GitLab

* Tensorflow MLM example

* Add CLM example

* Style fixes, adding missing checkpoint code from the CLM example

* Fix TPU training, avoid massive dataset warnings

* Fix incorrect training length calculation for multi-GPU training

* Fix incorrect training length calculation for multi-GPU training

* Refactors and nitpicks from the review

* Style pass

* Adding README

* fix_torch_device_generate_test

* remove @

* finish

* correct summary writer

* correct push to hub

* fix indent

* finish

* finish

* finish

* finish

* finish
Co-authored-by: Patrick von Platen <patrick@huggingface.co>

* fix_torch_device_generate_test

* remove @

* boom boom

* correct typos

* Apply suggestions from code review
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* Apply suggestions from code review
Co-authored-by: Suzana Ilić <io.suzanai@gmail.com>

* Apply suggestions from code review
Co-authored-by: Suraj Patil <surajp815@gmail.com>
Co-authored-by: Suzana Ilić <io.suzanai@gmail.com>

* added cotext manager to datasets map

* fixed style and spaces

* fixed warning of deprecation

* changed desc

Before the code could not be used for validation only because of this line:
extension = data_args.train_file.split(".")[-1]
was assuming that extension must be extracted from the training dataset. This line would run regardless of the training or validation options of the user. This would lead to an error if the user only wants to run an evaluation only and does not want to do train (because the training file does not exist). I modified it to extract extension from the training file if the user wants to do train and extract it from the validation file if the user wants to run eval. This way the code can be used for both training and validation separately.

* added log_level

* fix comment

* fixed log_level

* Trigger CI

* Unfied logging

* simplified args for log_level

* main_process_first context manager

* handle multi-node, add context description

* sync desc

* port bigbird script

* adapt script a bit

* change location

* adapt more

* save progress

* init commit

* style

* dataset script tested

* readme add

* fix_torch_device_generate_test

* remove @

* finish

* make style

* add summrization script

* fix arguments, preprocessing, metrics

* add generation and metrics

* auto model, prediction loop

* prettify

* label smoothing

* adress Sylvain and Patricks suggestions

* dynamically import shift_tokens_right

* fix shift_tokens_right_fn call

* bug fixes and a rename

* add extended DDP test

* fix_torch_device_generate_test

* remove @

* push

* finish

* some typos

* add more info on communication

* add suggestions

* set log level from CLI

* add log_level_replica + test + extended docs

* cleanup

* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* rename datasets objects to allow datasets module

* improve the doc

* style

* doc improve
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* New Tensorflow QA example!

* Style pass

* Updating README.md for the new example

* flake8 fixes

* Update examples/tensorflow/question-answering/README.md
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* make_student.py: fix to make student ProphetNet

* reformat

* update desc for map in all examples

* added plm

* suggestions

* [WIP] Model card defaults

* finetuned_from default value

* Add all mappings to the mapping file

* Be more defensive on finetuned_from arg

* Add default task tag

* Separate tags from tasks

* Edge case for dataset

* Apply suggestions from code review
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

Use a released version of optax rather than installing from Git

* consistent nn. and nn.functional: p4 examples

* restore

* [lm examples] Replicate --config_overrides addition to other LM examples

* Removing no trainer files changes

* Update README
Co-authored-by: Kumar Abhishek <kabhishek@expedia.com>

* Use text_column_name variable instead of "text"

`text_column_name` was already defined above where I made the changes and it was also used below where I made changes.

This is a very minor change. If a dataset does not use "text" as the column name, then the `tokenize_function` will now use whatever column is assigned to `text_column_name`. `text_column_name` is just the first column name if "text" is not a column name. It makes the function a little more robust, though I would assume that 90% + of datasets use "text" anyway.

* black formatting

* make style
Co-authored-by: Nicholas Broad <nicholas@nmbroad.com>

* Don't log anything before logging is setup in examples

* Last example

* fix_torch_device_generate_test

* remove @

* add colab links

* add readme for flax clm

* use section link for tokenizer

* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* update metrics
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* fix_torch_device_generate_test

* remove @

* upload

* Apply suggestions from code review

* Apply suggestions from code review

* Apply suggestions from code review

* Update examples/flax/language-modeling/README.md

* add more info

* finish

* fix
Co-authored-by: Patrick von Platen <patrick@huggingface.co>

* first draft

* max_seq_length => block_size

* fix arg names

* fix typos

* fix loss calculation

* add max examples, fix  train eval steps, metrics

* optimizer mask

* fix perpelexity, metric logging

* fix logging

* data_collator = > data_loader

* refactor loss_fn

* support single GPU

* pass distributed to write_metric

* fix jitting

* fix single device training

* fix single device metrics

* close inner progress bars once finished

* add overwrite_cache arg

* ifx dataset caching issue

* add more logs

* few small fixes,

* address nicholas suggestions

* fix docstr

* address patricks suggestions

* make flake happy

* pass new new_dropout_rng to apply_gradients

* reset train metrics after every epoc

* remove distributed logis, small fixes