[trainer] parametrize default output_dir

This PR:

* fixes trainer to have the logger agree with the actual default `output_dir`, but setting it one place and passing it as an argument to both places

@sgugger

.circleci/config.yml

@@ -3,27 +3,6 @@ orbs:
     gcp-gke: circleci/gcp-gke@1.0.4
     go: circleci/go@1.3.0
 
-commands:
-  skip-job-on-doc-only-changes:
-    description: "Do not continue this job and exit with success for PRs with only doc changes"
-    steps:
-
-      - run:
-          name: docs-only changes skip check
-          command: |
-            # pipeline.git.base_revision is not always defined, so only proceed if all external vars are defined
-            if test -n "<< pipeline.git.base_revision >>" && test -n "<< pipeline.git.revision >>" && test -n "$(git diff --name-only << pipeline.git.base_revision >>...<< pipeline.git.revision >>)"
-            then
-                if git diff --name-only << pipeline.git.base_revision >>...<< pipeline.git.revision >> | egrep -qv '\.(md|rst)$'
-                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
-            else
-                echo "Can't perform skipping check w/o base_revision defined, continuing the job"
-            fi
 
 # TPU REFERENCES
 references:
@@ -80,38 +59,27 @@ references:
                 kubectl get job | awk 'match($4,/[0-9]+[dh]/) {print $0}'
                 kubectl delete job $(kubectl get job | awk 'match($4,/[0-9]+[dh]/) {print $1}')
 
-setup: &setup
-    working_directory: ~/transformers
-    docker:
-        - image: circleci/python:3.6
-    environment:
-        OMP_NUM_THREADS: 1
-    resource_class: xlarge
-    parallelism: 1
 
-setup-small: &setup-small
-    working_directory: ~/transformers
-    docker:
-        - image: circleci/python:3.6
 
-store: &store
-    - store_artifacts:
-          path: ~/transformers/tests_output.txt
-    - store_artifacts:
-          path: ~/transformers/reports
 
 jobs:
     run_tests_torch_and_tf:
-        <<: *setup
+        working_directory: ~/transformers
+        docker:
+            - image: circleci/python:3.6
+        environment:
+            OMP_NUM_THREADS: 1
+        resource_class: xlarge
+        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,sentencepiece]
+            - run: pip install tapas torch-scatter -f https://pytorch-geometric.com/whl/torch-1.7.0+cpu.html
             - save_cache:
                 key: v0.4-{{ checksum "setup.py" }}
                 paths:
@@ -123,16 +91,22 @@ jobs:
                   path: ~/transformers/reports
 
     run_tests_torch:
-        <<: *setup
+        working_directory: ~/transformers
+        docker:
+            - image: circleci/python:3.7
+        environment:
+            OMP_NUM_THREADS: 1
+        resource_class: xlarge
+        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,sentencepiece]
+            - run: pip install tapas torch-scatter -f https://pytorch-geometric.com/whl/torch-1.7.0+cpu.html
             - save_cache:
                   key: v0.4-torch-{{ checksum "setup.py" }}
                   paths:
@@ -143,99 +117,16 @@ jobs:
             - store_artifacts:
                   path: ~/transformers/reports
 
-    run_tests_torch-1-3:
-        <<: *setup
-        steps:
-            - checkout
-            - skip-job-on-doc-only-changes
-            - restore_cache:
-                  keys:
-                      - v0.4-torch-1-3-{{ checksum "setup.py" }}
-                      - v0.4-{{ checksum "setup.py" }}
-            - run: pip install --upgrade pip
-            - run: pip install .[sklearn,testing,sentencepiece]
-            - run: pip install torch==1.3
-            - save_cache:
-                  key: v0.4-torch-1-3-{{ checksum "setup.py" }}
-                  paths:
-                      - '~/.cache/pip'
-            - run: python -m pytest -n 8 --dist=loadfile -s --make-reports=tests_torch ./tests/ | tee tests_output.txt
-            - store_artifacts:
-                  path: ~/transformers/tests_output.txt
-            - store_artifacts:
-                  path: ~/transformers/reports
-
-    run_tests_torch-1-4:
-        <<: *setup
-        steps:
-            - checkout
-            - skip-job-on-doc-only-changes
-            - restore_cache:
-                  keys:
-                      - v0.4-torch-1-4-{{ checksum "setup.py" }}
-                      - v0.4-{{ checksum "setup.py" }}
-            - run: pip install --upgrade pip
-            - run: pip install .[sklearn,testing,sentencepiece]
-            - run: pip install torch==1.4
-            - save_cache:
-                  key: v0.4-torch-1-4-{{ checksum "setup.py" }}
-                  paths:
-                      - '~/.cache/pip'
-            - run: python -m pytest -n 8 --dist=loadfile -s --make-reports=tests_torch ./tests/ | tee tests_output.txt
-            - store_artifacts:
-                  path: ~/transformers/tests_output.txt
-            - store_artifacts:
-                  path: ~/transformers/reports
-
-    run_tests_torch-1-5:
-        <<: *setup
-        steps:
-            - checkout
-            - skip-job-on-doc-only-changes
-            - restore_cache:
-                  keys:
-                      - v0.4-torch-1-5-{{ checksum "setup.py" }}
-                      - v0.4-{{ checksum "setup.py" }}
-            - run: pip install --upgrade pip
-            - run: pip install .[sklearn,testing,sentencepiece]
-            - run: pip install torch==1.5
-            - save_cache:
-                  key: v0.4-torch-1-5-{{ checksum "setup.py" }}
-                  paths:
-                      - '~/.cache/pip'
-            - run: python -m pytest -n 8 --dist=loadfile -s --make-reports=tests_torch ./tests/ | tee tests_output.txt
-            - store_artifacts:
-                  path: ~/transformers/tests_output.txt
-            - store_artifacts:
-                  path: ~/transformers/reports
-
-    run_tests_torch-1-6:
-        <<: *setup
-        steps:
-            - checkout
-            - skip-job-on-doc-only-changes
-            - restore_cache:
-                  keys:
-                      - v0.4-torch-1-6-{{ checksum "setup.py" }}
-                      - v0.4-{{ checksum "setup.py" }}
-            - run: pip install --upgrade pip
-            - run: pip install .[sklearn,testing,sentencepiece]
-            - run: pip install torch==1.6
-            - save_cache:
-                  key: v0.4-torch-1-6-{{ checksum "setup.py" }}
-                  paths:
-                      - '~/.cache/pip'
-            - run: python -m pytest -n 8 --dist=loadfile -s --make-reports=tests_torch ./tests/ | tee tests_output.txt
-            - store_artifacts:
-                  path: ~/transformers/tests_output.txt
-            - store_artifacts:
-                  path: ~/transformers/reports
-
     run_tests_tf:
-        <<: *setup
+        working_directory: ~/transformers
+        docker:
+            - image: circleci/python:3.7
+        environment:
+            OMP_NUM_THREADS: 1
+        resource_class: xlarge
+        parallelism: 1
         steps:
             - checkout
-            - skip-job-on-doc-only-changes
             - restore_cache:
                   keys:
                       - v0.4-tf-{{ checksum "setup.py" }}
@@ -253,10 +144,15 @@ jobs:
                   path: ~/transformers/reports
 
     run_tests_flax:
-        <<: *setup
+        working_directory: ~/transformers
+        docker:
+            - image: circleci/python:3.7
+        environment:
+            OMP_NUM_THREADS: 1
+        resource_class: xlarge
+        parallelism: 1
         steps:
             - checkout
-            - skip-job-on-doc-only-changes
             - restore_cache:
                 keys:
                     - v0.4-flax-{{ checksum "setup.py" }}
@@ -274,16 +170,22 @@ jobs:
                   path: ~/transformers/reports
 
     run_tests_pipelines_torch:
-        <<: *setup
+        working_directory: ~/transformers
+        docker:
+            - image: circleci/python:3.7
+        environment:
+            OMP_NUM_THREADS: 1
+        resource_class: xlarge
+        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,sentencepiece]
+            - run: pip install tapas torch-scatter -f https://pytorch-geometric.com/whl/torch-1.7.0+cpu.html
             - save_cache:
                   key: v0.4-torch-{{ checksum "setup.py" }}
                   paths:
@@ -295,10 +197,15 @@ jobs:
                   path: ~/transformers/reports
 
     run_tests_pipelines_tf:
-        <<: *setup
+        working_directory: ~/transformers
+        docker:
+            - image: circleci/python:3.7
+        environment:
+            OMP_NUM_THREADS: 1
+        resource_class: xlarge
+        parallelism: 1
         steps:
             - checkout
-            - skip-job-on-doc-only-changes
             - restore_cache:
                   keys:
                       - v0.4-tf-{{ checksum "setup.py" }}
@@ -316,10 +223,13 @@ jobs:
                   path: ~/transformers/reports
 
     run_tests_custom_tokenizers:
-        <<: *setup
+        working_directory: ~/transformers
+        docker:
+            - 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" }}
@@ -338,29 +248,52 @@ jobs:
                   path: ~/transformers/reports
 
     run_examples_torch:
-        <<: *setup
+        working_directory: ~/transformers
+        docker:
+            - image: circleci/python:3.6
+        environment:
+            OMP_NUM_THREADS: 1
+        resource_class: xlarge
+        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,sentencepiece,testing]
-            - run: pip install -r examples/requirements.txt
+            - run: pip install -r examples/_tests_requirements.txt
             - save_cache:
                   key: v0.4-torch_examples-{{ checksum "setup.py" }}
                   paths:
                       - '~/.cache/pip'
             - run: python -m pytest -n 8 --dist=loadfile -s --make-reports=examples_torch ./examples/ | tee examples_output.txt
             - store_artifacts:
-                  path: ~/transformers/tests_output.txt
+                  path: ~/transformers/examples_output.txt
             - store_artifacts:
                   path: ~/transformers/reports
 
+    run_tests_git_lfs:
+        working_directory: ~/transformers
+        docker:
+            - image: circleci/python:3.7
+        resource_class: xlarge
+        parallelism: 1
+        steps:
+            - checkout
+            - run: sudo apt-get install git-lfs
+            - run: |
+                git config --global user.email "ci@dummy.com"
+                git config --global user.name "ci"
+            - run: pip install --upgrade pip
+            - run: pip install .[testing]
+            - run: RUN_GIT_LFS_TESTS=1 python -m pytest -sv ./tests/test_hf_api.py -k "HfLargefilesTest"
+
     build_doc:
-        <<: *setup-small
+        working_directory: ~/transformers
+        docker:
+            - image: circleci/python:3.6
         steps:
             - checkout
             - restore_cache:
@@ -378,7 +311,9 @@ jobs:
                 path: ./docs/_build
 
     deploy_doc:
-        <<: *setup-small
+        working_directory: ~/transformers
+        docker:
+            - image: circleci/python:3.6
         steps:
             - add_ssh_keys:
                 fingerprints:
@@ -396,7 +331,11 @@ jobs:
             - run: ./.circleci/deploy.sh
 
     check_code_quality:
-        <<: *setup-small
+        working_directory: ~/transformers
+        docker:
+            - image: circleci/python:3.6
+        resource_class: medium
+        parallelism: 1
         steps:
             - checkout
             - restore_cache:
@@ -415,11 +354,16 @@ jobs:
             - run: flake8 examples tests src utils
             - run: python utils/style_doc.py src/transformers docs/source --max_len 119 --check_only
             - run: python utils/check_copies.py
+            - run: python utils/check_table.py
             - run: python utils/check_dummies.py
             - run: python utils/check_repo.py
 
     check_repository_consistency:
-        <<: *setup-small
+        working_directory: ~/transformers
+        docker:
+            - image: circleci/python:3.6
+        resource_class: small
+        parallelism: 1
         steps:
             - checkout
             - run: pip install requests
@@ -427,7 +371,12 @@ jobs:
 
 # TPU JOBS
     run_examples_tpu:
-        <<: *setup
+        docker:
+            - image: circleci/python:3.6
+        environment:
+            OMP_NUM_THREADS: 1
+        resource_class: xlarge
+        parallelism: 1
         steps:
             - checkout
             - go/install
@@ -465,14 +414,11 @@ workflows:
             - run_tests_custom_tokenizers
             - run_tests_torch_and_tf
             - run_tests_torch
-            - run_tests_torch-1-3
-            - run_tests_torch-1-4
-            - run_tests_torch-1-5
-            - run_tests_torch-1-6
             - run_tests_tf
             - run_tests_flax
             - run_tests_pipelines_torch
             - run_tests_pipelines_tf
+            - run_tests_git_lfs
             - build_doc
             - deploy_doc: *workflow_filters
     tpu_testing_jobs:

.circleci/deploy.sh

@@ -53,4 +53,5 @@ deploy_doc "3ebb1b3" v3.2.0
 deploy_doc "0613f05" v3.3.1
 deploy_doc "eb0e0ce" v3.4.0
 deploy_doc "818878d" v3.5.1
-deploy_doc "c781171" # v4.0.0 Latest stable release
+deploy_doc "c781171" v4.0.0
+deploy_doc "bfa4ccf" # v4.1.1 Latest stable release

.github/workflows/model-templates.yml

@@ -0,0 +1,67 @@
+name: Model templates runner
+
+on:
+  push:
+    paths:
+      - "src/**"
+      - "tests/**"
+      - ".github/**"
+      - "templates/**"
+
+jobs:
+  run_tests_templates:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v1
+
+      - name: Install Python
+        uses: actions/setup-python@v1
+        with:
+          python-version: 3.6
+
+      - name: Loading cache.
+        uses: actions/cache@v2
+        id: cache
+        with:
+          path: ~/.cache/pip
+          key: v1.2-tests_templates
+          restore-keys: |
+            v1.2-tests_templates-${{ hashFiles('setup.py') }}
+            v1.2-tests_templates
+
+      - name: Install dependencies
+        run: |
+          pip install --upgrade pip
+          pip install .[dev]
+      - name: Create model files
+        run: |
+          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/tf-seq-2-seq-bart-tokenizer.json --path=templates/adding_a_new_model
+          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/pt-seq-2-seq-bart-tokenizer.json --path=templates/adding_a_new_model
+          make style
+          python utils/check_table.py --fix_and_overwrite
+          python utils/check_dummies.py --fix_and_overwrite
+
+      - name: Run all non-slow tests
+        run: |
+          python -m pytest -n 2 --dist=loadfile -s --make-reports=tests_templates tests/*template*
+
+      - name: Run style changes
+        run: |
+          git fetch origin master:master
+          make fixup
+
+      - name: Failure short reports
+        if: ${{ always() }}
+        run: cat reports/tests_templates_failures_short.txt
+
+      - name: Test suite reports artifacts
+        if: ${{ always() }}
+        uses: actions/upload-artifact@v2
+        with:
+          name: run_all_tests_templates_test_reports
+          path: reports

.github/workflows/self-push.yml

@@ -50,6 +50,7 @@ jobs:
           pip install --upgrade pip
           pip install .[torch,sklearn,testing,onnxruntime,sentencepiece]
           pip install git+https://github.com/huggingface/datasets
+          pip install pandas torch-scatter -f https://pytorch-geometric.com/whl/torch-1.7.0+cu102.html
 
       - name: Are GPUs recognized by our DL frameworks
         run: |
@@ -187,6 +188,7 @@ jobs:
           pip install --upgrade pip
           pip install .[torch,sklearn,testing,onnxruntime,sentencepiece]
           pip install git+https://github.com/huggingface/datasets
+          pip install pandas torch-scatter -f https://pytorch-geometric.com/whl/torch-1.7.0+cu102.html
 
       - name: Are GPUs recognized by our DL frameworks
         run: |

.gitignore

@@ -159,3 +159,6 @@ tags
 
 # pre-commit
 .pre-commit*
+
+# .lock
+*.lock

CONTRIBUTING.md

@@ -1,3 +1,19 @@
+<!---
+Copyright 2020 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 # How to contribute to transformers?
 
 Everyone is welcome to contribute, and we value everybody's contribution. Code

LICENSE

@@ -1,3 +1,4 @@
+Copyright 2018- The Hugging Face team. All rights reserved.
 
                                  Apache License
                            Version 2.0, January 2004

Makefile

@@ -23,6 +23,7 @@ deps_table_update:
 
 extra_quality_checks: deps_table_update
 	python utils/check_copies.py
+	python utils/check_table.py
 	python utils/check_dummies.py
 	python utils/check_repo.py
 	python utils/style_doc.py src/transformers docs/source --max_len 119
@@ -50,6 +51,7 @@ fixup: modified_only_fixup extra_quality_checks
 
 fix-copies:
 	python utils/check_copies.py --fix_and_overwrite
+	python utils/check_table.py --fix_and_overwrite
 	python utils/check_dummies.py --fix_and_overwrite
 
 # Run tests for the library

README.md

@@ -1,3 +1,19 @@
+<!---
+Copyright 2020 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <p align="center">
     <br>
     <img src="https://raw.githubusercontent.com/huggingface/transformers/master/docs/source/imgs/transformers_logo_name.png" width="400"/>
@@ -31,9 +47,6 @@
 
 🤗 Transformers is backed by the two most popular deep learning libraries, [PyTorch](https://pytorch.org/) and [TensorFlow](https://www.tensorflow.org/), with a seamless integration between them, allowing you to train your models with one then load it for inference with the other.
 
-### Recent contributors
-[![](https://sourcerer.io/fame/clmnt/huggingface/transformers/images/0)](https://sourcerer.io/fame/clmnt/huggingface/transformers/links/0)[![](https://sourcerer.io/fame/clmnt/huggingface/transformers/images/1)](https://sourcerer.io/fame/clmnt/huggingface/transformers/links/1)[![](https://sourcerer.io/fame/clmnt/huggingface/transformers/images/2)](https://sourcerer.io/fame/clmnt/huggingface/transformers/links/2)[![](https://sourcerer.io/fame/clmnt/huggingface/transformers/images/3)](https://sourcerer.io/fame/clmnt/huggingface/transformers/links/3)[![](https://sourcerer.io/fame/clmnt/huggingface/transformers/images/4)](https://sourcerer.io/fame/clmnt/huggingface/transformers/links/4)[![](https://sourcerer.io/fame/clmnt/huggingface/transformers/images/5)](https://sourcerer.io/fame/clmnt/huggingface/transformers/links/5)[![](https://sourcerer.io/fame/clmnt/huggingface/transformers/images/6)](https://sourcerer.io/fame/clmnt/huggingface/transformers/links/6)[![](https://sourcerer.io/fame/clmnt/huggingface/transformers/images/7)](https://sourcerer.io/fame/clmnt/huggingface/transformers/links/7)
-
 ## Online demos
 
 You can test most of our models directly on their pages from the [model hub](https://huggingface.co/models). We also offer an [inference API](https://huggingface.co/pricing) to use those models.
@@ -200,6 +213,7 @@ Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
 1. **[LXMERT](https://huggingface.co/transformers/model_doc/lxmert.html)** (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.
 1. **[MarianMT](https://huggingface.co/transformers/model_doc/marian.html)** 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.
 1. **[MBart](https://huggingface.co/transformers/model_doc/mbart.html)** (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.
+1. **[MPNet](https://huggingface.co/transformers/model_doc/mpnet.html)** (from Microsoft Research) released with the paper [MPNet: Masked and Permuted Pre-training for Language Understanding](https://arxiv.org/abs/2004.09297) by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu.
 1. **[MT5](https://huggingface.co/transformers/model_doc/mt5.html)** (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.
 1. **[Pegasus](https://huggingface.co/transformers/model_doc/pegasus.html)** (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.
 1. **[ProphetNet](https://huggingface.co/transformers/model_doc/prophetnet.html)** (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.
@@ -208,6 +222,7 @@ Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
 ultilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers/tree/master/examples/distillation) and a German version of DistilBERT.
 1. **[SqueezeBert](https://huggingface.co/transformers/model_doc/squeezebert.html)** 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.
 1. **[T5](https://huggingface.co/transformers/model_doc/t5.html)** (from Google AI) released with the paper [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
+1. **[TAPAS](https://huggingface.co/transformers/master/model_doc/tapas.html)** (from Google AI) released with the paper [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://arxiv.org/abs/2004.02349) by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos.
 1. **[Transformer-XL](https://huggingface.co/transformers/model_doc/transformerxl.html)** (from Google/CMU) released with the paper [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
 1. **[XLM](https://huggingface.co/transformers/model_doc/xlm.html)** (from Facebook) released together with the paper [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample and Alexis Conneau.
 1. **[XLM-ProphetNet](https://huggingface.co/transformers/model_doc/xlmprophetnet.html)** (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.

docs/README.md

@@ -1,3 +1,19 @@
+<!---
+Copyright 2020 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 # Generating the documentation
 
 To generate the documentation, you first have to build it. Several packages are necessary to build the doc,

docs/source/_static/js/custom.js

@@ -1,10 +1,11 @@
 // These two things need to be updated at each release for the version selector.
 // Last stable version
-const stableVersion = "v4.0.0"
-// Dictionary doc folder to label
+const stableVersion = "v4.1.1"
+// Dictionary doc folder to label. The last stable version should have an empty key.
 const versionMapping = {
     "master": "master",
-    "v4.0.0": "v4.0.0",
+    "": "v4.1.1 (stable)",
+    "v4.0.1": "v4.0.0/v4.0.1",
     "v3.5.1": "v3.5.0/v3.5.1",
     "v3.4.0": "v3.4.0",
     "v3.3.1": "v3.3.0/v3.3.1",

docs/source/benchmarks.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 Benchmarks
 =======================================================================================================================
 

docs/source/bertology.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 BERTology
 -----------------------------------------------------------------------------------------------------------------------
 
@@ -22,5 +34,5 @@ help people access the inner representations, mainly adapted from the great work
   in https://arxiv.org/abs/1905.10650.
 
 To help you understand and use these features, we have added a specific example script: `bertology.py
-<https://github.com/huggingface/transformers/blob/master/examples/bertology/run_bertology.py>`_ while extract
-information and prune a model pre-trained on GLUE.
+<https://github.com/huggingface/transformers/blob/master/examples/research_projects/bertology/run_bertology.py>`_ while
+extract information and prune a model pre-trained on GLUE.

docs/source/conf.py

@@ -20,13 +20,13 @@ sys.path.insert(0, os.path.abspath('../../src'))
 # -- Project information -----------------------------------------------------
 
 project = u'transformers'
-copyright = u'2020, huggingface'
+copyright = u'2020, The Hugging Face Team, Licenced under the Apache License, Version 2.0'
 author = u'huggingface'
 
 # The short X.Y version
 version = u''
 # The full version, including alpha/beta/rc tags
-release = u'4.0.0'
+release = u'4.1.1'
 
 
 # -- General configuration ---------------------------------------------------

docs/source/converting_tensorflow_models.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 Converting Tensorflow Checkpoints
 =======================================================================================================================
 

docs/source/custom_datasets.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 Fine-tuning with custom datasets
 =======================================================================================================================
 

docs/source/glossary.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 Glossary
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
@@ -214,7 +226,7 @@ Contrary to RNNs that have the position of each token embedded within them, tran
 each token. Therefore, the position IDs (``position_ids``) are used by the model to identify each token's position in
 the list of tokens.
 
-They are an optional parameter. If no ``position_ids`` is passed to the model, the IDs are automatically created as
+They are an optional parameter. If no ``position_ids`` are passed to the model, the IDs are automatically created as
 absolute positional embeddings.
 
 Absolute positional embeddings are selected in the range ``[0, config.max_position_embeddings - 1]``. Some models use

docs/source/index.rst

@@ -22,6 +22,18 @@ State-of-the-art NLP for everyone:
 - Hands-on practitioners
 - AI/ML/NLP teachers and educators
 
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 Lower compute costs, smaller carbon footprint:
 
 - Researchers can share trained models instead of always retraining
@@ -139,41 +151,47 @@ and conversion utilities for the following models:
 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.
-23. :doc:`MT5 <model_doc/mt5>` (from Google AI) released with the paper `mT5: A massively multilingual pre-trained
+23. :doc:`MPNet <model_doc/mpnet>` (from Microsoft Research) released with the paper `MPNet: Masked and Permuted
+    Pre-training for Language Understanding <https://arxiv.org/abs/2004.09297>`__ by Kaitao Song, Xu Tan, Tao Qin,
+    Jianfeng Lu, Tie-Yan Liu.
+24. :doc:`MT5 <model_doc/mt5>` (from Google AI) released with the paper `mT5: A massively multilingual pre-trained
     text-to-text transformer <https://arxiv.org/abs/2010.11934>`__ by Linting Xue, Noah Constant, Adam Roberts, Mihir
     Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel.
-24. :doc:`Pegasus <model_doc/pegasus>` (from Google) released with the paper `PEGASUS: Pre-training with Extracted
+25. :doc:`Pegasus <model_doc/pegasus>` (from Google) released with the paper `PEGASUS: Pre-training with Extracted
     Gap-sentences for Abstractive Summarization <https://arxiv.org/abs/1912.08777>`__> by Jingqing Zhang, Yao Zhao,
     Mohammad Saleh and Peter J. Liu.
-25. :doc:`ProphetNet <model_doc/prophetnet>` (from Microsoft Research) released with the paper `ProphetNet: Predicting
+26. :doc:`ProphetNet <model_doc/prophetnet>` (from Microsoft Research) released with the paper `ProphetNet: Predicting
     Future N-gram for Sequence-to-Sequence Pre-training <https://arxiv.org/abs/2001.04063>`__ by Yu Yan, Weizhen Qi,
     Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
-26. :doc:`Reformer <model_doc/reformer>` (from Google Research) released with the paper `Reformer: The Efficient
+27. :doc:`Reformer <model_doc/reformer>` (from Google Research) released with the paper `Reformer: The Efficient
     Transformer <https://arxiv.org/abs/2001.04451>`__ by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
-27. :doc:`RoBERTa <model_doc/roberta>` (from Facebook), released together with the paper a `Robustly Optimized BERT
+28. :doc:`RoBERTa <model_doc/roberta>` (from Facebook), released together with the paper a `Robustly Optimized BERT
     Pretraining Approach <https://arxiv.org/abs/1907.11692>`__ by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar
     Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov. ultilingual BERT into `DistilmBERT
     <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__ and a German version of
     DistilBERT.
-28. :doc:`SqueezeBert <model_doc/squeezebert>` released with the paper `SqueezeBERT: What can computer vision teach NLP
+29. :doc:`SqueezeBert <model_doc/squeezebert>` released with the paper `SqueezeBERT: What can computer vision teach NLP
     about efficient neural networks? <https://arxiv.org/abs/2006.11316>`__ by Forrest N. Iandola, Albert E. Shaw, Ravi
     Krishna, and Kurt W. Keutzer.
-29. :doc:`T5 <model_doc/t5>` (from Google AI) released with the paper `Exploring the Limits of Transfer Learning with a
+30. :doc:`T5 <model_doc/t5>` (from Google AI) released with the paper `Exploring the Limits of Transfer Learning with a
     Unified Text-to-Text Transformer <https://arxiv.org/abs/1910.10683>`__ by Colin Raffel and Noam Shazeer and Adam
     Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
-30. :doc:`Transformer-XL <model_doc/transformerxl>` (from Google/CMU) released with the paper `Transformer-XL:
+31. `TAPAS <https://huggingface.co/transformers/master/model_doc/tapas.html>`__ (from Google AI) released with the
+    paper `TAPAS: Weakly Supervised Table Parsing via Pre-training <https://arxiv.org/abs/2004.02349>`__ by Jonathan
+    Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos.
+32. :doc:`Transformer-XL <model_doc/transformerxl>` (from Google/CMU) released with the paper `Transformer-XL:
     Attentive Language Models Beyond a Fixed-Length Context <https://arxiv.org/abs/1901.02860>`__ by Zihang Dai*,
     Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
-31. :doc:`XLM <model_doc/xlm>` (from Facebook) released together with the paper `Cross-lingual Language Model
+33. :doc:`XLM <model_doc/xlm>` (from Facebook) released together with the paper `Cross-lingual Language Model
     Pretraining <https://arxiv.org/abs/1901.07291>`__ by Guillaume Lample and Alexis Conneau.
-32. :doc:`XLM-ProphetNet <model_doc/xlmprophetnet>` (from Microsoft Research) released with the paper `ProphetNet:
+34. :doc:`XLM-ProphetNet <model_doc/xlmprophetnet>` (from Microsoft Research) released with the paper `ProphetNet:
     Predicting Future N-gram for Sequence-to-Sequence Pre-training <https://arxiv.org/abs/2001.04063>`__ by Yu Yan,
     Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
-33. :doc:`XLM-RoBERTa <model_doc/xlmroberta>` (from Facebook AI), released together with the paper `Unsupervised
+35. :doc:`XLM-RoBERTa <model_doc/xlmroberta>` (from Facebook AI), released together with the paper `Unsupervised
     Cross-lingual Representation Learning at Scale <https://arxiv.org/abs/1911.02116>`__ by Alexis Conneau*, Kartikay
     Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke
     Zettlemoyer and Veselin Stoyanov.
-34. :doc:`XLNet <model_doc/xlnet>` (from Google/CMU) released with the paper `​XLNet: Generalized Autoregressive
+36. :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.
 
@@ -228,6 +246,8 @@ TensorFlow and/or Flax.
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |         Longformer          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|            MPNet            |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
++-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |           Marian            |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |         MobileBERT          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
@@ -252,6 +272,8 @@ TensorFlow and/or Flax.
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |             T5              |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|            TAPAS            |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
++-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |       Transformer-XL        |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |             XLM             |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
@@ -267,7 +289,6 @@ TensorFlow and/or Flax.
 |             mT5             |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 
-
 .. toctree::
     :maxdepth: 2
     :caption: Get started
@@ -354,6 +375,7 @@ TensorFlow and/or Flax.
     model_doc/marian
     model_doc/mbart
     model_doc/mobilebert
+    model_doc/mpnet
     model_doc/mt5
     model_doc/gpt
     model_doc/gpt2
@@ -365,6 +387,7 @@ TensorFlow and/or Flax.
     model_doc/roberta
     model_doc/squeezebert
     model_doc/t5
+    model_doc/tapas
     model_doc/transformerxl
     model_doc/xlm
     model_doc/xlmprophetnet

docs/source/installation.md

@@ -1,3 +1,19 @@
+<!---
+Copyright 2020 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 # Installation
 
 🤗 Transformers is tested on Python 3.6+, and PyTorch 1.1.0+ or TensorFlow 2.0+.

docs/source/internal/generation_utils.rst

@@ -1,9 +1,22 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 Utilities for Generation
 -----------------------------------------------------------------------------------------------------------------------
 
 This page lists all the utility functions used by :meth:`~transformers.PretrainedModel.generate`,
 :meth:`~transformers.PretrainedModel.greedy_search`, :meth:`~transformers.PretrainedModel.sample`,
-:meth:`~transformers.PretrainedModel.beam_search`, and :meth:`~transformers.PretrainedModel.beam_sample`.
+:meth:`~transformers.PretrainedModel.beam_search`, :meth:`~transformers.PretrainedModel.beam_sample`, and
+:meth:`~transformers.PretrainedModel.group_beam_search`.
 
 Most of those are only useful if you are studying the code of the generate methods in the library.
 
@@ -19,6 +32,9 @@ generation.
 .. autoclass:: transformers.LogitsProcessorList
     :members: __call__
 
+.. autoclass:: transformers.LogitsWarper
+    :members: __call__
+
 .. autoclass:: transformers.MinLengthLogitsProcessor
     :members: __call__
 
@@ -40,6 +56,12 @@ generation.
 .. autoclass:: transformers.NoBadWordsLogitsProcessor
     :members: __call__
 
+.. autoclass:: transformers.PrefixConstrainedLogitsProcessor
+    :members: __call__
+
+.. autoclass:: transformers.HammingDiversityLogitsProcessor
+    :members: __call__
+
 BeamSearch
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -48,3 +70,10 @@ BeamSearch
 
 .. autoclass:: transformers.BeamSearchScorer
     :members: process, finalize
+
+Utilities
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autofunction:: transformers.top_k_top_p_filtering
+
+.. autofunction:: transformers.tf_top_k_top_p_filtering

docs/source/internal/modeling_utils.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 Custom Layers and Utilities
 -----------------------------------------------------------------------------------------------------------------------
 
@@ -79,8 +91,6 @@ TensorFlow loss functions
 TensorFlow Helper Functions
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autofunction:: transformers.modeling_tf_utils.cast_bool_to_primitive
-
 .. autofunction:: transformers.modeling_tf_utils.get_initializer
 
 .. autofunction:: transformers.modeling_tf_utils.keras_serializable

docs/source/internal/pipelines_utils.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 Utilities for pipelines
 -----------------------------------------------------------------------------------------------------------------------
 

docs/source/internal/tokenization_utils.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 Utilities for Tokenizers
 -----------------------------------------------------------------------------------------------------------------------
 

docs/source/internal/trainer_utils.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 Utilities for Trainer
 -----------------------------------------------------------------------------------------------------------------------
 
@@ -10,6 +22,8 @@ Utilities
 
 .. autoclass:: transformers.EvalPrediction
 
+.. autoclass:: transformers.EvaluationStrategy
+
 .. autofunction:: transformers.set_seed
 
 .. autofunction:: transformers.torch_distributed_zero_first
@@ -20,8 +34,15 @@ Callbacks internals
 
 .. autoclass:: transformers.trainer_callback.CallbackHandler
 
+
 Distributed Evaluation
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.trainer_pt_utils.DistributedTensorGatherer
     :members:
+
+
+Distributed Evaluation
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.HfArgumentParser

docs/source/main_classes/callback.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 Callbacks
 -----------------------------------------------------------------------------------------------------------------------
 

docs/source/main_classes/configuration.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 Configuration
 -----------------------------------------------------------------------------------------------------------------------
 

docs/source/main_classes/logging.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 Logging
 -----------------------------------------------------------------------------------------------------------------------
 

docs/source/main_classes/model.rst

@@ -1,9 +1,22 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 Models
 -----------------------------------------------------------------------------------------------------------------------
 
-The base classes :class:`~transformers.PreTrainedModel` and :class:`~transformers.TFPreTrainedModel` implement the
-common methods for loading/saving a model either from a local file or directory, or from a pretrained model
-configuration provided by the library (downloaded from HuggingFace's AWS S3 repository).
+The base classes :class:`~transformers.PreTrainedModel`, :class:`~transformers.TFPreTrainedModel`, and
+:class:`~transformers.FlaxPreTrainedModel` implement the common methods for loading/saving a model either from a local
+file or directory, or from a pretrained model configuration provided by the library (downloaded from HuggingFace's AWS
+S3 repository).
 
 :class:`~transformers.PreTrainedModel` and :class:`~transformers.TFPreTrainedModel` also implement a few methods which
 are common among all the models to:
@@ -45,6 +58,13 @@ TFModelUtilsMixin
     :members:
 
 
+FlaxPreTrainedModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaxPreTrainedModel
+    :members:
+
+
 Generation
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

docs/source/main_classes/optimizer_schedules.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 Optimization
 -----------------------------------------------------------------------------------------------------------------------
 
@@ -31,6 +43,10 @@ Schedules
 Learning Rate Schedules (Pytorch)
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
+.. autoclass:: transformers.SchedulerType
+
+.. autofunction:: transformers.get_scheduler
+
 .. autofunction:: transformers.get_constant_schedule
 
 
@@ -62,6 +78,10 @@ Learning Rate Schedules (Pytorch)
     :target: /imgs/warmup_linear_schedule.png
     :alt:
 
+
+.. autofunction:: transformers.get_polynomial_decay_schedule_with_warmup
+
+
 Warmup (TensorFlow)
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 

docs/source/main_classes/output.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 Model outputs
 -----------------------------------------------------------------------------------------------------------------------
 
@@ -114,13 +126,6 @@ CausalLMOutputWithCrossAttentions
     :members:
 
 
-CausalLMOutputWithPastAndCrossAttentions
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.modeling_outputs.CausalLMOutputWithPastAndCrossAttentions
-    :members:
-
-
 CausalLMOutputWithPast
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

docs/source/main_classes/pipelines.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 Pipelines
 -----------------------------------------------------------------------------------------------------------------------
 
@@ -22,6 +34,7 @@ There are two categories of pipeline abstractions to be aware about:
     - :class:`~transformers.TranslationPipeline`
     - :class:`~transformers.ZeroShotClassificationPipeline`
     - :class:`~transformers.Text2TextGenerationPipeline`
+    - :class:`~transformers.TableQuestionAnsweringPipeline`
 
 The pipeline abstraction
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -61,8 +74,9 @@ FillMaskPipeline
 NerPipeline
 =======================================================================================================================
 
-This class is an alias of the :class:`~transformers.TokenClassificationPipeline` defined below. Please refer to that
-pipeline for documentation and usage examples.
+.. autoclass:: transformers.NerPipeline
+
+See :class:`~transformers.TokenClassificationPipeline` for all details.
 
 QuestionAnsweringPipeline
 =======================================================================================================================
@@ -78,6 +92,13 @@ SummarizationPipeline
     :special-members: __call__
     :members:
 
+TableQuestionAnsweringPipeline
+=======================================================================================================================
+
+.. autoclass:: transformers.TableQuestionAnsweringPipeline
+    :special-members: __call__
+
+
 TextClassificationPipeline
 =======================================================================================================================
 
@@ -106,6 +127,13 @@ TokenClassificationPipeline
     :special-members: __call__
     :members:
 
+TranslationPipeline
+=======================================================================================================================
+
+.. autoclass:: transformers.TranslationPipeline
+    :special-members: __call__
+    :members:
+
 ZeroShotClassificationPipeline
 =======================================================================================================================
 

docs/source/main_classes/processors.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 Processors
 -----------------------------------------------------------------------------------------------------------------------
 

docs/source/main_classes/tokenizer.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 Tokenizer
 -----------------------------------------------------------------------------------------------------------------------
 

docs/source/main_classes/trainer.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 Trainer
 -----------------------------------------------------------------------------------------------------------------------
 
@@ -51,6 +63,13 @@ Trainer
     :members:
 
 
+Seq2SeqTrainer
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.Seq2SeqTrainer
+    :members: evaluate, predict
+
+
 TFTrainer
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -65,6 +84,13 @@ TrainingArguments
     :members:
 
 
+Seq2SeqTrainingArguments
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.Seq2SeqTrainingArguments
+    :members:
+
+
 TFTrainingArguments
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

docs/source/migration.md

@@ -1,3 +1,19 @@
+<!---
+Copyright 2020 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 # Migrating from previous packages
 
 ## Migrating from transformers `v3.x` to `v4.x`

docs/source/model_doc/albert.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 ALBERT
 -----------------------------------------------------------------------------------------------------------------------
 
@@ -48,6 +60,13 @@ AlbertTokenizer
         create_token_type_ids_from_sequences, save_vocabulary
 
 
+AlbertTokenizerFast
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.AlbertTokenizerFast
+    :members:
+
+
 Albert specific outputs
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

docs/source/model_doc/auto.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 Auto Classes
 -----------------------------------------------------------------------------------------------------------------------
 
@@ -102,6 +114,13 @@ AutoModelForQuestionAnswering
     :members:
 
 
+AutoModelForTableQuestionAnswering
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.AutoModelForTableQuestionAnswering
+    :members:
+
+
 TFAutoModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -163,3 +182,10 @@ TFAutoModelForQuestionAnswering
 
 .. autoclass:: transformers.TFAutoModelForQuestionAnswering
     :members:
+
+
+FlaxAutoModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaxAutoModel
+    :members:

docs/source/model_doc/bart.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 BART
 -----------------------------------------------------------------------------------------------------------------------
 
@@ -43,9 +55,8 @@ Implementation Notes
 
 - Bart doesn't use :obj:`token_type_ids` for sequence classification. Use :class:`~transformers.BartTokenizer` or
   :meth:`~transformers.BartTokenizer.encode` to get the proper splitting.
-- The forward pass of :class:`~transformers.BartModel` will create decoder inputs (using the helper function
-  :func:`transformers.models.bart.modeling_bart._prepare_bart_decoder_inputs`) if they are not passed. This is
-  different than some other modeling APIs.
+- The forward pass of :class:`~transformers.BartModel` will create the ``decoder_input_ids`` if they are not passed.
+  This is different than some other modeling APIs. A typical use case of this feature is mask filling.
 - Model predictions are intended to be identical to the original implementation when
   :obj:`force_bos_token_to_be_generated=True`. This only works, however, if the string you pass to
   :func:`fairseq.encode` starts with a space.
@@ -86,6 +97,12 @@ BartTokenizer
     :members:
 
 
+BartTokenizerFast
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.BartTokenizerFast
+    :members:
+
 
 BartModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -93,8 +110,6 @@ BartModel
 .. autoclass:: transformers.BartModel
     :members: forward
 
-.. autofunction:: transformers.models.bart.modeling_bart._prepare_bart_decoder_inputs
-
 
 BartForConditionalGeneration
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

docs/source/model_doc/barthez.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 BARThez
 -----------------------------------------------------------------------------------------------------------------------
 
@@ -39,3 +51,9 @@ BarthezTokenizer
 .. autoclass:: transformers.BarthezTokenizer
     :members:
 
+
+BarthezTokenizerFast
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.BarthezTokenizerFast
+    :members:

docs/source/model_doc/bert.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 BERT
 -----------------------------------------------------------------------------------------------------------------------
 
@@ -195,3 +207,10 @@ FlaxBertModel
 
 .. autoclass:: transformers.FlaxBertModel
     :members: __call__
+
+
+FlaxBertForMaskedLM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaxBertForMaskedLM
+    :members: __call__

docs/source/model_doc/bertgeneration.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 BertGeneration
 -----------------------------------------------------------------------------------------------------------------------
 

docs/source/model_doc/blenderbot.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 Blenderbot
 -----------------------------------------------------------------------------------------------------------------------
 
@@ -88,6 +100,15 @@ BlenderbotSmallTokenizer
     :members:
 
 
+BlenderbotModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+See :obj:`transformers.BartModel` for arguments to `forward` and `generate`
+
+.. autoclass:: transformers.BlenderbotModel
+    :members:
+
+
 BlenderbotForConditionalGeneration
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

docs/source/model_doc/camembert.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 CamemBERT
 -----------------------------------------------------------------------------------------------------------------------
 
@@ -42,6 +54,13 @@ CamembertTokenizer
         create_token_type_ids_from_sequences, save_vocabulary
 
 
+CamembertTokenizerFast
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.CamembertTokenizerFast
+    :members:
+
+
 CamembertModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

docs/source/model_doc/ctrl.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 CTRL
 -----------------------------------------------------------------------------------------------------------------------
 
@@ -85,3 +97,8 @@ TFCTRLLMHeadModel
 .. autoclass:: transformers.TFCTRLLMHeadModel
     :members: call
 
+TFCTRLForSequenceClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFCTRLForSequenceClassification
+    :members: call

docs/source/model_doc/deberta.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 DeBERTa
 -----------------------------------------------------------------------------------------------------------------------
 

docs/source/model_doc/dialogpt.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 DialoGPT
 -----------------------------------------------------------------------------------------------------------------------
 

docs/source/model_doc/distilbert.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 DistilBERT
 -----------------------------------------------------------------------------------------------------------------------
 

docs/source/model_doc/dpr.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 DPR
 -----------------------------------------------------------------------------------------------------------------------
 

docs/source/model_doc/electra.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 ELECTRA
 -----------------------------------------------------------------------------------------------------------------------
 

docs/source/model_doc/encoderdecoder.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 Encoder Decoder Models
 -----------------------------------------------------------------------------------------------------------------------
 

docs/source/model_doc/flaubert.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 FlauBERT
 -----------------------------------------------------------------------------------------------------------------------
 

docs/source/model_doc/fsmt.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 FSMT
 -----------------------------------------------------------------------------------------------------------------------
 

docs/source/model_doc/funnel.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 Funnel Transformer
 -----------------------------------------------------------------------------------------------------------------------
 

docs/source/model_doc/gpt.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 OpenAI GPT
 -----------------------------------------------------------------------------------------------------------------------
 
@@ -126,3 +138,9 @@ TFOpenAIGPTDoubleHeadsModel
 
 .. autoclass:: transformers.TFOpenAIGPTDoubleHeadsModel
     :members: call
+
+TFOpenAIGPTForSequenceClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFOpenAIGPTForSequenceClassification
+    :members: call

docs/source/model_doc/gpt2.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 OpenAI GPT2
 -----------------------------------------------------------------------------------------------------------------------
 
@@ -114,3 +126,15 @@ TFGPT2DoubleHeadsModel
 
 .. autoclass:: transformers.TFGPT2DoubleHeadsModel
     :members: call
+
+TFGPT2ForSequenceClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFGPT2ForSequenceClassification
+    :members: call
+
+TFSequenceClassifierOutputWithPast
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.modeling_tf_outputs.TFSequenceClassifierOutputWithPast
+    :members:

docs/source/model_doc/layoutlm.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 LayoutLM
 -----------------------------------------------------------------------------------------------------------------------
 
@@ -45,6 +57,13 @@ LayoutLMTokenizer
     :members:
 
 
+LayoutLMTokenizerFast
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.LayoutLMTokenizerFast
+    :members:
+
+
 LayoutLMModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

docs/source/model_doc/longformer.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 Longformer
 -----------------------------------------------------------------------------------------------------------------------
 
@@ -22,6 +34,12 @@ contrast to most prior work, we also pretrain Longformer and finetune it on a va
 pretrained Longformer consistently outperforms RoBERTa on long document tasks and sets new state-of-the-art results on
 WikiHop and TriviaQA.*
 
+Tips:
+
+- Since the Longformer is based on RoBERTa, it doesn't have :obj:`token_type_ids`. You don't need to indicate which
+  token belongs to which segment. Just separate your segments with the separation token :obj:`tokenizer.sep_token` (or
+  :obj:`</s>`).
+
 The Authors' code can be found `here <https://github.com/allenai/longformer>`__.
 
 Longformer Self Attention

docs/source/model_doc/lxmert.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 LXMERT
 -----------------------------------------------------------------------------------------------------------------------
 

docs/source/model_doc/marian.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 MarianMT
 -----------------------------------------------------------------------------------------------------------------------
 

docs/source/model_doc/mbart.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 MBart
 -----------------------------------------------------------------------------------------------------------------------
 
@@ -78,6 +90,20 @@ MBartTokenizer
     :members: build_inputs_with_special_tokens, prepare_seq2seq_batch
 
 
+MBartTokenizerFast
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.MBartTokenizerFast
+    :members:
+
+
+MBartModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.MBartModel
+    :members:
+
+
 MBartForConditionalGeneration
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

docs/source/model_doc/mobilebert.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 MobileBERT
 -----------------------------------------------------------------------------------------------------------------------
 

docs/source/model_doc/mpnet.rst

@@ -0,0 +1,149 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
+MPNet
+-----------------------------------------------------------------------------------------------------------------------
+
+Overview
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The MPNet model was proposed in `MPNet: Masked and Permuted Pre-training for Language Understanding
+<https://arxiv.org/abs/2004.09297>`__ by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu.
+
+MPNet adopts a novel pre-training method, named masked and permuted language modeling, to inherit the advantages of
+masked language modeling and permuted language modeling for natural language understanding.
+
+The abstract from the paper is the following:
+
+*BERT adopts masked language modeling (MLM) for pre-training and is one of the most successful pre-training models.
+Since BERT neglects dependency among predicted tokens, XLNet introduces permuted language modeling (PLM) for
+pre-training to address this problem. However, XLNet does not leverage the full position information of a sentence and
+thus suffers from position discrepancy between pre-training and fine-tuning. In this paper, we propose MPNet, a novel
+pre-training method that inherits the advantages of BERT and XLNet and avoids their limitations. MPNet leverages the
+dependency among predicted tokens through permuted language modeling (vs. MLM in BERT), and takes auxiliary position
+information as input to make the model see a full sentence and thus reducing the position discrepancy (vs. PLM in
+XLNet). We pre-train MPNet on a large-scale dataset (over 160GB text corpora) and fine-tune on a variety of
+down-streaming tasks (GLUE, SQuAD, etc). Experimental results show that MPNet outperforms MLM and PLM by a large
+margin, and achieves better results on these tasks compared with previous state-of-the-art pre-trained methods (e.g.,
+BERT, XLNet, RoBERTa) under the same model setting.*
+
+Tips:
+
+- MPNet doesn't have :obj:`token_type_ids`, you don't need to indicate which token belongs to which segment. just
+  separate your segments with the separation token :obj:`tokenizer.sep_token` (or :obj:`[sep]`).
+
+The original code can be found `here <https://github.com/microsoft/MPNet>`__.
+
+MPNetConfig
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.MPNetConfig
+    :members:
+
+
+MPNetTokenizer
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.MPNetTokenizer
+    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
+        create_token_type_ids_from_sequences, save_vocabulary
+
+
+MPNetTokenizerFast
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.MPNetTokenizerFast
+    :members:
+
+
+MPNetModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.MPNetModel
+    :members: forward
+
+
+MPNetForMaskedLM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.MPNetForMaskedLM
+    :members: forward
+
+
+MPNetForSequenceClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.MPNetForSequenceClassification
+    :members: forward
+
+
+MPNetForMultipleChoice
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.MPNetForMultipleChoice
+    :members: forward
+
+
+MPNetForTokenClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.MPNetForTokenClassification
+    :members: forward
+
+
+MPNetForQuestionAnswering
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.MPNetForQuestionAnswering
+    :members: forward
+
+
+TFMPNetModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFMPNetModel
+    :members: call
+
+
+TFMPNetForMaskedLM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFMPNetForMaskedLM
+    :members: call
+
+
+TFMPNetForSequenceClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFMPNetForSequenceClassification
+    :members: call
+
+
+TFMPNetForMultipleChoice
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFMPNetForMultipleChoice
+    :members: call
+
+
+TFMPNetForTokenClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFMPNetForTokenClassification
+    :members: call
+
+
+TFMPNetForQuestionAnswering
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFMPNetForQuestionAnswering
+    :members: call

docs/source/model_doc/mt5.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 MT5
 -----------------------------------------------------------------------------------------------------------------------
 
@@ -25,6 +37,22 @@ MT5Config
     :members:
 
 
+MT5Tokenizer
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.MT5Tokenizer
+
+See :class:`~transformers.T5Tokenizer` for all details.
+
+
+MT5TokenizerFast
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.MT5TokenizerFast
+
+See :class:`~transformers.T5TokenizerFast` for all details.
+
+
 MT5Model
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

docs/source/model_doc/pegasus.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 Pegasus
 -----------------------------------------------------------------------------------------------------------------------
 
@@ -100,6 +112,19 @@ warning: ``add_tokens`` does not work at the moment.
     :members: __call__, prepare_seq2seq_batch
 
 
+PegasusTokenizerFast
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.PegasusTokenizerFast
+    :members:
+
+
+PegasusModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.PegasusModel
+
+
 PegasusForConditionalGeneration
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

docs/source/model_doc/prophetnet.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 ProphetNet
 -----------------------------------------------------------------------------------------------------------------------
 

docs/source/model_doc/rag.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 RAG
 -----------------------------------------------------------------------------------------------------------------------
 

docs/source/model_doc/reformer.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 Reformer
 -----------------------------------------------------------------------------------------------------------------------
 
@@ -151,6 +163,13 @@ ReformerTokenizer
     :members: save_vocabulary
 
 
+ReformerTokenizerFast
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.ReformerTokenizerFast
+    :members:
+
+
 ReformerModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

docs/source/model_doc/retribert.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 RetriBERT
 -----------------------------------------------------------------------------------------------------------------------
 

docs/source/model_doc/roberta.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 RoBERTa
 -----------------------------------------------------------------------------------------------------------------------
 

docs/source/model_doc/squeezebert.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 SqueezeBERT
 -----------------------------------------------------------------------------------------------------------------------
 

docs/source/model_doc/t5.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 T5
 -----------------------------------------------------------------------------------------------------------------------
 
@@ -32,9 +44,9 @@ Tips:
 
   For more information about which prefix to use, it is easiest to look into Appendix D of the `paper
   <https://arxiv.org/pdf/1910.10683.pdf>`__. - For sequence-to-sequence generation, it is recommended to use
-  :obj:`T5ForConditionalGeneration.generate()``. This method takes care of feeding the encoded input via
-  cross-attention layers to the decoder and auto-regressively generates the decoder output. - T5 uses relative scalar
-  embeddings. Encoder input padding can be done on the left and on the right.
+  :obj:`T5ForConditionalGeneration.generate()`. This method takes care of feeding the encoded input via cross-attention
+  layers to the decoder and auto-regressively generates the decoder output. - T5 uses relative scalar embeddings.
+  Encoder input padding can be done on the left and on the right.
 
 The original code can be found `here <https://github.com/google-research/text-to-text-transfer-transformer>`__.
 
@@ -43,7 +55,7 @@ Training
 
 T5 is an encoder-decoder model and converts all NLP problems into a text-to-text format. It is trained using teacher
 forcing. This means that for training we always need an input sequence and a target sequence. The input sequence is fed
-to the model using :obj:`input_ids``. The target sequence is shifted to the right, i.e., prepended by a start-sequence
+to the model using :obj:`input_ids`. The target sequence is shifted to the right, i.e., prepended by a start-sequence
 token and fed to the decoder using the :obj:`decoder_input_ids`. In teacher-forcing style, the target sequence is then
 appended by the EOS token and corresponds to the :obj:`labels`. The PAD token is hereby used as the start-sequence
 token. T5 can be trained / fine-tuned both in a supervised and unsupervised fashion.
@@ -95,6 +107,13 @@ T5Tokenizer
         create_token_type_ids_from_sequences, prepare_seq2seq_batch, save_vocabulary
 
 
+T5TokenizerFast
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.T5TokenizerFast
+    :members:
+
+
 T5Model
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -112,7 +131,7 @@ T5EncoderModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.T5EncoderModel
-    :members: forward
+    :members: forward, parallelize, deparallelize
 
 TFT5Model
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

docs/source/model_doc/tapas.rst

@@ -0,0 +1,434 @@
+TAPAS
+-----------------------------------------------------------------------------------------------------------------------
+
+.. note::
+
+    This is a recently introduced model so the API hasn't been tested extensively. There may be some bugs or slight
+    breaking changes to fix them in the future.
+
+
+
+Overview
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The TAPAS model was proposed in `TAPAS: Weakly Supervised Table Parsing via Pre-training
+<https://www.aclweb.org/anthology/2020.acl-main.398>`__ by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller,
+Francesco Piccinno and Julian Martin Eisenschlos. It's a BERT-based model specifically designed (and pre-trained) for
+answering questions about tabular data. Compared to BERT, TAPAS uses relative position embeddings and has 7 token types
+that encode tabular structure. TAPAS is pre-trained on the masked language modeling (MLM) objective on a large dataset
+comprising millions of tables from English Wikipedia and corresponding texts. For question answering, TAPAS has 2 heads
+on top: a cell selection head and an aggregation head, for (optionally) performing aggregations (such as counting or
+summing) among selected cells. TAPAS has been fine-tuned on several datasets: `SQA
+<https://www.microsoft.com/en-us/download/details.aspx?id=54253>`__ (Sequential Question Answering by Microsoft), `WTQ
+<https://github.com/ppasupat/WikiTableQuestions>`__ (Wiki Table Questions by Stanford University) and `WikiSQL
+<https://github.com/salesforce/WikiSQL>`__ (by Salesforce). It achieves state-of-the-art on both SQA and WTQ, while
+having comparable performance to SOTA on WikiSQL, with a much simpler architecture.
+
+The abstract from the paper is the following:
+
+*Answering natural language questions over tables is usually seen as a semantic parsing task. To alleviate the
+collection cost of full logical forms, one popular approach focuses on weak supervision consisting of denotations
+instead of logical forms. However, training semantic parsers from weak supervision poses difficulties, and in addition,
+the generated logical forms are only used as an intermediate step prior to retrieving the denotation. In this paper, we
+present TAPAS, an approach to question answering over tables without generating logical forms. TAPAS trains from weak
+supervision, and predicts the denotation by selecting table cells and optionally applying a corresponding aggregation
+operator to such selection. TAPAS extends BERT's architecture to encode tables as input, initializes from an effective
+joint pre-training of text segments and tables crawled from Wikipedia, and is trained end-to-end. We experiment with
+three different semantic parsing datasets, and find that TAPAS outperforms or rivals semantic parsing models by
+improving state-of-the-art accuracy on SQA from 55.1 to 67.2 and performing on par with the state-of-the-art on WIKISQL
+and WIKITQ, but with a simpler model architecture. We additionally find that transfer learning, which is trivial in our
+setting, from WIKISQL to WIKITQ, yields 48.7 accuracy, 4.2 points above the state-of-the-art.*
+
+In addition, the authors have further pre-trained TAPAS to recognize **table entailment**, by creating a balanced
+dataset of millions of automatically created training examples which are learned in an intermediate step prior to
+fine-tuning. The authors of TAPAS call this further pre-training intermediate pre-training (since TAPAS is first
+pre-trained on MLM, and then on another dataset). They found that intermediate pre-training further improves
+performance on SQA, achieving a new state-of-the-art as well as state-of-the-art on `TabFact
+<https://github.com/wenhuchen/Table-Fact-Checking>`__, a large-scale dataset with 16k Wikipedia tables for table
+entailment (a binary classification task). For more details, see their follow-up paper: `Understanding tables with
+intermediate pre-training <https://www.aclweb.org/anthology/2020.findings-emnlp.27/>`__ by Julian Martin Eisenschlos,
+Syrine Krichene and Thomas Müller.
+
+The original code can be found `here <https://github.com/google-research/tapas>`__.
+
+Tips:
+
+- TAPAS is a model that uses relative position embeddings by default (restarting the position embeddings at every cell
+  of the table). Note that this is something that was added after the publication of the original TAPAS paper.
+  According to the authors, this usually results in a slightly better performance, and allows you to encode longer
+  sequences without running out of embeddings. This is reflected in the ``reset_position_index_per_cell`` parameter of
+  :class:`~transformers.TapasConfig`, which is set to ``True`` by default. The default versions of the models available
+  in the `model hub <https://huggingface.co/models?search=tapas>`_ all use relative position embeddings. You can still
+  use the ones with absolute position embeddings by passing in an additional argument ``revision="no_reset"`` when
+  calling the ``.from_pretrained()`` method. Note that it's usually advised to pad the inputs on the right rather than
+  the left.
+- TAPAS is based on BERT, so ``TAPAS-base`` for example corresponds to a ``BERT-base`` architecture. Of course,
+  TAPAS-large will result in the best performance (the results reported in the paper are from TAPAS-large). Results of
+  the various sized models are shown on the `original Github repository <https://github.com/google-research/tapas>`_.
+- TAPAS has checkpoints fine-tuned on SQA, which are capable of answering questions related to a table in a
+  conversational set-up. This means that you can ask follow-up questions such as "what is his age?" related to the
+  previous question. Note that the forward pass of TAPAS is a bit different in case of a conversational set-up: in that
+  case, you have to feed every table-question pair one by one to the model, such that the `prev_labels` token type ids
+  can be overwritten by the predicted `labels` of the model to the previous question. See "Usage" section for more
+  info.
+- TAPAS is similar to BERT and therefore relies on the masked language modeling (MLM) objective. It is therefore
+  efficient at predicting masked tokens and at NLU in general, but is not optimal for text generation. Models trained
+  with a causal language modeling (CLM) objective are better in that regard.
+
+
+Usage: fine-tuning
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Here we explain how you can fine-tune :class:`~transformers.TapasForQuestionAnswering` on your own dataset.
+
+**STEP 1: Choose one of the 3 ways in which you can use TAPAS - or experiment**
+
+Basically, there are 3 different ways in which one can fine-tune :class:`~transformers.TapasForQuestionAnswering`,
+corresponding to the different datasets on which Tapas was fine-tuned:
+
+1. SQA: if you're interested in asking follow-up questions related to a table, in a conversational set-up. For example
+   if you first ask "what's the name of the first actor?" then you can ask a follow-up question such as "how old is
+   he?". Here, questions do not involve any aggregation (all questions are cell selection questions).
+2. WTQ: if you're not interested in asking questions in a conversational set-up, but rather just asking questions
+   related to a table, which might involve aggregation, such as counting a number of rows, summing up cell values or
+   averaging cell values. You can then for example ask "what's the total number of goals Cristiano Ronaldo made in his
+   career?". This case is also called **weak supervision**, since the model itself must learn the appropriate
+   aggregation operator (SUM/COUNT/AVERAGE/NONE) given only the answer to the question as supervision.
+3. WikiSQL-supervised: this dataset is based on WikiSQL with the model being given the ground truth aggregation
+   operator during training. This is also called **strong supervision**. Here, learning the appropriate aggregation
+   operator is much easier.
+
+To summarize:
+
++------------------------------------+----------------------+-------------------------------------------------------------------------------------------------------------------+
+| **Task**                           | **Example dataset**  | **Description**                                                                                                   |
++------------------------------------+----------------------+-------------------------------------------------------------------------------------------------------------------+
+| Conversational                     | SQA                  | Conversational, only cell selection questions                                                                     |
++------------------------------------+----------------------+-------------------------------------------------------------------------------------------------------------------+
+| Weak supervision for aggregation   | WTQ                  | Questions might involve aggregation, and the model must learn this given only the answer as supervision           |
++------------------------------------+----------------------+-------------------------------------------------------------------------------------------------------------------+
+| Strong supervision for aggregation | WikiSQL-supervised   | Questions might involve aggregation, and the model must learn this given the gold aggregation operator            |
++------------------------------------+----------------------+-------------------------------------------------------------------------------------------------------------------+
+
+Initializing a model with a pre-trained base and randomly initialized classification heads from the model hub can be
+done as follows (be sure to have installed the `torch-scatter dependency <https://github.com/rusty1s/pytorch_scatter>`_
+for your environment):
+
+.. code-block::
+
+        >>> from transformers import TapasConfig, TapasForQuestionAnswering
+
+        >>> # for example, the base sized model with default SQA configuration
+        >>> model = TapasForQuestionAnswering.from_pretrained('google/tapas-base')
+
+        >>> # or, the base sized model with WTQ configuration
+        >>> config = TapasConfig.from_pretrained('google/tapas-base-finetuned-wtq')
+        >>> model = TapasForQuestionAnswering.from_pretrained('google/tapas-base', config=config)
+
+        >>> # or, the base sized model with WikiSQL configuration
+        >>> config = TapasConfig('google-base-finetuned-wikisql-supervised')
+        >>> model = TapasForQuestionAnswering.from_pretrained('google/tapas-base', config=config)
+
+
+Of course, you don't necessarily have to follow one of these three ways in which TAPAS was fine-tuned. You can also
+experiment by defining any hyperparameters you want when initializing :class:`~transformers.TapasConfig`, and then
+create a :class:`~transformers.TapasForQuestionAnswering` based on that configuration. For example, if you have a
+dataset that has both conversational questions and questions that might involve aggregation, then you can do it this
+way. Here's an example:
+
+.. code-block::
+
+        >>> from transformers import TapasConfig, TapasForQuestionAnswering
+
+        >>> # you can initialize the classification heads any way you want (see docs of TapasConfig)
+        >>> config = TapasConfig(num_aggregation_labels=3, average_logits_per_cell=True, select_one_column=False)
+        >>> # initializing the pre-trained base sized model with our custom classification heads
+        >>> model = TapasForQuestionAnswering.from_pretrained('google/tapas-base', config=config)
+
+What you can also do is start from an already fine-tuned checkpoint. A note here is that the already fine-tuned
+checkpoint on WTQ has some issues due to the L2-loss which is somewhat brittle. See `here
+<https://github.com/google-research/tapas/issues/91#issuecomment-735719340>`__ for more info.
+
+For a list of all pre-trained and fine-tuned TAPAS checkpoints available in the HuggingFace model hub, see `here
+<https://huggingface.co/models?search=tapas>`__.
+
+**STEP 2: Prepare your data in the SQA format**
+
+Second, no matter what you picked above, you should prepare your dataset in the `SQA format
+<https://www.microsoft.com/en-us/download/details.aspx?id=54253>`__. This format is a TSV/CSV file with the following
+columns:
+
+- ``id``: optional, id of the table-question pair, for bookkeeping purposes.
+- ``annotator``: optional, id of the person who annotated the table-question pair, for bookkeeping purposes.
+- ``position``: integer indicating if the question is the first, second, third,... related to the table. Only required
+  in case of conversational setup (SQA). You don't need this column in case you're going for WTQ/WikiSQL-supervised.
+- ``question``: string
+- ``table_file``: string, name of a csv file containing the tabular data
+- ``answer_coordinates``: list of one or more tuples (each tuple being a cell coordinate, i.e. row, column pair that is
+  part of the answer)
+- ``answer_text``: list of one or more strings (each string being a cell value that is part of the answer)
+- ``aggregation_label``: index of the aggregation operator. Only required in case of strong supervision for aggregation
+  (the WikiSQL-supervised case)
+- ``float_answer``: the float answer to the question, if there is one (np.nan if there isn't). Only required in case of
+  weak supervision for aggregation (such as WTQ and WikiSQL)
+
+The tables themselves should be present in a folder, each table being a separate csv file. Note that the authors of the
+TAPAS algorithm used conversion scripts with some automated logic to convert the other datasets (WTQ, WikiSQL) into the
+SQA format. The author explains this `here
+<https://github.com/google-research/tapas/issues/50#issuecomment-705465960>`__. Interestingly, these conversion scripts
+are not perfect (the ``answer_coordinates`` and ``float_answer`` fields are populated based on the ``answer_text``),
+meaning that WTQ and WikiSQL results could actually be improved.
+
+**STEP 3: Convert your data into PyTorch tensors using TapasTokenizer**
+
+Third, given that you've prepared your data in this TSV/CSV format (and corresponding CSV files containing the tabular
+data), you can then use :class:`~transformers.TapasTokenizer` to convert table-question pairs into :obj:`input_ids`,
+:obj:`attention_mask`, :obj:`token_type_ids` and so on. Again, based on which of the three cases you picked above,
+:class:`~transformers.TapasForQuestionAnswering` requires different inputs to be fine-tuned:
+
++------------------------------------+----------------------------------------------------------------------------------------------+
+| **Task**                           | **Required inputs**                                                                          |
++------------------------------------+----------------------------------------------------------------------------------------------+
+| Conversational                     | ``input_ids``, ``attention_mask``, ``token_type_ids``, ``labels``                            |
++------------------------------------+----------------------------------------------------------------------------------------------+
+| Weak supervision for aggregation   | ``input_ids``, ``attention_mask``, ``token_type_ids``, ``labels``, ``numeric_values``,       |
+|                                    | ``numeric_values_scale``, ``float_answer``                                                   |
++------------------------------------+----------------------------------------------------------------------------------------------+
+| Strong supervision for aggregation | ``input ids``, ``attention mask``, ``token type ids``, ``labels``, ``aggregation_labels``    |
++------------------------------------+----------------------------------------------------------------------------------------------+
+
+:class:`~transformers.TapasTokenizer` creates the ``labels``, ``numeric_values`` and ``numeric_values_scale`` based on
+the ``answer_coordinates`` and ``answer_text`` columns of the TSV file. The ``float_answer`` and ``aggregation_labels``
+are already in the TSV file of step 2. Here's an example:
+
+.. code-block::
+
+        >>> from transformers import TapasTokenizer
+        >>> import pandas as pd
+
+        >>> model_name = 'google/tapas-base'
+        >>> tokenizer = TapasTokenizer.from_pretrained(model_name)
+
+        >>> data = {'Actors': ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"], 'Number of movies': ["87", "53", "69"]}
+        >>> queries = ["What is the name of the first actor?", "How many movies has George Clooney played in?", "What is the total number of movies?"]
+        >>> answer_coordinates = [[(0, 0)], [(2, 1)], [(0, 1), (1, 1), (2, 1)]]
+        >>> answer_text = [["Brad Pitt"], ["69"], ["209"]]
+        >>> table = pd.DataFrame.from_dict(data)
+        >>> inputs = tokenizer(table=table, queries=queries, answer_coordinates=answer_coordinates, answer_text=answer_text, padding='max_length', return_tensors='pt')
+        >>> inputs
+        {'input_ids': tensor([[ ... ]]), 'attention_mask': tensor([[...]]), 'token_type_ids': tensor([[[...]]]),
+        'numeric_values': tensor([[ ... ]]), 'numeric_values_scale: tensor([[ ... ]]), labels: tensor([[ ... ]])}
+
+Note that :class:`~transformers.TapasTokenizer` expects the data of the table to be **text-only**. You can use
+``.astype(str)`` on a dataframe to turn it into text-only data. Of course, this only shows how to encode a single
+training example. It is advised to create a PyTorch dataset and a corresponding dataloader:
+
+.. code-block::
+
+        >>> import torch
+        >>> import pandas as pd
+
+        >>> tsv_path = "your_path_to_the_tsv_file"
+        >>> table_csv_path = "your_path_to_a_directory_containing_all_csv_files"
+
+        >>> class TableDataset(torch.utils.data.Dataset):
+        ...     def __init__(self, data, tokenizer):
+        ...         self.data = data
+        ...         self.tokenizer = tokenizer
+        ...
+        ...     def __getitem__(self, idx):
+        ...         item = data.iloc[idx]
+        ...         table = pd.read_csv(table_csv_path + item.table_file).astype(str) # be sure to make your table data text only
+        ...         encoding = self.tokenizer(table=table, 
+        ...                                   queries=item.question, 
+        ...                                   answer_coordinates=item.answer_coordinates, 
+        ...                                   answer_text=item.answer_text,
+        ...                                   truncation=True,
+        ...                                   padding="max_length",
+        ...                                   return_tensors="pt"
+        ...         )
+        ...         # remove the batch dimension which the tokenizer adds by default
+        ...         encoding = {key: val.squeeze(0) for key, val in encoding.items()}
+        ...         # add the float_answer which is also required (weak supervision for aggregation case)
+        ...         encoding["float_answer"] = torch.tensor(item.float_answer) 
+        ...         return encoding
+        ...
+        ...     def __len__(self):
+        ...        return len(self.data)
+
+        >>> data = pd.read_csv(tsv_path, sep='\t')
+        >>> train_dataset = TableDataset(data, tokenizer)
+        >>> train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=32)
+
+Note that here, we encode each table-question pair independently. This is fine as long as your dataset is **not
+conversational**. In case your dataset involves conversational questions (such as in SQA), then you should first group
+together the ``queries``, ``answer_coordinates`` and ``answer_text`` per table (in the order of their ``position``
+index) and batch encode each table with its questions. This will make sure that the ``prev_labels`` token types (see
+docs of :class:`~transformers.TapasTokenizer`) are set correctly. See `this notebook
+<https://github.com/NielsRogge/Transformers-Tutorials/blob/master/Fine_tuning_TapasForQuestionAnswering_on_SQA.ipynb>`__
+for more info.
+
+**STEP 4: Train (fine-tune) TapasForQuestionAnswering**
+
+You can then fine-tune :class:`~transformers.TapasForQuestionAnswering` using native PyTorch as follows (shown here for
+the weak supervision for aggregation case):
+
+.. code-block::
+
+        >>> from transformers import TapasConfig, TapasForQuestionAnswering, AdamW
+
+        >>> # this is the default WTQ configuration
+        >>> config = TapasConfig(
+        ...            num_aggregation_labels = 4,
+        ...            use_answer_as_supervision = True,
+        ...            answer_loss_cutoff = 0.664694,
+        ...            cell_selection_preference = 0.207951,
+        ...            huber_loss_delta = 0.121194,
+        ...            init_cell_selection_weights_to_zero = True,
+        ...            select_one_column = True,
+        ...            allow_empty_column_selection = False,
+        ...            temperature = 0.0352513,
+        ... )
+        >>> model = TapasForQuestionAnswering.from_pretrained("google/tapas-base", config=config)
+
+        >>> optimizer = AdamW(model.parameters(), lr=5e-5)
+
+        >>> for epoch in range(2):  # loop over the dataset multiple times
+        ...    for idx, batch in enumerate(train_dataloader):
+        ...         # get the inputs; 
+        ...         input_ids = batch["input_ids"]
+        ...         attention_mask = batch["attention_mask"]
+        ...         token_type_ids = batch["token_type_ids"]
+        ...         labels = batch["labels"]
+        ...         numeric_values = batch["numeric_values"]
+        ...         numeric_values_scale = batch["numeric_values_scale"]
+        ...         float_answer = batch["float_answer"]
+
+        ...         # zero the parameter gradients
+        ...         optimizer.zero_grad()
+
+        ...         # forward + backward + optimize
+        ...         outputs = model(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, 
+        ...                        labels=labels, numeric_values=numeric_values, numeric_values_scale=numeric_values_scale, 
+        ...                        float_answer=float_answer)
+        ...         loss = outputs.loss
+        ...         loss.backward()
+        ...         optimizer.step()
+
+Usage: inference
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Here we explain how you can use :class:`~transformers.TapasForQuestionAnswering` for inference (i.e. making predictions
+on new data). For inference, only ``input_ids``, ``attention_mask`` and ``token_type_ids`` (which you can obtain using
+:class:`~transformers.TapasTokenizer`) have to be provided to the model to obtain the logits. Next, you can use the
+handy ``convert_logits_to_predictions`` method of :class:`~transformers.TapasTokenizer` to convert these into predicted
+coordinates and optional aggregation indices.
+
+However, note that inference is **different** depending on whether or not the setup is conversational. In a
+non-conversational set-up, inference can be done in parallel on all table-question pairs of a batch. Here's an example
+of that:
+
+.. code-block::
+
+        >>> from transformers import TapasTokenizer, TapasForQuestionAnswering
+        >>> import pandas as pd 
+
+        >>> model_name = 'google/tapas-base-finetuned-wtq'
+        >>> model = TapasForQuestionAnswering.from_pretrained(model_name)
+        >>> tokenizer = TapasTokenizer.from_pretrained(model_name)
+
+        >>> data = {'Actors': ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"], 'Number of movies': ["87", "53", "69"]}
+        >>> queries = ["What is the name of the first actor?", "How many movies has George Clooney played in?", "What is the total number of movies?"]
+        >>> table = pd.DataFrame.from_dict(data)
+        >>> inputs = tokenizer(table=table, queries=queries, padding='max_length', return_tensors="pt") 
+        >>> outputs = model(**inputs)
+        >>> predicted_answer_coordinates, predicted_aggregation_indices = tokenizer.convert_logits_to_predictions(
+        ...         inputs, 
+        ...         outputs.logits.detach(), 
+        ...         outputs.logits_aggregation.detach()
+        ... )
+
+        >>> # let's print out the results:
+        >>> id2aggregation = {0: "NONE", 1: "SUM", 2: "AVERAGE", 3:"COUNT"}
+        >>> aggregation_predictions_string = [id2aggregation[x] for x in predicted_aggregation_indices]
+
+        >>> answers = []
+        >>> for coordinates in predicted_answer_coordinates:
+        ...   if len(coordinates) == 1:
+        ...     # only a single cell:
+        ...     answers.append(table.iat[coordinates[0]])
+        ...   else:
+        ...     # multiple cells
+        ...     cell_values = []
+        ...     for coordinate in coordinates:
+        ...        cell_values.append(table.iat[coordinate])
+        ...     answers.append(", ".join(cell_values))
+
+        >>> display(table)
+        >>> print("")
+        >>> for query, answer, predicted_agg in zip(queries, answers, aggregation_predictions_string):
+        ...   print(query)
+        ...   if predicted_agg == "NONE":
+        ...     print("Predicted answer: " + answer)
+        ...   else:
+        ...     print("Predicted answer: " + predicted_agg + " > " + answer)    
+        What is the name of the first actor?
+        Predicted answer: Brad Pitt
+        How many movies has George Clooney played in?
+        Predicted answer: COUNT > 69
+        What is the total number of movies?
+        Predicted answer: SUM > 87, 53, 69
+
+In case of a conversational set-up, then each table-question pair must be provided **sequentially** to the model, such
+that the ``prev_labels`` token types can be overwritten by the predicted ``labels`` of the previous table-question
+pair. Again, more info can be found in `this notebook
+<https://github.com/NielsRogge/Transformers-Tutorials/blob/master/Fine_tuning_TapasForQuestionAnswering_on_SQA.ipynb>`__.
+
+
+Tapas specific outputs
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.models.tapas.modeling_tapas.TableQuestionAnsweringOutput
+    :members:
+
+
+TapasConfig
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TapasConfig
+    :members:
+
+
+TapasTokenizer
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TapasTokenizer
+    :members: __call__, convert_logits_to_predictions, save_vocabulary
+
+
+TapasModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TapasModel
+    :members: forward
+
+
+TapasForMaskedLM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TapasForMaskedLM
+    :members: forward
+
+
+TapasForSequenceClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TapasForSequenceClassification
+    :members: forward
+
+
+TapasForQuestionAnswering
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TapasForQuestionAnswering
+    :members: forward

docs/source/model_doc/transformerxl.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 Transformer XL
 -----------------------------------------------------------------------------------------------------------------------
 
@@ -75,12 +87,14 @@ TransfoXLLMHeadModel
 .. autoclass:: transformers.TransfoXLLMHeadModel
     :members: forward
 
+
 TransfoXLForSequenceClassification
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TransfoXLForSequenceClassification
     :members: forward
 
+
 TFTransfoXLModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -93,3 +107,18 @@ TFTransfoXLLMHeadModel
 
 .. autoclass:: transformers.TFTransfoXLLMHeadModel
     :members: call
+
+
+TFTransfoXLForSequenceClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFTransfoXLForSequenceClassification
+    :members: call
+
+
+Internal Layers
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.AdaptiveEmbedding
+
+.. autoclass:: transformers.TFAdaptiveEmbedding

docs/source/model_doc/xlm.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 XLM
 -----------------------------------------------------------------------------------------------------------------------
 

docs/source/model_doc/xlmprophetnet.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 XLM-ProphetNet
 -----------------------------------------------------------------------------------------------------------------------
 

docs/source/model_doc/xlmroberta.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 XLM-RoBERTa
 -----------------------------------------------------------------------------------------------------------------------
 
@@ -50,6 +62,13 @@ XLMRobertaTokenizer
         create_token_type_ids_from_sequences, save_vocabulary
 
 
+XLMRobertaTokenizerFast
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.XLMRobertaTokenizerFast
+    :members:
+
+
 XLMRobertaModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

docs/source/model_doc/xlnet.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 XLNet
 -----------------------------------------------------------------------------------------------------------------------
 
@@ -50,6 +62,13 @@ XLNetTokenizer
         create_token_type_ids_from_sequences, save_vocabulary
 
 
+XLNetTokenizerFast
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.XLNetTokenizerFast
+    :members:
+
+
 XLNet specific outputs
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

docs/source/model_sharing.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 Model sharing and uploading
 =======================================================================================================================
 
@@ -48,7 +60,7 @@ Basic steps
 In order to upload a model, you'll need to first create a git repo. This repo will live on the model hub, allowing
 users to clone it and you (and your organization members) to push to it.
 
-You can create a model repo directly from the website, `here <https://huggingface.co/new>`.
+You can create a model repo **directly from `the /new page on the website <https://huggingface.co/new>`__.**
 
 Alternatively, you can use the ``transformers-cli``. The next steps describe that process:
 
@@ -70,12 +82,12 @@ This creates a repo on the model hub, which can be cloned.
 
 .. code-block:: bash
 
-    git clone https://huggingface.co/username/your-model-name
-
     # Make sure you have git-lfs installed
     # (https://git-lfs.github.com/)
     git lfs install
 
+    git clone https://huggingface.co/username/your-model-name
+
 When you have your local clone of your repo and lfs installed, you can then add/remove from that clone as you would
 with any other git repo.
 
@@ -86,8 +98,12 @@ with any other git repo.
     echo "hello" >> README.md
     git add . && git commit -m "Update from $USER"
 
-We are intentionally not wrapping git too much, so as to stay intuitive and easy-to-use.
+We are intentionally not wrapping git too much, so that you can go on with the workflow you're used to and the tools
+you already know.
 
+The only learning curve you might have compared to regular git is the one for git-lfs. The documentation at
+`git-lfs.github.com <https://git-lfs.github.com/>`__ is decent, but we'll work on a tutorial with some tips and tricks
+in the coming weeks!
 
 Make your model work on all frameworks
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -98,7 +114,7 @@ Make your model work on all frameworks
 You probably have your favorite framework, but so will other users! That's why it's best to upload your model with both
 PyTorch `and` TensorFlow checkpoints to make it easier to use (if you skip this step, users will still be able to load
 your model in another framework, but it will be slower, as it will have to be converted on the fly). Don't worry, it's
-super easy to do (and in a future version, it will all be automatic). You will need to install both PyTorch and
+super easy to do (and in a future version, it might all be automatic). You will need to install both PyTorch and
 TensorFlow for this step, but you don't need to worry about the GPU, so it should be very easy. Check the `TensorFlow
 installation page <https://www.tensorflow.org/install/pip#tensorflow-2.0-rc-is-available>`__ and/or the `PyTorch
 installation page <https://pytorch.org/get-started/locally/#start-locally>`__ to see how.
@@ -180,7 +196,7 @@ status`` command:
     git add --all
     git status
 
-Finally, the files should be comitted:
+Finally, the files should be committed:
 
 .. code-block:: bash
 
@@ -198,23 +214,20 @@ This will upload the folder containing the weights, tokenizer and configuration
 Add a model card
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-To make sure everyone knows what your model can do, what its limitations and potential bias or ethetical
-considerations, please add a README.md model card to the 🤗 Transformers repo under `model_cards/`. It should then be
-placed in a subfolder with your username or organization, then another subfolder named like your model
-(`awesome-name-you-picked`). Or just click on the "Create a model card on GitHub" button on the model page, it will get
-you directly to the right location. If you need one, `here <https://github.com/huggingface/model_card>`__ is a model
-card template (meta-suggestions are welcome).
+To make sure everyone knows what your model can do, what its limitations, potential bias or ethical considerations are,
+please add a README.md model card to your model repo. You can just create it, or there's also a convenient button
+titled "Add a README.md" on your model page. A model card template can be found `here
+<https://github.com/huggingface/model_card>`__ (meta-suggestions are welcome). model card template (meta-suggestions
+are welcome).
+
+.. note::
+
+    Model cards used to live in the 🤗 Transformers repo under `model_cards/`, but for consistency and scalability we
+    migrated every model card from the repo to its corresponding huggingface.co model repo.
 
 If your model is fine-tuned from another model coming from the model hub (all 🤗 Transformers pretrained models do),
 don't forget to link to its model card so that people can fully trace how your model was built.
 
-If you have never made a pull request to the 🤗 Transformers repo, look at the :doc:`contributing guide <contributing>`
-to see the steps to follow.
-
-.. note::
-
-    You can also send your model card in the folder you uploaded with the CLI by placing it in a `README.md` file
-    inside `path/to/awesome-name-you-picked/`.
 
 Using your model
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -250,7 +263,8 @@ First you need to install `git-lfs` in the environment used by the notebook:
 
     sudo apt-get install git-lfs
 
-Then you can use the :obj:`transformers-cli` to create your new repo:
+Then you can use either create a repo directly from `huggingface.co <https://huggingface.co/>`__ , or use the
+:obj:`transformers-cli` to create it:
 
 
 .. code-block:: bash
@@ -262,13 +276,14 @@ Once it's created, you can clone it and configure it (replace username by your u
 
 .. code-block:: bash
 
+    git lfs install
+
     git clone https://username:password@huggingface.co/username/your-model-name
     # Alternatively if you have a token,
     # you can use it instead of your password
     git clone https://username:token@huggingface.co/username/your-model-name
 
     cd your-model-name
-    git lfs install
     git config --global user.email "email@example.com"
     # Tip: using the same email than for your huggingface.co account will link your commits to your profile
     git config --global user.name "Your name"

docs/source/model_summary.rst

@@ -1,10 +1,22 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 Summary of the models
 =======================================================================================================================
 
 This is a summary of the models available in 🤗 Transformers. It assumes you’re familiar with the original `transformer
 model <https://arxiv.org/abs/1706.03762>`_. For a gentle introduction check the `annotated transformer
 <http://nlp.seas.harvard.edu/2018/04/03/attention.html>`_. Here we focus on the high-level differences between the
-models. You can check them more in detail in their respective documentation. Also checkout the :doc:`pretrained model
+models. You can check them more in detail in their respective documentation. Also check out the :doc:`pretrained model
 page </pretrained_models>` to see the checkpoints available for each type of model and all `the community models
 <https://huggingface.co/models>`_.
 
@@ -18,7 +30,7 @@ Each one of the models in the library falls into one of the following categories
 
 Autoregressive models are pretrained on the classic language modeling task: guess the next token having read all the
 previous ones. They correspond to the decoder of the original transformer model, and a mask is used on top of the full
-sentence so that the attention heads can only see what was before in the next, and not what’s after. Although those
+sentence so that the attention heads can only see what was before in the text, and not what’s after. Although those
 models can be fine-tuned and achieve great results on many tasks, the most natural application is text generation. A
 typical example of such models is GPT.
 
@@ -500,8 +512,8 @@ BART
 <https://arxiv.org/abs/1910.13461>`_, Mike Lewis et al.
 
 Sequence-to-sequence model with an encoder and a decoder. Encoder is fed a corrupted version of the tokens, decoder is
-fed the original tokens (but has a mask to hide the future words like a regular transformers decoder). For the encoder
-, on the pretraining tasks, a composition of the following transformations are applied:
+fed the original tokens (but has a mask to hide the future words like a regular transformers decoder). A composition of
+the following transformations are applied on the pretraining tasks for the encoder:
 
   * mask random tokens (like in BERT)
   * delete random tokens

docs/source/multilingual.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 Multi-lingual models
 =======================================================================================================================
 

docs/source/perplexity.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 Perplexity of fixed-length models
 =======================================================================================================================
 

docs/source/philosophy.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 Philosophy
 =======================================================================================================================
 
@@ -66,7 +78,7 @@ The library is built around three types of classes for each model:
 All these classes can be instantiated from pretrained instances and saved locally using two methods:
 
 - :obj:`from_pretrained()` lets you instantiate a model/configuration/tokenizer from a pretrained version either
-  provided by the library itself (the supported models are provided in the list :doc:`here <pretrained_models>` or
+  provided by the library itself (the supported models are provided in the list :doc:`here <pretrained_models>`) or
   stored locally (or on a server) by the user,
 - :obj:`save_pretrained()` lets you save a model/configuration/tokenizer locally so that it can be reloaded using
   :obj:`from_pretrained()`.

docs/source/preprocessing.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 Preprocessing data
 =======================================================================================================================
 
@@ -5,10 +17,10 @@ In this tutorial, we'll explore how to preprocess your data using 🤗 Transform
 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.
 
-As we saw in the :doc:`quicktour </quicktour>`, the tokenizer will first split a given text in words (or part of words,
-punctuation symbols, etc.) usually called `tokens`. Then it will convert those `tokens` into numbers, to be able to
-build a tensor out of them and feed them to the model. It will also add any additional inputs the model might expect to
-work properly.
+As we saw in the :doc:`quick tour </quicktour>`, the tokenizer will first split a given text in words (or part of
+words, punctuation symbols, etc.) usually called `tokens`. Then it will convert those `tokens` into numbers, to be able
+to build a tensor out of them and feed them to the model. It will also add any additional inputs the model might expect
+to work properly.
 
 .. note::
 
@@ -119,7 +131,7 @@ ones it should not (because they represent padding in this case).
 
 
 Note that if your model does not have a maximum length associated to it, the command above will throw a warning. You
-can safely ignore it. You can also pass ``verbose=False`` to stop the tokenizer to throw those kinds of warnings.
+can safely ignore it. You can also pass ``verbose=False`` to stop the tokenizer from throwing those kinds of warnings.
 
 .. _sentence-pairs:
 
@@ -204,7 +216,6 @@ Everything you always wanted to know about padding and truncation
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 We have seen the commands that will work for most cases (pad your batch to the length of the maximum sentence and
-
 truncate to the maximum length the mode can accept). However, the API supports more strategies if you need them. The
 three arguments you need to know for this are :obj:`padding`, :obj:`truncation` and :obj:`max_length`.
 

docs/source/pretrained_models.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 Pretrained models
 =======================================================================================================================
 

docs/source/quicktour.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 Quick tour
 =======================================================================================================================
 
@@ -146,7 +158,7 @@ Using the tokenizer
 
 We mentioned the tokenizer is responsible for the preprocessing of your texts. First, it will split a given text in
 words (or part of words, punctuation symbols, etc.) usually called `tokens`. There are multiple rules that can govern
-that process (you can learn more about them in the :doc:`tokenizer summary <tokenizer_summary>`, which is why we need
+that process (you can learn more about them in the :doc:`tokenizer summary <tokenizer_summary>`), which is why we need
 to instantiate the tokenizer using the name of the model, to make sure we use the same rules as when the model was
 pretrained.
 
@@ -182,6 +194,7 @@ and get tensors back. You can specify all of that to the tokenizer:
     ...     ["We are very happy to show you the 🤗 Transformers library.", "We hope you don't hate it."],
     ...     padding=True,
     ...     truncation=True,
+    ...     max_length=512,
     ...     return_tensors="pt"
     ... )
     >>> ## TENSORFLOW CODE
@@ -189,6 +202,7 @@ and get tensors back. You can specify all of that to the tokenizer:
     ...     ["We are very happy to show you the 🤗 Transformers library.", "We hope you don't hate it."],
     ...     padding=True,
     ...     truncation=True,
+    ...     max_length=512,
     ...     return_tensors="tf"
     ... )
 

docs/source/serialization.rst

@@ -1,4 +1,15 @@
-***********************************************************************************************************************
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 Exporting transformers models
 ***********************************************************************************************************************
 

docs/source/task_summary.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 Summary of the tasks
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
@@ -315,7 +327,7 @@ Masked Language Modeling
 Masked language modeling is the task of masking tokens in a sequence with a masking token, and prompting the model to
 fill that mask with an appropriate token. This allows the model to attend to both the right context (tokens on the
 right of the mask) and the left context (tokens on the left of the mask). Such a training creates a strong basis for
-downstream tasks, requiring bi-directional context such as SQuAD (question answering, see `Lewis, Lui, Goyal et al.
+downstream tasks requiring bi-directional context, such as SQuAD (question answering, see `Lewis, Lui, Goyal et al.
 <https://arxiv.org/abs/1910.13461>`__, part 4.2).
 
 Here is an example of using pipelines to replace a mask from a sequence:
@@ -645,7 +657,7 @@ Here are the expected results:
         {'word': 'Bridge', 'score': 0.990249514579773, 'entity': 'I-LOC'}
     ]
 
-Note, how the tokens of the sequence "Hugging Face" have been identified as an organisation, and "New York City",
+Note how the tokens of the sequence "Hugging Face" have been identified as an organisation, and "New York City",
 "DUMBO" and "Manhattan Bridge" have been identified as locations.
 
 Here is an example of doing named entity recognition, using a model and a tokenizer. The process is the following:

docs/source/testing.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 Testing
 =======================================================================================================================
 
@@ -1130,3 +1142,66 @@ To start a debugger at the point of the warning, do this:
 .. code-block:: bash
 
     pytest tests/test_logging.py -W error::UserWarning --pdb
+
+
+
+Testing Experimental CI Features
+-----------------------------------------------------------------------------------------------------------------------
+
+Testing CI features can be potentially problematic as it can interfere with the normal CI functioning. Therefore if a
+new CI feature is to be added, it should be done as following.
+
+1. Create a new dedicated job that tests what needs to be tested
+2. The new job must always succeed so that it gives us a green ✓ (details below).
+3. Let it run for some days to see that a variety of different PR types get to run on it (user fork branches,
+   non-forked branches, branches originating from github.com UI direct file edit, various forced pushes, etc. - there
+   are so many) while monitoring the experimental job's logs (not the overall job green as it's purposefully always
+   green)
+4. When it's clear that everything is solid, then merge the new changes into existing jobs.
+
+That way experiments on CI functionality itself won't interfere with the normal workflow.
+
+Now how can we make the job always succeed while the new CI feature is being developed?
+
+Some CIs, like TravisCI support ignore-step-failure and will report the overall job as successful, but CircleCI and
+Github Actions as of this writing don't support that.
+
+So the following workaround can be used:
+
+1. ``set +euo pipefail`` at the beginning of the run command to suppress most potential failures in the bash script.
+2. the last command must be a success: ``echo "done"`` or just ``true`` will do
+
+Here is an example:
+
+.. code-block:: yaml
+
+    - run:
+        name: run CI experiment
+        command: |
+            set +euo pipefail
+            echo "setting run-all-despite-any-errors-mode"
+            this_command_will_fail
+            echo "but bash continues to run"
+            # emulate another failure
+            false
+            # but the last command must be a success
+            echo "during experiment do not remove: reporting success to CI, even if there were failures"
+
+For simple commands you could also do:
+
+.. code-block:: bash
+
+    cmd_that_may_fail || true
+
+Of course, once satisfied with the results, integrate the experimental step or job with the rest of the normal jobs,
+while removing ``set +euo pipefail`` or any other things you may have added to ensure that the experimental job doesn't
+interfere with the normal CI functioning.
+
+This whole process would have been much easier if we only could set something like ``allow-failure`` for the
+experimental step, and let it fail without impacting the overall status of PRs. But as mentioned earlier CircleCI and
+Github Actions don't support it at the moment.
+
+You can vote for this feature and see where it is at at these CI-specific threads:
+
+* `Github Actions: <https://github.com/actions/toolkit/issues/399>`__
+* `CircleCI: <https://ideas.circleci.com/ideas/CCI-I-344>`__

docs/source/tokenizer_summary.rst

@@ -1,3 +1,15 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 Summary of the tokenizers
 -----------------------------------------------------------------------------------------------------------------------
 
@@ -6,7 +18,7 @@ On this page, we will have a closer look at tokenization. As we saw in :doc:`the
 look-up table. Converting words or subwords to ids is straightforward, so in this summary, we will focus on splitting a
 text into words or subwords (i.e. tokenizing a text). More specifically, we will look at the three main types of
 tokenizers used in 🤗 Transformers: :ref:`Byte-Pair Encoding (BPE) <byte-pair-encoding>`, :ref:`WordPiece <wordpiece>`,
-and :ref:`SentencePiece <sentencepiece>`, and show exemplary which tokenizer type is used by which model.
+and :ref:`SentencePiece <sentencepiece>`, and show examples of which tokenizer type is used by which model.
 
 Note that on each model page, you can look at the documentation of the associated tokenizer to know which tokenizer
 type was used by the pretrained model. For instance, if we look at :class:`~transformers.BertTokenizer`, we can see
@@ -60,7 +72,7 @@ greater than 50,000, especially if they are pretrained only on a single language
 So if simple space and punctuation tokenization is unsatisfactory, why not simply tokenize on characters? While
 character tokenization is very simple and would greatly reduce memory and time complexity it makes it much harder for
 the model to learn meaningful input representations. *E.g.* learning a meaningful context-independent representation
-for the letter ``"t"`` is much harder as learning a context-independent representation for the word ``"today"``.
+for the letter ``"t"`` is much harder than learning a context-independent representation for the word ``"today"``.
 Therefore, character tokenization is often accompanied by a loss of performance. So to get the best of both worlds,
 transformers models use a hybrid between word-level and character-level tokenization called **subword** tokenization.
 
@@ -190,10 +202,10 @@ WordPiece
 
 WordPiece is the subword tokenization algorithm used for :doc:`BERT <model_doc/bert>`, :doc:`DistilBERT
 <model_doc/distilbert>`, and :doc:`Electra <model_doc/electra>`. The algorithm was outlined in `Japanese and Korean
-Voice Seach (Schuster et al., 2012)
+Voice Search (Schuster et al., 2012)
 <https://static.googleusercontent.com/media/research.google.com/ja//pubs/archive/37842.pdf>`__ and is very similar to
 BPE. WordPiece first initializes the vocabulary to include every character present in the training data and
-progressively learn a given number of merge rules. In contrast to BPE, WordPiece does not choose the most frequent
+progressively learns a given number of merge rules. In contrast to BPE, WordPiece does not choose the most frequent
 symbol pair, but the one that maximizes the likelihood of the training data once added to the vocabulary.
 
 So what does this mean exactly? Referring to the previous example, maximizing the likelihood of the training data is

docs/source/training.rst

@@ -1,8 +1,20 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
 Training and fine-tuning
 =======================================================================================================================
 
 Model classes in 🤗 Transformers are designed to be compatible with native PyTorch and TensorFlow 2 and can be used
-seemlessly with either. In this quickstart, we will show how to fine-tune (or train from scratch) a model using the
+seamlessly with either. In this quickstart, we will show how to fine-tune (or train from scratch) a model using the
 standard training tools available in either framework. We will also show how to use our included
 :func:`~transformers.Trainer` class which handles much of the complexity of training for you.
 

examples/README.md

@@ -1,58 +1,110 @@
+<!---
+Copyright 2020 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 # Examples
 
-Version 2.9 of 🤗 Transformers introduced a new [`Trainer`](https://github.com/huggingface/transformers/blob/master/src/transformers/trainer.py) class for PyTorch, and its equivalent [`TFTrainer`](https://github.com/huggingface/transformers/blob/master/src/transformers/trainer_tf.py) for TF 2.
-Running the examples requires PyTorch 1.3.1+ or TensorFlow 2.2+.
-
-Here is the list of all our examples:
-- **grouped by task** (all official examples work for multiple models)
-- with information on whether they are **built on top of `Trainer`/`TFTrainer`** (if not, they still work, they might
-  just lack some features),
-- whether or not they leverage the [🤗 Datasets](https://github.com/huggingface/datasets) library.
-- links to **Colab notebooks** to walk through the scripts and run them easily,
-- links to **Cloud deployments** to be able to deploy large-scale trainings in the Cloud with little to no setup.
-
+This folder contains actively maintained examples of use of 🤗 Transformers organized along NLP tasks. If you are looking for an example that used to
+be in this folder, it may have moved to our [research projects](https://github.com/huggingface/transformers/tree/master/examples/research_projects) subfolder (which contains frozen snapshots of research projects).
 
 ## Important note
 
 **Important**
 
-To make sure you can successfully run the latest versions of the example scripts, you have to **install the library from source** and install some example-specific requirements.
-Execute the following steps in a new virtual environment:
-
+To make sure you can successfully run the latest versions of the example scripts, you have to **install the library from source** and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:
 ```bash
 git clone https://github.com/huggingface/transformers
 cd transformers
 pip install .
-pip install -r ./examples/requirements.txt
+```
+Then cd in the example folder of your choice and run
+```bash
+pip install -r requirements.txt
 ```
 
-Alternatively, you can run the version of the examples as they were for your current version of Transformers via (for instance with v3.4.0):
+Alternatively, you can run the version of the examples as they were for your current version of Transformers via (for instance with v3.5.1):
 ```bash
-git checkout tags/v3.4.0
+git checkout tags/v3.5.1
 ```
 
 ## The Big Table of Tasks
 
+Here is the list of all our examples:
+- with information on whether they are **built on top of `Trainer`/`TFTrainer`** (if not, they still work, they might
+  just lack some features),
+- whether or not they leverage the [🤗 Datasets](https://github.com/huggingface/datasets) library.
+- links to **Colab notebooks** to walk through the scripts and run them easily,
+<!--
+Coming soon!
+- links to **Cloud deployments** to be able to deploy large-scale trainings in the Cloud with little to no setup.
+-->
+
 | Task | Example datasets | Trainer support | TFTrainer support | 🤗 Datasets | Colab
 |---|---|:---:|:---:|:---:|:---:|
 | [**`language-modeling`**](https://github.com/huggingface/transformers/tree/master/examples/language-modeling)       | Raw text        | ✅ | -  | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/01_how_to_train.ipynb)
-| [**`text-classification`**](https://github.com/huggingface/transformers/tree/master/examples/text-classification)   | GLUE, XNLI      | ✅ | ✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://github.com/huggingface/notebooks/blob/master/examples/text_classification.ipynb)
-| [**`token-classification`**](https://github.com/huggingface/transformers/tree/master/examples/token-classification) | CoNLL NER       | ✅ | ✅ | ✅ | -
-| [**`multiple-choice`**](https://github.com/huggingface/transformers/tree/master/examples/multiple-choice)           | SWAG, RACE, ARC | ✅ | ✅ | - | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ViktorAlm/notebooks/blob/master/MPC_GPU_Demo_for_TF_and_PT.ipynb)
-| [**`question-answering`**](https://github.com/huggingface/transformers/tree/master/examples/question-answering)     | SQuAD           | ✅ | ✅ | - | -
-| [**`text-generation`**](https://github.com/huggingface/transformers/tree/master/examples/text-generation)           | -               | n/a | n/a | - | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/02_how_to_generate.ipynb)
-| [**`distillation`**](https://github.com/huggingface/transformers/tree/master/examples/distillation)                 | All             | - | -  | - | -
+| [**`multiple-choice`**](https://github.com/huggingface/transformers/tree/master/examples/multiple-choice)           | SWAG, RACE, ARC | ✅ | ✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ViktorAlm/notebooks/blob/master/MPC_GPU_Demo_for_TF_and_PT.ipynb)
+| [**`question-answering`**](https://github.com/huggingface/transformers/tree/master/examples/question-answering)     | SQuAD           | ✅ | ✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://github.com/huggingface/notebooks/blob/master/examples/question_answering.ipynb)
 | [**`summarization`**](https://github.com/huggingface/transformers/tree/master/examples/seq2seq)                     | CNN/Daily Mail  | ✅  | - | - | -
+| [**`text-classification`**](https://github.com/huggingface/transformers/tree/master/examples/text-classification)   | GLUE, XNLI      | ✅ | ✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://github.com/huggingface/notebooks/blob/master/examples/text_classification.ipynb)
+| [**`text-generation`**](https://github.com/huggingface/transformers/tree/master/examples/text-generation)           | -               | n/a | n/a | - | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/02_how_to_generate.ipynb)
+| [**`token-classification`**](https://github.com/huggingface/transformers/tree/master/examples/token-classification) | CoNLL NER       | ✅ | ✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://github.com/huggingface/notebooks/blob/master/examples/token_classification.ipynb)
 | [**`translation`**](https://github.com/huggingface/transformers/tree/master/examples/seq2seq)                       | WMT             | ✅  | - | - | -
-| [**`bertology`**](https://github.com/huggingface/transformers/tree/master/examples/bertology)                       | -               | - | - | - | -
-| [**`adversarial`**](https://github.com/huggingface/transformers/tree/master/examples/adversarial)                   | HANS            | ✅ | - | - | -
 
 
-<br>
-
+<!--
 ## One-click Deploy to Cloud (wip)
 
 **Coming soon!**
+-->
+
+## Distributed training and mixed precision
+
+All the PyTorch scripts mentioned above work out of the box with distributed training and mixed precision, thanks to
+the [Trainer API](https://huggingface.co/transformers/main_classes/trainer.html). To launch one of them on _n_ GPUS,
+use the following command:
+
+```bash
+python -m torch.distributed.launch \
+    --nproc_per_node number_of_gpu_you_have path_to_script.py \
+	--all_arguments_of_the_script 
+```
+
+As an example, here is how you would fine-tune the BERT large model (with whole word masking) on the text
+classification MNLI task using the `run_glue` script, with 8 GPUs:
+
+```bash
+python -m torch.distributed.launch \
+    --nproc_per_node 8 text-classification/run_glue.py \
+    --model_name_or_path bert-large-uncased-whole-word-masking \
+    --task_name mnli \
+    --do_train \
+    --do_eval \
+    --max_seq_length 128 \
+    --per_device_train_batch_size 8 \
+    --learning_rate 2e-5 \
+    --num_train_epochs 3.0 \
+    --output_dir /tmp/mnli_output/
+```
+
+If you have a GPU with mixed precision capabilities (architecture Pascal or more recent), you can use mixed precision
+training with PyTorch 1.6.0 or latest, or by installing the [Apex](https://github.com/NVIDIA/apex) library for previous
+versions. Just add the flag `--fp16` to your command launching one of the scripts mentioned above!
+
+Using mixed precision training usually results in 2x-speedup for training with the same final results (as shown in
+[this table](https://github.com/huggingface/transformers/tree/master/examples/text-classification#mixed-precision-training)
+for text classification).
 
 ## Running on TPUs
 
@@ -61,27 +113,34 @@ When using Tensorflow, TPUs are supported out of the box as a `tf.distribute.Str
 When using PyTorch, we support TPUs thanks to `pytorch/xla`. For more context and information on how to setup your TPU environment refer to Google's documentation and to the
 very detailed [pytorch/xla README](https://github.com/pytorch/xla/blob/master/README.md).
 
-In this repo, we provide a very simple launcher script named [xla_spawn.py](https://github.com/huggingface/transformers/tree/master/examples/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). 
-Note that this approach does not work for examples that use `pytorch-lightning`.
-
-For example for `run_glue`:
+In this repo, we provide a very simple launcher script named
+[xla_spawn.py](https://github.com/huggingface/transformers/tree/master/examples/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`):
 
 ```bash
-python examples/xla_spawn.py --num_cores 8 \
-	examples/text-classification/run_glue.py \
-	--model_name_or_path bert-base-cased \
-	--task_name mnli \
-	--data_dir ./data/glue_data/MNLI \
-	--output_dir ./models/tpu \
-	--overwrite_output_dir \
-	--do_train \
-	--do_eval \
-	--num_train_epochs 1 \
-	--save_steps 20000
+python xla_spawn.py --num_cores num_tpu_you_have \
+    path_to_script.py \
+	--all_arguments_of_the_script 
 ```
 
-Feedback and more use cases and benchmarks involving TPUs are welcome, please share with the community.
+As an example, here is how you would fine-tune the BERT large model (with whole word masking) on the text
+classification MNLI task using the `run_glue` script, with 8 TPUs:
+
+```bash
+python xla_spawn.py --num_cores 8 \
+    text-classification/run_glue.py \
+    --model_name_or_path bert-large-uncased-whole-word-masking \
+    --task_name mnli \
+    --do_train \
+    --do_eval \
+    --max_seq_length 128 \
+    --per_device_train_batch_size 8 \
+    --learning_rate 2e-5 \
+    --num_train_epochs 3.0 \
+    --output_dir /tmp/mnli_output/
+```
 
 ## Logging & Experiment tracking
 

examples/_tests_requirements.txt

@@ -0,0 +1,20 @@
+tensorboard
+scikit-learn
+seqeval
+psutil
+sacrebleu
+rouge-score
+tensorflow_datasets
+matplotlib
+git-python==1.0.3
+faiss-cpu
+streamlit
+elasticsearch
+nltk
+pandas
+datasets >= 1.1.3
+fire
+pytest
+conllu
+sentencepiece != 0.1.92
+protobuf

examples/benchmarking/README.md

@@ -1,3 +1,19 @@
+<!---
+Copyright 2020 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 # 🤗 Benchmark results
 
 Here, you can find a list of the different benchmark results created by the community.

examples/benchmarking/plot_csv_file.py

@@ -1,3 +1,17 @@
+# Copyright 2020 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import csv
 from collections import defaultdict
 from dataclasses import dataclass, field

examples/conftest.py

@@ -1,3 +1,17 @@
+# Copyright 2020 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 # tests directory-specific settings - this file is run automatically
 # by pytest before any tests are run
 

examples/contrib/README.md

@@ -1,5 +0,0 @@
-# Community contributed examples
-
-This folder contains examples which are not actively maintained (mostly contributed by the community).
-
-Using these examples together with a recent version of the library usually requires to make small (sometimes big) adaptations to get the scripts working.

examples/language-modeling/README.md

@@ -1,3 +1,19 @@
+<!---
+Copyright 2020 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 ## Language model training
 
 Fine-tuning (or training from scratch) the library models for language modeling on a text dataset for GPT, GPT-2,
@@ -9,8 +25,7 @@ objectives in our [model summary](https://huggingface.co/transformers/model_summ
 These scripts leverage the 🤗 Datasets library and the Trainer API. You can easily customize them to your needs if you
 need extra processing on your datasets.
 
-**Note:** The old script `run_language_modeling.py` is still available
-[here](https://github.com/huggingface/transformers/blob/master/examples/contrib/legacy/run_language_modeling.py).
+**Note:** The old script `run_language_modeling.py` is still available [here](https://github.com/huggingface/transformers/blob/master/examples/legacy/run_language_modeling.py).
 
 The following examples, will run on a datasets hosted on our [hub](https://huggingface.co/datasets) or with your own
 text files for training and validation. We give examples of both below.

examples/language-modeling/requirements.txt

@@ -0,0 +1,3 @@
+datasets >= 1.1.3
+sentencepiece != 0.1.92
+protobuf

examples/language-modeling/run_clm.py

@@ -102,8 +102,8 @@ class DataTrainingArguments:
         default=None,
         metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
     )
-    block_size: int = field(
-        default=-1,
+    block_size: Optional[int] = field(
+        default=None,
         metadata={
             "help": "Optional input sequence length after tokenization."
             "The training dataset will be truncated in block of this size for training."
@@ -113,6 +113,12 @@ class DataTrainingArguments:
     overwrite_cache: bool = field(
         default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
     )
+    validation_split_percentage: Optional[int] = field(
+        default=5,
+        metadata={
+            "help": "The percentage of the train set used as validation set in case there's no validation split"
+        },
+    )
     preprocessing_num_workers: Optional[int] = field(
         default=None,
         metadata={"help": "The number of processes to use for the preprocessing."},
@@ -188,6 +194,17 @@ def main():
     if data_args.dataset_name is not None:
         # Downloading and loading a dataset from the hub.
         datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
+        if "validation" not in datasets.keys():
+            datasets["validation"] = load_dataset(
+                data_args.dataset_name,
+                data_args.dataset_config_name,
+                split=f"train[:{data_args.validation_split_percentage}%]",
+            )
+            datasets["train"] = load_dataset(
+                data_args.dataset_name,
+                data_args.dataset_config_name,
+                split=f"train[{data_args.validation_split_percentage}%:]",
+            )
     else:
         data_files = {}
         if data_args.train_file is not None:
@@ -261,8 +278,14 @@ def main():
         load_from_cache_file=not data_args.overwrite_cache,
     )
 
-    if data_args.block_size <= 0:
+    if data_args.block_size is None:
         block_size = tokenizer.model_max_length
+        if block_size > 1024:
+            logger.warn(
+                f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). "
+                "Picking 1024 instead. You can change that default value by passing --block_size xxx."
+            )
+        block_size = 1024
     else:
         if data_args.block_size > tokenizer.model_max_length:
             logger.warn(
@@ -318,9 +341,20 @@ def main():
             if (model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path))
             else None
         )
-        trainer.train(model_path=model_path)
+        train_result = trainer.train(model_path=model_path)
         trainer.save_model()  # Saves the tokenizer too for easy upload
 
+        output_train_file = os.path.join(training_args.output_dir, "train_results.txt")
+        if trainer.is_world_process_zero():
+            with open(output_train_file, "w") as writer:
+                logger.info("***** Train results *****")
+                for key, value in sorted(train_result.metrics.items()):
+                    logger.info(f"  {key} = {value}")
+                    writer.write(f"{key} = {value}\n")
+
+            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
+            trainer.state.save_to_json(os.path.join(training_args.output_dir, "trainer_state.json"))
+
     # Evaluation
     results = {}
     if training_args.do_eval:
@@ -335,7 +369,7 @@ def main():
         if trainer.is_world_process_zero():
             with open(output_eval_file, "w") as writer:
                 logger.info("***** Eval results *****")
-                for key, value in results.items():
+                for key, value in sorted(results.items()):
                     logger.info(f"  {key} = {value}")
                     writer.write(f"{key} = {value}\n")