GitLab

* 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

* 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

* allow to not add special tokens

* remove print

Fixes bug reported in https://github.com/huggingface/transformers/issues/4669

See #3967 for context

* 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

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

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