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115 lines
4.1 KiB
Python
115 lines
4.1 KiB
Python
#!/usr/bin/env python
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# coding: utf8
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"""Example of training spaCy's named entity recognizer, starting off with an
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existing model or a blank model.
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For more details, see the documentation:
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* Training: https://alpha.spacy.io/usage/training
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* NER: https://alpha.spacy.io/usage/linguistic-features#named-entities
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Developed for: spaCy 2.0.0a18
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Last updated for: spaCy 2.0.0a18
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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.gold import GoldParse, biluo_tags_from_offsets
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# training data
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TRAIN_DATA = [
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('Who is Shaka Khan?', [(7, 17, 'PERSON')]),
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('I like London and Berlin.', [(7, 13, 'LOC'), (18, 24, 'LOC')])
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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=100):
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"""Load the model, set up the pipeline and train the entity recognizer."""
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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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# create the built-in pipeline components and add them to the pipeline
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# nlp.create_pipe works for built-ins that are registered with spaCy
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if 'ner' not in nlp.pipe_names:
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ner = nlp.create_pipe('ner')
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nlp.add_pipe(ner, last=True)
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# function that allows begin_training to get the training data
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get_data = lambda: reformat_train_data(nlp.tokenizer, TRAIN_DATA)
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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 != 'ner']
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with nlp.disable_pipes(*other_pipes): # only train NER
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optimizer = nlp.begin_training(get_data)
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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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for raw_text, entity_offsets in TRAIN_DATA:
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doc = nlp.make_doc(raw_text)
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gold = GoldParse(doc, entities=entity_offsets)
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nlp.update(
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[doc], # Batch of Doc objects
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[gold], # Batch of GoldParse objects
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drop=0.5, # Dropout -- make it harder to memorise data
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sgd=optimizer, # Callable to update weights
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losses=losses)
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print(losses)
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# test the trained model
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for text, _ in TRAIN_DATA:
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doc = nlp(text)
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print('Entities', [(ent.text, ent.label_) for ent in doc.ents])
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print('Tokens', [(t.text, t.ent_type_, t.ent_iob) 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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for text, _ in TRAIN_DATA:
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doc = nlp2(text)
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print('Entities', [(ent.text, ent.label_) for ent in doc.ents])
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print('Tokens', [(t.text, t.ent_type_, t.ent_iob) for t in doc])
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def reformat_train_data(tokenizer, examples):
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"""Reformat data to match JSON format.
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https://alpha.spacy.io/api/annotation#json-input
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tokenizer (Tokenizer): Tokenizer to process the raw text.
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examples (list): The trainig data.
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RETURNS (list): The reformatted training data."""
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output = []
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for i, (text, entity_offsets) in enumerate(examples):
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doc = tokenizer(text)
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ner_tags = biluo_tags_from_offsets(tokenizer(text), entity_offsets)
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words = [w.text for w in doc]
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tags = ['-'] * len(doc)
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heads = [0] * len(doc)
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deps = [''] * len(doc)
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sentence = (range(len(doc)), words, tags, heads, deps, ner_tags)
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output.append((text, [(sentence, [])]))
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return output
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if __name__ == '__main__':
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plac.call(main)
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