spaCy/examples/training/train_parser.py

#!/usr/bin/env python
# coding: utf8
"""Example of training spaCy dependency parser, starting off with an existing
model or a blank model. For more details, see the documentation:
* Training: https://spacy.io/usage/training
* Dependency Parse: https://spacy.io/usage/linguistic-features#dependency-parse

Compatible with: spaCy v2.0.0+
Last tested with: v2.1.0
"""
from __future__ import unicode_literals, print_function

import plac
import random
from pathlib import Path
import spacy
from spacy.gold import Example
from spacy.util import minibatch, compounding


# training data
TRAIN_DATA = [
    (
        "They trade mortgage-backed securities.",
        {
            "heads": [1, 1, 4, 4, 5, 1, 1],
            "deps": ["nsubj", "ROOT", "compound", "punct", "nmod", "dobj", "punct"],
        },
    ),
    (
        "I like London and Berlin.",
        {
            "heads": [1, 1, 1, 2, 2, 1],
            "deps": ["nsubj", "ROOT", "dobj", "cc", "conj", "punct"],
        },
    ),
]


@plac.annotations(
    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(model=None, output_dir=None, n_iter=15):
    """Load the model, set up the pipeline and train the parser."""
    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")

    # add the parser to the pipeline if it doesn't exist
    # nlp.create_pipe works for built-ins that are registered with spaCy
    if "parser" not in nlp.pipe_names:
        parser = nlp.create_pipe("parser")
        nlp.add_pipe(parser, first=True)
    # otherwise, get it, so we can add labels to it
    else:
        parser = nlp.get_pipe("parser")

    # add labels to the parser and create the Example objects
    train_examples = []
    for text, annotations in TRAIN_DATA:
        train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
        for dep in annotations.get("deps", []):
            parser.add_label(dep)

    with nlp.select_pipes(enable="parser"):  # only train parser
        optimizer = nlp.begin_training()
        for itn in range(n_iter):
            random.shuffle(train_examples)
            losses = {}
            # batch up the examples using spaCy's minibatch
            batches = minibatch(train_examples, size=compounding(4.0, 32.0, 1.001))
            for batch in batches:
                nlp.update(batch, sgd=optimizer, losses=losses)
            print("Losses", losses)

    # test the trained model
    test_text = "I like securities."
    doc = nlp(test_text)
    print("Dependencies", [(t.text, t.dep_, t.head.text) 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)
        doc = nlp2(test_text)
        print("Dependencies", [(t.text, t.dep_, t.head.text) for t in doc])


if __name__ == "__main__":
    plac.call(main)

    # expected result:
    # [
    #   ('I', 'nsubj', 'like'),
    #   ('like', 'ROOT', 'like'),
    #   ('securities', 'dobj', 'like'),
    #   ('.', 'punct', 'like')
    # ]