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110 lines
3.5 KiB
Python
110 lines
3.5 KiB
Python
#!/usr/bin/env python
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# coding: utf8
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"""Example of training spaCy dependency parser, starting off with an existing
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model or a blank model. For more details, see the documentation:
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* Training: https://spacy.io/usage/training
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* Dependency Parse: https://spacy.io/usage/linguistic-features#dependency-parse
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Compatible with: spaCy v2.0.0+
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"""
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from __future__ import unicode_literals, print_function
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import plac
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import random
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from pathlib import Path
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import spacy
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from spacy.util import minibatch, compounding
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# training data
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TRAIN_DATA = [
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(
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"They trade mortgage-backed securities.",
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{
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"heads": [1, 1, 4, 4, 5, 1, 1],
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"deps": ["nsubj", "ROOT", "compound", "punct", "nmod", "dobj", "punct"],
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},
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),
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(
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"I like London and Berlin.",
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{
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"heads": [1, 1, 1, 2, 2, 1],
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"deps": ["nsubj", "ROOT", "dobj", "cc", "conj", "punct"],
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},
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),
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]
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@plac.annotations(
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model=("Model name. Defaults to blank 'en' model.", "option", "m", str),
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output_dir=("Optional output directory", "option", "o", Path),
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n_iter=("Number of training iterations", "option", "n", int),
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)
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def main(model=None, output_dir=None, n_iter=10):
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"""Load the model, set up the pipeline and train the parser."""
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if model is not None:
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nlp = spacy.load(model) # load existing spaCy model
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print("Loaded model '%s'" % model)
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else:
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nlp = spacy.blank("en") # create blank Language class
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print("Created blank 'en' model")
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# add the parser to the pipeline if it doesn't exist
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# nlp.create_pipe works for built-ins that are registered with spaCy
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if "parser" not in nlp.pipe_names:
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parser = nlp.create_pipe("parser")
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nlp.add_pipe(parser, first=True)
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# otherwise, get it, so we can add labels to it
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else:
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parser = nlp.get_pipe("parser")
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# add labels to the parser
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for _, annotations in TRAIN_DATA:
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for dep in annotations.get("deps", []):
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parser.add_label(dep)
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# get names of other pipes to disable them during training
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other_pipes = [pipe for pipe in nlp.pipe_names if pipe != "parser"]
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with nlp.disable_pipes(*other_pipes): # only train parser
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optimizer = nlp.begin_training()
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for itn in range(n_iter):
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random.shuffle(TRAIN_DATA)
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losses = {}
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# batch up the examples using spaCy's minibatch
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batches = minibatch(TRAIN_DATA, size=compounding(4.0, 32.0, 1.001))
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for batch in batches:
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texts, annotations = zip(*batch)
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nlp.update(texts, annotations, sgd=optimizer, losses=losses)
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print("Losses", losses)
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# test the trained model
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test_text = "I like securities."
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doc = nlp(test_text)
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print("Dependencies", [(t.text, t.dep_, t.head.text) for t in doc])
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# save model to output directory
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if output_dir is not None:
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output_dir = Path(output_dir)
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if not output_dir.exists():
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output_dir.mkdir()
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nlp.to_disk(output_dir)
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print("Saved model to", output_dir)
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# test the saved model
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print("Loading from", output_dir)
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nlp2 = spacy.load(output_dir)
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doc = nlp2(test_text)
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print("Dependencies", [(t.text, t.dep_, t.head.text) for t in doc])
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if __name__ == "__main__":
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plac.call(main)
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# expected result:
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# [
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# ('I', 'nsubj', 'like'),
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# ('like', 'ROOT', 'like'),
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# ('securities', 'dobj', 'like'),
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# ('.', 'punct', 'like')
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# ]
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