GitLab

* Add more token classification examples

* POS tagging example

* Phrase chunking example

* PR review fixes

* Add conllu to third party list (used in token classification examples)

Otherwise, if label is not specified, the following error occurs:

	Traceback (most recent call last):
	  File "run_ner.py", line 303, in <module>
	    main()
	  File "run_ner.py", line 101, in main
	    model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
	  File "/home/user/anaconda3/envs/bert/lib/python3.7/site-packages/transformers/hf_argparser.py", line 159, in parse_json_file
	    obj = dtype(**inputs)
	TypeError: __init__() missing 1 required positional argument: 'labels'

* 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

* 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

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

* [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

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* remove run_tpu_glue.py as superseded by TPU support in Trainer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

* ner: parse args from .args file or JSON

* examples: mention json-based configuration file support for run_ner script

Close #3921

* 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

* Start cleaning examples

* Fixup

For more details, see https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate

* Update examples/ner/run_ner.py to use AutoModel

* Fix missing code and apply `make style` command

* fix n_gpu count when no_cuda flag is activated

* someone was left behind

* Added support for Albert in NER pipeline

* Added command-line options to examples/ner/run_ner.py to better control tokenization

* Added class AlbertForTokenClassification

* Changed output for NerPipeline to use .convert_ids_to_tokens(...) instead of .decode(...) to better reflect tokens

* initial pytorch lightning commit

* tested multigpu

* Fix learning rate schedule

* black formatting

* fix flake8

* isort

* isort

* .
Co-authored-by: Check your git settings! <chris@chris-laptop>

Every other file uses this pattern.

These libraries aren't always installed in the virtual environment where
isort is running. Declaring them properly avoids mixing these
third-party imports with local imports.

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.