mirror of
https://github.com/explosion/spaCy.git
synced 2024-09-21 19:39:13 +03:00
67 lines
2.0 KiB
Python
67 lines
2.0 KiB
Python
from spacy.pipeline.ner import DEFAULT_NER_MODEL
|
|
from spacy.training import Example
|
|
from spacy.pipeline import EntityRecognizer
|
|
from spacy.tokens import Span, Doc
|
|
from spacy import registry
|
|
import pytest
|
|
|
|
|
|
def _ner_example(ner):
|
|
doc = Doc(
|
|
ner.vocab,
|
|
words=["Joe", "loves", "visiting", "London", "during", "the", "weekend"],
|
|
)
|
|
gold = {"entities": [(0, 3, "PERSON"), (19, 25, "LOC")]}
|
|
return Example.from_dict(doc, gold)
|
|
|
|
|
|
def test_doc_add_entities_set_ents_iob(en_vocab):
|
|
text = ["This", "is", "a", "lion"]
|
|
doc = Doc(en_vocab, words=text)
|
|
config = {
|
|
"learn_tokens": False,
|
|
"min_action_freq": 30,
|
|
"update_with_oracle_cut_size": 100,
|
|
}
|
|
cfg = {"model": DEFAULT_NER_MODEL}
|
|
model = registry.resolve(cfg, validate=True)["model"]
|
|
ner = EntityRecognizer(en_vocab, model, **config)
|
|
ner.begin_training(lambda: [_ner_example(ner)])
|
|
ner(doc)
|
|
|
|
doc.ents = [("ANIMAL", 3, 4)]
|
|
assert [w.ent_iob_ for w in doc] == ["O", "O", "O", "B"]
|
|
|
|
doc.ents = [("WORD", 0, 2)]
|
|
assert [w.ent_iob_ for w in doc] == ["B", "I", "O", "O"]
|
|
|
|
|
|
def test_ents_reset(en_vocab):
|
|
"""Ensure that resetting doc.ents does not change anything"""
|
|
text = ["This", "is", "a", "lion"]
|
|
doc = Doc(en_vocab, words=text)
|
|
config = {
|
|
"learn_tokens": False,
|
|
"min_action_freq": 30,
|
|
"update_with_oracle_cut_size": 100,
|
|
}
|
|
cfg = {"model": DEFAULT_NER_MODEL}
|
|
model = registry.resolve(cfg, validate=True)["model"]
|
|
ner = EntityRecognizer(en_vocab, model, **config)
|
|
ner.begin_training(lambda: [_ner_example(ner)])
|
|
ner(doc)
|
|
orig_iobs = [t.ent_iob_ for t in doc]
|
|
doc.ents = list(doc.ents)
|
|
assert [t.ent_iob_ for t in doc] == orig_iobs
|
|
|
|
|
|
def test_add_overlapping_entities(en_vocab):
|
|
text = ["Louisiana", "Office", "of", "Conservation"]
|
|
doc = Doc(en_vocab, words=text)
|
|
entity = Span(doc, 0, 4, label=391)
|
|
doc.ents = [entity]
|
|
|
|
new_entity = Span(doc, 0, 1, label=392)
|
|
with pytest.raises(ValueError):
|
|
doc.ents = list(doc.ents) + [new_entity]
|