spaCy/examples/training/train_ner.py

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