* 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>
* 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
* 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 🎶
* Ensure tokens in never_split are not splitted when using basic tokenizer before wordpiece.
* never_split only use membership attempt to use a set() which is 10x faster for this operation.
* Use union to concatenate two sets.
* Updated docstring for never_split parameter.
* Avoid set.union() if never_split is None
* Added comments.
* Correct docstring format.
* Added links to more community notebooks
Added links to 3 more community notebooks from the git repo: https://github.com/abhimishra91/transformers-tutorials
Different Transformers models are fine tuned on Dataset using PyTorch
* Update README.md
* Update README.md
* Update README.md
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* [hf_api] Attach all unknown attributes for future-proof compatibility
* [Pipeline] NerPipeline is really a TokenClassificationPipeline
* modelcard.py: I don't think we need to force the download
* Remove config, tokenizer from SUPPORTED_TASKS as we're moving to one model = one weight + one tokenizer
* FillMaskPipeline: also output token in string form
* TextClassificationPipeline: option to return all scores, not just the argmax
* Update docs/source/main_classes/pipelines.rst
