mirror of
https://github.com/explosion/spaCy.git
synced 2025-11-01 00:17:44 +03:00
55 lines
1.9 KiB
Python
55 lines
1.9 KiB
Python
from spacy.kb import KnowledgeBase
|
|
from spacy.training import Example
|
|
from spacy.lang.en import English
|
|
|
|
|
|
# fmt: off
|
|
TRAIN_DATA = [
|
|
("Russ Cochran his reprints include EC Comics.",
|
|
{"links": {(0, 12): {"Q2146908": 1.0}},
|
|
"entities": [(0, 12, "PERSON")],
|
|
"sent_starts": [1, -1, 0, 0, 0, 0, 0, 0]})
|
|
]
|
|
# fmt: on
|
|
|
|
|
|
def test_partial_links():
|
|
# Test that having some entities on the doc without gold links, doesn't crash
|
|
nlp = English()
|
|
vector_length = 3
|
|
train_examples = []
|
|
for text, annotation in TRAIN_DATA:
|
|
doc = nlp(text)
|
|
train_examples.append(Example.from_dict(doc, annotation))
|
|
|
|
def create_kb(vocab):
|
|
# create artificial KB
|
|
mykb = KnowledgeBase(vocab, entity_vector_length=vector_length)
|
|
mykb.add_entity(entity="Q2146908", freq=12, entity_vector=[6, -4, 3])
|
|
mykb.add_alias("Russ Cochran", ["Q2146908"], [0.9])
|
|
return mykb
|
|
|
|
# Create and train the Entity Linker
|
|
entity_linker = nlp.add_pipe("entity_linker", last=True)
|
|
entity_linker.set_kb(create_kb)
|
|
optimizer = nlp.initialize(get_examples=lambda: train_examples)
|
|
for i in range(2):
|
|
losses = {}
|
|
nlp.update(train_examples, sgd=optimizer, losses=losses)
|
|
|
|
# adding additional components that are required for the entity_linker
|
|
nlp.add_pipe("sentencizer", first=True)
|
|
patterns = [
|
|
{"label": "PERSON", "pattern": [{"LOWER": "russ"}, {"LOWER": "cochran"}]},
|
|
{"label": "ORG", "pattern": [{"LOWER": "ec"}, {"LOWER": "comics"}]}
|
|
]
|
|
ruler = nlp.add_pipe("entity_ruler", before="entity_linker")
|
|
ruler.add_patterns(patterns)
|
|
|
|
# this will run the pipeline on the examples and shouldn't crash
|
|
results = nlp.evaluate(train_examples)
|
|
assert "PERSON" in results["ents_per_type"]
|
|
assert "PERSON" in results["nel_f_per_type"]
|
|
assert "ORG" in results["ents_per_type"]
|
|
assert "ORG" not in results["nel_f_per_type"]
|