mirror of https://github.com/explosion/spaCy.git
58 lines
1.8 KiB
Python
58 lines
1.8 KiB
Python
from spacy.pipeline import EntityRecognizer
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from spacy.tokens import Span
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from spacy import registry
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import pytest
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from ..util import get_doc
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from spacy.pipeline.ner import DEFAULT_NER_MODEL
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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 = get_doc(en_vocab, text)
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config = {
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"learn_tokens": False,
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"min_action_freq": 30,
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"update_with_oracle_cut_size": 100,
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}
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model = registry.make_from_config({"model": DEFAULT_NER_MODEL}, validate=True)["model"]
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ner = EntityRecognizer(en_vocab, model, **config)
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ner.begin_training([])
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ner(doc)
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assert len(list(doc.ents)) == 0
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assert [w.ent_iob_ for w in doc] == (["O"] * len(doc))
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doc.ents = [(doc.vocab.strings["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 = [(doc.vocab.strings["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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text = ["This", "is", "a", "lion"]
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doc = get_doc(en_vocab, text)
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config = {
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"learn_tokens": False,
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"min_action_freq": 30,
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"update_with_oracle_cut_size": 100,
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}
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model = registry.make_from_config({"model": DEFAULT_NER_MODEL}, validate=True)["model"]
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ner = EntityRecognizer(en_vocab, model, **config)
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ner.begin_training([])
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ner(doc)
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assert [t.ent_iob_ for t in doc] == (["O"] * len(doc))
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doc.ents = list(doc.ents)
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assert [t.ent_iob_ for t in doc] == (["O"] * len(doc))
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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 = get_doc(en_vocab, 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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