Upload v4.0.0 to conda

* Remove deprecated `evalutate_during_training`

* Update src/transformers/training_args_tf.py

Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

.circleci/config.yml

@@ -3,22 +3,6 @@ orbs:
     gcp-gke: circleci/gcp-gke@1.0.4
     go: circleci/go@1.3.0
 
-commands:
-  skip-job-on-doc-only-changes:
-    description: "Do not continue this job and exit with success for PRs with only doc changes"
-    steps:
-
-      - run:
-          name: docs-only changes skip check
-          command: |
-            if git diff --name-only << pipeline.git.base_revision >>...<< pipeline.git.revision >> | egrep -qv '\.(md|rst)$'
-            then
-                echo "Non-docs were modified in this PR, proceeding normally"
-            else
-                echo "Only docs were modified in this PR, quitting this job"
-                circleci step halt
-            fi
-
 # TPU REFERENCES
 references:
     checkout_ml_testing: &checkout_ml_testing
@@ -88,7 +72,6 @@ jobs:
         parallelism: 1
         steps:
             - checkout
-            - skip-job-on-doc-only-changes
             - restore_cache:
                   keys:
                       - v0.4-torch_and_tf-{{ checksum "setup.py" }}
@@ -115,7 +98,6 @@ jobs:
         parallelism: 1
         steps:
             - checkout
-            - skip-job-on-doc-only-changes
             - restore_cache:
                   keys:
                       - v0.4-torch-{{ checksum "setup.py" }}
@@ -142,7 +124,6 @@ jobs:
         parallelism: 1
         steps:
             - checkout
-            - skip-job-on-doc-only-changes
             - restore_cache:
                   keys:
                       - v0.4-tf-{{ checksum "setup.py" }}
@@ -169,7 +150,6 @@ jobs:
         parallelism: 1
         steps:
             - checkout
-            - skip-job-on-doc-only-changes
             - restore_cache:
                 keys:
                     - v0.4-flax-{{ checksum "setup.py" }}
@@ -196,7 +176,6 @@ jobs:
         parallelism: 1
         steps:
             - checkout
-            - skip-job-on-doc-only-changes
             - restore_cache:
                   keys:
                       - v0.4-torch-{{ checksum "setup.py" }}
@@ -223,7 +202,6 @@ jobs:
         parallelism: 1
         steps:
             - checkout
-            - skip-job-on-doc-only-changes
             - restore_cache:
                   keys:
                       - v0.4-tf-{{ checksum "setup.py" }}
@@ -243,12 +221,11 @@ jobs:
     run_tests_custom_tokenizers:
         working_directory: ~/transformers
         docker:
-            - image: circleci/python:3.7
+            - image: circleci/python:3.6
         environment:
             RUN_CUSTOM_TOKENIZERS: yes
         steps:
             - checkout
-            - skip-job-on-doc-only-changes
             - restore_cache:
                   keys:
                       - v0.4-custom_tokenizers-{{ checksum "setup.py" }}
@@ -276,7 +253,6 @@ jobs:
         parallelism: 1
         steps:
             - checkout
-            - skip-job-on-doc-only-changes
             - restore_cache:
                   keys:
                       - v0.4-torch_examples-{{ checksum "setup.py" }}

.circleci/deploy.sh

@@ -52,4 +52,5 @@ deploy_doc "4b3ee9c" v3.1.0
 deploy_doc "3ebb1b3" v3.2.0
 deploy_doc "0613f05" v3.3.1
 deploy_doc "eb0e0ce" v3.4.0
-deploy_doc "818878d" # v3.5.1 Latest stable release
+deploy_doc "818878d" v3.5.1
+deploy_doc "c781171" # v4.0.0 Latest stable release

.github/PULL_REQUEST_TEMPLATE.md

@@ -58,5 +58,5 @@ members/contributors which may be interested in your PR.
  tensorflow: @jplu
  examples/token-classification: @stefan-it
  documentation: @sgugger
- FSMT: @stas00
+ FSTM: @stas00
  -->

.github/conda/build.sh

@@ -0,0 +1 @@
+$PYTHON setup.py install     # Python command to install the script.

.github/conda/meta.yaml

@@ -0,0 +1,48 @@
+{% set name = "transformers" %}
+
+package:
+  name: "{{ name|lower }}"
+  version: "{{ TRANSFORMERS_VERSION }}"
+
+source:
+  path: ../../
+
+build:
+  noarch: python
+
+requirements:
+  host:
+    - python
+    - pip
+    - numpy
+    - dataclasses
+    - packaging
+    - filelock
+    - requests
+    - tqdm >=4.27
+    - sacremoses
+    - regex !=2019.12.17
+    - protobuf
+    - tokenizers ==0.9.4
+  run:
+    - python
+    - numpy
+    - dataclasses
+    - packaging
+    - filelock
+    - requests
+    - tqdm >=4.27
+    - sacremoses
+    - regex !=2019.12.17
+    - protobuf
+    - tokenizers ==0.9.4
+
+test:
+  imports:
+    - transformers
+
+about:
+  home: https://huggingface.co
+  license: Apache License 2.0
+  license_file: LICENSE
+  summary: "🤗Transformers: State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0."

.github/workflows/release-conda.yml

@@ -0,0 +1,46 @@
+name: Release - Conda
+
+on:
+  push:
+    tags:
+      - v*
+    branches:
+      - v4.0.0-release
+
+
+env:
+  ANACONDA_API_TOKEN: ${{ secrets.ANACONDA_API_TOKEN }}
+
+jobs:
+  build_and_package:
+    runs-on: ubuntu-latest
+    defaults:
+      run:
+        shell: bash -l {0}
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v1
+
+      - name: Install miniconda
+        uses: conda-incubator/setup-miniconda@v2
+        with:
+          auto-update-conda: true
+          auto-activate-base: false
+          activate-environment: "build-transformers"
+          channels: huggingface
+
+      - name: Setup conda env
+        run: |
+          conda install -c defaults anaconda-client conda-build
+
+      - name: Extract version
+        run: echo "TRANSFORMERS_VERSION=`python setup.py --version`" >> $GITHUB_ENV
+
+      - name: Build conda packages
+        run: |
+          conda info
+          conda build .github/conda
+
+      - name: Upload to Anaconda
+        run: anaconda upload `conda build .github/conda --output` --force

.github/workflows/self-push.yml

@@ -57,13 +57,13 @@ jobs:
           python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
           python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"
 
-#      - name: Create model files
-#        run: |
-#          source .env/bin/activate
-#          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/encoder-bert-tokenizer.json --path=templates/adding_a_new_model
-#          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/pt-encoder-bert-tokenizer.json --path=templates/adding_a_new_model
-#          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/standalone.json --path=templates/adding_a_new_model
-#          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/tf-encoder-bert-tokenizer.json --path=templates/adding_a_new_model
+      - name: Create model files
+        run: |
+          source .env/bin/activate
+          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/encoder-bert-tokenizer.json --path=templates/adding_a_new_model
+          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/pt-encoder-bert-tokenizer.json --path=templates/adding_a_new_model
+          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/standalone.json --path=templates/adding_a_new_model
+          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/tf-encoder-bert-tokenizer.json --path=templates/adding_a_new_model
 
       - name: Run all non-slow tests on GPU
         env:
@@ -129,10 +129,10 @@ jobs:
       - name: Create model files
         run: |
           source .env/bin/activate
-#          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/encoder-bert-tokenizer.json --path=templates/adding_a_new_model
-#          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/pt-encoder-bert-tokenizer.json --path=templates/adding_a_new_model
-#          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/standalone.json --path=templates/adding_a_new_model
-#          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/tf-encoder-bert-tokenizer.json --path=templates/adding_a_new_model
+          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/encoder-bert-tokenizer.json --path=templates/adding_a_new_model
+          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/pt-encoder-bert-tokenizer.json --path=templates/adding_a_new_model
+          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/standalone.json --path=templates/adding_a_new_model
+          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/tf-encoder-bert-tokenizer.json --path=templates/adding_a_new_model
 
       - name: Run all non-slow tests on GPU
         env:

CONTRIBUTING.md

@@ -125,7 +125,7 @@ Follow these steps to start contributing:
    $ git checkout -b a-descriptive-name-for-my-changes
    ```
 
-   **Do not** work on the `master` branch.
+   **do not** work on the `master` branch.
 
 4. Set up a development environment by running the following command in a virtual environment:
 
@@ -317,16 +317,3 @@ One way one can run the make command on Window is to pass by MSYS2:
 1. [Download MSYS2](https://www.msys2.org/), we assume to have it installed in C:\msys64
 2. Open the command line C:\msys64\msys2.exe (it should be available from the start menu)
 3. Run in the shell: `pacman -Syu` and install make with `pacman -S make`
-
-### Syncing forked master with upstream (HuggingFace) master
-
-To avoid pinging the upstream repository which adds reference notes to each upstream PR and sends unnessary notifications to the developers involved in these PRs, 
-when syncing the master branch of a forked repository, please, follow these steps:
-1. When possible, avoid syncing with the upstream using a branch and PR on the forked repository. Instead merge directly into the forked master.
-2. If a PR is absolutely necessary, use the following steps after checking out your branch:
-```
-$ git checkout -b your-branch-for-syncing
-$ git pull --squash --no-commit upstream master
-$ git commit -m '<your message without GitHub references>'
-$ git push --set-upstream origin your-branch-for-syncing
-```

Makefile

@@ -1,4 +1,4 @@
-.PHONY: deps_table_update modified_only_fixup extra_quality_checks quality style fixup fix-copies test test-examples docs
+.PHONY: modified_only_fixup extra_quality_checks quality style fixup fix-copies test test-examples docs
 
 
 check_dirs := examples tests src utils
@@ -14,14 +14,9 @@ modified_only_fixup:
 		echo "No library .py files were modified"; \
 	fi
 
-# Update src/transformers/dependency_versions_table.py
-
-deps_table_update:
-	@python setup.py deps_table_update
-
 # Check that source code meets quality standards
 
-extra_quality_checks: deps_table_update
+extra_quality_checks:
 	python utils/check_copies.py
 	python utils/check_dummies.py
 	python utils/check_repo.py
@@ -37,7 +32,7 @@ quality:
 
 # Format source code automatically and check is there are any problems left that need manual fixing
 
-style: deps_table_update
+style:
 	black $(check_dirs)
 	isort $(check_dirs)
 	python utils/style_doc.py src/transformers docs/source --max_len 119

README.md

@@ -137,14 +137,16 @@ The model itself is a regular [Pytorch `nn.Module`](https://pytorch.org/docs/sta
 
 ## Installation
 
+### With pip
+
 This repository is tested on Python 3.6+, PyTorch 1.0.0+ (PyTorch 1.3.1+ for [examples](https://github.com/huggingface/transformers/tree/master/examples)) and TensorFlow 2.0.
 
 You should install 🤗 Transformers in a [virtual environment](https://docs.python.org/3/library/venv.html). If you're unfamiliar with Python virtual environments, check out the [user guide](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/).
 
 First, create a virtual environment with the version of Python you're going to use and activate it.
 
-Then, you will need to install one of, or both, TensorFlow 2.0 and PyTorch.
-Please refer to [TensorFlow installation page](https://www.tensorflow.org/install/pip#tensorflow-2.0-rc-is-available) and/or [PyTorch installation page](https://pytorch.org/get-started/locally/#start-locally) regarding the specific install command for your platform.
+Then, you will need to install at least one of TensorFlow 2.0, PyTorch or Flax.
+Please refer to [TensorFlow installation page](https://www.tensorflow.org/install/pip#tensorflow-2.0-rc-is-available), [PyTorch installation page](https://pytorch.org/get-started/locally/#start-locally) regarding the specific install command for your platform and/or [Flax installation page](https://github.com/google/flax#quick-install).
 
 When TensorFlow 2.0 and/or PyTorch has been installed, 🤗 Transformers can be installed using pip as follows:
 
@@ -154,13 +156,24 @@ pip install transformers
 
 If you'd like to play with the examples, you must [install the library from source](https://huggingface.co/transformers/installation.html#installing-from-source).
 
+### With conda
+
+Since Transformers version v4.0.0, we now have a conda channel: `huggingface`.
+
+🤗 Transformers can be installed using conda as follows:
+
+```shell script
+conda install -c huggingface transformers
+```
+
+Follow the installation pages of TensorFlow, PyTorch or Flax to see how to install them with conda. 
+
 ## Models architectures
 
 🤗 Transformers currently provides the following architectures (see [here](https://huggingface.co/transformers/model_summary.html) for a high-level summary of each them):
 
 1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.
 1. **[BART](https://huggingface.co/transformers/model_doc/bart.html)** (from Facebook) released with the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/pdf/1910.13461.pdf) by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer.
-1. **[BARThez](https://huggingface.co/transformers/model_doc/barthez.html)** (from École polytechnique) released with the paper [BARThez: a Skilled Pretrained French Sequence-to-Sequence Model](https://arxiv.org/abs/2010.12321) by Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis.
 1. **[BERT](https://huggingface.co/transformers/model_doc/bert.html)** (from Google) released with the paper [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.
 1. **[BERT For Sequence Generation](https://huggingface.co/transformers/model_doc/bertgeneration.html)** (from Google) released with the paper [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
 1. **[Blenderbot](https://huggingface.co/transformers/model_doc/blenderbot.html)** (from Facebook) released with the paper [Recipes for building an open-domain chatbot](https://arxiv.org/abs/2004.13637) by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
@@ -198,6 +211,8 @@ ultilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers/
 1. **[Other community models](https://huggingface.co/models)**, contributed by the [community](https://huggingface.co/users).
 1. Want to contribute a new model? We have added a **detailed guide and templates** to guide you in the process of adding a new model. You can find them in the [`templates`](./templates) folder of the repository. Be sure to check the [contributing guidelines](./CONTRIBUTING.md) and contact the maintainers or open an issue to collect feedbacks before starting your PR.
 
+To cehck if each model has an implementation in PyTorch/TensorFlow/Flax or has an associated tokenizer backed by the 🤗 Tokenizers library, refer to [this table](https://huggingface.co/transformers/index.html#bigtable)
+
 These implementations have been tested on several datasets (see the example scripts) and should match the performances of the original implementations. You can find more details on the performances in the Examples section of the [documentation](https://huggingface.co/transformers/examples.html).
 
 
@@ -215,17 +230,13 @@ These implementations have been tested on several datasets (see the example scri
 
 ## Citation
 
-We now have a [paper](https://www.aclweb.org/anthology/2020.emnlp-demos.6/) you can cite for the 🤗 Transformers library:
+We now have a [paper](https://arxiv.org/abs/1910.03771) you can cite for the 🤗 Transformers library:
 ```bibtex
-@inproceedings{wolf-etal-2020-transformers,
-    title = "Transformers: State-of-the-Art Natural Language Processing",
-    author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush",
-    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
-    month = oct,
-    year = "2020",
-    address = "Online",
-    publisher = "Association for Computational Linguistics",
-    url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6",
-    pages = "38--45"
+@article{Wolf2019HuggingFacesTS,
+  title={HuggingFace's Transformers: State-of-the-art Natural Language Processing},
+  author={Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush},
+  journal={ArXiv},
+  year={2019},
+  volume={abs/1910.03771}
 }
 ```

docs/source/_static/js/custom.js

@@ -1,14 +1,15 @@
 // These two things need to be updated at each release for the version selector.
 // Last stable version
-const stableVersion = "v3.5.0"
+const stableVersion = "v4.0.0"
 // Dictionary doc folder to label
 const versionMapping = {
     "master": "master",
-    "": "v3.5.0/v3.5.1",
+    "v4.0.0": "v4.0.0",
+    "v3.5.1": "v3.5.0/v3.5.1",
     "v3.4.0": "v3.4.0",
     "v3.3.1": "v3.3.0/v3.3.1",
     "v3.2.0": "v3.2.0",
-    "v3.1.0": "v3.1.0 (stable)",
+    "v3.1.0": "v3.1.0",
     "v3.0.2": "v3.0.0/v3.0.1/v3.0.2",
     "v2.11.0": "v2.11.0",
     "v2.10.0": "v2.10.0",

docs/source/conf.py

@@ -26,7 +26,7 @@ author = u'huggingface'
 # The short X.Y version
 version = u''
 # The full version, including alpha/beta/rc tags
-release = u'3.5.0'
+release = u'4.0.0'
 
 
 # -- General configuration ---------------------------------------------------

docs/source/index.rst

@@ -68,110 +68,109 @@ and conversion utilities for the following models:
    Pre-training for Natural Language Generation, Translation, and Comprehension
    <https://arxiv.org/pdf/1910.13461.pdf>`__ by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman
    Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer.
-3. :doc:`BARThez <model_doc/barthez>` (from École polytechnique) released with the paper `BARThez: a Skilled Pretrained
-   French Sequence-to-Sequence Model <https://arxiv.org/abs/2010.12321>`__ by Moussa Kamal Eddine, Antoine J.-P.
-   Tixier, Michalis Vazirgiannis.
-4. :doc:`BERT <model_doc/bert>` (from Google) released with the paper `BERT: Pre-training of Deep Bidirectional
+3. :doc:`BERT <model_doc/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.
-5. :doc:`BERT For Sequence Generation <model_doc/bertgeneration>` (from Google) released with the paper `Leveraging
+4. :doc:`BERT For Sequence Generation <model_doc/bertgeneration>` (from Google) released with the paper `Leveraging
    Pre-trained Checkpoints for Sequence Generation Tasks <https://arxiv.org/abs/1907.12461>`__ by Sascha Rothe, Shashi
    Narayan, Aliaksei Severyn.
-6. :doc:`Blenderbot <model_doc/blenderbot>` (from Facebook) released with the paper `Recipes for building an
+5. :doc:`Blenderbot <model_doc/blenderbot>` (from Facebook) released with the paper `Recipes for building an
    open-domain chatbot <https://arxiv.org/abs/2004.13637>`__ by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary
    Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
-7. :doc:`CamemBERT <model_doc/camembert>` (from Inria/Facebook/Sorbonne) released with the paper `CamemBERT: a Tasty
+6. :doc:`CamemBERT <model_doc/camembert>` (from Inria/Facebook/Sorbonne) released with the paper `CamemBERT: a Tasty
    French Language Model <https://arxiv.org/abs/1911.03894>`__ by Louis Martin*, Benjamin Muller*, Pedro Javier Ortiz
    Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
-8. :doc:`CTRL <model_doc/ctrl>` (from Salesforce) released with the paper `CTRL: A Conditional Transformer Language
+7. :doc:`CTRL <model_doc/ctrl>` (from Salesforce) released with the paper `CTRL: A Conditional Transformer Language
    Model for Controllable Generation <https://arxiv.org/abs/1909.05858>`__ by Nitish Shirish Keskar*, Bryan McCann*,
    Lav R. Varshney, Caiming Xiong and Richard Socher.
-9. :doc:`DeBERTa <model_doc/deberta>` (from Microsoft Research) released with the paper `DeBERTa: Decoding-enhanced
+8. :doc:`DeBERTa <model_doc/deberta>` (from Microsoft Research) released with the paper `DeBERTa: Decoding-enhanced
    BERT with Disentangled Attention <https://arxiv.org/abs/2006.03654>`__ by Pengcheng He, Xiaodong Liu, Jianfeng Gao,
    Weizhu Chen.
-10. :doc:`DialoGPT <model_doc/dialogpt>` (from Microsoft Research) released with the paper `DialoGPT: Large-Scale
-    Generative Pre-training for Conversational Response Generation <https://arxiv.org/abs/1911.00536>`__ by Yizhe
-    Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan.
-11. :doc:`DistilBERT <model_doc/distilbert>` (from HuggingFace), released together with the paper `DistilBERT, a
+9. :doc:`DialoGPT <model_doc/dialogpt>` (from Microsoft Research) released with the paper `DialoGPT: Large-Scale
+   Generative Pre-training for Conversational Response Generation <https://arxiv.org/abs/1911.00536>`__ by Yizhe Zhang,
+   Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan.
+10. :doc:`DistilBERT <model_doc/distilbert>` (from HuggingFace), released together with the paper `DistilBERT, a
     distilled version of BERT: smaller, faster, cheaper and lighter <https://arxiv.org/abs/1910.01108>`__ by Victor
     Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into `DistilGPT2
     <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__, RoBERTa into `DistilRoBERTa
     <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__, Multilingual BERT into
     `DistilmBERT <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__ and a German
     version of DistilBERT.
-12. :doc:`DPR <model_doc/dpr>` (from Facebook) released with the paper `Dense Passage Retrieval for Open-Domain
+11. :doc:`DPR <model_doc/dpr>` (from Facebook) released with the paper `Dense Passage Retrieval for Open-Domain
     Question Answering <https://arxiv.org/abs/2004.04906>`__ by Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick
     Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
-13. :doc:`ELECTRA <model_doc/electra>` (from Google Research/Stanford University) released with the paper `ELECTRA:
+12. :doc:`ELECTRA <model_doc/electra>` (from Google Research/Stanford University) released with the paper `ELECTRA:
     Pre-training text encoders as discriminators rather than generators <https://arxiv.org/abs/2003.10555>`__ by Kevin
     Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning.
-14. :doc:`FlauBERT <model_doc/flaubert>` (from CNRS) released with the paper `FlauBERT: Unsupervised Language Model
+13. :doc:`FlauBERT <model_doc/flaubert>` (from CNRS) released with the paper `FlauBERT: Unsupervised Language Model
     Pre-training for French <https://arxiv.org/abs/1912.05372>`__ by Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne,
     Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, Didier Schwab.
-15. :doc:`Funnel Transformer <model_doc/funnel>` (from CMU/Google Brain) released with the paper `Funnel-Transformer:
+14. :doc:`Funnel Transformer <model_doc/funnel>` (from CMU/Google Brain) released with the paper `Funnel-Transformer:
     Filtering out Sequential Redundancy for Efficient Language Processing <https://arxiv.org/abs/2006.03236>`__ by
     Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le.
-16. :doc:`GPT <model_doc/gpt>` (from OpenAI) released with the paper `Improving Language Understanding by Generative
+15. :doc:`GPT <model_doc/gpt>` (from OpenAI) released with the paper `Improving Language Understanding by Generative
     Pre-Training <https://blog.openai.com/language-unsupervised/>`__ by Alec Radford, Karthik Narasimhan, Tim Salimans
     and Ilya Sutskever.
-17. :doc:`GPT-2 <model_doc/gpt2>` (from OpenAI) released with the paper `Language Models are Unsupervised Multitask
+16. :doc:`GPT-2 <model_doc/gpt2>` (from OpenAI) released with the paper `Language Models are Unsupervised Multitask
     Learners <https://blog.openai.com/better-language-models/>`__ by Alec Radford*, Jeffrey Wu*, Rewon Child, David
     Luan, Dario Amodei** and Ilya Sutskever**.
-18. :doc:`LayoutLM <model_doc/layoutlm>` (from Microsoft Research Asia) released with the paper `LayoutLM: Pre-training
+17. :doc:`LayoutLM <model_doc/layoutlm>` (from Microsoft Research Asia) released with the paper `LayoutLM: Pre-training
     of Text and Layout for Document Image Understanding <https://arxiv.org/abs/1912.13318>`__ by Yiheng Xu, Minghao Li,
     Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou.
-19. :doc:`Longformer <model_doc/longformer>` (from AllenAI) released with the paper `Longformer: The Long-Document
+18. :doc:`Longformer <model_doc/longformer>` (from AllenAI) released with the paper `Longformer: The Long-Document
     Transformer <https://arxiv.org/abs/2004.05150>`__ by Iz Beltagy, Matthew E. Peters, Arman Cohan.
-20. :doc:`LXMERT <model_doc/lxmert>` (from UNC Chapel Hill) released with the paper `LXMERT: Learning Cross-Modality
+19. :doc:`LXMERT <model_doc/lxmert>` (from UNC Chapel Hill) released with the paper `LXMERT: Learning Cross-Modality
     Encoder Representations from Transformers for Open-Domain Question Answering <https://arxiv.org/abs/1908.07490>`__
     by Hao Tan and Mohit Bansal.
-21. :doc:`MarianMT <model_doc/marian>` Machine translation models trained using `OPUS <http://opus.nlpl.eu/>`__ data by
+20. :doc:`MarianMT <model_doc/marian>` Machine translation models trained using `OPUS <http://opus.nlpl.eu/>`__ data by
     Jörg Tiedemann. The `Marian Framework <https://marian-nmt.github.io/>`__ is being developed by the Microsoft
     Translator Team.
-22. :doc:`MBart <model_doc/mbart>` (from Facebook) released with the paper `Multilingual Denoising Pre-training for
+21. :doc:`MBart <model_doc/mbart>` (from Facebook) released with the paper `Multilingual Denoising Pre-training for
     Neural Machine Translation <https://arxiv.org/abs/2001.08210>`__ by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li,
     Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
-23. :doc:`MT5 <model_doc/mt5>` (from Google AI) released with the paper `mT5: A massively multilingual pre-trained
+22. :doc:`MT5 <model_doc/mt5>` (from Google AI) released with the paper `mT5: A massively multilingual pre-trained
     text-to-text transformer <https://arxiv.org/abs/2010.11934>`__ by Linting Xue, Noah Constant, Adam Roberts, Mihir
     Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel.
-24. :doc:`Pegasus <model_doc/pegasus>` (from Google) released with the paper `PEGASUS: Pre-training with Extracted
+23. :doc:`Pegasus <model_doc/pegasus>` (from Google) released with the paper `PEGASUS: Pre-training with Extracted
     Gap-sentences for Abstractive Summarization <https://arxiv.org/abs/1912.08777>`__> by Jingqing Zhang, Yao Zhao,
     Mohammad Saleh and Peter J. Liu.
-25. :doc:`ProphetNet <model_doc/prophetnet>` (from Microsoft Research) released with the paper `ProphetNet: Predicting
+24. :doc:`ProphetNet <model_doc/prophetnet>` (from Microsoft Research) released with the paper `ProphetNet: Predicting
     Future N-gram for Sequence-to-Sequence Pre-training <https://arxiv.org/abs/2001.04063>`__ by Yu Yan, Weizhen Qi,
     Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
-26. :doc:`Reformer <model_doc/reformer>` (from Google Research) released with the paper `Reformer: The Efficient
+25. :doc:`Reformer <model_doc/reformer>` (from Google Research) released with the paper `Reformer: The Efficient
     Transformer <https://arxiv.org/abs/2001.04451>`__ by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
-27. :doc:`RoBERTa <model_doc/roberta>` (from Facebook), released together with the paper a `Robustly Optimized BERT
+26. :doc:`RoBERTa <model_doc/roberta>` (from Facebook), released together with the paper a `Robustly Optimized BERT
     Pretraining Approach <https://arxiv.org/abs/1907.11692>`__ by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar
     Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov. ultilingual BERT into `DistilmBERT
     <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__ and a German version of
     DistilBERT.
-28. :doc:`SqueezeBert <model_doc/squeezebert>` released with the paper `SqueezeBERT: What can computer vision teach NLP
+27. :doc:`SqueezeBert <model_doc/squeezebert>` released with the paper `SqueezeBERT: What can computer vision teach NLP
     about efficient neural networks? <https://arxiv.org/abs/2006.11316>`__ by Forrest N. Iandola, Albert E. Shaw, Ravi
     Krishna, and Kurt W. Keutzer.
-29. :doc:`T5 <model_doc/t5>` (from Google AI) released with the paper `Exploring the Limits of Transfer Learning with a
+28. :doc:`T5 <model_doc/t5>` (from Google AI) released with the paper `Exploring the Limits of Transfer Learning with a
     Unified Text-to-Text Transformer <https://arxiv.org/abs/1910.10683>`__ by Colin Raffel and Noam Shazeer and Adam
     Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
-30. :doc:`Transformer-XL <model_doc/transformerxl>` (from Google/CMU) released with the paper `Transformer-XL:
+29. :doc:`Transformer-XL <model_doc/transformerxl>` (from Google/CMU) released with the paper `Transformer-XL:
     Attentive Language Models Beyond a Fixed-Length Context <https://arxiv.org/abs/1901.02860>`__ by Zihang Dai*,
     Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
-31. :doc:`XLM <model_doc/xlm>` (from Facebook) released together with the paper `Cross-lingual Language Model
+30. :doc:`XLM <model_doc/xlm>` (from Facebook) released together with the paper `Cross-lingual Language Model
     Pretraining <https://arxiv.org/abs/1901.07291>`__ by Guillaume Lample and Alexis Conneau.
-32. :doc:`XLM-ProphetNet <model_doc/xlmprophetnet>` (from Microsoft Research) released with the paper `ProphetNet:
+31. :doc:`XLM-ProphetNet <model_doc/xlmprophetnet>` (from Microsoft Research) released with the paper `ProphetNet:
     Predicting Future N-gram for Sequence-to-Sequence Pre-training <https://arxiv.org/abs/2001.04063>`__ by Yu Yan,
     Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
-33. :doc:`XLM-RoBERTa <model_doc/xlmroberta>` (from Facebook AI), released together with the paper `Unsupervised
+32. :doc:`XLM-RoBERTa <model_doc/xlmroberta>` (from Facebook AI), released together with the paper `Unsupervised
     Cross-lingual Representation Learning at Scale <https://arxiv.org/abs/1911.02116>`__ by Alexis Conneau*, Kartikay
     Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke
     Zettlemoyer and Veselin Stoyanov.
-34. :doc:`XLNet <model_doc/xlnet>` (from Google/CMU) released with the paper `​XLNet: Generalized Autoregressive
+33. :doc:`XLNet <model_doc/xlnet>` (from Google/CMU) released with the paper `​XLNet: Generalized Autoregressive
     Pretraining for Language Understanding <https://arxiv.org/abs/1906.08237>`__ by Zhilin Yang*, Zihang Dai*, Yiming
     Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
-35. `Other community models <https://huggingface.co/models>`__, contributed by the `community
+34. `Other community models <https://huggingface.co/models>`__, contributed by the `community
     <https://huggingface.co/users>`__.
 
 
+.. _bigtable:
+
 The table below represents the current support in the library for each of those models, whether they have a Python
 tokenizer (called "slow"). A "fast" tokenizer backed by the 🤗 Tokenizers library, whether they have support in PyTorch,
 TensorFlow and/or Flax.
@@ -325,7 +324,6 @@ TensorFlow and/or Flax.
     model_doc/albert
     model_doc/auto
     model_doc/bart
-    model_doc/barthez
     model_doc/bert
     model_doc/bertgeneration
     model_doc/blenderbot

docs/source/installation.md

@@ -12,9 +12,10 @@ must install it from source.
 ## Installation with pip
 
 First you need to install one of, or both, TensorFlow 2.0 and PyTorch.
-Please refer to [TensorFlow installation page](https://www.tensorflow.org/install/pip#tensorflow-2.0-rc-is-available) 
-and/or [PyTorch installation page](https://pytorch.org/get-started/locally/#start-locally) regarding the specific 
-install command for your platform.
+Please refer to [TensorFlow installation page](https://www.tensorflow.org/install/pip#tensorflow-2.0-rc-is-available), 
+[PyTorch installation page](https://pytorch.org/get-started/locally/#start-locally) and/or 
+[Flax installation page](https://github.com/google/flax#quick-install)
+regarding the specific install command for your platform.
 
 When TensorFlow 2.0 and/or PyTorch has been installed, 🤗 Transformers can be installed using pip as follows:
 
@@ -34,6 +35,12 @@ or 🤗 Transformers and TensorFlow 2.0 in one line with:
 pip install transformers[tf-cpu]
 ```
 
+or 🤗 Transformers and Flax in one line with:
+
+```bash
+pip install transformers[flax]
+```
+
 To check 🤗 Transformers is properly installed, run the following command:
 
 ```bash
@@ -66,6 +73,19 @@ python -c "from transformers import pipeline; print(pipeline('sentiment-analysis
 
 to check 🤗 Transformers is properly installed.
 
+
+## With conda
+
+Since Transformers version v4.0.0, we now have a conda channel: `huggingface`.
+
+🤗 Transformers can be installed using conda as follows:
+
+```
+conda install -c huggingface transformers
+```
+
+Follow the installation pages of TensorFlow, PyTorch or Flax to see how to install them with conda. 
+
 ## Caching models
 
 This library provides pretrained models that will be downloaded and cached locally. Unless you specify a location with

docs/source/main_classes/callback.rst

@@ -44,8 +44,6 @@ Here is the list of the available :class:`~transformers.TrainerCallback` in the
 
 .. autoclass:: transformers.ProgressCallback
 
-.. autoclass:: transformers.EarlyStoppingCallback
-
 .. autoclass:: transformers.integrations.TensorBoardCallback
 
 .. autoclass:: transformers.integrations.WandbCallback

docs/source/model_doc/barthez.rst

@@ -1,41 +0,0 @@
-BARThez
------------------------------------------------------------------------------------------------------------------------
-
-Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The BARThez model was proposed in `BARThez: a Skilled Pretrained French Sequence-to-Sequence Model`
-<https://arxiv.org/abs/2010.12321>`__ by Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis on 23 Oct,
-2020.
-
-The abstract of the paper:
-
-
-*Inductive transfer learning, enabled by self-supervised learning, have taken the entire Natural Language Processing
-(NLP) field by storm, with models such as BERT and BART setting new state of the art on countless natural language
-understanding tasks. While there are some notable exceptions, most of the available models and research have been
-conducted for the English language. In this work, we introduce BARThez, the first BART model for the French language
-(to the best of our knowledge). BARThez was pretrained on a very large monolingual French corpus from past research
-that we adapted to suit BART's perturbation schemes. Unlike already existing BERT-based French language models such as
-CamemBERT and FlauBERT, BARThez is particularly well-suited for generative tasks, since not only its encoder but also
-its decoder is pretrained. In addition to discriminative tasks from the FLUE benchmark, we evaluate BARThez on a novel
-summarization dataset, OrangeSum, that we release with this paper. We also continue the pretraining of an already
-pretrained multilingual BART on BARThez's corpus, and we show that the resulting model, which we call mBARTHez,
-provides a significant boost over vanilla BARThez, and is on par with or outperforms CamemBERT and FlauBERT.*
-
-The Authors' code can be found `here <https://github.com/moussaKam/BARThez>`__.
-
-
-Examples
-_______________________________________________________________________________________________________________________
-
-- BARThez can be fine-tuned on sequence-to-sequence tasks in a similar way as BART, check: `examples/seq2seq/
-  <https://github.com/huggingface/transformers/blob/master/examples/seq2seq/README.md>`__.
-
-
-BarthezTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BarthezTokenizer
-    :members:
-

docs/source/model_doc/conv_bert.rst

@@ -1,81 +0,0 @@
-CONV_BERT
------------------------------------------------------------------------------------------------------------------------
-
-Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The ConvBERT model was proposed in `<INSERT PAPER NAME HERE> <<INSERT PAPER LINK HERE>>`__ by <INSERT AUTHORS HERE>.
-<INSERT SHORT SUMMARY HERE>
-
-The abstract from the paper is the following:
-
-*<INSERT PAPER ABSTRACT HERE>*
-
-Tips:
-
-<INSERT TIPS ABOUT MODEL HERE>
-
-ConvBertConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.ConvBertConfig
-    :members:
-
-
-ConvBertTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.ConvBertTokenizer
-    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
-        create_token_type_ids_from_sequences, save_vocabulary
-
-
-ConvBertTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.ConvBertTokenizerFast
-    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
-        create_token_type_ids_from_sequences, save_vocabulary
-
-
-ConvBertModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.ConvBertModel
-    :members: forward
-
-
-ConvBertForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.ConvBertForMaskedLM
-    :members: forward
-
-
-ConvBertForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.ConvBertForSequenceClassification
-    :members: forward
-
-
-ConvBertForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.ConvBertForMultipleChoice
-    :members:
-
-
-ConvBertForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.ConvBertForTokenClassification
-    :members: forward
-
-
-ConvBertForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.ConvBertForQuestionAnswering
-    :members: forward
-

docs/source/model_doc/gpt2.rst

@@ -71,14 +71,14 @@ GPT2Model
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.GPT2Model
-    :members: forward, parallelize, deparallelize
+    :members: forward
 
 
 GPT2LMHeadModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.GPT2LMHeadModel
-    :members: forward, parallelize, deparallelize
+    :members: forward
 
 
 GPT2DoubleHeadsModel

docs/source/model_doc/mt5.rst

@@ -39,13 +39,6 @@ MT5ForConditionalGeneration
     :members:
 
 
-MT5EncoderModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MT5EncoderModel
-    :members:
-
-
 TFMT5Model
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -58,10 +51,3 @@ TFMT5ForConditionalGeneration
 
 .. autoclass:: transformers.TFMT5ForConditionalGeneration
     :members:
-
-
-TFMT5EncoderModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFMT5EncoderModel
-    :members:

docs/source/model_doc/t5.rst

@@ -99,21 +99,16 @@ T5Model
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.T5Model
-    :members: forward, parallelize, deparallelize
+    :members: forward
 
 
 T5ForConditionalGeneration
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.T5ForConditionalGeneration
-    :members: forward, parallelize, deparallelize
-
-T5EncoderModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.T5EncoderModel
     :members: forward
 
+
 TFT5Model
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -126,9 +121,3 @@ TFT5ForConditionalGeneration
 
 .. autoclass:: transformers.TFT5ForConditionalGeneration
     :members: call
-
-TFT5EncoderModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFT5EncoderModel
-    :members: call

docs/source/preprocessing.rst

@@ -2,6 +2,7 @@ Preprocessing data
 =======================================================================================================================
 
 In this tutorial, we'll explore how to preprocess your data using 🤗 Transformers. The main tool for this is what we
+
 call a :doc:`tokenizer <main_classes/tokenizer>`. You can build one using the tokenizer class associated to the model
 you would like to use, or directly with the :class:`~transformers.AutoTokenizer` class.
 
@@ -51,7 +52,7 @@ The tokenizer can decode a list of token ids in a proper sentence:
     "[CLS] Hello, I'm a single sentence! [SEP]"
 
 As you can see, the tokenizer automatically added some special tokens that the model expects. Not all models need
-special tokens; for instance, if we had used `gpt2-medium` instead of `bert-base-cased` to create our tokenizer, we
+special tokens; for instance, if we had used` gtp2-medium` instead of `bert-base-cased` to create our tokenizer, we
 would have seen the same sentence as the original one here. You can disable this behavior (which is only advised if you
 have added those special tokens yourself) by passing ``add_special_tokens=False``.
 

docs/source/pretrained_models.rst

@@ -333,12 +333,6 @@ For a list that includes all community-uploaded models, refer to `https://huggin
 |                    | ``facebook/bart-large-cnn``                                | | 24-layer, 1024-hidden, 16-heads, 406M parameters       (same as large)                                                              |
 |                    |                                                            | | bart-large base architecture finetuned on cnn summarization task                                                                    |
 +--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| BARThez            | ``moussaKam/barthez``                                      | | 12-layer,  768-hidden, 12-heads, 216M parameters                                                                                    |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/moussaKam/BARThez>`__)                                                                              |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``moussaKam/mbarthez``                                     | | 24-layer, 1024-hidden, 16-heads, 561M parameters                                                                                    |
-+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 | DialoGPT           | ``DialoGPT-small``                                         | | 12-layer, 768-hidden, 12-heads, 124M parameters                                                                                     |
 |                    |                                                            | | Trained on English text: 147M conversation-like exchanges extracted from Reddit.                                                    |
 |                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+

docs/source/quicktour.rst

@@ -240,9 +240,7 @@ activations of the model.
            [ 0.08181786, -0.04179301]], dtype=float32)>,)
 
 The model can return more than just the final activations, which is why the output is a tuple. Here we only asked for
-the final activations, so we get a tuple with one element.
-
-.. note::
+the final activations, so we get a tuple with one element. .. note::
 
     All 🤗 Transformers models (PyTorch or TensorFlow) return the activations of the model *before* the final activation
     function (like SoftMax) since this final activation function is often fused with the loss.

docs/source/serialization.rst

@@ -70,8 +70,8 @@ inference.
     optimizations afterwards.
 
 .. note::
-    For more information about the optimizations enabled by ONNXRuntime, please have a look at the `ONNXRuntime Github
-    <https://github.com/microsoft/onnxruntime/tree/master/onnxruntime/python/tools/transformers>`_.
+    For more information about the optimizations enabled by ONNXRuntime, please have a look at the (`ONNXRuntime Github
+    <https://github.com/microsoft/onnxruntime/tree/master/onnxruntime/python/tools/transformers>`_)
 
 Quantization
 -----------------------------------------------------------------------------------------------------------------------

docs/source/testing.rst

@@ -909,10 +909,9 @@ pipelines), then we should run that test in the non-slow test suite. If it's foc
 such as the documentation or the examples, then we should run these tests in the slow test suite. And then, to refine
 this approach we should have exceptions:
 
-* All tests that need to download a heavy set of weights or a dataset that is larger than ~50MB (e.g., model or
-  tokenizer integration tests, pipeline integration tests) should be set to slow. If you're adding a new model, you
-  should create and upload to the hub a tiny version of it (with random weights) for integration tests. This is
-  discussed in the following paragraphs.
+* All tests that need to download a heavy set of weights (e.g., model or tokenizer integration tests, pipeline
+  integration tests) should be set to slow. If you're adding a new model, you should create and upload to the hub a
+  tiny version of it (with random weights) for integration tests. This is discussed in the following paragraphs.
 * All tests that need to do a training not specifically optimized to be fast should be set to slow.
 * We can introduce exceptions if some of these should-be-non-slow tests are excruciatingly slow, and set them to
   ``@slow``. Auto-modeling tests, which save and load large files to disk, are a good example of tests that are marked

examples/language-modeling/run_plm.py

@@ -93,11 +93,11 @@ class DataTrainingArguments:
     overwrite_cache: bool = field(
         default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
     )
-    max_seq_length: int = field(
-        default=512,
+    max_seq_length: Optional[int] = field(
+        default=None,
         metadata={
             "help": "The maximum total input sequence length after tokenization. Sequences longer "
-            "than this will be truncated."
+            "than this will be truncated. Default to the max input length of the model."
         },
     )
     preprocessing_num_workers: Optional[int] = field(
@@ -286,12 +286,15 @@ def main():
             load_from_cache_file=not data_args.overwrite_cache,
         )
 
-        if data_args.max_seq_length > tokenizer.model_max_length:
-            logger.warn(
-                f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"
-                f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
-            )
-        max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)
+        if data_args.max_seq_length is None:
+            max_seq_length = tokenizer.model_max_length
+        else:
+            if data_args.max_seq_length > tokenizer.model_max_length:
+                logger.warn(
+                    f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"
+                    f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
+                )
+            max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)
 
         # Main data processing function that will concatenate all texts from our dataset and generate chunks of
         # max_seq_length.

examples/lightning_base.py

@@ -4,9 +4,11 @@ import os
 from pathlib import Path
 from typing import Any, Dict
 
+import packaging
 import pytorch_lightning as pl
 from pytorch_lightning.utilities import rank_zero_info
 
+import pkg_resources
 from transformers import (
     AdamW,
     AutoConfig,
@@ -28,12 +30,21 @@ from transformers.optimization import (
     get_linear_schedule_with_warmup,
     get_polynomial_decay_schedule_with_warmup,
 )
-from transformers.utils.versions import require_version_examples
 
 
 logger = logging.getLogger(__name__)
 
-require_version_examples("pytorch_lightning>=1.0.4")
+
+def require_min_ver(pkg, min_ver):
+    got_ver = pkg_resources.get_distribution(pkg).version
+    if packaging.version.parse(got_ver) < packaging.version.parse(min_ver):
+        logger.warning(
+            f"{pkg}>={min_ver} is required for a normal functioning of this module, but found {pkg}=={got_ver}. "
+            "Try: pip install -r examples/requirements.txt"
+        )
+
+
+require_min_ver("pytorch_lightning", "1.0.4")
 
 MODEL_MODES = {
     "base": AutoModel,
@@ -373,8 +384,6 @@ def generic_train(
         train_params["distributed_backend"] = "ddp"
 
     train_params["accumulate_grad_batches"] = args.accumulate_grad_batches
-    train_params["accelerator"] = extra_train_kwargs.get("accelerator", None)
-    train_params["profiler"] = extra_train_kwargs.get("profiler", None)
 
     trainer = pl.Trainer.from_argparse_args(
         args,

examples/question-answering/README.md

@@ -159,81 +159,6 @@ Larger batch size may improve the performance while costing more memory.
 }
 ```
 
-#### Fine-tuning BERT on SQuAD1.0 with relative position embeddings
-
-The following examples show how to fine-tune BERT models with different relative position embeddings. The BERT model 
-`bert-base-uncased` was pre-trained with default absolute position embeddings. We provide the following pre-trained 
-models which were pre-trained on the same training data (BooksCorpus and English Wikipedia) as in the BERT model 
-training, but with different relative position embeddings. 
-
-* `zhiheng-huang/bert-base-uncased-embedding-relative-key`, trained from scratch with relative embedding proposed by 
-Shaw et al., [Self-Attention with Relative Position Representations](https://arxiv.org/abs/1803.02155)
-* `zhiheng-huang/bert-base-uncased-embedding-relative-key-query`, trained from scratch with relative embedding method 4 
-in Huang et al. [Improve Transformer Models with Better Relative Position Embeddings](https://arxiv.org/abs/2009.13658)
-* `zhiheng-huang/bert-large-uncased-whole-word-masking-embedding-relative-key-query`, fine-tuned from model 
-`bert-large-uncased-whole-word-masking` with 3 additional epochs with relative embedding method 4 in Huang et al. 
-[Improve Transformer Models with Better Relative Position Embeddings](https://arxiv.org/abs/2009.13658)
-
-
-##### Base models fine-tuning
-
-```bash
-export SQUAD_DIR=/path/to/SQUAD
-output_dir=relative_squad
-export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
-python -m torch.distributed.launch --nproc_per_node=8 ./examples/question-answering/run_squad.py \
-    --model_type bert \
-    --model_name_or_path zhiheng-huang/bert-base-uncased-embedding-relative-key-query \
-    --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 512 \
-    --doc_stride 128 \
-    --output_dir ${output_dir} \
-    --per_gpu_eval_batch_size=60 \
-    --per_gpu_train_batch_size=6
-```
-Training with the above command leads to the following results. It boosts the BERT default from f1 score of 88.52 to 90.54.
-
-```bash
-'exact': 83.6802270577105, 'f1': 90.54772098174814
-```
-
-The change of `max_seq_length` from 512 to 384 in the above command leads to the f1 score of 90.34. Replacing the above 
-model `zhiheng-huang/bert-base-uncased-embedding-relative-key-query` with 
-`zhiheng-huang/bert-base-uncased-embedding-relative-key` leads to the f1 score of 89.51. The changing of 8 gpus to one 
-gpu training leads to the f1 score of 90.71.
-
-##### Large models fine-tuning
-
-```bash
-export SQUAD_DIR=/path/to/SQUAD
-output_dir=relative_squad
-export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
-python -m torch.distributed.launch --nproc_per_node=8 ./examples/question-answering/run_squad.py \
-    --model_type bert \
-    --model_name_or_path zhiheng-huang/bert-large-uncased-whole-word-masking-embedding-relative-key-query \
-    --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 512 \
-    --doc_stride 128 \
-    --output_dir ${output_dir} \
-    --per_gpu_eval_batch_size=6 \
-    --per_gpu_train_batch_size=2 \
-    --gradient_accumulation_steps 3
-```
-Training with the above command leads to the f1 score of 93.52, which is slightly better than the f1 score of 93.15 for 
-`bert-large-uncased-whole-word-masking`.
-
 ## SQuAD with the Tensorflow Trainer
 
 ```bash

examples/rag/README.md

@@ -7,9 +7,9 @@ to the retriever to extract relevant context documents. The documents are then p
 Such contextualized inputs are passed to the generator.
 
 Read more about RAG  at https://arxiv.org/abs/2005.11401.
-
 # Finetuning
 
+
 Our finetuning logic is based on scripts from [`examples/seq2seq`](https://github.com/huggingface/transformers/tree/master/examples/seq2seq). We accept training data in the same format as specified there - we expect a directory consisting of 6 text files:
 ```bash
 train.source
@@ -20,10 +20,10 @@ test.source
 test.target
 ```
 
-A sample finetuning command (run ` ./examples/rag/finetune_rag.py --help` to list all available options):
+A sample finetuning command (run ` ./examples/rag/finetune.py --help` to list all available options):
 
 ```bash
-python examples/rag/finetune_rag.py \
+python examples/rag/finetune.py \
     --data_dir $DATA_DIR \
     --output_dir $OUTPUT_DIR \
     --model_name_or_path $MODEL_NAME_OR_PATH \
@@ -45,7 +45,7 @@ python examples/rag/consolidate_rag_checkpoint.py \
     --question_encoder_name_or_path facebook/dpr-question_encoder-single-nq-base \
     --dest path/to/checkpoint
 ```
-You will then be able to pass `path/to/checkpoint` as `model_name_or_path` to the `finetune_rag.py` script.
+You will then be able to pass `path/to/checkpoint` as `model_name_or_path` to the `finetune.py` script.
 
 
 # Evaluation
@@ -130,29 +130,3 @@ python examples/rag/eval_rag.py \
     --print_predictions \
     --recalculate \ # adding this parameter will force recalculating predictions even if predictions_path already exists
 ```
-
-# Use your own knowledge source
-
-By default, RAG uses the English Wikipedia as a knowledge source, known as the 'wiki_dpr' dataset.
-With `use_custom_knowledge_dataset.py` you can build your own knowledge source, *e.g.* for RAG.
-
-For instance, if documents are serialized as tab-separated csv files with the columns "title" and "text", one can use `use_own_knowledge_dataset.py` as follows:
-```bash
-python examples/rag/use_own_knowledge_dataset.py \
-    --csv_path path/to/my_csv \
-    --output_dir path/to/my_knowledge_dataset \
-```
-
-The created outputs in `path/to/my_knowledge_dataset` can then be used to finetune RAG as follows:
-```bash
-python examples/rag/finetune_rag.py \
-    --data_dir $DATA_DIR \
-    --output_dir $OUTPUT_DIR \
-    --model_name_or_path $MODEL_NAME_OR_PATH \
-    --model_type rag_sequence \
-    --fp16 \
-    --gpus 8
-    --index_name custom
-    --passages_path path/to/data/my_knowledge_dataset
-    --index_path path/to/my_knowledge_dataset_hnsw_index.faiss
-```

examples/rag/callbacks.py

@@ -8,7 +8,7 @@ import torch
 from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
 from pytorch_lightning.utilities import rank_zero_only
 
-from utils_rag import save_json
+from utils import save_json
 
 
 def count_trainable_parameters(model):
@@ -38,7 +38,7 @@ def get_checkpoint_callback(output_dir, metric):
         monitor=f"val_{metric}",
         mode="max",
         save_top_k=3,
-        period=1,  # maybe save a checkpoint every time val is run, not just end of epoch.
+        period=0,  # maybe save a checkpoint every time val is run, not just end of epoch.
     )
     return checkpoint_callback
 

examples/rag/distributed_retriever.py

@@ -40,6 +40,7 @@ class RagPyTorchDistributedRetriever(RagRetriever):
             generator_tokenizer=generator_tokenizer,
             index=index,
         )
+
         self.process_group = None
 
     def init_retrieval(self, distributed_port: int):

examples/rag/eval_rag.py

@@ -153,7 +153,7 @@ def get_args():
     parser.add_argument(
         "--index_name",
         default=None,
-        choices=["exact", "compressed", "legacy"],
+        choices=["hf", "legacy"],
         type=str,
         help="RAG model retriever type",
     )

examples/rag/finetune.py

@@ -1,10 +1,12 @@
 """Finetuning script for RAG models. Adapted from examples.seq2seq.finetune.py"""
 
 import argparse
+import glob
 import logging
 import os
 import sys
 import time
+import warnings
 from collections import defaultdict
 from pathlib import Path
 from typing import Any, Dict, List, Tuple
@@ -13,31 +15,29 @@ import numpy as np
 import pytorch_lightning as pl
 import torch
 import torch.distributed as dist
-from pytorch_lightning.accelerators.ddp_accelerator import DDPAccelerator
-from pytorch_lightning.cluster_environments import TorchElasticEnvironment
 from torch.utils.data import DataLoader
 
 from transformers import (
     AutoConfig,
     AutoTokenizer,
     BartForConditionalGeneration,
-    BatchEncoding,
     RagConfig,
     RagSequenceForGeneration,
     RagTokenForGeneration,
     RagTokenizer,
     T5ForConditionalGeneration,
+    get_linear_schedule_with_warmup,
 )
 from transformers import logging as transformers_logging
 
 
-from callbacks_rag import (  # noqa: E402 # isort:skipq
+from callbacks import (  # noqa: E402 # isort:skipq
     get_checkpoint_callback,
     get_early_stopping_callback,
     Seq2SeqLoggingCallback,
 )
 from distributed_retriever import RagPyTorchDistributedRetriever  # noqa: E402 # isort:skip
-from utils_rag import (  # noqa: E402 # isort:skip
+from utils import (  # noqa: E402 # isort:skip
     calculate_exact_match,
     flatten_list,
     get_git_info,
@@ -67,30 +67,6 @@ class AttrDict(dict):
         self.__dict__ = self
 
 
-# In PTL >v1.0, `init_ddp_connection` method in the `LightningModule`
-# is no longer used, and is moved into DDPAccelerator instead.
-# We override DDPAccelerator to add our custom logic for initializing the
-# retriever.
-# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/tests/backends/test_accelerator_connector.py
-
-
-class CustomAccel(DDPAccelerator):
-    def __init__(self, trainer=None, **kwargs):
-        # Trainer is set later.
-        super().__init__(trainer, **kwargs)
-
-    def init_ddp_connection(self, global_rank: int, world_size: int, is_slurm_managing_tasks: bool = True):
-        logger.info("Custom init_ddp_connection.")
-        module = self.trainer.model
-        if self.cluster_environment is None:
-            self.cluster_environment = TorchElasticEnvironment()
-        self.distributed_port = module.hparams.distributed_port
-        os.environ["MASTER_PORT"] = str(self.distributed_port)
-        super().init_ddp_connection(global_rank, world_size, is_slurm_managing_tasks)
-        if module.is_rag_model:
-            module.model.rag.retriever.init_retrieval(self.distributed_port)
-
-
 class GenerativeQAModule(BaseTransformer):
     mode = "generative_qa"
     loss_names = ["loss"]
@@ -115,24 +91,23 @@ class GenerativeQAModule(BaseTransformer):
         config = config_class.from_pretrained(hparams.model_name_or_path)
 
         # set retriever parameters
-        config.index_name = hparams.index_name or config.index_name
-        config.passages_path = hparams.passages_path or config.passages_path
-        config.index_path = hparams.index_path or config.index_path
-        config.use_dummy_dataset = hparams.use_dummy_dataset
+        config.index_name = args.index_name or config.index_name
+        config.passages_path = args.passages_path or config.passages_path
+        config.index_path = args.index_path or config.index_path
 
         # set extra_model_params for generator configs and load_model
         extra_model_params = ("encoder_layerdrop", "decoder_layerdrop", "attention_dropout", "dropout")
         if self.is_rag_model:
-            if hparams.prefix is not None:
-                config.generator.prefix = hparams.prefix
+            if args.prefix is not None:
+                config.generator.prefix = args.prefix
             config.label_smoothing = hparams.label_smoothing
             hparams, config.generator = set_extra_model_params(extra_model_params, hparams, config.generator)
             retriever = RagPyTorchDistributedRetriever.from_pretrained(hparams.model_name_or_path, config=config)
             model = self.model_class.from_pretrained(hparams.model_name_or_path, config=config, retriever=retriever)
             prefix = config.question_encoder.prefix
         else:
-            if hparams.prefix is not None:
-                config.prefix = hparams.prefix
+            if args.prefix is not None:
+                config.prefix = args.prefix
             hparams, config = set_extra_model_params(extra_model_params, hparams, config)
             model = self.model_class.from_pretrained(hparams.model_name_or_path, config=config)
             prefix = config.prefix
@@ -177,9 +152,11 @@ class GenerativeQAModule(BaseTransformer):
         self.num_workers = hparams.num_workers
         self.distributed_port = self.hparams.distributed_port
 
-        # For single GPU training, init_ddp_connection is not called.
-        # So we need to initialize the retrievers here.
-        if hparams.gpus <= 1:
+    def init_ddp_connection(self, global_rank: int, world_size: int, is_slurm_managing_tasks: bool = True):
+        logger.info("Custom init_ddp_connection.")
+        os.environ["MASTER_PORT"] = str(self.distributed_port)
+        super().init_ddp_connection(global_rank, world_size, is_slurm_managing_tasks)
+        if self.is_rag_model:
             self.model.retriever.init_retrieval(self.distributed_port)
 
     def forward(self, input_ids, **kwargs):
@@ -293,7 +270,6 @@ class GenerativeQAModule(BaseTransformer):
 
     def _generative_step(self, batch: dict) -> dict:
         start_time = time.time()
-        batch = BatchEncoding(batch).to(device=self.model.device)
         generated_ids = self.model.generate(
             batch["input_ids"],
             attention_mask=batch["attention_mask"],
@@ -346,6 +322,17 @@ class GenerativeQAModule(BaseTransformer):
 
     def train_dataloader(self) -> DataLoader:
         dataloader = self.get_dataloader("train", batch_size=self.hparams.train_batch_size, shuffle=True)
+        t_total = (
+            (len(dataloader.dataset) // (self.hparams.train_batch_size * max(1, self.hparams.gpus)))
+            // self.hparams.accumulate_grad_batches
+            * float(self.hparams.max_epochs)
+        )
+        scheduler = get_linear_schedule_with_warmup(
+            self.opt, num_warmup_steps=self.hparams.warmup_steps, num_training_steps=t_total
+        )
+        if max(scheduler.get_last_lr()) > 0:
+            warnings.warn("All learning rates are 0")
+        self.lr_scheduler = scheduler
         return dataloader
 
     def val_dataloader(self) -> DataLoader:
@@ -442,24 +429,10 @@ class GenerativeQAModule(BaseTransformer):
             default=None,
             help="Path to the faiss index for custom index. More info about custom indexes in the RagRetriever documentation as well as in `examples/rag/use_own_knowledge_dataset.py`",
         )
-        parser.add_argument(
-            "--use_dummy_dataset",
-            type=bool,
-            default=False,
-            help="Whether to use the dummy version of the dataset index. More info about custom indexes in the RagRetriever documentation as well as in `examples/rag/use_own_knowledge_dataset.py`",
-        )
         return parser
 
 
-def main(args=None, model=None) -> GenerativeQAModule:
-
-    parser = argparse.ArgumentParser()
-    parser = pl.Trainer.add_argparse_args(parser)
-    parser = GenerativeQAModule.add_model_specific_args(parser, os.getcwd())
-    parser = GenerativeQAModule.add_retriever_specific_args(parser)
-
-    args = args or parser.parse_args()
-
+def main(args, model=None) -> GenerativeQAModule:
     Path(args.output_dir).mkdir(exist_ok=True)
     if model is None:
         model: GenerativeQAModule = GenerativeQAModule(args)
@@ -488,7 +461,6 @@ def main(args=None, model=None) -> GenerativeQAModule:
         if args.early_stopping_patience >= 0
         else False
     )
-
     trainer: pl.Trainer = generic_train(
         model,
         args,
@@ -496,17 +468,31 @@ def main(args=None, model=None) -> GenerativeQAModule:
         checkpoint_callback=get_checkpoint_callback(args.output_dir, model.val_metric),
         early_stopping_callback=es_callback,
         logger=logger,
-        accelerator=CustomAccel() if args.gpus > 1 else None,
     )
     pickle_save(model.hparams, model.output_dir / "hparams.pkl")
 
     if not args.do_predict:
         return model
 
+    model.hparams.test_checkpoint = ""
+    checkpoints = list(sorted(glob.glob(os.path.join(args.output_dir, "*.ckpt"), recursive=True)))
+    if checkpoints:
+        model.hparams.test_checkpoint = checkpoints[-1]
+        trainer.resume_from_checkpoint = checkpoints[-1]  # best checkpoint
+    trainer.logger.log_hyperparams(model.hparams)
+
     # test() without a model tests using the best checkpoint automatically
     trainer.test()
+
     return model
 
 
 if __name__ == "__main__":
-    main()
+    parser = argparse.ArgumentParser()
+    parser = pl.Trainer.add_argparse_args(parser)
+    parser = GenerativeQAModule.add_model_specific_args(parser, os.getcwd())
+    parser = GenerativeQAModule.add_retriever_specific_args(parser)
+
+    args = parser.parse_args()
+
+    main(args)

examples/rag/finetune.sh

@@ -4,7 +4,7 @@ export PYTHONPATH="../":"${PYTHONPATH}"
 # A sample finetuning run, you need to specify data_dir, output_dir and model_name_or_path
 # run ./examples/rag/finetune.sh --help to see all the possible options
 
-python examples/rag/finetune_rag.py \
+python examples/rag/finetune.py \
     --data_dir $DATA_DIR \
     --output_dir $OUTPUT_DIR \
     --model_name_or_path $MODEL_NAME_OR_PATH \

examples/rag/test_finetune_rag.py

@@ -1,96 +0,0 @@
-import json
-import logging
-import os
-import sys
-from pathlib import Path
-
-import finetune_rag
-from transformers.file_utils import is_apex_available
-from transformers.testing_utils import (
-    TestCasePlus,
-    execute_subprocess_async,
-    require_torch_gpu,
-    require_torch_multi_gpu,
-)
-
-
-logging.basicConfig(level=logging.DEBUG)
-logger = logging.getLogger()
-
-
-class RagFinetuneExampleTests(TestCasePlus):
-    def _create_dummy_data(self, data_dir):
-        os.makedirs(data_dir, exist_ok=True)
-        contents = {"source": "What is love ?", "target": "life"}
-        n_lines = {"train": 12, "val": 2, "test": 2}
-        for split in ["train", "test", "val"]:
-            for field in ["source", "target"]:
-                content = "\n".join([contents[field]] * n_lines[split])
-                with open(os.path.join(data_dir, f"{split}.{field}"), "w") as f:
-                    f.write(content)
-
-    def _run_finetune(self, gpus: int):
-        stream_handler = logging.StreamHandler(sys.stdout)
-        logger.addHandler(stream_handler)
-
-        tmp_dir = self.get_auto_remove_tmp_dir()
-        output_dir = os.path.join(tmp_dir, "output")
-        data_dir = os.path.join(tmp_dir, "data")
-        self._create_dummy_data(data_dir=data_dir)
-
-        testargs = f"""
-                --data_dir {data_dir} \
-                --output_dir {output_dir} \
-                --model_name_or_path facebook/rag-sequence-base \
-                --model_type rag_sequence \
-                --do_train \
-                --do_predict \
-                --n_val -1 \
-                --val_check_interval 1.0 \
-                --train_batch_size 2 \
-                --eval_batch_size 1 \
-                --max_source_length 25 \
-                --max_target_length 25 \
-                --val_max_target_length 25 \
-                --test_max_target_length 25 \
-                --label_smoothing 0.1 \
-                --dropout 0.1 \
-                --attention_dropout 0.1 \
-                --weight_decay 0.001 \
-                --adam_epsilon 1e-08 \
-                --max_grad_norm 0.1 \
-                --lr_scheduler polynomial \
-                --learning_rate 3e-04 \
-                --num_train_epochs 1 \
-                --warmup_steps 4 \
-                --gradient_accumulation_steps 1 \
-                --distributed-port 8787 \
-                --use_dummy_dataset 1 \
-            """.split()
-
-        if gpus > 0:
-            testargs.append(f"--gpus={gpus}")
-            if is_apex_available():
-                testargs.append("--fp16")
-        else:
-            testargs.append("--gpus=0")
-            testargs.append("--distributed_backend=ddp_cpu")
-            testargs.append("--num_processes=2")
-
-        cmd = [sys.executable, str(Path(finetune_rag.__file__).resolve())] + testargs
-        execute_subprocess_async(cmd, env=self.get_env())
-
-        metrics_save_path = os.path.join(output_dir, "metrics.json")
-        with open(metrics_save_path) as f:
-            result = json.load(f)
-        return result
-
-    @require_torch_gpu
-    def test_finetune_gpu(self):
-        result = self._run_finetune(gpus=1)
-        self.assertGreaterEqual(result["test"][0]["test_avg_em"], 0.2)
-
-    @require_torch_multi_gpu
-    def test_finetune_multigpu(self):
-        result = self._run_finetune(gpus=2)
-        self.assertGreaterEqual(result["test"][0]["test_avg_em"], 0.2)

examples/rag/use_own_knowledge_dataset.py

@@ -7,7 +7,7 @@ from tempfile import TemporaryDirectory
 from typing import List, Optional
 
 import torch
-from datasets import Features, Sequence, Value, load_dataset
+from datasets import load_dataset
 
 import faiss
 from transformers import (
@@ -82,14 +82,10 @@ def main(
     # And compute the embeddings
     ctx_encoder = DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name).to(device=device)
     ctx_tokenizer = DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name)
-    new_features = Features(
-        {"text": Value("string"), "title": Value("string"), "embeddings": Sequence(Value("float32"))}
-    )  # optional, save as float32 instead of float64 to save space
     dataset = dataset.map(
         partial(embed, ctx_encoder=ctx_encoder, ctx_tokenizer=ctx_tokenizer),
         batched=True,
         batch_size=processing_args.batch_size,
-        features=new_features,
     )
 
     # And finally save your dataset

examples/requirements.txt

@@ -13,7 +13,7 @@ streamlit
 elasticsearch
 nltk
 pandas
-datasets >= 1.1.3
+datasets
 fire
 pytest
 conllu

examples/seq2seq/README.md

@@ -203,30 +203,30 @@ model = AutoModelForSeq2SeqLM.from_pretrained(f'{output_dir}/best_tfmr')
 ```
 
 ### Fine-tuning using Seq2SeqTrainer
-To use `Seq2SeqTrainer` for fine-tuning you should use the `finetune_trainer.py` script. It subclasses `Trainer` to extend it for seq2seq training. Except the `Trainer`-related `TrainingArguments`, it shares the same argument names as that of `finetune.py` file. One notable difference is that calculating generative metrics (BLEU, ROUGE) is optional and is controlled using the `--predict_with_generate` argument.
+To use `Seq2SeqTrainer` for fine-tuning you should use the `finetune_trainer.py` script. It subclasses `Trainer` to extend it for seq2seq training. Except the `Trainer` releated `TrainingArguments`, it shares the same argument names as that of `finetune.py` file. One notable difference is that, calculating generative metrics (BLEU, ROUGE) is optional and is controlled using the `--predict_with_generate` argument, set this argument to calculate BLEU and ROUGE metrics.
 
 With PyTorch 1.6+ it'll automatically use `native AMP` when `--fp16` is set.
 
 To see all the possible command line options, run:
 
 ```bash
-python finetune_trainer.py --help
+./builtin_trainer/finetune.sh --help # This calls python finetune_trainer.py --help
 ```
 
 **At the moment, `Seq2SeqTrainer` does not support *with teacher* distillation.**
 
-All `Seq2SeqTrainer`-based fine-tuning scripts are included in the `builtin_trainer` directory.
+All `Seq2SeqTrainer` based fine-tuning scripts are included in the `builtin_trainer` directory.
 
 #### TPU Training
 `Seq2SeqTrainer` supports TPU training with few caveats
-1. As `generate` method does not work on TPU at the moment, `predict_with_generate` cannot be used. You should use `--prediction_loss_only` to only calculate loss, and do not set `--do_predict` and `--predict_with_generate`.
-2. All sequences should be padded to be of equal length to avoid extremely slow training. (`finetune_trainer.py` does this automatically when running on TPU.)
+1. As `generate` method does not work on TPU at the moment, `predict_with_generate` can not be used. You should use `--prediction_loss_only` to only calculate loss, and do not set `--do_predict` and `--predict_with_generate`.
+2. All sequences should be padded to be of equal length otherwise it leads to extremely slow training. (`finetune_trainer.py` does this automatically when running on TPU.)
 
-We provide a very simple launcher script named `xla_spawn.py` that lets you run our example scripts on multiple TPU cores without any boilerplate. Just pass a `--num_cores` flag to this script, then your regular training script with its arguments (this is similar to the `torch.distributed.launch` helper for `torch.distributed`).
+We provide a very simple launcher script named `xla_spawn.py` that lets you run our example scripts on multiple TPU cores without any boilerplate. Just pass a --num_cores flag to this script, then your regular training script with its arguments (this is similar to the torch.distributed.launch helper for torch.distributed).
 
 `builtin_trainer/finetune_tpu.sh` script provides minimal arguments needed for TPU training.
 
-The following command fine-tunes `sshleifer/student_marian_en_ro_6_3` on TPU V3-8 and should complete one epoch in ~5-6 mins.
+Following command fine-tunes `sshleifer/student_marian_en_ro_6_3` on TPU V3-8 and should complete one epoch in ~5-6 mins.
 
 ```bash
 ./builtin_trainer/train_distil_marian_enro_tpu.sh

examples/seq2seq/builtin_trainer/finetune.sh

@@ -3,7 +3,8 @@
 python finetune_trainer.py \
     --learning_rate=3e-5 \
     --fp16 \
-    --do_train --do_eval --do_predict --evaluate_during_training \
+    --do_train --do_eval --do_predict \
+    --evaluation_strategy steps \
     --predict_with_generate \
     --n_val 1000 \
     "$@"

examples/seq2seq/builtin_trainer/finetune_tpu.sh

@@ -5,7 +5,8 @@ export TPU_NUM_CORES=8
 python xla_spawn.py --num_cores $TPU_NUM_CORES \
     finetune_trainer.py \
     --learning_rate=3e-5 \
-    --do_train --do_eval --evaluate_during_training \
+    --do_train --do_eval \
+    --evaluation_strategy steps \
     --prediction_loss_only \
     --n_val 1000 \
     "$@"

examples/seq2seq/builtin_trainer/train_distil_marian_enro.sh

@@ -16,7 +16,8 @@ python finetune_trainer.py \
     --num_train_epochs=6 \
     --save_steps 3000 --eval_steps 3000 \
     --max_source_length $MAX_LEN --max_target_length $MAX_LEN --val_max_target_length $MAX_LEN --test_max_target_length $MAX_LEN \
-    --do_train --do_eval --do_predict --evaluate_during_training\
+    --do_train --do_eval --do_predict \
+    --evaluation_strategy steps \
     --predict_with_generate --logging_first_step \
     --task translation --label_smoothing 0.1 \
     "$@"

examples/seq2seq/builtin_trainer/train_distil_marian_enro_tpu.sh

@@ -17,7 +17,8 @@ python xla_spawn.py --num_cores $TPU_NUM_CORES \
     --save_steps 500 --eval_steps 500 \
     --logging_first_step --logging_steps 200 \
     --max_source_length $MAX_LEN --max_target_length $MAX_LEN --val_max_target_length $MAX_LEN --test_max_target_length $MAX_LEN \
-    --do_train --do_eval --evaluate_during_training \
+    --do_train --do_eval \
+    --evaluation_strategy steps \
     --prediction_loss_only \
     --task translation --label_smoothing 0.1 \
     "$@"

examples/seq2seq/builtin_trainer/train_distilbart_cnn.sh

@@ -19,6 +19,7 @@ python finetune_trainer.py \
     --save_steps 3000 --eval_steps 3000 \
     --logging_first_step \
     --max_target_length 56 --val_max_target_length $MAX_TGT_LEN --test_max_target_length $MAX_TGT_LEN \
-    --do_train --do_eval --do_predict --evaluate_during_training \
+    --do_train --do_eval --do_predict \
+    --evaluation_strategy steps \
     --predict_with_generate --sortish_sampler \
     "$@"

examples/seq2seq/builtin_trainer/train_mbart_cc25_enro.sh

@@ -15,7 +15,8 @@ python finetune_trainer.py \
     --sortish_sampler \
     --num_train_epochs 6 \
     --save_steps 25000 --eval_steps 25000 --logging_steps 1000 \
-    --do_train --do_eval --do_predict --evaluate_during_training \
+    --do_train --do_eval --do_predict \
+    --evaluation_strategy steps \
     --predict_with_generate --logging_first_step \
     --task translation \
     "$@"

examples/seq2seq/callbacks.py

@@ -1,4 +1,5 @@
 import logging
+import os
 from pathlib import Path
 
 import numpy as np
@@ -97,8 +98,7 @@ def get_checkpoint_callback(output_dir, metric, save_top_k=1, lower_is_better=Fa
         )
 
     checkpoint_callback = ModelCheckpoint(
-        dirpath=output_dir,
-        filename=exp,
+        filepath=os.path.join(output_dir, exp),
         monitor=f"val_{metric}",
         mode="min" if "loss" in metric else "max",
         save_top_k=save_top_k,

examples/seq2seq/seq2seq_trainer.py

@@ -189,7 +189,7 @@ class Seq2SeqTrainer(Trainer):
         }
 
         if self.args.predict_with_generate and not self.args.prediction_loss_only:
-            generated_tokens = self.model.generate(
+            generated_tokens = model.generate(
                 inputs["input_ids"],
                 attention_mask=inputs["attention_mask"],
                 **gen_kwargs,

examples/seq2seq/test_finetune_trainer.py

@@ -4,7 +4,13 @@ from unittest.mock import patch
 
 from transformers import BertTokenizer, EncoderDecoderModel
 from transformers.file_utils import is_datasets_available
-from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, slow
+from transformers.testing_utils import (
+    TestCasePlus,
+    execute_subprocess_async,
+    get_gpu_count,
+    require_torch_non_multi_gpu_but_fix_me,
+    slow,
+)
 from transformers.trainer_callback import TrainerState
 from transformers.trainer_utils import set_seed
 
@@ -46,6 +52,7 @@ class TestFinetuneTrainer(TestCasePlus):
         assert "test_results.json" in contents
 
     @slow
+    @require_torch_non_multi_gpu_but_fix_me
     def test_finetune_bert2bert(self):
         if not is_datasets_available():
             return

examples/text-generation/pplm/run_pplm.py

@@ -154,8 +154,7 @@ def perturb_past(
         # Compute hidden using perturbed past
         perturbed_past = list(map(add, past, curr_perturbation))
         _, _, _, curr_length, _ = curr_perturbation[0].shape
-        lm_output = model(last, past_key_values=perturbed_past)
-        all_logits, all_hidden = lm_output["logits"], lm_output["hidden_states"]
+        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)
@@ -180,8 +179,7 @@ def perturb_past(
             wte = model.resize_token_embeddings()
             for _ in range(horizon_length):
                 inputs_embeds = torch.matmul(curr_probs, wte.weight.data)
-                lm_output = model(past_key_values=curr_unpert_past, inputs_embeds=inputs_embeds)
-                curr_unpert_past, curr_all_hidden = lm_output["past_key_values"], lm_output["hidden_states"]
+                _, 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)
 
@@ -464,14 +462,9 @@ def generate_text_pplm(
         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])["past_key_values"]
+                _, past, _ = model(output_so_far[:, :-1])
 
-        lm_output = model(output_so_far)
-        unpert_logits, unpert_past, unpert_all_hidden = (
-            lm_output["logits"],
-            lm_output["past_key_values"],
-            lm_output["hidden_states"],
-        )
+        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
@@ -514,11 +507,7 @@ def generate_text_pplm(
             else:
                 pert_past = past
 
-        lm_output = model(last, past_key_values=pert_past)
-        pert_logits, past = (
-            lm_output["logits"],
-            lm_output["past_key_values"],
-        )
+        pert_logits, past, pert_all_hidden = model(last, past=pert_past)
         pert_logits = pert_logits[:, -1, :] / temperature  # + SMALL_CONST
 
         for token_idx in set(output_so_far[0].tolist()):

examples/text-generation/pplm/run_pplm_discrim_train.py

@@ -64,7 +64,7 @@ class Discriminator(torch.nn.Module):
 
     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)["last_hidden_state"]
+        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

examples/token-classification/run_ner.py

@@ -15,8 +15,7 @@
 """
 Fine-tuning the library models for token classification.
 """
-# You can also adapt this script on your own token classification task and datasets. Pointers for this are left as
-# comments.
+# You can also adapt this script on your own token classification task and datasets. Pointers for this are left as comments.
 
 import logging
 import os
@@ -25,7 +24,7 @@ from dataclasses import dataclass, field
 from typing import Optional
 
 import numpy as np
-from datasets import ClassLabel, load_dataset
+from datasets import load_dataset
 from seqeval.metrics import accuracy_score, f1_score, precision_score, recall_score
 
 import transformers
@@ -199,17 +198,12 @@ def main():
 
     if training_args.do_train:
         column_names = datasets["train"].column_names
-        features = datasets["train"].features
     else:
         column_names = datasets["validation"].column_names
-        features = datasets["validation"].features
-    text_column_name = "tokens" if "tokens" in column_names else column_names[0]
-    label_column_name = (
-        f"{data_args.task_name}_tags" if f"{data_args.task_name}_tags" in column_names else column_names[1]
-    )
+    text_column_name = "words" if "words" in column_names else column_names[0]
+    label_column_name = data_args.task_name if data_args.task_name in column_names else column_names[1]
 
-    # In the event the labels are not a `Sequence[ClassLabel]`, we will need to go through the dataset to get the
-    # unique labels.
+    # Labeling (this part will be easier when https://github.com/huggingface/datasets/issues/797 is solved)
     def get_label_list(labels):
         unique_labels = set()
         for label in labels:
@@ -218,13 +212,8 @@ def main():
         label_list.sort()
         return label_list
 
-    if isinstance(features[label_column_name].feature, ClassLabel):
-        label_list = features[label_column_name].feature.names
-        # No need to convert the labels since they are already ints.
-        label_to_id = {i: i for i in range(len(label_list))}
-    else:
-        label_list = get_label_list(datasets["train"][label_column_name])
-        label_to_id = {l: i for i, l in enumerate(label_list)}
+    label_list = get_label_list(datasets["train"][label_column_name])
+    label_to_id = {l: i for i, l in enumerate(label_list)}
     num_labels = len(label_list)
 
     # Load pretrained model and tokenizer

model_cards/Cinnamon/electra-small-japanese-discriminator/README.md

@@ -1,20 +0,0 @@
----
-language: ja
-license: apache-2.0
----
-
-## Japanese ELECTRA-small
-
-We provide a Japanese **ELECTRA-Small** model, as described in [ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators](https://openreview.net/pdf?id=r1xMH1BtvB).
-
-Our pretraining process employs subword units derived from the [Japanese Wikipedia](https://dumps.wikimedia.org/jawiki/latest), using the [Byte-Pair Encoding](https://www.aclweb.org/anthology/P16-1162.pdf) method and building on an initial tokenization with [mecab-ipadic-NEologd](https://github.com/neologd/mecab-ipadic-neologd). For optimal performance, please take care to set your MeCab dictionary appropriately.
-
-## How to use the discriminator in `transformers`
-
-```
-from transformers import BertJapaneseTokenizer, ElectraForPreTraining
-
-tokenizer = BertJapaneseTokenizer.from_pretrained('Cinnamon/electra-small-japanese-discriminator', mecab_kwargs={"mecab_option": "-d /usr/lib/x86_64-linux-gnu/mecab/dic/mecab-ipadic-neologd"})
-
-model = ElectraForPreTraining.from_pretrained('Cinnamon/electra-small-japanese-discriminator')
-```

model_cards/Geotrend/bert-base-15lang-cased/README.md

@@ -4,16 +4,6 @@ language: multilingual
 datasets: wikipedia
 
 license: apache-2.0
-
-widget:
-- text: "Google generated 46 billion [MASK] in revenue."
-- text: "Paris is the capital of [MASK]."
-- text: "Algiers is the largest city in [MASK]."
-- text: "Paris est la [MASK] de la France."
-- text: "Paris est la capitale de la [MASK]."
-- text: "L'élection américaine a eu [MASK] en novembre 2020."
-- text: "تقع سويسرا في [MASK] أوروبا"
-- text: "إسمي محمد وأسكن في [MASK]."
 ---
 
 # bert-base-15lang-cased
@@ -43,8 +33,6 @@ model = AutoModel.from_pretrained("Geotrend/bert-base-15lang-cased")
 
 ```
 
-To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
-
 ### How to cite
 
 ```bibtex

model_cards/Geotrend/bert-base-ar-cased/README.md

@@ -4,10 +4,6 @@ language: ar
 datasets: wikipedia
 
 license: apache-2.0
-
-widget:
-- text: "تقع سويسرا في [MASK] أوروبا"
-- text: "إسمي محمد وأسكن في [MASK]."
 ---
 
 # bert-base-ar-cased
@@ -29,8 +25,6 @@ model = AutoModel.from_pretrained("Geotrend/bert-base-ar-cased")
 
 ```
 
-To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
-
 ### How to cite
 
 ```bibtex

model_cards/Geotrend/bert-base-bg-cased/README.md

@@ -24,8 +24,6 @@ model = AutoModel.from_pretrained("Geotrend/bert-base-bg-cased")
 
 ```
 
-To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
-
 ### How to cite
 
 ```bibtex

model_cards/Geotrend/bert-base-de-cased/README.md

@@ -24,8 +24,6 @@ model = AutoModel.from_pretrained("Geotrend/bert-base-de-cased")
 
 ```
 
-To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
-
 ### How to cite
 
 ```bibtex

model_cards/Geotrend/bert-base-el-cased/README.md

@@ -24,8 +24,6 @@ model = AutoModel.from_pretrained("Geotrend/bert-base-el-cased")
 
 ```
 
-To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
-
 ### How to cite
 
 ```bibtex

model_cards/Geotrend/bert-base-en-ar-cased/README.md

@@ -4,13 +4,6 @@ language: multilingual
 datasets: wikipedia
 
 license: apache-2.0
-
-widget:
-- text: "Google generated 46 billion [MASK] in revenue."
-- text: "Paris is the capital of [MASK]."
-- text: "Algiers is the largest city in [MASK]."
-- text: "تقع سويسرا في [MASK] أوروبا"
-- text: "إسمي محمد وأسكن في [MASK]."
 ---
 
 # bert-base-en-ar-cased
@@ -31,8 +24,6 @@ model = AutoModel.from_pretrained("Geotrend/bert-base-en-ar-cased")
 
 ```
 
-To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
-
 ### How to cite
 
 ```bibtex

model_cards/Geotrend/bert-base-en-bg-cased/README.md

@@ -4,11 +4,6 @@ language: multilingual
 datasets: wikipedia
 
 license: apache-2.0
-
-widget:
-- text: "Google generated 46 billion [MASK] in revenue."
-- text: "Paris is the capital of [MASK]."
-- text: "Algiers is the largest city in [MASK]."
 ---
 
 # bert-base-en-bg-cased
@@ -29,8 +24,6 @@ model = AutoModel.from_pretrained("Geotrend/bert-base-en-bg-cased")
 
 ```
 
-To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
-
 ### How to cite
 
 ```bibtex

model_cards/Geotrend/bert-base-en-cased/README.md

@@ -4,11 +4,6 @@ language: en
 datasets: wikipedia
 
 license: apache-2.0
-
-widget:
-- text: "Google generated 46 billion [MASK] in revenue."
-- text: "Paris is the capital of [MASK]."
-- text: "Algiers is the largest city in [MASK]."
 ---
 
 # bert-base-en-cased
@@ -29,8 +24,6 @@ model = AutoModel.from_pretrained("Geotrend/bert-base-en-cased")
 
 ```
 
-To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
-
 ### How to cite
 
 ```bibtex

model_cards/Geotrend/bert-base-en-de-cased/README.md

@@ -4,11 +4,6 @@ language: multilingual
 datasets: wikipedia
 
 license: apache-2.0
-
-widget:
-- text: "Google generated 46 billion [MASK] in revenue."
-- text: "Paris is the capital of [MASK]."
-- text: "Algiers is the largest city in [MASK]."
 ---
 
 # bert-base-en-de-cased
@@ -29,8 +24,6 @@ model = AutoModel.from_pretrained("Geotrend/bert-base-en-de-cased")
 
 ```
 
-To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
-
 ### How to cite
 
 ```bibtex

model_cards/Geotrend/bert-base-en-el-cased/README.md

@@ -4,11 +4,6 @@ language: multilingual
 datasets: wikipedia
 
 license: apache-2.0
-
-widget:
-- text: "Google generated 46 billion [MASK] in revenue."
-- text: "Paris is the capital of [MASK]."
-- text: "Algiers is the largest city in [MASK]."
 ---
 
 # bert-base-en-el-cased
@@ -29,8 +24,6 @@ model = AutoModel.from_pretrained("Geotrend/bert-base-en-el-cased")
 
 ```
 
-To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
-
 ### How to cite
 
 ```bibtex

model_cards/Geotrend/bert-base-en-es-cased/README.md

@@ -4,11 +4,6 @@ language: multilingual
 datasets: wikipedia
 
 license: apache-2.0
-
-widget:
-- text: "Google generated 46 billion [MASK] in revenue."
-- text: "Paris is the capital of [MASK]."
-- text: "Algiers is the largest city in [MASK]."
 ---
 
 # bert-base-en-es-cased
@@ -29,8 +24,6 @@ model = AutoModel.from_pretrained("Geotrend/bert-base-en-es-cased")
 
 ```
 
-To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
-
 ### How to cite
 
 ```bibtex

model_cards/Geotrend/bert-base-en-fr-cased/README.md

@@ -4,14 +4,6 @@ language: multilingual
 datasets: wikipedia
 
 license: apache-2.0
-
-widget:
-- text: "Google generated 46 billion [MASK] in revenue."
-- text: "Paris is the capital of [MASK]."
-- text: "Algiers is the largest city in [MASK]."
-- text: "Paris est la [MASK] de la France."
-- text: "Paris est la capitale de la [MASK]."
-- text: "L'élection américaine a eu [MASK] en novembre 2020."
 ---
 
 # bert-base-en-fr-cased
@@ -32,8 +24,6 @@ model = AutoModel.from_pretrained("Geotrend/bert-base-en-fr-cased")
 
 ```
 
-To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
-
 ### How to cite
 
 ```bibtex

model_cards/Geotrend/bert-base-en-hi-cased/README.md

@@ -4,11 +4,6 @@ language: multilingual
 datasets: wikipedia
 
 license: apache-2.0
-
-widget:
-- text: "Google generated 46 billion [MASK] in revenue."
-- text: "Paris is the capital of [MASK]."
-- text: "Algiers is the largest city in [MASK]."
 ---
 
 # bert-base-en-hi-cased
@@ -29,8 +24,6 @@ model = AutoModel.from_pretrained("Geotrend/bert-base-en-hi-cased")
 
 ```
 
-To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
-
 ### How to cite
 
 ```bibtex

model_cards/Geotrend/bert-base-en-ru-cased/README.md

@@ -4,11 +4,6 @@ language: multilingual
 datasets: wikipedia
 
 license: apache-2.0
-
-widget:
-- text: "Google generated 46 billion [MASK] in revenue."
-- text: "Paris is the capital of [MASK]."
-- text: "Algiers is the largest city in [MASK]."
 ---
 
 # bert-base-en-ru-cased
@@ -29,8 +24,6 @@ model = AutoModel.from_pretrained("Geotrend/bert-base-en-ru-cased")
 
 ```
 
-To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
-
 ### How to cite
 
 ```bibtex

model_cards/Geotrend/bert-base-en-sw-cased/README.md

@@ -4,11 +4,6 @@ language: multilingual
 datasets: wikipedia
 
 license: apache-2.0
-
-widget:
-- text: "Google generated 46 billion [MASK] in revenue."
-- text: "Paris is the capital of [MASK]."
-- text: "Algiers is the largest city in [MASK]."
 ---
 
 # bert-base-en-sw-cased
@@ -29,8 +24,6 @@ model = AutoModel.from_pretrained("Geotrend/bert-base-en-sw-cased")
 
 ```
 
-To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
-
 ### How to cite
 
 ```bibtex

model_cards/Geotrend/bert-base-en-th-cased/README.md

@@ -4,11 +4,6 @@ language: multilingual
 datasets: wikipedia
 
 license: apache-2.0
-
-widget:
-- text: "Google generated 46 billion [MASK] in revenue."
-- text: "Paris is the capital of [MASK]."
-- text: "Algiers is the largest city in [MASK]."
 ---
 
 # bert-base-en-th-cased
@@ -29,8 +24,6 @@ model = AutoModel.from_pretrained("Geotrend/bert-base-en-th-cased")
 
 ```
 
-To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
-
 ### How to cite
 
 ```bibtex

model_cards/Geotrend/bert-base-en-tr-cased/README.md

@@ -4,11 +4,6 @@ language: multilingual
 datasets: wikipedia
 
 license: apache-2.0
-
-widget:
-- text: "Google generated 46 billion [MASK] in revenue."
-- text: "Paris is the capital of [MASK]."
-- text: "Algiers is the largest city in [MASK]."
 ---
 
 # bert-base-en-tr-cased
@@ -29,8 +24,6 @@ model = AutoModel.from_pretrained("Geotrend/bert-base-en-tr-cased")
 
 ```
 
-To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
-
 ### How to cite
 
 ```bibtex

model_cards/Geotrend/bert-base-en-ur-cased/README.md

@@ -4,11 +4,6 @@ language: multilingual
 datasets: wikipedia
 
 license: apache-2.0
-
-widget:
-- text: "Google generated 46 billion [MASK] in revenue."
-- text: "Paris is the capital of [MASK]."
-- text: "Algiers is the largest city in [MASK]."
 ---
 
 # bert-base-en-ur-cased
@@ -29,8 +24,6 @@ model = AutoModel.from_pretrained("Geotrend/bert-base-en-ur-cased")
 
 ```
 
-To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
-
 ### How to cite
 
 ```bibtex

model_cards/Geotrend/bert-base-en-vi-cased/README.md

@@ -4,11 +4,6 @@ language: multilingual
 datasets: wikipedia
 
 license: apache-2.0
-
-widget:
-- text: "Google generated 46 billion [MASK] in revenue."
-- text: "Paris is the capital of [MASK]."
-- text: "Algiers is the largest city in [MASK]."
 ---
 
 # bert-base-en-vi-cased
@@ -29,8 +24,6 @@ model = AutoModel.from_pretrained("Geotrend/bert-base-en-vi-cased")
 
 ```
 
-To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
-
 ### How to cite
 
 ```bibtex

model_cards/Geotrend/bert-base-en-zh-cased/README.md

@@ -4,11 +4,6 @@ language: multilingual
 datasets: wikipedia
 
 license: apache-2.0
-
-widget:
-- text: "Google generated 46 billion [MASK] in revenue."
-- text: "Paris is the capital of [MASK]."
-- text: "Algiers is the largest city in [MASK]."
 ---
 
 # bert-base-en-zh-cased
@@ -29,8 +24,6 @@ model = AutoModel.from_pretrained("Geotrend/bert-base-en-zh-cased")
 
 ```
 
-To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
-
 ### How to cite
 
 ```bibtex

model_cards/Geotrend/bert-base-es-cased/README.md

@@ -24,8 +24,6 @@ model = AutoModel.from_pretrained("Geotrend/bert-base-es-cased")
 
 ```
 
-To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
-
 ### How to cite
 
 ```bibtex

model_cards/Geotrend/bert-base-fr-cased/README.md

@@ -4,11 +4,6 @@ language: fr
 datasets: wikipedia
 
 license: apache-2.0
-
-widget:
-- text: "Paris est la [MASK] de la France."
-- text: "Paris est la capitale de la [MASK]."
-- text: "L'élection américaine a eu [MASK] en novembre 2020."
 ---
 
 # bert-base-fr-cased
@@ -29,8 +24,6 @@ model = AutoModel.from_pretrained("Geotrend/bert-base-fr-cased")
 
 ```
 
-To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
-
 ### How to cite
 
 ```bibtex

model_cards/Geotrend/bert-base-hi-cased/README.md

@@ -24,8 +24,6 @@ model = AutoModel.from_pretrained("Geotrend/bert-base-hi-cased")
 
 ```
 
-To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
-
 ### How to cite
 
 ```bibtex

model_cards/Geotrend/bert-base-ru-cased/README.md

@@ -24,8 +24,6 @@ model = AutoModel.from_pretrained("Geotrend/bert-base-ru-cased")
 
 ```
 
-To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
-
 ### How to cite
 
 ```bibtex

model_cards/Geotrend/bert-base-sw-cased/README.md

@@ -24,8 +24,6 @@ model = AutoModel.from_pretrained("Geotrend/bert-base-sw-cased")
 
 ```
 
-To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
-
 ### How to cite
 
 ```bibtex

model_cards/Geotrend/bert-base-th-cased/README.md

@@ -24,8 +24,6 @@ model = AutoModel.from_pretrained("Geotrend/bert-base-th-cased")
 
 ```
 
-To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
-
 ### How to cite
 
 ```bibtex

model_cards/Geotrend/bert-base-tr-cased/README.md

@@ -24,8 +24,6 @@ model = AutoModel.from_pretrained("Geotrend/bert-base-tr-cased")
 
 ```
 
-To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
-
 ### How to cite
 
 ```bibtex

model_cards/Geotrend/bert-base-ur-cased/README.md

@@ -24,8 +24,6 @@ model = AutoModel.from_pretrained("Geotrend/bert-base-ur-cased")
 
 ```
 
-To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
-
 ### How to cite
 
 ```bibtex

model_cards/Geotrend/bert-base-vi-cased/README.md

@@ -24,8 +24,6 @@ model = AutoModel.from_pretrained("Geotrend/bert-base-vi-cased")
 
 ```
 
-To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
-
 ### How to cite
 
 ```bibtex

model_cards/Geotrend/bert-base-zh-cased/README.md

@@ -24,8 +24,6 @@ model = AutoModel.from_pretrained("Geotrend/bert-base-zh-cased")
 
 ```
 
-To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
-
 ### How to cite
 
 ```bibtex

model_cards/NlpHUST/vibert4news-base-cased/README.md

@@ -1,38 +0,0 @@
----
-language: vn
----
-
-# BERT for Vietnamese is trained on more 20 GB news dataset
-
-Apply for task sentiment analysis on using [AIViVN's comments dataset](https://www.aivivn.com/contests/6)
-
-The model achieved 0.90268 on the public leaderboard, (winner's score is 0.90087)
-Bert4news is used for a toolkit Vietnames(segmentation and Named Entity Recognition) at ViNLPtoolkit(https://github.com/bino282/ViNLP)
-
-***************New Mar 11 , 2020 ***************
-
-**[BERT](https://github.com/google-research/bert)** (from Google Research and the Toyota Technological Institute at Chicago) released with the paper [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805).
-
-We use word sentencepiece, use basic bert tokenization and same config with bert base with lowercase = False.
-
-You can download trained model:
-- [tensorflow](https://drive.google.com/file/d/1X-sRDYf7moS_h61J3L79NkMVGHP-P-k5/view?usp=sharing).
-- [pytorch](https://drive.google.com/file/d/11aFSTpYIurn-oI2XpAmcCTccB_AonMOu/view?usp=sharing).
-
-
-
-Run training with base config
-
-``` bash
-
-python train_pytorch.py \
-  --model_path=bert4news.pytorch \
-  --max_len=200 \
-  --batch_size=16 \
-  --epochs=6 \
-  --lr=2e-5
-
-```
-
-### Contact information
-For personal communication related to this project, please contact Nha Nguyen Van (nha282@gmail.com).

model_cards/bert-base-multilingual-cased-README.md

@@ -5,7 +5,7 @@ datasets:
 - wikipedia
 ---
 
-# BERT multilingual base model (cased)
+# BERT multilingual base model (uncased)
 
 Pretrained model on the top 104 languages with the largest Wikipedia using a masked language modeling (MLM) objective.
 It was introduced in [this paper](https://arxiv.org/abs/1810.04805) and first released in

model_cards/e-tony/gpt2-rnm/README.md

@@ -1,20 +0,0 @@
-### How to use
-
-You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we
-set a seed for reproducibility:
-
-```python
->>> from transformers import pipeline, set_seed
->>> generator = pipeline('text-generation', model='e-tony/gpt2-rnm')
->>> set_seed(42)
->>> generator("Rick: I turned myself into a pickle, Morty!\nMorty: ", max_length=50, num_return_sequences=5)
-
-[{'generated_text': "Rick: I turned myself into a pickle, Morty!\nMorty:  I didn't want to have children. It was my fate! I'll pay my mom and dad.\nSnuffles:  Well, at least we"}, 
- {'generated_text': "Rick: I turned myself into a pickle, Morty!\nMorty:  you know what happened?\n(Steven begins dragging people down the toilet with his hand. As Steven falls) The whole thing starts.\nA man approaches Steven"}, 
- {'generated_text': "Rick: I turned myself into a pickle, Morty!\nMorty:  Oh wait! And do you remember what I did to you?\nJerry:  Uh, it didn't hurt. It should have hurt a lot since I"}, 
- {'generated_text': "Rick: I turned myself into a pickle, Morty!\nMorty:  Rick!\nKraven:  Wait! [wary gasp] What the hell are you doing this time?!\nJerry:  Hey, are you"}, 
- {'generated_text': "Rick: I turned myself into a pickle, Morty!\nMorty:  Uh.\nJerry:  You don't have to put your finger on me today, do you?\nRick:  It's just, what do you"}]
-```
-
-### Training data
-We used the original `gpt2` model and fine-tuned it on [Rick and Morty transcripts](https://rickandmorty.fandom.com/wiki/Category:Transcripts).

model_cards/julien-c/EsperBERTo-small-pos/README.md

@@ -1,6 +1,6 @@
 ---
 language: eo
-thumbnail: https://huggingface.co/blog/assets/01_how-to-train/EsperBERTo-thumbnail-v2.png
+thumbnail: https://huggingface.co/blog/assets/EsperBERTo-thumbnail-v2.png
 widget:
 - text: "Mi estas viro kej estas tago varma."
 ---
@@ -15,7 +15,7 @@ widget:
 - machine name: `galinette`
 
 
-![](https://huggingface.co/blog/assets/01_how-to-train/EsperBERTo-thumbnail-v2.png)
+![](https://huggingface.co/blog/assets/EsperBERTo-thumbnail-v2.png)
 
 ## Example pipeline
 

model_cards/julien-c/EsperBERTo-small/README.md

@@ -1,6 +1,6 @@
 ---
 language: eo
-thumbnail: https://huggingface.co/blog/assets/01_how-to-train/EsperBERTo-thumbnail-v2.png
+thumbnail: https://huggingface.co/blog/assets/EsperBERTo-thumbnail-v2.png
 widget:
 - text: "Jen la komenco de bela <mask>."
 - text: "Uno du <mask>"
@@ -17,7 +17,7 @@ widget:
 - machine name: `galinette`
 
 
-![](https://huggingface.co/blog/assets/01_how-to-train/EsperBERTo-thumbnail-v2.png)
+![](https://huggingface.co/blog/assets/EsperBERTo-thumbnail-v2.png)
 
 ## Example pipeline
 

model_cards/monilouise/ner_pt_br/README.md

@@ -1,92 +0,0 @@
----
-language: 
-- pt
-tags:
-- ner
-metrics:
-- f1
-- accuracy
-- precision
-- recall
----
-
-# RiskData Brazilian Portuguese NER
-
-## Model description
-
-This is a finetunned version from [Neuralmind BERTimbau] (https://github.com/neuralmind-ai/portuguese-bert/blob/master/README.md) for Portuguese language.
-
-For more details, please see, (https://github.com/SecexSaudeTCU/noticias_ner).
-
-## Intended uses & limitations
-
-#### How to use
-
-#### Limitations and bias
-
-- The finetunned model was trained on a corpus with around 180 news articles crawled from Google News.  The original project's purpose was to recognize named entities in news 
-related to fraud and corruption, classifying these entities in four classes: PERSON, ORGANIZATION, PUBLIC INSITUITION and LOCAL (PESSOA, ORGANIZAÇÃO, INSTITUIÇÃO PÚBLICA and LOCAL).
-
-## Training data
-
-The training data can be found at (https://github.com/SecexSaudeTCU/noticias_ner/blob/master/dados/labeled_4_labels.jsonl).
-
-
-## Training procedure
-
-
-## Eval results
-
-accuracy: 0.98, 
-precision: 0.86 
-recall: 0.91
-f1: 0.88
-
-
-The score was calculated using this code:
-
-```python
-    def align_predictions(predictions: np.ndarray, label_ids: np.ndarray) -> Tuple[List[int], List[int]]:
-        preds = np.argmax(predictions, axis=2)
-        batch_size, seq_len = preds.shape
-        out_label_list = [[] for _ in range(batch_size)]
-        preds_list = [[] for _ in range(batch_size)]
-
-        for i in range(batch_size):
-            for j in range(seq_len):
-                if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
-                    out_label_list[i].append(id2tag[label_ids[i][j]])
-                    preds_list[i].append(id2tag[preds[i][j]])
-
-        return preds_list, out_label_list
-
-    def compute_metrics(p: EvalPrediction) -> Dict:
-        preds_list, out_label_list = align_predictions(p.predictions, p.label_ids)
-        return {
-            "accuracy_score": accuracy_score(out_label_list, preds_list),
-            "precision": precision_score(out_label_list, preds_list),
-            "recall": recall_score(out_label_list, preds_list),
-            "f1": f1_score(out_label_list, preds_list),
-        }
-```
-
-### BibTeX entry and citation info
-
-For further information about BERTimbau language model:
-
-```bibtex
-@inproceedings{souza2020bertimbau,
-    author    = {Souza, F{\'a}bio and Nogueira, Rodrigo and Lotufo, Roberto},
-    title     = {{BERT}imbau: pretrained {BERT} models for {B}razilian {P}ortuguese},
-    booktitle = {9th Brazilian Conference on Intelligent Systems, {BRACIS}, Rio Grande do Sul, Brazil, October 20-23 (to appear)},
-    year      = {2020}
-}
-
-@article{souza2019portuguese,
-    title={Portuguese Named Entity Recognition using BERT-CRF},
-    author={Souza, F{\'a}bio and Nogueira, Rodrigo and Lotufo, Roberto},
-    journal={arXiv preprint arXiv:1909.10649},
-    url={http://arxiv.org/abs/1909.10649},
-    year={2019}
-}
-```

model_cards/mrm8488/bert-mini2bert-mini-finetuned-cnn_daily_mail-summarization/README.md

@@ -1,52 +0,0 @@
----
-language: en
-license: apache-2.0
-datasets:
-- cnn_dailymail
-tags:
-- summarization
----
-
-# Bert-mini2Bert-mini Summarization with 🤗EncoderDecoder Framework
-
-This model is a warm-started *BERT2BERT* ([mini](https://huggingface.co/google/bert_uncased_L-4_H-256_A-4)) model fine-tuned on the *CNN/Dailymail* summarization dataset.
-
-The model achieves a **16.51** ROUGE-2 score on *CNN/Dailymail*'s test dataset.
-
-For more details on how the model was fine-tuned, please refer to 
-[this](https://colab.research.google.com/drive/1Ekd5pUeCX7VOrMx94_czTkwNtLN32Uyu?usp=sharing) notebook.
-
-## Results on test set 📝
-
-| Metric | # Value   |
-| ------ | --------- |
-| **ROUGE-2** | **16.51** |
-
-
-
-## Model in Action 🚀
-
-```python
-from transformers import BertTokenizerFast, EncoderDecoderModel
-import torch
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-tokenizer = BertTokenizerFast.from_pretrained('mrm8488/bert-mini2bert-mini-finetuned-cnn_daily_mail-summarization')
-model = EncoderDecoderModel.from_pretrained('mrm8488/bert-mini2bert-mini-finetuned-cnn_daily_mail-summarization').to(device)
-
-def generate_summary(text):
-    # cut off at BERT max length 512
-    inputs = tokenizer([text], padding="max_length", truncation=True, max_length=512, return_tensors="pt")
-    input_ids = inputs.input_ids.to(device)
-    attention_mask = inputs.attention_mask.to(device)
-
-    output = model.generate(input_ids, attention_mask=attention_mask)
-
-    return tokenizer.decode(output[0], skip_special_tokens=True)
-  
-text = "your text to be summarized here..."
-generate_summary(text)
-```
-
-> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/)
-
-> Made with <span style="color: #e25555;">&hearts;</span> in Spain

model_cards/mrm8488/bert-small2bert-small-finetuned-cnn_daily_mail-summarization/README.md

@@ -1,52 +0,0 @@
----
-language: en
-license: apache-2.0
-datasets:
-- cnn_dailymail
-tags:
-- summarization
----
-
-# Bert-small2Bert-small Summarization with 🤗EncoderDecoder Framework
-
-This model is a warm-started *BERT2BERT* ([small](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8)) model fine-tuned on the *CNN/Dailymail* summarization dataset.
-
-The model achieves a **17.37** ROUGE-2 score on *CNN/Dailymail*'s test dataset.
-
-For more details on how the model was fine-tuned, please refer to 
-[this](https://colab.research.google.com/drive/1Ekd5pUeCX7VOrMx94_czTkwNtLN32Uyu?usp=sharing) notebook.
-
-## Results on test set 📝
-
-| Metric | # Value   |
-| ------ | --------- |
-| **ROUGE-2** | **17.37** |
-
-
-
-## Model in Action 🚀
-
-```python
-from transformers import BertTokenizerFast, EncoderDecoderModel
-import torch
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-tokenizer = BertTokenizerFast.from_pretrained('mrm8488/bert-small2bert-small-finetuned-cnn_daily_mail-summarization')
-model = EncoderDecoderModel.from_pretrained('mrm8488/bert-small2bert-small-finetuned-cnn_daily_mail-summarization').to(device)
-
-def generate_summary(text):
-    # cut off at BERT max length 512
-    inputs = tokenizer([text], padding="max_length", truncation=True, max_length=512, return_tensors="pt")
-    input_ids = inputs.input_ids.to(device)
-    attention_mask = inputs.attention_mask.to(device)
-
-    output = model.generate(input_ids, attention_mask=attention_mask)
-
-    return tokenizer.decode(output[0], skip_special_tokens=True)
-  
-text = "your text to be summarized here..."
-generate_summary(text)
-```
-
-> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/)
-
-> Made with <span style="color: #e25555;">&hearts;</span> in Spain

model_cards/mrm8488/mT5-small-finetuned-tydiqa-for-xqa/README.md

@@ -1,85 +0,0 @@
----
-language: multilingual
-datasets:
-- tydiqa
-pipeline_tag: question-answering
----
-
-# mT5-small fine-tuned on TyDiQA for multilingual QA 🗺📖❓
-[Google's mT5-small](https://huggingface.co/google/mt5-small) fine-tuned on [TyDi QA](https://huggingface.co/nlp/viewer/?dataset=tydiqa&config=secondary_task) (secondary task) for **multingual Q&A** downstream task.
-
-## Details of mT5
-
-[Google's mT5](https://github.com/google-research/multilingual-t5)
-
-mT5 is pretrained on the [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual) corpus, covering 101 languages:
-
-Afrikaans, Albanian, Amharic, Arabic, Armenian, Azerbaijani, Basque, Belarusian, Bengali, Bulgarian, Burmese, Catalan, Cebuano, Chichewa, Chinese, Corsican, Czech, Danish, Dutch, English, Esperanto, Estonian, Filipino, Finnish, French, Galician, Georgian, German, Greek, Gujarati, Haitian Creole, Hausa, Hawaiian, Hebrew, Hindi, Hmong, Hungarian, Icelandic, Igbo, Indonesian, Irish, Italian, Japanese, Javanese, Kannada, Kazakh, Khmer, Korean, Kurdish, Kyrgyz, Lao, Latin, Latvian, Lithuanian, Luxembourgish, Macedonian, Malagasy, Malay, Malayalam, Maltese, Maori, Marathi, Mongolian, Nepali, Norwegian, Pashto, Persian, Polish, Portuguese, Punjabi, Romanian, Russian, Samoan, Scottish Gaelic, Serbian, Shona, Sindhi, Sinhala, Slovak, Slovenian, Somali, Sotho, Spanish, Sundanese, Swahili, Swedish, Tajik, Tamil, Telugu, Thai, Turkish, Ukrainian, Urdu, Uzbek, Vietnamese, Welsh, West Frisian, Xhosa, Yiddish, Yoruba, Zulu.
-
-**Note**: mT5 was only pre-trained on mC4 excluding any supervised training. Therefore, this model has to be fine-tuned before it is useable on a downstream task.
-
-Pretraining Dataset: [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual)
-
-Other Community Checkpoints: [here](https://huggingface.co/models?search=mt5)
-
-Paper: [mT5: A massively multilingual pre-trained text-to-text transformer](https://arxiv.org/abs/2010.11934)
-
-Authors: *Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel* 
-
-
-## Details of the dataset 📚 
-
-**TyDi QA** is a question answering dataset covering 11 typologically diverse languages with 204K question-answer pairs. The languages of TyDi QA are diverse with regard to their typology -- the set of linguistic features that each language expresses -- such that we expect models performing well on this set to generalize across a large number of the languages in the world. It contains language phenomena that would not be found in English-only corpora. To provide a realistic information-seeking task and avoid priming effects, questions are written by people who want to know the answer, but don’t know the answer yet, (unlike SQuAD and its descendents) and the data is collected directly in each language without the use of translation (unlike MLQA and XQuAD).
-
-| Dataset  | Task | Split | # samples |
-| -------- | ----- |------| --------- |
-| TyDi QA | GoldP  | train| 49881     |
-| TyDi QA | GoldP  | valid| 5077      | 
-
-
-
-## Results on validation dataset 📝
-
-| Metric | # Value   |
-| ------ | --------- |
-| **EM** | **41.65** |
-
-
-
-## Model in Action 🚀
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import torch
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-tokenizer = AutoTokenizer.from_pretrained("mrm8488/mT5-small-finetuned-tydiqa-for-xqa")
-model = AutoModelForCausalLM.from_pretrained("mrm8488/mT5-small-finetuned-tydiqa-for-xqa").to(device)
-
-def get_response(question, context, max_length=32):
-  input_text = 'question: %s  context: %s' % (question, context)
-  features = tokenizer([input_text], return_tensors='pt')
-
-  output = model.generate(input_ids=features['input_ids'].to(device), 
-               attention_mask=features['attention_mask'].to(device),
-               max_length=max_length)
-
-  return tokenizer.decode(output[0])
-  
-# Some examples in different languages
-
-context = 'HuggingFace won the best Demo paper at EMNLP2020.'
-question = 'What won HuggingFace?'
-get_response(question, context)
-
-context = 'HuggingFace ganó la mejor demostración con su paper en la EMNLP2020.'
-question = 'Qué ganó HuggingFace?'
-get_response(question, context)
-
-context = 'HuggingFace выиграл лучшую демонстрационную работу на EMNLP2020.'
-question = 'Что победило в HuggingFace?'
-get_response(question, context)
-```
-
-> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/)
-
-> Made with <span style="color: #e25555;">&hearts;</span> in Spain

model_cards/nlpaueb/bert-base-greek-uncased-v1/README.md

@@ -91,7 +91,7 @@ print(tokenizer_greek.convert_ids_to_tokens(outputs[0, 5].max(0)[1].item()))
 # ================ EXAMPLE 2 ================
 text_2 = 'Είναι ένας [MASK] άνθρωπος.'
 # EN: 'He is a [MASK] person.'
-input_ids = tokenizer_greek.encode(text_2)
+input_ids = tokenizer_greek.encode(text_1)
 print(tokenizer_greek.convert_ids_to_tokens(input_ids))
 # ['[CLS]', 'ειναι', 'ενας', '[MASK]', 'ανθρωπος', '.', '[SEP]']
 outputs = lm_model_greek(torch.tensor([input_ids]))[0]

model_cards/sagorsarker/bangla-bert-sentiment/README.md

@@ -1,61 +0,0 @@
----
-language:
-- bn
-datasets:
-- socian
-- bangla-sentiment-benchmark
-license: mit
-tags:
-- bengali
-- bengali-sentiment
-- sentiment-analysis
----
-
-# bangla-bert-sentiment
-`bangla-bert-sentiment` is a pretrained model for bengali **Sentiment Analysis** using [bangla-bert-base](https://huggingface.co/sagorsarker/bangla-bert-base) model.
-
-## Datasets Details
-This model was trained with two combined datasets
-* [socian sentiment data](https://github.com/socian-ai/socian-bangla-sentiment-dataset-labeled)
-* [bangla classification dataset](https://github.com/rezacsedu/Classification_Benchmarks_Benglai_NLP)
-
-|||
-|--|--|
-|Data Size| 10889 |
-|Positive| 4999 |
-|Negative| 5890 |
-|Train | 8711 |
-| Test | 2178 |
-
-## Training Details
-Model trained with [simpletransformers](https://github.com/ThilinaRajapakse/simpletransformers) binary classification script with total of **3 epochs** in `google colab gpu`.
-
-
-## Evaluation Details
-Model evaluate with 2178 sentences
-
-Here is the evaluation result details in table
-
-
-|Eval Loss | TP | TN | FP | FN | F1 Score |
-| -------- | -- | -- | -- | -- | -------- |
-| 0.3289 | 880 | 1158 | 59 | 81 | 92.63 |
-
-## Usage
-
-Calculate sentiment from given sentence
-
-```py
-
-from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
-
-tokenizer = AutoTokenizer.from_pretrained("sagorsarker/bangla-bert-sentiment")
-
-model = AutoModelForSequenceClassification.from_pretrained("sagorsarker/bangla-bert-sentiment")
-
-nlp = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
-sentence = "বাংলার ঘরে ঘরে আজ নবান্নের উৎসব"
-nlp(sentence)
-
-```
-

notebooks/02-transformers.ipynb

@@ -87,7 +87,7 @@
    "outputs": [],
    "source": [
     "!pip install transformers\n",
-    "!pip install --upgrade tensorflow"
+    "!pip install tensorflow==2.1.0"
    ]
   },
   {
@@ -559,4 +559,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

setup.py

@@ -47,9 +47,7 @@ To create the package for pypi.
 """
 
 import os
-import re
 import shutil
-from distutils.core import Command
 from pathlib import Path
 
 from setuptools import find_packages, setup
@@ -71,166 +69,57 @@ if stale_egg_info.exists():
     shutil.rmtree(stale_egg_info)
 
 
-# IMPORTANT:
-# 1. all dependencies should be listed here with their version requirements if any
-# 2. once modified, run: `make deps_table_update` to update src/transformers/dependency_versions_table.py
-_deps = [
-    "black>=20.8b1",
-    "cookiecutter==1.7.2",
-    "dataclasses",
-    "datasets",
-    "faiss-cpu",
-    "fastapi",
-    "filelock",
-    "flake8>=3.8.3",
-    "flax==0.2.2",
-    "fugashi>=1.0",
-    "ipadic>=1.0.0,<2.0",
-    "isort>=5.5.4",
-    "jax>=0.2.0",
-    "jaxlib==0.1.55",
-    "keras2onnx",
-    "numpy",
-    "onnxconverter-common",
-    "onnxruntime-tools>=1.4.2",
-    "onnxruntime>=1.4.0",
-    "packaging",
-    "parameterized",
-    "protobuf",
-    "psutil",
-    "pydantic",
-    "pytest",
-    "pytest-xdist",
-    "python>=3.6.0",
-    "recommonmark",
-    "regex!=2019.12.17",
-    "requests",
-    "sacremoses",
-    "scikit-learn",
-    "sentencepiece==0.1.91",
-    "sphinx-copybutton",
-    "sphinx-markdown-tables",
-    "sphinx-rtd-theme==0.4.3",  # sphinx-rtd-theme==0.5.0 introduced big changes in the style.
-    "sphinx==3.2.1",
-    "starlette",
-    "tensorflow-cpu>=2.0",
-    "tensorflow>=2.0",
-    "timeout-decorator",
-    "tokenizers==0.9.4",
-    "torch>=1.0",
-    "tqdm>=4.27",
-    "unidic>=1.0.2",
-    "unidic_lite>=1.0.7",
-    "uvicorn",
-]
-
-
-# tokenizers: "tokenizers==0.9.4" lookup table
-# support non-versions file too so that they can be checked at run time
-deps = {b: a for a, b in (re.findall(r"^(([^!=<>]+)(?:[!=<>].*)?$)", x)[0] for x in _deps)}
-
-
-def deps_list(*pkgs):
-    return [deps[pkg] for pkg in pkgs]
-
-
-class DepsTableUpdateCommand(Command):
-    """
-    A custom distutils command that updates the dependency table.
-    usage: python setup.py deps_table_update
-    """
-
-    description = "build runtime dependency table"
-    user_options = [
-        # format: (long option, short option, description).
-        ("dep-table-update", None, "updates src/transformers/dependency_versions_table.py"),
-    ]
-
-    def initialize_options(self):
-        pass
-
-    def finalize_options(self):
-        pass
-
-    def run(self):
-        entries = "\n".join([f'    "{k}": "{v}",' for k, v in deps.items()])
-        content = [
-            "# THIS FILE HAS BEEN AUTOGENERATED. To update:",
-            "# 1. modify the `_deps` dict in setup.py",
-            "# 2. run `make deps_table_update``",
-            "deps = {",
-            entries,
-            "}",
-            ""
-        ]
-        target = "src/transformers/dependency_versions_table.py"
-        print(f"updating {target}")
-        with open(target, "w", encoding="utf-8", newline="\n") as f:
-            f.write("\n".join(content))
-
-
 extras = {}
 
-extras["ja"] = deps_list("fugashi", "ipadic", "unidic_lite", "unidic")
-extras["sklearn"] = deps_list("scikit-learn")
+extras["ja"] = ["fugashi>=1.0", "ipadic>=1.0.0,<2.0", "unidic_lite>=1.0.7", "unidic>=1.0.2"]
+extras["sklearn"] = ["scikit-learn"]
 
-extras["tf"] = deps_list("tensorflow", "onnxconverter-common", "keras2onnx")
-extras["tf-cpu"] = deps_list("tensorflow-cpu", "onnxconverter-common", "keras2onnx")
-
-extras["torch"] = deps_list("torch")
+# keras2onnx and onnxconverter-common version is specific through a commit until 1.7.0 lands on pypi
+extras["tf"] = [
+    "tensorflow>=2.0",
+    "onnxconverter-common",
+    "keras2onnx"
+    # "onnxconverter-common @ git+git://github.com/microsoft/onnxconverter-common.git@f64ca15989b6dc95a1f3507ff6e4c395ba12dff5#egg=onnxconverter-common",
+    # "keras2onnx @ git+git://github.com/onnx/keras-onnx.git@cbdc75cb950b16db7f0a67be96a278f8d2953b48#egg=keras2onnx",
+]
+extras["tf-cpu"] = [
+    "tensorflow-cpu>=2.0",
+    "onnxconverter-common",
+    "keras2onnx"
+    # "onnxconverter-common @ git+git://github.com/microsoft/onnxconverter-common.git@f64ca15989b6dc95a1f3507ff6e4c395ba12dff5#egg=onnxconverter-common",
+    # "keras2onnx @ git+git://github.com/onnx/keras-onnx.git@cbdc75cb950b16db7f0a67be96a278f8d2953b48#egg=keras2onnx",
+]
+extras["torch"] = ["torch>=1.0"]
 
 if os.name == "nt":  # windows
-    extras["retrieval"] = deps_list("datasets")  # faiss is not supported on windows
-    extras["flax"] = []  # jax is not supported on windows
+    extras["retrieval"] = ["datasets"] # faiss is not supported on windows
+    extras["flax"] = [] # jax is not supported on windows
 else:
-    extras["retrieval"] = deps_list("faiss-cpu", "datasets")
-    extras["flax"] = deps_list("jax", "jaxlib", "flax")
+    extras["retrieval"] = ["faiss-cpu", "datasets"]
+    extras["flax"] = ["jaxlib==0.1.55", "jax>=0.2.0", "flax==0.2.2"]
 
-extras["tokenizers"] = deps_list("tokenizers")
-extras["onnxruntime"] = deps_list("onnxruntime", "onnxruntime-tools")
-extras["modelcreation"] = deps_list("cookiecutter")
+extras["tokenizers"] = ["tokenizers==0.9.4"]
+extras["onnxruntime"] = ["onnxruntime>=1.4.0", "onnxruntime-tools>=1.4.2"]
+extras["modelcreation"] = ["cookiecutter==1.7.2"]
 
-extras["serving"] = deps_list("pydantic", "uvicorn", "fastapi", "starlette")
+extras["serving"] = ["pydantic", "uvicorn", "fastapi", "starlette"]
+
+extras["sentencepiece"] = ["sentencepiece==0.1.91", "protobuf"]
+extras["retrieval"] = ["faiss-cpu", "datasets"]
+extras["testing"] = ["pytest", "pytest-xdist", "timeout-decorator", "parameterized", "psutil"] + extras["retrieval"] + extras["modelcreation"]
+# sphinx-rtd-theme==0.5.0 introduced big changes in the style.
+extras["docs"] = ["recommonmark", "sphinx==3.2.1", "sphinx-markdown-tables", "sphinx-rtd-theme==0.4.3", "sphinx-copybutton"]
+extras["quality"] = ["black >= 20.8b1", "isort >= 5.5.4", "flake8 >= 3.8.3"]
 
-extras["sentencepiece"] = deps_list("sentencepiece", "protobuf")
-extras["retrieval"] = deps_list("faiss-cpu", "datasets")
-extras["testing"] = (
-    deps_list("pytest", "pytest-xdist", "timeout-decorator", "parameterized", "psutil")
-    + extras["retrieval"]
-    + extras["modelcreation"]
-)
-extras["docs"] = deps_list("recommonmark", "sphinx", "sphinx-markdown-tables", "sphinx-rtd-theme", "sphinx-copybutton")
-extras["quality"] = deps_list("black", "isort", "flake8")
 
 extras["all"] = extras["tf"] + extras["torch"] + extras["flax"] + extras["sentencepiece"] + extras["tokenizers"]
 
-extras["dev"] = (
-    extras["all"]
-    + extras["testing"]
-    + extras["quality"]
-    + extras["ja"]
-    + extras["docs"]
-    + extras["sklearn"]
-    + extras["modelcreation"]
-)
+extras["dev"] = extras["all"] + extras["testing"] + extras["quality"] + extras["ja"] + extras["docs"] + extras["sklearn"] + extras["modelcreation"]
 
 
-# when modifying the following list, make sure to update src/transformers/dependency_versions_check.py
-install_requires = [
-    deps["dataclasses"] + ";python_version<'3.7'",  # dataclasses for Python versions that don't have it
-    deps["filelock"],   # filesystem locks, e.g., to prevent parallel downloads
-    deps["numpy"],
-    deps["packaging"],  # utilities from PyPA to e.g., compare versions
-    deps["regex"],      # for OpenAI GPT
-    deps["requests"],   # for downloading models over HTTPS
-    deps["sacremoses"], # for XLM
-    deps["tokenizers"],
-    deps["tqdm"],       # progress bars in model download and training scripts
-]
-
 setup(
     name="transformers",
-    version="4.0.0-rc-1",
+    version="4.0.0",
     author="Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Sam Shleifer, Patrick von Platen, Sylvain Gugger, Google AI Language Team Authors, Open AI team Authors, Facebook AI Authors, Carnegie Mellon University Authors",
     author_email="thomas@huggingface.co",
     description="State-of-the-art Natural Language Processing for TensorFlow 2.0 and PyTorch",
@@ -241,10 +130,27 @@ setup(
     url="https://github.com/huggingface/transformers",
     package_dir={"": "src"},
     packages=find_packages("src"),
+    install_requires=[
+        "numpy",
+        "tokenizers == 0.9.4",
+        # dataclasses for Python versions that don't have it
+        "dataclasses;python_version<'3.7'",
+        # utilities from PyPA to e.g. compare versions
+        "packaging",
+        # filesystem locks e.g. to prevent parallel downloads
+        "filelock",
+        # for downloading models over HTTPS
+        "requests",
+        # progress bars in model download and training scripts
+        "tqdm >= 4.27",
+        # for OpenAI GPT
+        "regex != 2019.12.17",
+        # for XLM
+        "sacremoses",
+    ],
     extras_require=extras,
     entry_points={"console_scripts": ["transformers-cli=transformers.commands.transformers_cli:main"]},
     python_requires=">=3.6.0",
-    install_requires=install_requires,
     classifiers=[
         "Development Status :: 5 - Production/Stable",
         "Intended Audience :: Developers",
@@ -257,5 +163,4 @@ setup(
         "Programming Language :: Python :: 3.7",
         "Topic :: Scientific/Engineering :: Artificial Intelligence",
     ],
-    cmdclass={"deps_table_update": DepsTableUpdateCommand},
 )

src/transformers/__init__.py

@@ -2,7 +2,7 @@
 # There's no way to ignore "F401 '...' imported but unused" warnings in this
 # module, but to preserve other warnings. So, don't check this module at all.
 
-__version__ = "4.0.0-rc-1"
+__version__ = "4.0.0"
 
 # Work around to update TensorFlow's absl.logging threshold which alters the
 # default Python logging output behavior when present.
@@ -17,7 +17,15 @@ else:
     absl.logging.set_stderrthreshold("info")
     absl.logging._warn_preinit_stderr = False
 
-from . import dependency_versions_check
+# Integrations: this needs to come before other ml imports
+# in order to allow any 3rd-party code to initialize properly
+from .integrations import (  # isort:skip
+    is_comet_available,
+    is_optuna_available,
+    is_ray_available,
+    is_tensorboard_available,
+    is_wandb_available,
+)
 
 # Configuration
 from .configuration_utils import PretrainedConfig
@@ -113,7 +121,6 @@ from .models.blenderbot import (
     BlenderbotTokenizer,
 )
 from .models.camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig
-from .models.conv_bert import CONV_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig
 from .models.ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig, CTRLTokenizer
 from .models.deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaTokenizer
 from .models.distilbert import DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, DistilBertConfig, DistilBertTokenizer
@@ -197,20 +204,8 @@ from .tokenization_utils_base import (
 )
 
 
-# Integrations: this needs to come before other ml imports
-# in order to allow any 3rd-party code to initialize properly
-from .integrations import (  # isort:skip
-    is_comet_available,
-    is_optuna_available,
-    is_ray_available,
-    is_tensorboard_available,
-    is_wandb_available,
-)
-
-
 if is_sentencepiece_available():
     from .models.albert import AlbertTokenizer
-    from .models.barthez import BarthezTokenizer
     from .models.bert_generation import BertGenerationTokenizer
     from .models.camembert import CamembertTokenizer
     from .models.marian import MarianTokenizer
@@ -227,7 +222,6 @@ else:
 if is_tokenizers_available():
     from .models.albert import AlbertTokenizerFast
     from .models.bart import BartTokenizerFast
-    from .models.barthez import BarthezTokenizerFast
     from .models.bert import BertTokenizerFast
     from .models.camembert import CamembertTokenizerFast
     from .models.distilbert import DistilBertTokenizerFast
@@ -260,7 +254,6 @@ else:
 # Trainer
 from .trainer_callback import (
     DefaultFlowCallback,
-    EarlyStoppingCallback,
     PrinterCallback,
     ProgressCallback,
     TrainerCallback,
@@ -392,18 +385,6 @@ if is_torch_available():
         CamembertForTokenClassification,
         CamembertModel,
     )
-    from .models.conv_bert import (
-        CONV_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
-        ConvBertForMaskedLM,
-        ConvBertForMultipleChoice,
-        ConvBertForQuestionAnswering,
-        ConvBertForSequenceClassification,
-        ConvBertForTokenClassification,
-        ConvBertLayer,
-        ConvBertModel,
-        ConvBertPreTrainedModel,
-        load_tf_weights_in_conv_bert,
-    )
     from .models.ctrl import CTRL_PRETRAINED_MODEL_ARCHIVE_LIST, CTRLLMHeadModel, CTRLModel, CTRLPreTrainedModel
     from .models.deberta import (
         DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
@@ -519,7 +500,7 @@ if is_torch_available():
         MobileBertPreTrainedModel,
         load_tf_weights_in_mobilebert,
     )
-    from .models.mt5 import MT5EncoderModel, MT5ForConditionalGeneration, MT5Model
+    from .models.mt5 import MT5ForConditionalGeneration, MT5Model
     from .models.openai import (
         OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
         OpenAIGPTDoubleHeadsModel,
@@ -574,7 +555,6 @@ if is_torch_available():
     )
     from .models.t5 import (
         T5_PRETRAINED_MODEL_ARCHIVE_LIST,
-        T5EncoderModel,
         T5ForConditionalGeneration,
         T5Model,
         T5PreTrainedModel,
@@ -817,7 +797,7 @@ if is_tf_available():
         TFMobileBertModel,
         TFMobileBertPreTrainedModel,
     )
-    from .models.mt5 import TFMT5EncoderModel, TFMT5ForConditionalGeneration, TFMT5Model
+    from .models.mt5 import TFMT5ForConditionalGeneration, TFMT5Model
     from .models.openai import (
         TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
         TFOpenAIGPTDoubleHeadsModel,
@@ -840,7 +820,6 @@ if is_tf_available():
     )
     from .models.t5 import (
         TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
-        TFT5EncoderModel,
         TFT5ForConditionalGeneration,
         TFT5Model,
         TFT5PreTrainedModel,

src/transformers/configuration_utils.py

@@ -19,7 +19,7 @@
 import copy
 import json
 import os
-from typing import Any, Dict, Tuple, Union
+from typing import Any, Dict, Tuple
 
 from .file_utils import CONFIG_NAME, cached_path, hf_bucket_url, is_remote_url
 from .utils import logging
@@ -262,13 +262,13 @@ class PretrainedConfig(object):
         self.id2label = {i: "LABEL_{}".format(i) for i in range(num_labels)}
         self.label2id = dict(zip(self.id2label.values(), self.id2label.keys()))
 
-    def save_pretrained(self, save_directory: Union[str, os.PathLike]):
+    def save_pretrained(self, save_directory: str):
         """
         Save a configuration object to the directory ``save_directory``, so that it can be re-loaded using the
         :func:`~transformers.PretrainedConfig.from_pretrained` class method.
 
         Args:
-            save_directory (:obj:`str` or :obj:`os.PathLike`):
+            save_directory (:obj:`str`):
                 Directory where the configuration JSON file will be saved (will be created if it does not exist).
         """
         if os.path.isfile(save_directory):
@@ -281,13 +281,13 @@ class PretrainedConfig(object):
         logger.info("Configuration saved in {}".format(output_config_file))
 
     @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
+    def from_pretrained(cls, pretrained_model_name_or_path: str, **kwargs) -> "PretrainedConfig":
         r"""
         Instantiate a :class:`~transformers.PretrainedConfig` (or a derived class) from a pretrained model
         configuration.
 
         Args:
-            pretrained_model_name_or_path (:obj:`str` or :obj:`os.PathLike`):
+            pretrained_model_name_or_path (:obj:`str`):
                 This can be either:
 
                 - a string, the `model id` of a pretrained model configuration hosted inside a model repo on
@@ -297,7 +297,7 @@ class PretrainedConfig(object):
                   :func:`~transformers.PretrainedConfig.save_pretrained` method, e.g., ``./my_model_directory/``.
                 - a path or url to a saved configuration JSON `file`, e.g.,
                   ``./my_model_directory/configuration.json``.
-            cache_dir (:obj:`str` or :obj:`os.PathLike`, `optional`):
+            cache_dir (:obj:`str`, `optional`):
                 Path to a directory in which a downloaded pretrained model configuration should be cached if the
                 standard cache should not be used.
             force_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
@@ -346,15 +346,13 @@ class PretrainedConfig(object):
         return cls.from_dict(config_dict, **kwargs)
 
     @classmethod
-    def get_config_dict(
-        cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs
-    ) -> Tuple[Dict[str, Any], Dict[str, Any]]:
+    def get_config_dict(cls, pretrained_model_name_or_path: str, **kwargs) -> Tuple[Dict[str, Any], Dict[str, Any]]:
         """
         From a ``pretrained_model_name_or_path``, resolve to a dictionary of parameters, to be used for instantiating a
         :class:`~transformers.PretrainedConfig` using ``from_dict``.
 
         Parameters:
-            pretrained_model_name_or_path (:obj:`str` or :obj:`os.PathLike`):
+            pretrained_model_name_or_path (:obj:`str`):
                 The identifier of the pre-trained checkpoint from which we want the dictionary of parameters.
 
         Returns:
@@ -368,7 +366,6 @@ class PretrainedConfig(object):
         local_files_only = kwargs.pop("local_files_only", False)
         revision = kwargs.pop("revision", None)
 
-        pretrained_model_name_or_path = str(pretrained_model_name_or_path)
         if os.path.isdir(pretrained_model_name_or_path):
             config_file = os.path.join(pretrained_model_name_or_path, CONFIG_NAME)
         elif os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path):
@@ -454,12 +451,12 @@ class PretrainedConfig(object):
             return config
 
     @classmethod
-    def from_json_file(cls, json_file: Union[str, os.PathLike]) -> "PretrainedConfig":
+    def from_json_file(cls, json_file: str) -> "PretrainedConfig":
         """
         Instantiates a :class:`~transformers.PretrainedConfig` from the path to a JSON file of parameters.
 
         Args:
-            json_file (:obj:`str` or :obj:`os.PathLike`):
+            json_file (:obj:`str`):
                 Path to the JSON file containing the parameters.
 
         Returns:
@@ -470,7 +467,7 @@ class PretrainedConfig(object):
         return cls(**config_dict)
 
     @classmethod
-    def _dict_from_json_file(cls, json_file: Union[str, os.PathLike]):
+    def _dict_from_json_file(cls, json_file: str):
         with open(json_file, "r", encoding="utf-8") as reader:
             text = reader.read()
         return json.loads(text)
@@ -540,12 +537,12 @@ class PretrainedConfig(object):
             config_dict = self.to_dict()
         return json.dumps(config_dict, indent=2, sort_keys=True) + "\n"
 
-    def to_json_file(self, json_file_path: Union[str, os.PathLike], use_diff: bool = True):
+    def to_json_file(self, json_file_path: str, use_diff: bool = True):
         """
         Save this instance to a JSON file.
 
         Args:
-            json_file_path (:obj:`str` or :obj:`os.PathLike`):
+            json_file_path (:obj:`str`):
                 Path to the JSON file in which this configuration instance's parameters will be saved.
             use_diff (:obj:`bool`, `optional`, defaults to :obj:`True`):
                 If set to ``True``, only the difference between the config instance and the default

src/transformers/convert_pytorch_checkpoint_to_tf2.py

@@ -302,7 +302,7 @@ def convert_pt_checkpoint_to_tf(
         pytorch_checkpoint_url = hf_bucket_url(pytorch_checkpoint_path, filename=WEIGHTS_NAME)
         pytorch_checkpoint_path = cached_path(pytorch_checkpoint_url, force_download=not use_cached_models)
     # Load PyTorch checkpoint in tf2 model:
-    tf_model = load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path, config)
+    tf_model = load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path)
 
     if compare_with_pt_model:
         tfo = tf_model(tf_model.dummy_inputs, training=False)  # build the network

src/transformers/convert_slow_tokenizer.py

@@ -382,22 +382,6 @@ class AlbertConverter(SpmConverter):
         )
 
 
-class BarthezConverter(SpmConverter):
-    def unk_id(self, proto):
-        unk_id = 3
-        return unk_id
-
-    def post_processor(self):
-        return processors.TemplateProcessing(
-            single="<s> $A </s>",
-            pair="<s> $A </s> </s> $B </s>",
-            special_tokens=[
-                ("<s>", self.original_tokenizer.convert_tokens_to_ids("<s>")),
-                ("</s>", self.original_tokenizer.convert_tokens_to_ids("</s>")),
-            ],
-        )
-
-
 class CamembertConverter(SpmConverter):
     def vocab(self, proto):
         vocab = [
@@ -547,12 +531,10 @@ class BertGenerationConverter(SpmConverter):
 class PegasusConverter(SpmConverter):
     def vocab(self, proto):
         vocab = [
-            (self.original_tokenizer.pad_token, 0.0),
-            (self.original_tokenizer.eos_token, 0.0),
-            (self.original_tokenizer.mask_token_sent, 0.0),
-            (self.original_tokenizer.mask_token, 0.0),
+            (self.original_tokenizer.pad_token, 0),
+            (self.original_tokenizer.eos_token, 0),
         ]
-        vocab += [(f"<unk_{i}>", -100.0) for i in range(2, self.original_tokenizer.offset)]
+        vocab += [(f"unk_{i}", -100) for i in range(2, 2 + self.original_tokenizer.offset)]
         vocab += [(piece.piece, piece.score) for piece in proto.pieces[2:]]
         return vocab
 
@@ -561,10 +543,13 @@ class PegasusConverter(SpmConverter):
 
     def post_processor(self):
         eos = self.original_tokenizer.eos_token
-        special_tokens = [
-            (eos, self.original_tokenizer.eos_token_id),
-        ]
-        return processors.TemplateProcessing(single=["$A", eos], pair=["$A", "$B", eos], special_tokens=special_tokens)
+        return processors.TemplateProcessing(
+            single=["$A", eos],
+            pair=["$A", "$B", eos],
+            special_tokens=[
+                (eos, self.original_tokenizer.eos_token_id),
+            ],
+        )
 
 
 class T5Converter(SpmConverter):
@@ -587,7 +572,6 @@ class T5Converter(SpmConverter):
 SLOW_TO_FAST_CONVERTERS = {
     "AlbertTokenizer": AlbertConverter,
     "BartTokenizer": RobertaConverter,
-    "BarthezTokenizer": BarthezConverter,
     "BertTokenizer": BertConverter,
     "CamembertTokenizer": CamembertConverter,
     "DistilBertTokenizer": BertConverter,

src/transformers/data/data_collator.py

@@ -20,14 +20,14 @@ DataCollator = NewType("DataCollator", Callable[[List[InputDataClass]], Dict[str
 
 def default_data_collator(features: List[InputDataClass]) -> Dict[str, torch.Tensor]:
     """
-    Very simple data collator that simply collates batches of dict-like objects and performs special handling for
+    Very simple data collator that simply collates batches of dict-like objects and erforms special handling for
     potential keys named:
 
         - ``label``: handles a single value (int or float) per object
         - ``label_ids``: handles a list of values per object
 
-    Does not do any additional preprocessing: property names of the input object will be used as corresponding inputs
-    to the model. See glue and ner for example of how it's useful.
+    Des not do any additional preprocessing: property names of the input object will be used as corresponding inputs to
+    the model. See glue and ner for example of how it's useful.
     """
 
     # In this function we'll make the assumption that all `features` in the batch