* Delete valohai.yaml
* NLP => ML
* typo
* website supports https
* datasets
* 60k + modalities
* unrelated link fixing for accelerate
* Ok those links were actually broken
* Fix link
* Make `AutoTokenizer` auto-link
* wording tweak
* add at least one non-nlp task
* Add logits_processor parameter, used by `generate`, to `Seq2SeqTrainer` methods `evaluate` and `predict`
* Add all generate parameters to `Seq2SeqTrainer`, and also to `QuestionAnsweringSeq2SeqTrainer` which overrides it
* Remove `self._num_beams` from trainer classes
* - Run fixup
- Fix "Constraint" not exposed
- Fix synced_gpus to actually read from param
* Use kwargs
* Copy kwargs before making changes to it
* Fix style issues unused imports
* Add examples telemetry
* Alternative approach
* Add to all other examples
* Add to templates as well
* Put framework separately
* Same for TensorFlow
* Fix length in no_trainer examples
* Add setup and teardown
* Use new accelerator config generator to automatically make tests able to run based on environment
- Add --ignore_mismatched_sizes argument to classification examples
- Expand the error message when loading a model whose head dimensions are different from expected dimensions
1. Fixes evaluation errors popping up when you train/eval on squad v2 (one was newly encountered and one that was previously reported Running SQuAD 1.0 sample command raises IndexError #15401 but not completely fixed).
2. Removes boolean arguments that don't use store_true. Please, don't use these: *ANY non-empty string is being converted to True in this case and this clearly is not the desired behavior (and it creates a LOT of confusion).
3. All no-trainer test scripts are now saving metric values in the same way (with the right prefix eval_), which is consistent with the trainer-based versions.
4. Adds forgotten model.eval() in the no-trainer versions. This improved some results, but not everything (see the discussion in the end). Please, see the F1 scores and the discussion below.
* Add first draft
* Improve script and README
* Improve README
* Apply suggestions from code review
* Improve script, add link to resulting model
* Add corresponding test
* Adjust learning rate
* Add first draft
* Improve README and run fixup
* Make script aligned with other scripts, improve README
* Improve script and add test
* Remove print statement
* Apply suggestions from code review
* Add num_labels to make test pass
* Improve README
* Add first draft from previous PR
* First draft
* Improve README and remove num_labels
* Make script more aligned with other scripts
* Improve README and apply suggestion from code review
