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78 lines
2.6 KiB
Plaintext
78 lines
2.6 KiB
Plaintext
include ../../_includes/_mixins
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p
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| This guide describes how to train new statistical models for spaCy's
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| part-of-speech tagger, named entity recognizer and dependency parser.
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| Once the model is trained, you can then
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| #[+a("/docs/usage/saving-loading") save and load] it.
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+h(2, "101") Training 101
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include _spacy-101/_training
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+h(2, "train-pos-tagger") Training the part-of-speech tagger
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+code.
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from spacy.vocab import Vocab
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from spacy.tagger import Tagger
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from spacy.tokens import Doc
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from spacy.gold import GoldParse
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vocab = Vocab(tag_map={'N': {'pos': 'NOUN'}, 'V': {'pos': 'VERB'}})
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tagger = Tagger(vocab)
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doc = Doc(vocab, words=['I', 'like', 'stuff'])
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gold = GoldParse(doc, tags=['N', 'V', 'N'])
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tagger.update(doc, gold)
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p
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+button(gh("spaCy", "examples/training/train_tagger.py"), false, "secondary") Full example
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+h(2, "train-entity") Training the named entity recognizer
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+code.
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from spacy.vocab import Vocab
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from spacy.pipeline import EntityRecognizer
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from spacy.tokens import Doc
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vocab = Vocab()
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entity = EntityRecognizer(vocab, entity_types=['PERSON', 'LOC'])
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doc = Doc(vocab, words=['Who', 'is', 'Shaka', 'Khan', '?'])
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entity.update(doc, ['O', 'O', 'B-PERSON', 'L-PERSON', 'O'])
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p
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+button(gh("spaCy", "examples/training/train_ner.py"), false, "secondary") Full example
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+h(2, "extend-entity") Extending the named entity recognizer
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p
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| All #[+a("/docs/usage/models") spaCy models] support online learning, so
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| you can update a pre-trained model with new examples. You can even add
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| new classes to an existing model, to recognise a new entity type,
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| part-of-speech, or syntactic relation. Updating an existing model is
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| particularly useful as a "quick and dirty solution", if you have only a
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| few corrections or annotations.
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p.o-inline-list
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+button(gh("spaCy", "examples/training/train_new_entity_type.py"), true, "secondary") Full example
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+button("/docs/usage/training-ner", false, "secondary") Usage guide
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+h(2, "train-dependency") Training the dependency parser
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+code.
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from spacy.vocab import Vocab
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from spacy.pipeline import DependencyParser
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from spacy.tokens import Doc
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vocab = Vocab()
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parser = DependencyParser(vocab, labels=['nsubj', 'compound', 'dobj', 'punct'])
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doc = Doc(vocab, words=['Who', 'is', 'Shaka', 'Khan', '?'])
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parser.update(doc, [(1, 'nsubj'), (1, 'ROOT'), (3, 'compound'), (1, 'dobj'),
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(1, 'punct')])
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p
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+button(gh("spaCy", "examples/training/train_parser.py"), false, "secondary") Full example
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