GitLab

* Only resize embeddings when necessary

* Add comment

* fixed typo for SQuAD

* Fixed the preprocess_validation_function function for the labels to reflect the remaining truncated instances

* Rolled back the trainer_seq2seq_qa.py for UnboundLocalError: local variable 'metrics' referenced before assignment
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* fix args option

* fix trainer eval log

* fix out of memory qa script

* do isort, black, flake

* fix tokenize target

* take it back.

* fix: 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`

* 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 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

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

* do tf and flax

* Update postporcessing accordingly to use SQuAD metric.

* Update assets accordingly based on SQuAD metrics.

* Fix function naming error.

* Add seq2seq example for QnA on SQuAD Dataset.

* Changes from review - Fixing styling mistakes.

* Added how to example in README, simplified the access to dataset's preprocess function.

* Added tests for the seq2seq QA example.

* Change dataset column name to fix tests.

* Fix test command mistake.

* Add missing argument 'ignore_pad_token_for_loss' from DataTrainingArguments.

* Add missing argument 'num_beams' from DataTrainingArguments.

* Fix processing of output predicted token ids so that tokenizer decode gets appropriate input. Updated assertion conditions on the tests.

* Update run_glue.py

* Update run_glue.py

* Add model creation snippet to other scripts

* Fix style

* finish

* delete bogus file

* correct some stuff

* finish

* finish