* Feed forward chunking for Distilbert & Albert
* Added ff chunking for many other models
* Change model signature
* Added chunking for XLM
* Cleaned up by removing some variables.
* remove test_chunking flag
Co-authored-by: patrickvonplaten <patrick.v.platen@gmail.com>
* cleanup torch unittests: part 2
* remove trailing comma added by isort, and which breaks flake
* one more comma
* revert odd balls
* part 3: odd cases
* more ["key"] -> .key refactoring
* .numpy() is not needed
* more unncessary .numpy() removed
* more simplification
* allow using tokenizer.pad as a collate_fn in pytorch
* allow using tokenizer.pad as a collate_fn in pytorch
* Add documentation and tests
* Make attention mask the right shape
* Better test
Co-authored-by: Thomas Wolf <thomwolf@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
* [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>
* Chunked feed forward for Bert
This is an initial implementation to test applying feed forward chunking for BERT.
Will need additional modifications based on output and benchmark results.
* Black and cleanup
* Feed forward chunking in BertLayer class.
* Isort
* add chunking for all models
* fix docs
* Fix typo
Co-authored-by: patrickvonplaten <patrick.v.platen@gmail.com>
* improve names and tests longformer
* more and better tests for longformer
* add first tf test
* finalize tf basic op functions
* fix merge
* tf shape test passes
* narrow down discrepancies
* make longformer local attn tf work
* correct tf longformer
* add first global attn function
* add more global longformer func
* advance tf longformer
* finish global attn
* upload big model
* finish all tests
* correct false any statement
* fix common tests
* make all tests pass except keras save load
* fix some tests
* fix torch test import
* finish tests
* fix test
* fix torch tf tests
* add docs
* finish docs
* Update src/transformers/modeling_longformer.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Update src/transformers/modeling_tf_longformer.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* apply Lysandres suggestions
* reverse to assert statement because function will fail otherwise
* applying sylvains recommendations
* Update src/transformers/modeling_longformer.py
Co-authored-by: Sam Shleifer <sshleifer@gmail.com>
* Update src/transformers/modeling_tf_longformer.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Sam Shleifer <sshleifer@gmail.com>
* Add a script to check all models are tested and documented
* Apply suggestions from code review
Co-authored-by: Kevin Canwen Xu <canwenxu@126.com>
* Address comments
Co-authored-by: Kevin Canwen Xu <canwenxu@126.com>
* Add strip_accents to basic tokenizer
* Add tests for strip_accents.
* fix style with black
* Fix strip_accents test
* empty commit to trigger CI
* Improved strip_accents check
* Add code quality with is not False
* TF outputs and test on BERT
* Albert to DistilBert
* All remaining TF models except T5
* Documentation
* One file forgotten
* TF outputs and test on BERT
* Albert to DistilBert
* All remaining TF models except T5
* Documentation
* One file forgotten
* Add new models and fix issues
* Quality improvements
* Add T5
* A bit of cleanup
* Fix for slow tests
* Style
* Add SequenceClassification and MultipleChoice TF models to Electra
* Apply style
* Add summary_proj_to_labels to Electra config
* Finally mirroring the PT version of these models
* Apply style
* Fix Electra test
* improve unit tests
this is a sample of one test according to the request in https://github.com/huggingface/transformers/issues/5973
before I apply it to the rest
* batch 1
* batch 2
* batch 3
* batch 4
* batch 5
* style
* non-tf template
* last deletion of check_loss_output
* Fix TF Serving when output_hidden_states and output_attentions are True
* Add tests for saved model creation + bug fix for multiple choices models
* remove unused import
* Fix the input for several layers
* Fix test
* Fix conflict printing
* Apply style
* Fix XLM and Flaubert for TensorFlow
* Apply style
* Fix TF check version
* Apply style
* Trigger CI
* 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
* initial commit for pipeline implementation
Addition of input processing and history concatenation
* Conversation pipeline tested and working for single & multiple conversation inputs
* Added docstrings for dialogue pipeline
* Addition of dialogue pipeline integration tests
* Delete test_t5.py
* Fixed max code length
* Updated styling
* Fixed test broken by formatting tools
* Removed unused import
* Added unit test for DialoguePipeline
* Fixed Tensorflow compatibility
* Fixed multi-framework support using framework flag
* - Fixed docstring
- Added `min_length_for_response` as an initialization parameter
- Renamed `*args` to `conversations`, `conversations` being a `Conversation` or a `List[Conversation]`
- Updated truncation to truncate entire segments of conversations, instead of cutting in the middle of a user/bot input
* - renamed pipeline name from dialogue to conversational
- removed hardcoded default value of 1000 and use config.max_length instead
- added `append_response` and `set_history` method to the Conversation class to avoid direct fields mutation
- fixed bug in history truncation method
* - Updated ConversationalPipeline to accept only active conversations (otherwise a ValueError is raised)
* - Simplified input tensor conversion
* - Updated attention_mask value for Tensorflow compatibility
* - Updated last dialogue reference to conversational & fixed integration tests
* Fixed conflict with master
* Updates following review comments
* Updated formatting
* Added Conversation and ConversationalPipeline to the library __init__, addition of docstrings for Conversation, added both to the docs
* Update src/transformers/pipelines.py
Updated docsting following review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* 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>
* Added capability to quantize a model while exporting through ONNX.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
We do not support multiple extensions
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Reformat files
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* More quality
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Ensure test_generate_identified_name compares the same object types
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Added documentation everywhere on ONNX exporter
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Use pathlib.Path instead of plain-old string
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Use f-string everywhere
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Use the correct parameters for black formatting
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Use Python 3 super() style.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Use packaging.version to ensure installed onnxruntime version match requirements
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Fixing imports sorting order.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Missing raise(s)
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Added quantization documentation
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Fix some spelling.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Fix bad list header format
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* 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
* 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
Slightly breaking change, changes functionality for `use_cache` in XLNet: if use_cache is True and mem_len is 0 or None (which is the case in the base model config), the model behaves like GPT-2 and returns mems to be used as past in generation. At training time `use_cache` is overriden and always True.
Slightly breaking change, changes functionality for `use_cache` in XLNet: if use_cache is True and mem_len is 0 or None (which is the case in the base model config), the model behaves like GPT-2 and returns mems to be used as past in generation. At training time `use_cache` is overriden and always True.
Slightly breaking change, changes functionality for `use_cache` in XLNet: if use_cache is True and mem_len is 0 or None (which is the case in the base model config), the model behaves like GPT-2 and returns mems to be used as past in generation. At training time `use_cache` is overriden and always True.
* fix merge rebase
* add intermediate reformer code
* save intermediate caching results
* save intermediate
* save intermediate results
* save intermediate
* upload next step
* fix generate tests
* make tests work
* add named tuple output
* Apply suggestions from code review
* fix use_cache for False case
* fix tensor to gpu
* fix tensor to gpu
* refactor
* refactor and make style
* Reformer model head classification implementation for text classification
* Reformat the reformer model classification code
* PR review comments, and test case implementation for reformer for classification head changes
* CI/CD reformer for classification head test import error fix
* CI/CD test case implementation added ReformerForSequenceClassification to all_model_classes
* Code formatting- fixed
* Normal test cases added for reformer classification head
* Fix test cases implementation for the reformer classification head
* removed token_type_id parameter from the reformer classification head
* fixed the test case for reformer classification head
* merge conflict with master fixed
* merge conflict, changed reformer classification to accept the choice_label parameter added in latest code
* refactored the the reformer classification head test code
* reformer classification head, common transform test cases fixed
* final set of the review comment, rearranging the reformer classes and docstring add to classification forward method
* fixed the compilation error and text case fix for reformer classification head
* Apply suggestions from code review
Remove unnecessary dup
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Add B I handling to grouping
* Add fix to include separate entity as last token
* move last_idx definition outside loop
* Use first entity in entity group as reference for entity type
* Add test cases
* Take out extra class accidentally added
* Return tf ner grouped test to original
* Take out redundant last entity
* Get last_idx safely
Co-authored-by: ColleterVi <36503688+ColleterVi@users.noreply.github.com>
* Fix first entity comment
* Create separate functions for group_sub_entities and group_entities (splitting call method to testable functions)
* Take out unnecessary last_idx
* Remove additional forward pass test
* Move token classification basic tests to separate class
* Move token classification basic tests back to monocolumninputtestcase
* Move base ner tests to nerpipelinetests
* Take out unused kwargs
* Add back mandatory_keys argument
* Add unitary tests for group_entities in _test_ner_pipeline
* Fix last entity handling
* Fix grouping fucntion used
* Add typing to group_sub_entities and group_entities
Co-authored-by: ColleterVi <36503688+ColleterVi@users.noreply.github.com>
* Default decoder inputs to encoder ones for T5 if neither are specified.
* Fixing typo, now all tests are passing.
* Changing einsum to operations supported by onnx
* Adding a test to ensure T5 can be exported to onnx op>9
* Modified test for onnx export to make it faster
* Styling changes.
* Styling changes.
* Changing notation for matrix multiplication
Co-authored-by: Abel Riboulot <tkai@protomail.com>
* 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
* Exposing prepare_for_model for both slow & fast tokenizers
* Update method signature
* The traditional style commit
* Hide the warnings behind the verbose flag
* update default truncation strategy and prepare_for_model
* fix tests and prepare_for_models methods
Co-authored-by: Thomas Wolf <thomwolf@users.noreply.github.com>
* Added PipelineException
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* fill-mask pipeline raises exception when more than one mask_token detected.
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Put everything in a function.
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Added tests on pipeline fill-mask when input has != 1 mask_token
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Fix numel() computation for TF
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Addressing PR comments.
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Remove function typing to avoid import on specific framework.
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Quality.
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Retry typing with @julien-c tip.
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Quality².
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Simplify fill-mask mask_token checking.
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Trigger CI
* 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 new parameter `pad_to_multiple_of` on tokenizers.
* unittest for pad_to_multiple_of
* Add .name when logging enum.
* Fix missing .items() on dict in tests.
* Add special check + warning if the tokenizer doesn't have proper pad_token.
* Use the correct logger format specifier.
* Ensure tokenizer with no pad_token do not modify the underlying padding strategy.
* Skip test if tokenizer doesn't have pad_token
* Fix RobertaTokenizer on empty input
* Format.
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* fix and updating to simpler API
Co-authored-by: Thomas Wolf <thomwolf@users.noreply.github.com>
* avoid recursion in id checks for fast tokenizers
* better typings and fix#5232
* align slow and fast tokenizers behaviors for Roberta and GPT2
* style and quality
* fix tests - improve typings
* fix-5181
Padding to max sequence length while truncation to another length was wrong on slow tokenizers
* clean up and fix#5155
* fix XLM test
* Fix tests for Transfo-XL
* logging only above WARNING in tests
* switch slow tokenizers tests in @slow
* fix Marian truncation tokenization test
* style and quality
* make the test a lot faster by limiting the sequence length used in tests
* Add return lengths
* make pad a bit more flexible so it can be used as collate_fn
* check all kwargs sent to encoding method are known
* fixing kwargs in encodings
* New AddedToken class in python
This class let you specify specifique tokenization behaviors for some special tokens. Used in particular for GPT2 and Roberta, to control how white spaces are stripped around special tokens.
* style and quality
* switched to hugginface tokenizers library for AddedTokens
* up to tokenizer 0.8.0-rc3 - update API to use AddedToken state
* style and quality
* do not raise an error on additional or unused kwargs for tokenize() but only a warning
* transfo-xl pretrained model requires torch
* Update src/transformers/tokenization_utils.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* fix#5081 and improve backward compatibility (slightly)
* add nlp to setup.cfg - style and quality
* align default to previous default
* remove test that doesn't generalize
* Configure all models to use output_hidden_states as argument passed to foward()
* Pass all tests
* Remove cast_bool_to_primitive in TF Flaubert model
* correct tf xlnet
* add pytorch test
* add tf test
* Fix broken tests
* Configure all models to use output_hidden_states as argument passed to foward()
* Pass all tests
* Remove cast_bool_to_primitive in TF Flaubert model
* correct tf xlnet
* add pytorch test
* add tf test
* Fix broken tests
* Refactor output_hidden_states for mobilebert
* Reset and remerge to master
Co-authored-by: Joseph Liu <joseph.liu@coinflex.com>
Co-authored-by: patrickvonplaten <patrick.v.platen@gmail.com>
* Fixed resize_token_embeddings for transfo_xl model
* Fixed resize_token_embeddings for transfo_xl.
Added custom methods to TransfoXLPreTrainedModel for resizing layers of
the AdaptiveEmbedding.
* Updated docstring
* Fixed resizinhg cutoffs; added check for new size of embedding layer.
* Added test for resize_token_embeddings
* Fixed code quality
* Fixed unchanged cutoffs in model.config
* Added feature to move added tokens in tokenizer.
* Fixed code quality
* Added feature to move added tokens in tokenizer.
* Fixed code quality
* Fixed docstring, renamed sym to oken.
Co-authored-by: Rafael Weingartner <rweingartner.its-b2015@fh-salzburg.ac.at>
* add ElectraForMultipleChoice
* add test_for_multiple_choice
* add ElectraForMultipleChoice in auto model
* add ElectraForMultipleChoice in all_model_classes
* add SequenceSummary related parameters
* get rid pooler, use SequenceSummary instead
* add electra multiple choice test
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Added is_fast property on BatchEncoding to indicate if the object comes from a Fast Tokenizer.
* Added __get_state__() & __set_state__() to be pickable.
* Correct tokens() return type from List[int] to List[str]
* Added unittest for BatchEncoding pickle/unpickle
* Added unittest for BatchEncoding is_fast
* More careful checking on BatchEncoding unpickle tests.
* Formatting.
* is_fast should assertTrue on Rust tokenizers.
* Ensure tensorflow has correct way of checking array_equal
* More formatting.
* Fixed resize_token_embeddings for transfo_xl model
* Fixed resize_token_embeddings for transfo_xl.
Added custom methods to TransfoXLPreTrainedModel for resizing layers of
the AdaptiveEmbedding.
* Updated docstring
* Fixed resizinhg cutoffs; added check for new size of embedding layer.
* Added test for resize_token_embeddings
* Fixed code quality
* Fixed unchanged cutoffs in model.config
Co-authored-by: Rafael Weingartner <rweingartner.its-b2015@fh-salzburg.ac.at>
* ElectraForQuestionAnswering
* udate __init__
* add test for electra qa model
* add ElectraForQuestionAnswering in auto models
* add ElectraForQuestionAnswering in all_model_classes
* fix outputs, input_ids defaults to None
* add ElectraForQuestionAnswering in docs
* remove commented line
* DOC: Replace instances of ``config.output_attentions`` with function argument ``output_attentions``
* DOC: Apply Black Formatting
* Fix errors where output_attentions was undefined
* Remove output_attentions in classes per review
* Fix regressions on tests having `output_attention`
* Fix further regressions in tests relating to `output_attentions`
Ensure proper propagation of `output_attentions` as a function parameter
to all model subclasses
* Fix more regressions in `test_output_attentions`
* Fix issues with BertEncoder
* Rename related variables to `output_attentions`
* fix pytorch tests
* fix bert and gpt2 tf
* Fix most TF tests for `test_output_attentions`
* Fix linter errors and more TF tests
* fix conflicts
* DOC: Apply Black Formatting
* Fix errors where output_attentions was undefined
* Remove output_attentions in classes per review
* Fix regressions on tests having `output_attention`
* fix conflicts
* fix conflicts
* fix conflicts
* fix conflicts
* fix pytorch tests
* fix conflicts
* fix conflicts
* Fix linter errors and more TF tests
* fix tf tests
* make style
* fix isort
* improve output_attentions
* improve tensorflow
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* add tpu and torchscipt for benchmark
* fix name in tests
* "fix email"
* make style
* better log message for tpu
* add more print and info for tpu
* allow possibility to print tpu metrics
* correct cpu usage
* fix test for non-install
* remove bugus file
* include psutil in testing
* run a couple of times before tracing in torchscript
* do not allow tpu memory tracing for now
* make style
* add torchscript to env
* better name for torch tpu
Co-authored-by: Patrick von Platen <patrick@huggingface.co>
* Better None gradients handling
* Apply Style
* Apply Style
* Create a loss class per task to compute its respective loss
* Add loss classes to the ALBERT TF models
* Add loss classes to the BERT TF models
* Add question answering and multiple choice to TF Camembert
* Remove prints
* Add multiple choice model to TF DistilBERT + loss computation
* Add question answering model to TF Electra + loss computation
* Add token classification, question answering and multiple choice models to TF Flaubert
* Add multiple choice model to TF Roberta + loss computation
* Add multiple choice model to TF XLM + loss computation
* Add multiple choice and question answering models to TF XLM-Roberta
* Add multiple choice model to TF XLNet + loss computation
* Remove unused parameters
* Add task loss classes
* Reorder TF imports + add new model classes
* Add new model classes
* Bugfix in TF T5 model
* Bugfix for TF T5 tests
* Bugfix in TF T5 model
* Fix TF T5 model tests
* Fix T5 tests + some renaming
* Fix inheritance issue in the AutoX tests
* Add tests for TF Flaubert and TF XLM Roberta
* Add tests for TF Flaubert and TF XLM Roberta
* Remove unused piece of code in the TF trainer
* bugfix and remove unused code
* Bugfix for TF 2.2
* Apply Style
* Divide TFSequenceClassificationAndMultipleChoiceLoss into their two respective name
* Apply style
* Mirror the PT Trainer in the TF one: fp16, optimizers and tb_writer as class parameter and better dataset handling
* Fix TF optimizations tests and apply style
* Remove useless parameter
* Bugfix and apply style
* Fix TF Trainer prediction
* Now the TF models return the loss such as their PyTorch couterparts
* Apply Style
* Ignore some tests output
* Take into account the SQuAD cls_index, p_mask and is_impossible parameters for the QuestionAnswering task models.
* Fix names for SQuAD data
* Apply Style
* Fix conflicts with 2.11 release
* Fix conflicts with 2.11
* Fix wrongname
* Add better documentation on the new create_optimizer function
* Fix isort
* logging_dir: use same default as PyTorch
Co-authored-by: Julien Chaumond <chaumond@gmail.com>
* Refactor tensor creation in tokenizers.
* Make sure to convert string to TensorType
* Refactor convert_to_tensors_
* Introduce numpy tensor creation
* Format
* Add unittest for TensorType creation from str
* sorting imports
* Added unittests for numpy tensor conversion.
* Do not use in-place version for squeeze as numpy doesn't provide such feature.
* Added extra parameter prepend_batch_axis: bool on prepare_for_model.
* Ensure test_np_encode_plus_sent_to_model is not executed if encoder/decoder model.
* style.
* numpy tests require_torch for now while flax not merged.
* Hopefully will make flake8 happy.
* One more time 🎶
* 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
* pass on tokenizer to pipeline
* order input names when convert to onnx
* update style
* remove unused imports
* make ordered inputs list needs to be mutable
* add test custom bert model
* remove unused imports
* better api
* improve automatic setting of global attention mask
* fix longformer bug
* fix global attention mask in test
* fix global attn mask flatten
* fix slow tests
* update docstring
* update docs and make more robust
* improve attention mask
* add multiple choice for longformer
* add models to docs
* adapt docstring
* add test to longformer
* add longformer for mc in init and modeling auto
* fix tests
* added LongformerForQuestionAnswering
* add LongformerForQuestionAnswering
* fix import for LongformerForMaskedLM
* add LongformerForQuestionAnswering
* hardcoded sep_token_id
* compute attention_mask if not provided
* combine global_attention_mask with attention_mask when provided
* update example in docstring
* add assert error messages, better attention combine
* add test for longformerForQuestionAnswering
* typo
* cast gloabl_attention_mask to long
* make style
* Update src/transformers/configuration_longformer.py
* Update src/transformers/configuration_longformer.py
* fix the code quality
* Merge branch 'longformer-for-question-answering' of https://github.com/patil-suraj/transformers into longformer-for-question-answering
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* 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>
* first commit
* bug fixes
* better examples
* undo padding
* remove wrong VOCAB_FILES_NAMES
* License
* make style
* make isort happy
* unit tests
* integration test
* make `black` happy by undoing `isort` changes!!
* lint
* no need for the padding value
* batch_size not bsz
* remove unused type casting
* seqlen not seq_len
* staticmethod
* `bert` selfattention instead of `n2`
* uint8 instead of bool + lints
* pad inputs_embeds using embeddings not a constant
* black
* unit test with padding
* fix unit tests
* remove redundant unit test
* upload model weights
* resolve todo
* simpler _mask_invalid_locations without lru_cache + backward compatible masked_fill_
* increase unittest coverage
* 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 index to be returned by NerPipeline to allow for the creation of
* Add entity groups
* Convert entity list to dict
* Add entity to entity_group_disagg atfter updating entity gorups
* Change 'group' parameter to 'grouped_entities'
* Add unit tests for grouped NER pipeline case
* Correct variable name typo for NER_FINETUNED_MODELS
* Sync grouped tests to recent test updates
* Added generic ONNX conversion script for PyTorch model.
* WIP initial TF support.
* TensorFlow/Keras ONNX export working.
* Print framework version info
* Add possibility to check the model is correctly loading on ONNX runtime.
* Remove quantization option.
* Specify ONNX opset version when exporting.
* Formatting.
* Remove unused imports.
* Make functions more generally reusable from other part of the code.
* isort happy.
* flake happy
* Export only feature-extraction for now
* Correctly check inputs order / filter before export.
* Removed task variable
* Fix invalid args call in load_graph_from_args.
* Fix invalid args call in convert.
* Fix invalid args call in infer_shapes.
* Raise exception and catch in caller function instead of exit.
* Add 04-onnx-export.ipynb notebook
* More WIP on the notebook
* Remove unused imports
* Simplify & remove unused constants.
* Export with constant_folding in PyTorch
* Let's try to put function args in the right order this time ...
* Disable external_data_format temporary
* ONNX notebook draft ready.
* Updated notebooks charts + wording
* Correct error while exporting last chart in notebook.
* Adressing @LysandreJik comment.
* Set ONNX opset to 11 as default value.
* Set opset param mandatory
* Added ONNX export unittests
* Quality.
* flake8 happy
* Add keras2onnx dependency on extras["tf"]
* Pin keras2onnx on github master to v1.6.5
* Second attempt.
* Third attempt.
* Use the right repo URL this time ...
* Do the same for onnxconverter-common
* Added keras2onnx and onnxconveter-common to 1.7.0 to supports TF2.2
* Correct commit hash.
* Addressing PR review: Optimization are enabled by default.
* Addressing PR review: small changes in the notebook
* setup.py comment about keras2onnx versioning.
* 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
