GitLab

* first commit

* bug fixes

* better examples

* undo padding

* remove wrong VOCAB_FILES_NAMES

* License

* make style

* make isort happy

* unit tests

* integration test

* make `black` happy by undoing `isort` changes!!

* lint

* no need for the padding value

* batch_size not bsz

* remove unused type casting

* seqlen not seq_len

* staticmethod

* `bert` selfattention instead of `n2`

* uint8 instead of bool + lints

* pad inputs_embeds using embeddings not a constant

* black

* unit test with padding

* fix unit tests

* remove redundant unit test

* upload model weights

* resolve todo

* simpler _mask_invalid_locations without lru_cache + backward compatible masked_fill_

* increase unittest coverage

* Map optimizer to correct device after loading from checkpoint.

* Make style test pass
Co-authored-by: Julien Chaumond <chaumond@gmail.com>

* Distributed eval: SequentialDistributedSampler + gather all results

* For consistency only write to disk from world_master

Close https://github.com/huggingface/transformers/issues/4272

* Working distributed eval

* Hook into scripts

* Fix #3721 again

* TPU.mesh_reduce: stay in tensor space

Thanks @jysohn23

* Just a small comment

* whitespace

* torch.hub: pip install packaging

* Add test scenarii

* Test case for #3936

* multigpu tests pass on pytorch 1.4.0

* Fixup

* multigpu tests pass on pytorch 1.5.0

* Update src/transformers/modeling_utils.py

* Update src/transformers/modeling_utils.py

* rename multigpu to require_multigpu

* mode doc

* makes fetching last learning late in trainer backward compatible

* split comment to multiple lines

* fixes black styling issue

* uses version to create a more explicit logic

* Create README.md

* Create README.md

* Update README.md

* Update README.md

* Apply suggestions from code review
Co-authored-by: Julien Chaumond <chaumond@gmail.com>

- add a citation.
- modify the table of the BLUE benchmark.

The table of the first version was not displayed correctly on https://huggingface.co/seiya/oubiobert-base-uncased.
Could you please confirm that this fix will allow you to display it correctly?

I followed the google example of usage for its electra small model but i have seen it is not meaningful, so i created a better example

* add model card for t5-base-squad

* Update model_cards/valhalla/t5-base-squad/README.md
Co-authored-by: Julien Chaumond <chaumond@gmail.com>

* add bert bahasa readme

* update readme

* update readme

* added xlnet

* added tiny-bert and fix xlnet readme

* added albert base

* added albert tiny

* added electra model

* added gpt2 117m bahasa readme

* added gpt2 345m bahasa readme

* added t5-base-bahasa

* fix readme

* Update model_cards/huseinzol05/t5-base-bahasa-cased/README.md
Co-authored-by: Julien Chaumond <chaumond@gmail.com>

* Adding optimizations block from ONNXRuntime.

* Turn off external data format by default for PyTorch export.

* Correct the way use_external_format is passed through the cmdline args.

* Update README.md

* Update README.md

* Update README.md

* Update README.md

* fix fp16 in t5

* make style

* refactor invert_attention_mask fn

* fix typo

see https://github.com/huggingface/transformers/pull/4367#discussion_r426356693

Hat/tip @girishponkiya

* Add index to be returned by NerPipeline to allow for the creation of

* Add entity groups

* Convert entity list to dict

* Add entity to entity_group_disagg atfter updating entity gorups

* Change 'group' parameter to 'grouped_entities'

* Add unit tests for grouped NER pipeline case

* Correct variable name typo for NER_FINETUNED_MODELS

* Sync grouped tests to recent test updates

* Better p_mask building

* Adressing @mfuntowicz comments

* Added generic ONNX conversion script for PyTorch model.

* WIP initial TF support.

* TensorFlow/Keras ONNX export working.

* Print framework version info

* Add possibility to check the model is correctly loading on ONNX runtime.

* Remove quantization option.

* Specify ONNX opset version when exporting.

* Formatting.

* Remove unused imports.

* Make functions more generally reusable from other part of the code.

* isort happy.

* flake happy

* Export only feature-extraction for now

* Correctly check inputs order / filter before export.

* Removed task variable

* Fix invalid args call in load_graph_from_args.

* Fix invalid args call in convert.

* Fix invalid args call in infer_shapes.

* Raise exception and catch in caller function instead of exit.

* Add 04-onnx-export.ipynb notebook

* More WIP on the notebook

* Remove unused imports

* Simplify & remove unused constants.

* Export with constant_folding in PyTorch

* Let's try to put function args in the right order this time ...

* Disable external_data_format temporary

* ONNX notebook draft ready.

* Updated notebooks charts + wording

* Correct error while exporting last chart in notebook.

* Adressing @LysandreJik comment.

* Set ONNX opset to 11 as default value.

* Set opset param mandatory

* Added ONNX export unittests

* Quality.

* flake8 happy

* Add keras2onnx dependency on extras["tf"]

* Pin keras2onnx on github master to v1.6.5

* Second attempt.

* Third attempt.

* Use the right repo URL this time ...

* Do the same for onnxconverter-common

* Added keras2onnx and onnxconveter-common to 1.7.0 to supports TF2.2

* Correct commit hash.

* Addressing PR review: Optimization are enabled by default.

* Addressing PR review: small changes in the notebook

* setup.py comment about keras2onnx versioning.