NerPipeline (TokenClassification) now outputs offsets of words (#8781)
* NerPipeline (TokenClassification) now outputs offsets of words
- It happens that the offsets are missing, it forces the user to pattern
match the "word" from his input, which is not always feasible.
For instance if a sentence contains the same word twice, then there
is no way to know which is which.
- This PR proposes to fix that by outputting 2 new keys for this
pipelines outputs, "start" and "end", which correspond to the string
offsets of the word. That means that we should always have the
invariant:
```python
input[entity["start"]: entity["end"]] == entity["entity_group"]
# or entity["entity"] if not grouped
```
* Fixing doc style
This commit is contained in:
Nicolas Patry
GitHub
parent
5fd3d81ec9
commit
d8fc26e919
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@ -1420,9 +1420,14 @@ class TokenClassificationPipeline(Pipeline):
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- **word** (:obj:`str`) -- The token/word classified.
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- **score** (:obj:`float`) -- The corresponding probability for :obj:`entity`.
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- **entity** (:obj:`str`) -- The entity predicted for that token/word.
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- **entity** (:obj:`str`) -- The entity predicted for that token/word (it is named `entity_group` when
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`grouped_entities` is set to True.
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- **index** (:obj:`int`, only present when ``self.grouped_entities=False``) -- The index of the
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corresponding token in the sentence.
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- **start** (:obj:`int`, `optional`) -- The index of the start of the corresponding entity in the sentence.
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Only exists if the offsets are available within the tokenizer
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- **end** (:obj:`int`, `optional`) -- The index of the end of the corresponding entity in the sentence.
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Only exists if the offsets are available within the tokenizer
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"""
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inputs, offset_mappings = self._args_parser(inputs, **kwargs)
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@ -1486,11 +1491,16 @@ class TokenClassificationPipeline(Pipeline):
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else:
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word = self.tokenizer.convert_ids_to_tokens(int(input_ids[idx]))
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start_ind = None
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end_ind = None
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entity = {
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"word": word,
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"score": score[idx][label_idx].item(),
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"entity": self.model.config.id2label[label_idx],
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"index": idx,
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"start": start_ind,
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"end": end_ind,
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}
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if self.grouped_entities and self.ignore_subwords:
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@ -1524,6 +1534,8 @@ class TokenClassificationPipeline(Pipeline):
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"entity_group": entity,
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"score": np.mean(scores),
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"word": self.tokenizer.convert_tokens_to_string(tokens),
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"start": entities[0]["start"],
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"end": entities[-1]["end"],
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}
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return entity_group
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@ -2,7 +2,7 @@ import unittest
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from transformers import AutoTokenizer, pipeline
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from transformers.pipelines import Pipeline, TokenClassificationArgumentHandler
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from transformers.testing_utils import require_tf, require_torch
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from transformers.testing_utils import require_tf, require_torch, slow
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from .test_pipelines_common import CustomInputPipelineCommonMixin
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@ -18,55 +18,207 @@ class NerPipelineTests(CustomInputPipelineCommonMixin, unittest.TestCase):
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large_models = [] # Models tested with the @slow decorator
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def _test_pipeline(self, nlp: Pipeline):
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output_keys = {"entity", "word", "score"}
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output_keys = {"entity", "word", "score", "start", "end"}
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if nlp.grouped_entities:
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output_keys = {"entity_group", "word", "score"}
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output_keys = {"entity_group", "word", "score", "start", "end"}
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ungrouped_ner_inputs = [
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[
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{"entity": "B-PER", "index": 1, "score": 0.9994944930076599, "is_subword": False, "word": "Cons"},
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{"entity": "B-PER", "index": 2, "score": 0.8025449514389038, "is_subword": True, "word": "##uelo"},
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{"entity": "I-PER", "index": 3, "score": 0.9993102550506592, "is_subword": False, "word": "Ara"},
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{"entity": "I-PER", "index": 4, "score": 0.9993743896484375, "is_subword": True, "word": "##új"},
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{"entity": "I-PER", "index": 5, "score": 0.9992871880531311, "is_subword": True, "word": "##o"},
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{"entity": "I-PER", "index": 6, "score": 0.9993029236793518, "is_subword": False, "word": "No"},
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{"entity": "I-PER", "index": 7, "score": 0.9981776475906372, "is_subword": True, "word": "##guera"},
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{"entity": "B-PER", "index": 15, "score": 0.9998136162757874, "is_subword": False, "word": "Andrés"},
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{"entity": "I-PER", "index": 16, "score": 0.999740719795227, "is_subword": False, "word": "Pas"},
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{"entity": "I-PER", "index": 17, "score": 0.9997414350509644, "is_subword": True, "word": "##tran"},
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{"entity": "I-PER", "index": 18, "score": 0.9996136426925659, "is_subword": True, "word": "##a"},
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{"entity": "B-ORG", "index": 28, "score": 0.9989739060401917, "is_subword": False, "word": "Far"},
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{"entity": "I-ORG", "index": 29, "score": 0.7188422083854675, "is_subword": True, "word": "##c"},
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{
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"entity": "B-PER",
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"index": 1,
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"score": 0.9994944930076599,
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"is_subword": False,
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"word": "Cons",
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"start": 0,
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"end": 4,
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},
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{
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"entity": "B-PER",
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"index": 2,
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"score": 0.8025449514389038,
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"is_subword": True,
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"word": "##uelo",
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"start": 4,
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"end": 8,
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},
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{
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"entity": "I-PER",
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"index": 3,
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"score": 0.9993102550506592,
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"is_subword": False,
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"word": "Ara",
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"start": 9,
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"end": 11,
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},
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{
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"entity": "I-PER",
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"index": 4,
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"score": 0.9993743896484375,
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"is_subword": True,
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"word": "##új",
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"start": 11,
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"end": 13,
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},
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{
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"entity": "I-PER",
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"index": 5,
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"score": 0.9992871880531311,
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"is_subword": True,
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"word": "##o",
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"start": 13,
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"end": 14,
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},
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{
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"entity": "I-PER",
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"index": 6,
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"score": 0.9993029236793518,
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"is_subword": False,
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"word": "No",
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"start": 15,
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"end": 17,
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},
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{
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"entity": "I-PER",
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"index": 7,
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"score": 0.9981776475906372,
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"is_subword": True,
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"word": "##guera",
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"start": 17,
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"end": 22,
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},
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{
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"entity": "B-PER",
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"index": 15,
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"score": 0.9998136162757874,
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"is_subword": False,
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"word": "Andrés",
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"start": 23,
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"end": 28,
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},
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{
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"entity": "I-PER",
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"index": 16,
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"score": 0.999740719795227,
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"is_subword": False,
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"word": "Pas",
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"start": 29,
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"end": 32,
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},
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{
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"entity": "I-PER",
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"index": 17,
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"score": 0.9997414350509644,
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"is_subword": True,
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"word": "##tran",
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"start": 32,
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"end": 36,
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},
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{
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"entity": "I-PER",
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"index": 18,
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"score": 0.9996136426925659,
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"is_subword": True,
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"word": "##a",
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"start": 36,
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"end": 37,
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},
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{
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"entity": "B-ORG",
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"index": 28,
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"score": 0.9989739060401917,
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"is_subword": False,
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"word": "Far",
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"start": 39,
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"end": 42,
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},
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{
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"entity": "I-ORG",
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"index": 29,
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"score": 0.7188422083854675,
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"is_subword": True,
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"word": "##c",
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"start": 42,
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"end": 43,
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},
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],
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[
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{"entity": "I-PER", "index": 1, "score": 0.9968166351318359, "is_subword": False, "word": "En"},
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{"entity": "I-PER", "index": 2, "score": 0.9957635998725891, "is_subword": True, "word": "##zo"},
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{"entity": "I-ORG", "index": 7, "score": 0.9986497163772583, "is_subword": False, "word": "UN"},
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{
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"entity": "I-PER",
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"index": 1,
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"score": 0.9968166351318359,
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"is_subword": False,
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"word": "En",
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"start": 0,
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"end": 2,
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},
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{
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"entity": "I-PER",
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"index": 2,
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"score": 0.9957635998725891,
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"is_subword": True,
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"word": "##zo",
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"start": 2,
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"end": 4,
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},
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{
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"entity": "I-ORG",
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"index": 7,
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"score": 0.9986497163772583,
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"is_subword": False,
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"word": "UN",
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"start": 11,
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"end": 13,
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},
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],
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]
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expected_grouped_ner_results = [
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[
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{"entity_group": "PER", "score": 0.999369223912557, "word": "Consuelo Araújo Noguera"},
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{"entity_group": "PER", "score": 0.9997771680355072, "word": "Andrés Pastrana"},
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{"entity_group": "ORG", "score": 0.9989739060401917, "word": "Farc"},
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{
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"entity_group": "PER",
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"score": 0.999369223912557,
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"word": "Consuelo Araújo Noguera",
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"start": 0,
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"end": 22,
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},
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{
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"entity_group": "PER",
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"score": 0.9997771680355072,
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"word": "Andrés Pastrana",
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"start": 23,
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"end": 37,
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},
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{"entity_group": "ORG", "score": 0.9989739060401917, "word": "Farc", "start": 39, "end": 43},
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],
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[
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{"entity_group": "PER", "score": 0.9968166351318359, "word": "Enzo"},
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{"entity_group": "ORG", "score": 0.9986497163772583, "word": "UN"},
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{"entity_group": "PER", "score": 0.9968166351318359, "word": "Enzo", "start": 0, "end": 4},
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{"entity_group": "ORG", "score": 0.9986497163772583, "word": "UN", "start": 11, "end": 13},
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],
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]
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expected_grouped_ner_results_w_subword = [
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[
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{"entity_group": "PER", "score": 0.9994944930076599, "word": "Cons"},
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{"entity_group": "PER", "score": 0.9663328925768534, "word": "##uelo Araújo Noguera"},
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{"entity_group": "PER", "score": 0.9997273534536362, "word": "Andrés Pastrana"},
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{"entity_group": "ORG", "score": 0.8589080572128296, "word": "Farc"},
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{"entity_group": "PER", "score": 0.9994944930076599, "word": "Cons", "start": 0, "end": 4},
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{
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"entity_group": "PER",
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"score": 0.9663328925768534,
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"word": "##uelo Araújo Noguera",
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"start": 4,
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"end": 22,
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},
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{
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"entity_group": "PER",
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"score": 0.9997273534536362,
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"word": "Andrés Pastrana",
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"start": 23,
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"end": 37,
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},
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{"entity_group": "ORG", "score": 0.8589080572128296, "word": "Farc", "start": 39, "end": 43},
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],
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[
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{"entity_group": "PER", "score": 0.9962901175022125, "word": "Enzo"},
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{"entity_group": "ORG", "score": 0.9986497163772583, "word": "UN"},
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{"entity_group": "PER", "score": 0.9962901175022125, "word": "Enzo", "start": 0, "end": 4},
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{"entity_group": "ORG", "score": 0.9986497163772583, "word": "UN", "start": 11, "end": 13},
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],
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]
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@ -164,6 +316,34 @@ class NerPipelineTests(CustomInputPipelineCommonMixin, unittest.TestCase):
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nlp = pipeline(task="ner", model=model_name)
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self._test_pipeline(nlp)
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@slow
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@require_torch
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def test_simple(self):
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nlp = pipeline(task="ner", model="dslim/bert-base-NER", grouped_entities=True)
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output = nlp("Hello Sarah Jessica Parker who Jessica lives in New York")
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def simplify(output):
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for i in range(len(output)):
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output[i]["score"] = round(output[i]["score"], 3)
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return output
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output = simplify(output)
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self.assertEqual(
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output,
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[
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{
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"entity_group": "PER",
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"score": 0.996,
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"word": "Sarah Jessica Parker",
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"start": 6,
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"end": 26,
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},
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{"entity_group": "PER", "score": 0.977, "word": "Jessica", "start": 31, "end": 38},
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{"entity_group": "LOC", "score": 0.999, "word": "New York", "start": 48, "end": 56},
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],
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)
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@require_torch
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def test_pt_small_ignore_subwords_available_for_fast_tokenizers(self):
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for model_name in self.small_models:
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