* remove output_past from pt
* make style
* add optional input length for gpt2
* add use cache to prepare input
* save memory in gpt2
* correct gpt2 test inputs
* make past input optional for gpt2
* finish use_cache for all models
* make style
* delete modeling_gpt2 change in test file
* correct docstring
* correct is true statements for gpt2
* [examples] Generate argparsers from type hints on dataclasses
* [HfArgumentParser] way simpler API
* Restore run_language_modeling.py for easier diff
* [HfArgumentParser] final tweaks from code review
* Renamed num_added_tokens to num_special_tokens_to_add
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Cherry-Pick: Partially fix space only input without special tokens added to the output #3091
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Added property is_fast on PretrainedTokenizer and PretrainedTokenizerFast
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Make fast tokenizers unittests work on Windows.
* Entirely refactored unittest for tokenizers fast.
* Remove ABC class for CommonFastTokenizerTest
* Added embeded_special_tokens tests from allenai @dirkgr
* Make embeded_special_tokens tests from allenai more generic
* Uniformize vocab_size as a property for both Fast and normal tokenizers
* Move special tokens handling out of PretrainedTokenizer (SpecialTokensMixin)
* Ensure providing None input raise the same ValueError than Python tokenizer + tests.
* Fix invalid input for assert_padding when testing batch_encode_plus
* Move add_special_tokens from constructor to tokenize/encode/[batch_]encode_plus methods parameter.
* Ensure tokenize() correctly forward add_special_tokens to rust.
* Adding None checking on top on encode / encode_batch for TransfoXLTokenizerFast.
Avoid stripping on None values.
* unittests ensure tokenize() also throws a ValueError if provided None
* Added add_special_tokens unittest for all supported models.
* Style
* Make sure TransfoXL test run only if PyTorch is provided.
* Split up tokenizers tests for each model type.
* Fix invalid unittest with new tokenizers API.
* Filter out Roberta openai detector models from unittests.
* Introduce BatchEncoding on fast tokenizers path.
This new structure exposes all the mappings retrieved from Rust.
It also keeps the current behavior with model forward.
* Introduce BatchEncoding on slow tokenizers path.
Backward compatibility.
* Improve error message on BatchEncoding for slow path
* Make add_prefix_space True by default on Roberta fast to match Python in majority of cases.
* Style and format.
* Added typing on all methods for PretrainedTokenizerFast
* Style and format
* Added path for feeding pretokenized (List[str]) input to PretrainedTokenizerFast.
* Style and format
* encode_plus now supports pretokenized inputs.
* Remove user warning about add_special_tokens when working on pretokenized inputs.
* Always go through the post processor.
* Added support for pretokenized input pairs on encode_plus
* Added is_pretokenized flag on encode_plus for clarity and improved error message on input TypeError.
* Added pretokenized inputs support on batch_encode_plus
* Update BatchEncoding methods name to match Encoding.
* Bump setup.py tokenizers dependency to 0.7.0rc1
* Remove unused parameters in BertTokenizerFast
* Make sure Roberta returns token_type_ids for unittests.
* Added missing typings
* Update add_tokens prototype to match tokenizers side and allow AddedToken
* Bumping tokenizers to 0.7.0rc2
* Added documentation for BatchEncoding
* Added (unused) is_pretokenized parameter on PreTrainedTokenizer encode_plus/batch_encode_plus methods.
* Added higher-level typing for tokenize / encode_plus / batch_encode_plus.
* Fix unittests failing because add_special_tokens was defined as a constructor parameter on Rust Tokenizers.
* Fix text-classification pipeline using the wrong tokenizer
* Make pipelines works with BatchEncoding
* Turn off add_special_tokens on tokenize by default.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Remove add_prefix_space from tokenize call in unittest.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Style and quality
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Correct message for batch_encode_plus none input exception.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Fix invalid list comprehension for offset_mapping overriding content every iteration.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* TransfoXL uses Strip normalizer.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Bump tokenizers dependency to 0.7.0rc3
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Support AddedTokens for special_tokens and use left stripping on mask for Roberta.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* SpecilaTokenMixin can use slots to faster access to underlying attributes.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Remove update_special_tokens from fast tokenizers.
* Ensure TransfoXL unittests are run only when torch is available.
* Style.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Style
* Style 🙏🙏
* Remove slots on SpecialTokensMixin, need deep dive into pickle protocol.
* Remove Roberta warning on __init__.
* Move documentation to Google style.
Co-authored-by: LysandreJik <lysandre.debut@reseau.eseo.fr>
* add some t5 integration tests
* finish summarization and translation integration tests for T5 - results loook good
* add tf test
* fix == vs is bug
* fix tf beam search error and make tf t5 tests pass
* make decoder input ids optional for t5 training
* lm_lables should not be shifted in t5
* add tests
* finish shift right functionality for PT T5
* move shift right to correct class
* cleaner code
* replace -100 values with pad token id
* add assert statement
* remove unnecessary for loop
* make style
* Add the missing token classification for XLM
* fix styling
* Add XLMForTokenClassification to AutoModelForTokenClassification class
* Fix docstring typo for non-existing class
* Add the missing token classification for XLM
* fix styling
* fix styling
* Add XLMForTokenClassification to AutoModelForTokenClassification class
* Fix docstring typo for non-existing class
* Add missing description for AlbertForTokenClassification
* fix styling
* Add missing docstring for AlBert
* Slow tests should be slow
Co-authored-by: Sakares Saengkaew <s.sakares@gmail.com>
Co-authored-by: LysandreJik <lysandre.debut@reseau.eseo.fr>
* fix conflicts
* update bart max length test
* correct spelling mistakes
* implemented model specific encode function
* fix merge conflicts
* better naming
* save intermediate state -> need to rethink strucuture a bit
* leave tf problem as it is for now
* current version
* add layers.pop
* remove ipdb
* make style
* clean return cut decoding
* remove ipdbs
* Fix restoring layers in the decoders that doesnt exists.
* push good intermediate solution for now
* fix conflicts
* always good to refuse to merge conflicts when rebasing
* fix small bug
* improve function calls
* remove unused file
* add correct scope behavior for t5_generate
Co-authored-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* first commit
* work in progress
* make language generation task pass
* update to working version for LM
* delete print
* remove dead code
* make style
* passing
* Undo stupid chg
* docs
* undo rename
* delete-cruft
* only import if you have torch
* Dont rely on dict ordering
* Fix dict ordering upstream
* docstring link
* docstring link
* remove trailing comma for 3.5 compat
* new name
* delegate kwarging
* Update kwargs
