GitLab

* Replace -100s in predictions by the pad token

* Style

* Try to catch them all

allow only test_file in pytorch and flax summarization

* Override the decoding parameters of Seq2SeqTrainer

* Fix quality

* Fix max_length parameter

* Fix quality

* Remove redundant parameter max_length

* Separate the preprocess of train and validation to use different max_target_length

* Respect documentation on passive log level

* Fix test and set log level in examples

* Add doc

* Result of black 23.1

* Update target to Python 3.7

* Switch flake8 to ruff

* Configure isort

* Configure isort

* Apply isort with line limit

* Put the right black version

* adapt black in check copies

* Fix copies

* Only resize embeddings when necessary

* Add comment

* zero chance anyone's using that constant no?

* `transformers-cli login` => `huggingface-cli login`

* `transformers-cli repo create` => `huggingface-cli repo create`

* `make style`

* Fix ROUGE add example check and update README

* Stay consistent in values

* Migrate metric to Evaluate in pytorch examples

* Remove unused imports

* Preliminary work on tokenizers

* Quality + fix tests

* Treat processors

* Fix pad

* Remove all uses of  in tests, docs and examples

* Replace all as_target_tokenizer

* Fix tests

* Fix quality

* Update examples/flax/image-captioning/run_image_captioning_flax.py
Co-authored-by: amyeroberts <amy@huggingface.co>

* Style
Co-authored-by: amyeroberts <amy@huggingface.co>

* Add summarization name mapping for MultiNews

* Add summarization name mapping for MultiNews

* Add examples telemetry

* Alternative approach

* Add to all other examples

* Add to templates as well

* Put framework separately

* Same for TensorFlow

* Black preview

* Fixup too!

* Fix check copies

* Use the same version as the CI

* Bump black

* fix formatting and remove use_auth

* Add use_auth_token to Flax examples

* [examples] max samples can't be bigger than then len of dataset

* do tf and flax

* Split file_utils in several submodules

* Fixes

* Add back more objects

* More fixes

* Who exactly decided to import that from there?

* Second suggestion to code with code review

* Revert wront move

* Fix imports

* Adapt all imports

* Adapt all imports everywhere

* Revert this import, will fix in a separate commit

Remove input and target reset after preprocessing

* Update run_summarization.py

* Fixed languages and added missing code

* fixed obj, docs, removed source_lang and target_lang

* make style, run_summarization.py reformatted

* [examples/summarization] deal with None in data records

* rewrite to use a simpler (slower) variant