GitLab

* TFDeberta

moved weights to build and fixed name scope

added missing ,

bug fixes to enable graph mode execution

updated setup.py

fixing typo

fix imports

embedding mask fix

added layer names avoid autmatic incremental names

+XSoftmax

cleanup

added names to layer

disable keras_serializable
Distangled attention output shape hidden_size==None
using symbolic inputs

test for Deberta tf

make style

Update src/transformers/models/deberta/modeling_tf_deberta.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

Update src/transformers/models/deberta/modeling_tf_deberta.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

Update src/transformers/models/deberta/modeling_tf_deberta.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

Update src/transformers/models/deberta/modeling_tf_deberta.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

Update src/transformers/models/deberta/modeling_tf_deberta.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

Update src/transformers/models/deberta/modeling_tf_deberta.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

Update src/transformers/models/deberta/modeling_tf_deberta.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

removed tensorflow-probability

removed blank line

* removed tf experimental api
+torch_gather tf implementation from @Rocketknight1

* layername DeBERTa --> deberta

* copyright fix

* added docs for TFDeberta & make style

* layer_name change to fix load from pt model

* layer_name change as pt model

* SequenceClassification layername change,
to same as pt model

* switched to keras built-in LayerNormalization

* added `TFDeberta` prefix most layer classes

* updated to tf.Tensor in the docstring

* Doctests

* Limit to 4 decimals

* Try with separate PT/TF tests

* Remove test for TF

* Ellips the predictions

* Doctest continue on failure
Co-authored-by: Sylvain Gugger <sylvain.gugger@gmail.com>

Classification head of AlbertForMultipleChoice uses `hidden_dropout_prob` instead of `classifier_dropout_prob`.  This
is not desirable as we cannot change classifer head dropout probability without changing the dropout probabilities of
the whole model.

* Install git

* Add TF tests

* And last TF test

* Add in commented code too
Co-authored-by: Sylvain Gugger <sylvain.gugger@gmail.com>

* Initialize VisualBERT demo

* Update demo

* Add commented URL

* Update README

* Update README

* Fix doctests for quicktour

* Adapt causal LM exemple

* Remove space

* Fix until summarization

* End of task summary

* Style

* With last changes in quicktour

* Use original key for label in DataCollatorForTokenClassification

DataCollatorForTokenClassification accepts either `label` or `labels` as key for label in it's input. However after padding the label it assigns the padded labels to key `labels`. If originally `label` was used as key than the original upadded labels still remains in the batch. Then at line 192 when we try to convert the batch elements to torch tensor than these original unpadded labels cannot be converted as the labels for different samples have different lengths.

* Fixed style.

* Use test fetcher for push tests as well

* Force diff with last commit for circleCI on master

* Fix syntax error

* Style

* Schedule nightly tests

* Fix ModelOutput instantiation form dictionaries

* Style

* Adding HuggingArtists to Community Notebooks

* Adding HuggingArtists to Community Notebooks

* Adding HuggingArtists to Community Notebooks

* docs: add HuggingArtists to community notebooks
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Add try-catch for torch_scatter

* Update modeling_tapas.py

* Add to ONNX docs

* Add MBART example

* Update docs/source/serialization.rst
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Add MBART to models exportable with ONNX

* unittest mock

* Add tests

* Misc fixes

* fix_torch_device_generate_test

* remove @

* improve docs for clm

* speed-ups

* correct t5 example as well

* push final touches

* Update examples/flax/language-modeling/README.md

* correct docs for mlm

* Update examples/flax/language-modeling/README.md
Co-authored-by: Patrick von Platen <patrick@huggingface.co>

* Use min version for huggingface-hub dependency

* Update dependency version table

* Fix tied weights on TPU

* Manually tie weights in no trainer examples

* Fix for test

* One last missing

* Gettning owned by my scripts

* Address review comments

* Fix test

* Fix tests

* Fix reformer tests

T5 with past ONNX export, and more explicit past_key_values inputs and outputs names for ONNX model
Authored-by: Michael Benayoun <michael@huggingface.co>

Changed the way dynamically inserted submodules are named and the method used to insert them
Authored-by: Michael Benayoun <michael@huggingface.co>

* Initial work

* All auto models

* All tf auto models

* All flax auto models

* Tokenizers

* Add feature extractors

* Fix typos

* Fix other typo

* Use the right config

* Remove old mapping names and update logic in AutoTokenizer

* Update check_table

* Fix copies and check_repo script

* Fix last test

* Add back name

* clean up

* Update template

* Update template

* Forgot a )

* Use alternative to fixup

* Fix TF model template

* Address review comments

* Address review comments

* Style

* fix_torch_device_generate_test

* remove @

* update

* up

* fix

* remove f-stings

* correct readme

* up
Co-authored-by: Patrick von Platen <patrick@huggingface.co>

* finish PR

* add tests

* correct tests

* finish

* correct other flax tests

* better naming

* correct naming

* finish

* apply sylvains suggestions

GPT-Neo ONNX export and task / feature refactoring
Authored-by: Michael Benayoun <michael@huggingface.co>

Updating the import for load_dataset

* First pass

* Make conversion script work

* Improve conversion script

* Fix bug, conversion script working

* Improve conversion script, implement BEiTFeatureExtractor

* Make conversion script work based on URL

* Improve conversion script

* Add tests, add documentation

* Fix bug in conversion script

* Fix another bug

* Add support for converting masked image modeling model

* Add support for converting masked image modeling

* Fix bug

* Add print statement for debugging

* Fix another bug

* Make conversion script finally work for masked image modeling models

* Move id2label for datasets to JSON files on the hub

* Make sure id's are read in as integers

* Add integration tests

* Make style & quality

* Fix test, add BEiT to README

* Apply suggestions from @sgugger's review

* Apply suggestions from code review

* Make quality

* Replace nielsr by microsoft in tests, add docs

* Rename BEiT to Beit

* Minor fix

* Fix docs of BeitForMaskedImageModeling
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>