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* remove run_tpu_glue.py as superseded by TPU support in Trainer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
* 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 827d6d6ef0
* First pass on utility classes and python tokenizers
* finishing cleanup pass
* style and quality
* Fix tests
* Updating following @mfuntowicz comment
* style and quality
* Fix Roberta
* fix batch_size/seq_length inBatchEncoding
* add alignement methods + tests
* Fix OpenAI and Transfo-XL tokenizers
* adding trim_offsets=True default for GPT2 et RoBERTa
* style and quality
* fix tests
* add_prefix_space in roberta
* bump up tokenizers to rc7
* style
* unfortunately tensorfow does like these - removing shape/seq_len for now
* Update src/transformers/tokenization_utils.py
Co-Authored-By: Stefan Schweter <stefan@schweter.it>
* Adding doc and docstrings
* making flake8 happy
Co-authored-by: Stefan Schweter <stefan@schweter.it>
* Refactored use of newstest2013 to newstest2014. Fixed bug where argparse consumed first command line argument as model_size argument rather than using default model_size by forcing explicit --model_size flag inclusion
* More pythonic file handling through 'with' context
* COSMETIC - ran Black and isort
* Fixed reference to number of lines in newstest2014
* Fixed failing test. More pythonic file handling
* finish PR from tholiao
* remove outcommented lines
* make style
* make isort happy
Co-authored-by: Thomas Liao <tholiao@gmail.com>
* remove output_past from pt
* make style
* add optional input length for gpt2
* add use cache to prepare input
* save memory in gpt2
* correct gpt2 test inputs
* make past input optional for gpt2
* finish use_cache for all models
* make style
* delete modeling_gpt2 change in test file
* correct docstring
* correct is true statements for gpt2
* Initial commit to get BERT + run_glue.py on TPU
* Add README section for TPU and address comments.
* Cleanup TPU bits from run_glue.py (#3)
TPU runner is currently implemented in:
https://github.com/pytorch-tpu/transformers/blob/tpu/examples/run_glue_tpu.py.
We plan to upstream this directly into `huggingface/transformers`
(either `master` or `tpu`) branch once it's been more thoroughly tested.
* Cleanup TPU bits from run_glue.py
TPU runner is currently implemented in:
https://github.com/pytorch-tpu/transformers/blob/tpu/examples/run_glue_tpu.py.
We plan to upstream this directly into `huggingface/transformers`
(either `master` or `tpu`) branch once it's been more thoroughly tested.
* No need to call `xm.mark_step()` explicitly (#4)
Since for gradient accumulation we're accumulating on batches from
`ParallelLoader` instance which on next() marks the step itself.
* Resolve R/W conflicts from multiprocessing (#5)
* Add XLNet in list of models for `run_glue_tpu.py` (#6)
* Add RoBERTa to list of models in TPU GLUE (#7)
* Add RoBERTa and DistilBert to list of models in TPU GLUE (#8)
* Use barriers to reduce duplicate work/resources (#9)
* Shard eval dataset and aggregate eval metrics (#10)
* Shard eval dataset and aggregate eval metrics
Also, instead of calling `eval_loss.item()` every time do summation with
tensors on device.
* Change defaultdict to float
* Reduce the pred, label tensors instead of metrics
As brought up during review some metrics like f1 cannot be aggregated
via averaging. GLUE task metrics depends largely on the dataset, so
instead we sync the prediction and label tensors so that the metrics can
be computed accurately on those instead.
* Only use tb_writer from master (#11)
* Apply huggingface black code formatting
* Style
* Remove `--do_lower_case` as example uses cased
* Add option to specify tensorboard logdir
This is needed for our testing framework which checks regressions
against key metrics writtern by the summary writer.
* Using configuration for `xla_device`
* Prefix TPU specific comments.
* num_cores clarification and namespace eval metrics
* Cache features file under `args.cache_dir`
Instead of under `args.data_dir`. This is needed as our test infra uses
data_dir with a read-only filesystem.
* Rename `run_glue_tpu` to `run_tpu_glue`
Co-authored-by: LysandreJik <lysandre.debut@reseau.eseo.fr>
