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40 lines
1.1 KiB
Python
40 lines
1.1 KiB
Python
# coding: utf8
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from __future__ import unicode_literals
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from collections import defaultdict
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from spacy.pipeline import EntityRecognizer
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from spacy.lang.en import English
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from spacy.tokens import Span
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def test_issue4313():
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""" This should not crash or exit with some strange error code """
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beam_width = 16
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beam_density = 0.0001
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nlp = English()
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ner = EntityRecognizer(nlp.vocab)
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ner.add_label("SOME_LABEL")
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ner.begin_training([])
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nlp.add_pipe(ner)
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# add a new label to the doc
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doc = nlp("What do you think about Apple ?")
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assert len(ner.labels) == 1
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assert "SOME_LABEL" in ner.labels
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apple_ent = Span(doc, 5, 6, label="MY_ORG")
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doc.ents = list(doc.ents) + [apple_ent]
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# ensure the beam_parse still works with the new label
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docs = [doc]
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beams = nlp.entity.beam_parse(
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docs, beam_width=beam_width, beam_density=beam_density
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)
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for doc, beam in zip(docs, beams):
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entity_scores = defaultdict(float)
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for score, ents in nlp.entity.moves.get_beam_parses(beam):
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for start, end, label in ents:
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entity_scores[(start, end, label)] += score
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