 'module') else model  # Only save the model it-self
+    output_model_file = os.path.join(args.output_dir, "pytorch_model.bin")
+    torch.save(model_to_save.state_dict(), output_model_file)
+
+    # Load a trained model that you have fine-tuned
+    model_state_dict = torch.load(output_model_file)
+    model = BertForSequenceClassification.from_pretrained(args.bert_model, state_dict=model_state_dict)
+    model.to(device)
+
+    if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
+        eval_examples = processor.get_dev_examples(args.data_dir)
+        eval_features = convert_examples_to_features(
+            eval_examples, label_list, args.max_seq_length, tokenizer)
+        logger.info("***** Running evaluation *****")
+        logger.info("  Num examples = %d", len(eval_examples))
+        logger.info("  Batch size = %d", args.eval_batch_size)
+        all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long)
+        all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long)
+        all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long)
+        all_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.long)
+        eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)
+        # Run prediction for full data
+        eval_sampler = SequentialSampler(eval_data)
+        eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size)
+
+        model.eval()
+        eval_loss, eval_accuracy = 0, 0
+        nb_eval_steps, nb_eval_examples = 0, 0
+        start_time = datetime.now()
+        for input_ids, input_mask, segment_ids, label_ids in eval_dataloader:
+            input_ids = input_ids.to(device)
+            input_mask = input_mask.to(device)
+            segment_ids = segment_ids.to(device)
+            label_ids = label_ids.to(device)
+
+            with torch.no_grad():
+                tmp_eval_loss = model(input_ids, segment_ids, input_mask, label_ids)
+                logits = model(input_ids, segment_ids, input_mask)
+
+            logits = logits.detach().cpu().numpy()
+            label_ids = label_ids.to('cpu').numpy()
+            tmp_eval_accuracy = accuracy(logits, label_ids)
+
+            eval_loss += tmp_eval_loss.mean().item()
+            eval_accuracy += tmp_eval_accuracy
+
+            nb_eval_examples += input_ids.size(0)
+            nb_eval_steps += 1
+
+        total_time = datetime.now() - start_time
+        logger.info("Total time: %d seconds — Number of examples: %d — Examples/second: %f" % (
+            total_time.total_seconds(), len(eval_data), len(eval_data)/total_time.total_seconds()))
+
+        eval_loss = eval_loss / nb_eval_steps
+        eval_accuracy = eval_accuracy / nb_eval_examples
+
+        result = {'eval_loss': eval_loss,
+                  'eval_accuracy': eval_accuracy,
+                  'global_step': global_step,
+                  'loss': tr_loss/nb_tr_steps}
+
+        output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
+        with open(output_eval_file, "w") as writer:
+            logger.info("***** Eval results *****")
+            for key in sorted(result.keys()):
+                logger.info("  %s = %s", key, str(result[key]))
+                writer.write("%s = %s\n" % (key, str(result[key])))
+
+if __name__ == "__main__":
+    main()
@@ -1,264 +0,0 @@
-#!/usr/bin/env python3
-# coding=utf-8
-# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University 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.
-""" Conditional text generation with the auto-regressive models of the library (GPT/GPT-2/CTRL/Transformer-XL/XLNet)
-"""
-from __future__ import absolute_import, division, print_function, unicode_literals
-
-import argparse
-import logging
-from tqdm import trange
-
-import torch
-import torch.nn.functional as F
-import numpy as np
-
-from transformers import GPT2Config, OpenAIGPTConfig, XLNetConfig, TransfoXLConfig, XLMConfig, CTRLConfig
-
-from transformers import GPT2LMHeadModel, GPT2Tokenizer
-from transformers import OpenAIGPTLMHeadModel, OpenAIGPTTokenizer
-from transformers import XLNetLMHeadModel, XLNetTokenizer
-from transformers import TransfoXLLMHeadModel, TransfoXLTokenizer
-from transformers import CTRLLMHeadModel, CTRLTokenizer
-from transformers import XLMWithLMHeadModel, XLMTokenizer
-
-
-logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s',
-                    datefmt = '%m/%d/%Y %H:%M:%S',
-                    level = logging.INFO)
-logger = logging.getLogger(__name__)
-
-MAX_LENGTH = int(10000)  # Hardcoded max length to avoid infinite loop
-
-ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) for conf in (GPT2Config, OpenAIGPTConfig, XLNetConfig, TransfoXLConfig, XLMConfig, CTRLConfig)), ())
-
-MODEL_CLASSES = {
-    'gpt2': (GPT2LMHeadModel, GPT2Tokenizer),
-    'ctrl': (CTRLLMHeadModel, CTRLTokenizer),
-    'openai-gpt': (OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
-    'xlnet': (XLNetLMHeadModel, XLNetTokenizer),
-    'transfo-xl': (TransfoXLLMHeadModel, TransfoXLTokenizer),
-    'xlm': (XLMWithLMHeadModel, XLMTokenizer),
-}
-
-# 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
-(except for Alexei and Maria) are discovered.
-The voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the
-remainder of the story. 1883 Western Siberia,
-a young Grigori Rasputin is asked by his father and a group of men to perform magic.
-Rasputin has a vision and denounces one of the men as a horse thief. Although his
-father initially slaps him for making such an accusation, Rasputin watches as the
-man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
-the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,
-with people, even a bishop, begging for his blessing. <eod> </s> <eos>"""
-
-
-def set_seed(args):
-    np.random.seed(args.seed)
-    torch.manual_seed(args.seed)
-    if args.n_gpu > 0:
-        torch.cuda.manual_seed_all(args.seed)
-
-
-def top_k_top_p_filtering(logits, top_k=0, top_p=0.0, filter_value=-float('Inf')):
-    """ Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
-        Args:
-            logits: logits distribution shape (batch size x vocabulary size)
-            top_k > 0: keep only top k tokens with highest probability (top-k filtering).
-            top_p > 0.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering).
-                Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
-        From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317
-    """
-    top_k = min(top_k, logits.size(-1))  # Safety check
-    if top_k > 0:
-        # Remove all tokens with a probability less than the last token of the top-k
-        indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
-        logits[indices_to_remove] = filter_value
-
-    if top_p > 0.0:
-        sorted_logits, sorted_indices = torch.sort(logits, descending=True)
-        cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
-
-        # Remove tokens with cumulative probability above the threshold
-        sorted_indices_to_remove = cumulative_probs > top_p
-        # Shift the indices to the right to keep also the first token above the threshold
-        sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
-        sorted_indices_to_remove[..., 0] = 0
-
-        # scatter sorted tensors to original indexing
-        indices_to_remove = sorted_indices_to_remove.scatter(dim=1, index=sorted_indices, src=sorted_indices_to_remove)
-        logits[indices_to_remove] = filter_value
-    return logits
-
-
-def sample_sequence(model, length, context, num_samples=1, temperature=1, top_k=0, top_p=0.0, repetition_penalty=1.0,
-                    is_xlnet=False, is_xlm_mlm=False, xlm_mask_token=None, xlm_lang=None, device='cpu'):
-    context = torch.tensor(context, dtype=torch.long, device=device)
-    context = context.unsqueeze(0).repeat(num_samples, 1)
-    generated = context
-    with torch.no_grad():
-        for _ in trange(length):
-
-            inputs = {'input_ids': generated}
-            if is_xlnet: 
-                # XLNet is a direct (predict same token, not next token) and bi-directional model by default
-                # => need one additional dummy token in the input (will be masked), attention mask and target mapping (see model docstring)
-                input_ids = torch.cat((generated, torch.zeros((1, 1), dtype=torch.long, device=device)), dim=1)
-                perm_mask = torch.zeros((1, input_ids.shape[1], input_ids.shape[1]), dtype=torch.float, device=device)
-                perm_mask[:, :, -1] = 1.0  # Previous tokens don't see last token
-                target_mapping = torch.zeros((1, 1, input_ids.shape[1]), dtype=torch.float, device=device)
-                target_mapping[0, 0, -1] = 1.0  # predict last token
-                inputs = {'input_ids': input_ids, 'perm_mask': perm_mask, 'target_mapping': target_mapping}
-
-            if is_xlm_mlm and xlm_mask_token:
-                # XLM MLM models are direct models (predict same token, not next token)
-                # => need one additional dummy token in the input (will be masked and guessed)
-                input_ids = torch.cat((generated, torch.full((1, 1), xlm_mask_token, dtype=torch.long, device=device)), dim=1)
-                inputs = {'input_ids': input_ids}
-
-            if xlm_lang is not None:
-                inputs["langs"] = torch.tensor([xlm_lang] * inputs["input_ids"].shape[1], device=device).view(1, -1)
-
-            outputs = model(**inputs)  # Note: we could also use 'past' with GPT-2/Transfo-XL/XLNet/CTRL (cached hidden-states)
-            next_token_logits = outputs[0][:, -1, :] / (temperature if temperature > 0 else 1.)
-
-            # repetition penalty from CTRL (https://arxiv.org/abs/1909.05858)
-            for i in range(num_samples):
-                for _ in set(generated[i].tolist()):
-                    next_token_logits[i, _] /= repetition_penalty
-                
-            filtered_logits = top_k_top_p_filtering(next_token_logits, top_k=top_k, top_p=top_p)
-            if temperature == 0: # greedy sampling:
-                next_token = torch.argmax(filtered_logits, dim=-1).unsqueeze(-1)
-            else:
-                next_token = torch.multinomial(F.softmax(filtered_logits, dim=-1), num_samples=1)
-            generated = torch.cat((generated, next_token), dim=1)
-    return generated
-
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--model_type", default=None, type=str, required=True,
-                        help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()))
-    parser.add_argument("--model_name_or_path", default=None, type=str, required=True,
-                        help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS))
-    parser.add_argument("--prompt", type=str, default="")
-    parser.add_argument("--padding_text", type=str, default="")
-    parser.add_argument("--xlm_lang", type=str, default="", help="Optional language when used with the XLM model.")
-    parser.add_argument("--length", type=int, default=20)
-    parser.add_argument("--num_samples", type=int, default=1)
-    parser.add_argument("--temperature", type=float, default=1.0,
-                        help="temperature of 0 implies greedy sampling")
-    parser.add_argument("--repetition_penalty", type=float, default=1.0,
-                        help="primarily useful for CTRL model; in that case, use 1.2")
-    parser.add_argument("--top_k", type=int, default=0)
-    parser.add_argument("--top_p", type=float, default=0.9)
-    parser.add_argument("--no_cuda", action='store_true',
-                        help="Avoid using CUDA when available")
-    parser.add_argument('--seed', type=int, default=42,
-                        help="random seed for initialization")
-    parser.add_argument('--stop_token', type=str, default=None,
-                        help="Token at which text generation is stopped")
-    args = parser.parse_args()
-
-    args.device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
-    args.n_gpu = torch.cuda.device_count()
-
-    set_seed(args)
-
-    args.model_type = args.model_type.lower()
-    model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
-    tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path)
-    model = model_class.from_pretrained(args.model_name_or_path)
-    model.to(args.device)
-    model.eval()
-
-    if args.length < 0 and model.config.max_position_embeddings > 0:
-        args.length = model.config.max_position_embeddings
-    elif 0 < model.config.max_position_embeddings < args.length:
-        args.length = model.config.max_position_embeddings  # No generation bigger than model size 
-    elif args.length < 0:
-        args.length = MAX_LENGTH  # avoid infinite loop
-
-    logger.info(args)
-    if args.model_type in ["ctrl"]:
-        if args.temperature > 0.7:
-            logger.info('CTRL typically works better with lower temperatures (and lower top_k).')
-
-    while True:
-        xlm_lang = None
-        # XLM Language usage detailed in the issues #1414
-        if args.model_type in ["xlm"] and hasattr(tokenizer, 'lang2id') and hasattr(model.config, 'use_lang_emb') \
-                and model.config.use_lang_emb:
-            if args.xlm_lang:
-                language = args.xlm_lang
-            else:
-                language = None
-                while language not in tokenizer.lang2id.keys():
-                    language = input("Using XLM. Select language in " + str(list(tokenizer.lang2id.keys())) + " >>> ")
-            xlm_lang = tokenizer.lang2id[language]
-
-        # XLM masked-language modeling (MLM) models need masked token (see details in sample_sequence)
-        is_xlm_mlm = args.model_type in ["xlm"] and 'mlm' in args.model_name_or_path
-        if is_xlm_mlm:
-            xlm_mask_token = tokenizer.mask_token_id
-        else:
-            xlm_mask_token = None
-
-        raw_text = args.prompt if args.prompt else input("Model prompt >>> ")
-        if args.model_type in ["transfo-xl", "xlnet"]:
-            # Models with memory likes to have a long prompt for short inputs.
-            raw_text = (args.padding_text if args.padding_text else PADDING_TEXT) + raw_text
-        context_tokens = tokenizer.encode(raw_text, add_special_tokens=False)
-        if args.model_type == "ctrl":
-            if not any(context_tokens[0] == x for x in tokenizer.control_codes.values()):
-                logger.info("WARNING! You are not starting your generation from a control code so you won't get good results")
-        out = sample_sequence(
-            model=model,
-            context=context_tokens,
-            num_samples=args.num_samples,
-            length=args.length,
-            temperature=args.temperature,
-            top_k=args.top_k,
-            top_p=args.top_p,
-            repetition_penalty=args.repetition_penalty,
-            is_xlnet=bool(args.model_type == "xlnet"),
-            is_xlm_mlm=is_xlm_mlm,
-            xlm_mask_token=xlm_mask_token,
-            xlm_lang=xlm_lang,
-            device=args.device,
-        )
-        out = out[:, len(context_tokens):].tolist()
-        for o in out:
-            text = tokenizer.decode(o, clean_up_tokenization_spaces=True)
-            if args.stop_token:
-                index =  text.find(args.stop_token)
-                if index == -1:
-                    index = None
-                text = text[:index]
-
-            print(text)
-
-        if args.prompt:
-            break
-    return text
-
-
-if __name__ == '__main__':
-    main()
@@ -1,540 +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 json
-
-import numpy as np
-import torch
-from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
-                              TensorDataset)
-from torch.utils.data.distributed import DistributedSampler
-
-try:
-    from torch.utils.tensorboard import SummaryWriter
-except:
-    from tensorboardX import SummaryWriter
-
-from tqdm import tqdm, trange
-
-from transformers import (WEIGHTS_NAME, BertConfig,
-                                  BertForSequenceClassification, BertTokenizer,
-                                  RobertaConfig,
-                                  RobertaForSequenceClassification,
-                                  RobertaTokenizer,
-                                  XLMConfig, XLMForSequenceClassification,
-                                  XLMTokenizer, XLNetConfig,
-                                  XLNetForSequenceClassification,
-                                  XLNetTokenizer,
-                                  DistilBertConfig,
-                                  DistilBertForSequenceClassification,
-                                  DistilBertTokenizer,
-                                  AlbertConfig,
-                                  AlbertForSequenceClassification, 
-                                  AlbertTokenizer,
-                                )
-
-from transformers import AdamW, get_linear_schedule_with_warmup
-
-from transformers import glue_compute_metrics as compute_metrics
-from transformers import glue_output_modes as output_modes
-from transformers import glue_processors as processors
-from transformers import glue_convert_examples_to_features as convert_examples_to_features
-
-logger = logging.getLogger(__name__)
-
-ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) for conf in (BertConfig, XLNetConfig, XLMConfig, 
-                                                                                RobertaConfig, DistilBertConfig)), ())
-
-MODEL_CLASSES = {
-    'bert': (BertConfig, BertForSequenceClassification, BertTokenizer),
-    'xlnet': (XLNetConfig, XLNetForSequenceClassification, XLNetTokenizer),
-    'xlm': (XLMConfig, XLMForSequenceClassification, XLMTokenizer),
-    'roberta': (RobertaConfig, RobertaForSequenceClassification, RobertaTokenizer),
-    'distilbert': (DistilBertConfig, DistilBertForSequenceClassification, DistilBertTokenizer),
-    'albert': (AlbertConfig, AlbertForSequenceClassification, AlbertTokenizer)
-}
-
-
-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 = 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()
-            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)
-
-        eval_loss = eval_loss / nb_eval_steps
-        if args.output_mode == "classification":
-            preds = np.argmax(preds, axis=1)
-        elif args.output_mode == "regression":
-            preds = np.squeeze(preds)
-        result = compute_metrics(eval_task, preds, out_label_ids)
-        results.update(result)
-
-        output_eval_file = os.path.join(eval_output_dir, prefix, "eval_results.txt")
-        with open(output_eval_file, "w") as writer:
-            logger.info("***** Eval results {} *****".format(prefix))
-            for key in sorted(result.keys()):
-                logger.info("  %s = %s", key, str(result[key]))
-                writer.write("%s = %s\n" % (key, str(result[key])))
-
-    return 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)))
-    if os.path.exists(cached_features_file) and not args.overwrite_cache:
-        logger.info("Loading features from cached file %s", cached_features_file)
-        features = torch.load(cached_features_file)
-    else:
-        logger.info("Creating features from dataset file at %s", args.data_dir)
-        label_list = processor.get_labels()
-        if task in ['mnli', 'mnli-mm'] and args.model_type in ['roberta']:
-            # HACK(label indices are swapped in RoBERTa pretrained model)
-            label_list[1], label_list[2] = label_list[2], label_list[1] 
-        examples = processor.get_dev_examples(args.data_dir) if evaluate else processor.get_train_examples(args.data_dir)
-        features = convert_examples_to_features(examples,
-                                                tokenizer,
-                                                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)
- 
-    dataset = TensorDataset(all_input_ids, all_attention_mask, all_token_type_ids, all_labels)
-    return dataset
-
-
-def main():
-    parser = argparse.ArgumentParser()
-
-    ## Required parameters
-    parser.add_argument("--data_dir", default=None, type=str, required=True,
-                        help="The input data dir. Should contain the .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 = 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()
@@ -1,594 +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.
-"""
-Fine-tuning the library models for language modeling on a text file (GPT, GPT-2, BERT, RoBERTa).
-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.
-"""
-
-from __future__ import absolute_import, division, print_function
-
-import argparse
-import glob
-import logging
-import os
-import pickle
-import random
-import re
-import shutil
-
-import numpy as np
-import torch
-from torch.utils.data import DataLoader, Dataset, SequentialSampler, RandomSampler
-from torch.utils.data.distributed import DistributedSampler
-
-try:
-    from torch.utils.tensorboard import SummaryWriter
-except:
-    from tensorboardX import SummaryWriter
-
-from tqdm import tqdm, trange
-
-from transformers import (WEIGHTS_NAME, AdamW, get_linear_schedule_with_warmup,
-                                  BertConfig, BertForMaskedLM, BertTokenizer,
-                                  GPT2Config, GPT2LMHeadModel, GPT2Tokenizer,
-                                  OpenAIGPTConfig, OpenAIGPTLMHeadModel, OpenAIGPTTokenizer,
-                                  RobertaConfig, RobertaForMaskedLM, RobertaTokenizer,
-                                  DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer,
-                                  CamembertConfig, CamembertForMaskedLM, CamembertTokenizer)
-
-
-logger = logging.getLogger(__name__)
-
-
-MODEL_CLASSES = {
-    'gpt2': (GPT2Config, GPT2LMHeadModel, GPT2Tokenizer),
-    'openai-gpt': (OpenAIGPTConfig, OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
-    'bert': (BertConfig, BertForMaskedLM, BertTokenizer),
-    'roberta': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
-    'distilbert': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
-    'camembert': (CamembertConfig, CamembertForMaskedLM, CamembertTokenizer)
-}
-
-
-class TextDataset(Dataset):
-    def __init__(self, tokenizer, args, file_path='train', block_size=512):
-        assert os.path.isfile(file_path)
-        directory, filename = os.path.split(file_path)
-        cached_features_file = os.path.join(directory, args.model_name_or_path + '_cached_lm_' + str(block_size) + '_' + filename)
-
-        if os.path.exists(cached_features_file) and not args.overwrite_cache:
-            logger.info("Loading features from cached file %s", cached_features_file)
-            with open(cached_features_file, 'rb') as handle:
-                self.examples = pickle.load(handle)
-        else:
-            logger.info("Creating features from dataset file at %s", directory)
-
-            self.examples = []
-            with open(file_path, encoding="utf-8") as f:
-                text = f.read()
-
-            tokenized_text = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(text))
-
-            for i in range(0, len(tokenized_text)-block_size+1, block_size): # Truncate in block of block_size
-                self.examples.append(tokenizer.build_inputs_with_special_tokens(tokenized_text[i:i+block_size]))
-            # Note that we are loosing the last truncated example here for the sake of simplicity (no padding)
-            # If your dataset is small, first you should loook for a bigger one :-) and second you
-            # can change this behavior by adding (model specific) padding.
-
-            logger.info("Saving features into cached file %s", cached_features_file)
-            with open(cached_features_file, 'wb') as handle:
-                pickle.dump(self.examples, handle, protocol=pickle.HIGHEST_PROTOCOL)
-
-    def __len__(self):
-        return len(self.examples)
-
-    def __getitem__(self, item):
-        return torch.tensor(self.examples[item])
-
-
-def load_and_cache_examples(args, tokenizer, evaluate=False):
-    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):
-    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 _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))
-        else:
-            regex_match = re.match('.*{}-([0-9]+)'.format(checkpoint_prefix), path)
-            if regex_match and regex_match.groups():
-                ordering_and_checkpoint_path.append((int(regex_match.groups()[0]), path))
-
-    checkpoints_sorted = sorted(ordering_and_checkpoint_path)
-    checkpoints_sorted = [checkpoint[1] for checkpoint in checkpoints_sorted]
-    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:
-        logger.info("Deleting older checkpoint [{}] due to args.save_total_limit".format(checkpoint))
-        shutil.rmtree(checkpoint)
-
-
-def mask_tokens(inputs, tokenizer, args):
-    """ Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original. """
-    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)
-    special_tokens_mask = [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)
-    masked_indices = torch.bernoulli(probability_matrix).bool()
-    labels[~masked_indices] = -1  # We only compute loss on masked tokens
-
-    # 80% of the time, we replace masked input tokens with tokenizer.mask_token ([MASK])
-    indices_replaced = torch.bernoulli(torch.full(labels.shape, 0.8)).bool() & masked_indices
-    inputs[indices_replaced] = tokenizer.convert_tokens_to_ids(tokenizer.mask_token)
-
-    # 10% of the time, we replace masked input tokens with random word
-    indices_random = torch.bernoulli(torch.full(labels.shape, 0.5)).bool() & masked_indices & ~indices_replaced
-    random_words = torch.randint(len(tokenizer), labels.shape, dtype=torch.long)
-    inputs[indices_random] = random_words[indices_random]
-
-    # The rest of the time (10% of the time) we keep the masked input tokens unchanged
-    return inputs, labels
-
-
-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)
-
-    # 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)
-    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
-    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):
-        # 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)
-
-    tr_loss, logging_loss = 0.0, 0.0
-
-    model_to_resize = model.module if hasattr(model, 'module') else model  # Take care of distributed/parallel training
-    model_to_resize.resize_token_embeddings(len(tokenizer))
-
-    model.zero_grad()
-    train_iterator = trange(epochs_trained, int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0])
-    set_seed(args)  # Added here for reproducibility (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):
-            
-            # Skip past any already trained steps if resuming training
-            if steps_trained_in_current_epoch > 0:
-                steps_trained_in_current_epoch -= 1
-                continue
-
-            inputs, labels = mask_tokens(batch, tokenizer, args) if args.mlm else (batch, batch)
-            inputs = inputs.to(args.device)
-            labels = labels.to(args.device)
-            model.train()
-            outputs = model(inputs, masked_lm_labels=labels) if args.mlm else model(inputs, labels=labels)
-            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:
-                    # 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)
-                    tb_writer.add_scalar('lr', scheduler.get_lr()[0], global_step)
-                    tb_writer.add_scalar('loss', (tr_loss - logging_loss)/args.logging_steps, global_step)
-                    logging_loss = tr_loss
-
-                if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
-                    checkpoint_prefix = 'checkpoint'
-                    # Save model checkpoint
-                    output_dir = os.path.join(args.output_dir, '{}-{}'.format(checkpoint_prefix, 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)
-                    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)
-
-                    _rotate_checkpoints(args, checkpoint_prefix)
-
-                    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
-        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_output_dir = args.output_dir
-
-    eval_dataset = load_and_cache_examples(args, tokenizer, evaluate=True)
-
-    if not os.path.exists(eval_output_dir) and args.local_rank in [-1, 0]:
-        os.makedirs(eval_output_dir)
-
-    args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
-    # Note that DistributedSampler samples randomly
-    eval_sampler = SequentialSampler(eval_dataset)
-    eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
-
-    # multi-gpu evaluate
-    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
-    model.eval()
-
-    for batch in tqdm(eval_dataloader, desc="Evaluating"):
-        inputs, labels = mask_tokens(batch, tokenizer, args) if args.mlm else (batch, batch)
-        inputs = inputs.to(args.device)
-        labels = labels.to(args.device)
-
-        with torch.no_grad():
-            outputs = model(inputs, masked_lm_labels=labels) if args.mlm else model(inputs, labels=labels)
-            lm_loss = outputs[0]
-            eval_loss += lm_loss.mean().item()
-        nb_eval_steps += 1
-
-    eval_loss = eval_loss / nb_eval_steps
-    perplexity = torch.exp(torch.tensor(eval_loss))
-
-    result = {
-        "perplexity": perplexity
-    }
-
-    output_eval_file = os.path.join(eval_output_dir, prefix, "eval_results.txt")
-    with open(output_eval_file, "w") as writer:
-        logger.info("***** Eval results {} *****".format(prefix))
-        for key in sorted(result.keys()):
-            logger.info("  %s = %s", key, str(result[key]))
-            writer.write("%s = %s\n" % (key, str(result[key])))
-
-    return result
-
-
-def main():
-    parser = argparse.ArgumentParser()
-
-    ## Required parameters
-    parser.add_argument("--train_data_file", default=None, type=str, required=True,
-                        help="The input training data file (a text file).")
-    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("--eval_data_file", default=None, type=str,
-                        help="An optional input evaluation data file to evaluate the perplexity on (a text file).")
-
-    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="bert-base-cased", type=str,
-                        help="The model checkpoint for weights initialization.")
-
-    parser.add_argument("--mlm", action='store_true',
-                        help="Train with masked-language modeling loss instead of language modeling.")
-    parser.add_argument("--mlm_probability", type=float, default=0.15,
-                        help="Ratio of tokens to mask for masked language modeling loss")
-
-    parser.add_argument("--config_name", default="", type=str,
-                        help="Optional pretrained config name or path if not the same as model_name_or_path")
-    parser.add_argument("--tokenizer_name", default="", type=str,
-                        help="Optional pretrained tokenizer name or path if not the same as model_name_or_path")
-    parser.add_argument("--cache_dir", default="", type=str,
-                        help="Optional directory to store the pre-trained models downloaded from s3 (instread of the default one)")
-    parser.add_argument("--block_size", default=-1, type=int,
-                        help="Optional input sequence length after tokenization."
-                             "The training dataset will be truncated in block of this size for training."
-                             "Default to the model max input length for single sentence inputs (take into account special tokens).")
-    parser.add_argument("--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.")
-    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("--per_gpu_eval_batch_size", default=4, type=int,
-                        help="Batch size per GPU/CPU for evaluation.")
-    parser.add_argument('--gradient_accumulation_steps', type=int, default=1,
-                        help="Number of updates steps to accumulate before performing a backward/update pass.")
-    parser.add_argument("--learning_rate", default=5e-5, type=float,
-                        help="The initial learning rate for Adam.")
-    parser.add_argument("--weight_decay", default=0.0, type=float,
-                        help="Weight deay if we apply some.")
-    parser.add_argument("--adam_epsilon", default=1e-8, type=float,
-                        help="Epsilon for Adam optimizer.")
-    parser.add_argument("--max_grad_norm", default=1.0, type=float,
-                        help="Max gradient norm.")
-    parser.add_argument("--num_train_epochs", default=1.0, 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('--save_total_limit', type=int, default=None,
-                        help='Limit the total amount of checkpoints, delete the older checkpoints in the output_dir, does not delete by default')
-    parser.add_argument("--eval_all_checkpoints", action='store_true',
-                        help="Evaluate all checkpoints starting with the same prefix as model_name_or_path 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 args.model_type in ["bert", "roberta", "distilbert", "camembert"] and not args.mlm:
-        raise ValueError("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:
-        raise ValueError("Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file "
-                         "or remove the --do_eval argument.")
-
-    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 = torch.cuda.device_count()
-    else:  # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
-        torch.cuda.set_device(args.local_rank)
-        device = torch.device("cuda", args.local_rank)
-        torch.distributed.init_process_group(backend='nccl')
-        args.n_gpu = 1
-    args.device = device
-
-    # Setup logging
-    logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s',
-                        datefmt = '%m/%d/%Y %H:%M:%S',
-                        level = logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
-    logger.warning("Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
-                    args.local_rank, device, args.n_gpu, bool(args.local_rank != -1), args.fp16)
-
-    # Set seed
-    set_seed(args)
-
-    # Load pretrained model and tokenizer
-    if args.local_rank not in [-1, 0]:
-        torch.distributed.barrier()  # Barrier to make sure only the first process in distributed training download model & vocab
-
-    config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
-    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_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:
-        torch.distributed.barrier()  # End of barrier to make sure only the first process in distributed training download model & vocab
-
-    logger.info("Training/evaluation parameters %s", args)
-
-    # Training
-    if args.do_train:
-        if args.local_rank not in [-1, 0]:
-            torch.distributed.barrier()  # Barrier to make sure only the first process in distributed training process the dataset, and the others will use the cache
-
-        train_dataset = load_and_cache_examples(args, tokenizer, evaluate=False)
-
-        if args.local_rank == 0:
-            torch.distributed.barrier()
-
-        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 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 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, do_lower_case=args.do_lower_case)
-        model.to(args.device)
-
-
-    # Evaluation
-    results = {}
-    if args.do_eval and args.local_rank in [-1, 0]:
-        checkpoints = [args.output_dir]
-        if args.eval_all_checkpoints:
-            checkpoints = list(os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME, recursive=True)))
-            logging.getLogger("transformers.modeling_utils").setLevel(logging.WARN)  # Reduce logging
-        logger.info("Evaluate the following checkpoints: %s", checkpoints)
-        for checkpoint in checkpoints:
-            global_step = checkpoint.split('-')[-1] if len(checkpoints) > 1 else ""
-            prefix = checkpoint.split('/')[-1] if checkpoint.find('checkpoint') != -1 else ""
-            
-            model = 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()
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
thomwolf	0046635551	update to store uncompressed model on S3	2018-12-19 11:18:17 +01:00
thomwolf	466ed99196	do not save model if no training	2018-12-18 23:26:49 +01:00
thomwolf	ba910fd8a0	predict_fp16	2018-12-18 23:12:44 +01:00
thomwolf	59f9cc21bf	add do_benchmark	2018-12-18 22:38:03 +01:00
thomwolf	3eb54d6ded	added prediction timing to run_squad and run_classifier	2018-12-18 22:02:32 +01:00