* adapt wav2vec2
* add example
* add files
* adapt
* remove bogus file
* Apply suggestions from code review
* adapt files more
* upload changes
* del old files
* up
* up
* up
* up
* up
* correct gradient checkpoitning
* add readme
* finish
* finish
* up
* more fixes
* up
* up
* add demo run to readme
* up
* Tmp.
* Fixing BC for question answering with long context.
* Capping model_max_length to avoid tf overflow.
* Bad workaround bugged roberta.
* Fixing name.
* Symbolic trace dynamic axes support for BERT like models (albert, bert, distilbert, mobilebert, electra, megatron-bert)
* Sanity checks before tracing that make sure the model to trace is supported
* Adapted to PyTorch 1.9
Co-authored-by: Michael Benayoun <michael@huggingface.co>
* update no_* argument
Changes the order so that the no_* argument is created after the original argument AND sets the default for this no_* argument to False
* import copy
* update test
* make style
* Use kwargs to set default=False
* make style
* add sigopt hpo to transformers.
Signed-off-by: Ding, Ke <ke.ding@intel.com>
* extend sigopt changes to test code and others..
Signed-off-by: Ding, Ke <ke.ding@intel.com>
* Style.
* fix style for sigopt integration.
Signed-off-by: Ding, Ke <ke.ding@intel.com>
* Add necessary information to run unittests on SigOpt.
Co-authored-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Use fp16 checkpoints
* Style
* Fix outputs and disable OOM tests
* Correct another output
* Use a random smaller model for generation tests
* repo quickfix
* fix gradient checkpointing
* Make gradient_checkpointing a training argument
* Update src/transformers/modeling_utils.py
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
* Update src/transformers/configuration_utils.py
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
* Fix tests
* Style
* document Gradient Checkpointing as a performance feature
* Small rename
* PoC for not using the config
* Adapt BC to new PoC
* Forgot to save
* Rollout changes to all other models
* Fix typo
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
Co-authored-by: Stas Bekman <stas@stason.org>
* Add support for exporting PyTorch LayoutLM to ONNX
* Added tests for converting LayoutLM to ONNX
* Add support for exporting PyTorch LayoutLM to ONNX
* Added tests for converting LayoutLM to ONNX
* cleanup
* Removed regression/ folder
* Add support for exporting PyTorch LayoutLM to ONNX
* Added tests for converting LayoutLM to ONNX
* cleanup
* Fixed import error
* Remove unnecessary import statements
* Changed max_2d_positions from class variable to instance variable of the config class
* Add support for exporting PyTorch LayoutLM to ONNX
* Added tests for converting LayoutLM to ONNX
* cleanup
* Add support for exporting PyTorch LayoutLM to ONNX
* cleanup
* Fixed import error
* Changed max_2d_positions from class variable to instance variable of the config class
* Use super class generate_dummy_inputs method
Co-authored-by: Michael Benayoun <mickbenayoun@gmail.com>
* Add support for Masked LM, sequence classification and token classification
Co-authored-by: Michael Benayoun <mickbenayoun@gmail.com>
* Removed uncessary import and method
* Fixed code styling
* Raise error if PyTorch is not installed
* Remove unnecessary import statement
Co-authored-by: Michael Benayoun <mickbenayoun@gmail.com>
* beit-flax
* updated FLAX_BEIT_MLM_DOCSTRING
* removed bool_masked_pos from classification
* updated Copyright
* code refactoring: x -> embeddings
* updated test: rm from_pt
* Update docs/source/model_doc/beit.rst
* model code dtype updates and
other changes according to review
* relative_position_bias
revert back to pytorch design
* Init FNet
* Update config
* Fix config
* Update model classes
* Update tokenizers to use sentencepiece
* Fix errors in model
* Fix defaults in config
* Remove position embedding type completely
* Fix typo and take only real numbers
* Fix type vocab size in configuration
* Add projection layer to embeddings
* Fix position ids bug in embeddings
* Add minor changes
* Add conversion script and remove CausalLM vestiges
* Fix conversion script
* Fix conversion script
* Remove CausalLM Test
* Update checkpoint names to dummy checkpoints
* Add tokenizer mapping
* Fix modeling file and corresponding tests
* Add tokenization test file
* Add PreTraining model test
* Make style and quality
* Make tokenization base tests work
* Update docs
* Add FastTokenizer tests
* Fix fast tokenizer special tokens
* Fix style and quality
* Remove load_tf_weights vestiges
* Add FNet to main README
* Fix configuration example indentation
* Comment tokenization slow test
* Fix style
* Add changes from review
* Fix style
* Remove bos and eos tokens from tokenizers
* Add tokenizer slow test, TPU transforms, NSP
* Add scipy check
* Add scipy availabilty check to test
* Fix tokenizer and use correct inputs
* Remove remaining TODOs
* Fix tests
* Fix tests
* Comment Fourier Test
* Uncomment Fourier Test
* Change to google checkpoint
* Add changes from review
* Fix activation function
* Fix model integration test
* Add more integration tests
* Add comparison steps to MLM integration test
* Fix style
* Add masked tokenization fix
* Improve mask tokenization fix
* Fix index docs
* Add changes from review
* Fix issue
* Fix failing import in test
* some more fixes
* correct fast tokenizer
* finalize
* make style
* Remove additional tokenization logic
* Set do_lower_case to False
* Allow keeping accents
* Fix tokenization test
* Fix FNet Tokenizer Fast
* fix tests
* make style
* Add tips to FNet docs
Co-authored-by: patrickvonplaten <patrick.v.platen@gmail.com>
* Fix special tokens not correctly tokenized
* Add testing
* Fix
* Fix
* Use user workflows instead of directly assigning variables
* Enable test of fast tokenizers
* Update test of canine tokenizer
