* Allow tests in examples to use cuda or fp16,if they are available
The tests in examples didn't use the cuda or fp16 even if they where available.
- The text classification example (`run_glue.py`) didn't use the fp16 even if it was available but
the device was take based on the availablity(cuda/cpu).
- The language-modeling example (`run_language_modeling.py`) was having `--no_cuda` argument
which made the test to work without cuda. This example is having issue when running with fp16
thus it not enabled (got an assertion error for perplexity due to it higher value).
- The cuda and fp16 is not enabled for question-answering example (`run_squad.py`) as it is having a
difference in the f1 score.
- The text-generation example (`run_generation.py`) will take the cuda or fp16 whenever it is available.
Resolves some of: #5057
* Unwanted import of is_apex_available was removed
* Made changes to test examples file to have the pass --fp16 only if cuda and apex is avaliable
- run_glue.py: Removed the check for cuda and fp16.
- run_generation.py: Removed the check for cuda and fp16 also removed unwanted flag creation.
* Incorrectly sorted imports fixed
* The model needs to be converted to half precision
* Formatted single line if condition statement to multiline
* The torch_device also needed to be checked before running the test on examples
- The tests in examples which uses cuda should also depend from the USE_CUDA flag,
similarly to the rest of the test suite. Even if we decide to set USE_CUDA to
True by default, setting USE_CUDA to False should result in the examples not using CUDA
* Format some of the code in test_examples file
* The improper import of is_apex_available was sorted
* Formatted the code to keep the style standards
* The comma at the end of list giving a flake8 issue was fixed
* Import sort was fixed
* Removed the clean_test_dir function as its not used right now
* [testing] switch to a new TestCasePlus + get_auto_remove_tmp_dir() for auto-removal of tmp dirs
* respect after=True for tempfile, simplify code
* comments
* comment fix
* put `before` last in args, so can make debug even faster
* Add more token classification examples
* POS tagging example
* Phrase chunking example
* PR review fixes
* Add conllu to third party list (used in token classification examples)
* replace capsys with the more refined CaptureStderr/CaptureStdout
* Update examples/seq2seq/test_seq2seq_examples.py
Co-authored-by: Sam Shleifer <sshleifer@gmail.com>
* [wip] add get_polynomial_decay_schedule_with_warmup
* style
* add assert
* change lr_end to a much smaller default number
* check for exact equality
* [model_cards] electra-base-turkish-cased-ner (#6350)
* for electra-base-turkish-cased-ner
* Add metadata
Co-authored-by: Julien Chaumond <chaumond@gmail.com>
* Temporarily de-activate TPU CI
* Update modeling_tf_utils.py (#6372)
fix typo: ckeckpoint->checkpoint
* the test now works again (#6371)
* correct pl link in readme (#6364)
* refactor almost identical tests (#6339)
* refactor almost identical tests
* important to add a clear assert error message
* make the assert error even more descriptive than the original bt
* Small docfile fixes (#6328)
* Patch models (#6326)
* TFAlbertFor{TokenClassification, MultipleChoice}
* Patch models
* BERT and TF BERT info
s
* Update check_repo
* Ci GitHub caching (#6382)
* Cache Github Actions CI
* Remove useless file
* Colab button (#6389)
* Add colab button
* Add colab link for tutorials
* Fix links for open in colab (#6391)
* Update src/transformers/optimization.py
consistently use lr_end=1e-7 default
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* [wip] add get_polynomial_decay_schedule_with_warmup
* style
* add assert
* change lr_end to a much smaller default number
* check for exact equality
* Update src/transformers/optimization.py
consistently use lr_end=1e-7 default
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* remove dup (leftover from merge)
* convert the test into the new refactored format
* stick to using the current_step as is, without ++
Co-authored-by: M. Yusuf Sarıgöz <yusufsarigoz@gmail.com>
Co-authored-by: Julien Chaumond <chaumond@gmail.com>
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
Co-authored-by: Alexander Measure <ameasure@gmail.com>
Co-authored-by: Rohit Gupta <rohitgr1998@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* 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
as discussed with @sshleifer, removing this TODO to switch to a tiny model, since it won't be able to test the results of the evaluation (i.e. the results are meaningless).
* Support for Comet.ml
* Need to import comet first
* Log this model, not the one in the backprop step
* Log args as hyperparameters; use framework to allow fine control
* Log hyperparameters with context
* Apply black formatting
* isort fix integrations
* isort fix __init__
* Update src/transformers/trainer.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/trainer.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/trainer_tf.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Address review comments
* Style + Quality, remove Tensorboard import test
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
* support --lr_scheduler with multiple possibilities
* correct the error message
* add a note about supported schedulers
* cleanup
* cleanup2
* needs the argument default
* style
* add another assert in the test
* implement requested changes
* cleanups
* fix relative import
* cleanup
* enable easy checkout switch
allow having multiple repository checkouts and not needing to remember to rerun 'pip install -e .[dev]' when switching between checkouts and running tests.
* make isort happy
* examples needs one too
* Switch from return_tuple to return_dict
* Fix test
* [WIP] Test TF Flaubert + Add {XLM, Flaubert}{TokenClassification, MultipleC… (#5614)
* Test TF Flaubert + Add {XLM, Flaubert}{TokenClassification, MultipleChoice} models and tests
* AutoModels
Tiny tweaks
* Style
* Final changes before merge
* Re-order for simpler review
* Final fixes
* Addressing @sgugger's comments
* Test MultipleChoice
* Rework TF trainer (#6038)
* 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
* Switch from return_tuple to return_dict
* Fix test
* Add recent model
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Julien Plu <plu.julien@gmail.com>
* 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
* DataParallel fixes:
1. switched to a more precise check
- if self.args.n_gpu > 1:
+ if isinstance(model, nn.DataParallel):
2. fix tests - require the same fixup under DataParallel as the training module
* another fix
* Add deebert code
* Add readme of deebert
* Add test for deebert
Update test for Deebert
* Update DeeBert (README, class names, function refactoring); remove requirements.txt
* Format update
* Update test
* Update readme and model init methods
* 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
Otherwise, if label is not specified, the following error occurs:
Traceback (most recent call last):
File "run_ner.py", line 303, in <module>
main()
File "run_ner.py", line 101, in main
model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
File "/home/user/anaconda3/envs/bert/lib/python3.7/site-packages/transformers/hf_argparser.py", line 159, in parse_json_file
obj = dtype(**inputs)
TypeError: __init__() missing 1 required positional argument: 'labels'
* Fix the bug 'Attempted relative import with no known parent package' when using the bertabs example. Also change the used model from bertabs-finetuned-cnndm, since it seems not be accessible anymore
* Update run_summarization.py
Co-authored-by: Kevin Canwen Xu <canwenxu@126.com>
* remove references to old API in docstring - update data processors
* style
* fix tests - better type checking error messages
* better type checking
* include awesome fix by @LysandreJik for #5310
* updated doc and examples
* Add BERT Loses Patience (Patience-based Early Exit)
* update model archive
* update format
* sort import
* flake8
* Add results
* full results
* align the table
* refactor to inherit
* default per gpu eval = 1
* Formatting
* Formatting
* isort
* modify readme
* Add check
* Fix format
* Fix format
* Doc strings
* ALBERT & BERT for sequence classification don't inherit from the original anymore
* Remove incorrect comments
* Remove incorrect comments
* Remove incorrect comments
* Sync up with new code
* Sync up with new code
* Add a test
* Add a test
* Add a test
* Add a test
* Add a test
* Add a test
* Finishing up!
* 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
* ner: add preprocessing script for examples that splits longer sentences
* ner: example shell scripts use local preprocessing now
* ner: add new example section for WNUT’17 NER task. Remove old English CoNLL-03 results
* ner: satisfy black and isort
* 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>
* Kill model archive maps
* Fixup
* Also kill model_archive_map for MaskedBertPreTrainedModel
* Unhook config_archive_map
* Tokenizers: align with model id changes
* make style && make quality
* Fix CI
The option `--do_lower_case` is currently required by the uncased models (i.e., bert-base-uncased, bert-large-uncased).
Results:
BERT-BASE without --do_lower_case: 'exact': 73.83, 'f1': 82.22
BERT-BASE with --do_lower_case: 'exact': 81.02, 'f1': 88.34
* 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>
* Distributed eval: SequentialDistributedSampler + gather all results
* For consistency only write to disk from world_master
Close https://github.com/huggingface/transformers/issues/4272
* Working distributed eval
* Hook into scripts
* Fix#3721 again
* TPU.mesh_reduce: stay in tensor space
Thanks @jysohn23
* Just a small comment
* whitespace
* torch.hub: pip install packaging
* Add test scenarii
* Add QA trainer example for TF
* Make data_dir optional
* Fix parameter logic
* Fix feature convert
* Update the READMEs to add the question-answering task
* Apply style
* Change 'sequence-classification' to 'text-classification' and prefix with 'eval' all the metric names
* Apply style
* Apply style
* 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>
* catch gpu len 1 set to gpu0
* Add mpc to trainer
* Add MPC for TF
* fix TF automodel for MPC and add Albert
* Apply style
* Fix import
* Note to self: double check
* Make shape None, None for datasetgenerator output shapes
* Add from_pt bool which doesnt seem to work
* Original checkpoint dir
* Fix docstrings for automodel
* Update readme and apply style
* Colab should probably not be from users
* Colabs should probably not be from users
* Add colab
* Update README.md
* Update README.md
* Cleanup __intit__
* Cleanup flake8 trailing comma
* Update src/transformers/training_args_tf.py
* Update src/transformers/modeling_tf_auto.py
Co-authored-by: Viktor Alm <viktoralm@pop-os.localdomain>
Co-authored-by: Julien Chaumond <chaumond@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
* First commit to add a TF version of the trainer.
* Make the TF trainer closer to what looks the PT trainer
* Refactoring common code between the PT and TF trainer into an util file.
* Some bugfix + better similarity with the PT trainer
* Add missing class in transformers init
* Bugfix over prediction + use classification report instead of simple metrics
* Fix name error
* Fix optimization tests + style
* Apply style
* Several bugfix for multi-gpu training
* Apply style
* Apply style
* Add glue example for the TF trainer
* Several bugix + address the reviews
* Fix on the TF training args file
* Add a debug mode
* Bugfix in utils_ner.py when segment_ids is None
* Apply style
* Apply style
* Add TPU strategy
* Fix selection strategy
