mirror of https://github.com/explosion/spaCy.git
91 lines
2.6 KiB
Python
91 lines
2.6 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
|
|
from spacy.tagger import Tagger
|
|
|
|
|
|
def train_ner(nlp, train_data, entity_types):
|
|
# Add new words to vocab.
|
|
for raw_text, _ in train_data:
|
|
doc = nlp.make_doc(raw_text)
|
|
for word in doc:
|
|
_ = nlp.vocab[word.orth]
|
|
|
|
# Train NER.
|
|
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 save_model(ner, model_dir):
|
|
model_dir = pathlib.Path(model_dir)
|
|
if not model_dir.exists():
|
|
model_dir.mkdir()
|
|
assert model_dir.is_dir()
|
|
|
|
with (model_dir / 'config.json').open('w') as file_:
|
|
json.dump(ner.cfg, file_)
|
|
ner.model.dump(str(model_dir / 'model'))
|
|
if not (model_dir / 'vocab').exists():
|
|
(model_dir / 'vocab').mkdir()
|
|
ner.vocab.dump(str(model_dir / 'vocab' / 'lexemes.bin'))
|
|
with (model_dir / 'vocab' / 'strings.json').open('w', encoding='utf8') as file_:
|
|
ner.vocab.strings.dump(file_)
|
|
|
|
|
|
def main(model_dir=None):
|
|
nlp = spacy.load('en', parser=False, entity=False, add_vectors=False)
|
|
|
|
# v1.1.2 onwards
|
|
if nlp.tagger is None:
|
|
print('---- WARNING ----')
|
|
print('Data directory not found')
|
|
print('please run: `python -m spacy.en.download --force all` for better performance')
|
|
print('Using feature templates for tagging')
|
|
print('-----------------')
|
|
nlp.tagger = Tagger(nlp.vocab, features=Tagger.feature_templates)
|
|
|
|
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.orth, word.lower, word.tag_, word.ent_type_, word.ent_iob)
|
|
|
|
if model_dir is not None:
|
|
save_model(ner, model_dir)
|
|
|
|
|
|
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main('ner')
|
|
# Who "" 2
|
|
# is "" 2
|
|
# Shaka "" PERSON 3
|
|
# Khan "" PERSON 1
|
|
# ? "" 2
|