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70 lines
2.2 KiB
Python
70 lines
2.2 KiB
Python
from __future__ import unicode_literals, print_function
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import random
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from spacy.lang.en import English
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from spacy.gold import GoldParse, biluo_tags_from_offsets
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def reformat_train_data(tokenizer, examples):
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"""Reformat data to match JSON format"""
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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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def main(model_dir=None):
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train_data = [
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(
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'Who is Shaka Khan?',
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[(len('Who is '), len('Who is Shaka Khan'), 'PERSON')]
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),
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(
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'I like London and Berlin.',
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[(len('I like '), len('I like London'), 'LOC'),
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(len('I like London and '), len('I like London and Berlin'), 'LOC')]
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)
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]
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nlp = English(pipeline=['tensorizer', 'ner'])
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get_data = lambda: reformat_train_data(nlp.tokenizer, train_data)
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optimizer = nlp.begin_training(get_data)
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for itn in range(100):
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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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print("Save to", model_dir)
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nlp.to_disk(model_dir)
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print("Load from", model_dir)
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nlp = spacy.lang.en.English(pipeline=['tensorizer', 'ner'])
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nlp.from_disk(model_dir)
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for raw_text, _ in train_data:
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doc = nlp(raw_text)
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for word in doc:
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print(word.text, word.ent_type_, word.ent_iob_)
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if __name__ == '__main__':
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import plac
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plac.call(main)
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# Who "" 2
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# is "" 2
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# Shaka "" PERSON 3
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# Khan "" PERSON 1
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# ? "" 2
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