train_ner_mod.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+

Taken from https://github.com/explosion/spaCy/blob/master/examples/training/train_ner.py,
and modified to accept JSON file input
"""
from __future__ import unicode_literals, print_function

import plac
import random
from pathlib import Path
import spacy
import json
from tqdm import tqdm


@plac.annotations(
    data=("JSON of training data", "option", "d", Path),
    model=("Model name. Defaults to blank 'en' model.", "option", "m", str),
    output_dir=("Optional output directory", "option", "o", Path),
    n_iter=("Number of training iterations", "option", "n", int))
def main(data, model=None, output_dir=None, n_iter=100):
    """Load the model, set up the pipeline and train the entity recognizer."""
    with open(data, "r") as f:
        TRAIN_DATA = json.load(f)
    if output_dir is not None:
        output_dir = Path(output_dir)
        if not output_dir.exists():
            output_dir.mkdir()
    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 _, annotations in TRAIN_DATA:
        for ent in annotations.get('entities'):
            ner.add_label(ent[2])
    print("Starting training")
    # 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):
            print("Iteration ", itn)
            random.shuffle(TRAIN_DATA)
            losses = {}
            for text, annotations in tqdm(TRAIN_DATA):
                nlp.update(
                    [text],  # 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)
            nlp.to_disk(output_dir)

    TEST_DATA = [
        ('Who is Shaka Khan?', {
            'entities': [(7, 17, 'PERSON')]
        }),
        ('I like London and Berlin.', {
            'entities': [(7, 13, 'LOC'), (18, 24, 'LOC')]
        })
    ]


    # test the trained model
    for text, _ in TEST_DATA:
        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

   # 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__':
    plac.call(main)

    # Expected output:
    # Entities [('Shaka Khan', 'PERSON')]
    # Tokens [('Who', '', 2), ('is', '', 2), ('Shaka', 'PERSON', 3),
    # ('Khan', 'PERSON', 1), ('?', '', 2)]
    # Entities [('London', 'LOC'), ('Berlin', 'LOC')]
    # Tokens [('I', '', 2), ('like', '', 2), ('London', 'LOC', 3),
    # ('and', '', 2), ('Berlin', 'LOC', 3), ('.', '', 2)]