GitLab

* added predict stage

* added test keyword in exception message

* removed example specific saving predictions

* fixed f-string error

* removed extra line
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>

* Add minimum version check in examples

* Style

* No need for new line maybe?

* Add helpful comment

* Add new GLUE example with no Trainer.

* Style

* Address review comments

Fixes an issue in `text-classification` where MNLI eval/test datasets are not being preprocessed. (#10621)

* Fix MNLI tests

* Linter fix

* reverted changes of logging and saving metrics

* added max_sample arguments

* fixed code

* white space diff

* reformetting code

* reformatted code

This reverts commit f3660613.

* updated logging and saving metrics

* space removal

* remove xnli_compute_metrics, add load_dataset, load_metric, set_seed,metric.compute,load_metric

* fix

* fix

* fix

* push

* fix

* everything works

* fix init

* fix

* special treatment for sepconv1d

* style

* 🙏🏽

* add doc and cleanup


* fix doc

* fix doc again

* fix doc again

* Apply suggestions from code review

* make style

* Proposal that should work

* Remove needless code

* Fix test

* Apply suggestions from code review

* remove xnli_compute_metrics, add load_dataset, load_metric, set_seed,metric.compute,load_metric

* amend README

* removed data_args.task_name and replaced with task_name = "xnli"; use split function to load train and validation dataset separately; remove __post_init__; remove flag --task_name from README.

* removed dict task_to_keys, use str "xnli" instead of variable task_name, change preprocess_function to use examples["premise"], examples["hypothesis"] directly, remove sentence1_key and sentence2_key, change compute_metrics function to cater only to accuracy metric, add condition for train_langauge is None when using dataset.load_dataset()

* removed `torch.distributed.barrier()` and `import torch` as `from_pretrained` is able to do the work; amend README

* make executable

* make executable

* same for the template

* cleanup

* change tokenizer requirement

* split line

* Correct typo from list to str

* improve style

* make other function pretty as well

* add comment

* correct typo

* add new test

* pass tests for tok without padding token

* Apply suggestions from code review

* Pad to 8x for fp16 multiple choice example (#9752)

* Pad to 8x for fp16 squad trainer example (#9752)

* Pad to 8x for fp16 ner example (#9752)

* Pad to 8x for fp16 swag example (#9752)

* Pad to 8x for fp16 qa beam search example (#9752)

* Pad to 8x for fp16 qa example (#9752)

* Pad to 8x for fp16 seq2seq example (#9752)

* Pad to 8x for fp16 glue example (#9752)

* Pad to 8x for fp16 new ner example (#9752)

* update script template #9752

* Update examples/multiple-choice/run_swag.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update examples/question-answering/run_qa.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update examples/question-answering/run_qa_beam_search.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* improve code quality #9752
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Auto-resume training from checkpoint

* Update examples/text-classification/run_glue.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Roll out to other examples
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Update run_glue for do_predict with local test data (#9442)

* Update run_glue (#9442): fix comments ('files' to 'a file')

* Update run_glue (#9442): reflect the code review

* Update run_glue (#9442): auto format

* Update run_glue (#9442): reflect the code review

Co-authored-by: Pavel Tarashkevich <Pavel.Tarashkievich@orange.com>

* Allow example to use a revision and work with private models

* Copy to other examples and template

* Styling

* Update the README of the text classification example

* Update examples/README.md
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Adapt comment from review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* add speed metrics

* suggestions

* 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>

Without this fix, training a `BARTForSequenceClassification` model with `run_pl_glue.py` gives `TypeError: forward() got an unexpected keyword argument 'token_type_ids'`, because BART does not have token_type_ids. I've solved this issue in the same way as it's solved for the "distilbert" model, and I can train BART models on SNLI without errors now.

* <small>tiny typo</small>

* Tokenizers: ability to load from model subfolder

* use subfolder for local files as well

* Uniformize model shortcut name => model id

* from s3 => from huggingface.co
Co-authored-by: Quentin Lhoest <lhoest.q@gmail.com>

* Finish the cleanup of the language-modeling examples

* Update main README

* Apply suggestions from code review
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Apply suggestions from code review
Co-authored-by: Thomas Wolf <thomwolf@users.noreply.github.com>

* Propagate changes
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Thomas Wolf <thomwolf@users.noreply.github.com>

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

* New run_clm script

* Formatting

* More comments

* Remove unused imports

* Apply suggestions from code review
Co-authored-by: Thomas Wolf <thomwolf@users.noreply.github.com>

* Address review comments

* Change link to the hub
Co-authored-by: Thomas Wolf <thomwolf@users.noreply.github.com>

* Start simplification

* More progress

* Finished script

* Address comments and update tests instructions

* Wrong test

* Accept files as inputs and fix test

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

* Fix labels and add combined score

* Add special labels

* Update TPU command

* Revert to old label strategy

* Use model labels

* Fix for STT-B

* Styling

* Apply suggestions from code review
Co-authored-by: Thomas Wolf <thomwolf@users.noreply.github.com>

* Code styling

* Fix review comments
Co-authored-by: Julien Chaumond <chaumond@gmail.com>
Co-authored-by: Thomas Wolf <thomwolf@users.noreply.github.com>

* Don't use `store_xxx` on optional bools

* Refine test

* Refine test

* Fix test

* fix generic text classification

* fix test

* Fix tests

* Fix test

* Fix cardinality issue

* Fix test

* Add new example with nlp

* Update README

* replace nlp by datasets

* Update examples/text-classification/README.md

Add Lysandre's suggestion.
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>