GitLab

* doc

* [tests] Add sample files for a regression task

* [HUGE] Trainer

* Feedback from @sshleifer

* Feedback from @thomwolf + logging tweak

* [file_utils] when downloading concurrently, get_from_cache will use the cached file for subsequent processes

* [glue] Use default max_seq_length of 128 like before

* [glue] move DataTrainingArguments around

* [ner] Change interface of InputExample, and align run_{tf,pl}

* Re-align the pl scripts a little bit

* ner

* [ner] Add integration test

* Fix language_modeling with API tweak

* [ci] Tweak loss target

* Don't break console output

* amp.initialize: model must be on right device before

* [multiple-choice] update for Trainer

* Re-align to 827d6d6e

* [examples] Generate argparsers from type hints on dataclasses

* [HfArgumentParser] way simpler API

* Restore run_language_modeling.py for easier diff

* [HfArgumentParser] final tweaks from code review

* 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

* solving bug where for small epochs and large gradient_accumulation_steps we never train

* black formatting

* no need to change these files

That's the same fix applied in https://github.com/huggingface/transformers/issues/2258 , but for the GLUE example

* fix n_gpu count when no_cuda flag is activated

* someone was left behind

* Add support for Albert and XLMRoberta for the Glue example

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.

deay -> decay

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

add DistilBert model shortcut into ALL_MODELS