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

* 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.

* fix fp16 in t5

* make style

* refactor invert_attention_mask fn

* fix typo

* 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.

* fix loss calculation in evaluation

* fix evaluation on TPU when prediction_loss_only is True

covers torch and tf. Also fixes a failing @slow test

* Fix: unpin flake8 and fix cs errors

* Ok we still need to quote those

see context in https://github.com/huggingface/transformers/pull/4223

[Marian Fixes] prevent predicting pad_token_id before softmax, support language codes, name multilingual models (#4290)

* Add QA trainer example for TF

* Make data_dir optional

* Fix parameter logic

* Fix feature convert

* Update the READMEs to add the question-answering task

* Apply style

* Change 'sequence-classification' to 'text-classification' and prefix with 'eval' all the metric names

* Apply style

* Apply style

Fix for #3865. PretrainedTokenizer mapped " do not" into " don't" when .decode(...) is called. Removed the " do not" --> " don't" mapping from clean_up_tokenization(...). (#4024)

* Improvements to the wandb integration

* small reorg + no global necessary

* feat(trainer): log epoch and final metrics

* Simplify logging a bit

* Fixup

* Fix crash when just running eval
Co-authored-by: Chris Van Pelt <vanpelt@gmail.com>
Co-authored-by: Boris Dayma <boris.dayma@gmail.com>

* Allow BatchEncoding to be initialized empty.

This is required by recent changes introduced in TF 2.2.

* Attempt to unpin Tensorflow to 2.2 with the previous commit.

* catch gpu len 1 set to gpu0

* Add mpc to trainer

* Add MPC for TF

* fix TF automodel for MPC and add Albert

* Apply style

* Fix import

* Note to self: double check

* Make shape None, None for datasetgenerator output shapes

* Add from_pt bool which doesnt seem to work

* Original checkpoint dir

* Fix docstrings for automodel

* Update readme and apply style

* Colab should probably not be from users

* Colabs should probably not be from users

* Add colab

* Update README.md

* Update README.md

* Cleanup __intit__

* Cleanup flake8 trailing comma

* Update src/transformers/training_args_tf.py

* Update src/transformers/modeling_tf_auto.py
Co-authored-by: Viktor Alm <viktoralm@pop-os.localdomain>
Co-authored-by: Julien Chaumond <chaumond@gmail.com>

* simplify cache vars and allow for TRANSFORMERS_CACHE env

As it currently stands, "TRANSFORMERS_CACHE" is not an accepted variable. It seems that the these variables were not updated when moving from version pytorch_transformers to transformers. In addition, the fallback procedure could be improved. and simplified. Pathlib seems redundant here.

* Update file_utils.py

* allow gpt2 to be exported to valid ONNX model

* cast size from int to float explictly