wip

2020-12-11 14:23:07 -08:00 · 2020-12-11 14:20:02 -08:00 · 2020-12-11 14:17:17 -08:00 · 2020-12-11 13:53:53 -08:00 · 2020-12-11 13:53:12 -08:00 · 2020-12-11 12:58:01 -08:00
279 changed files with 5726 additions and 12382 deletions
@@ -3,28 +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"
-                    # disable skipping for now, as circleCI's base_revision is inconsistent leading to invalid ranges
-                    # circleci step halt
-                fi
-            else
-                echo "Can't perform skipping check w/o base_revision defined, continuing the job"
-            fi

 # TPU REFERENCES
 references:
@@ -85,281 +63,6 @@ references:


 jobs:
-    run_tests_torch_and_tf:
-        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:
-                    - '~/.cache/pip'
-            - run: RUN_PT_TF_CROSS_TESTS=1 python -m pytest -n 8 --dist=loadfile -rA -s --make-reports=tests_torch_and_tf ./tests/ -m is_pt_tf_cross_test --durations=0 | tee tests_output.txt
-            - store_artifacts:
-                  path: ~/transformers/tests_output.txt
-            - store_artifacts:
-                  path: ~/transformers/reports
-
-    run_tests_torch:
-        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:
-                      - '~/.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:
-        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" }}
-                      - v0.4-{{ checksum "setup.py" }}
-            - run: pip install --upgrade pip
-            - run: pip install .[sklearn,tf-cpu,testing,sentencepiece]
-            - save_cache:
-                  key: v0.4-tf-{{ checksum "setup.py" }}
-                  paths:
-                      - '~/.cache/pip'
-            - run: python -m pytest -n 8 --dist=loadfile -rA -s --make-reports=tests_tf ./tests/ | tee tests_output.txt
-            - store_artifacts:
-                  path: ~/transformers/tests_output.txt
-            - store_artifacts:
-                  path: ~/transformers/reports
-
-    run_tests_flax:
-        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" }}
-                    - v0.4-{{ checksum "setup.py" }}
-            - run: pip install --upgrade pip
-            - run: sudo pip install .[flax,sklearn,torch,testing,sentencepiece]
-            - save_cache:
-                  key: v0.4-flax-{{ checksum "setup.py" }}
-                  paths:
-                      - '~/.cache/pip'
-            - run: python -m pytest -n 8 --dist=loadfile -rA -s --make-reports=tests_flax ./tests/ | tee tests_output.txt
-            - store_artifacts:
-                  path: ~/transformers/tests_output.txt
-            - store_artifacts:
-                  path: ~/transformers/reports
-
-    run_tests_pipelines_torch:
-        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:
-                      - '~/.cache/pip'
-            - run: RUN_PIPELINE_TESTS=1 python -m pytest -n 8 --dist=loadfile -rA -s --make-reports=tests_pipelines_torch -m is_pipeline_test ./tests/ | tee tests_output.txt
-            - store_artifacts:
-                  path: ~/transformers/tests_output.txt
-            - store_artifacts:
-                  path: ~/transformers/reports
-
-    run_tests_pipelines_tf:
-        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" }}
-                      - v0.4-{{ checksum "setup.py" }}
-            - run: pip install --upgrade pip
-            - run: pip install .[sklearn,tf-cpu,testing,sentencepiece]
-            - save_cache:
-                  key: v0.4-tf-{{ checksum "setup.py" }}
-                  paths:
-                      - '~/.cache/pip'
-            - run: RUN_PIPELINE_TESTS=1 python -m pytest -n 8 --dist=loadfile -rA -s --make-reports=tests_pipelines_tf ./tests/ -m is_pipeline_test | tee tests_output.txt
-            - store_artifacts:
-                  path: ~/transformers/tests_output.txt
-            - store_artifacts:
-                  path: ~/transformers/reports
-
-    run_tests_custom_tokenizers:
-        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" }}
-                      - v0.4-{{ checksum "setup.py" }}
-            - run: pip install --upgrade pip
-            - run: pip install .[ja,testing,sentencepiece]
-            - run: python -m unidic download
-            - save_cache:
-                  key: v0.4-custom_tokenizers-{{ checksum "setup.py" }}
-                  paths:
-                      - '~/.cache/pip'
-            - run: python -m pytest -s --make-reports=tests_custom_tokenizers ./tests/test_tokenization_bert_japanese.py | tee tests_output.txt
-            - store_artifacts:
-                  path: ~/transformers/tests_output.txt
-            - store_artifacts:
-                  path: ~/transformers/reports
-
-    run_examples_torch:
-        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
-            - 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/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:
-        working_directory: ~/transformers
-        docker:
-            - image: circleci/python:3.6
-        steps:
-            - checkout
-            - restore_cache:
-                  keys:
-                      - v0.4-build_doc-{{ checksum "setup.py" }}
-                      - v0.4-{{ checksum "setup.py" }}
-            - run: pip install --upgrade pip
-            - run: pip install ."[all, docs]"
-            - save_cache:
-                  key: v0.4-build_doc-{{ checksum "setup.py" }}
-                  paths:
-                      - '~/.cache/pip'
-            - run: cd docs && make html SPHINXOPTS="-W"
-            - store_artifacts:
-                path: ./docs/_build
-
-    deploy_doc:
-        working_directory: ~/transformers
-        docker:
-            - image: circleci/python:3.6
-        steps:
-            - add_ssh_keys:
-                fingerprints:
-                    - "5b:7a:95:18:07:8c:aa:76:4c:60:35:88:ad:60:56:71"
-            - checkout
-            - restore_cache:
-                  keys:
-                      - v0.4-deploy_doc-{{ checksum "setup.py" }}
-                      - v0.4-{{ checksum "setup.py" }}
-            - run: pip install ."[all,docs]"
-            - save_cache:
-                  key: v0.4-deploy_doc-{{ checksum "setup.py" }}
-                  paths:
-                      - '~/.cache/pip'
-            - run: ./.circleci/deploy.sh
-
    check_code_quality:
        working_directory: ~/transformers
        docker:
@@ -373,20 +76,22 @@ jobs:
                      - v0.4-code_quality-{{ checksum "setup.py" }}
                      - v0.4-{{ checksum "setup.py" }}
            - run: pip install --upgrade pip
-            - run: pip install isort
-            - run: pip install .[all,quality]
-            - save_cache:
-                  key: v0.4-code_quality-{{ checksum "setup.py" }}
-                  paths:
-                      - '~/.cache/pip'
-            - run: black --check examples tests src utils
-            - run: isort --check-only examples tests src utils
-            - 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
+            - run:
+                command: |
+                    set +e
+                    echo "my experiment is here"
+                    # emulate failure
+                    false
+                    echo "it can safely fail"
+                    some non existing command
+                    echo "should still reach here"
+                    some non existing command again
+                    echo "should still reach here too"
+                    false
+            - run:
+                when: always
+                command: |
+                    echo "forcing success for this experiment"

    check_repository_consistency:
        working_directory: ~/transformers
@@ -398,37 +103,7 @@ jobs:
            - checkout
            - run: pip install requests
            - run: python ./utils/link_tester.py
-
-# TPU JOBS
-    run_examples_tpu:
-        docker:
-            - image: circleci/python:3.6
-        environment:
-            OMP_NUM_THREADS: 1
-        resource_class: xlarge
-        parallelism: 1
-        steps:
-            - checkout
-            - go/install
-            - *checkout_ml_testing
-            - gcp-gke/install
-            - gcp-gke/update-kubeconfig-with-credentials:
-                  cluster: $GKE_CLUSTER
-                  perform-login: true
-            - setup_remote_docker
-            - *build_push_docker
-            - *deploy_cluster
-
-    cleanup-gke-jobs:
-        docker:
-            - image: circleci/python:3.6
-        steps:
-            - gcp-gke/install
-            - gcp-gke/update-kubeconfig-with-credentials:
-                  cluster: $GKE_CLUSTER
-                  perform-login: true
-            - *delete_gke_jobs
-
+            
 workflow_filters: &workflow_filters
    filters:
        branches:
@@ -440,26 +115,5 @@ workflows:
        jobs:
            - check_code_quality
            - check_repository_consistency
-            - run_examples_torch
-            - run_tests_custom_tokenizers
-            - run_tests_torch_and_tf
-            - run_tests_torch
-            - 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:
-        triggers:
-            - schedule:
-                # Set to run at the first minute of every hour.
-                cron: "0 8 * * *"
-                filters:
-                    branches:
-                        only:
-                            - master
-        jobs:
-            - cleanup-gke-jobs
-            - run_examples_tpu
+
+            
@@ -222,7 +222,6 @@ 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)** 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.
@@ -176,22 +176,19 @@ and conversion utilities for the following models:
 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.
-31. `TAPAS <https://huggingface.co/transformers/master/model_doc/tapas.html>`__ 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:
+31. :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.
-33. :doc:`XLM <model_doc/xlm>` (from Facebook) released together with the paper `Cross-lingual Language Model
+32. :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.
-34. :doc:`XLM-ProphetNet <model_doc/xlmprophetnet>` (from Microsoft Research) released with the paper `ProphetNet:
+33. :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.
-35. :doc:`XLM-RoBERTa <model_doc/xlmroberta>` (from Facebook AI), released together with the paper `Unsupervised
+34. :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.
-36. :doc:`XLNet <model_doc/xlnet>` (from Google/CMU) released with the paper `XLNet: Generalized Autoregressive
+35. :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.

@@ -272,8 +269,6 @@ TensorFlow and/or Flax.
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |             T5              |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|            TAPAS            |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |       Transformer-XL        |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |             XLM             |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
@@ -387,7 +382,6 @@ 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
@@ -32,6 +32,9 @@ generation.
 .. autoclass:: transformers.LogitsProcessorList
    :members: __call__

+.. autoclass:: transformers.LogitsWarper
+    :members: __call__
+
 .. autoclass:: transformers.MinLengthLogitsProcessor
    :members: __call__

@@ -67,3 +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
@@ -22,6 +22,8 @@ Utilities

 .. autoclass:: transformers.EvalPrediction

+.. autoclass:: transformers.EvaluationStrategy
+
 .. autofunction:: transformers.set_seed

 .. autofunction:: transformers.torch_distributed_zero_first
@@ -32,8 +34,15 @@ Callbacks internals

 .. autoclass:: transformers.trainer_callback.CallbackHandler

+
 Distributed Evaluation
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.trainer_pt_utils.DistributedTensorGatherer
    :members:
+
+
+Distributed Evaluation
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.HfArgumentParser
@@ -74,6 +74,10 @@ Learning Rate Schedules (Pytorch)
    :target: /imgs/warmup_linear_schedule.png
    :alt:

+
+.. autofunction:: transformers.get_polynomial_decay_schedule_with_warmup
+
+
 Warmup (TensorFlow)
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

@@ -73,8 +73,9 @@ 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
 =======================================================================================================================
@@ -118,6 +119,13 @@ TokenClassificationPipeline
    :special-members: __call__
    :members:

+TranslationPipeline
+=======================================================================================================================
+
+.. autoclass:: transformers.TranslationPipeline
+    :special-members: __call__
+    :members:
+
 ZeroShotClassificationPipeline
 =======================================================================================================================

@@ -60,6 +60,13 @@ AlbertTokenizer
        create_token_type_ids_from_sequences, save_vocabulary


+AlbertTokenizerFast
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.AlbertTokenizerFast
+    :members:
+
+
 Albert specific outputs
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -175,3 +175,10 @@ TFAutoModelForQuestionAnswering

 .. autoclass:: transformers.TFAutoModelForQuestionAnswering
    :members:
+
+
+FlaxAutoModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaxAutoModel
+    :members:
@@ -98,6 +98,12 @@ BartTokenizer
    :members:


+BartTokenizerFast
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.BartTokenizerFast
+    :members:
+

 BartModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -51,3 +51,9 @@ BarthezTokenizer
 .. autoclass:: transformers.BarthezTokenizer
    :members:

+
+BarthezTokenizerFast
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.BarthezTokenizerFast
+    :members:
@@ -207,3 +207,10 @@ FlaxBertModel

 .. autoclass:: transformers.FlaxBertModel
    :members: __call__
+
+
+FlaxBertForMaskedLM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaxBertForMaskedLM
+    :members: __call__
@@ -54,6 +54,13 @@ CamembertTokenizer
        create_token_type_ids_from_sequences, save_vocabulary


+CamembertTokenizerFast
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.CamembertTokenizerFast
+    :members:
+
+
 CamembertModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -57,6 +57,13 @@ LayoutLMTokenizer
    :members:


+LayoutLMTokenizerFast
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.LayoutLMTokenizerFast
+    :members:
+
+
 LayoutLMModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -90,6 +90,13 @@ MBartTokenizer
    :members: build_inputs_with_special_tokens, prepare_seq2seq_batch


+MBartTokenizerFast
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.MBartTokenizerFast
+    :members:
+
+
 MBartForConditionalGeneration
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -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.
+
 MPNet
 -----------------------------------------------------------------------------------------------------------------------

@@ -37,6 +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
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -112,6 +112,13 @@ warning: ``add_tokens`` does not work at the moment.
    :members: __call__, prepare_seq2seq_batch


+PegasusTokenizerFast
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.PegasusTokenizerFast
+    :members:
+
+
 PegasusForConditionalGeneration
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -163,6 +163,13 @@ ReformerTokenizer
    :members: save_vocabulary


+ReformerTokenizerFast
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.ReformerTokenizerFast
+    :members:
+
+
 ReformerModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -107,6 +107,13 @@ T5Tokenizer
        create_token_type_ids_from_sequences, prepare_seq2seq_batch, save_vocabulary


+T5TokenizerFast
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.T5TokenizerFast
+    :members:
+
+
 T5Model
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -1,395 +0,0 @@
-TAPAS
-----------------------------------------------------------------------------------------------------------------------
-
-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 (Sequential Question Answering by
-Microsoft), WTQ (Wiki Table Questions by Stanford University) and 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, 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://arxiv.org/abs/2010.00571>`__ by Julian Martin Eisenschlos, Syrine Krichene and
-Thomas Müller.
-
-The original code can be found `here <https://github.com/google-research/tapas>`__. This page also includes links to
-the datasets (SQA, WTQ, WikiSQL and TabFact).
-
-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 latest 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 a certain version when calling the `.from_pretrained`
-  method as explained in the model cards. 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). Metrics 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 training example one by one to the model, such that the `prev_labels` token type ids can
-  be overwritten by the predicted `label_ids` 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 datasets** | **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 is as
-easy as:
-
-.. code-block::
-
-        >>> from transformers import TapasForQuestionAnswering
-
-        >>> # for example, the base sized model 
-        >>> model = TapasForQuestionAnswering.from_pretrained('google/tapas-base-uncased')
-
-
-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-uncased', 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 :class:`~transformers.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``, ``label_ids``                         |
-+------------------------------------+----------------------------------------------------------------------------------------------+
-| Weak supervision for aggregation   | ``input_ids``, ``attention_mask``, ``token_type_ids``, ``label_ids``, ``numeric_values``,    |
-|                                    | ``numeric_values_scale``, ``float_answer``                                                   |
-+------------------------------------+----------------------------------------------------------------------------------------------+
-| Strong supervision for aggregation | ``input ids``, ``attention mask``, ``token type ids``, ``label ids``, ``aggregation_labels`` |
-+------------------------------------+----------------------------------------------------------------------------------------------+
-
-:class:`~transformers.TapasTokenizer` creates the ``label_ids``, ``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-uncased'
-        >>> 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)], [(1, 0)], [(0, 2), (1, 2), (2, 2)]]
-        >>> answer_text = [["Brad Pitt"], ["69"], ["209"]]
-        >>> table = pd.Dataframe(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([[ ... ]]), label_ids: 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)
-        ...         encoding = self.tokenizer(table=table, 
-        ...                                 queries=item.question, 
-        ...                                 answer_coordinates=item.answer_coordinates, 
-        ...                                 answer_text=item.answer_text,
-        ...                                 padding="max_length",
-        ...                                 return_tensors="pt"
-        ...         )
-        ...         # we add the float_answer which is also required (weak supervision for aggregation)
-        ...         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.
-
-=================================================== STEP 4: Train (fine-tune) TapasForQuestionAnswering
-=======================================================================================================================
-
-You can then fine-tune :class:`~transformers.TapasForQuestionAnswering` using native PyTorch as follows:
-
-.. code-block::
-
-        >>> from transformers import TapasForQuestionAnswering
-
-        >>> model = TapasForQuestionAnswering.from_pretrained("google/tapas-base-uncased")
-
-        >>> for epoch in range(2):  # loop over the dataset multiple times
-        ...    for idx, batch in enumerate(train_dataloader):
-        ...         # get the inputs; 
-        ...         input_ids, attention_mask, token_type_ids, label_ids, numeric_values, numeric_values_scale, float_answer = batch
-
-        ...         # 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, 
-        ...                        label_ids=label_ids, 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-uncased-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(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, 
-        ...         output.logits, 
-        ...         outputs.logits_aggregation
-        ...)
-
-        >>> # 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(df.iat[coordinates[0]])
-        ...   else:
-        ...     # multiple cells
-        ...     cell_values = []
-        ...     for coordinate in coordinates:
-        ...        cell_values.append(df.iat[coordinate])
-        ...     answers.append(", ".join(cell_values))
-
-        >>> display(df)
-        >>> 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)    
-        When was Brad Pitt born?
-        Predicted answer: 18 december 1963
-        Which actor appeared in the least number of movies?
-        Predicted answer: Leonardo Di Caprio
-        What is the average number of movies?
-        Predicted answer: AVERAGE > 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 ``label_ids`` of the previous table-question
-pair.
-
-
-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:
-
-
-TapasForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TapasForMaskedLM
-    :members:
-
-
-TapasForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TapasForSequenceClassification
-    :members: forward
-
-
-TapasForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TapasForQuestionAnswering
-    :members:
@@ -105,3 +105,11 @@ TFTransfoXLLMHeadModel

 .. autoclass:: transformers.TFTransfoXLLMHeadModel
    :members: call
+
+
+Internal Layers
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.AdaptiveEmbedding
+
+.. autoclass:: transformers.TFAdaptiveEmbedding
@@ -62,6 +62,13 @@ XLMRobertaTokenizer
        create_token_type_ids_from_sequences, save_vocabulary


+XLMRobertaTokenizerFast
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.XLMRobertaTokenizerFast
+    :members:
+
+
 XLMRobertaModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -62,6 +62,13 @@ XLNetTokenizer
        create_token_type_ids_from_sequences, save_vocabulary


+XLNetTokenizerFast
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.XLNetTokenizerFast
+    :members:
+
+
 XLNet specific outputs
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -16,59 +16,58 @@ 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             | - | -  | - | -
 | [**`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       | ✅ | ✅ | ✅ | -
 | [**`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!**
+-->

 ## Running on TPUs

@@ -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
@@ -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.
@@ -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
@@ -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

@@ -1,5 +0,0 @@
-# Community contributed examples
-
-This folder contains examples which are not actively maintained (mostly contributed by the community).
-
-Using these examples together with a recent version of the library usually requires to make small (sometimes big) adaptations to get the scripts working.
@@ -1,3 +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,
@@ -0,0 +1,3 @@
+datasets >= 1.1.3
+sentencepiece != 0.1.92
+protobuf
@@ -281,7 +281,7 @@ def main():
    # Add the chinese references if provided
    if data_args.train_ref_file is not None:
        tokenized_datasets["train"] = add_chinese_references(tokenized_datasets["train"], data_args.train_ref_file)
-    if data_args.valid_ref_file is not None:
+    if data_args.validation_ref_file is not None:
        tokenized_datasets["validation"] = add_chinese_references(
            tokenized_datasets["validation"], data_args.validation_ref_file
        )
@@ -0,0 +1,21 @@
+<!---
+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.
+-->
+
+# Legacy 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.
@@ -21,7 +21,7 @@ mkdir -p $OUTPUT_DIR
 # Add parent directory to python path to access lightning_base.py
 export PYTHONPATH="../":"${PYTHONPATH}"

-python3 run_pl_glue.py --gpus 1 --data_dir $DATA_DIR \
+python3 run_glue.py --gpus 1 --data_dir $DATA_DIR \
 --task $TASK \
 --model_name_or_path $BERT_MODEL \
 --output_dir $OUTPUT_DIR \
@@ -31,7 +31,7 @@ mkdir -p $OUTPUT_DIR
 # Add parent directory to python path to access lightning_base.py
 export PYTHONPATH="../":"${PYTHONPATH}"

-python3 run_pl_ner.py --data_dir ./ \
+python3 run_ner.py --data_dir ./ \
 --labels ./labels.txt \
 --model_name_or_path $BERT_MODEL \
 --output_dir $OUTPUT_DIR \
@@ -26,7 +26,7 @@ export SEED=1
 # Add parent directory to python path to access lightning_base.py
 export PYTHONPATH="../":"${PYTHONPATH}"

-python3 run_pl_ner.py --data_dir ./ \
+python3 run_ner.py --data_dir ./ \
 --task_type POS \
 --model_name_or_path $BERT_MODEL \
 --output_dir $OUTPUT_DIR \
@@ -0,0 +1,229 @@
+## Token classification
+
+Based on the scripts [`run_ner.py`](https://github.com/huggingface/transformers/blob/master/examples/contrib/legacy/token-classification/run_ner.py).
+
+The following examples are covered in this section:
+
+* NER on the GermEval 2014 (German NER) dataset
+* Emerging and Rare Entities task: WNUT’17 (English NER) dataset
+
+Details and results for the fine-tuning provided by @stefan-it.
+
+### GermEval 2014 (German NER) dataset
+
+#### Data (Download and pre-processing steps)
+
+Data can be obtained from the [GermEval 2014](https://sites.google.com/site/germeval2014ner/data) shared task page.
+
+Here are the commands for downloading and pre-processing train, dev and test datasets. The original data format has four (tab-separated) columns, in a pre-processing step only the two relevant columns (token and outer span NER annotation) are extracted:
+
+```bash
+curl -L 'https://drive.google.com/uc?export=download&id=1Jjhbal535VVz2ap4v4r_rN1UEHTdLK5P' \
+| grep -v "^#" | cut -f 2,3 | tr '\t' ' ' > train.txt.tmp
+curl -L 'https://drive.google.com/uc?export=download&id=1ZfRcQThdtAR5PPRjIDtrVP7BtXSCUBbm' \
+| grep -v "^#" | cut -f 2,3 | tr '\t' ' ' > dev.txt.tmp
+curl -L 'https://drive.google.com/uc?export=download&id=1u9mb7kNJHWQCWyweMDRMuTFoOHOfeBTH' \
+| grep -v "^#" | cut -f 2,3 | tr '\t' ' ' > test.txt.tmp
+```
+
+The GermEval 2014 dataset contains some strange "control character" tokens like `'\x96', '\u200e', '\x95', '\xad' or '\x80'`.
+One problem with these tokens is, that `BertTokenizer` returns an empty token for them, resulting in misaligned `InputExample`s.
+The `preprocess.py` script located in the `scripts` folder a) filters these tokens and b) splits longer sentences into smaller ones (once the max. subtoken length is reached).
+
+Let's define some variables that we need for further pre-processing steps and training the model:
+
+```bash
+export MAX_LENGTH=128
+export BERT_MODEL=bert-base-multilingual-cased
+```
+
+Run the pre-processing script on training, dev and test datasets:
+
+```bash
+python3 scripts/preprocess.py train.txt.tmp $BERT_MODEL $MAX_LENGTH > train.txt
+python3 scripts/preprocess.py dev.txt.tmp $BERT_MODEL $MAX_LENGTH > dev.txt
+python3 scripts/preprocess.py test.txt.tmp $BERT_MODEL $MAX_LENGTH > test.txt
+```
+
+The GermEval 2014 dataset has much more labels than CoNLL-2002/2003 datasets, so an own set of labels must be used:
+
+```bash
+cat train.txt dev.txt test.txt | cut -d " " -f 2 | grep -v "^$"| sort | uniq > labels.txt
+```
+
+#### Prepare the run
+
+Additional environment variables must be set:
+
+```bash
+export OUTPUT_DIR=germeval-model
+export BATCH_SIZE=32
+export NUM_EPOCHS=3
+export SAVE_STEPS=750
+export SEED=1
+```
+
+#### Run the Pytorch version
+
+To start training, just run:
+
+```bash
+python3 run_ner.py --data_dir ./ \
+--labels ./labels.txt \
+--model_name_or_path $BERT_MODEL \
+--output_dir $OUTPUT_DIR \
+--max_seq_length  $MAX_LENGTH \
+--num_train_epochs $NUM_EPOCHS \
+--per_device_train_batch_size $BATCH_SIZE \
+--save_steps $SAVE_STEPS \
+--seed $SEED \
+--do_train \
+--do_eval \
+--do_predict
+```
+
+If your GPU supports half-precision training, just add the `--fp16` flag. After training, the model will be both evaluated on development and test datasets.
+
+#### JSON-based configuration file
+
+Instead of passing all parameters via commandline arguments, the `run_ner.py` script also supports reading parameters from a json-based configuration file:
+
+```json
+{
+    "data_dir": ".",
+    "labels": "./labels.txt",
+    "model_name_or_path": "bert-base-multilingual-cased",
+    "output_dir": "germeval-model",
+    "max_seq_length": 128,
+    "num_train_epochs": 3,
+    "per_device_train_batch_size": 32,
+    "save_steps": 750,
+    "seed": 1,
+    "do_train": true,
+    "do_eval": true,
+    "do_predict": true
+}
+```
+
+It must be saved with a `.json` extension and can be used by running `python3 run_ner.py config.json`.
+
+#### Evaluation
+
+Evaluation on development dataset outputs the following for our example:
+
+```bash
+10/04/2019 00:42:06 - INFO - __main__ -   ***** Eval results  *****
+10/04/2019 00:42:06 - INFO - __main__ -     f1 = 0.8623348017621146
+10/04/2019 00:42:06 - INFO - __main__ -     loss = 0.07183869666975543
+10/04/2019 00:42:06 - INFO - __main__ -     precision = 0.8467916366258111
+10/04/2019 00:42:06 - INFO - __main__ -     recall = 0.8784592370979806
+```
+
+On the test dataset the following results could be achieved:
+
+```bash
+10/04/2019 00:42:42 - INFO - __main__ -   ***** Eval results  *****
+10/04/2019 00:42:42 - INFO - __main__ -     f1 = 0.8614389652384803
+10/04/2019 00:42:42 - INFO - __main__ -     loss = 0.07064602487454782
+10/04/2019 00:42:42 - INFO - __main__ -     precision = 0.8604651162790697
+10/04/2019 00:42:42 - INFO - __main__ -     recall = 0.8624150210424085
+```
+
+### Emerging and Rare Entities task: WNUT’17 (English NER) dataset
+
+Description of the WNUT’17 task from the [shared task website](http://noisy-text.github.io/2017/index.html):
+
+> The WNUT’17 shared task focuses on identifying unusual, previously-unseen entities in the context of emerging discussions.
+> Named entities form the basis of many modern approaches to other tasks (like event clustering and summarization), but recall on
+> them is a real problem in noisy text - even among annotators. This drop tends to be due to novel entities and surface forms.
+
+Six labels are available in the dataset. An overview can be found on this [page](http://noisy-text.github.io/2017/files/).
+
+#### Data (Download and pre-processing steps)
+
+The dataset can be downloaded from the [official GitHub](https://github.com/leondz/emerging_entities_17) repository.
+
+The following commands show how to prepare the dataset for fine-tuning:
+
+```bash
+mkdir -p data_wnut_17
+
+curl -L 'https://github.com/leondz/emerging_entities_17/raw/master/wnut17train.conll'  | tr '\t' ' ' > data_wnut_17/train.txt.tmp
+curl -L 'https://github.com/leondz/emerging_entities_17/raw/master/emerging.dev.conll' | tr '\t' ' ' > data_wnut_17/dev.txt.tmp
+curl -L 'https://raw.githubusercontent.com/leondz/emerging_entities_17/master/emerging.test.annotated' | tr '\t' ' ' > data_wnut_17/test.txt.tmp
+```
+
+Let's define some variables that we need for further pre-processing steps:
+
+```bash
+export MAX_LENGTH=128
+export BERT_MODEL=bert-large-cased
+```
+
+Here we use the English BERT large model for fine-tuning.
+The `preprocess.py` scripts splits longer sentences into smaller ones (once the max. subtoken length is reached):
+
+```bash
+python3 scripts/preprocess.py data_wnut_17/train.txt.tmp $BERT_MODEL $MAX_LENGTH > data_wnut_17/train.txt
+python3 scripts/preprocess.py data_wnut_17/dev.txt.tmp $BERT_MODEL $MAX_LENGTH > data_wnut_17/dev.txt
+python3 scripts/preprocess.py data_wnut_17/test.txt.tmp $BERT_MODEL $MAX_LENGTH > data_wnut_17/test.txt
+```
+
+In the last pre-processing step, the `labels.txt` file needs to be generated. This file contains all available labels:
+
+```bash
+cat data_wnut_17/train.txt data_wnut_17/dev.txt data_wnut_17/test.txt | cut -d " " -f 2 | grep -v "^$"| sort | uniq > data_wnut_17/labels.txt
+```
+
+#### Run the Pytorch version
+
+Fine-tuning with the PyTorch version can be started using the `run_ner.py` script. In this example we use a JSON-based configuration file.
+
+This configuration file looks like:
+
+```json
+{
+    "data_dir": "./data_wnut_17",
+    "labels": "./data_wnut_17/labels.txt",
+    "model_name_or_path": "bert-large-cased",
+    "output_dir": "wnut-17-model-1",
+    "max_seq_length": 128,
+    "num_train_epochs": 3,
+    "per_device_train_batch_size": 32,
+    "save_steps": 425,
+    "seed": 1,
+    "do_train": true,
+    "do_eval": true,
+    "do_predict": true,
+    "fp16": false
+}
+```
+
+If your GPU supports half-precision training, please set `fp16` to `true`.
+
+Save this JSON-based configuration under `wnut_17.json`. The fine-tuning can be started with `python3 run_ner_old.py wnut_17.json`.
+
+#### Evaluation
+
+Evaluation on development dataset outputs the following:
+
+```bash
+05/29/2020 23:33:44 - INFO - __main__ -   ***** Eval results *****
+05/29/2020 23:33:44 - INFO - __main__ -     eval_loss = 0.26505235286212275
+05/29/2020 23:33:44 - INFO - __main__ -     eval_precision = 0.7008264462809918
+05/29/2020 23:33:44 - INFO - __main__ -     eval_recall = 0.507177033492823
+05/29/2020 23:33:44 - INFO - __main__ -     eval_f1 = 0.5884802220680084
+05/29/2020 23:33:44 - INFO - __main__ -     epoch = 3.0
+```
+
+On the test dataset the following results could be achieved:
+
+```bash
+05/29/2020 23:33:44 - INFO - transformers.trainer -   ***** Running Prediction *****
+05/29/2020 23:34:02 - INFO - __main__ -     eval_loss = 0.30948806500973547
+05/29/2020 23:34:02 - INFO - __main__ -     eval_precision = 0.5840108401084011
+05/29/2020 23:34:02 - INFO - __main__ -     eval_recall = 0.3994439295644115
+05/29/2020 23:34:02 - INFO - __main__ -     eval_f1 = 0.47440836543753434
+```
+
+WNUT’17 is a very difficult task. Current state-of-the-art results on this dataset can be found [here](http://nlpprogress.com/english/named_entity_recognition.html).
@@ -20,7 +20,7 @@ export NUM_EPOCHS=3
 export SAVE_STEPS=750
 export SEED=1

-python3 run_ner_old.py \
+python3 run_ner.py \
 --task_type NER \
 --data_dir . \
 --labels ./labels.txt \
@@ -21,7 +21,7 @@ export NUM_EPOCHS=3
 export SAVE_STEPS=750
 export SEED=1

-python3 run_ner_old.py \
+python3 run_ner.py \
 --task_type Chunk \
 --data_dir . \
 --model_name_or_path $BERT_MODEL \
@@ -21,7 +21,7 @@ export NUM_EPOCHS=3
 export SAVE_STEPS=750
 export SEED=1

-python3 run_ner_old.py \
+python3 run_ner.py \
 --task_type POS \
 --data_dir . \
 --model_name_or_path $BERT_MODEL \
@@ -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.
+-->
+
 ## Multiple Choice

 Based on the script [`run_multiple_choice.py`]().
@@ -0,0 +1,2 @@
+sentencepiece != 0.1.92
+protobuf
@@ -1,8 +1,29 @@
+<!---
+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.
+-->

 ## SQuAD

-Based on the script [`run_squad.py`](https://github.com/huggingface/transformers/blob/master/examples/question-answering/run_squad.py).
+Based on the script [`run_qa.py`](https://github.com/huggingface/transformers/blob/master/examples/question-answering/run_qa.py).
+
+**Note:** This script only works with models that have a fast tokenizer (backed by the 🤗 Tokenizers library) as it
+uses special features of those tokenizers. You can check if your favorite model has a fast tokenizer in
+[this table](https://huggingface.co/transformers/index.html#bigtable), if it doesn't you can still use the old version
+of the script.
+
+The old version of this script can be found [here](https://github.com/huggingface/transformers/blob/master/examples/contrib/legacy/question-answering/run_squad.py).

 #### Fine-tuning BERT on SQuAD1.0

@@ -0,0 +1 @@
+datasets >= 1.1.3
@@ -0,0 +1,28 @@
+<!---
+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.
+-->
+
+# Research projects
+
+This folder contains various research projects using 🤗 Transformers. They are not maintained and require a specific
+version of 🤗 Transformers that is indicated in the requirements file of each folder. Updating them to the most recent version of the library will require some work.
+
+To use any of them, just run the command
+```
+pip install -r requirements.txt
+```
+inside the folder of your choice.
+
+If you need help with any of those, contact the author(s), indicated at the top of the `README` of each folder.
@@ -0,0 +1 @@
+transformers == 3.5.1
@@ -0,0 +1 @@
+transformers == 3.5.1
@@ -1,4 +1,4 @@
-transformers
+transformers == 3.5.1

 # For ROUGE
 nltk
@@ -0,0 +1 @@
+transformers == 3.5.1
@@ -0,0 +1 @@
+transformers == 3.5.1
@@ -1,5 +1,7 @@
 # Distil*

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

 **January 20, 2020 - Bug fixing** We have recently discovered and fixed [a bug](https://github.com/huggingface/transformers/commit/48cbf267c988b56c71a2380f748a3e6092ccaed3) in the evaluation of our `run_*.py` scripts that caused the reported metrics to be over-estimated on average. We have updated all the metrics with the latest runs.
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Stas Bekman	5ea787a2d8	wip	2020-12-11 14:23:07 -08:00
Stas Bekman	a019424ff0	wip	2020-12-11 14:20:02 -08:00
Stas Bekman	49f7744aa3	wip	2020-12-11 14:17:17 -08:00
Stas Bekman	5455191606	shorten job	2020-12-11 13:53:53 -08:00
Stas Bekman	8507bb7352	set +e	2020-12-11 13:53:12 -08:00
Stas Bekman	f339c16a38	wip	2020-12-11 12:58:01 -08:00
Stas Bekman	40a6227fe1	workflows	2020-12-11 12:56:33 -08:00
Stas Bekman	09cd2464db	workflow restore	2020-12-11 12:55:15 -08:00
Stas Bekman	6a2d0b4e50	test with just 2 jobs	2020-12-11 12:53:54 -08:00
Stas Bekman	eacbd879c9	[wip] [ci] experiment for documentation please ignore	2020-12-11 12:49:31 -08:00
Patrick von Platen	9cc9f4122e	Make ProphetNetModel really compatible with EncoderDecoder (#9033 ) * improve * finish * upload model * fix lm head * fix test	2020-12-11 16:59:54 +01:00
dependabot[bot]	24f6cdeab6	Bump notebook in /examples/research_projects/movement-pruning/lxmert (#9062 ) Bumps [notebook](https://github.com/jupyter/jupyterhub) from 6.1.4 to 6.1.5. - [Release notes](https://github.com/jupyter/jupyterhub/releases) - [Changelog](https://github.com/jupyterhub/jupyterhub/blob/master/CHECKLIST-Release.md) - [Commits](https://github.com/jupyter/jupyterhub/commits) Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>	2020-12-11 10:32:43 -05:00
Lysandre Debut	91fa707217	Remove docs only check (#9065 )	2020-12-11 10:27:31 -05:00
Sylvain Gugger	70527ba694	Fix PreTrainedTokenizer.pad when first inputs are empty (#9018 ) * Fix PreTrainedTokenizer.pad when first inputs are empty * Handle empty inputs case	2020-12-11 10:25:00 -05:00
Sylvain Gugger Lysandre Debut Thomas Wolf	783d7d2629	Reorganize examples (#9010 ) * Reorganize example folder * Continue reorganization * Change requirements for tests * Final cleanup * Finish regroup with tests all passing * Copyright * Requirements and readme * Make a full link for the documentation * Address review comments * Apply suggestions from code review Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * Add symlink * Reorg again * Apply suggestions from code review Co-authored-by: Thomas Wolf <thomwolf@users.noreply.github.com> * Adapt title * Update to new strucutre * Remove test * Update READMEs Co-authored-by: Lysandre Debut <lysandre@huggingface.co> Co-authored-by: Thomas Wolf <thomwolf@users.noreply.github.com>	2020-12-11 10:07:02 -05:00
Suraj Patil	86896de064	update tatoeba workflow (#9051 )	2020-12-11 20:29:15 +05:30
Ganesh KharadandJulien Chaumond	7c8f5f6487	Create README.md (#8096 ) * Create README.md * Fix model card Co-authored-by: Julien Chaumond <julien@huggingface.co>	2020-12-11 09:45:12 -05:00
RamonMamonandJulien Chaumond	5527f78721	Create README.md (#8281 ) * Create README.md * Update model_cards/kiri-ai/distiluse-base-multilingual-cased-et/README.md Co-authored-by: Julien Chaumond <chaumond@gmail.com>	2020-12-11 09:41:29 -05:00
joanginesandJulien Chaumond	c615df7422	Create README.md (#8751 ) * Create README.md * Update model_cards/Cinnamon/electra-small-japanese-generator/README.md Co-authored-by: Julien Chaumond <chaumond@gmail.com>	2020-12-11 09:40:14 -05:00
Ahmed Abdelali Ahmed Abdelali Julien Chaumond	76df559383	QARiB Arabic and dialects models (#8796 ) * Add QARiB models * fix README.md * Fix README.md * Fix README.md * Fix README.md * Fix QARiB files * add models card for QARiB models 860k, 1790k, and 1970k * try to fix PR * re-add files * links aren't allowed here :) Co-authored-by: Ahmed Abdelali <aabdelali@hbku.edu.qa> Co-authored-by: Julien Chaumond <julien@huggingface.co>	2020-12-11 09:38:38 -05:00
moniquebm	b161f1ae54	Update README.md (#8820 )	2020-12-11 09:24:21 -05:00
Panggi Libersa Jasri Akadol	649d389dab	Initial README for `t5-base-indonesian-summarization-cased` model (#9028 ) * Create README.md Initial README for `t5-base-indonesian-summarization-cased` model * Update README for t5-base-indonesian-summarization-cased Typo in README, change from `small` to `base`	2020-12-11 09:18:16 -05:00
Panggi Libersa Jasri Akadol	5e794b6628	Create README.md (#9030 ) Initial README for `t5-small-indonesian-summarization-cased` model	2020-12-11 09:17:29 -05:00
Cola	935e346959	🎨 Change nn.dropout to layer.Dropout (#9047 )	2020-12-11 10:40:25 +01:00
Julien Plu	b01ddc9577	Remove value error (#8985 ) * Remove value error * Try a fix for parameter ordering * Restore previous behavior * Add documentation * Review the comment	2020-12-10 17:17:19 -05:00
NatLun137	91ab02af28	Fix typo #9012 (#1 ) (#9038 ) There is a tiny typo in the code "transformers/examples/language-modeling/run_mlm_wwm.py" at line 284. [Details.](https://github.com/huggingface/transformers/issues/9012)	2020-12-10 16:41:00 -05:00
Sylvain Gugger	8d4bb02056	Refactor FLAX tests (#9034 )	2020-12-10 15:57:39 -05:00
Sylvain Gugger	1310e1a758	Enforce all objects in the main init are documented (#9014 )	2020-12-10 11:57:12 -05:00
Sylvain Gugger	51e81e5895	MPNet copyright files (#9015 )	2020-12-10 09:29:38 -05:00
Sylvain Gugger	35bffd70e2	Fix documention of book in LayoutLM (#9017 )	2020-12-10 09:28:49 -05:00
Cola	c95de29e31	✏️ Fix typo (#9020 )	2020-12-10 08:22:52 +01:00
Stas Bekman	5e637e6c69	[wip] [ci] doc-job-skip take #4 dry-run (#8980 ) * ci-doc-job-skip-take-4 * wip * wip * wip * wip * skip yaml * wip * wip * wip * wip * wip * wip * wip * wip * wip * wip * wip * wip * ready to test * yet another way * trying with HEAD * trying with head.sha * trying with head.sha fix * trying with head.sha fix wip * undo * try to switch to sha * current branch * current branch * PR number check * joy ride * joy ride * joy ride * joy ride * joy ride * joy ride * joy ride * joy ride * joy ride * joy ride * joy ride * joy ride	2020-12-09 15:36:36 -05:00