GitLab

* 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>

Co-authored-by: Hamza Harkous <harkous@google.com>

* Add XLMRobertaForQuestionAnswering

* Formatting

* Make test happy

* Clean documentation

* Align checkpoint dir with the PT trainer

* Use args for max to keep checkpoints

* fix flaky beam search

* fix typo

* Expose classes used in documentation

* Format code

Co-authored-by: Daniel Shan <daniel.shan@workday.com>

https://github.com/huggingface/transformers/issues/4776

* 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