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<!--- Provide a general summary of your changes in the title. --> ## Description Update the training examples in `/examples/training` to show usage of spaCy's `minibatch` and `compounding` helpers ([see here](https://spacy.io/usage/training#tips-batch-size) for details). The lack of batching in the examples has caused some confusion in the past, especially for beginners who would copy-paste the examples, update them with large training sets and experienced slow and unsatisfying results. ### Types of change enhancements ## Checklist <!--- Before you submit the PR, go over this checklist and make sure you can tick off all the boxes. [] -> [x] --> - [x] I have submitted the spaCy Contributor Agreement. - [x] I ran the tests, and all new and existing tests passed. - [x] My changes don't require a change to the documentation, or if they do, I've added all required information.
103 lines
3.4 KiB
Python
103 lines
3.4 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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("They trade mortgage-backed securities.", {
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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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("I like London and Berlin.", {
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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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@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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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., 32., 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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