GitLab

* adapt convert script

* update convert script

* finish

* fix marian pretrained docs

- MarianSentencepieceTokenizer - > MarianTokenizer
- Start using unk token.
- add docs page
- add better generation params to MarianConfig
- more conversion utilities

* [TPU] Doc, fix xla_spawn.py, only preprocess dataset once

* Update examples/README.md

* [xla_spawn] Add `_mp_fn` to other Trainer scripts

* [TPU] Fix: eval dataloader was None

* example updated to use generation pipeline

* Update model_cards/LorenzoDeMattei/GePpeTto/README.md
Co-authored-by: Julien Chaumond <chaumond@gmail.com>

* Create README.md

* Adding code fence around code block

model card for my De Novo Drug discovery model using MLM

* fix PR

* move tests to correct place

* Add AlbertForPreTraining and TFAlbertForPreTraining models.

* PyTorch conversion

* TensorFlow conversion

* style
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>

* README

* Update README.md

* Created using Colaboratory

* [examples] reorganize files

* remove run_tpu_glue.py as superseded by TPU support in Trainer

* Bugfix: int, not tuple

* move files around

cc @patrickvonplaten @thomwolf

As per https://github.com/huggingface/transformers/pull/3934#discussion_r421307659

* wip

* wip

* a last wip

* Better logging when using TPUs

* Correct argument name

* Tests

* fix

* Metrics in evaluation

* Update src/transformers/training_args.py

* [tpu] Use launcher script instead

* [tpu] lots of tweaks

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

Ensure fast tokenizer can construct tensor without pad token if only one sample is provided. (#4201)

* Rewritten batch support in pipelines.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>

* Fix imports sorting 🔧

Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>

* Set pad_to_max_length=True by default on Pipeline.

* Set pad_to_max_length=False for generation pipelines.

Most of generation models doesn't have padding token.

* Address @joeddav review comment: Uniformized *args.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>

* Address @joeddav review comment: Uniformized *args (second).
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>

* fix example reformer

* fix error message and example docstring

* improved error message

* first copy & past commit from Bert and morgans LSH code

* add easy way to compare to trax original code

* translate most of function

* make trax lsh self attention deterministic with numpy seed + copy paste code

* add same config

* add same config

* make layer init work

* implemented hash_vectors function for lsh attention

* continue reformer translation

* hf LSHSelfAttentionLayer gives same output as trax layer

* refactor code

* refactor code

* refactor code

* refactor

* refactor + add reformer config

* delete bogus file

* split reformer attention layer into two layers

* save intermediate step

* save intermediate step

* make test work

* add complete reformer block layer

* finish reformer layer

* implement causal and self mask

* clean reformer test and refactor code

* fix merge conflicts

* fix merge conflicts

* update init

* fix device for GPU

* fix chunk length init for tests

* include morgans optimi...

* First commit to add a TF version of the trainer.

* Make the TF trainer closer to what looks the PT trainer

* Refactoring common code between the PT and TF trainer into an util file.

* Some bugfix + better similarity with the PT trainer

* Add missing class in transformers init

* Bugfix over prediction + use classification report instead of simple metrics

* Fix name error

* Fix optimization tests + style

* Apply style

* Several bugfix for multi-gpu training

* Apply style

* Apply style

* Add glue example for the TF trainer

* Several bugix + address the reviews

* Fix on the TF training args file

* Add a debug mode

* Bugfix in utils_ner.py when segment_ids is None

* Apply style

* Apply style

* Add TPU strategy

* Fix selection strategy

Description for the model card describing the camembert-large-fquad model.