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76 lines
2.6 KiB
Python
76 lines
2.6 KiB
Python
from spacy.kb import KnowledgeBase
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from spacy.lang.en import English
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from spacy.training import Example
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def test_issue7065():
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text = "Kathleen Battle sang in Mahler 's Symphony No. 8 at the Cincinnati Symphony Orchestra 's May Festival."
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nlp = English()
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nlp.add_pipe("sentencizer")
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ruler = nlp.add_pipe("entity_ruler")
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patterns = [{"label": "THING", "pattern": [{"LOWER": "symphony"}, {"LOWER": "no"}, {"LOWER": "."}, {"LOWER": "8"}]}]
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ruler.add_patterns(patterns)
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doc = nlp(text)
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sentences = [s for s in doc.sents]
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assert len(sentences) == 2
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sent0 = sentences[0]
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ent = doc.ents[0]
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assert ent.start < sent0.end < ent.end
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assert sentences.index(ent.sent) == 0
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def test_issue7065_b():
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# Test that the NEL doesn't crash when an entity crosses a sentence boundary
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nlp = English()
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vector_length = 3
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nlp.add_pipe("sentencizer")
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text = "Mahler 's Symphony No. 8 was beautiful."
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entities = [(0, 6, "PERSON"), (10, 24, "WORK")]
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links = {(0, 6): {"Q7304": 1.0, "Q270853": 0.0},
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(10, 24): {"Q7304": 0.0, "Q270853": 1.0}}
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sent_starts = [1, -1, 0, 0, 0, 0, 0, 0, 0]
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doc = nlp(text)
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example = Example.from_dict(doc, {"entities": entities, "links": links, "sent_starts": sent_starts})
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train_examples = [example]
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def create_kb(vocab):
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# create artificial KB
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mykb = KnowledgeBase(vocab, entity_vector_length=vector_length)
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mykb.add_entity(entity="Q270853", freq=12, entity_vector=[9, 1, -7])
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mykb.add_alias(
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alias="No. 8",
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entities=["Q270853"],
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probabilities=[1.0],
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)
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mykb.add_entity(entity="Q7304", freq=12, entity_vector=[6, -4, 3])
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mykb.add_alias(
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alias="Mahler",
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entities=["Q7304"],
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probabilities=[1.0],
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)
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return mykb
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# Create the Entity Linker component and add it to the pipeline
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entity_linker = nlp.add_pipe("entity_linker", last=True)
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entity_linker.set_kb(create_kb)
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# train the NEL pipe
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optimizer = nlp.initialize(get_examples=lambda: train_examples)
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for i in range(2):
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losses = {}
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nlp.update(train_examples, sgd=optimizer, losses=losses)
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# Add a custom rule-based component to mimick NER
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patterns = [
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{"label": "PERSON", "pattern": [{"LOWER": "mahler"}]},
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{"label": "WORK", "pattern": [{"LOWER": "symphony"}, {"LOWER": "no"}, {"LOWER": "."}, {"LOWER": "8"}]}
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]
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ruler = nlp.add_pipe("entity_ruler", before="entity_linker")
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ruler.add_patterns(patterns)
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# test the trained model - this should not throw E148
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doc = nlp(text)
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assert doc
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