spaCy/examples/training/train_ner.py

64 lines
1.7 KiB
Python

from __future__ import unicode_literals, print_function
import json
import pathlib
import random
import spacy
from spacy.pipeline import EntityRecognizer
from spacy.gold import GoldParse
def train_ner(nlp, train_data, entity_types):
ner = EntityRecognizer(nlp.vocab, entity_types=entity_types)
for itn in range(5):
random.shuffle(train_data)
for raw_text, entity_offsets in train_data:
doc = nlp.make_doc(raw_text)
gold = GoldParse(doc, entities=entity_offsets)
ner.update(doc, gold)
ner.model.end_training()
return ner
def main(model_dir=None):
if model_dir is not None:
model_dir = pathlb.Path(model_dir)
if not model_dir.exists():
model_dir.mkdir()
assert model_dir.isdir()
nlp = spacy.load('en', parser=False, entity=False, vectors=False)
train_data = [
(
'Who is Shaka Khan?',
[(len('Who is '), len('Who is Shaka Khan'), 'PERSON')]
),
(
'I like London and Berlin.',
[(len('I like '), len('I like London'), 'LOC'),
(len('I like London and '), len('I like London and Berlin'), 'LOC')]
)
]
ner = train_ner(nlp, train_data, ['PERSON', 'LOC'])
doc = nlp.make_doc('Who is Shaka Khan?')
nlp.tagger(doc)
ner(doc)
for word in doc:
print(word.text, word.tag_, word.ent_type_, word.ent_iob)
if model_dir is not None:
with (model_dir / 'config.json').open('wb') as file_:
json.dump(ner.cfg, file_)
ner.model.dump(str(model_dir / 'model'))
if __name__ == '__main__':
main()
# Who "" 2
# is "" 2
# Shaka "" PERSON 3
# Khan "" PERSON 1
# ? "" 2