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6f5e308d17
* Support a cfg field in transition system * Make NER 'has gold' check use right alignment for span * Pass 'negative_samples_key' property into NER transition system * Add field for negative samples to NER transition system * Check neg_key in NER has_gold * Support negative examples in NER oracle * Test for negative examples in NER * Fix name of config variable in NER * Remove vestiges of old-style partial annotation * Remove obsolete tests * Add comment noting lack of support for negative samples in parser * Additions to "neg examples" PR (#8201) * add custom error and test for deprecated format * add test for unlearning an entity * add break also for Begin's cost * add negative_samples_key property on Parser * rename * extend docs & fix some older docs issues * add subclass constructors, clean up tests, fix docs * add flaky test with ValueError if gold parse was not found * remove ValueError if n_gold == 0 * fix docstring * Hack in environment variables to try out training * Remove hack * Remove NER hack, and support 'negative O' samples * Fix O oracle * Fix transition parser * Remove 'not O' from oracle * Fix NER oracle * check for spans in both gold.ents and gold.spans and raise if so, to prevent memory access violation * use set instead of list in consistency check Co-authored-by: svlandeg <sofie.vanlandeghem@gmail.com> Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
57 lines
1.8 KiB
Python
57 lines
1.8 KiB
Python
from spacy.pipeline.ner import DEFAULT_NER_MODEL
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from spacy.training import Example
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from spacy.pipeline import EntityRecognizer
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from spacy.tokens import Span, Doc
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from spacy import registry
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import pytest
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def _ner_example(ner):
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doc = Doc(
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ner.vocab,
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words=["Joe", "loves", "visiting", "London", "during", "the", "weekend"],
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)
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gold = {"entities": [(0, 3, "PERSON"), (19, 25, "LOC")]}
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return Example.from_dict(doc, gold)
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def test_doc_add_entities_set_ents_iob(en_vocab):
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text = ["This", "is", "a", "lion"]
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doc = Doc(en_vocab, words=text)
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cfg = {"model": DEFAULT_NER_MODEL}
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model = registry.resolve(cfg, validate=True)["model"]
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ner = EntityRecognizer(en_vocab, model)
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ner.initialize(lambda: [_ner_example(ner)])
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ner(doc)
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doc.ents = [("ANIMAL", 3, 4)]
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assert [w.ent_iob_ for w in doc] == ["O", "O", "O", "B"]
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doc.ents = [("WORD", 0, 2)]
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assert [w.ent_iob_ for w in doc] == ["B", "I", "O", "O"]
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def test_ents_reset(en_vocab):
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"""Ensure that resetting doc.ents does not change anything"""
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text = ["This", "is", "a", "lion"]
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doc = Doc(en_vocab, words=text)
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cfg = {"model": DEFAULT_NER_MODEL}
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model = registry.resolve(cfg, validate=True)["model"]
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ner = EntityRecognizer(en_vocab, model)
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ner.initialize(lambda: [_ner_example(ner)])
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ner(doc)
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orig_iobs = [t.ent_iob_ for t in doc]
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doc.ents = list(doc.ents)
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assert [t.ent_iob_ for t in doc] == orig_iobs
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def test_add_overlapping_entities(en_vocab):
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text = ["Louisiana", "Office", "of", "Conservation"]
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doc = Doc(en_vocab, words=text)
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entity = Span(doc, 0, 4, label=391)
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doc.ents = [entity]
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new_entity = Span(doc, 0, 1, label=392)
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with pytest.raises(ValueError):
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doc.ents = list(doc.ents) + [new_entity]
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