* 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* remove run_tpu_glue.py as superseded by TPU support in Trainer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>
* [examples] Generate argparsers from type hints on dataclasses
* [HfArgumentParser] way simpler API
* Restore run_language_modeling.py for easier diff
* [HfArgumentParser] final tweaks from code review
* Big cleanup of `glue_convert_examples_to_features`
* Use batch_encode_plus
* Cleaner wrapping of glue_convert_examples_to_features for TF
@lysandrejik
* Cleanup syntax, thanks to @mfuntowicz
* Raise explicit error in case of user error
* Fix RoBERTa/XLNet Pad Token in run_multiple_choice.py
`convert_examples_to_fes atures` sets `pad_token=0` by default, which is correct for BERT but incorrect for RoBERTa (`pad_token=1`) and XLNet (`pad_token=5`). I think the other arguments to `convert_examples_to_features` are correct, but it might be helpful if someone checked who is more familiar with this part of the codebase.
* Simplifying change to match recent commits
* Using loaded checkpoint with --do_predict
Without this fix, I'm getting near-random validation performance for a trained model, and the validation performance differs per validation run. I think this happens since the `model` variable isn't set with the loaded checkpoint, so I'm using a randomly initialized model. Looking at the model activations, they differ each time I run evaluation (but they don't with this fix).
* Update checkpoint loading
* Fixing model loading
* Update the NER TF script to remove the softmax and make the pad token label id to -1
* Reformat the quality and style
Co-authored-by: Julien Plu <julien.plu@adevinta.com>
* force bleu
* fix wrong file name
* rename file
* different filenames for each example test
* test files should clean up after themselves
* test files should clean up after themselves
* do not force bleu
* correct typo
* fix isort
* Use tokenizer.num_added_tokens to count number of added special_tokens instead of hardcoded numbers.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* run_ner.py - Do not add a label to the labels_ids if word_tokens is empty.
This can happen when using bert-base-multilingual-cased with an input containing an unique space.
In this case, the tokenizer will output just an empty word_tokens thus leading to an non-consistent behavior
over the labels_ids tokens adding one more tokens than tokens vector.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* ✨ Alter base pl transformer to use automodels
* 🐛 Add batch size env variable to function call
* 💄 Apply black code style from Makefile
* 🚚 Move lightning base out of ner directory
* ✨ Add lightning glue example
* 💄 self
* move _feature_file to base class
* ✨ Move eval logging to custom callback
* 💄 Apply black code style
* 🐛 Add parent to pythonpath, remove copy command
* 🐛 Add missing max_length kwarg
* memory benchmark rss
* have both forward pass and line-by-line mem tracing
* cleaned up tracing
* refactored and cleaning up API
* no f-strings yet...
* add GPU mem logging
* fix GPU memory monitoring
* style and quality
* clean up and doc
* update with comments
* Switching to python 3.6+
* fix quality
* Rename and improve example
* Add test
* slightly faster test
* style
* This breaks remy prolly
* shorter test string
* no slow
* newdir structure
* New tree
* Style
* shorter
* docs
* clean
* Attempt future import
* more import hax
* * Added support for Albert when fine-tuning for NER
* Added support for Albert in NER pipeline
* Added command-line options to examples/ner/run_ner.py to better control tokenization
* Added class AlbertForTokenClassification
* Changed output for NerPipeline to use .convert_ids_to_tokens(...) instead of .decode(...) to better reflect tokens
* Added ,
* Now passes style guide enforcement
* Changes from reviews.
* Code now passes style enforcement
* Added test for AlbertForTokenClassification
* Added test for AlbertForTokenClassification
* add preprocessing to add space before punctuation for transfo_xl
* improve warning messages
* make style
* compile regex at instantination of tokenizer object
* Added support for Albert in NER pipeline
* Added command-line options to examples/ner/run_ner.py to better control tokenization
* Added class AlbertForTokenClassification
* Changed output for NerPipeline to use .convert_ids_to_tokens(...) instead of .decode(...) to better reflect tokens
* improving generation
* finalized special token behaviour for no_beam_search generation
* solved modeling_utils merge conflict
* solve merge conflicts in modeling_utils.py
* add run_generation improvements from PR #2749
* adapted language generation to not use hardcoded -1 if no padding token is available
* remove the -1 removal as hard coded -1`s are not necessary anymore
* add lightweight language generation testing for randomely initialized models - just checking whether no errors are thrown
* add slow language generation tests for pretrained models using hardcoded output with pytorch seed
* delete ipdb
* check that all generated tokens are valid
* renaming
* renaming Generation -> Generate
* make style
* updated so that generate_beam_search has same token behavior than generate_no_beam_search
* consistent return format for run_generation.py
* deleted pretrain lm generate tests -> will be added in another PR
* cleaning of unused if statements and renaming
* run_generate will always return an iterable
* make style
* consistent renaming
* improve naming, make sure generate function always returns the same tensor, add docstring
* add slow tests for all lmhead models
* make style and improve example comments modeling_utils
* better naming and refactoring in modeling_utils
* improving generation
* finalized special token behaviour for no_beam_search generation
* solved modeling_utils merge conflict
* solve merge conflicts in modeling_utils.py
* add run_generation improvements from PR #2749
* adapted language generation to not use hardcoded -1 if no padding token is available
* remove the -1 removal as hard coded -1`s are not necessary anymore
* add lightweight language generation testing for randomely initialized models - just checking whether no errors are thrown
* add slow language generation tests for pretrained models using hardcoded output with pytorch seed
* delete ipdb
* check that all generated tokens are valid
* renaming
* renaming Generation -> Generate
* make style
* updated so that generate_beam_search has same token behavior than generate_no_beam_search
* consistent return format for run_generation.py
* deleted pretrain lm generate tests -> will be added in another PR
* cleaning of unused if statements and renaming
* run_generate will always return an iterable
* make style
* consistent renaming
* improve naming, make sure generate function always returns the same tensor, add docstring
* add slow tests for all lmhead models
* make style and improve example comments modeling_utils
* better naming and refactoring in modeling_utils
* changed fast random lm generation testing design to more general one
* delete in old testing design in gpt2
* correct old variable name
* temporary fix for encoder_decoder lm generation tests - has to be updated when t5 is fixed
* adapted all fast random generate tests to new design
* better warning description in modeling_utils
* better comment
* better comment and error message
Co-authored-by: Thomas Wolf <thomwolf@users.noreply.github.com>
