train_ner.py

#!/usr/bin/env python
# coding: utf8
"""Example of training spaCy's named entity recognizer, starting off with an
existing model or a blank model.
For more details, see the documentation:
* Training: https://spacy.io/usage/training
* NER: https://spacy.io/usage/linguistic-features#named-entities
Compatible with: spaCy v2.0.0+
"""
from __future__ import unicode_literals, print_function

import random
from pathlib import Path
import spacy
from spacy.util import minibatch, compounding
import json

LABELS = ['FIRSTNAME', 'LASTNAME', 'ADDR', 'DATE']
with open("training_data.json") as fd:
    TRAIN_DATA = json.load(fd)[:200]


def main(model=None, output_dir=None, n_iter=100):
    """Load the model, set up the pipeline and train the entity recognizer."""
    if model is not None:
        nlp = spacy.load(model)  # load existing spaCy model
        print("Loaded model '%s'" % model)
    else:
        nlp = spacy.blank('en')  # create blank Language class
        print("Created blank 'en' model")

    # create the built-in pipeline components and add them to the pipeline
    # nlp.create_pipe works for built-ins that are registered with spaCy
    if 'ner' not in nlp.pipe_names:
        ner = nlp.create_pipe('ner')
        nlp.add_pipe(ner, last=True)
    # otherwise, get it so we can add labels
    else:
        ner = nlp.get_pipe('ner')

    # add labels
    for ent in LABELS:
        print("Label", ent)
        ner.add_label(ent)

    # get names of other pipes to disable them during training
    other_pipes = [pipe for pipe in nlp.pipe_names if pipe != 'ner']
    with nlp.disable_pipes(*other_pipes):  # only train NER
        optimizer = nlp.begin_training()
        for itn in range(n_iter):
            random.shuffle(TRAIN_DATA)
            losses = {}
            # batch up the examples using spaCy's minibatch
            batches = minibatch(TRAIN_DATA, size=compounding(4., 32., 1.001))
            for batch in batches:
                texts, annotations = zip(*batch)
                nlp.update(
                    texts,  # batch of texts
                    annotations,  # batch of annotations
                    drop=0.5,  # dropout - make it harder to memorise data
                    sgd=optimizer,  # callable to update weights
                    losses=losses)
            print('Losses', itn, losses)

    # test the trained model
    for text, _ in TRAIN_DATA[:1]:
        doc = nlp(text)
        print('Entities', [(ent.text, ent.label_) for ent in doc.ents])
        print('Tokens', [(t.text, t.ent_type_, t.ent_iob) for t in doc])

    # save model to output directory
    if output_dir is not None:
        output_dir = Path(output_dir)
        if not output_dir.exists():
            output_dir.mkdir()
        nlp.to_disk(output_dir)
        print("Saved model to", output_dir)

        # test the saved model
        print("Loading from", output_dir)
        nlp2 = spacy.load(output_dir)
        for text, _ in TRAIN_DATA:
            doc = nlp2(text)
            print('Entities', [(ent.text, ent.label_) for ent in doc.ents])
            #print('Tokens', [(t.text, t.ent_type_, t.ent_iob) for t in doc])


if __name__=='__main__':

    main(n_iter=100, output_dir="models")