[WIP] Ner pipeline grouped_entities fixes (#5970)
* Bug fix: NER pipeline shouldn't group separate entities of same type
* style fix
* [Bug Fix] Shouldn't group entities that are both 'B' even if they are same type
(B-type1 B-type1) != (B-type1 I-type1)
[Bug Fix] add an option `ignore_subwords` to ignore subsequent ##wordpieces in predictions. Because some models train on only the first token of a word and not on the subsequent wordpieces (BERT NER default). So it makes sense doing the same thing at inference time.
The simplest fix is to just group the subwords with the first wordpiece.
[TODO] how to handle ignored scores? just set them to 0 and calculate zero invariant mean ?
[TODO] handle different wordpiece_prefix ## ? possible approaches:
get it from tokenizer? but currently most tokenizers dont have a wordpiece_prefix property?
have an _is_subword(token)
[Feature add] added option to `skip_special_tokens`. Cause It was harder to remove them after grouping.
[Additional Changes] remove B/I prefix on returned grouped_entities
[Feature Request/TODO] Return indexes?
[Bug TODO] can't use fast tokenizer with grouped_entities ('BertTokenizerFast' object has no attribute 'convert_tokens_to_string')
* use offset_mapping to fix [UNK] token problem
* ignore score for subwords
* modify ner_pipeline test
* modify ner_pipeline test
* modify ner_pipeline test
* ner_pipeline change ignore_subwords default to true
* add ner_pipeline ignore_subword=False test case
* fix offset_mapping index
* fix style again duh
* change is_subword and convert_tokens_to_string logic
* merge tests with new test structure
* change test names
* remove old tests
* ner tests for fast tokenizer
* fast tokenizers have convert_tokens_to_string
* Fix the incorrect merge
Co-authored-by: Ceyda Cinarel <snu-ceyda@users.noreply.github.com>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
