include ../../_includes/_mixins

p
    |  This workflow describes how to train new statistical models for spaCy's
    |  part-of-speech tagger, named entity recognizer and dependency parser.
    |  Once the model is trained, you can then
    |  #[+a("/docs/usage/saving-loading") save and load] it.

+h(2, "101") Training 101

include _spacy-101/_training

+h(2, "train-pos-tagger") Training the part-of-speech tagger

+code.
    from spacy.vocab import Vocab
    from spacy.tagger import Tagger
    from spacy.tokens import Doc
    from spacy.gold import GoldParse


    vocab = Vocab(tag_map={'N': {'pos': 'NOUN'}, 'V': {'pos': 'VERB'}})
    tagger = Tagger(vocab)

    doc = Doc(vocab, words=['I', 'like', 'stuff'])
    gold = GoldParse(doc, tags=['N', 'V', 'N'])
    tagger.update(doc, gold)

    tagger.model.end_training()

p
    +button(gh("spaCy", "examples/training/train_tagger.py"), false, "secondary") Full example

+h(2, "train-entity") Training the named entity recognizer

+code.
    from spacy.vocab import Vocab
    from spacy.pipeline import EntityRecognizer
    from spacy.tokens import Doc

    vocab = Vocab()
    entity = EntityRecognizer(vocab, entity_types=['PERSON', 'LOC'])

    doc = Doc(vocab, words=['Who', 'is', 'Shaka', 'Khan', '?'])
    entity.update(doc, ['O', 'O', 'B-PERSON', 'L-PERSON', 'O'])

    entity.model.end_training()

p
    +button(gh("spaCy", "examples/training/train_ner.py"), false, "secondary") Full example

+h(2, "extend-entity") Extending the named entity recognizer

p
    |  All #[+a("/docs/usage/models") spaCy models] support online learning, so
    |  you can update a pre-trained model with new examples. You can even add
    |  new classes to an existing model, to recognise a new entity type,
    |  part-of-speech, or syntactic relation. Updating an existing model is
    |  particularly useful as a "quick and dirty solution", if you have only a
    |  few corrections or annotations.

p.o-inline-list
    +button(gh("spaCy", "examples/training/train_new_entity_type.py"), true, "secondary") Full example
    +button("/docs/usage/training-ner", false, "secondary") Usage Workflow

+h(2, "train-dependency") Training the dependency parser

+code.
    from spacy.vocab import Vocab
    from spacy.pipeline import DependencyParser
    from spacy.tokens import Doc

    vocab = Vocab()
    parser = DependencyParser(vocab, labels=['nsubj', 'compound', 'dobj', 'punct'])

    doc = Doc(vocab, words=['Who', 'is', 'Shaka', 'Khan', '?'])
    parser.update(doc, [(1, 'nsubj'), (1, 'ROOT'), (3, 'compound'), (1, 'dobj'),
                        (1, 'punct')])

    parser.model.end_training()

p
    +button(gh("spaCy", "examples/training/train_parser.py"), false, "secondary") Full example