GitLab

* 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

* Support multiple choice in tf common model tests

* Add the input_embeds test

* Fixed resize_token_embeddings for transfo_xl model

* Fixed resize_token_embeddings for transfo_xl.

Added custom methods to TransfoXLPreTrainedModel for resizing layers of
the AdaptiveEmbedding.

* Updated docstring

* Fixed resizinhg cutoffs; added check for new size of embedding layer.

* Added test for resize_token_embeddings

* Fixed code quality

* Fixed unchanged cutoffs in model.config
Co-authored-by: Rafael Weingartner <rweingartner.its-b2015@fh-salzburg.ac.at>

* Split LMBert model in two

* Fix example

* Remove lm_labels

* Adapt tests, refactor prepare_for_generation

* Fix merge

* Hide BeartLMHeadModel

* check type before logging to ensure it's a scalar

* log when Trainer attempts to add a non-scalar value using TensorboardX's writer.add_scalar so we know what kinds of fixes are appropriate

* black it

* rephrase log message to clarify attribute was dropped
Co-authored-by: Julien Chaumond <chaumond@gmail.com>
Co-authored-by: Julien Chaumond <chaumond@gmail.com>

* Run a single wandb instance per TPU run

* wandb: self.is_world_master

* make style
Co-authored-by: Julien Chaumond <chaumond@gmail.com>

* ElectraForQuestionAnswering

* udate __init__

* add test for electra qa model

* add ElectraForQuestionAnswering in auto models

* add ElectraForQuestionAnswering in all_model_classes

* fix outputs, input_ids defaults to None

* add ElectraForQuestionAnswering in docs

* remove commented line