def runspacymodel(sentences, tagger, model):
# model = 'en_core_web_sm'
nlp = spacy.load(model)
ner = EntityRecognizer(nlp.vocab)
ner.from_disk(tagger)
result = []
for sentence in sentences:
doc = spacy.tokens.doc.Doc(nlp.vocab, words=sentence)
# run ner against every sentence
processed = ner(doc)
for token in processed:
result.append([token.text, token.ent_type_])
return result
def test_issue4042_bug2():
"""
Test that serialization of an NER works fine when new labels were added.
This is the second bug of two bugs underlying the issue 4042.
"""
nlp1 = English()
vocab = nlp1.vocab
# add ner pipe
ner1 = nlp1.create_pipe("ner")
ner1.add_label("SOME_LABEL")
nlp1.add_pipe(ner1)
nlp1.begin_training()
# add a new label to the doc
doc1 = nlp1("What do you think about Apple ?")
assert len(ner1.labels) == 1
assert "SOME_LABEL" in ner1.labels
apple_ent = Span(doc1, 5, 6, label="MY_ORG")
doc1.ents = list(doc1.ents) + [apple_ent]
# reapply the NER - at this point it should resize itself
ner1(doc1)
assert len(ner1.labels) == 2
assert "SOME_LABEL" in ner1.labels
assert "MY_ORG" in ner1.labels
with make_tempdir() as d:
# assert IO goes fine
output_dir = ensure_path(d)
if not output_dir.exists():
output_dir.mkdir()
ner1.to_disk(output_dir)
nlp2 = English(vocab)
ner2 = EntityRecognizer(vocab)
ner2.from_disk(output_dir)
assert len(ner2.labels) == 2
import spacy
from spacy.pipeline import EntityRecognizer
nlp = spacy.load('en', disable=['ner'])
ner = EntityRecognizer(nlp.vocab)
ner.from_disk('/usr/to/ner')
nlp.add_pipe(ner, "custom_ner")
print(nlp.meta['pipeline'])
doc = nlp(u'Could you pick me up at Solnce?')
for ent in doc.ents:
print(ent.text, ent.label_)
