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* Add initial script for finetuning MLM models with accelerate

* Add evaluation metric calculation

* Fix bugs

* Use no_grad on evaluation

* update script docstring

* Update examples/language-modeling/run_mlm_no_trainer.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* PR feedback

* Fix CI failure

* Update examples/language-modeling/run_mlm_no_trainer.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* First third

* Styling and fix mistake

* Quality

* All the rest

* Treat %s and %d

* typo

* Missing )

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

* Fixed typos

* Removed legacy colab notebook from readme
Co-authored-by: WybeKoper <WybeKoper@users.noreply.github.com>

* added py7zr

* comment out check_min for sagemaker test

* added min version again

Fixes https://github.com/huggingface/transformers/security/dependabot/examples/research_projects/lxmert/requirements.txt/Pygments/open

@LysandreJik

* Initial commit

* Another bunch of updates

* make style quliaty + delete debug arg from bash script

* Use compue_metrics func

* Do a few fixes

* Add copyright

* Fix typos

* Add NER example with accelerate library

* This commit contains the first (yet really unfinished)
version of a script for showing how to train HuggingFace model
with their new accelerate library.

* Fix metric calculation

* make style quality

* mv ner_no_trainer to token-classification dir

* Delete --debug flag from running script

* hf_datasets -> raw_datasets

* Make a few slight adjustments

* Add an informative comment + rewrite a help comment

* Change header

* Fix a few things

* Enforce to use fast tokenizers only

* DataCollatorWithPadding -> DataCollatorForTokenClassification

* Change bash script: python3 -> accelerate launch

* make style

* Add a few missing things (see below)

* Add a max-lenghth padding to predictions and labels to
enable accelerate gather functionality

* Add PyTorch no trainer example to the example README.md

* Remove --do-train from args as being redundant for now

* DataCollatorWithPadding -> DataCollatorForTokenClassification

* Remove some obsolete args.do_train conditions from the script

* Delete --do_train from bash running script

* Delete use_slow_tokenizer from args

* Add unintentionally removed flag --label_all_tokens

* Delete --debug flag from running script

Co-authored-by: WybeKoper <WybeKoper@users.noreply.github.com>

this PR fixes https://github.com/huggingface/transformers/security/dependabot/examples/research_projects/lxmert/requirements.txt/PyYAML/open

Use the correct variable (raw_datasets) instead of the module (datasets)
where appropriate.

* added predict stage

* added test keyword in exception message

* removed example specific saving predictions

* fixed f-string error

* removed extra line
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>

Co-authored-by: Eliza <eliza@habanero.tiger.com.pl>

* feat: ensure unique artifact id

* feat: allow manual init

* fix: simplify reinit logic

* fix: no dropped value + immediate commits

* fix: wandb use in sagemaker

* docs: improve documenation and formatting

* fix: typos

* docs: improve formatting

Takes care of:
https://github.com/huggingface/transformers/security/dependabot/examples/research_projects/lxmert/requirements.txt/jinja2/open



@LysandreJik
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

Bumps [jinja2](https://github.com/pallets/jinja) from 2.11.2 to 2.11.3.
- [Release notes](https://github.com/pallets/jinja/releases)
- [Changelog](https://github.com/pallets/jinja/blob/master/CHANGES.rst)
- [Commits](https://github.com/pallets/jinja/compare/2.11.2...2.11.3

)
Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>

Fix typo.

* wording/typos tweaks

* Make model upload instructions simpler

* Expand a bit the presentation of examples

* Apply suggestions from code review
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>

* Address review comments
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>

* added prediction stage and eval fix

* style correction

* removed extra lines

* finish

* fix

* fix

* fix

* fix

Following up on a security alert:
https://github.com/huggingface/transformers/security/dependabot/examples/research_projects/lxmert/requirements.txt/Pillow/open

* upload

* upload fine-tuning script

* improve

* adapt

* Apply suggestions from code review

* correct

* upload

* finalize

* remove @

* correct typos

* [examples/seq2seq] fix t5 examples

This PR:
* fixes T5 examples to include `--source_prefix` - it's **not** optional. If you give it a try you will see that you get 10x worse bleu scores w/o it. w/ `27.6849`, w/ `2.374`
* added a normal translation example w/o the peculiarities of MBart and T5
* reduces the default max samples to 50 so it's much faster to test quickly

summarization seems to be broken for t5 score-wise: https://github.com/huggingface/transformers/issues/10733

@sgugger

* specify explicitly the t5 models requiring the special handling

* one more

* update the t5 summarization example to use cnn_dailymail

* move max*samples into the top level README.md

* better wording

* better wording

* do not gobble certain kinds of requests.ConnectionError

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

* add initial script

* finish script

* add shell script example

* accept chars_to_ignor as cl arg

* align the script with other example scripts

* add torchaudio dep

* wav2vec2: support datasets other than LibriSpeech

* Formatting run_asr.py to pass code quality test

* bundled orthography options and added verbose logs

* fixing a typo in timit fine-tuning script

* update comment for clarity

* resize_lm_head and load custom vocab from file

* adding a max_duration_in_seconds filter

* do not assign `duration_filter` lambda, use a def

* log untransliterated text as well

* fix base model for arabic

* fix duration filter when target_sr is not set

* drop duration_in_seconds when unneeded

* script for wav2vec2-large-lv60-timit-asr

* fix for "tha" in arabic corpus (huggingface#10581)

* adding more options to work with common_voice

* PR feedback (huggingface#10581)

* small README change

* document resuming in examples

* fix

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

* put trainer code last, adjust notes
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>