GitLab

* add eli5 examples

* add dense query script

* query_di

* merging

* merging

* add_utils

* adds nearest neighbor wikipedia

* batch queries

* training_retriever

* new notebooks

* moved retriever traiing script

* finished wiki40b

* max_len_fix

* train_s2s

* retriever_batch_checkpointing

* cleanup

* merge

* dim_fix

* fix_indexer

* fix_wiki40b_snippets

* fix_embed_for_r

* fp32 index

* fix_sparse_q

* joint_training

* remove obsolete datasets

* add_passage_nn_results

* add_passage_nn_results

* add_batch_nn

* add_batch_nn

* add_data_scripts

* notebook

* notebook

* notebook

* fix_multi_gpu

* add_app

* full_caching

* full_caching

* notebook

* sparse_done

* images

* notebook

* add_image_gif

* with_Gif

* add_contr_image

* notebook

* notebook

* notebook

* train_functions

* notebook

* min_retrieval_length

* pandas_option

* notebook

* min_retrieval_length

* notebook

* notebook

* eval_Retriever

* notebook

* images

* notebook

* add_example

* add_example

* notebook

* fireworks

* notebook

* notebook

* joe's notebook comments

* app_update

* notebook

* notebook_link

* captions

* notebook

* assing RetriBert model

* add RetriBert to Auto

* change AutoLMHead to AutoSeq2Seq

* notebook downloads from hf models

* style_black

* style_black

* app_update

* app_update

* fix_app_update

* style

* style

* isort

* Delete WikiELI5training.ipynb

* Delete evaluate_eli5.py

* Delete WikiELI5explore.ipynb

* Delete ExploreWikiELI5Support.html

* Delete explainlikeimfive.py

* Delete wiki_snippets.py

* children before parent

* children before parent

* style_black

* style_black_only

* isort

* isort_new

* Update src/transformers/modeling_retribert.py
Co-authored-by: Julien Chaumond <chaumond@gmail.com>

* typo fixes

* app_without_asset

* cleanup

* Delete ELI5animation.gif

* Delete ELI5contrastive.svg

* Delete ELI5wiki_index.svg

* Delete choco_bis.svg

* Delete fireworks.gif

* Delete huggingface_logo.jpg

* Delete huggingface_logo.svg

* Delete Long_Form_Question_Answering_with_ELI5_and_Wikipedia.ipynb

* Delete eli5_app.py

* Delete eli5_utils.py

* readme

* Update README.md

* unused imports

* moved_info

* default_beam

* ftuned model

* disclaimer

* Update src/transformers/modeling_retribert.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* black

* add_doc

* names

* isort_Examples

* isort_Examples

* Add doc to index
Co-authored-by: Julien Chaumond <chaumond@gmail.com>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>

* Convert hans to Trainer

* Tick box

Co-authored-by: Sam Shleifer <sshleifer@gmail.com>

* Add reference to NLP dataset

* Update README.md
Co-authored-by: Julien Chaumond <chaumond@gmail.com>

* Add reference to NLP (package) dataset

* Update README.md
Co-authored-by: Julien Chaumond <chaumond@gmail.com>

* Added is_fast property on BatchEncoding to indicate if the object comes from a Fast Tokenizer.

* Added __get_state__() & __set_state__() to be pickable.

* Correct tokens() return type from List[int] to List[str]

* Added unittest for BatchEncoding pickle/unpickle

* Added unittest for BatchEncoding is_fast

* More careful checking on BatchEncoding unpickle tests.

* Formatting.

* is_fast should assertTrue on Rust tokenizers.

* Ensure tensorflow has correct way of checking array_equal

* More formatting.

* Add `DistilBertForMultipleChoice`

[HUGE] Refactoring tokenizers backend - padding - truncation - pre-tokenized pipeline - fast tokenizers - tests (#4510)

* Use tokenizers pre-tokenized pipeline

* failing pretrokenized test

* Fix is_pretokenized in python

* add pretokenized tests

* style and quality

* better tests for batched pretokenized inputs

* tokenizers clean up - new padding_strategy - split the files

* [HUGE] refactoring tokenizers - padding - truncation - tests

* style and quality

* bump up requied tokenizers version to 0.8.0-rc1

* switched padding/truncation API - simpler better backward compat

* updating tests for custom tokenizers

* style and quality - tests on pad

* fix QA pipeline

* fix backward compatibility for max_length only

* style and quality

* Various cleans up - add verbose

* fix tests

* update docstrings

* Fix tests

* Docs reformatted

* __call__ method documented
Co-authored-by: Thomas Wolf <thomwolf@users.noreply.github.com>
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>

* fix test

* Update tests/test_modeling_common.py

* Update tests/test_modeling_common.py

* feat(tftrainer): improve logging

* fix(trainer): consider case with evaluation only

* refactor(tftrainer): address comments

* refactor(tftrainer): move self.epoch_logging to __init__

* Increase pipeline support for ONNX export.

* Style.

* Make DataCollator a callable

* Update src/transformers/data/data_collator.py
Co-authored-by: Julien Chaumond <chaumond@gmail.com>

* manually set device in trainer args

* check if current device is cuda before set_device

* Explicitly set GPU ID when using single GPU

This addresses https://github.com/huggingface/transformers/issues/4657#issuecomment-642228099

* Create README.md

* Update model_cards/ipuneetrathore/bert-base-cased-finetuned-finBERT/README.md
Co-authored-by: Julien Chaumond <chaumond@gmail.com>

* utils_ner: do not add extra sep token for RoBERTa model

* run_pl_ner: do not add extra sep token for RoBERTa model

* [model card] model card for bart-large-finetuned-squadv1

* add metadata link to the dataset

* Improve ONNX export logging to give more information about the generated graph.

* Correctly handle input and output in the logging.

* Update hans data to be able to use Trainer

* Fixes

* Deal with tokenizer that don't have token_ids

* Clean up things

* Simplify data use

* Fix the input dict

* Formatting + proper path in README

see corresponding model pages to see how it's rendered

* Add AlbertForMultipleChoice

* Make up to date and add all models to common tests

* first commit

* add new auto models

* better naming

* fix bert automodel

* fix automodel for pretraining

* add models to init

* fix name typo

* fix typo

* better naming

* future warning instead of depreciation warning