GitLab

* 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

* Better filtering of the model outputs in Trainer

* Fix examples tests

* Add test for Lysandre

* 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...

Improved TensorBoard and Wandb integration, as well as optuna and ray/tune support, with minor modifications to trainer core code.

* 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>

* Add eval_accumulation_step and clean distributed eval

* Add TPU test

* Add TPU stuff

* Fix arg name

* Fix Seq2SeqTrainer

* Fix total_size

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

* Doc and add test to TPU

* Add unit test

* Adapt name
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* 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

* Add automatic best model loading to Trainer

* Some small fixes

* Formatting

* Make big downloads as slow

* Add import

* Right order for slow decorator

* More slow tests

* Trainer accep multiple labels

* Missing import

* Fix dosctrings

* 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

* Allow multiple outputs

* Formatting

* Move the unwrapping before metrics

* Fix typo

* Add test for non-supported config options

* Fix reproducible tests in Trainer

* Deal with multiple GPUs

* Temporarily skip failing tests due to dependency change

* Remove trace

* these tests require non-multigpu env

* cleanup

* clarify

* nlp -> datasets

* More nlp -> datasets

* Woopsie

* More nlp -> datasets

* One last

* [testing] replace hardcoded paths to allow running tests from anywhere

* fix the merge conflict

* More tests to Trainer

* Add warning in the doc

* Add tests to Trainer

* Test if removing long breaks everything

* Remove ugly hack

* Fix distributed test

* Use float for number of epochs

* 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

* allow tensor label inputs to default collator

* replace try/except with type check

* Make default_data_collator more flexible

* Accept tensors for all features

* Document code

* Refactor

* Formatting

* Make DataCollator a callable

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

* Fix argument label

* Fix test

Adds predict stage for glue tasks, and generate result files which can be submitted to gluebenchmark.com (#4463)

* 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>

* Improvements to the wandb integration

* small reorg + no global necessary

* feat(trainer): log epoch and final metrics

* Simplify logging a bit

* Fixup

* Fix crash when just running eval
Co-authored-by: Chris Van Pelt <vanpelt@gmail.com>
Co-authored-by: Boris Dayma <boris.dayma@gmail.com>

* Created using Colaboratory

* [examples] reorganize files

* remove run_tpu_glue.py as superseded by TPU support in Trainer

* Bugfix: int, not tuple

* move files around

* doc

* [tests] Add sample files for a regression task

* [HUGE] Trainer

* Feedback from @sshleifer

* Feedback from @thomwolf + logging tweak

* [file_utils] when downloading concurrently, get_from_cache will use the cached file for subsequent processes

* [glue] Use default max_seq_length of 128 like before

* [glue] move DataTrainingArguments around

* [ner] Change interface of InputExample, and align run_{tf,pl}

* Re-align the pl scripts a little bit

* ner

* [ner] Add integration test

* Fix language_modeling with API tweak

* [ci] Tweak loss target

* Don't break console output

* amp.initialize: model must be on right device before

* [multiple-choice] update for Trainer

* Re-align to 827d6d6e