GitLab

* Add badges for models and docs

* automatically set decoder config to decoder

* add more tests

* Fix argument label

* Fix test

* Add model summary

* Add link to pretrained models

* No silent error when d_head already in the configuration

* Update src/transformers/configuration_xlnet.py
Co-authored-by: Julien Chaumond <chaumond@gmail.com>
Co-authored-by: Julien Chaumond <chaumond@gmail.com>

* 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>

https://huggingface.co/tblard/tf-allocine

* 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

* Model cad for SanBERTa

Model Card for RoBERTa trained on Sanskrit

* Model card for SanBERTa

model card for RoBERTa trained on Sanskrit

* Codecov setup

* Understanding codecov

* 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.

* Deprecate masked_lm_labels argument

* Apply to all models

* Better error message

* 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

* Create README.md

* Create README.md

* Create README.md

* improve handling of short inputs for reformer

* correct typo in assert statement

* fix other tests

* Glue task cleaup

* Enable writing cache to cache_dir in case dataset lives in readOnly
filesystem.
* Differentiate match vs mismatch for MNLI metrics.

* Style

* Fix pytype

* Fix type

* Use cache_dir in mnli mismatch eval dataset

* Small Tweaks
Co-authored-by: Julien Chaumond <chaumond@gmail.com>

* 🐛 Fix model ids for BART and Flaubert