* 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
* 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
* 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>
* 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>
* 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>
* add pl_glue example test
* for now just test that it runs, next validate results of eval or predict?
* complete the run_pl_glue test to validate the actual outcome
* worked on my machine, CI gets less accuracy - trying higher epochs
* match run_pl.sh hparms
* more epochs?
* trying higher lr
* for now just test that the script runs to a completion
* correct the comment
* if cuda is available, add --fp16 --gpus=1 to cover more bases
* style
* Fully rework training/prediction loops
* fix method name
* Fix variable name
* Fix property name
* Fix scope
* Fix method name
* Fix tuple index
* Fix tuple index
* Fix indentation
* Fix variable name
* fix eval before log
* Add drop remainder for test dataset
* Fix step number + fix logging datetime
* fix eval loss value
* use global step instead of step + fix logging at step 0
* Fix logging datetime
* Fix global_step usage
* Fix breaking loop + logging datetime
* Fix step in prediction loop
* Fix step breaking
* Fix train/test loops
* Force TF at least 2.2 for the trainer
* Use assert_cardinality to facilitate the dataset size computation
* Log steps per epoch
* Make tfds compliant with TPU
* Make tfds compliant with TPU
* Use TF dataset enumerate instead of the Python one
* revert previous commit
* Fix data_dir
* Apply style
* rebase on master
* Address Sylvain's comments
* Address Sylvain's and Lysandre comments
* Trigger CI
* Remove unused import
* Glue task cleaup
* Enable writing cache to cache_dir in case dataset lives in readOnly
filesystem.
* Differentiate match vs mismatch for MNLI metrics.
* Style
* Fix pytype
* Fix type
* Use cache_dir in mnli mismatch eval dataset
* Small Tweaks
Co-authored-by: Julien Chaumond <chaumond@gmail.com>
