* [trainer] fix ignored columns logger
This PR fixes a confusing log entry that says:
```
The following columns in the evaluation set don't have a corresponding argument in `T5ForConditionalGeneration.forward` and have been ignored: .
```
when everything is in order.
* Update src/transformers/trainer.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* fix run_seq2seq.py; porting DeepSpeed tests to it
* unrefactor
* defensive programming
* defensive programming 2
* port the rest of the trainer tests
* style
* a cleaner scripts dir finder
* cleanup
* Add check-ops script
* Finish to implement check_tf_ops and start the test
* Make the test mandatory only for BERT
* Update tf_ops folder
* Remove useless classes
* Add the ONNX test for GPT2 and BART
* Add a onnxruntime slow test + better opset flexibility
* Fix test + apply style
* fix tests
* Switch min opset from 12 to 10
* Update src/transformers/file_utils.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Fix GPT2
* Remove extra shape_list usage
* Fix GPT2
* Address Morgan's comments
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Conversion from slow to fast for BPE spm vocabs contained an error.
- There is only 1 test currently (tokenizers + slow) that used the modified path
and it's reformer, which does not contain any ids modification so the
bug was silent for now.
- The real issue is that vocab variable was overloaded by
SentencePieceExtractor, leading to Slow specific vocab oddities to be
completely ignored
- The bug was reported here https://github.com/huggingface/transformers/issues/9518
- Ran the complete tokenization test suite with slow without error
(`RUN_SLOW=1 pytest -sv tests/test_tokenization_*`)
* Remove rebase error.
* Adding the fixture.
* remove xnli_compute_metrics, add load_dataset, load_metric, set_seed,metric.compute,load_metric
* fix
* fix
* fix
* push
* fix
* everything works
* fix init
* fix
* special treatment for sepconv1d
* style
* 🙏🏽
* add doc and cleanup
* fix doc
* fix doc again
* fix doc again
* Apply suggestions from code review
* make style
* Proposal that should work
* Remove needless code
* Fix test
* Apply suggestions from code review
* remove xnli_compute_metrics, add load_dataset, load_metric, set_seed,metric.compute,load_metric
* amend README
* removed data_args.task_name and replaced with task_name = "xnli"; use split function to load train and validation dataset separately; remove __post_init__; remove flag --task_name from README.
* removed dict task_to_keys, use str "xnli" instead of variable task_name, change preprocess_function to use examples["premise"], examples["hypothesis"] directly, remove sentence1_key and sentence2_key, change compute_metrics function to cater only to accuracy metric, add condition for train_langauge is None when using dataset.load_dataset()
* removed `torch.distributed.barrier()` and `import torch` as `from_pretrained` is able to do the work; amend README
