Checking output format + check raises ValueError (#8986 )

Docs
Update documentation
2020-12-08 12:25:58 -05:00 · 2020-11-30 17:59:23 -05:00 · 2020-11-30 17:58:40 -05:00 · 2020-11-30 17:39:57 -05:00 · 2020-11-30 14:25:10 -05:00 · 2020-11-30 14:25:10 -05:00
301 changed files with 10207 additions and 4193 deletions
@@ -3,6 +3,22 @@ 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
@@ -72,12 +88,13 @@ jobs:
        parallelism: 1
        steps:
            - checkout
+            # - skip-job-on-doc-only-changes
            - restore_cache:
                  keys:
                      - v0.4-torch_and_tf-{{ checksum "setup.py" }}
                      - v0.4-{{ checksum "setup.py" }}
            - run: pip install --upgrade pip
-            - run: pip install .[sklearn,tf-cpu,torch,testing]
+            - run: pip install .[sklearn,tf-cpu,torch,testing,sentencepiece]
            - save_cache:
                key: v0.4-{{ checksum "setup.py" }}
                paths:
@@ -98,12 +115,13 @@ jobs:
        parallelism: 1
        steps:
            - checkout
+            # - skip-job-on-doc-only-changes
            - restore_cache:
                  keys:
                      - v0.4-torch-{{ checksum "setup.py" }}
                      - v0.4-{{ checksum "setup.py" }}
            - run: pip install --upgrade pip
-            - run: pip install .[sklearn,torch,testing]
+            - run: pip install .[sklearn,torch,testing,sentencepiece]
            - save_cache:
                  key: v0.4-torch-{{ checksum "setup.py" }}
                  paths:
@@ -124,12 +142,13 @@ jobs:
        parallelism: 1
        steps:
            - checkout
+            # - skip-job-on-doc-only-changes
            - restore_cache:
                  keys:
                      - v0.4-tf-{{ checksum "setup.py" }}
                      - v0.4-{{ checksum "setup.py" }}
            - run: pip install --upgrade pip
-            - run: pip install .[sklearn,tf-cpu,testing]
+            - run: pip install .[sklearn,tf-cpu,testing,sentencepiece]
            - save_cache:
                  key: v0.4-tf-{{ checksum "setup.py" }}
                  paths:
@@ -150,12 +169,13 @@ jobs:
        parallelism: 1
        steps:
            - checkout
+            # - skip-job-on-doc-only-changes
            - restore_cache:
                keys:
                    - v0.4-flax-{{ checksum "setup.py" }}
                    - v0.4-{{ checksum "setup.py" }}
            - run: pip install --upgrade pip
-            - run: sudo pip install .[flax,sklearn,torch,testing]
+            - run: sudo pip install .[flax,sklearn,torch,testing,sentencepiece]
            - save_cache:
                  key: v0.4-flax-{{ checksum "setup.py" }}
                  paths:
@@ -176,12 +196,13 @@ jobs:
        parallelism: 1
        steps:
            - checkout
+            # - skip-job-on-doc-only-changes
            - restore_cache:
                  keys:
                      - v0.4-torch-{{ checksum "setup.py" }}
                      - v0.4-{{ checksum "setup.py" }}
            - run: pip install --upgrade pip
-            - run: pip install .[sklearn,torch,testing]
+            - run: pip install .[sklearn,torch,testing,sentencepiece]
            - save_cache:
                  key: v0.4-torch-{{ checksum "setup.py" }}
                  paths:
@@ -202,12 +223,13 @@ jobs:
        parallelism: 1
        steps:
            - checkout
+            # - skip-job-on-doc-only-changes
            - restore_cache:
                  keys:
                      - v0.4-tf-{{ checksum "setup.py" }}
                      - v0.4-{{ checksum "setup.py" }}
            - run: pip install --upgrade pip
-            - run: pip install .[sklearn,tf-cpu,testing]
+            - run: pip install .[sklearn,tf-cpu,testing,sentencepiece]
            - save_cache:
                  key: v0.4-tf-{{ checksum "setup.py" }}
                  paths:
@@ -221,17 +243,18 @@ jobs:
    run_tests_custom_tokenizers:
        working_directory: ~/transformers
        docker:
-            - image: circleci/python:3.6
+            - image: circleci/python:3.7
        environment:
            RUN_CUSTOM_TOKENIZERS: yes
        steps:
            - checkout
+            # - skip-job-on-doc-only-changes
            - restore_cache:
                  keys:
                      - v0.4-custom_tokenizers-{{ checksum "setup.py" }}
                      - v0.4-{{ checksum "setup.py" }}
            - run: pip install --upgrade pip
-            - run: pip install .[ja,testing]
+            - run: pip install .[ja,testing,sentencepiece]
            - run: python -m unidic download
            - save_cache:
                  key: v0.4-custom_tokenizers-{{ checksum "setup.py" }}
@@ -253,12 +276,13 @@ jobs:
        parallelism: 1
        steps:
            - checkout
+            # - skip-job-on-doc-only-changes
            - restore_cache:
                  keys:
                      - v0.4-torch_examples-{{ checksum "setup.py" }}
                      - v0.4-{{ checksum "setup.py" }}
            - run: pip install --upgrade pip
-            - run: pip install .[sklearn,torch,testing]
+            - run: pip install .[sklearn,torch,sentencepiece,testing]
            - run: pip install -r examples/requirements.txt
            - save_cache:
                  key: v0.4-torch_examples-{{ checksum "setup.py" }}
@@ -324,7 +348,7 @@ jobs:
                      - v0.4-{{ checksum "setup.py" }}
            - run: pip install --upgrade pip
            - run: pip install isort
-            - run: pip install .[tf,torch,flax,quality]
+            - run: pip install .[all,quality]
            - save_cache:
                  key: v0.4-code_quality-{{ checksum "setup.py" }}
                  paths:
@@ -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
@@ -58,5 +58,5 @@ members/contributors which may be interested in your PR.
 tensorflow: @jplu
 examples/token-classification: @stefan-it
 documentation: @sgugger
- FSTM: @stas00
+ FSMT: @stas00
 -->
@@ -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:
@@ -9,7 +9,6 @@ on:
  push:
    branches:
      - ci_*
-      - framework-agnostic-tokenizers
  repository_dispatch:
  schedule:
    - cron: "0 0 * * *"
@@ -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:

@@ -317,3 +317,16 @@ 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
+```
@@ -1,4 +1,4 @@
-.PHONY: modified_only_fixup extra_quality_checks quality style fixup fix-copies test test-examples docs
+.PHONY: deps_table_update modified_only_fixup extra_quality_checks quality style fixup fix-copies test test-examples docs


 check_dirs := examples tests src utils
@@ -14,9 +14,14 @@ 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:
+extra_quality_checks: deps_table_update
 	python utils/check_copies.py
 	python utils/check_dummies.py
 	python utils/check_repo.py
@@ -32,7 +37,7 @@ quality:

 # Format source code automatically and check is there are any problems left that need manual fixing

-style:
+style: deps_table_update
 	black $(check_dirs)
 	isort $(check_dirs)
 	python utils/style_doc.py src/transformers docs/source --max_len 119
@@ -160,6 +160,7 @@ If you'd like to play with the examples, you must [install the library from sour

 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.
@@ -197,6 +198,8 @@ 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).


@@ -214,13 +217,17 @@ These implementations have been tested on several datasets (see the example scri

 ## Citation

-We now have a [paper](https://arxiv.org/abs/1910.03771) you can cite for the 🤗 Transformers library:
+We now have a [paper](https://www.aclweb.org/anthology/2020.emnlp-demos.6/) you can cite for the 🤗 Transformers library:
 ```bibtex
-@article{Wolf2019HuggingFacesTS,
-  title={HuggingFace's Transformers: State-of-the-art Natural Language Processing},
-  author={Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and 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}
+@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"
 }
 ```
@@ -2,6 +2,15 @@

 /* Colab dropdown */

+table.center-aligned-table td {
+    text-align: center;
+}
+
+table.center-aligned-table th {
+    text-align: center;
+    vertical-align: middle;
+}
+
 .colab-dropdown {
    position: relative;
    display: inline-block;
@@ -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",
@@ -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 ---------------------------------------------------
@@ -35,6 +35,8 @@ Choose the right framework for every part of a model's lifetime:
 - Move a single model between TF2.0/PyTorch frameworks at will
 - Seamlessly pick the right framework for training, evaluation, production

+Experimental support for Flax with a few models right now, expected to grow in the coming months.
+
 Contents
 -----------------------------------------------------------------------------------------------------------------------

@@ -44,7 +46,7 @@ The documentation is organized in five parts:
  and a glossary.
 - **USING 🤗 TRANSFORMERS** contains general tutorials on how to use the library.
 - **ADVANCED GUIDES** contains more advanced guides that are more specific to a given script or part of the library.
- **RESEARCH** focuses on tutorials that have less to do with how to use the library but more about general resarch in
+- **RESEARCH** focuses on tutorials that have less to do with how to use the library but more about general research in
  transformers model
 - The three last section contain the documentation of each public class and function, grouped in:

@@ -52,8 +54,8 @@ The documentation is organized in five parts:
    - **MODELS** for the classes and functions related to each model implemented in the library.
    - **INTERNAL HELPERS** for the classes and functions we use internally.

-The library currently contains PyTorch and Tensorflow implementations, pre-trained model weights, usage scripts and
-conversion utilities for the following models:
+The library currently contains PyTorch, Tensorflow and Flax implementations, pretrained model weights, usage scripts
+and conversion utilities for the following models:

 ..
    This list is updated automatically from the README with `make fix-copies`. Do not update manually!
@@ -66,106 +68,200 @@ 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:`BERT <model_doc/bert>` (from Google) released with the paper `BERT: Pre-training of Deep Bidirectional
+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
   Transformers for Language Understanding <https://arxiv.org/abs/1810.04805>`__ by Jacob Devlin, Ming-Wei Chang,
   Kenton Lee and Kristina Toutanova.
-4. :doc:`BERT For Sequence Generation <model_doc/bertgeneration>` (from Google) released with the paper `Leveraging
+5. :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.
-5. :doc:`Blenderbot <model_doc/blenderbot>` (from Facebook) released with the paper `Recipes for building an
+6. :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.
-6. :doc:`CamemBERT <model_doc/camembert>` (from Inria/Facebook/Sorbonne) released with the paper `CamemBERT: a Tasty
+7. :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.
-7. :doc:`CTRL <model_doc/ctrl>` (from Salesforce) released with the paper `CTRL: A Conditional Transformer Language
+8. :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.
-8. :doc:`DeBERTa <model_doc/deberta>` (from Microsoft Research) released with the paper `DeBERTa: Decoding-enhanced
+9. :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.
-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
+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
    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.
-11. :doc:`DPR <model_doc/dpr>` (from Facebook) released with the paper `Dense Passage Retrieval for Open-Domain
+12. :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.
-12. :doc:`ELECTRA <model_doc/electra>` (from Google Research/Stanford University) released with the paper `ELECTRA:
+13. :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.
-13. :doc:`FlauBERT <model_doc/flaubert>` (from CNRS) released with the paper `FlauBERT: Unsupervised Language Model
+14. :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.
-14. :doc:`Funnel Transformer <model_doc/funnel>` (from CMU/Google Brain) released with the paper `Funnel-Transformer:
+15. :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.
-15. :doc:`GPT <model_doc/gpt>` (from OpenAI) released with the paper `Improving Language Understanding by Generative
+16. :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.
-16. :doc:`GPT-2 <model_doc/gpt2>` (from OpenAI) released with the paper `Language Models are Unsupervised Multitask
+17. :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**.
-17. :doc:`LayoutLM <model_doc/layoutlm>` (from Microsoft Research Asia) released with the paper `LayoutLM: Pre-training
+18. :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.
-18. :doc:`Longformer <model_doc/longformer>` (from AllenAI) released with the paper `Longformer: The Long-Document
+19. :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.
-19. :doc:`LXMERT <model_doc/lxmert>` (from UNC Chapel Hill) released with the paper `LXMERT: Learning Cross-Modality
+20. :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.
-20. :doc:`MarianMT <model_doc/marian>` Machine translation models trained using `OPUS <http://opus.nlpl.eu/>`__ data by
+21. :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.
-21. :doc:`MBart <model_doc/mbart>` (from Facebook) released with the paper `Multilingual Denoising Pre-training for
+22. :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.
-22. :doc:`MT5 <model_doc/mt5>` (from Google AI) released with the paper `mT5: A massively multilingual pre-trained
+23. :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.
-23. :doc:`Pegasus <model_doc/pegasus>` (from Google) released with the paper `PEGASUS: Pre-training with Extracted
+24. :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.
-24. :doc:`ProphetNet <model_doc/prophetnet>` (from Microsoft Research) released with the paper `ProphetNet: Predicting
+25. :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.
-25. :doc:`Reformer <model_doc/reformer>` (from Google Research) released with the paper `Reformer: The Efficient
+26. :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.
-26. :doc:`RoBERTa <model_doc/roberta>` (from Facebook), released together with the paper a `Robustly Optimized BERT
+27. :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.
-27. :doc:`SqueezeBert <model_doc/squeezebert>` released with the paper `SqueezeBERT: What can computer vision teach NLP
+28. :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.
-28. :doc:`T5 <model_doc/t5>` (from Google AI) released with the paper `Exploring the Limits of Transfer Learning with a
+29. :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.
-29. :doc:`Transformer-XL <model_doc/transformerxl>` (from Google/CMU) released with the paper `Transformer-XL:
+30. :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.
-30. :doc:`XLM <model_doc/xlm>` (from Facebook) released together with the paper `Cross-lingual Language Model
+31. :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.
-31. :doc:`XLM-ProphetNet <model_doc/xlmprophetnet>` (from Microsoft Research) released with the paper `ProphetNet:
+32. :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.
-32. :doc:`XLM-RoBERTa <model_doc/xlmroberta>` (from Facebook AI), released together with the paper `Unsupervised
+33. :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.
-33. :doc:`XLNet <model_doc/xlnet>` (from Google/CMU) released with the paper `XLNet: Generalized Autoregressive
+34. :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.
-34. `Other community models <https://huggingface.co/models>`__, contributed by the `community
+35. `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.
+
+..
+    This table is updated automatically from the auto modules with `make fix-copies`. Do not update manually!
+
+.. rst-class:: center-aligned-table
+
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|            Model            | Tokenizer slow | Tokenizer fast | PyTorch support | TensorFlow support | Flax Support |
+=============================+================+================+=================+====================+==============+
+|           ALBERT            |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|            BART             |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|            BERT             |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|       Bert Generation       |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|         Blenderbot          |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|            CTRL             |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|          CamemBERT          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|             DPR             |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|           DeBERTa           |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|         DistilBERT          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|           ELECTRA           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|       Encoder decoder       |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+| FairSeq Machine-Translation |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|          FlauBERT           |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|     Funnel Transformer      |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|           LXMERT            |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|          LayoutLM           |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|         Longformer          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|           Marian            |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|         MobileBERT          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|         OpenAI GPT          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|        OpenAI GPT-2         |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|           Pegasus           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|         ProphetNet          |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|             RAG             |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|          Reformer           |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|          RetriBERT          |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|           RoBERTa           |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|         SqueezeBERT         |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|             T5              |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|       Transformer-XL        |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|             XLM             |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|         XLM-RoBERTa         |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|        XLMProphetNet        |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|            XLNet            |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|            mBART            |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|             mT5             |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+
+
 .. toctree::
    :maxdepth: 2
    :caption: Get started
@@ -231,6 +327,7 @@ conversion utilities for the following models:
    model_doc/albert
    model_doc/auto
    model_doc/bart
+    model_doc/barthez
    model_doc/bert
    model_doc/bertgeneration
    model_doc/blenderbot
@@ -70,15 +70,15 @@ to check 🤗 Transformers is properly installed.

 This library provides pretrained models that will be downloaded and cached locally. Unless you specify a location with
 `cache_dir=...` when you use methods like `from_pretrained`, these models will automatically be downloaded in the
-folder given by the shell environment variable ``TRANSFORMERS_CACHE``. The default value for it will be the PyTorch
-cache home followed by ``/transformers/`` (even if you don't have PyTorch installed). This is (by order of priority):
+folder given by the shell environment variable ``TRANSFORMERS_CACHE``. The default value for it will be the Hugging
+Face cache home followed by ``/transformers/``. This is (by order of priority):

-  * shell environment variable ``TORCH_HOME``
-  * shell environment variable ``XDG_CACHE_HOME`` + ``/torch/``
-  * default: ``~/.cache/torch/``
+  * shell environment variable ``HF_HOME`` 
+  * shell environment variable ``XDG_CACHE_HOME`` + ``/huggingface/``
+  * default: ``~/.cache/huggingface/``

 So if you don't have any specific environment variable set, the cache directory will be at
-``~/.cache/torch/transformers/``.
+``~/.cache/huggingface/transformers/``.

 **Note:** If you have set a shell environment variable for one of the predecessors of this library
 (``PYTORCH_TRANSFORMERS_CACHE`` or ``PYTORCH_PRETRAINED_BERT_CACHE``), those will be used if there is no shell
@@ -97,6 +97,6 @@ You should check out our [swift-coreml-transformers](https://github.com/huggingf
 It contains a set of tools to convert PyTorch or TensorFlow 2.0 trained Transformer models (currently contains `GPT-2`, 
 `DistilGPT-2`, `BERT`, and `DistilBERT`) to CoreML models that run on iOS devices.

-At some point in the future, you'll be able to seamlessly move from pre-training or fine-tuning models in PyTorch or
+At some point in the future, you'll be able to seamlessly move from pretraining or fine-tuning models in PyTorch or
 TensorFlow 2.0 to productizing them in CoreML, or prototype a model or an app in CoreML then research its
 hyperparameters or architecture from PyTorch or TensorFlow 2.0. Super exciting!
@@ -44,6 +44,8 @@ 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
@@ -1,5 +1,170 @@
 # Migrating from previous packages

+## Migrating from transformers `v3.x` to `v4.x`
+
+A couple of changes were introduced when the switch from version 3 to version 4 was done. Below is a summary of the
+expected changes:
+
+#### 1. AutoTokenizers and pipelines now use fast (rust) tokenizers by default.
+
+The python and rust tokenizers have roughly the same API, but the rust tokenizers have a more complete feature set. 
+
+This introduces two breaking changes:
+- The handling of overflowing tokens between the python and rust tokenizers is different.
+- The rust tokenizers do not accept integers in the encoding methods.
+
+##### How to obtain the same behavior as v3.x in v4.x
+
+- The pipelines now contain additional features out of the box. See the [token-classification pipeline with the `grouped_entities` flag](https://huggingface.co/transformers/main_classes/pipelines.html?highlight=textclassification#tokenclassificationpipeline).
+- The auto-tokenizers now return rust tokenizers. In order to obtain the python tokenizers instead, the user may use the `use_fast` flag by setting it to `False`:
+
+In version `v3.x`:
+```py
+from transformers import AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
+```
+to obtain the same in version `v4.x`:
+```py
+from transformers import AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("bert-base-cased", use_fast=False)
+```
+
+#### 2. SentencePiece is removed from the required dependencies
+
+The requirement on the SentencePiece dependency has been lifted from the `setup.py`. This is done so that we may have a channel on anaconda cloud without relying on `conda-forge`. This means that the tokenizers that depend on the SentencePiece library will not be available with a standard `transformers` installation.
+
+This includes the **slow** versions of:
+- `XLNetTokenizer`
+- `AlbertTokenizer`
+- `CamembertTokenizer`
+- `MBartTokenizer`
+- `PegasusTokenizer`
+- `T5Tokenizer`
+- `ReformerTokenizer`
+- `XLMRobertaTokenizer`
+
+##### How to obtain the same behavior as v3.x in v4.x
+
+In order to obtain the same behavior as version `v3.x`, you should install `sentencepiece` additionally:
+
+In version `v3.x`:
+```bash
+pip install transformers
+```
+to obtain the same in version `v4.x`:
+```bash
+pip install transformers[sentencepiece]
+```
+or
+```bash
+pip install transformers sentencepiece
+```
+#### 3. The architecture of the repo has been updated so that each model resides in its folder
+
+The past and foreseeable addition of new models means that the number of files in the directory `src/transformers` keeps growing and becomes harder to navigate and understand. We made the choice to put each model and the files accompanying it in their own sub-directories.
+
+This is a breaking change as importing intermediary layers using a model's module directly needs to be done via a different path.
+
+##### How to obtain the same behavior as v3.x in v4.x
+
+In order to obtain the same behavior as version `v3.x`, you should update the path used to access the layers. 
+
+In version `v3.x`:
+```bash
+from transformers.modeling_bert import BertLayer
+```
+to obtain the same in version `v4.x`:
+```bash
+from transformers.models.bert.modeling_bert import BertLayer
+```
+
+#### 4. Switching the `return_dict` argument to `True` by default
+
+The [`return_dict` argument](https://huggingface.co/transformers/main_classes/output.html) enables the return of dict-like python objects containing the model outputs, instead of the standard tuples. This object is self-documented as keys can be used to retrieve values, while also behaving as a tuple as users may retrieve objects by index or by slice.
+
+This is a breaking change as the limitation of that tuple is that it cannot be unpacked: `value0, value1 = outputs` will not work.
+
+##### How to obtain the same behavior as v3.x in v4.x
+
+In order to obtain the same behavior as version `v3.x`, you should specify the `return_dict` argument to `False`, either in the model configuration or during the forward pass.
+
+In version `v3.x`:
+```bash
+model = BertModel.from_pretrained("bert-base-cased")
+outputs = model(**inputs)
+```
+to obtain the same in version `v4.x`:
+```bash
+model = BertModel.from_pretrained("bert-base-cased")
+outputs = model(**inputs, return_dict=False)
+```
+or
+```bash
+model = BertModel.from_pretrained("bert-base-cased", return_dict=False)
+outputs = model(**inputs)
+```
+
+#### 5. Removed some deprecated attributes
+
+Attributes that were deprecated have been removed if they had been deprecated for at least a month. The full list of deprecated attributes can be found in [#8604](https://github.com/huggingface/transformers/pull/8604).
+
+Here is a list of these attributes/methods/arguments and what their replacements should be:
+
+In several models, the labels become consistent with the other models:
+- `masked_lm_labels` becomes `labels` in `AlbertForMaskedLM` and `AlbertForPreTraining`.
+- `masked_lm_labels` becomes `labels` in `BertForMaskedLM` and `BertForPreTraining`.
+- `masked_lm_labels` becomes `labels` in `DistilBertForMaskedLM`.
+- `masked_lm_labels` becomes `labels` in `ElectraForMaskedLM`.
+- `masked_lm_labels` becomes `labels` in `LongformerForMaskedLM`.
+- `masked_lm_labels` becomes `labels` in `MobileBertForMaskedLM`.
+- `masked_lm_labels` becomes `labels` in `RobertaForMaskedLM`.
+- `lm_labels` becomes `labels` in `BartForConditionalGeneration`.
+- `lm_labels` becomes `labels` in `GPT2DoubleHeadsModel`.
+- `lm_labels` becomes `labels` in `OpenAIGPTDoubleHeadsModel`.
+- `lm_labels` becomes `labels` in `T5ForConditionalGeneration`.
+
+In several models, the caching mechanism becomes consistent with the other models:
+- `decoder_cached_states` becomes `past_key_values` in all BART-like, FSMT and T5 models.
+- `decoder_past_key_values` becomes `past_key_values` in all BART-like, FSMT and T5 models.
+- `past` becomes `past_key_values` in all CTRL models.
+- `past` becomes `past_key_values` in all GPT-2 models.
+
+Regarding the tokenizer classes:
+- The tokenizer attribute `max_len` becomes `model_max_length`.
+- The tokenizer attribute `return_lengths` becomes `return_length`.
+- The tokenizer encoding argument `is_pretokenized` becomes `is_split_into_words`.
+
+Regarding the `Trainer` class:
+- The `Trainer` argument `tb_writer` is removed in favor of the callback `TensorBoardCallback(tb_writer=...)`.
+- The `Trainer` argument `prediction_loss_only` is removed in favor of the class argument `args.prediction_loss_only`.
+- The `Trainer` attribute `data_collator` should be a callable.
+- The `Trainer` method `_log` is deprecated in favor of `log`.
+- The `Trainer` method `_training_step` is deprecated in favor of `training_step`.
+- The `Trainer` method `_prediction_loop` is deprecated in favor of `prediction_loop`.
+- The `Trainer` method `is_local_master` is deprecated in favor of `is_local_process_zero`.
+- The `Trainer` method `is_world_master` is deprecated in favor of `is_world_process_zero`.
+
+Regarding the `TFTrainer` class:
+- The `TFTrainer` argument `prediction_loss_only` is removed in favor of the class argument `args.prediction_loss_only`.
+- The `Trainer` method `_log` is deprecated in favor of `log`.
+- The `TFTrainer` method `_prediction_loop` is deprecated in favor of `prediction_loop`.
+- The `TFTrainer` method `_setup_wandb` is deprecated in favor of `setup_wandb`.
+- The `TFTrainer` method `_run_model` is deprecated in favor of `run_model`.
+
+Regarding the `TrainerArgument` class:
+- The `TrainerArgument` argument `evaluate_during_training` is deprecated in favor of `evaluation_strategy`.
+
+Regarding the Transfo-XL model:
+- The Transfo-XL configuration attribute `tie_weight` becomes `tie_words_embeddings`.
+- The Transfo-XL modeling method `reset_length` becomes `reset_memory_length`.
+
+Regarding pipelines:
+- The `FillMaskPipeline` argument `topk` becomes `top_k`.
+
+
+
 ## Migrating from pytorch-transformers to 🤗 Transformers

 Here is a quick summary of what you should take care of when migrating from `pytorch-transformers` to 🤗 Transformers.
@@ -0,0 +1,41 @@
+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:
+
@@ -10,7 +10,7 @@ Tasks <https://arxiv.org/abs/1907.12461>`__ by Sascha Rothe, Shashi Narayan, Ali

 The abstract from the paper is the following:

-*Unsupervised pre-training of large neural models has recently revolutionized Natural Language Processing. By
+*Unsupervised pretraining of large neural models has recently revolutionized Natural Language Processing. By
 warm-starting from the publicly released checkpoints, NLP practitioners have pushed the state-of-the-art on multiple
 benchmarks while saving significant amounts of compute time. So far the focus has been mainly on the Natural Language
 Understanding tasks. In this paper, we demonstrate the efficacy of pre-trained checkpoints for Sequence Generation. We
@@ -20,8 +20,8 @@ disentangled attention mechanism, where each word is represented using two vecto
 position, respectively, and the attention weights among words are computed using disentangled matrices on their
 contents and relative positions. Second, an enhanced mask decoder is used to replace the output softmax layer to
 predict the masked tokens for model pretraining. We show that these two techniques significantly improve the efficiency
-of model pre-training and performance of downstream tasks. Compared to RoBERTa-Large, a DeBERTa model trained on half
-of the training data performs consistently better on a wide range of NLP tasks, achieving improvements on MNLI by +0.9%
+of model pretraining and performance of downstream tasks. Compared to RoBERTa-Large, a DeBERTa model trained on half of
+the training data performs consistently better on a wide range of NLP tasks, achieving improvements on MNLI by +0.9%
 (90.2% vs. 91.1%), on SQuAD v2.0 by +2.3% (88.4% vs. 90.7%) and RACE by +3.6% (83.2% vs. 86.8%). The DeBERTa code and
 pre-trained models will be made publicly available at https://github.com/microsoft/DeBERTa.*

@@ -18,9 +18,9 @@ operating these large models in on-the-edge and/or under constrained computation
 remains challenging. In this work, we propose a method to pre-train a smaller general-purpose language representation
 model, called DistilBERT, which can then be fine-tuned with good performances on a wide range of tasks like its larger
 counterparts. While most prior work investigated the use of distillation for building task-specific models, we leverage
-knowledge distillation during the pre-training phase and show that it is possible to reduce the size of a BERT model by
+knowledge distillation during the pretraining phase and show that it is possible to reduce the size of a BERT model by
 40%, while retaining 97% of its language understanding capabilities and being 60% faster. To leverage the inductive
-biases learned by larger models during pre-training, we introduce a triple loss combining language modeling,
+biases learned by larger models during pretraining, we introduce a triple loss combining language modeling,
 distillation and cosine-distance losses. Our smaller, faster and lighter model is cheaper to pre-train and we
 demonstrate its capabilities for on-device computations in a proof-of-concept experiment and a comparative on-device
 study.*
@@ -5,7 +5,7 @@ Overview
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 Dense Passage Retrieval (DPR) is a set of tools and models for state-of-the-art open-domain Q&A research. It was
-intorduced in `Dense Passage Retrieval for Open-Domain Question Answering <https://arxiv.org/abs/2004.04906>`__ by
+introduced in `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, Wen-tau Yih.

 The abstract from the paper is the following:
@@ -12,14 +12,14 @@ identify which tokens were replaced by the generator in the sequence.

 The abstract from the paper is the following:

-*Masked language modeling (MLM) pre-training methods such as BERT corrupt the input by replacing some tokens with
-[MASK] and then train a model to reconstruct the original tokens. While they produce good results when transferred to
+*Masked language modeling (MLM) pretraining methods such as BERT corrupt the input by replacing some tokens with [MASK]
+and then train a model to reconstruct the original tokens. While they produce good results when transferred to
 downstream NLP tasks, they generally require large amounts of compute to be effective. As an alternative, we propose a
-more sample-efficient pre-training task called replaced token detection. Instead of masking the input, our approach
+more sample-efficient pretraining task called replaced token detection. Instead of masking the input, our approach
 corrupts it by replacing some tokens with plausible alternatives sampled from a small generator network. Then, instead
 of training a model that predicts the original identities of the corrupted tokens, we train a discriminative model that
 predicts whether each token in the corrupted input was replaced by a generator sample or not. Thorough experiments
-demonstrate this new pre-training task is more efficient than MLM because the task is defined over all input tokens
+demonstrate this new pretraining task is more efficient than MLM because the task is defined over all input tokens
 rather than just the small subset that was masked out. As a result, the contextual representations learned by our
 approach substantially outperform the ones learned by BERT given the same model size, data, and compute. The gains are
 particularly strong for small models; for example, we train a model on one GPU for 4 days that outperforms GPT (trained
@@ -19,7 +19,7 @@ representations (Dai and Le, 2015; Peters et al., 2018; Howard and Ruder, 2018;
 heterogeneous French corpus. Models of different sizes are trained using the new CNRS (French National Centre for
 Scientific Research) Jean Zay supercomputer. We apply our French language models to diverse NLP tasks (text
 classification, paraphrasing, natural language inference, parsing, word sense disambiguation) and show that most of the
-time they outperform other pre-training approaches. Different versions of FlauBERT as well as a unified evaluation
+time they outperform other pretraining approaches. Different versions of FlauBERT as well as a unified evaluation
 protocol for the downstream tasks, called FLUE (French Language Understanding Evaluation), are shared to the research
 community for further reproducible experiments in French NLP.*

@@ -14,7 +14,7 @@ The abstract from the paper is the following:
 *Natural language understanding comprises a wide range of diverse tasks such as textual entailment, question answering,
 semantic similarity assessment, and document classification. Although large unlabeled text corpora are abundant,
 labeled data for learning these specific tasks is scarce, making it challenging for discriminatively trained models to
-perform adequately. We demonstrate that large gains on these tasks can be realized by generative pre-training of a
+perform adequately. We demonstrate that large gains on these tasks can be realized by generative pretraining of a
 language model on a diverse corpus of unlabeled text, followed by discriminative fine-tuning on each specific task. In
 contrast to previous approaches, we make use of task-aware input transformations during fine-tuning to achieve
 effective transfer while requiring minimal changes to the model architecture. We demonstrate the effectiveness of our
@@ -71,14 +71,14 @@ GPT2Model
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.GPT2Model
-    :members: forward
+    :members: forward, parallelize, deparallelize


 GPT2LMHeadModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.GPT2LMHeadModel
-    :members: forward
+    :members: forward, parallelize, deparallelize


 GPT2DoubleHeadsModel
@@ -6,19 +6,19 @@ Overview

 The LayoutLM model was proposed in 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, and
-Ming Zhou. It's a simple but effective pre-training method of text and layout for document image understanding and
+Ming Zhou. It's a simple but effective pretraining method of text and layout for document image understanding and
 information extraction tasks, such as form understanding and receipt understanding.

 The abstract from the paper is the following:

 *Pre-training techniques have been verified successfully in a variety of NLP tasks in recent years. Despite the
-widespread use of pre-training models for NLP applications, they almost exclusively focus on text-level manipulation,
+widespread use of pretraining models for NLP applications, they almost exclusively focus on text-level manipulation,
 while neglecting layout and style information that is vital for document image understanding. In this paper, we propose
 the \textbf{LayoutLM} to jointly model interactions between text and layout information across scanned document images,
 which is beneficial for a great number of real-world document image understanding tasks such as information extraction
 from scanned documents. Furthermore, we also leverage image features to incorporate words' visual information into
 LayoutLM. To the best of our knowledge, this is the first time that text and layout are jointly learned in a single
-framework for document-level pre-training. It achieves new state-of-the-art results in several downstream tasks,
+framework for document-level pretraining. It achieves new state-of-the-art results in several downstream tasks,
 including form understanding (from 70.72 to 79.27), receipt understanding (from 94.02 to 95.24) and document image
 classification (from 93.07 to 94.42).*

@@ -19,7 +19,7 @@ Encoder Representations from Transformers) framework to learn these vision-and-l
 build a large-scale Transformer model that consists of three encoders: an object relationship encoder, a language
 encoder, and a cross-modality encoder. Next, to endow our model with the capability of connecting vision and language
 semantics, we pre-train the model with large amounts of image-and-sentence pairs, via five diverse representative
-pre-training tasks: masked language modeling, masked object prediction (feature regression and label classification),
+pretraining tasks: masked language modeling, masked object prediction (feature regression and label classification),
 cross-modality matching, and image question answering. These tasks help in learning both intra-modality and
 cross-modality relationships. After fine-tuning from our pretrained parameters, our model achieves the state-of-the-art
 results on two visual question answering datasets (i.e., VQA and GQA). We also show the generalizability of our
@@ -13,7 +13,7 @@ The MBart model was presented in `Multilingual Denoising Pre-training for Neural
 Ghazvininejad, Mike Lewis, Luke Zettlemoyer.

 According to the abstract, MBART is a sequence-to-sequence denoising auto-encoder pretrained on large-scale monolingual
-corpora in many languages using the BART objective. mBART is one of the first methods for pre-training a complete
+corpora in many languages using the BART objective. mBART is one of the first methods for pretraining a complete
 sequence-to-sequence model by denoising full texts in multiple languages, while previous approaches have focused only
 on the encoder, decoder, or reconstructing parts of the text.

@@ -39,6 +39,13 @@ MT5ForConditionalGeneration
    :members:


+MT5EncoderModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.MT5EncoderModel
+    :members:
+
+
 TFMT5Model
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -51,3 +58,10 @@ TFMT5ForConditionalGeneration

 .. autoclass:: transformers.TFMT5ForConditionalGeneration
    :members:
+
+
+TFMT5EncoderModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFMT5EncoderModel
+    :members:
@@ -17,7 +17,7 @@ the next token.

 The abstract from the paper is the following:

-*In this paper, we present a new sequence-to-sequence pre-training model called ProphetNet, which introduces a novel
+*In this paper, we present a new sequence-to-sequence pretraining model called ProphetNet, which introduces a novel
 self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism. Instead of
 the optimization of one-step ahead prediction in traditional sequence-to-sequence model, the ProphetNet is optimized by
 n-step ahead prediction which predicts the next n tokens simultaneously based on previous context tokens at each time
@@ -25,7 +25,7 @@ step. The future n-gram prediction explicitly encourages the model to plan for t
 overfitting on strong local correlations. We pre-train ProphetNet using a base scale dataset (16GB) and a large scale
 dataset (160GB) respectively. Then we conduct experiments on CNN/DailyMail, Gigaword, and SQuAD 1.1 benchmarks for
 abstractive summarization and question generation tasks. Experimental results show that ProphetNet achieves new
-state-of-the-art results on all these datasets compared to the models using the same scale pre-training corpus.*
+state-of-the-art results on all these datasets compared to the models using the same scale pretraining corpus.*

 The Authors' code can be found `here <https://github.com/microsoft/ProphetNet>`__.

@@ -17,7 +17,7 @@ The abstract from the paper is the following:
 task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning
 has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of
 transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a
-text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer
+text-to-text format. Our systematic study compares pretraining objectives, architectures, unlabeled datasets, transfer
 approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration
 with scale and our new "Colossal Clean Crawled Corpus", we achieve state-of-the-art results on many benchmarks covering
 summarization, question answering, text classification, and more. To facilitate future work on transfer learning for
@@ -99,15 +99,20 @@ T5Model
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.T5Model
-    :members: forward
+    :members: forward, parallelize, deparallelize


 T5ForConditionalGeneration
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.T5ForConditionalGeneration
-    :members: forward
+    :members: forward, parallelize, deparallelize

+T5EncoderModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.T5EncoderModel
+    :members: forward

 TFT5Model
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -121,3 +126,9 @@ TFT5ForConditionalGeneration

 .. autoclass:: transformers.TFT5ForConditionalGeneration
    :members: call
+
+TFT5EncoderModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFT5EncoderModel
+    :members: call
@@ -19,7 +19,7 @@ just the next token. Its architecture is identical to ProhpetNet, but the model

 The abstract from the paper is the following:

-*In this paper, we present a new sequence-to-sequence pre-training model called ProphetNet, which introduces a novel
+*In this paper, we present a new sequence-to-sequence pretraining model called ProphetNet, which introduces a novel
 self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism. Instead of
 the optimization of one-step ahead prediction in traditional sequence-to-sequence model, the ProphetNet is optimized by
 n-step ahead prediction which predicts the next n tokens simultaneously based on previous context tokens at each time
@@ -27,7 +27,7 @@ step. The future n-gram prediction explicitly encourages the model to plan for t
 overfitting on strong local correlations. We pre-train ProphetNet using a base scale dataset (16GB) and a large scale
 dataset (160GB) respectively. Then we conduct experiments on CNN/DailyMail, Gigaword, and SQuAD 1.1 benchmarks for
 abstractive summarization and question generation tasks. Experimental results show that ProphetNet achieves new
-state-of-the-art results on all these datasets compared to the models using the same scale pre-training corpus.*
+state-of-the-art results on all these datasets compared to the models using the same scale pretraining corpus.*

 The Authors' code can be found `here <https://github.com/microsoft/ProphetNet>`__.

@@ -527,10 +527,10 @@ Pegasus
 <https://arxiv.org/pdf/1912.08777.pdf>`_, Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019.

 Sequence-to-sequence model with the same encoder-decoder model architecture as BART. Pegasus is pre-trained jointly on
-two self-supervised objective functions: Masked Language Modeling (MLM) and a novel summarization specific pre-training
+two self-supervised objective functions: Masked Language Modeling (MLM) and a novel summarization specific pretraining
 objective, called Gap Sentence Generation (GSG).

-  * MLM: encoder input tokens are randomely replaced by a mask tokens and have to be predicted by the encoder (like in
+  * MLM: encoder input tokens are randomly replaced by a mask tokens and have to be predicted by the encoder (like in
    BERT)
  * GSG: whole encoder input sentences are replaced by a second mask token and fed to the decoder, but which has a
    causal mask to hide the future words like a regular auto-regressive transformer decoder.
@@ -609,7 +609,7 @@ MT5
 `mT5: A massively multilingual pre-trained text-to-text transformer <https://arxiv.org/abs/2010.11934>`_, Linting Xue
 et al.

-The model architecture is same as T5. mT5's pre-training objective includes T5's self-supervised training, but not T5's
+The model architecture is same as T5. mT5's pretraining objective includes T5's self-supervised training, but not T5's
 supervised training. mT5 is trained on 101 languages.

 The library provides a version of this model for conditional generation.
@@ -630,8 +630,8 @@ MBart
 `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.

-The model architecture and pre-training objective is same as BART, but MBart is trained on 25 languages and is intended
-for supervised and unsupervised machine translation. MBart is one of the first methods for pre-training a complete
+The model architecture and pretraining objective is same as BART, but MBart is trained on 25 languages and is intended
+for supervised and unsupervised machine translation. MBart is one of the first methods for pretraining a complete
 sequence-to-sequence model by denoising full texts in multiple languages,

 The library provides a version of this model for conditional generation.
@@ -658,7 +658,7 @@ ProphetNet
 `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, Ming Zhou.

-ProphetNet introduces a novel *sequence-to-sequence* pre-training objective, called *future n-gram prediction*. In
+ProphetNet introduces a novel *sequence-to-sequence* pretraining objective, called *future n-gram prediction*. In
 future n-gram prediction, the model predicts the next n tokens simultaneously based on previous context tokens at each
 time step instead instead of just the single next token. The future n-gram prediction explicitly encourages the model
 to plan for the future tokens and prevent overfitting on strong local correlations. The model architecture is based on
@@ -683,8 +683,8 @@ XLM-ProphetNet
 `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, Ming Zhou.

-XLM-ProphetNet's model architecture and pre-training objective is same as ProphetNet, but XLM-ProphetNet was
-pre-trained on the cross-lingual dataset `XGLUE <https://arxiv.org/abs/2004.01401>`__.
+XLM-ProphetNet's model architecture and pretraining objective is same as ProphetNet, but XLM-ProphetNet was pre-trained
+on the cross-lingual dataset `XGLUE <https://arxiv.org/abs/2004.01401>`__.

 The library provides a pre-trained version of this model for multi-lingual conditional generation and fine-tuned
 versions for headline generation and question generation, respectively.
@@ -109,7 +109,7 @@ XLM-RoBERTa
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

 XLM-RoBERTa was trained on 2.5TB of newly created clean CommonCrawl data in 100 languages. It provides strong gains
-over previously released multi-lingual models like mBERT or XLM on downstream taks like classification, sequence
+over previously released multi-lingual models like mBERT or XLM on downstream tasks like classification, sequence
 labeling and question answering.

 Two XLM-RoBERTa checkpoints can be used for multi-lingual tasks:
@@ -62,7 +62,7 @@ sliding the context window so that the model has more context when making each p
 This is a closer approximation to the true decomposition of the sequence probability and will typically yield a more
 favorable score. The downside is that it requires a separate forward pass for each token in the corpus. A good
 practical compromise is to employ a strided sliding window, moving the context by larger strides rather than sliding by
-1 token a time. This allows computation to procede much faster while still giving the model a large context to make
+1 token a time. This allows computation to proceed much faster while still giving the model a large context to make
 predictions at each step.

 Example: Calculating perplexity with GPT-2 in 🤗 Transformers
@@ -2,7 +2,6 @@ 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.

@@ -52,7 +51,7 @@ 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` gtp2-medium` instead of `bert-base-cased` to create our tokenizer, we
+special tokens; for instance, if we had used `gpt2-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``.

@@ -333,6 +333,12 @@ 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.                                                    |
 |                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
@@ -240,7 +240,9 @@ 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.
@@ -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
 -----------------------------------------------------------------------------------------------------------------------
@@ -305,7 +305,7 @@ Language modeling is the task of fitting a model to a corpus, which can be domai
 transformer-based models are trained using a variant of language modeling, e.g. BERT with masked language modeling,
 GPT-2 with causal language modeling.

-Language modeling can be useful outside of pre-training as well, for example to shift the model distribution to be
+Language modeling can be useful outside of pretraining as well, for example to shift the model distribution to be
 domain-specific: using a language model trained over a very large corpus, and then fine-tuning it to a news dataset or
 on scientific papers e.g. `LysandreJik/arxiv-nlp <https://huggingface.co/lysandre/arxiv-nlp>`__.

@@ -909,9 +909,10 @@ 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 (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 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 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
@@ -25,7 +25,7 @@ class PlotArguments:
    )
    plot_along_batch: bool = field(
        default=False,
-        metadata={"help": "Whether to plot along batch size or sequence lengh. Defaults to sequence length."},
+        metadata={"help": "Whether to plot along batch size or sequence length. Defaults to sequence length."},
    )
    is_time: bool = field(
        default=False,
@@ -17,7 +17,7 @@ This folder contains the original code used to train Distil* as well as examples

 ## What is Distil*

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

 We have applied the same method to other Transformer architectures and released the weights:
 - GPT2: on the [WikiText-103](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/) benchmark, GPT2 reaches a perplexity on the test set of 16.3 compared to 21.1 for **DistilGPT2** (after fine-tuning on the train set).
@@ -57,7 +57,7 @@ Here are the results on the *test* sets for 6 of the languages available in XNLI

 This part of the library has only be tested with Python3.6+. There are few specific dependencies to install before launching a distillation, you can install them with the command `pip install -r requirements.txt`.

-**Important note:** The training scripts have been updated to support PyTorch v1.2.0 (there are breakings changes compared to v1.1.0).
+**Important note:** The training scripts have been updated to support PyTorch v1.2.0 (there are breaking changes compared to v1.1.0).


 ## How to use DistilBERT
@@ -111,7 +111,7 @@ python scripts/binarized_data.py \
    --dump_file data/binarized_text
 ```

-Our implementation of masked language modeling loss follows [XLM](https://github.com/facebookresearch/XLM)'s one and smoothes the probability of masking with a factor that put more emphasis on rare words. Thus we count the occurrences of each tokens in the data:
+Our implementation of masked language modeling loss follows [XLM](https://github.com/facebookresearch/XLM)'s one and smooths the probability of masking with a factor that put more emphasis on rare words. Thus we count the occurrences of each tokens in the data:

 ```bash
 python scripts/token_counts.py \
@@ -173,7 +173,7 @@ python -m torch.distributed.launch \
        --token_counts data/token_counts.bert-base-uncased.pickle
 ```

-**Tips:** Starting distillated training with good initialization of the model weights is crucial to reach decent performance. In our experiments, we initialized our model from a few layers of the teacher (Bert) itself! Please refer to `scripts/extract.py` and `scripts/extract_distilbert.py` to create a valid initialization checkpoint and use `--student_pretrained_weights` argument to use this initialization for the distilled training!
+**Tips:** Starting distilled training with good initialization of the model weights is crucial to reach decent performance. In our experiments, we initialized our model from a few layers of the teacher (Bert) itself! Please refer to `scripts/extract.py` and `scripts/extract_distilbert.py` to create a valid initialization checkpoint and use `--student_pretrained_weights` argument to use this initialization for the distilled training!

 Happy distillation!

@@ -188,7 +188,7 @@ class Distiller:

    def prepare_batch_mlm(self, batch):
        """
-        Prepare the batch: from the token_ids and the lenghts, compute the attention mask and the masked label for MLM.
+        Prepare the batch: from the token_ids and the lengths, compute the attention mask and the masked label for MLM.

        Input:
        ------
@@ -200,7 +200,7 @@ class Distiller:
        -------
            token_ids: `torch.tensor(bs, seq_length)` - The token ids after the modifications for MLM.
            attn_mask: `torch.tensor(bs, seq_length)` - The attention mask for the self-attention.
-            mlm_labels: `torch.tensor(bs, seq_length)` - The masked languge modeling labels. There is a -100 where there is nothing to predict.
+            mlm_labels: `torch.tensor(bs, seq_length)` - The masked language modeling labels. There is a -100 where there is nothing to predict.
        """
        token_ids, lengths = batch
        token_ids, lengths = self.round_batch(x=token_ids, lengths=lengths)
@@ -253,7 +253,7 @@ class Distiller:

    def prepare_batch_clm(self, batch):
        """
-        Prepare the batch: from the token_ids and the lenghts, compute the attention mask and the labels for CLM.
+        Prepare the batch: from the token_ids and the lengths, compute the attention mask and the labels for CLM.

        Input:
        ------
@@ -86,7 +86,7 @@ if __name__ == "__main__":
            compressed_sd[f"vocab_layer_norm.{w}"] = state_dict[f"cls.predictions.transform.LayerNorm.{w}"]

    print(f"N layers selected for distillation: {std_idx}")
-    print(f"Number of params transfered for distillation: {len(compressed_sd.keys())}")
+    print(f"Number of params transferred for distillation: {len(compressed_sd.keys())}")

-    print(f"Save transfered checkpoint to {args.dump_checkpoint}.")
+    print(f"Save transferred checkpoint to {args.dump_checkpoint}.")
    torch.save(compressed_sd, args.dump_checkpoint)
@@ -93,11 +93,11 @@ class DataTrainingArguments:
    overwrite_cache: bool = field(
        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
    )
-    max_seq_length: Optional[int] = field(
-        default=None,
+    max_seq_length: int = field(
+        default=512,
        metadata={
            "help": "The maximum total input sequence length after tokenization. Sequences longer "
-            "than this will be truncated. Default to the max input length of the model."
+            "than this will be truncated."
        },
    )
    preprocessing_num_workers: Optional[int] = field(
@@ -286,15 +286,12 @@ def main():
            load_from_cache_file=not data_args.overwrite_cache,
        )

-        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)
+        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.
@@ -4,11 +4,9 @@ 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,
@@ -30,21 +28,12 @@ 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__)

-
-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")
+require_version_examples("pytorch_lightning>=1.0.4")

 MODEL_MODES = {
    "base": AutoModel,
@@ -384,6 +373,8 @@ 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,
@@ -21,7 +21,7 @@ You can also have a look at this fun *Explain Like I'm Five* introductory [slide

 One promise of extreme pruning is to obtain extremely small models that can be easily sent (and stored) on edge devices. By setting weights to 0., we reduce the amount of information we need to store, and thus decreasing the memory size. We are able to obtain extremely sparse fine-pruned models with movement pruning: ~95% of the dense performance with ~5% of total remaining weights in the BERT encoder.

-In [this notebook](https://github.com/huggingface/transformers/blob/master/examples/movement-pruning/Saving_PruneBERT.ipynb), we showcase how we can leverage standard tools that exist out-of-the-box to efficiently store an extremely sparse question answering model (only 6% of total remaining weights in the encoder). We are able to reduce the memory size of the encoder **from the 340MB (the orignal dense BERT) to 11MB**, without any additional training of the model (every operation is performed *post fine-pruning*). It is sufficiently small to store it on a [91' floppy disk](https://en.wikipedia.org/wiki/Floptical) 📎!
+In [this notebook](https://github.com/huggingface/transformers/blob/master/examples/movement-pruning/Saving_PruneBERT.ipynb), we showcase how we can leverage standard tools that exist out-of-the-box to efficiently store an extremely sparse question answering model (only 6% of total remaining weights in the encoder). We are able to reduce the memory size of the encoder **from the 340MB (the original dense BERT) to 11MB**, without any additional training of the model (every operation is performed *post fine-pruning*). It is sufficiently small to store it on a [91' floppy disk](https://en.wikipedia.org/wiki/Floptical) 📎!

 While movement pruning does not directly optimize for memory footprint (but rather the number of non-null weights), we hypothetize that further memory compression ratios can be achieved with specific quantization aware trainings (see for instance [Q8BERT](https://arxiv.org/abs/1910.06188), [And the Bit Goes Down](https://arxiv.org/abs/1907.05686) or [Quant-Noise](https://arxiv.org/abs/2004.07320)).

@@ -14,7 +14,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """
-Binarizers take a (real value) matrice as input and produce a binary (values in {0,1}) mask of the same shape.
+Binarizers take a (real value) matrix as input and produce a binary (values in {0,1}) mask of the same shape.
 """

 import torch
@@ -159,6 +159,81 @@ 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
@@ -7,8 +7,8 @@ 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

+# 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
@@ -20,10 +20,10 @@ test.source
 test.target
 ```

-A sample finetuning command (run ` ./examples/rag/finetune.py --help` to list all available options):
+A sample finetuning command (run ` ./examples/rag/finetune_rag.py --help` to list all available options):

 ```bash
-python examples/rag/finetune.py \
+python examples/rag/finetune_rag.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.py` script.
+You will then be able to pass `path/to/checkpoint` as `model_name_or_path` to the `finetune_rag.py` script.


 # Evaluation
@@ -130,3 +130,29 @@ 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
+```
@@ -8,7 +8,7 @@ import torch
 from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
 from pytorch_lightning.utilities import rank_zero_only

-from utils import save_json
+from utils_rag 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=0,  # maybe save a checkpoint every time val is run, not just end of epoch.
+        period=1,  # maybe save a checkpoint every time val is run, not just end of epoch.
    )
    return checkpoint_callback

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

    def init_retrieval(self, distributed_port: int):
@@ -153,7 +153,7 @@ def get_args():
    parser.add_argument(
        "--index_name",
        default=None,
-        choices=["hf", "legacy"],
+        choices=["exact", "compressed", "legacy"],
        type=str,
        help="RAG model retriever type",
    )
@@ -1,12 +1,10 @@
 """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
@@ -15,29 +13,31 @@ 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 import (  # noqa: E402 # isort:skipq
+from callbacks_rag import (  # noqa: E402 # isort:skipq
    get_checkpoint_callback,
    get_early_stopping_callback,
    Seq2SeqLoggingCallback,
 )
 from distributed_retriever import RagPyTorchDistributedRetriever  # noqa: E402 # isort:skip
-from utils import (  # noqa: E402 # isort:skip
+from utils_rag import (  # noqa: E402 # isort:skip
    calculate_exact_match,
    flatten_list,
    get_git_info,
@@ -67,6 +67,30 @@ 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"]
@@ -91,23 +115,24 @@ class GenerativeQAModule(BaseTransformer):
        config = config_class.from_pretrained(hparams.model_name_or_path)

        # set retriever parameters
-        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
+        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

        # 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 args.prefix is not None:
-                config.generator.prefix = args.prefix
+            if hparams.prefix is not None:
+                config.generator.prefix = hparams.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 args.prefix is not None:
-                config.prefix = args.prefix
+            if hparams.prefix is not None:
+                config.prefix = hparams.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
@@ -152,11 +177,9 @@ class GenerativeQAModule(BaseTransformer):
        self.num_workers = hparams.num_workers
        self.distributed_port = self.hparams.distributed_port

-    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:
+        # For single GPU training, init_ddp_connection is not called.
+        # So we need to initialize the retrievers here.
+        if hparams.gpus <= 1:
            self.model.retriever.init_retrieval(self.distributed_port)

    def forward(self, input_ids, **kwargs):
@@ -270,6 +293,7 @@ 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"],
@@ -322,17 +346,6 @@ 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:
@@ -429,10 +442,24 @@ 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, model=None) -> GenerativeQAModule:
+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()
+
    Path(args.output_dir).mkdir(exist_ok=True)
    if model is None:
        model: GenerativeQAModule = GenerativeQAModule(args)
@@ -461,6 +488,7 @@ def main(args, model=None) -> GenerativeQAModule:
        if args.early_stopping_patience >= 0
        else False
    )
+
    trainer: pl.Trainer = generic_train(
        model,
        args,
@@ -468,31 +496,17 @@ def main(args, 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__":
-    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)
+    main()
@@ -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.py \
+python examples/rag/finetune_rag.py \
    --data_dir $DATA_DIR \
    --output_dir $OUTPUT_DIR \
    --model_name_or_path $MODEL_NAME_OR_PATH \
@@ -0,0 +1,96 @@
+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)
@@ -7,7 +7,7 @@ from tempfile import TemporaryDirectory
 from typing import List, Optional

 import torch
-from datasets import load_dataset
+from datasets import Features, Sequence, Value, load_dataset

 import faiss
 from transformers import (
@@ -82,10 +82,14 @@ 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
@@ -13,7 +13,7 @@ streamlit
 elasticsearch
 nltk
 pandas
-datasets
+datasets >= 1.1.3
 fire
 pytest
 conllu
@@ -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` 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.
+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.

 With PyTorch 1.6+ it'll automatically use `native AMP` when `--fp16` is set.

 To see all the possible command line options, run:

 ```bash
-./builtin_trainer/finetune.sh --help # This calls python finetune_trainer.py --help
+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` 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.)
+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.)

-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.

-Following command fine-tunes `sshleifer/student_marian_en_ro_6_3` on TPU V3-8 and should complete one epoch in ~5-6 mins.
+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.

 ```bash
 ./builtin_trainer/train_distil_marian_enro_tpu.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 \
    "$@"
@@ -5,7 +5,8 @@ export TPU_NUM_CORES=8
 python xla_spawn.py --num_cores $TPU_NUM_CORES \
    finetune_trainer.py \
    --learning_rate=3e-5 \
-    --do_train --do_eval --evaluate_during_training \
+    --do_train --do_eval \
+    --evaluation_strategy steps \
    --prediction_loss_only \
    --n_val 1000 \
    "$@"
@@ -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 $MAX_LEN \
-    --do_train --do_eval --do_predict --evaluate_during_training\
+    --do_train --do_eval --do_predict \
+    --evaluation_strategy steps \
    --predict_with_generate --logging_first_step \
    --task translation --label_smoothing 0.1 \
    "$@"
@@ -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 $MAX_LEN \
-    --do_train --do_eval --evaluate_during_training \
+    --do_train --do_eval \
+    --evaluation_strategy steps \
    --prediction_loss_only \
    --task translation --label_smoothing 0.1 \
    "$@"
@@ -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 $MAX_TGT_LEN \
-    --do_train --do_eval --do_predict --evaluate_during_training \
+    --do_train --do_eval --do_predict \
+    --evaluation_strategy steps \
    --predict_with_generate --sortish_sampler \
    "$@"
@@ -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 \
-    --predict_with_generate --logging_first_step 
+    --do_train --do_eval --do_predict \
+    --evaluation_strategy steps \
+    --predict_with_generate --logging_first_step \
    --task translation \
    "$@"
@@ -1,5 +1,4 @@
 import logging
-import os
 from pathlib import Path

 import numpy as np
@@ -98,7 +97,8 @@ def get_checkpoint_callback(output_dir, metric, save_top_k=1, lower_is_better=Fa
        )

    checkpoint_callback = ModelCheckpoint(
-        filepath=os.path.join(output_dir, exp),
+        dirpath=output_dir,
+        filename=exp,
        monitor=f"val_{metric}",
        mode="min" if "loss" in metric else "max",
        save_top_k=save_top_k,
@@ -189,7 +189,7 @@ class Seq2SeqTrainer(Trainer):
        }

        if self.args.predict_with_generate and not self.args.prediction_loss_only:
-            generated_tokens = model.generate(
+            generated_tokens = self.model.generate(
                inputs["input_ids"],
                attention_mask=inputs["attention_mask"],
                **gen_kwargs,
@@ -4,13 +4,7 @@ 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,
-    require_torch_non_multi_gpu_but_fix_me,
-    slow,
-)
+from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, slow
 from transformers.trainer_callback import TrainerState
 from transformers.trainer_utils import set_seed

@@ -52,7 +46,6 @@ 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
@@ -154,7 +154,8 @@ def perturb_past(
        # Compute hidden using perturbed past
        perturbed_past = list(map(add, past, curr_perturbation))
        _, _, _, curr_length, _ = curr_perturbation[0].shape
-        all_logits, _, all_hidden = model(last, past=perturbed_past)
+        lm_output = model(last, past_key_values=perturbed_past)
+        all_logits, all_hidden = lm_output["logits"], lm_output["hidden_states"]
        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)
@@ -179,7 +180,8 @@ def perturb_past(
            wte = model.resize_token_embeddings()
            for _ in range(horizon_length):
                inputs_embeds = torch.matmul(curr_probs, wte.weight.data)
-                _, curr_unpert_past, curr_all_hidden = model(past=curr_unpert_past, inputs_embeds=inputs_embeds)
+                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_hidden = curr_all_hidden[-1]
                new_accumulated_hidden = new_accumulated_hidden + torch.sum(curr_hidden, dim=1)

@@ -462,9 +464,14 @@ 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 = model(output_so_far[:, :-1])["past_key_values"]

-        unpert_logits, unpert_past, unpert_all_hidden = model(output_so_far)
+        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_last_hidden = unpert_all_hidden[-1]

        # check if we are abowe grad max length
@@ -507,7 +514,11 @@ def generate_text_pplm(
            else:
                pert_past = past

-        pert_logits, past, pert_all_hidden = model(last, past=pert_past)
+        lm_output = model(last, past_key_values=pert_past)
+        pert_logits, past = (
+            lm_output["logits"],
+            lm_output["past_key_values"],
+        )
        pert_logits = pert_logits[:, -1, :] / temperature  # + SMALL_CONST

        for token_idx in set(output_so_far[0].tolist()):
@@ -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)
+        hidden = self.encoder.transformer(x)["last_hidden_state"]
        masked_hidden = hidden * mask
        avg_hidden = torch.sum(masked_hidden, dim=1) / (torch.sum(mask, dim=1).detach() + EPSILON)
        return avg_hidden
@@ -15,7 +15,8 @@
 """
 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
@@ -24,7 +25,7 @@ from dataclasses import dataclass, field
 from typing import Optional

 import numpy as np
-from datasets import load_dataset
+from datasets import ClassLabel, load_dataset
 from seqeval.metrics import accuracy_score, f1_score, precision_score, recall_score

 import transformers
@@ -198,12 +199,17 @@ def main():

    if training_args.do_train:
        column_names = datasets["train"].column_names
+        features = datasets["train"].features
    else:
        column_names = datasets["validation"].column_names
-    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]
+        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]
+    )

-    # Labeling (this part will be easier when https://github.com/huggingface/datasets/issues/797 is solved)
+    # In the event the labels are not a `Sequence[ClassLabel]`, we will need to go through the dataset to get the
+    # unique labels.
    def get_label_list(labels):
        unique_labels = set()
        for label in labels:
@@ -212,8 +218,13 @@ def main():
        label_list.sort()
        return label_list

-    label_list = get_label_list(datasets["train"][label_column_name])
-    label_to_id = {l: i for i, l in enumerate(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)}
    num_labels = len(label_list)

    # Load pretrained model and tokenizer
@@ -358,7 +369,7 @@ def main():
        ]

        output_test_results_file = os.path.join(training_args.output_dir, "test_results.txt")
-        if trainer.is_world_master():
+        if trainer.is_world_process_zero():
            with open(output_test_results_file, "w") as writer:
                for key, value in metrics.items():
                    logger.info(f"  {key} = {value}")
@@ -366,7 +377,7 @@ def main():

        # Save predictions
        output_test_predictions_file = os.path.join(training_args.output_dir, "test_predictions.txt")
-        if trainer.is_world_master():
+        if trainer.is_world_process_zero():
            with open(output_test_predictions_file, "w") as writer:
                for prediction in true_predictions:
                    writer.write(" ".join(prediction) + "\n")
@@ -291,7 +291,7 @@ def main():
        preds_list, _ = align_predictions(predictions, label_ids)

        output_test_results_file = os.path.join(training_args.output_dir, "test_results.txt")
-        if trainer.is_world_master():
+        if trainer.is_world_process_zero():
            with open(output_test_results_file, "w") as writer:
                for key, value in metrics.items():
                    logger.info("  %s = %s", key, value)
@@ -299,7 +299,7 @@ def main():

        # Save predictions
        output_test_predictions_file = os.path.join(training_args.output_dir, "test_predictions.txt")
-        if trainer.is_world_master():
+        if trainer.is_world_process_zero():
            with open(output_test_predictions_file, "w") as writer:
                with open(os.path.join(data_args.data_dir, "test.txt"), "r") as f:
                    token_classification_task.write_predictions_to_file(writer, f, preds_list)
@@ -0,0 +1,20 @@
+---
+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')
+```
@@ -4,6 +4,16 @@ 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
@@ -33,6 +43,8 @@ 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
@@ -4,6 +4,10 @@ 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
@@ -25,6 +29,8 @@ 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
@@ -24,6 +24,8 @@ 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
@@ -24,6 +24,8 @@ 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
@@ -24,6 +24,8 @@ 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
@@ -4,6 +4,13 @@ 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
@@ -24,6 +31,8 @@ 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
@@ -4,6 +4,11 @@ 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
@@ -24,6 +29,8 @@ 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
@@ -4,6 +4,11 @@ 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
@@ -24,6 +29,8 @@ 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
@@ -4,6 +4,11 @@ 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
@@ -24,6 +29,8 @@ 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
@@ -4,6 +4,11 @@ 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
@@ -24,6 +29,8 @@ 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
@@ -4,6 +4,11 @@ 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
@@ -24,6 +29,8 @@ 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
@@ -4,6 +4,14 @@ 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
@@ -24,6 +32,8 @@ 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
@@ -4,6 +4,11 @@ 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
@@ -24,6 +29,8 @@ 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
@@ -4,6 +4,11 @@ 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
@@ -24,6 +29,8 @@ 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
@@ -4,6 +4,11 @@ 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
@@ -24,6 +29,8 @@ 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
@@ -4,6 +4,11 @@ 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
@@ -24,6 +29,8 @@ 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
@@ -4,6 +4,11 @@ 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
@@ -24,6 +29,8 @@ 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
@@ -4,6 +4,11 @@ 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
@@ -24,6 +29,8 @@ 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
@@ -4,6 +4,11 @@ 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
@@ -24,6 +29,8 @@ 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
@@ -4,6 +4,11 @@ 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
@@ -24,6 +29,8 @@ 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
@@ -24,6 +24,8 @@ 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
@@ -4,6 +4,11 @@ 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
@@ -24,6 +29,8 @@ 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
@@ -24,6 +24,8 @@ 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
@@ -24,6 +24,8 @@ 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
@@ -24,6 +24,8 @@ 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
@@ -24,6 +24,8 @@ 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
--- a/Show More
+++ b/Show More