GitLab

* Add deebert code

* Add readme of deebert

* Add test for deebert

Update test for Deebert

* Update DeeBert (README, class names, function refactoring); remove requirements.txt

* Format update

* Update test

* Update readme and model init methods

* all save_pretrained methods mkdir if not os.path.exists

* Fix PABEE division by zero error

* patience=0 by default

* Add BERT Loses Patience (Patience-based Early Exit)

* update model archive

* update format

* sort import

* flake8

* Add results

* full results

* align the table

* refactor to inherit

* default per gpu eval = 1

* Formatting

* Formatting

* isort

* modify readme

* Add check

* Fix format

* Fix format

* Doc strings

* ALBERT & BERT for sequence classification don't inherit from the original anymore

* Remove incorrect comments

* Remove incorrect comments

* Remove incorrect comments

* Sync up with new code

* Sync up with new code

* Add a test

* Add a test

* Add a test

* Add a test

* Add a test

* Add a test

* Finishing up!

* 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

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* remove run_tpu_glue.py as superseded by TPU support in Trainer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

* Big cleanup of `glue_convert_examples_to_features`

* Use batch_encode_plus

* Cleaner wrapping of glue_convert_examples_to_features for TF

@lysandrejik

* Cleanup syntax, thanks to @mfuntowicz

* Raise explicit error in case of user error

* Start cleaning examples

* Fixup

* fix n_gpu count when no_cuda flag is activated

* someone was left behind

This is the result of:

    $ isort --recursive examples templates transformers utils hubconf.py setup.py

This is the result of:

    $ black --line-length 119 examples templates transformers utils hubconf.py setup.py

There's a lot of fairly long lines in the project. As a consequence, I'm
picking the longest widely accepted line length, 119 characters.

This is also Thomas' preference, because it allows for explicit variable
names, to make the code easier to understand.

TPU runner is currently implemented in:
https://github.com/pytorch-tpu/transformers/blob/tpu/examples/run_glue_tpu.py.

We plan to upstream this directly into `huggingface/transformers`
(either `master` or `tpu`) branch once it's been more thoroughly tested.

gradient norm clipping should be done right before calling the optimiser - fixing run_glue and run_ner as well