* Add early stopping patience and minimum threshold metric must improve to prevent early stopping to pytorch trainer
* Add early stopping test
* Set patience counter to 0 if best metric not defined yet
* Make early stopping a callback. Add callback event for updating the best metric for early stopping callback to trigger on.
* Run make style
* make funciton name sensible
* Improve new argument docstring wording and hope that flakey CI test passes.
* Use on_evaluation callback instead of custom. Remove some debug printing
* Move early stopping arguments and state into early stopping callback
* Run make style
* Remove old code
* Fix docs formatting. make style went rogue on me.
* Remove copied attributes and fix variable
* Add assertions on training arguments instead of mutating them. Move comment out of public docs.
* Make separate test for early stopping callback. Add test of invalid arguments.
* Run make style... I remembered before CI this time!
* appease flake8
* Add EarlyStoppingCallback to callback docs
* Make docstring EarlyStoppingCallabck match other callbacks.
* Fix typo in docs
* Make Trainer evaluation handle dynamic seq_length
* Document behavior.
* Fix test
* Better fix
* Fixes for realsies this time
* Address review comments
* Without forgetting to save...
* fix 5990
* accomodate iterable dataset without predefined length
* set it as 1 use case: provide max_steps, and NO num_epochs
* Is a merge of master and PR 5995
* fix trainer test under TF
* fix only for torch
* TF trainer untouched
* trainer tests are skipped when no torch
* address comments
* fix quality checks
* remove torch.dataset from test_trainer
* unnecessary inheritance
* RegressionDataset implements all needed methods __len__ and __getitem__
* fix quality checks
* restore RegressionDataset
* was wrongly under is_torch_available()
* splitting fast and slow tokenizers [WIP]
* [WIP] splitting sentencepiece and tokenizers dependencies
* update dummy objects
* add name_or_path to models and tokenizers
* prefix added to file names
* prefix
* styling + quality
* spliting all the tokenizer files - sorting sentencepiece based ones
* update tokenizer version up to 0.9.0
* remove hard dependency on sentencepiece 🎉
* and removed hard dependency on tokenizers 🎉
* update conversion script
* update missing models
* fixing tests
* move test_tokenization_fast to main tokenization tests - fix bugs
* bump up tokenizers
* fix bert_generation
* update ad fix several tokenizers
* keep sentencepiece in deps for now
* fix funnel and deberta tests
* fix fsmt
* fix marian tests
* fix layoutlm
* fix squeezebert and gpt2
* fix T5 tokenization
* fix xlnet tests
* style
* fix mbart
* bump up tokenizers to 0.9.2
* fix model tests
* fix tf models
* fix seq2seq examples
* fix tests without sentencepiece
* fix slow => fast conversion without sentencepiece
* update auto and bert generation tests
* fix mbart tests
* fix auto and common test without tokenizers
* fix tests without tokenizers
* clean up tests lighten up when tokenizers + sentencepiece are both off
* style quality and tests fixing
* add sentencepiece to doc/examples reqs
* leave sentencepiece on for now
* style quality split hebert and fix pegasus
* WIP Herbert fast
* add sample_text_no_unicode and fix hebert tokenization
* skip FSMT example test for now
* fix style
* fix fsmt in example tests
* update following Lysandre and Sylvain's comments
* Update src/transformers/testing_utils.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/testing_utils.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/tokenization_utils_base.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/tokenization_utils_base.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Trainer should not modify its TrainingArguments
* Trainer should not modify its TrainingArguments
* Trainer should not modify its TrainingArguments
* Add test of resumed training
* Fixes
* Non multiGPU test
* Clean Trainer state
* Add more to the state
* Documentation
* One last test
* Make resume training test more complete
* Unwanted changes
* fix ZeroDivisionError and epoch counting
* Add test for num_train_epochs calculation in trainer.py
* Remove @require_non_multigpu for test_num_train_epochs_in_training
* Don't pass sampler for iterable dataset
* Added check for test and eval dataloaders.
* Formatting
* Don't pass sampler for iterable dataset
* Added check for test and eval dataloaders.
* Formatting
* Cleaner if nesting.
* Added test for trainer and iterable dataset
* Formatting for test
* Fixed import when torch is available only.
* Added require torch decorator to helper class
* Moved dataset class inside unittest
* Removed nested if and changed model in test
* Checking torch availability for IterableDataset
* Added data collator for XLNet language modeling and related calls
Added DataCollatorForXLNetLanguageModeling in data/data_collator.py
to generate necessary inputs for language modeling training with
XLNetLMHeadModel. Also added related arguments, logic and calls in
examples/language-modeling/run_language_modeling.py.
Resolves: #4739, #2008 (partially)
* Changed name to `DataCollatorForPermutationLanguageModeling`
Changed the name of `DataCollatorForXLNetLanguageModeling` to the more general `DataCollatorForPermutationLanguageModelling`.
Removed the `--mlm` flag requirement for the new collator and defined a separate `--plm_probability` flag for its use.
CTRL uses a CLM loss just like GPT and GPT-2, so should work out of the box with this script (provided `past` is taken care of
similar to `mems` for XLNet).
Changed calls and imports appropriately.
* Added detailed comments, changed variable names
Added more detailed comments to `DataCollatorForPermutationLanguageModeling` in `data/data_collator.py` to explain working. Also cleaned up variable names and made them more informative.
* Added tests for new data collator
Added tests in `tests/test_trainer.py` for DataCollatorForPermutationLanguageModeling based on those in DataCollatorForLanguageModeling. A specific test has been added to check for odd-length sequences.
* Fixed styling issues
* Adds predict stage for glue tasks, and generate result files which could be submitted to gluebenchmark.com website.
* Use Split enum + always output the label name
Co-authored-by: Julien Chaumond <chaumond@gmail.com>
