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23 lines
772 B
Python
23 lines
772 B
Python
# Load NER
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from __future__ import unicode_literals
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import spacy
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import pathlib
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from spacy.pipeline import EntityRecognizer
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from spacy.vocab import Vocab
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def load_model(model_dir):
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model_dir = pathlib.Path(model_dir)
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nlp = spacy.load('en', parser=False, entity=False, add_vectors=False)
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with (model_dir / 'vocab' / 'strings.json').open('r', encoding='utf8') as file_:
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nlp.vocab.strings.load(file_)
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nlp.vocab.load_lexemes(model_dir / 'vocab' / 'lexemes.bin')
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ner = EntityRecognizer.load(model_dir, nlp.vocab, require=True)
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return (nlp, ner)
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(nlp, ner) = load_model('ner')
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doc = nlp.make_doc('Who is Shaka Khan?')
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nlp.tagger(doc)
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ner(doc)
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for word in doc:
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print(word.text, word.orth, word.lower, word.tag_, word.ent_type_, word.ent_iob)
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