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55 lines
1.9 KiB
Python
55 lines
1.9 KiB
Python
from spacy.kb import KnowledgeBase
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from spacy.training import Example
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from spacy.lang.en import English
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# fmt: off
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TRAIN_DATA = [
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("Russ Cochran his reprints include EC Comics.",
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{"links": {(0, 12): {"Q2146908": 1.0}},
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"entities": [(0, 12, "PERSON")],
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"sent_starts": [1, -1, 0, 0, 0, 0, 0, 0]})
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]
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# fmt: on
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def test_partial_links():
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# Test that having some entities on the doc without gold links, doesn't crash
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nlp = English()
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vector_length = 3
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train_examples = []
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for text, annotation in TRAIN_DATA:
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doc = nlp(text)
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train_examples.append(Example.from_dict(doc, annotation))
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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="Q2146908", freq=12, entity_vector=[6, -4, 3])
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mykb.add_alias("Russ Cochran", ["Q2146908"], [0.9])
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return mykb
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# Create and train the Entity Linker
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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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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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# adding additional components that are required for the entity_linker
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nlp.add_pipe("sentencizer", first=True)
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patterns = [
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{"label": "PERSON", "pattern": [{"LOWER": "russ"}, {"LOWER": "cochran"}]},
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{"label": "ORG", "pattern": [{"LOWER": "ec"}, {"LOWER": "comics"}]}
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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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# this will run the pipeline on the examples and shouldn't crash
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results = nlp.evaluate(train_examples)
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assert "PERSON" in results["ents_per_type"]
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assert "PERSON" in results["nel_f_per_type"]
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assert "ORG" in results["ents_per_type"]
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assert "ORG" not in results["nel_f_per_type"]
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