* Optimizing away the `fill-mask` pipeline.
- Don't send anything to the tokenizer unless needed. Vocab check is
much faster
- Keep BC by sending data to the tokenizer when needed. User handling warning messages will see performance benefits again
- Make `targets` and `top_k` work together better `top_k` cannot be
higher than `len(targets)` but can be smaller still.
- Actually simplify the `target_ids` in case of duplicate (it can happen
because we're parsing raw strings)
- Removed useless code to fail on empty strings. It works only if empty
string is in first position, moved to ignoring them instead.
- Changed the related tests as only the tests would fail correctly
(having incorrect value in first position)
* Make tests compatible for 2 different vocabs... (at the price of a
warning).
Co-authored-by: @EtaoinWu
* ValueError working globally
* Update src/transformers/pipelines/fill_mask.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* `tokenizer.vocab` -> `tokenizer.get_vocab()` for more compatiblity +
fallback.
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Replace conditional generation example (fixes#12268)
* Replace model in summarization example with finetuned checkpoint, adapt example text
* Fix typo in new summarization example
* Fix docstring formatting, add missing import statement to example
* registering a buffer for token_type_ids, to pass the error of device-id getting hardcoded when tracing
* sytle format
* adding persistent flag to the resgitered buffers that prevent from adding them to the state_dict and addresses the Backward compatibility issue
* adding the try catch to the fix as persistent flag is only available from PT >1.6
* adding version check
* added the condition to only use the token_type_ids buffer when its autogenerated not passed by user
* adding comments and making the conidtion where token_type_ids are None to use the registered buffer
* taking out position-embeddding from the if block
* adding comments
* handling the case if buffer for position_ids was not registered
* reverted the changes on position_ids, fix the issue with size of token_type_ids buffer, moved the modification for generated token_type_ids to Bertmodel, instead of Embeddings
* reverting the token_type_ids in case of None to the previous version
* reverting changes on position_ids adding back the if block
* changes added by running make fix-copies
* changes added by running make fix-copies and added the import version as it was getting used
* changes added by running make fix-copies
* changes added by running make fix-copies
* fixing the import format
* fixing the import format
* modified to use temp tensor for trimed and expanded token_type_ids buffer
* changes made by fix-copies after temp tensor modifications
* changes made by fix-copies after temp tensor modifications
* changes made by fix-copies after temp tensor modifications
* clean up
* clean up
* clean up
* clean up
* Nit
* Nit
* Nit
* modified according to support device conversion on traced models
* modified according to support device conversion on traced models
* modified according to support device conversion on traced models
* modified according to support device conversion on traced models
* changes based on latest in master
* Adapt templates
* Add version import
Co-authored-by: Ubuntu <ubuntu@ip-172-31-32-81.us-west-2.compute.internal>
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
* boom boom
* remove flax clip example
* allow loading head model with base model weights
* add test
* fix imports
* disable save, load test for clip
* add test_save_load_to_base
* [run_clm.py] restore caching
* style
* [t5 doc] make the example work out of the box
This PR expands the training example to include the correct model type for the example to work, e.g. with `T5Model` this example will break.
* Update docs/source/model_doc/t5.rst
Co-authored-by: Suraj Patil <surajp815@gmail.com>
* expand the other example
Co-authored-by: Suraj Patil <surajp815@gmail.com>
* AutoTokenizer: infer the class from the tokenizer config if possible
* Add tests
* Update src/transformers/models/auto/tokenization_auto.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Temporarily deactivate torch-scatter while we wait for new release
* torch-1.8.1 binary for scatter
* Revert to 1.8.0
* Pin torch dependency
* torchaudio and torchvision
* [WIP] Model card defaults
* finetuned_from default value
* Add all mappings to the mapping file
* Be more defensive on finetuned_from arg
* Add default task tag
* Separate tags from tasks
* Edge case for dataset
* Apply suggestions from code review
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
- Convert use of deprecated AutoModelWithLMHead to AutoModelForSeq2SeqLM
- Add newly required `truncation=True` to `tokenizer.encode` with `max_length`
This silences all warnings.
